Sample sizes

Well-being

Anxiety
Depression
Stress
Loneliness
Child Externalizing
Child Internalizing

Work benefits

Income
Sick Leave

Employment

Unemployed
Lost employment
Among unemployed

Have access to free food
Lost free lunch for child(ren)

Childcare

Plans for next month

Same as right now
Different arrangement
Same as during pandemic
Don’t know

Lost type of childcare
Type of care for the next month

Center-based
Care by relatives
Professional (non-centerbased)

Responses ARE NOT WEIGHTED by race.

Rural communities are defined as zipcodes with density smaller than 500 people per square mile. Urban areas are zipcodes with density greater than 1000 people per square mile. Zipcodes between these two extremes are omitted.

Sample sizes

Comparison	Group 1	Group 2
1	Black	White
	771	6541
2	Black + LatinX	White
	2186	5797
3	POC (minus Asian)	White
	2554	5797
4	POC	White
	2875	5797

Well-being

Anxiety

## 
##  Welch Two Sample t-test
## 
## data:  anxiety by compare1
## t = 5.8653, df = 957.37, p-value = 6.168e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  4.739698 9.506118
## sample estimates:
## mean in group 0 mean in group 1 
##        41.73164        34.60873

## 
##  Welch Two Sample t-test
## 
## data:  anxiety by compare2
## t = 6.2848, df = 4003, p-value = 3.634e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  3.396554 6.476471
## sample estimates:
## mean in group 0 mean in group 1 
##        42.05911        37.12260

## 
##  Welch Two Sample t-test
## 
## data:  anxiety by compare3
## t = 5.6188, df = 4950.8, p-value = 2.028e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  2.728334 5.652483
## sample estimates:
## mean in group 0 mean in group 1 
##        42.05911        37.86870

## 
##  Welch Two Sample t-test
## 
## data:  anxiety by compare4
## t = 6.8855, df = 5858.9, p-value = 6.362e-12
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  3.508842 6.302134
## sample estimates:
## mean in group 0 mean in group 1 
##        42.05911        37.15362

## Analysis of Variance Table
## 
## Response: anxiety
##              Df  Sum Sq Mean Sq F value    Pr(>F)    
## poverty150    1   58223   58223  58.794 1.962e-14 ***
## Residuals  7811 7735119     990                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Response: anxiety
##             Df  Sum Sq Mean Sq F value Pr(>F)
## region       3     377  125.63  0.1261 0.9447
## Residuals 8761 8730421  996.51

## 
##  Welch Two Sample t-test
## 
## data:  anxiety by rural
## t = -0.12772, df = 3755.3, p-value = 0.8984
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.910430  1.676755
## sample estimates:
## mean in group rural mean in group urban 
##            41.21829            41.33513

Depression

## 
##  Welch Two Sample t-test
## 
## data:  depress by compare1
## t = 0.61504, df = 946.45, p-value = 0.5387
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.523277  2.913885
## sample estimates:
## mean in group 0 mean in group 1 
##        30.04077        29.34547

## 
##  Welch Two Sample t-test
## 
## data:  depress by compare2
## t = 0.79911, df = 3898, p-value = 0.4243
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.8487723  2.0167131
## sample estimates:
## mean in group 0 mean in group 1 
##        30.02358        29.43961

## 
##  Welch Two Sample t-test
## 
## data:  depress by compare3
## t = 0.39147, df = 4839.9, p-value = 0.6955
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.084310  1.625393
## sample estimates:
## mean in group 0 mean in group 1 
##        30.02358        29.75304

## 
##  Welch Two Sample t-test
## 
## data:  depress by compare4
## t = 1.6078, df = 5735.7, p-value = 0.1079
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.232457  2.352504
## sample estimates:
## mean in group 0 mean in group 1 
##        30.02358        28.96356

## Analysis of Variance Table
## 
## Response: depress
##              Df  Sum Sq Mean Sq F value    Pr(>F)    
## poverty150    1  138824  138824  170.14 < 2.2e-16 ***
## Residuals  7809 6371710     816                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Response: depress
##             Df  Sum Sq Mean Sq F value Pr(>F)
## region       3    2860  953.24  1.1412 0.3309
## Residuals 8757 7314594  835.29

