# FuzzyPovertyR

library(FuzzyPovertyR)
library(kableExtra)

## Introduction

FuzzyPovertyR is a package for estimating fuzzy poverty indexes. Broadly speaking, a fuzzy poverty index is an index that ranges in the set $$Q=[0,1]$$ (Alkire et al. (2015); Silber (2023)). A fuzzy indexes assigns to each statistical unit a value in this interval according to a given membership function (mf) $$\mu(x_i)\in Q$$ where $$x$$ is a poverty predicate. The higher the value of $$\mu$$, the more the individual is regarded as “poor” with respect to the poverty predicate $$x$$. In socio-economic surveys $$x$$ may be the equivalised disposable income, or expenditure. However, in principle $$x$$ may be a generic poverty predicate that the researcher needs to analyse, for example this could be a variable that relates to access to transports, services and other facilities.

Below we distinguish between monetary and supplementary poverty indexes (or measures). A fuzzy monetary poverty measure is calculated over a numeric vector of length $$n$$ (the available sample size). A supplementary poverty index is calculated on a data.frame of items of a questionnaire that relates to other dimensions of poverty other than monetary.

The dataset coming from the package is loaded in the environment with

data(eusilc)

## Fuzzy Monetary poverty measures

The package lets the user choose among different membership functions trough the argument fm of the fm_construct function. The membership function available are:

• fm="verma" (see Cheli and Lemmi (1995))

$\mu_i=\left(1-F_X(x_i)\right)^{\alpha-1} = \left(\frac{\sum_{j=i+1} w_j|x_j> x_i}{\sum_{j\ge 2} w_j|x_j> x_1}\right)^{\alpha-1}$

where $$F_X$$ is the empirical cumulative distribution function of $$X$$ calculated for the $$i-th$$ individual and $$w_i$$ is the sampling weight of statistical unit $$i$$. The parameter $$\alpha\ge 2$$ is chosen so that the average over the function equals the head count ratio (i.e. the proportion of units whose equivalised disposable income falls below the poverty line) of the whole population.

• fm="verma1999" (see Betti and Verma (1999))

$\mu_i=(1-L_X(x_i))^{\alpha-1} = \left(\frac{\sum_{j=i+1} w_jx_j|x_j> x_i}{\sum_{j\ge 2} w_jx_j|x_j> x_1}\right)^{\alpha-1}$

where $$L_X$$ is the Lorenz Curve of $$X$$ calculated for the $$i-th$$ individual and $$w_i$$ is the sampling weight of statistical unit $$i$$. Again, the parameter $$\alpha\ge 2$$ is chosen once again so that the average over the function equals the head count ratio of the whole population or its estimate.

• fm="verma" (see Betti and Verma (2008))

$\begin{split} \mu_i&=\left(1-F_X(x_i)\right)^{\alpha-1}\left(1-L_X(x_i)\right)=\\ &=\left(\frac{\sum_{j=i+1} w_j|x_j> x_i}{\sum_{j\ge 2} w_j|x_j> x_1}\right)^{\alpha-1} \left(\frac{\sum_{j=i+1} w_jx_j|x_j> x_i}{\sum_{j\ge 2} w_jx_j|x_j> x_1}\right) \end{split}$

Again, the parameter $$\alpha\ge 2$$ is chosen once again so that the average over the function equals the head count ratio of the whole population or its estimate.

• fm="chakravarty" (see Chakravarty (2006))

$\mu_i = \begin{cases} 1 & x_i = 0\\ \frac{z - x_i}{z} & 0 \le x_i < z\\ 0 & x_i \ge z \end{cases}$ where $$z$$ is a threshold to be chosen by the researcher.

• fm="cerioli" (see Cerioli and Zani (1990)) $$_i= $\mu_i = \begin{cases} 1, \quad 0<x_i<z_1\\ \frac{z_2-x_i}{z_2-z_1}, \quad z_1\le x_i<z_2\\ 0 \quad x_i\ge z_2 \end{cases}$$$ the values $$z_1$$ and $$z_2$$ have to be chosen by the researcher.

• fm="ZBM" (see Zedini and Belhadj (2015) and Belhadj and Matoussi (2010))

$\mu_i = \begin{cases} 1 & a \le x_i < b\\ \frac{-x_i}{c-b} + \frac{c}{c-b} & b \le x_i < c\\ 0 & x_i < a \cup x_i \ge c\\ \end{cases}$ where $$a,c,b$$ are percentiles estimated with via the bootstrap technique.

• fm="belhadj2015" (see Besma (2015))

$\mu_i = \begin{cases} 1 & x_i < z_1\\ \mu^1 = 1-\frac{1}{2}\left(\frac{x_i-z_1}{z_1}\right)^b & z_1 \le x_i < z\\ \mu^2 = 1-\frac{1}{2}\left(\frac{z_2 - x_i}{z_2}\right)^b & z \le x_i < z_2\\ 0 & x_i \ge z_2 \end{cases}$ where $$z^*$$ is the flex point of $$\mu_i$$, $$z_1$$ and $$z_2$$ have to be chosen by the researcher, and $$b\ge 1$$ is a shape parameter ruling the degree of convexity of the function. In particular, when $$b=1$$ the trend is linear.

