11

Micro-level and Macro-level Effects of Micro-level and Macro-level Effects of Household Poverty in theHousehold Poverty in the

Texas Borderland & Texas Borderland & Lower Mississippi Delta:Lower Mississippi Delta:

United States, 2006United States, 2006

Dudley L. Poston, Jr.Dudley L. Poston, Jr.d-poston@tamu.edud-poston@tamu.edu

Department of SociologyDepartment of SociologyTexas A&M UniversityTexas A&M University

College Station, Texas, USACollege Station, Texas, USA

Presented at Presented at ““Census Microdata: Findings and Futures”Census Microdata: Findings and Futures”

Conference,Conference,University of ManchesterUniversity of Manchester

1-3 September 20081-3 September 2008

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IntroductionIntroduction The Texas Borderland and the Lower The Texas Borderland and the Lower

Mississippi Delta are the two poorest Mississippi Delta are the two poorest regions in the United States.regions in the United States.

Both regions are predominately rural.Both regions are predominately rural.

The Borderland is characterized by a The Borderland is characterized by a high concentration of Latinos.high concentration of Latinos.

The Delta is characterized by a high The Delta is characterized by a high concentration of blacks.concentration of blacks.

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44

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Table 1Percent Poor for the United States, the Delta, and the Borderland

Total Population White Black Latino

United States 12.4 9.1 24.9 22.6

Delta 22.6 12.9 37.8 --

Borderland 29.5 10.0 17.2 34.0

Source: 2000 Census Summary Files

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How is “Poverty” measured?How is “Poverty” measured?

The next 2 slides show an example The next 2 slides show an example calculation, and the minimum poverty calculation, and the minimum poverty thresholds (in US$ in 2006), according to thresholds (in US$ in 2006), according to the size of household, and the number of the size of household, and the number of children in the household. children in the household.

Each household is assigned a poverty Each household is assigned a poverty value of between 1 and 501 depending on value of between 1 and 501 depending on its household income. If the hh income its household income. If the hh income equals the threshold value, the hh equals the threshold value, the hh receives a poverty value of 100.receives a poverty value of 100.

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•Household A has five members: •two children, their mother, father, and great-aunt.

•Their threshold was $24,662 dollars in 2006. (See poverty thresholds for 2006)

•Suppose the members' incomes in 2006 were: Mother: $10,000

Father: 5,000

Great-aunt: 10,000

First child: 0

Second child: 0

Total family income:

$25,000

THE CALCULATION OF POVERTY VALUE:Compare total family income with the family's threshold.      Income / Threshold = $25,000 / $24,662 = 1.01 * 100 = 101

EXAMPLE OF CALCULATION OF POVERTY VALUE FOR A HOUSEHOLD

Poverty Thresholds for 2006 by Size of Family and # of Related Children <18 # of Related children under 18 Size of family unit Weighted Eight average None One Two Three Four Five Six Seven or more thresholds

One person 10,294

Under 65 years 10,488

10,488

65 years and over 9,669

9,669

Two people............................

13,167

Householder under 65 13,569

13,500

13,896

Householder 65 years 12,201

12,186

13,843

Three people........................ 16,079

15,769

16,227

16,242

Four people.......................... 20,614

20,794

21,134

20,444

20,516

Five people.......................... 24,382

25,076

25,441

24,662

24,059

23,691

Six people............................ 27,560

28,842

28,957

28,360

27,788

26,938 26,434

Seven people....................... 31,205

33,187

33,394

32,680

32,182

31,254 30,172 28,985

Eight people......................... 34,774

37,117

37,444

36,770

36,180

35,342 34,278 33,171 32,890

Nine people + 41,499

44,649

44,865

44,269

43,768

42,945 41,813 40,790 40,536 38,975

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Three Poverty Measures at Three Poverty Measures at Level-1:Level-1:

The Dependent VariablesThe Dependent Variables 1. Whether household is in deep poverty 1. Whether household is in deep poverty

(poverty score of 50 or less) (yes = 1)(poverty score of 50 or less) (yes = 1)

2. Whether household is in poverty 2. Whether household is in poverty (poverty score of 100 or less) (yes = 1)(poverty score of 100 or less) (yes = 1)

3. Whether household is near or in 3. Whether household is near or in poverty (poverty score of 150 or less) poverty (poverty score of 150 or less) (yes = 1)(yes = 1)

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DataDataThe multi-level analyses reported are based on micro-The multi-level analyses reported are based on micro-

data (level-1) of household heads living in data (level-1) of household heads living in households in the 10 PUMAs in the Texas households in the 10 PUMAs in the Texas Borderland, and in the 34 PUMAs in the Lower Borderland, and in the 34 PUMAs in the Lower Mississippi Delta; we use macro-data (level-2) for Mississippi Delta; we use macro-data (level-2) for the 44 PUMAs.the 44 PUMAs.

From the 1% PUMS of the 2006 American Community From the 1% PUMS of the 2006 American Community Survey, we have developed a sample of 26,425 Survey, we have developed a sample of 26,425 households in the Texas Borderland and Mississippi households in the Texas Borderland and Mississippi Delta; these are one-family households. These are Delta; these are one-family households. These are the level-1 data; the household heads are between the level-1 data; the household heads are between the ages of 20 and 79.the ages of 20 and 79.

