Tract-Level Geocoding Analysis: Identifying

Communities With Low CalFresh Access

M. Akhtar Khan, PhD.Research Services Branch

Chief

Aynalem Adugna, Ph.D.Research Program

Specialist

Research Services BranchCalifornia Department of Social Services

August 20, 2014

Presentation Outline SNAP-CalFresh Access: Big picture

overview.

SNAP-CalFresh Access : As it is measured.

Geo-coding: Looking below county level.

Once below the county level, ability to measure CalFresh access at neighborhood and community levels.

New proposed methodology for identifying “true hot spots” for targeted outreach efforts.

Highlight measurement issues uncovered.

Application of adjusted access measure.

* CalFresh Program Overview Legislative Analyst’s Office, March 2014

California*

Average monthly households 1,890,1

29

Average monthly individuals 4,124,3

73

Average household size 2.2Average monthly benefit per household $333 Average monthly benefit per person $153

Household Characteristics— Income*

Eligible to Receive CalFresh

Income Below Certain Thresholds. • Gross income - 130 percent of federal poverty guidelines

(also known as the federal poverty level, or FPL). Example: $2,008/month for a household of three (2012).

• Net income - income of less than 100 percent of the FPL after certain deductions are applied. Example: $1,545/month for a household of three (2012).

Additional Eligibility Criteria for College Students

Ineligible to Receive CalFresh Citizenship/Immigration Status – the

largest group Drug Felony Convictions SSI/SSP Recipients Ineligible Due to “Cash-

Out.”

Source: CalFresh Program Overview Legislative Analyst’s Office, March 2014

=

Food Distribution Program on Indian Reservations; SSI is Supplemental Security Income

Measuring Program Access

By this measure: California is almost at the bottom in the Union. 3.9 million eligible Californians are not receiving

CalFresh. Questions that come to mind…?• Why is participation low in California?• How counties within the State fare?

• Is it unemployment rate at local level• Is it poverty level at local level?• Rural-Urban divide, or something else

• Any issues with the way program access is measured?

The Program Access Index (PAI): USDA/FNS

6

The search for answers:What sub-county-level geographies should be used to identify areas of low participation?

Regression analyses show the low predictive value of unemployment rates and poverty levels of counties in estimating calFresh access.

6 8 10 12 14 16 18 20 22 24 260

0.20.40.60.8

1

f(x) = 0.0113534671966928 x + 0.361448967437047R² = 0.147718986873891

County % in Poverty vs. % Receiving CalFresh (2011)

% in Poverty, 2011 % R

ece

ivin

g C

alF

resh

, 2

01

1

Distribution by proportions of non-English speakers points to:

o Language as an important factoro The possible role of immigration status

A measure that takes into account ineligibility for CalFresh due to citizenship status is necessary

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% -

0.20 0.40 0.60 0.80 1.00

f(x) = 1.56602477 x + 0.329909794R² = 0.171480819663686

County % Unemployed vs. % Receiving CalFresh (2011)

Unemployment Rate, 2011% R

eceiv

ing C

alF

resh,

2011

Preliminary - not for reproduction 7

So the Issue!

• Socio-economic indicators at the county level rarely show significant correlations with CalFresh access.

• Explanation to this phenomena may exist at below county level – neighborhoods and communities.

• The question – how do we get down to the neighborhood and community levels.

• Need local level data and tools…

Preliminary - not for reproduction 8

Geo-mapping Analytics Geocoding analytics allow us to

assemble a rich data set using a variety of resources.

Show differences in CalFresh access at below county levels – census tracts, zip codes, neighborhoods, etc. using geocoding analytics.

Highlight population subgroups having CalFresh access lower than expected based on poverty levels using geocoding analytics.

Preliminary - not for reproduction 9

  Gecoding is a technique that can help us:

I. map recipient level data, and

II. assess CalFresh reach at community levels.

Based on the poverty estimates for an area, this spatial analytical tool will enable us to identify places:

where potential CalFresh eligibles reside,

where CalFresh reach is low, and more importantly,

where more effective and targeted outreach strategies could be directed.

Geocoding

Why geocode?

Image source: http://jags-webdesign.com/home-clipart-image-stick-family-

123 Main Street (geocoded)

CalFresh recipient

Poverty level of census tract

Neighborhood characteristics

Languages spoken

Demographic characteristics

Environmental characteristics

Service delivery

Neighborhood organizations

Social/environmental activism

Outreach activities

Geocoding helps us gain a holistic view of the environments surrounding each CalFresh recipient address.