• fm="belhadj2011" (see Belhadj (2011))

$\mu_i = \begin{cases} 1 & 0 < x_i < z_{min} \\ \frac{-x_i}{z_{\max} - z_{\min}} + \frac{-z_{\max}}{z_{\max} - z_{\min}} & z_{min} \le x_i < z_{max}\\ 0 & x_i \ge z_{max} \end{cases}$ where $$z_{min}$$ and $$z_{MAX}$$ have to be chosen by the researcher

For each of the functions below the breakdown argument can be specified in case the user’s want to obtain estimates for given sub-domains.

### Example using fm=verma, fm=verma1999 and fm=ZBM

The computation of a fuzzy poverty index that uses the fm="verma"argument goes trough the following steps:

1. Estimation of the Head Count Ratio (HCR). The package FuzzyPovertyR provides the function HCR to estimate the Head Count Ratio from data. It outputs a list of three elements: a classification of units into being poor or not poor, the poverty line, and the value itself.
hcr = HCR(predicate = eusilc$eq_income, weight = eusilc$DB090, p = 0.5, q = 0.6)$HCR # add poverty threshold if needed, the package has a built-in function eq_predicate to calculate the equivalised disposable income using some equivalence scales. 1. Construction of the Fuzzy Monetary measure. #> [1] "verma" #> [1] "FuzzyMonetary" #> Fuzzy monetary results: #> #> Summary of verma membership function: #> #> Quantiles: #> #> 0% 20% 40% 60% 80% 100% #> 0 0.004 0.036 0.194 0.528 1 #> #> Estimate(s): #> #> [1] 0.246 #> #> Parameter(s): #> #> alpha #> 3.787 When alpha = NULL (the default) this function solves a non-linear equation finding the value $$\alpha$$ in interval that equates the expected value of the poverty measure to the Head Count Ratio calculated above (see #eq-betti2006). This can be avoided by specifying a numeric value of $$\alpha$$. verma = fm_construct(predicate = eusilc$eq_income, fm = "verma", weight = eusilc$DB090, ID = NULL, interval = c(1,10), alpha = 2) The result of fm_construct using fm="verma" is a list containing • a data.frame of individuals’ membership functions sorted in descending order (i.e. from most poor to least poor) head(verma$results)
#>    ID predicate    weight        mu
#> 1  44  3.225806  465.3585 1.0000000
#> 2 450  6.666667 1010.5060 0.9946732
#> 3 372 12.903226  304.7067 0.9930663
#> 4 490 17.857143 1177.4120 0.9868551
#> 5 245 20.000000  853.0255 0.9823546
#> 6 130 42.424242 1141.3410 0.9763241
• The estimated FM measure (note that this equals the HCR if breakdown=NULL). However, one can obtain estimates for sub-domains using the breakdown argument as follows
verma.break = fm_construct(predicate = eusilc$eq_income, weight = eusilc$DB090, ID = NULL, HCR = hcr, interval = c(1,10), alpha = NULL, breakdown = eusilc$db040, fm="verma") summary(verma.break) #> Fuzzy monetary results: #> #> Summary of verma membership function: #> #> Quantiles: #> #> 0% 20% 40% 60% 80% 100% #> 0 0.004 0.036 0.194 0.528 1 #> #> Estimate(s): #> #> a b c d e f g h i j k l m #> 0.189 0.165 0.166 0.268 0.217 0.178 0.363 0.294 0.186 0.155 0.157 0.272 0.327 #> n o p q r s t u v w x y z #> 0.247 0.237 0.255 0.356 0.163 0.375 0.076 0.509 0.242 0.206 0.271 0.313 0.213 #> #> Parameter(s): #> #> alpha #> 3.787 verma.break$estimate
#>          a          b          c          d          e          f          g
#> 0.18866339 0.16538025 0.16562162 0.26821706 0.21695323 0.17829490 0.36323070
#>          h          i          j          k          l          m          n
#> 0.29418642 0.18600937 0.15484057 0.15722022 0.27162839 0.32717227 0.24665062
#>          o          p          q          r          s          t          u
#> 0.23685247 0.25459850 0.35638295 0.16349880 0.37543084 0.07613034 0.50897058
#>          v          w          x          y          z
#> 0.24174548 0.20595260 0.27131691 0.31315140 0.21313753
• The alpha parameter.
alpha = verma$parameters$alpha
alpha
#> [1] 2

With almost identical procedures is also possible to compute the index for fm="verma1999" and fm="TFR".

#> [1] "verma1999"
#> [1] "FuzzyMonetary"
#> Fuzzy monetary results:
#>
#>  Summary of verma1999 membership function:
#>
#>  Quantiles:
#>
#>  0% 20%   40%   60%   80% 100%
#>   0   0 0.006 0.131 0.591    1
#>
#>  Estimate(s):
#>
#> [1] 0.246
#>
#>  Parameter(s):
#>
#>  alpha
#> 12.067

#> [1] "TFR"
#> [1] "FuzzyMonetary"
#> Fuzzy monetary results:
#>
#>  Summary of TFR membership function:
#>
#>  Quantiles:
#>
#>  0%   20%   40%   60%  80% 100%
#>   0 0.005 0.042 0.199 0.52    1
#>
#>  Estimate(s):
#>
#> [1] 0.246
#>
#>  Parameter(s):
#>
#> alpha
#> 3.062

### Example using fm=belhadj2015 and fm=cerioli

The construction of a fuzzy index using the membership function as Besma (2015) or Cerioli and Zani (1990) is obtained by specifying fm="belhadj2015" or fm="cerioli". Let us begin by fm="belhadj2015". For this mf the arguments z1, z2 and b need user’s specification. The value z that correspond to the flex points of the mf or to the point where the two mf touch together is calculated by the function.