From other data sources, we developed macro-level From other data sources, we developed macro-level data for the 44 Borderland and Delta PUMAs. These data for the 44 Borderland and Delta PUMAs. These are the level-2 units.are the level-2 units.

1111

American Community American Community SurveySurveyWe use data from the American Community Survey, 2006 sampleWe use data from the American Community Survey, 2006 sample

– 1-in-100 national random sample of the population.1-in-100 national random sample of the population.– The data do not include persons in group quarters.The data do not include persons in group quarters.– This is a weighted sample. Weights This is a weighted sample. Weights areare used to produce used to produce

accurate statistics, esp. standard errors.accurate statistics, esp. standard errors.– The smallest identifiable geographic unit is the PUMA (Public The smallest identifiable geographic unit is the PUMA (Public

Use Microdata Area), containing at least 100,000 persons. Use Microdata Area), containing at least 100,000 persons. PUMAs do not cross state boundaries.PUMAs do not cross state boundaries.

– Approximately 1,344,000 households and 2,970,000 person Approximately 1,344,000 households and 2,970,000 person records.records.

Our sample Our sample ddata of 26,425 households were extracted from this ata of 26,425 households were extracted from this ACS sample ACS sample ofof households in the households in the 44 PUMAs of the Borderland 44 PUMAs of the Borderland and Deltaand Delta

(Above description taken from: http://usa.ipums.org/usa/)(Above description taken from: http://usa.ipums.org/usa/)

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The RegionsThe Regions

The next five slides show the PUMAs The next five slides show the PUMAs (Public Use Microdata Areas) we use in (Public Use Microdata Areas) we use in our analysis. our analysis.

Each PUMA contains one or more Each PUMA contains one or more counties.counties.

These PUMAs define the “Borderland” These PUMAs define the “Borderland” and “Delta” regions we are studying.and “Delta” regions we are studying.

Each PUMA contains samples of Each PUMA contains samples of households.households.

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PUMA Counties in Borderland and DeltaPUMA Counties in Borderland and Delta

TX

AR

LA

MS

TX

AR

LA

MS

Metro PUMAs

Nonmetro PUMAs

Figure 2. PUMA Counties in Micro-analyses of Poverty in Borderland and Delta

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15

■ PUMA 400

■ Jonesboro

■ West Memphis

■ PUMA 700

■ PUMA 800

■ Little Rock

■ Pine Bluff

■ PUMA 1700

■ PUMA 1800

Arkansas

16

■ Monroe

■ PUMA 500 t

■ PUMA 600

■ PUMA 700 t

■ PUMA 1200

■ PUMA 1300 t

■ PUMA 2500

■ PUMA 2100

■ Baton Rouge

■ PUMA 1600

■ PUMA 1700

■ PUMA 2400

■ PUMA 1905 L

■ Kenner-Metairie L

■ New Orleans L *t= top of map, L= lower part of map

Louisiana

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■ PUMA 100

■ PUMA 200

■ PUMA 500

■ PUMA 600

■ PUMA 700

■ PUMA 1600

■ PUMA 800

■ Jackson

■ PUMA 1300

■ PUMA 1700

■ PUMA 200

Mississippi

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What is Multilevel What is Multilevel Analysis?Analysis?

Multilevel research had its start Multilevel research had its start in the field of education. in the field of education.

In education, students are In education, students are grouped in classes, and both grouped in classes, and both students and the classes have students and the classes have characteristics of interest. characteristics of interest.

In our research, we are looking at In our research, we are looking at households that are grouped households that are grouped (aggregated) in PUMAs.(aggregated) in PUMAs.

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Households and PUMAsHouseholds and PUMAs

We hypothesize that when it comes We hypothesize that when it comes to predicting the log likelihood of a to predicting the log likelihood of a household being in poverty, that household being in poverty, that characteristics of the household characteristics of the household (and/or household head), (and/or household head), as well asas well as characteristics of the PUMA in which characteristics of the PUMA in which the household is located, will be the household is located, will be important independent variables in important independent variables in the model.the model.

2020

Past ResearchPast Research

Traditionally, multilevel research Traditionally, multilevel research has been conducted in two ways, has been conducted in two ways, both of which are statistically both of which are statistically incorrect.incorrect.

2121

The first technique is to disaggregate all the contextual level variables down to the level of the households, and use OLS to solve the equation.

The problem with this approach is that if we know that households are from the same PUMA, then we also know that those households have the same values on the various PUMA characteristics.

“Thus we cannot use the assumption of independence of observations that is basic for the use of classic statistical techniques” (de Leeuw, 1992: xiv).

2222

An alternate technique is to An alternate technique is to

aggregateaggregate the household-level the household-level characteristics up to the characteristics up to the contextual level, say of the contextual level, say of the PUMA, and to conduct the PUMA, and to conduct the analysis at the aggregate (PUMA) analysis at the aggregate (PUMA) level.level.

The main problem here is that we The main problem here is that we discard all the within-group (i.e., discard all the within-group (i.e., within-PUMA) variation; as much within-PUMA) variation; as much as 80-90 percent of the variation as 80-90 percent of the variation could be thrown away before the could be thrown away before the analysis begins.analysis begins.

