analysis.md

The Baltimore Sun analyzed data on child support cases in the public child support system (also known as IV-D cases) across Maryland during the 2018 federal fiscal year provided by the Maryland Department of Human Service in response to a public records request for a Feb. 26, 2020 story titled “xxx”.

Here are the key data elements reported in the story. Note the raw data is saved in the input/ folder and was pre-processed using the cleaning.Rmd script prior to analysis.

Child support arrears debt by ZIP code: - Several Baltimore city ZIP codes owe millions in back child support, including one West Baltimore ZIP code which owes $23 million in arrears. - In 10 city ZIP codes, where about 15,000 parents collectively owe more than $233 million.

Child support as welfare cost recovery: - Maryland has about 16,500 cases set up for welfare cost recovery, almost half of them in Baltimore. - Collectively in the state, non-custodial parents owe the government $156 million in back child support to repay welfare benefits, including $73 million in Baltimore.

License suspensions by Maryland’s child support agency: - Nearly 40,000 people had their driver’s license suspended by Maryland’s child support agency as of September 2018. More than a third of those drivers lived in Baltimore. - Some 1,900 professional and recreational licenses had been suspended by the state, including 750 in Baltimore, in September 2018. - Statewide, about 320 suspended professional licenses were for certified medication technicians. Another 150 were rideshare licenses. About 225 barbers and 120 certified nursing assistants had their occupational privileges blocked.

Child support debt among older parents: - Among noncustodial parents 62 or older, two-thirds had back payments in September 2018.

Load R libraries

library('tidyverse')
library('tidycensus')
library('sf')

Read in the pre-processed caseload data

See the pre-processing code at cleaning.Rmd for detail on how the data was cleaned prior to analysis.

caseload <- read_rds('output/caseload.rds') # statewide caseload
baci_caseload <- read_rds('output/baci_caseload.rds') # caseload for Baltimore city

Child support arrears debt by ZIP code

*Finding: Several Baltimore city ZIP codes owe millions in back child support, including one West Baltimore ZIP code which owes $23 million in arrears.

Finding: In 10 city ZIP codes, where about 15,000 parents collectively owe more than $233 million.*

Create a data frame, baci_arrears.zips, that groups the Baltimore city caseload data by ZIP code to get the arrears that area owed by ZIP code. Merge with baci_caseload.ncps.zips, which provides the number of non-custodial parents in each city ZIP code that owe arrears.

baci_caseload.ncps.zips <- baci_caseload %>% filter(arrears_owed_total_september_2018 > 0) %>%
  select(ncp_zip, ncp_id) %>% 
  distinct() %>% 
  group_by(ncp_zip) %>%
  summarise(number_of_ncps = n()) 
  
baci_arrears.zips <- 
baci_caseload %>% group_by(ncp_zip) %>% 
  summarise(arrears = sum(arrears_owed_total_september_2018, 
                          na.rm = T)) %>% 
  arrange(desc(arrears)) %>% 
  ungroup() %>%
  mutate(cumulative = cumsum(arrears)) %>% 
  merge(baci_caseload.ncps.zips) %>%
  arrange(desc(arrears)) %>% 
  mutate(cumulative_ncps = cumsum(number_of_ncps))

Filter baci_arrears.zips to see the 10 ZIP codes that owe the most arrears. As shown below, the arrears in these ZIP codes are 15+ million.

In West Baltimore ZIP code 21216, $23.4 million is owed in child support arrears.

baci_arrears.zips %>% head(10)

##    ncp_zip  arrears cumulative number_of_ncps cumulative_ncps
## 1    21215 33165588   33165588           2145            2145
## 2    21217 27896524   61062112           1706            3851
## 3    21213 26404097   87466208           1589            5440
## 4    21218 24143885  111610093           1509            6949
## 5    21229 23745388  135355481           1522            8471
## 6    21216 23414161  158769643           1504            9975
## 7    21223 20814035  179583678           1285           11260
## 8    21206 20239512  199823191           1464           12724
## 9    21207 18120026  217943216           1252           13976
## 10   21225 15561124  233504340           1036           15012

Use only state-owed arrears

baci_caseload.ncps.zips <- baci_caseload %>% filter(state_owed_arrears_total_september_2018 > 0) %>%
  select(ncp_zip, ncp_id) %>% 
  distinct() %>% 
  group_by(ncp_zip) %>%
  summarise(number_of_ncps = n()) 
  
baci_arrears.zips <- 
baci_caseload %>% group_by(ncp_zip) %>% 
  summarise(arrears = sum(state_owed_arrears_total_september_2018, 
                          na.rm = T)) %>% 
  arrange(desc(arrears)) %>% 
  ungroup() %>%
  mutate(cumulative = cumsum(arrears)) %>% 
  merge(baci_caseload.ncps.zips) %>%
  arrange(desc(arrears)) %>% 
  mutate(cumulative_ncps = cumsum(number_of_ncps))