The parameter b $$(>=1)$$ rules the shape of the membership functions (set b=1 for linearity)

z1 = 20000; z2 = 70000; b = 2
belhadj = fm_construct(predicate = eusilc$eq_income, weight = eusilc$DB090, fm = "belhadj2015", z1 = z1, z2 = z2, b = b) 
#> Fuzzy monetary results:
#>
#>  Summary of belhadj2015 membership function:
#>
#>  Quantiles:
#>
#>  0%   20% 40% 60% 80% 100%
#>   0 0.991   1   1   1    1
#>
#>  Estimate(s):
#>
#> [1] 0.939
#>
#>  Parameter(s):
#>
#>       z1       z2        z        b
#> 20000.00 70000.00 47547.17     2.00

Using fm="cerioli". Again we have to set the values of z1, z2 as follows:

z1 = 10000; z2 = 70000
cerioli = fm_construct(predicate = eusilc$eq_income, weight = eusilc$DB090, fm = "cerioli", z1 = z1, z2 = z2) 
#> Fuzzy monetary results:
#>
#>  Summary of cerioli membership function:
#>
#>  Quantiles:
#>
#>  0%   20%   40% 60% 80% 100%
#>   0 0.788 0.935   1   1    1
#>
#>  Estimate(s):
#>
#> [1] 0.895
#>
#>  Parameter(s):
#>
#>    z1    z2
#> 10000 70000

### Example using fm=chakravarty and fm=belhadj2011

Chakravarty (2006) fuzzy index is obtained setting fm = "chakravarty". The argument z needs user’s specification as follows:

z = 60000
chakravarty = fm_construct(predicate = eusilc$eq_income, weight = eusilc$DB090, fm = "chakravarty", z = z)
#> Fuzzy monetary results:
#>
#>  Summary of chakravarty membership function:
#>
#>  Quantiles:
#>
#>  0%   20%   40%  60%   80% 100%
#>   0 0.622 0.768 0.84 0.903    1
#>
#>  Estimate(s):
#>
#> [1] 0.761
#>
#>  Parameter(s):
#>
#>     z
#> 60000

again is is possible to specify the breakdown argument to obtain estimates at sub-domains.

## Fuzzy supplementary

The package include also a multidimensional fuzzy poverty index known as Fuzzy Supplementary (FS) index (see Betti and Verma (2008) and Betti, Gagliardi, and Verma (2018)). This index is defined with multiple steps. The package has an ad-hoc function for each step (excluding the third one). The steps are:

• Step 1 - Identification

• Step 2 - Transformation

• Step 3 - Factor analysis to identify dimensions of poverty

• Step 4 - Calculation of weights

• Step 5 - Calculation of scores in dimensions

• Step 6 - Calculation of the $$\alpha$$ parameter

• Step 7 - Construction of the FS measure for each dimension

### Step 1 - Identification

This step is pretty simple. The user has to select the columns of the data that correspond to the items that he/she has decided to keep in the analysis.

Step 1 is done with the following selection

# eusilc = na.omit(eusilc)
step1 = eusilc[,4:23]

If the data in the dataset are not “ordered” in the right way, i.e. the highest values represents the highest deprivation it is possible to invert them using the following function:

#Create a dataframe in which the variable X is not ordered in the right way:
data=data.frame("X"=rep(c(1,2,3,4),20), "Y"=rep(c(7,8,9,1),20))

#Crete vec_order a vector of length n with TRUE or FALSE. True if the order of the variable is to be inverted, False otherwise

vec_order=c(TRUE,FALSE)

#>   X Y
#> 1 4 7
#> 2 3 8
#> 3 2 9
#> 4 1 1
#> 5 4 7
#> 6 3 8

### Step 2 - Transformation

In this step the items are mapped from their original space to the $$[0,1]$$ interval using the function fs_transform (see Betti, Gagliardi, and Verma (2018) and Betti and Verma (2008)). For each item, a positive score $$s_{ij}$$ is determined as follows

$s_{ij}= 1- d_{ij} = 1-\frac{1-F(c_{ij})}{1-F(1)},\quad i=1,\dots,n \quad \text{and}\quad j=1,\dots,k$

where $$c_{ij}$$ is the value of the category of the $$j$$-th item for the $$i$$-th household and $$F(c_{ij})$$ is the value of the $$j$$-th item cumulative function for the $$i$$-th household. This step is done as follows using the function fs_transform:

step2 = fs_transform(step1, weight = eusilc$DB090, ID = eusilc$ID); class(step2)
#> [1] "FuzzySupplementary"
summary(step2$step2) #> ID HS040 HS050 HS060 #> Min. : 1.0 Min. :0.000 Min. :0.000 Min. :0.000 #> 1st Qu.:125.8 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:0.000 #> Median :250.5 Median :1.000 Median :1.000 Median :1.000 #> Mean :250.5 Mean :0.608 Mean :0.976 Mean :0.538 #> 3rd Qu.:375.2 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 #> Max. :500.0 Max. :1.000 Max. :1.000 Max. :1.000 #> HS070 HS080 HS090 HS100 #> Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 #> 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:1.0000 #> Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000 #> Mean :0.9929 Mean :0.9874 Mean :0.7279 Mean :0.9758 #> 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 #> Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000 #> HS110 HS120 HS160 HS170 #> Min. :0.0000 Min. :0.0000 Min. :0.00 Min. :0.000 #> 1st Qu.:1.0000 1st Qu.:0.1201 1st Qu.:0.00 1st Qu.:0.000 #> Median :1.0000 Median :0.3791 Median :0.00 Median :0.000 #> Mean :0.8274 Mean :0.4420 Mean :0.05 Mean :0.122 #> 3rd Qu.:1.0000 3rd Qu.:0.7960 3rd Qu.:0.00 3rd Qu.:0.000 #> Max. :1.0000 Max. :1.0000 Max. :1.00 Max. :1.000 #> HS180 HS190 HH010 HH020 #> Min. :0.000 Min. :0.000 Min. :0.00000 Min. :0.0000 #> 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.08545 1st Qu.:0.6244 #> Median :0.000 Median :0.000 Median :0.08545 Median :1.0000 #> Mean :0.058 Mean :0.086 Mean :0.45947 Mean :0.8080 #> 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:1.00000 3rd Qu.:1.0000 #> Max. :1.000 Max. :1.000 Max. :1.00000 Max. :1.0000 #> HH040 HH050 HH081 HH091 #> Min. :0.000 Min. :0.000 Min. :0.0000 Min. :0.00 #> 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:1.0000 1st Qu.:1.00 #> Median :0.000 Median :1.000 Median :1.0000 Median :1.00 #> Mean :0.128 Mean :0.954 Mean :0.9865 Mean :0.99 #> 3rd Qu.:0.000 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.00 #> Max. :1.000 Max. :1.000 Max. :1.0000 Max. :1.00 #> HX040 #> Min. :0.0000 #> 1st Qu.:0.1537 #> Median :0.5762 #> Mean :0.5511 #> 3rd Qu.:1.0000 #> Max. :1.0000 # step2.1 = fs_transform(step1, weight = eusilc$DB090, ID = eusilc$ID, depr.score = "d") which outputs knitr::kable(head(step2$step2), digits = 3, align = "c", caption = "Transformed items")
Transformed items
ID HS040 HS050 HS060 HS070 HS080 HS090 HS100 HS110 HS120 HS160 HS170 HS180 HS190 HH010 HH020 HH040 HH050 HH081 HH091 HX040
1 0 1 0 1 1 1.000 1 1 1.000 0 0 0 0 1.000 1.000 0 1 1 1 0.576
2 0 0 0 1 1 0.776 1 1 0.796 0 0 0 0 0.085 0.080 0 0 1 1 0.154
3 0 1 1 1 1 1.000 1 1 0.796 0 0 0 0 0.085 0.080 1 1 1 1 0.154
4 1 1 1 1 1 0.000 0 0 0.120 0 1 0 1 0.005 0.624 0 1 1 1 1.000
5 1 1 0 1 1 1.000 1 1 0.379 0 0 0 0 0.005 0.624 0 1 1 1 1.000
6 0 1 0 1 1 1.000 1 1 0.379 0 0 0 0 0.085 0.080 0 1 1 1 0.040

### Step 3 - Factor Analysis

This fuzzy supplementary measure use factor analysis to undercover latent dimension in the data. There are multiple approaches to get factor analysis in R which we do not cover in this vignette, however the user can check for example the lavaan package. Anyways, factor analysis is not mandatory and the user may wish to undertake a different approach to undercover a latent structure in the data. Indeed, it is possible to skip factor analysis or to use a personal assignment of columns in dimensions.

Regardless of the chosen method, to go trough Step 3 the user has to specify a numeric vector of the same length of the number of items selected in Step 1 that assigns each column to a given dimension.

dimensions = c(1,1,1,1,2,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5)

### Steps 4 and 5 - Calculation of weights and measures within dimensions

In this step, the weights to be assigned to each item, belonging to a given dimension $$h$$, are determined separately within each dimension. Such weighting procedure takes into account of two different aspects: the dispersion of the deprivation indicator and its correlation with other deprivation indicators in the given dimension. The weight of item $$j$$ belonging to dimension $$h$$ is taken as

$\omega_{hj}=\omega^a_{hj}\times \omega^b_{hj}, \quad h=1,\dots,H \quad \text{and}\quad j=1,\dots,k_h$

$$\omega_{hj}^a$$ is taken as proportional to the coefficient of variation of the complement to one of the positive score for the variable concerned

$\omega^a_{hj}\propto \frac{\sigma_{s_{hj}}}{1-\bar{s}_{hj}}$

where $$\sigma_{s_{hj}}$$ is the standard deviation of the deprivation score $$s$$ for item $$j$$ in dimension $$h$$ and $$\bar s_{hj}$$ its sample mean. The second factor is