Over two hundred tract-level data elements are linked to each dot.

Example:

Total tract population.

% Below poverty level.

% Non-native. Number of

Hispanics. Number of families

with children under 18.

Number of Female-headed households.

% Speaking languages other than English.

EBT access.

Preliminary - not for reproduction 11

Fresno County CalFresh Distributions (July, 2013)

12

L.A. County: Number of CalFresh Recipients in Tracts with Reported Zero Poverty Levels

13

Monterey County: CalFresh Recipient Addresses and Distance from EBT Locations

(Zip Codes 93905 and 93906)

286 EBT locations7168 / 39167 locations (18 percent) within 0.1 mile18624 / 39167 locations (47 percent) within 0.2 mile

14

Where:FDPRI: Food Distribution Program on Indian ReservationsSSI: Supplemental Security Income Source: http://www.fns.usda.gov/sites/default/files/PAI2011.pdf

PRI=

Program Reach Index (PRI) *

Program Access Index (PAI)

* Geography-based or population-based

Preliminary - not for reproduction 15

Advantages of Using PRI Measure CalFresh access below

county levels.

Measure differences in access among population subgroups.

Use results to devise targeted CalFresh outreach activities.

Help uncover the limitations of PAI as measurement methodology.

Preliminary - not for reproduction 16

Application Example: LA County on the Map

Potential Eligible – Below 130 percent Poverty

vs.

CalFresh Access – PRI

Preliminary - not for reproduction 17

MEDIUM

HIGH

LOW

Preliminary - not for reproduction 18

MEDIUM

HIGH

HIGH

Preliminary - not for reproduction 19

L.A. County Tracts: Percent Speaking Languages other than English by Position Above or Below Median (Median = 58.3)

Preliminary - not for reproduction 20

L.A. County Tracts: Percent Below Poverty by Position Above or Below Median

(Median = 11.2)

21

9th

8th

6th

4th

3rd

2nd

1st 88.2-80.9

80.8-73.8

73.7-66.3

62.2-58.6

99.4-88.3

50.4-40.8

40.7-31.6

23.3-

0.0*

10th

7th

5th* Deciles width/ interval ( percent)

58.5-50.5

31.5-23.4

Population subgroups: LA County language deciles

Preliminary - not for reproduction 22

23

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10

5.1 5.57.4

10.612.8 13.0

16.0

18.8

22.4

27.0

L.A. County: Average Poverty Level Tract by Language (other than English) Deciles

Language deciles

Avera

ge p

overt

y

Application: County Level PRI, 2011High PRI Low PRI

Calaveras 0.90 Santa Barbara 0.38Del Norte 0.83 San Luis Obispo 0.37San Bernardino 0.77 Napa 0.37Fresno 0.75 Marin 0.35Tulare 0.73 Yolo 0.34Sacramento 0.71 Colusa 0.34Yuba 0.71 San Mateo 0.32Solano 0.71 Mono 0.22

1. The Program Reach Index – PRI Raises eligibility from 125 % FPL to the State’s poverty

threshold of 130%FPL

Applying PRI as New Measures of Access

Preliminary - not for reproduction 25

The Program Reach Index (PRI) is as useful an indicator of CalFresh access at the county level as the Program Access Index (PAI).

The ability to apply PRI at below county levels makes it much more valuable in accessing CalFresh reach at community and neighborhood levels.

The PRI can help target outreach activities through mapping techniques that highlight areas needing benefits the most.

HOWEVER, does it help us in identifying “true hot spots” where outreach efforts should be targeted???

Some observations so far!

Preliminary - not for reproduction 26

Nu

mb

er

of

pers

on

s r

eceiv

ing

C

alF

resh

Poverty level of tracts

Federal law

Languages other than

English

Visualphotos.com

????

2. Adjusted Program Reach Index – APRI

Keeps the above adjustment Removes the estimated number of

undocumented persons using data on child-only CalFresh households as proxy

We refer to this method as the Child-only method

Proposed New Measures of Access …contd.

The child-only method is an indirect method of estimating the number of persons ineligible to receive CalFresh due to citizenship status.

Child-only CalFresh households are households in which all participants are minors and all adults:

• receive SSI• are convicted felons

• undocumented immigrants

The Child-Only Method

Proposed New Measures of Access

.