## 
##  Welch Two Sample t-test
## 
## data:  depress by rural
## t = 1.485, df = 3801.1, p-value = 0.1376
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.396176  2.870366
## sample estimates:
## mean in group rural mean in group urban 
##             30.8747             29.6376

Stress

## 
##  Welch Two Sample t-test
## 
## data:  stress by compare1
## t = 4.5915, df = 924.04, p-value = 5.01e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  3.434278 8.561756
## sample estimates:
## mean in group 0 mean in group 1 
##        56.06363        50.06562

## 
##  Welch Two Sample t-test
## 
## data:  stress by compare2
## t = 2.9924, df = 3723.1, p-value = 0.002786
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.8661328 4.1578448
## sample estimates:
## mean in group 0 mean in group 1 
##        56.05049        53.53850

## 
##  Welch Two Sample t-test
## 
## data:  stress by compare3
## t = 2.1381, df = 4621.6, p-value = 0.03256
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.1408149 3.2490806
## sample estimates:
## mean in group 0 mean in group 1 
##        56.05049        54.35554

## 
##  Welch Two Sample t-test
## 
## data:  stress by compare4
## t = 3.53, df = 5443.1, p-value = 0.000419
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  1.190769 4.165283
## sample estimates:
## mean in group 0 mean in group 1 
##        56.05049        53.37246

## Analysis of Variance Table
## 
## Response: stress
##              Df  Sum Sq Mean Sq F value    Pr(>F)    
## poverty150    1   76001   76001  72.926 < 2.2e-16 ***
## Residuals  7758 8085062    1042                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Response: stress
##             Df  Sum Sq Mean Sq F value Pr(>F)
## region       3    2800   933.3  0.8766 0.4523
## Residuals 8707 9270291  1064.7

## 
##  Welch Two Sample t-test
## 
## data:  stress by rural
## t = 0.295, df = 3722, p-value = 0.768
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.563473  2.117292
## sample estimates:
## mean in group rural mean in group urban 
##            55.82796            55.55105

Loneliness

## 
##  Welch Two Sample t-test
## 
## data:  lonely by compare1
## t = 6.4494, df = 925.78, p-value = 1.806e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  5.047386 9.462751
## sample estimates:
## mean in group 0 mean in group 1 
##        49.56026        42.30519

## 
##  Welch Two Sample t-test
## 
## data:  lonely by compare2
## t = 6.2014, df = 3655.6, p-value = 6.218e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  3.048005 5.866303
## sample estimates:
## mean in group 0 mean in group 1 
##        49.85761        45.40046

## 
##  Welch Two Sample t-test
## 
## data:  lonely by compare3
## t = 5.5112, df = 4513.2, p-value = 3.762e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  2.413907 5.079529
## sample estimates:
## mean in group 0 mean in group 1 
##        49.85761        46.11089

## 
##  Welch Two Sample t-test
## 
## data:  lonely by compare4
## t = 6.5765, df = 5326.7, p-value = 5.275e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  2.996714 5.542039
## sample estimates:
## mean in group 0 mean in group 1 
##        49.85761        45.58824

## Analysis of Variance Table
## 
## Response: lonely
##              Df  Sum Sq Mean Sq F value    Pr(>F)    
## poverty150    1   46774   46774  62.336 3.291e-15 ***
## Residuals  7810 5860239     750                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Response: lonely
##             Df  Sum Sq Mean Sq F value Pr(>F)
## region       3    3998 1332.69  1.7376 0.1569
## Residuals 8757 6716295  766.96

## 
##  Welch Two Sample t-test
## 
## data:  lonely by rural
## t = 2.3755, df = 3714.2, p-value = 0.01757
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.3307875 3.4569118
## sample estimates:
## mean in group rural mean in group urban 
##            49.71409            47.82024