$\omega^b_{hj}\propto\Biggl(\frac{1}{1+\sum_{j=1}^{k_h}\rho_{e_{hjhj^{\ast}}}|\rho_{e_{hjhj^{\ast}}}<r^\ast_{e_{hj}}}\Biggr)\times \Biggl(\frac{1}{1+\sum_{j=1}^{k_h}\rho_{e_{hjhj^{\ast}}}|\rho_{e_{hjhj^{\ast}}}\ge r^\ast_{e_{hj}}}\Biggr)$

where $$\rho_{e_{hjhj^{\ast}}}$$ is the kendall’s correlation coefficient between deprivation indicators corresponding to items j and $$j^{\ast}$$ in the $$h$$-dimension and $$r^\ast_{e_{hj}}$$ is a critical value. Aggregation over a group of items in a particular dimension is given by a weighted mean taken over the items in that dimension $$s_{hi}=\sum w_{hj} s_{hj,i}/w_{hj}$$, where $$w_{hj}$$ is the sampling weight of the $$j$$-th deprivation item in the $$h$$-th dimension. An overall score for the $$i$$-th individual is calculated as the un-weighted mean:

$s_i=\frac{\sum_h s_{hi}}{H}$ Those two steps are implemented in the package with the function fs_weight. It is necessary to define a value $$\rho$$ which is a critical value to be used for calculation of weights in the Kendall correlation matrix. If NULL, i.e. not defined, rho is set equal to the point of largest gap between the ordered set of correlation values encountered (see Betti and Verma, 2008).

#> [1] "FuzzySupplementary"
#> # A tibble: 20 × 5
#>    Dimension Item     w_a   w_b      w
#>        <dbl> <chr>  <dbl> <dbl>  <dbl>
#>  1         1 HS040 0.804  0.483 0.388
#>  2         1 HS050 0.157  0.688 0.108
#>  3         1 HS060 0.928  0.508 0.471
#>  4         1 HS070 0.0807 0.504 0.0407
#>  5         2 HS080 0.111  0.611 0.0677
#>  6         2 HS090 0.597  0.586 0.350
#>  7         2 HS100 0.157  0.478 0.0751
#>  8         2 HS110 0.419  0.578 0.242
#>  9         2 HS120 0.759  0.883 0.670
#> 10         3 HS160 4.36   0.807 3.52
#> 11         3 HS170 2.69   0.630 1.69
#> 12         3 HS180 4.03   0.686 2.77
#> 13         3 HS190 3.26   1.32  4.29
#> 14         4 HH010 0.986  0.929 0.916
#> 15         4 HH020 0.427  0.900 0.384
#> 16         4 HH040 2.61   0.933 2.44
#> 17         4 HH050 0.220  0.980 0.215
#> 18         5 HH081 0.105  0.553 0.0583
#> 19         5 HH091 0.101  0.590 0.0593
#> 20         5 HX040 0.624  0.903 0.563

The output is a longitudinal data frame that contains the weights $$w_a, w_b, w = w_a\times w_b$$ , the deprivation score $$s_{hi}$$ for unit $$i$$ and dimension $$j$$, and the overall score $$s_i$$ (the average over dimensions).

knitr::kable(head(steps4_5$steps4_5), digits = 4, caption = "Results from Steps 4 and 5.") Results from Steps 4 and 5. ID Item s Dimension w_a w_b w s_hi s_i 1 HS040 0 1 0.8038 0.4828 0.3881 0.1476 0.4536 2 HS040 0 1 0.8038 0.4828 0.3881 0.0404 0.2703 3 HS040 0 1 0.8038 0.4828 0.3881 0.6149 0.5334 4 HS040 1 1 0.8038 0.4828 0.3881 1.0000 0.4972 5 HS040 1 1 0.8038 0.4828 0.3881 0.5327 0.4558 6 HS040 0 1 0.8038 0.4828 0.3881 0.1476 0.2527 ### Step 6 - Calculation of the parameter $$\alpha$$ This step is equivalent to that discussed in the Fuzzy Monetary section when fm="verma", in-fact the mf is defined as: $\mu_i=(1-F_S(s_i)^{\alpha-1}(1-L_S(s_i)).$ The function fs_equate is used to find the value of $$\alpha$$ such that the expected value of the mf equals the HCR. alpha = fs_equate(steps4_5 = steps4_5, weight = eusilc$DB090, HCR = hcr, interval = c(1,10))
#> trying with alpha:  1  Expected Value:  0.5559
#> trying with alpha:  10  Expected Value:  0.0926
#> trying with alpha:  7.0204  Expected Value:  0.1285
#> trying with alpha:  4.0102  Expected Value:  0.2098
#> trying with alpha:  2.951  Expected Value:  0.2691
#> trying with alpha:  3.364  Expected Value:  0.2424
#> trying with alpha:  3.3089  Expected Value:  0.2457
#> trying with alpha:  3.3038  Expected Value:  0.246
#> trying with alpha:  3.3039  Expected Value:  0.246
#> trying with alpha:  3.3038  Expected Value:  0.246
#> Done.

(alternatively a user’s defined specification of the alpha argument can be used as well.)

### Step 7 - Construction of the Fuzzy Supplementary measure.

The parameter $$\alpha$$ estimated is used to calculate the fuzzy supplementary mf for each dimension of deprivation separately as follows. The FS indicator for the $$h-th$$ deprivation dimension for the $$i-th$$ individual is defined as combination of the $$(1-F_{(S),hi})$$ indicator and the $$(1-L_{(S),hi})$$ indicator.