Three important questions:

Q. 1 What proportions of child-only households are headed by parents/adults who are ineligible to receive CalFresh due to citizenship status?

Q. 2 How many undocumented adults live in a child-only household?

Q. 3 How many undocumented adults live in households where there are no children?

The Child-only method…contd.

30

County*

Proportion of child-only

households (June, 2013)

Amador 0.0360Bute 0.0365

Calaveras 0.0369Del Norte 0.0379Humbolt 0.0415Inyo 0.0507Lake 0.0521Lassen 0.0564Mariposa 0.0585Mendocino 0.0618Modoc 0.0689Nevada 0.0746Plumas 0.0837Shasta 0.0961Siskiyou 0.1005Tehama 0.1075Tuolumne 0.1108

Average 0.0653Median 0.0585

Nor

th a

nd Mou

ntain

Bay A

rea

Farm

Bel

tL.

A.

South

ern (

w/o

L.A

.)

0.7269

0.5452

0.7075

0.59 0.6237

PAI by Region*, 2010

Answer 1: 6 percent of child-only cases in every county are due to non-immigration causes such as or parents on SSI and that the remaining 94 percent are due to immigration status

*Alpine, Mono, Sierra and Trinity are excluded

Regions are per. Thomas MaCurdy, Mancuso and Margaret O’Brien-Strain, The Rise and Fall of C a l i f o r n i a ’s We l f a r e Caseload: Types and R e g i o n s , 1 9 80– 1 9 9 9, Public Policy Institute of California, PPIC, 2000.

31

Number of adults in child-only CalFresh households

• Answer 2: There are 1.77 undocumented adults in each child-only household.

• Answer 3: Number of adults in non-participating non-child-only households

For every 177undocumented adults residing in child-only households there are 124 undocumented persons residing in households without children.

Undocumented Households with Children

Number of households

(000s)

Adultsper

householdTotal

adults

2-parent 507 2 1,014

1-parent 155 1 155

Other 9 2 18

Total 671 1,187

Average 1.77

Undocumented Households without Children

Number of households (000s)

Personsper

householdTotal

persons

Married couple 118 2 236

Other families 29 3 87

Solo adult men 435 1 435

Solo adult women 137 1 137

Total 719 895

Average 1.24

32

Calculation Objective Result15,136 x 0.94 Estimate the number of households that are

child-only due to the parents’ citizenship status

14,228

14,228 x 1.77 Estimate the number of undocumented adults residing in child-only households

25,184

25,184 + (25,184 x (124/177)) Estimate the total number of undocumented persons (those residing in households with

children + those residing in households without children)

42,827

Validation

23.4 ÷ 15.07 Fresno’s poverty rate relative to the statewide average 1.55

1.55 x 32.5* Estimate the number of undocumented persons in Fresno that are CalFresh-poor (130% FPL or below) for

every 100 undocumented persons in the county

50.5

42,812 x (100/50.5) Estimate the total number of undocumented persons in Fresno County

84,858

*This is a minimum percentage for all counties (based on simulation)

Validating the Child-Only Method Fresno County, 2011 (15,136 Child-Only Households)

33

Validation: Ten largest child-only counties, 2011 Total Number of

Undocumented Persons

Child-Only Households

Undocumented (130% FPL)

Child-Only, 2011

PPIC (2011)*

Los Angeles 119,837

338,954

964,501 916,000

Orange 29,734

84,101

258,774 289,000

San Bernardino 21,870

61,858

179,320 150,000

San Diego 17,967

50,819

156,366 198,000

Riverside 17,706

50,081

154,095 146,000

Fresno 15,136

42,812

84,858 49,000

Santa Clara 13,468

38,094

117,211 180,000

Kern 11,845

33,503

72,614 46,000

Sacramento 11,260

31,848

97,995 65,000

Alameda 10,808

30,570

94,062 124,000

Statewide 356,627 1,008,705

2,864,504 2,874,500

34

The Child-Only Method vs. PPIC’s method

The Child-Only Method (poverty-based) appears to give a better estimate of undocumented persons than PPIC’s:

o for counties with higher poverty level than the statewide average

o in general, for counties where agriculture is the predominant economy

The PPIC method (tax-return-based) appears to give a better estimate of undocumented persons than the Child-only method:

o for counties with lower poverty level than the statewide average

o for counties with predominantly non-agricultural economies

35

SUMMARY: Statewide PAI Gain Using Different Estimates

Statewide Program AccessUnder Six Scenarios

Numerator 2011

Denominator 2011

%Receivin

g

%age Point GAIN

FNS FNS (2011) 3,760,866 7,684,310 49

--

Adjusted Program Reach Index - APRI

Child-Only

0.94 x 356,627 3,760,866 7,349.091 51

2

Pew 

0.94 x 356,627 x 1.78 3,760,866 7,087,60

2 53

4

0.94 x 356,627 x 1.78 x 1.94

3,760,866 6,526,696 58

9

     Urban Institute

0.94 x 356,627 x 1.77 3,760,866 7,090,954 53

4

0.94 x 356,627 x 1.77 x 1.70

3,760,866 6,675,605 56

7

36

L.A. County (June, 2013) True + False “hot-spots”

LA County: Program Reach Index - PRI, June 2013

37

True “hot-spots”

LA County: Adjusted Program Reach Index -

APRI, June 2013

38

Child-o

nly HHs

Receiving

CalFr

esh

0-49

% F

PL

50% -

99% F

PL

100%

-124

% F

PL

SSIS

SP

Poten

tial C

F (o

n Med

i-Cal)

English

spea

kers

Non

-Eng

lish

17616178082

92731

911516977559

L.A. Northwest (58 Zip codes)

Child-o

nly HHs

Receiving

CalFr

esh

0-49

% F

PL

50% -

99% F

PL

100%

-124

% F

PL

SSIS

SP

Poten

tial C

F (o

n Med

i-Cal)

English

spea

kers

Non-E

nglis

h

541797912

37530

360254342963

L.A. Northeast (30 Zip codes)

Child

-onl

y HHs

Recei

ving

Cal

Fres

h

0-49

% F

PL

50%

- 99

% F

PL

100%

-124

% F

PL

SSIS

SP

Pote

ntia

l CF

(on

Med

i-Cal

)

Engl

ish

spea

kers

Non

-Eng

lish

125129528 15362

763154

350498

L.A. Southwest (47 Zip codes)

Child

-onl

y HHs

Recei

ving

Cal

Fres

h

0-49

% F

PL

50%

- 99

% F

PL

100%

-124

% F

PL

SSIS

SP

Pote

ntia

l CF

(on

Med

i-Cal

)

Engl

ish

spea

kers

Non

-Eng

lish

86656

842578

363803

2003667

3566000

L.A. Southeast (152 Zip codes)

PRI = 0.64APRI = 0.72

PRI = 0.47APRI = 0.54

PRI = 0.21APRI = 0.22

PRI = 0.53APRI = 0.63

L.A. County Regions

39

Kern

40

41

LIMITATIONS OF THE CHILD-ONLY METHODNumerator and Denominator Data Issues

The reliability of APRI is affected by percentage of unmatched addresses due to P. O. Box addresses.

The denominator becomes negative for tracts with 0 eligibles and where the eligibles based on 130% poverty (formula below) is lower than the SSI component and/or the child-only households component.

Due to small sample sizes and large margins of error, the ACS shows many tracts with fewer eligible persons than the number of persons receiving CalFresh; this leads to APRI greater than1.

A single address is used to provide CalFresh benefits to hundreds or thousands of beneficiaries (over 6,100 recipients in one L.A. County address and 5,100 recipients in one Fresno County address) making it difficult to interpret APRI maps for surrounding areas.

APRI =

42

The reliability of APRI increaseswith increasing geographic scale

Tract Zip code CalFresh region

County

Denominator margin of error

Numerator includes recipients from outside of

geographic unit

LIMITATIONS….Summary

43

Conclusions Geocoding enables us to analyze CalFresh data in the

context of the environments in which recipients live.

Estimates of undocumented persons from the Child-Only Method are broadly consistent with county-level estimates from PPIC and state-level estimates from DHS and PPIC.

o Any discrepancies most likely reflect differences in methodological focus - persons receiving public assistance (Child-Only) vs. persons receiving taxable income (PPIC).

The Child-Only Method can be used with confidence at county levels and for regions within a county.

o In some instances, Zip code or tract-level analysis may be feasible.

It appears that in places where non-English speakers are a minority (example: LA Southwest) the participation rate is significantly lower (APRI = 0.22) than in places where they are a majority.

THANK YOU