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Multilevel Models and Some Multilevel Models and Some TerminologyTerminology

The approaches known as HLM are The approaches known as HLM are also known sometimes by other also known sometimes by other names, multilevel linear and names, multilevel linear and nonlinear models (sociology), nonlinear models (sociology), mixed-effects models and random-mixed-effects models and random-effects models (biometry), random-effects models (biometry), random-coefficient regression models coefficient regression models (econometrics), and covariance (econometrics), and covariance components models (statistics).components models (statistics).

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The Logic and MethodologyThe Logic and Methodology Here is a 2-level model predicting a level-1 Here is a 2-level model predicting a level-1

dichotomous outcome (Pdichotomous outcome (Pijij ) for the i ) for the ithth Household in the jHousehold in the jthth PUMA, with two PUMA, with two independent variables, Windependent variables, Wj j and and XXij ij (WWj j is a characteristic of the PUMA and XXijij is a characteristic of the Household):

PPijij = = 0000 + + 0101WWj j + + 1010 X Xijij + + 1111WWjj (X(Xijij ) )

+ u+ u00jj + u+ u11jj (X(Xijij - X-bar - X-bar.j.j) + r) + rijij

In this, the macrolevel variable, WIn this, the macrolevel variable, Wjj, , interacts with the microlevel variable, Xinteracts with the microlevel variable, Xijij (as (as in in 1111), and the error structure contains both ), and the error structure contains both microlevel terms and macrolevel terms microlevel terms and macrolevel terms

2525

We couldn’t estimate the preceding with 1-level We couldn’t estimate the preceding with 1-level logit models because the models require that logit models because the models require that the errors be the errors be independentindependent, , normally normally distributeddistributed, and , and have constant variance.have constant variance.

In contrast, the random error in the above In contrast, the random error in the above equation is of a more complex form, namely equation is of a more complex form, namely

uu00jj + u+ u11jj (X(Xijij-X-bar-X-bar.j.j)+r)+rijij. . Such errors are dependent within each level-2 Such errors are dependent within each level-2

(PUMA) unit because the components u(PUMA) unit because the components u00jj and uand u11jj are common to every Household (or level-1 are common to every Household (or level-1 unit) within each of the unit) within each of the j j level-2 PUMA units. level-2 PUMA units.

The errors also have unequal variances; this is The errors also have unequal variances; this is because “ubecause “u00jj+u+u11jj (X(Xijij -X-bar -X-bar.j.j)” depends upon u)” depends upon u00j j

and uand u11jj which vary across the level-2 PUMA which vary across the level-2 PUMA units; it also depends on the value of “(Xunits; it also depends on the value of “(Xijij),” ),” which varies across the level-1 Household which varies across the level-1 Household units. units.

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In the Borderland area there are 10 PUMAs containing 49 counties. Five of the PUMAs are metro PUMAs (El Paso, Laredo, Corpus Christi, Brownsville, and McAllen); the other five are nonmetro PUMAs.

In the Delta there are 34 PUMAs containing 133 counties/parishes. Our multi-level analyses combine the Delta PUMAs with the Borderland PUMAs.

2727

Our models examine three kinds of effects on the individual outcome of poverty: “b” effects, i.e., level-1 direct effects; “G” effects, i.e., level-2 direct effects; and “g” effects, i.e., cross level interactions of the level-2 variables on the slopes.

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Descriptive statistics: Descriptive statistics: three poverty measures, three poverty measures,

households, households, Borderland & Delta,Borderland & Delta,

20062006

Variable | Obs Mean Std. Dev.Variable | Obs Mean Std. Dev.

deeppov | 26,425 .0652791 .2470223 deeppov | 26,425 .0652791 .2470223

pov100 | 26,425 .1808136 .384871 pov100 | 26,425 .1808136 .384871

nearpov | 26,425 .2977483 .4572769nearpov | 26,425 .2977483 .4572769

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Descriptive statistics: Descriptive statistics: three poverty measures, households, three poverty measures, households, Delta-specific & Borderland-specific, Delta-specific & Borderland-specific,

20062006Variable | Obs Mean Std. Dev.Variable | Obs Mean Std. Dev.

Delta HouseholdsDelta Households deeppov | 19,182 .059 .236 deeppov | 19,182 .059 .236 pov100 | 19,182 .160 .368 pov100 | 19,182 .160 .368 nearpov | 19,182 .270 .444nearpov | 19,182 .270 .444

Borderland HouseholdsBorderland Households deeppov | 7,243 .081 .274 deeppov | 7,243 .081 .274 pov100 | 7,243 .233 .422 pov100 | 7,243 .233 .422 nearpov | 7,243 .370 .482nearpov | 7,243 .370 .482

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Correlations: Correlations: three poverty measures, households, three poverty measures, households,

Borderland & Delta,Borderland & Delta,20062006

(obs=26,425)(obs=26,425)

| deeppov pov100 nearpov| deeppov pov100 nearpov -------------+----------------------------------------+--------------------------- deeppov | 1.0000deeppov | 1.0000 pov100 | 0.5625 1.0000pov100 | 0.5625 1.0000 nearpov | 0.4059 0.7215 1.0000nearpov | 0.4059 0.7215 1.0000

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Is there Variance at level-2?Is there Variance at level-2?

Multilevel analysis is only appropriate when Multilevel analysis is only appropriate when there is a statistically significant amount of there is a statistically significant amount of variance in the dependent variable at level-2, variance in the dependent variable at level-2, i.e., among the 44 PUMAs.i.e., among the 44 PUMAs.