$\begin{split} \mu_{hi}&=\biggl(1-F_{S_{h}}(s_{hi})\Biggr)^{\alpha-1}\biggl(1-L_{S_h}(s_{hi})\Biggr)=\\ &=\Biggl[\frac{\sum_{\gamma=i+1}w_{h\gamma}|s_{h\gamma}>s_{hi}}{\sum_{\gamma\ge 2}w_{h\gamma}|s_{h\gamma}>s_{h1}}\Biggr]^{\alpha-1}\Biggl[\frac{\sum_{\gamma=i+1}w_{h\gamma}s_{h\gamma}|s_{h\gamma}>s_{hi}}{\sum_{\gamma\ge 2}w_{h\gamma}s_{h\gamma}|s_{h\gamma}>s_{h1}}\Biggr] \end{split}$ The function fs_construct is used to compute the FS for each dimension and overall as follows:

#> Fuzzy supplementary results:
#>
#>  Summary of the membership functions:
#>
#>  Quantiles:
#>
#>         0%    25%    50%    75% 100%
#> FS1      0 0.0000 0.1028 0.3146    1
#> FS2      0 0.0234 0.1761 0.4886    1
#> FS3      0 1.0000 1.0000 1.0000    1
#> FS4      0 0.0049 0.1082 0.1082    1
#> FS5      0 0.0000 0.0113 0.3381    1
#> Overall  0 0.0074 0.0975 0.3643    1
#>
#>  Estimate(s):
#>
#>     FS1     FS2     FS3     FS4     FS5 Overall
#>   0.161   0.224   0.829   0.200   0.191   0.246
#>
#>  Parameter(s):
#>
#>  Alpha:
#>
#> [1] 3.304
#>          FS1        FS2       FS3        FS4        FS5    Overall
#> a 0.16017778 0.15721420 0.7504151 0.11843430 0.13690382 0.17803697
#> b 0.17203766 0.13353783 0.8603701 0.18226841 0.35306548 0.16150876
#> c 0.17790861 0.20949808 0.8747891 0.23864519 0.22568769 0.30225670
#> d 0.18430484 0.38429991 0.8634134 0.17047030 0.06218540 0.24525107
#> e 0.13157099 0.21602939 0.9432241 0.35550492 0.30772389 0.45379840
#> f 0.11635064 0.25468798 0.7003120 0.18457563 0.15552302 0.18651992
#> g 0.19789906 0.19517219 0.7868058 0.16063463 0.08637581 0.27243247
#> h 0.10378979 0.19398696 0.8526735 0.21615485 0.22137989 0.18955852
#> i 0.19944239 0.10733043 0.9561498 0.16923227 0.29106818 0.21228732
#> j 0.10950019 0.22043135 0.8774224 0.05680170 0.18878432 0.19766477
#> k 0.20243807 0.07183648 0.9791507 0.09036923 0.27431162 0.32986760
#> l 0.18773841 0.23105579 0.9185245 0.15254579 0.12587426 0.16756316
#> m 0.19459905 0.08661289 0.8812583 0.19715665 0.19712955 0.16909045
#> n 0.08403138 0.23751028 0.8101947 0.24143510 0.05576150 0.08021327
#> o 0.10717430 0.15153461 0.6907420 0.07810733 0.12316342 0.09665732
#> p 0.12462294 0.26074356 0.7678514 0.28953421 0.29440486 0.21144141
#> q 0.10731824 0.28134009 0.8083268 0.16137948 0.26594294 0.22523560
#> r 0.13292525 0.13075056 0.9532498 0.36621517 0.41196933 0.38421113
#> s 0.26416295 0.28100851 0.9387913 0.26507953 0.21010476 0.41753517
#> t 0.16740211 0.41950301 0.8725724 0.11691019 0.04229702 0.35450250
#> u 0.22746666 0.22160734 0.6666813 0.11469800 0.21659332 0.22168511
#> v 0.17997018 0.20148115 0.7284268 0.18935129 0.22035156 0.24724376
#> w 0.19114979 0.24834051 0.7252039 0.37794904 0.13056837 0.24049684
#> x 0.15863892 0.23342891 0.8680189 0.15527000 0.14885089 0.24134930
#> y 0.11294887 0.29472989 0.8436033 0.14557032 0.09870329 0.25374784
#> z 0.21039040 0.30499191 0.7309410 0.25553623 0.20458509 0.44289583

The output of the fs_construct function is a list containing:

• membership a list containing the FS measures for each statistical unit in the sample. Results for each dimension can be obtained by