Child Externalizing

## 
##  Welch Two Sample t-test
## 
## data:  fussy by compare1
## t = 5.2345, df = 948.51, p-value = 2.037e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  4.292275 9.441015
## sample estimates:
## mean in group 0 mean in group 1 
##        52.42800        45.56136

## 
##  Welch Two Sample t-test
## 
## data:  fussy by compare2
## t = 3.4869, df = 3797.6, p-value = 0.0004943
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  1.327078 4.736509
## sample estimates:
## mean in group 0 mean in group 1 
##        52.54926        49.51746

## 
##  Welch Two Sample t-test
## 
## data:  fussy by compare3
## t = 3.0938, df = 4690.5, p-value = 0.001988
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.9338376 4.1646761
## sample estimates:
## mean in group 0 mean in group 1 
##        52.54926        50.00000

## 
##  Welch Two Sample t-test
## 
## data:  fussy by compare4
## t = 4.0536, df = 5528.3, p-value = 5.113e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  1.650638 4.742487
## sample estimates:
## mean in group 0 mean in group 1 
##        52.54926        49.35269

## Analysis of Variance Table
## 
## Response: fussy
##              Df  Sum Sq Mean Sq F value    Pr(>F)    
## poverty150    1   51018   51018  44.242 3.098e-11 ***
## Residuals  7791 8984293    1153                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Response: fussy
##              Df  Sum Sq Mean Sq F value    Pr(>F)    
## poverty150    1   51018   51018  44.242 3.098e-11 ***
## Residuals  7791 8984293    1153                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
##  Welch Two Sample t-test
## 
## data:  fussy by rural
## t = 1.8004, df = 3739.7, p-value = 0.07188
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.1562683  3.6689042
## sample estimates:
## mean in group rural mean in group urban 
##            53.13024            51.37392

Child Internalizing

## 
##  Welch Two Sample t-test
## 
## data:  fear by compare1
## t = 2.5303, df = 967.79, p-value = 0.01155
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.7050749 5.5782944
## sample estimates:
## mean in group 0 mean in group 1 
##        26.83620        23.69452

## 
##  Welch Two Sample t-test
## 
## data:  fear by compare2
## t = -0.90295, df = 3822.2, p-value = 0.3666
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.436724  0.899994
## sample estimates:
## mean in group 0 mean in group 1 
##        26.48129        27.24965

## 
##  Welch Two Sample t-test
## 
## data:  fear by compare3
## t = -1.3103, df = 4723.8, p-value = 0.1902
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.6390822  0.5246155
## sample estimates:
## mean in group 0 mean in group 1 
##        26.48129        27.53852

## 
##  Welch Two Sample t-test
## 
## data:  fear by compare4
## t = -0.41279, df = 5598.5, p-value = 0.6798
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.825851  1.190675
## sample estimates:
## mean in group 0 mean in group 1 
##        26.48129        26.79888

## Analysis of Variance Table
## 
## Response: fear
##              Df  Sum Sq Mean Sq F value    Pr(>F)    
## poverty150    1   29558 29557.7   26.73 2.398e-07 ***
## Residuals  7780 8602974  1105.8                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Response: fear
##              Df  Sum Sq Mean Sq F value    Pr(>F)    
## poverty150    1   29558 29557.7   26.73 2.398e-07 ***
## Residuals  7780 8602974  1105.8                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
##  Welch Two Sample t-test
## 
## data:  fear by rural
## t = 1.1802, df = 3773.5, p-value = 0.238
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.7472624  3.0076203
## sample estimates:
## mean in group rural mean in group urban 
##            27.22300            26.09282

Work benefits

Income

## 
##  Welch Two Sample t-test
## 
## data:  income by compare1
## t = 2.4216, df = 755.97, p-value = 0.01569
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   5164.122 49382.310
## sample estimates:
## mean in group 0 mean in group 1 
##        88849.34        61576.13

## 
##  Welch Two Sample t-test
## 
## data:  income by compare2
## t = 3.4932, df = 3904, p-value = 0.0004827
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  13122.79 46697.80
## sample estimates:
## mean in group 0 mean in group 1 
##        91045.53        61135.23