The level-2 variance values, known as The level-2 variance values, known as ττ0000, for , for the three poverty dependent variables are the three poverty dependent variables are shown on the next slide, along with their shown on the next slide, along with their respective respective χχ2 2 values and significance levels.values and significance levels.

We see that each We see that each ττ00 00 is statisticallyis statistically significant, justifying the multilevel analysis significant, justifying the multilevel analysis of each of the three poverty dependent of each of the three poverty dependent variables.variables.

One-way ANOVAs for Non-linear One-way ANOVAs for Non-linear

Logistic Regression Multilevel ModelsLogistic Regression Multilevel Models

Model τ00 χ2 P-value τ00/ (τ00 + Π2/3)

deep poverty 0.186 344.9 0.000 0.054

100% poverty 0.194 789.3 0.000 0.056

near poverty 0.193 1057.0 0.000 0.056

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Intra-class CorrelationIntra-class Correlation

The intra-class correlation is the ratio of level-2 variance The intra-class correlation is the ratio of level-2 variance (referred to as (referred to as ττ0000) to the total variance in the dependent ) to the total variance in the dependent variable, and tells us the proportion of variance that occurs variable, and tells us the proportion of variance that occurs at level 2. In a nonlinear model, however, the variance at at level 2. In a nonlinear model, however, the variance at level-1 is heteroscedastic so can’t be used per se in the level-1 is heteroscedastic so can’t be used per se in the denominator. Statisticians, e.g., Scott Long and others, denominator. Statisticians, e.g., Scott Long and others, recommend thinking about the level-1 model and the recommend thinking about the level-1 model and the dependent variable, i.e., being in poverty (yes or no), in dependent variable, i.e., being in poverty (yes or no), in terms of a latent (unmeasured) variable, and to consider its terms of a latent (unmeasured) variable, and to consider its variance as variance as ΠΠ22/3, i.e., the constant variance of the /3, i.e., the constant variance of the unmeasured latent variable of 3.29.unmeasured latent variable of 3.29.

Thus the intra-class correlation,Thus the intra-class correlation,ρρ, is calculated as:, is calculated as:

ρρ = = ττ0000/ (/ (ττ0000 + + ΠΠ22/3)./3).

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The three poverty dependent The three poverty dependent variables have statistically variables have statistically

significant variances at level-significant variances at level-22

Deep poverty = 5.4% of variance Deep poverty = 5.4% of variance occurs at level-2occurs at level-2

100% poverty = 5.6% of variance 100% poverty = 5.6% of variance occurs at level-2occurs at level-2

Near poverty = 5.6% variance Near poverty = 5.6% variance occurs at level-2 occurs at level-2

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Descriptive Statistics:Descriptive Statistics:Five Level-1 Independent Variables,Five Level-1 Independent Variables,

26,425 Households, Delta & Borderland, 200626,425 Households, Delta & Borderland, 2006

Variable | Mean Std. Dev. MinVariable | Mean Std. Dev. Min MaxMax ----------------------------------------------------------------------------------------------------Sex of Head (+)| 1.45Sex of Head (+)| 1.45 .50 .50 1 1 2 2 m=1, f=2 m=1, f=2

Educ of Head(-) 10.5 3.1Educ of Head(-) 10.5 3.1 1 1 17 17

Duncan SEI (-)Duncan SEI (-) 33.233.2 28.1 28.1 0 0 96 96

AgeAge (-) 51.1(-) 51.1 15.2 15.2 20 20 79 79

Black or Black or HispanicHispanicYes =1Yes =1 (+) .39(+) .39 .49 .49 0 1 0 1

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Descriptive Statistics:Descriptive Statistics:Five Level-2 Independent Variables,Five Level-2 Independent Variables,

44 PUMAs, Delta & Borderland, @200044 PUMAs, Delta & Borderland, @2000

Variable | Mean Std. Dev. MinVariable | Mean Std. Dev. MinMaxMax

----------------------------------------------------------------------------------------------------% FIRE | 4.83 1.50 3.00 9.00% FIRE | 4.83 1.50 3.00 9.00 (-)(-)% < 9% < 9thth grade | 27.37 7.29 10.80 40.00 grade | 27.37 7.29 10.80 40.00 (+)(+)% in poverty | 12.32 4.00 4.00 22.20% in poverty | 12.32 4.00 4.00 22.20 (+) (+) % rural | 39.92 22.80 1.00 84.00% rural | 39.92 22.80 1.00 84.00 (-)(-)% fem HH,no H | 22.00 6.43 11.00 40.00% fem HH,no H | 22.00 6.43 11.00 40.00 (+)(+)

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HLM coefficientsHLM coefficients

The (gamma) The (gamma) coefficients are coefficients are the major indicators of the the major indicators of the effects of level-1 effects of level-1 characteristics and level-2 characteristics and level-2 characteristics on the characteristics on the outcome, as well as the outcome, as well as the effects of level-2 effects of level-2 characteristics on the level-1 characteristics on the level-1 slopes. slopes.

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First consider the effects of First consider the effects of the level-1 variables on each the level-1 variables on each

of the three poverty of the three poverty outcomes, controlling for outcomes, controlling for whether the PUMA is in whether the PUMA is in

Texas.Texas.