• estimate the average of the membership function for each dimension

FS$estimate #> FS1 FS2 FS3 FS4 FS5 Overall #> a 0.16017778 0.15721420 0.7504151 0.11843430 0.13690382 0.17803697 #> b 0.17203766 0.13353783 0.8603701 0.18226841 0.35306548 0.16150876 #> c 0.17790861 0.20949808 0.8747891 0.23864519 0.22568769 0.30225670 #> d 0.18430484 0.38429991 0.8634134 0.17047030 0.06218540 0.24525107 #> e 0.13157099 0.21602939 0.9432241 0.35550492 0.30772389 0.45379840 #> f 0.11635064 0.25468798 0.7003120 0.18457563 0.15552302 0.18651992 #> g 0.19789906 0.19517219 0.7868058 0.16063463 0.08637581 0.27243247 #> h 0.10378979 0.19398696 0.8526735 0.21615485 0.22137989 0.18955852 #> i 0.19944239 0.10733043 0.9561498 0.16923227 0.29106818 0.21228732 #> j 0.10950019 0.22043135 0.8774224 0.05680170 0.18878432 0.19766477 #> k 0.20243807 0.07183648 0.9791507 0.09036923 0.27431162 0.32986760 #> l 0.18773841 0.23105579 0.9185245 0.15254579 0.12587426 0.16756316 #> m 0.19459905 0.08661289 0.8812583 0.19715665 0.19712955 0.16909045 #> n 0.08403138 0.23751028 0.8101947 0.24143510 0.05576150 0.08021327 #> o 0.10717430 0.15153461 0.6907420 0.07810733 0.12316342 0.09665732 #> p 0.12462294 0.26074356 0.7678514 0.28953421 0.29440486 0.21144141 #> q 0.10731824 0.28134009 0.8083268 0.16137948 0.26594294 0.22523560 #> r 0.13292525 0.13075056 0.9532498 0.36621517 0.41196933 0.38421113 #> s 0.26416295 0.28100851 0.9387913 0.26507953 0.21010476 0.41753517 #> t 0.16740211 0.41950301 0.8725724 0.11691019 0.04229702 0.35450250 #> u 0.22746666 0.22160734 0.6666813 0.11469800 0.21659332 0.22168511 #> v 0.17997018 0.20148115 0.7284268 0.18935129 0.22035156 0.24724376 #> w 0.19114979 0.24834051 0.7252039 0.37794904 0.13056837 0.24049684 #> x 0.15863892 0.23342891 0.8680189 0.15527000 0.14885089 0.24134930 #> y 0.11294887 0.29472989 0.8436033 0.14557032 0.09870329 0.25374784 #> z 0.21039040 0.30499191 0.7309410 0.25553623 0.20458509 0.44289583 • alpha the parameter $$\alpha$$ estimated from data. Again, it is possible to obtain results for sub-domains by specifying the breakdown argument FS1 FS2 FS3 FS4 FS5 Overall a 0.1602 0.1572 0.7504 0.1184 0.1369 0.1780 b 0.1720 0.1335 0.8604 0.1823 0.3531 0.1615 c 0.1779 0.2095 0.8748 0.2386 0.2257 0.3023 d 0.1843 0.3843 0.8634 0.1705 0.0622 0.2453 e 0.1316 0.2160 0.9432 0.3555 0.3077 0.4538 f 0.1164 0.2547 0.7003 0.1846 0.1555 0.1865 g 0.1979 0.1952 0.7868 0.1606 0.0864 0.2724 h 0.1038 0.1940 0.8527 0.2162 0.2214 0.1896 i 0.1994 0.1073 0.9561 0.1692 0.2911 0.2123 j 0.1095 0.2204 0.8774 0.0568 0.1888 0.1977 k 0.2024 0.0718 0.9792 0.0904 0.2743 0.3299 l 0.1877 0.2311 0.9185 0.1525 0.1259 0.1676 m 0.1946 0.0866 0.8813 0.1972 0.1971 0.1691 n 0.0840 0.2375 0.8102 0.2414 0.0558 0.0802 o 0.1072 0.1515 0.6907 0.0781 0.1232 0.0967 p 0.1246 0.2607 0.7679 0.2895 0.2944 0.2114 q 0.1073 0.2813 0.8083 0.1614 0.2659 0.2252 r 0.1329 0.1308 0.9532 0.3662 0.4120 0.3842 s 0.2642 0.2810 0.9388 0.2651 0.2101 0.4175 t 0.1674 0.4195 0.8726 0.1169 0.0423 0.3545 u 0.2275 0.2216 0.6667 0.1147 0.2166 0.2217 v 0.1800 0.2015 0.7284 0.1894 0.2204 0.2472 w 0.1911 0.2483 0.7252 0.3779 0.1306 0.2405 x 0.1586 0.2334 0.8680 0.1553 0.1489 0.2413 y 0.1129 0.2947 0.8436 0.1456 0.0987 0.2537 z 0.2104 0.3050 0.7309 0.2555 0.2046 0.4429 The package contains also a function named fs_construct_all which constructs the fuzzy supplementary poverty measure based without step-by-step functions. ## Variance Estimation The variance of each Fuzzy Monetary measure can be estimated either via Bootstrap or Jackknife Repeated Replications. We recommend the former each time the user has no knowledge of the sampling design, while we recommend the Jackknife when there is full information on the design and of the PSUs (see Betti, Gagliardi, and Verma (2018)). In the following, for the unidimensional indices, we report only the examples linked with fm=verma. For the other specification of the mf the function is identical the only changes are linked with the parameters required by the mf (see fm_construct). ### Example using fm=verma In case of fm="verma", we recommend the user to use the value of alpha from obtained from the function fm_construct. It is possible to specify different values of the parameter (i.e. alpha=2). We do not recommend to leave the argument alpha=NULL for the computation of variance. alpha = fm_construct(predicate = eusilc$eq_income, weight = eusilc$DB090, ID = NULL, HCR = 0.12, interval = c(1,10), alpha = NULL)$alpha
boot.var = fm_var(predicate = eusilc$eq_income, weight = eusilc$DB090, fm = "verma",  type = "bootstrap", HCR = .12, alpha = alpha, verbose = F, R = 10)

# plot(boot.var)

fm_var(predicate = eusilc$eq_income, weight = eusilc$DB090, fm = "verma", type = "jackknife", HCR = .12, alpha = 9, stratum = eusilc$stratum, psu = eusilc$psu, verbose = F)
#> $variance #> [1] 5.324184e-06 #> #>$type
#> [1] "jackknife"
#>
#> attr(,"class")
#> [1] "FuzzyMonetary"

which gives the bootstrap estimate or the jackknife estimate.