## 
##  Welch Two Sample t-test
## 
## data:  income by compare3
## t = 3.7733, df = 3904.6, p-value = 0.0001635
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  15161.87 47959.16
## sample estimates:
## mean in group 0 mean in group 1 
##        91045.53        59485.01

## 
##  Welch Two Sample t-test
## 
## data:  income by compare4
## t = 2.3625, df = 4131, p-value = 0.0182
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   3470.649 37324.641
## sample estimates:
## mean in group 0 mean in group 1 
##        91045.53        70647.88

## Analysis of Variance Table
## 
## Response: income
##             Df     Sum Sq    Mean Sq F value  Pr(>F)  
## region       3 1.0970e+12 3.6567e+11  2.6299 0.04848 *
## Residuals 4273 5.9412e+14 1.3904e+11                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
##  Welch Two Sample t-test
## 
## data:  income by rural
## t = -3.2647, df = 952.12, p-value = 0.001135
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -54647.98 -13614.37
## sample estimates:
## mean in group rural mean in group urban 
##            63034.15            97165.32

Sick Leave

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$sick_leave and scored$compare1
## X-squared = 1.4212, df = 1, p-value = 0.2332

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$sick_leave and scored$compare2
## X-squared = 0.27466, df = 1, p-value = 0.6002

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$sick_leave and scored$compare3
## X-squared = 0.00024085, df = 1, p-value = 0.9876

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$sick_leave and scored$compare4
## X-squared = 0.074761, df = 1, p-value = 0.7845

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$sick_leave and scored$poverty150
## X-squared = 407.09, df = 1, p-value < 2.2e-16

## 
##  Pearson's Chi-squared test
## 
## data:  scored$sick_leave and scored$region
## X-squared = 58.52, df = 3, p-value = 1.217e-12

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$sick_leave and scored$rural
## X-squared = 21.556, df = 1, p-value = 3.436e-06

Employment

Unemployed

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$unemployed and scored$compare1
## X-squared = 4.8389, df = 1, p-value = 0.02783

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$unemployed and scored$compare2
## X-squared = 15.485, df = 1, p-value = 8.315e-05

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$unemployed and scored$compare3
## X-squared = 17.15, df = 1, p-value = 3.453e-05

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$unemployed and scored$compare4
## X-squared = 14.95, df = 1, p-value = 0.0001104

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$unemployed and scored$poverty150
## X-squared = 219.93, df = 1, p-value < 2.2e-16

## 
##  Pearson's Chi-squared test
## 
## data:  scored$unemployed and scored$region
## X-squared = 27.241, df = 3, p-value = 5.241e-06

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$unemployed and scored$rural
## X-squared = 8.2554, df = 1, p-value = 0.004063

Lost employment

“Has your level of employment decreased due to the coronavirus (COVID-19) pandemic?”

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$employment_decreased and scored$compare1
## X-squared = 27.033, df = 1, p-value = 2e-07

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$employment_decreased and scored$compare2
## X-squared = 74.038, df = 1, p-value < 2.2e-16

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$employment_decreased and scored$compare3
## X-squared = 81.018, df = 1, p-value < 2.2e-16

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$employment_decreased and scored$compare4
## X-squared = 72.955, df = 1, p-value < 2.2e-16

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$employment_decreased and scored$poverty150
## X-squared = 181.22, df = 1, p-value < 2.2e-16

## 
##  Pearson's Chi-squared test
## 
## data:  scored$employment_decreased and scored$region
## X-squared = 1.9427, df = 3, p-value = 0.5844

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$employment_decreased and scored$rural
## X-squared = 2.5619, df = 1, p-value = 0.1095

Among unemployed

Comparison	Group 1	Group 2
1	Black	White
	176	1272
2	Black + LatinX	White
	507	1113
3	POC (minus Asian)	White
	592	1113
4	POC	White
	654	1113

Have access to free food

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  unemployed$free_food and unemployed$compare1
## X-squared = 11.313, df = 1, p-value = 0.0007695

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  unemployed$free_food and unemployed$compare2
## X-squared = 7.0257, df = 1, p-value = 0.008035