Tables 1-3 report these results for deep Tables 1-3 report these results for deep poverty, 100% poverty, and near poverty.poverty, 100% poverty, and near poverty.

Regarding the odds of being in poverty: the Regarding the odds of being in poverty: the effect of age is negative, sex (being effect of age is negative, sex (being

female) is positive, education is negative, female) is positive, education is negative, Duncan’s SEI is negative, and being a main Duncan’s SEI is negative, and being a main

minority is positive. minority is positive.

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Re. the cross-level Re. the cross-level interactions:interactions:

Positive effect of being female on all Positive effect of being female on all three poverty outcomes is weaker in three poverty outcomes is weaker in Texas than in the Delta.Texas than in the Delta.

Age is negative on poverty, and so is Age is negative on poverty, and so is education, and so is SEI, in the same education, and so is SEI, in the same ways in both Texas and the Delta.ways in both Texas and the Delta.

Positive effect of being a minority is Positive effect of being a minority is less on all three poverty outcomes in less on all three poverty outcomes in Texas compared to in the Delta.Texas compared to in the Delta.

Table 1Level-1 Variables and Whether PUMA is in Texas, Predicting Deep Poverty:26,425 Households, 44 PUMAs, Borderland and Delta, 2006

Variable Gamma (γ) Logit Coef Odds Ratio t-ratio

Intercept γ00 -3.06 0.05 -72.97

Texas PUMA γ01 0.25 1.29 2.01

Age γ10 -0.05 0.95 -32.24Texas PUMA (CLI) γ11 0.00 1 -0.47

Sex γ20 0.92 2.52 26.54Texas PUMA (CLI) γ21 -0.17 0.84 -2.01

Education γ30 -0.12 0.89 -16.99Texas PUMA (CLI) γ31 0.00 1 -0.23

Duncan SEI γ40 -0.02 0.98 -20.82Texas PUMA (CLI) γ41 0.00 1 -1.11

Main-Minority γ50 0.38 1.46 9.32Texas PUMA (CLI) γ51 -0.48 0.62 -7.01

Table 2Level-1 Variables and Whether PUMA is in Texas, Predicting 100% Poverty:26,425 Households, 44 PUMAs, Borderland and Delta, 2006

Variable Gamma (γ) Logit Coef Odds Ratio t-ratio

Intercept γ00 -1.91 0.15 -36.23

Texas PUMA γ01 0.49 1.63 3.36

Age γ10 -0.03 0.97 -23.02Texas PUMA (CLI) γ11 0.00 1 0.65

Sex γ20 0.99 2.69 27.83Texas PUMA (CLI) γ21 -0.36 0.71 -4.34

Education γ30 -0.17 0.84 -27.25Texas PUMA (CLI) γ31 0.02 1.02 1.72

Duncan SEI γ40 -0.03 0.97 -28.49Texas PUMA (CLI) γ41 0.00 1 0.77

Main-Minority γ50 0.71 2.04 15.68Texas PUMA (CLI) γ51 -0.20 0.82 -1.93

Table 3Level-1 Variables and Whether PUMA is in Texas, Predicting Near Poverty:26,425 Households, 44 PUMAs, Borderland and Delta, 2006

Variable Gamma (γ) Logit Coef Odds Ratio t-ratio

Intercept γ00 -1.08 0.34 -17.37

Texas PUMA γ01 0.48 1.62 2.96

Age γ10 -0.02 0.98 -16.18Texas PUMA (CLI) γ11 0.00 1 -0.87

Sex γ20 0.97 2.63 27.19Texas PUMA (CLI) γ21 -0.36 0.7 -3.65

Education γ30 -0.20 0.82 -32.17Texas PUMA (CLI) γ31 0.01 1.01 1.03

Duncan SEI γ40 -0.02 0.98 -30.46Texas PUMA (CLI) γ41 0.00 1 1.31

Main-Minority γ50 0.74 2.09 18.49Texas PUMA (CLI) γ51 -0.25 0.78 -3.3

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Now examine level-2 effects Now examine level-2 effects of % of PUMA with less than of % of PUMA with less than 99thth grade education; and % grade education; and %

rural rural (Tables 4-6).(Tables 4-6).

The direct effect on poverty of % less than The direct effect on poverty of % less than 99thth grade is positive on all three poverty grade is positive on all three poverty outcomes; the direct effect of % rural is outcomes; the direct effect of % rural is

negative only for deep poverty.negative only for deep poverty.The indirect effects of these two level-2 The indirect effects of these two level-2 variables are sometimes significant, and variables are sometimes significant, and

sometimes insignificant.sometimes insignificant.