If there are multiple sub-domains or sub-populations that need variance estimation, the user can specify the breakdown to the breakdown argument of the function fm_var. For example:

#> Variance of Fuzzy monetary results:
#>
#>  Type of estimator:
#>
#>  bootstrap
#>
#>  Estimate(s):
#>
#>        a        b        c        d        e        f        g        h
#> 0.005956 0.005558 0.007428 0.008145 0.004224 0.004601 0.011222 0.008221
#>        i        j        k        l        m        n        o        p
#> 0.003389 0.006326 0.007336 0.003591 0.008333 0.003932 0.007797 0.005855
#>        q        r        s        t        u        v        w        x
#> 0.014911 0.005027 0.004477 0.000405 0.006955 0.006844 0.006655 0.003209
#>        y        z
#> 0.008839 0.006192
#> Variance of Fuzzy monetary results:
#>
#>  Type of estimator:
#>
#>  jackknife
#>
#>  Estimate(s):
#>
#>        a        b        c        d        e        f        g        h
#> 0.006067 0.009761 0.009576 0.012358 0.013549 0.003032 0.014162 0.024544
#>        i        j        k        l        m        n        o        p
#> 0.005101 0.006252 0.014782 0.003836 0.016164 0.002989 0.008660 0.007709
#>        q        r        s        t        u        v        w        x
#> 0.022870 0.008810 0.007936 0.000672 0.008556 0.010271 0.007848 0.007644
#>        y        z
#> 0.009668 0.005607

### Fuzzy supplementary

variance = fs_var(data = eusilc[,4:23], weight = eusilc$DB090, ID = NULL, dimensions = dimensions, breakdown = NULL, HCR = 0.12, alpha = 2, rho = NULL, type = 'bootstrap', M = NULL, R = 2, verbose = F) summary(variance) The following uses the Jackknife fs_var(data = eusilc[,4:23], weight = eusilc$DB090, ID = NULL, dimensions = dimensions,
stratum = eusilc$stratum, psu = eusilc$psu, verbose = F, f = .01,
breakdown = eusilc\$db040, alpha = 3, rho = NULL, type = "jackknife")%>%summary()
Alkire, Sabina, José Manuel Roche, Paola Ballon, James Foster, Maria Emma Santos, and Suman Seth. 2015. Multidimensional Poverty Measurement and Analysis. Oxford University Press, USA.
Belhadj, Besma. 2011. “A New Fuzzy Unidimensional Poverty Index from an Information Theory Perspective.” Empirical Economics 40 (3): 687–704. https://doi.org/10.1007/s00181-010-0368-5.
Belhadj, Besma, and Mohamed Salah Matoussi. 2010. “Poverty in Tunisia: A Fuzzy Measurement Approach.” Swiss Journal of Economics and Statistics 146 (2): 431–50.
Besma, Belhadj. 2015. “Employment Measure in Developing Countries via Minimum Wage and Poverty New Fuzzy Approach.” Opsearch 52: 329–39.
Betti, Gianni, Francesca Gagliardi, and Vijay Verma. 2018. “Simplified Jackknife Variance Estimates for Fuzzy Measures of Multidimensional Poverty.” International Statistical Review 86 (1): 68–86.
Betti, Gianni, and Vijay Verma. 1999. “Measuring the Degree of Poverty in a Dynamic and Comparative Context: A Multi-Dimensional Approach Using Fuzzy Set Theory.” In Proceedings, Iccs-Vi, 11:289.
———. 2008. “Fuzzy Measures of the Incidence of Relative Poverty and Deprivation: A Multi-Dimensional Perspective.” Statistical Methods and Applications 17: 225–50.
Cerioli, Andrea, and Sergio Zani. 1990. “A Fuzzy Approach to the Measurement of Poverty.” In Income and Wealth Distribution, Inequality and Poverty, 272–84. Springer.
Chakravarty, Satya R. 2006. “An Axiomatic Approach to Multidimensional Poverty Measurement via Fuzzy Sets.” Poverty, Social Exclusion and Stochastic Dominance, 123–41.
Cheli, Bruno, and Achille Lemmi. 1995. “A’totally’fuzzy and Relative Approach to the Multidimensional Analysis of Poverty.”
Silber, Jacques. 2023. Research Handbook on Measuring Poverty and Deprivation. Edward Elgar Publishing, UK.
Zedini, Asma, and Besma Belhadj. 2015. “A New Approach to Unidimensional Poverty Analysis: Application to the Tunisian Case.” Review of Income and Wealth 61 (3): 465–76.