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  unemployed$free_food and unemployed$compare3
## X-squared = 11.58, df = 1, p-value = 0.0006665

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  unemployed$free_food and unemployed$compare4
## X-squared = 5.3255, df = 1, p-value = 0.02102

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  unemployed$free_food and unemployed$poverty150
## X-squared = 244.22, df = 1, p-value < 2.2e-16

## 
##  Pearson's Chi-squared test
## 
## data:  unemployed$free_food and unemployed$region
## X-squared = 4.7261, df = 3, p-value = 0.193

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  unemployed$free_food and unemployed$rural
## X-squared = 0.30867, df = 1, p-value = 0.5785

Lost free lunch for child(ren)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  unemployed$lost_free_lunch and unemployed$compare1
## X-squared = 3.7391, df = 1, p-value = 0.05315

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  unemployed$lost_free_lunch and unemployed$compare2
## X-squared = 2.7094, df = 1, p-value = 0.09976

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  unemployed$lost_free_lunch and unemployed$compare3
## X-squared = 3.825, df = 1, p-value = 0.05049

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  unemployed$lost_free_lunch and unemployed$compare4
## X-squared = 2.2655, df = 1, p-value = 0.1323

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  unemployed$lost_free_lunch and unemployed$poverty150
## X-squared = 23.075, df = 1, p-value = 1.558e-06

## 
##  Chi-squared test for given probabilities
## 
## data:  unemployed$lost_free_lunch
## X-squared = 1686, df = 1806, p-value = 0.9789

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  unemployed$lost_free_lunch and unemployed$rural
## X-squared = 0.85934, df = 1, p-value = 0.3539

Childcare

Plans for next month

Same as right now

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_samenow and scored$compare1
## X-squared = 20.81, df = 1, p-value = 5.073e-06

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_samenow and scored$compare2
## X-squared = 22.375, df = 1, p-value = 2.243e-06

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_samenow and scored$compare3
## X-squared = 21.889, df = 1, p-value = 2.888e-06

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_samenow and scored$compare4
## X-squared = 17.105, df = 1, p-value = 3.536e-05

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_samenow and scored$poverty150
## X-squared = 28.128, df = 1, p-value = 1.136e-07

## 
##  Pearson's Chi-squared test
## 
## data:  scored$plan_cc_samenow and scored$region
## X-squared = 3.7043, df = 3, p-value = 0.2952

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_samenow and scored$rural
## X-squared = 0.0015298, df = 1, p-value = 0.9688

Different arrangement

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_differ and scored$compare1
## X-squared = 9.9784, df = 1, p-value = 0.001584

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_differ and scored$compare2
## X-squared = 10.671, df = 1, p-value = 0.001088

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_differ and scored$compare3
## X-squared = 7.5562, df = 1, p-value = 0.00598

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_differ and scored$compare4
## X-squared = 6.1717, df = 1, p-value = 0.01298

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_differ and scored$poverty150
## X-squared = 0.1158, df = 1, p-value = 0.7336

## 
##  Pearson's Chi-squared test
## 
## data:  scored$plan_cc_differ and scored$region
## X-squared = 1.4694, df = 3, p-value = 0.6894

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_differ and scored$rural
## X-squared = 4.4619, df = 1, p-value = 0.03466

Same as during pandemic

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_sameCOVID and scored$compare1
## X-squared = 0.67446, df = 1, p-value = 0.4115

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_sameCOVID and scored$compare2
## X-squared = 0.504, df = 1, p-value = 0.4777

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_sameCOVID and scored$compare3
## X-squared = 0.6699, df = 1, p-value = 0.4131

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_sameCOVID and scored$compare4
## X-squared = 0.90868, df = 1, p-value = 0.3405

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_sameCOVID and scored$poverty150
## X-squared = 0.086435, df = 1, p-value = 0.7688

## 
##  Pearson's Chi-squared test
## 
## data:  scored$plan_cc_sameCOVID and scored$region
## X-squared = 3.0154, df = 3, p-value = 0.3893