Table 4Level-1 & Level-2 Variables and Whether PUMA is in Texas, Predicting Deep Poverty:26,425 Households, 44 PUMAs, Borderland and Delta, 2006

Variable Gamma (γ) Logit Coef Odds Ratio t-ratio

Intercept γ00 -3.18 0.04 -90.73

%<9th grade γ01 0.05 1.05 9.06% Rural γ02 -0.01 0.99 -3.18Texas PUMA γ03 0.28 1.33 3.04

Age γ10 -0.05 0.95 -29.62%<9th grade (CLI) γ11 0.00 1 -1.14% Rural (CLI) γ12 0.00 1 2.19Texas PUMA (CLI) γ13 0.00 1 0.49

Sex γ20 0.99 2.72 23.48%<9th grade (CLI) γ21 -0.01 0.99 -0.6% Rural (CLI) γ22 0.00 1 1.11Texas PUMA (CLI) γ23 -0.12 0.89 -1.08

Education γ30 -0.12 0.89 -13.37%<9th grade (CLI) γ31 0.00 1 -1.15% Rural (CLI) γ32 0.00 1 1.37Texas PUMA (CLI) γ33 0.01 1.02 0.83

Duncan SEI γ40 -0.02 0.98 -18.51%<9th grade (CLI) γ41 0.00 1 0.22% Rural (CLI) γ42 0.00 1 1.67Texas PUMA (CLI) γ43 0.00 1 0.31

Main-Minority γ50 0.45 1.57 9.93%<9th grade (CLI) γ51 0.00 1 -0.64% Rural (CLI) γ52 0.01 1.01 2.85Texas PUMA (CLI) γ53 -0.29 0.75 -2.9

Table 5Level-1 & Level-2 Variables and Whether PUMA is in Texas, Predicting 100% Poverty:26,425 Households, 44 PUMAs, Borderland and Delta, 2006

Variable Gamma (γ) Logit Coef Odds Ratio t-ratio

Intercept γ00 -2.00 0.13 -57.46

%<9th grade γ01 0.06 1.06 11.19% Rural γ02 0.00 1 -1.02Texas PUMA γ03 0.69 2 8.75

Age γ10 -0.03 0.97 -24.71%<9th grade (CLI) γ11 0.00 1 -0.3% Rural (CLI) γ12 0.00 1 2.68Texas PUMA (CLI) γ13 0.01 1.01 1.81

Sex γ20 1.04 2.84 28.88%<9th grade (CLI) γ21 0.00 1 0.09% Rural (CLI) γ22 0.00 1 0.68Texas PUMA (CLI) γ23 -0.32 0.73 -2.85

Education γ30 -0.18 0.83 -25.21%<9th grade (CLI) γ31 0.00 1 0.85% Rural (CLI) γ32 0.00 1 1.27Texas PUMA (CLI) γ33 0.04 1.04 2.25

Duncan SEI γ40 -0.03 0.97 -26.03%<9th grade (CLI) γ41 0.00 1 -0.09% Rural (CLI) γ42 0.00 1 0.85Texas PUMA (CLI) γ43 0.00 1 1.03

Main-Minority γ50 0.71 2.03 14.81%<9th grade (CLI) γ51 0.02 1.02 2.64% Rural (CLI) γ52 0.00 1 0.28Texas PUMA (CLI) γ53 -0.11 0.9 -0.97

Table 6Level-1 & Level-2 Variables and Whether PUMA is in Texas, Predicting Near Poverty:26,425 Households, 44 PUMAs, Borderland and Delta, 2006

Variable Gamma (γ) Logit Coef Odds Ratio t-ratio

Intercept γ00 -1.14 0.32 -31.03

%<9th grade γ01 0.06 1.06 11.77% Rural γ02 0.00 1 -0.28Texas PUMA γ03 0.76 2.14 9.75

Age γ10 -0.03 0.97 -17.77%<9th grade (CLI) γ11 0.00 1 -1.22% Rural (CLI) γ12 0.00 1 2.87Texas PUMA (CLI) γ13 0.00 1 0.32

Sex γ20 1.03 2.8 27.58%<9th grade (CLI) γ21 0.00 1 0.15% Rural (CLI) γ22 0.00 1 -0.27Texas PUMA (CLI) γ23 -0.39 0.7 -3.32

Education γ30 -0.21 0.81 -30.73%<9th grade (CLI) γ31 0.00 1 -1.11% Rural (CLI) γ32 0.00 1 0.5Texas PUMA (CLI) γ33 0.02 1.02 1.26

Duncan SEI γ40 -0.03 0.97 -29.09%<9th grade (CLI) γ41 0.00 1 -0.01% Rural (CLI) γ42 0.00 1 0.77Texas PUMA (CLI) γ43 0.00 1 1.39

Main-Minority γ50 0.76 2.14 18.89%<9th grade (CLI) γ51 0.02 1.02 2.72% Rural (CLI) γ52 0.00 1 0.46Texas PUMA (CLI) γ53 -0.16 0.85 -1.79

4747

Now examine level-2 effects Now examine level-2 effects of % of PUMA in poverty; and of % of PUMA in poverty; and

% rural % rural (Tables 7-9).(Tables 7-9).

The direct effect on poverty of % of PUMA in The direct effect on poverty of % of PUMA in poverty is positive on all three poverty poverty is positive on all three poverty outcomes; the direct effect of % rural is outcomes; the direct effect of % rural is

negative only for deep poverty.negative only for deep poverty.The indirect effects of these two level-2 The indirect effects of these two level-2 variables are sometimes significant, and variables are sometimes significant, and

sometimes insignificant.sometimes insignificant.

Table 7Level-1 & Level-2 Variables and Whether PUMA is in Texas, Predicting Deep Poverty:26,425 Households, 44 PUMAs, Borderland and Delta, 2006

Variable Gamma (γ) Logit Coef Odds Ratio t-ratio

Intercept γ00 -3.24 0.04 -101.31

% INPOV γ01 0.12 1.12 11.7% Rural γ02 0.00 1 -2.19Texas PUMA γ03 -0.39 0.68 -3.43

Age γ10 -0.05 0.95 -27.52% INPOV (CLI) γ11 0.00 1 -0.63% Rural (CLI) γ12 0.00 1 1.82Texas PUMA (CLI) γ13 0.00 1 0.8

Sex γ20 1.02 2.77 22.21% INPOV (CLI) γ21 0.00 1 -0.28% Rural (CLI) γ22 0.00 1 0.9Texas PUMA (CLI) γ23 -0.10 0.9 -0.59

Education γ30 -0.12 0.88 -12.73% INPOV (CLI) γ31 0.00 1 -0.62% Rural (CLI) γ32 0.00 1 1.19Texas PUMA (CLI) γ33 0.03 1.03 0.91

Duncan SEI γ40 -0.03 0.98 -18.02% INPOV (CLI) γ41 0.00 1 0.61% Rural (CLI) γ42 0.00 1 1.5Texas PUMA (CLI) γ43 0.00 1 -0.09

Main-Minority γ50 0.48 1.62 9.56% INPOV (CLI) γ51 -0.02 0.98 -1.01% Rural (CLI) γ52 0.01 1 2.68Texas PUMA (CLI) γ53 -0.16 0.85 -1.06

Table 8Level-1 & Level-2 Variables and Whether PUMA is in Texas, Predicting 100% Poverty:26,425 Households, 44 PUMAs, Borderland and Delta, 2006

Variable Gamma (γ) Logit Coef Odds Ratio t-ratio

Intercept γ00 -2.01 0.13 -72.35

% INPOV γ01 0.13 1.13 17.95% Rural γ02 0.00 1 0.61Texas PUMA γ03 -0.06 0.94 -0.76

Age γ10 -0.03 0.97 -23.54% INPOV (CLI) γ11 0.00 1 -1.27% Rural (CLI) γ12 0.00 1 2.84Texas PUMA (CLI) γ13 0.01 1.01 2.05

Sex γ20 1.05 2.85 27.55% INPOV (CLI) γ21 0.00 1 0.05% Rural (CLI) γ22 0.00 1 0.76Texas PUMA (CLI) γ23 -0.35 0.7 -1.85

Education γ30 -0.19 0.83 -25.2% INPOV (CLI) γ31 0.00 1 0.17% Rural (CLI) γ32 0.00 1 1.32Texas PUMA (CLI) γ33 0.04 1.04 1.58

Duncan SEI γ40 -0.03 0.97 -26.12% INPOV (CLI) γ41 0.00 1 -0.72% Rural (CLI) γ42 0.00 1 0.84Texas PUMA (CLI) γ43 0.00 1 1.04

Main-Minority γ50 0.70 2.01 14.45% INPOV (CLI) γ51 0.04 1.04 2.45% Rural (CLI) γ52 0.00 1 0.61Texas PUMA (CLI) γ53 -0.33 0.72 -2.53

Table 9Level-1 & Level-2 Variables and Whether PUMA is in Texas, Predicting Near Poverty:26,425 Households, 44 PUMAs, Borderland and Delta, 2006

Variable Gamma (γ) Logit Coef Odds Ratio t-ratio

Intercept γ00 -1.15 0.32 -38.14

% INPOV γ01 0.14 1.14 15.46% Rural γ02 0.00 1 1.72Texas PUMA γ03 -0.06 0.94 -0.58

Age γ10 -0.03 0.97 -18.38% INPOV (CLI) γ11 0.00 1 -2.77% Rural (CLI) γ12 0.00 1 3.09Texas PUMA (CLI) γ13 0.01 1.01 1.59

Sex γ20 1.04 2.82 26.78% INPOV (CLI) γ21 0.00 1 0.05% Rural (CLI) γ22 0.00 1 -0.32Texas PUMA (CLI) γ23 -0.41 0.66 -2.43

Education γ30 -0.21 0.81 -31.18% INPOV (CLI) γ31 0.00 1 -1.44% Rural (CLI) γ32 0.00 1 0.49Texas PUMA (CLI) γ33 0.03 1.03 2.07

Duncan SEI γ40 -0.03 0.97 -28.94% INPOV (CLI) γ41 0.00 1 -0.02% Rural (CLI) γ42 0.00 1 0.74Texas PUMA (CLI) γ43 0.00 1 1.08

Main-Minority γ50 0.75 2.12 18.87% INPOV (CLI) γ51 0.04 1.04 3.05% Rural (CLI) γ52 0.00 1 0.38Texas PUMA (CLI) γ53 -0.37 0.69 -2.82

5151

Now examine level-2 effects of % Now examine level-2 effects of % of PUMA in FIRE; and % of PUMA of PUMA in FIRE; and % of PUMA

with female HHs, no husband with female HHs, no husband present present

(Tables 10-12).(Tables 10-12).

The direct effect on poverty of % of PUMA in The direct effect on poverty of % of PUMA in FIRE is negative on all three poverty FIRE is negative on all three poverty

outcomes; the direct effect of % female outcomes; the direct effect of % female households is positive on all three poverty households is positive on all three poverty

outcomes.outcomes.The indirect effects of these two level-2 The indirect effects of these two level-2 variables are sometimes significant, and variables are sometimes significant, and

sometimes insignificant.sometimes insignificant.