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_sameCOVID and scored$rural
## X-squared = 4.5959, df = 1, p-value = 0.03205

Don’t know

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_dontknow and scored$compare1
## X-squared = 14.216, df = 1, p-value = 0.000163

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_dontknow and scored$compare2
## X-squared = 13.412, df = 1, p-value = 0.00025

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_dontknow and scored$compare3
## X-squared = 16.14, df = 1, p-value = 5.883e-05

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_dontknow and scored$compare4
## X-squared = 14.656, df = 1, p-value = 0.000129

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_dontknow and scored$poverty150
## X-squared = 33.766, df = 1, p-value = 6.214e-09

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_dontknow and scored$poverty150
## X-squared = 33.766, df = 1, p-value = 6.214e-09

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_dontknow and scored$rural
## X-squared = 2.7611, df = 1, p-value = 0.09658

Lost type of childcare

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$lostCC and scored$compare1
## X-squared = 0.40809, df = 1, p-value = 0.5229

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$lostCC and scored$compare2
## X-squared = 1.7122, df = 1, p-value = 0.1907

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$lostCC and scored$compare3
## X-squared = 1.1796, df = 1, p-value = 0.2774

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$lostCC and scored$compare4
## X-squared = 0.74367, df = 1, p-value = 0.3885

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$plan_cc_dontknow and scored$poverty150
## X-squared = 33.766, df = 1, p-value = 6.214e-09

## 
##  Pearson's Chi-squared test
## 
## data:  scored$plan_cc_dontknow and scored$region
## X-squared = 1.1743, df = 3, p-value = 0.7592

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$lostCC and scored$rural
## X-squared = 0.56361, df = 1, p-value = 0.4528

Type of care for the next month

Center-based

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_center and scored$compare1
## X-squared = 0.73597, df = 1, p-value = 0.391

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_center and scored$compare2
## X-squared = 2.6121, df = 1, p-value = 0.106

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_center and scored$compare3
## X-squared = 1.2627, df = 1, p-value = 0.2611

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_center and scored$compare4
## X-squared = 0.93472, df = 1, p-value = 0.3336

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_center and scored$poverty150
## X-squared = 1.6588, df = 1, p-value = 0.1978

## 
##  Pearson's Chi-squared test
## 
## data:  scored$expect_center and scored$region
## X-squared = 2.4723, df = 3, p-value = 0.4803

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_center and scored$rural
## X-squared = 0.013, df = 1, p-value = 0.9092

Care by relatives

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_relative and scored$compare1
## X-squared = 0.028474, df = 1, p-value = 0.866

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_relative and scored$compare2
## X-squared = 2.3755, df = 1, p-value = 0.1233

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_relative and scored$compare3
## X-squared = 2.4451, df = 1, p-value = 0.1179

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_relative and scored$compare4
## X-squared = 1.6884, df = 1, p-value = 0.1938

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_relative and scored$poverty150
## X-squared = 3.1149, df = 1, p-value = 0.07758

## 
##  Pearson's Chi-squared test
## 
## data:  scored$expect_relative and scored$region
## X-squared = 0.51307, df = 3, p-value = 0.916

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_relative and scored$rural
## X-squared = 0.005328, df = 1, p-value = 0.9418

Professional (non-centerbased)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_pro and scored$compare1
## X-squared = 3.448e-27, df = 1, p-value = 1

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_pro and scored$compare2
## X-squared = 0.091306, df = 1, p-value = 0.7625

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_pro and scored$compare3
## X-squared = 2.0345e-26, df = 1, p-value = 1

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_pro and scored$compare4
## X-squared = 2.7225e-27, df = 1, p-value = 1

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_pro and scored$poverty150
## X-squared = 6.1753e-27, df = 1, p-value = 1

## 
##  Pearson's Chi-squared test
## 
## data:  scored$expect_pro and scored$region
## X-squared = 3.631, df = 3, p-value = 0.3042

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scored$expect_pro and scored$rural
## X-squared = 0.029907, df = 1, p-value = 0.8627

Racial inequality begins in early childhood