Table 10Level-1 & Level-2 Variables and Whether PUMA is in Texas, Predicting Deep Poverty:26,425 Households, 44 PUMAs, Borderland and Delta, 2006

Variable Gamma (γ) Logit Coef Odds Ratio t-ratio

Intercept γ00 -3.17 0.04 -85.32

% FIRE γ01 -0.13 0.87 -6.16% fem HH, no H γ02 0.04 1.04 6.63Texas PUMA γ03 0.41 1.51 3.82

Age γ10 -0.05 0.95 -29.46% FIRE (CLI) γ11 0.00 1 -0.27% fem HH, no H (CLI) γ12 0.00 1 -0.51Texas PUMA (CLI) γ13 0.00 1 -0.58

Sex γ20 0.93 2.53 26.09% FIRE (CLI) γ21 -0.04 0.96 -1.38% fem HH, no H (CLI) γ22 0.01 1.01 1.16Texas PUMA (CLI) γ23 -0.13 0.88 -1.29

Education γ30 -0.12 0.88 -15.09% FIRE (CLI) γ31 0.00 1 0.51% fem HH, no H (CLI) γ32 0.00 1 1.13Texas PUMA (CLI) γ33 0.01 1.01 0.54

Duncan SEI γ40 -0.03 0.97 -20.83% FIRE (CLI) γ41 0.00 1 -3.62% fem HH, no H (CLI) γ42 0.00 1 0.35Texas PUMA (CLI) γ43 0.00 1 -0.86

Main-Minority γ50 0.40 1.49 9.15% FIRE (CLI) γ51 -0.05 0.96 -1.76% fem HH, no H (CLI) γ52 0.00 1 -0.21Texas PUMA (CLI) γ53 -0.53 0.59 -5.46

Table 11Level-1 & Level-2 Variables and Whether PUMA is in Texas, Predicting 100% Poverty:26,425 Households, 44 PUMAs, Borderland and Delta, 2006

Variable Gamma (γ) Logit Coef Odds Ratio t-ratio

Intercept γ00 -1.99 0.14 -46.81

% FIRE γ01 -0.18 0.83 -5.56% fem HH, no H γ02 0.04 1.04 5.08Texas PUMA γ03 0.63 1.89 5.24

Age γ10 -0.03 0.97 -23.56% FIRE (CLI) γ11 0.00 1 -1.6% fem HH, no H (CLI) γ12 0.00 1 -2.01Texas PUMA (CLI) γ13 0.00 1 0.16

Sex γ20 1.03 2.8 29.77% FIRE (CLI) γ21 -0.01 0.99 -0.3% fem HH, no H (CLI) γ22 0.00 1 0.4Texas PUMA (CLI) γ23 -0.36 0.69 -4.05

Education γ30 -0.18 0.83 -25.88% FIRE (CLI) γ31 0.00 1 0.33% fem HH, no H (CLI) γ32 0.00 1 0.31Texas PUMA (CLI) γ33 0.03 1.03 2.09

Duncan SEI γ40 -0.03 0.97 -27.49% FIRE (CLI) γ41 0.00 1 -3.23% fem HH, no H (CLI) γ42 0.00 1 -0.62Texas PUMA (CLI) γ43 0.00 1 0.35

Main-Minority γ50 0.70 2 14.6% FIRE (CLI) γ51 -0.07 0.93 -2.43% fem HH, no H (CLI) γ52 0.01 1.01 1.83Texas PUMA (CLI) γ53 -0.15 0.86 -1.28

Table 12Level-1 & Level-2 Variables and Whether PUMA is in Texas, Predicting Near Poverty:26,425 Households, 44 PUMAs, Borderland and Delta, 2006

Variable Gamma (γ) Logit Coef Odds Ratio t-ratio

Intercept γ00 -1.14 0.32 -25.91

% FIRE γ01 -0.23 0.8 -6.68% fem HH, no H γ02 0.04 1.04 4.62Texas PUMA γ03 0.62 1.85 5.05

Age γ10 -0.03 0.97 -17.51% FIRE (CLI) γ11 0.00 1 -1.67% fem HH, no H (CLI) γ12 0.00 1 -2.78Texas PUMA (CLI) γ13 -0.01 0.99 -1.86

Sex γ20 1.03 2.8 27.65% FIRE (CLI) γ21 0.01 1.01 0.36% fem HH, no H (CLI) γ22 0.00 1 0.52Texas PUMA (CLI) γ23 -0.36 0.69 -3.39

Education γ30 -0.21 0.81 -33.14% FIRE (CLI) γ31 0.00 1 0.79% fem HH, no H (CLI) γ32 0.00 1 0.31Texas PUMA (CLI) γ33 0.02 1.02 1.6

Duncan SEI γ40 -0.03 0.97 -30.03% FIRE (CLI) γ41 0.00 1 -3.38% fem HH, no H (CLI) γ42 0.00 1 -0.18Texas PUMA (CLI) γ43 0.00 1 1.02

Main-Minority γ50 0.74 2.1 18.86% FIRE (CLI) γ51 -0.05 0.95 -2.06% fem HH, no H (CLI) γ52 0.02 1.02 1.46Texas PUMA (CLI) γ53 -0.18 0.84 -1.82

5555

END of PRESENTATIONEND of PRESENTATION