SIOP 2020 Lale extension

Codebooks located at

5-year follow-up to Lale findings (which go through 2013; Lale (2015))

Lale

In 2013, 6.8 million workers in the United States held more than one job. Twenty years before, the figure was 7.5 million, although the total number of workers with a job was lower by 15.9 million. The multiple-jobholding rate-the proportion of multiple jobholders among all employed workers-rose from 6.2 percent in 1994 to a high of 6.8 percent during the summer of 1995. It has declined steadily since then and was at 5.0 percent by the end of 2013. Inspection of data from the Current Population Survey (CPS, see accompanying box) reveals that the downward trend holds across various sociodemographic groups of the working-age population (those 16 to 64 years old).

Lale Figure 1.

General linear downward effect has continued, $R^2$ = 0.4654888. If we just focus on last few years, has this percentage of second job holders leveled off? (not sure best way analytically to test for this - polynomial? [8/3/19])

Note. Tried the 3-month floating average (“moving average.R”) but plot is extremely ragged (8/3/19)

Lale Figure 2a.

This is very ragged without geom_smooth

Smoothing across 3-month rolling average

Lale Figure 2C.

Maybe point out that it looks like White and Asian women who are predominantly driving the gender effect?

Lale Figure 2C.

Education with 3-month moving average estimate.

Age with 3-month moving average estimate.

Marital status with 3-month moving average estimate.

NOTE. Subsetting to only 2010 $\rightarrow$ results in rMarkdown errors - will have to copy chunks separately for Lale (2015) graph continuation…

Recession data was taken from the National Bureau of Economic Research, which identified an 18-month United States recession lasting from December 2007 to June 2009. The categories of “Pre-recession”, “During”, and “Post-recession” are defined by this 18 month window.

Percentage second job holders per occupational category.

Overall percentages of second job-holders was seemingly not affected by the recession, with percentages being 6.04% Pre-recession, 5.81% During recession, and 5.4% Post-recession.

Regarding hours worked per week, first job reported figures ranged from 0 to 99, while self-reported other job earnings (from all other jobs [than the primary]) ranged from 0 to 99. We adjusted for seemingly out-of-range values by truncating cases to caps of 90 for primary job and 70 for “other” jobs. Summing the two variables resulted in total number of hours worked the previous week (across all jobs) estimates ranging from 1 to 130.

## `summarise()` regrouping output by 'second' (override with `.groups` argument)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `summarise()` regrouping output by 'second' (override with `.groups` argument)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `summarise()` regrouping output by 'second' (override with `.groups` argument)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Sleep variable is:

lale$t010101 (Sleeping)

Socializing variable is:

lale$t120101 (Socializing and communicating with others)
lale$t120301 (Relaxing, thinking)
lale$t120302 (Tobacco and drug use)
lale$t120303 (Television and movies (not religious))
lale$t120304 (Television (religious))
lale$t120305 (Listening to the radio)
lale$t120306 (Listening to/playing music (not radio))
lale$t120307 (Playing games)
lale$t120308 (Computer use for leisure (exc. Games))
lale$t120309 (Arts and crafts as a hobby)
lale$t120310 (Collecting as a hobby)
lale$t120311 (Hobbies, except arts & crafts and collecting)
lale$t120312 (Reading for personal interest)
lale$t120313 (Writing for personal interest)

Exercise variable is:

lale$t130101 (Doing aerobics)
lale$t130102 (Playing baseball)
lale$t130103 (Playing basketball)
lale$t130104 (Biking)
lale$t130105 (Playing billiards)
lale$t130106 (Boating)
lale$t130107 (Bowling)
lale$t130108 (Climbing, spelunking, caving)
lale$t130109 (Dancing)
lale$t130110 (Participating in equestrian sports)
lale$t130111 (Fencing)
lale$t130112 (Fishing)
lale$t130113 (Playing football)
lale$t130114 (Golfing)
lale$t130115 (Doing gymnastics)
lale$t130116 (Hiking)
lale$t130117 (Playing hockey)
lale$t130118 (Hunting)
lale$t130119 (Participating in martial arts)
lale$t130120 (Playing racquet sports)
lale$t130121 (Participating in rodeo competitions)
lale$t130122 (Rollerblading)
lale$t130123 (Playing rugby)
lale$t130124 (Running)
lale$t130125 (Skiing, ice skating, snowboarding)
lale$t130126 (Playing soccer)
lale$t130127 (Softball)
lale$t130128 (Using cardiovascular equipment)
lale$t130129 (Vehicle touring/racing)
lale$t130130 (Playing volleyball)
lale$t130131 (Walking)
lale$t130132 (Participating in water sports)
lale$t130133 (Weightlifting/strength training)
lale$t130134 (Working out, unspecified)
lale$t130135 (Wrestling)
lale$t130136 (Doing yoga)

Looking at seasonal patterns (month variable is HRMONTH): Time spent on activities that are considered resources or recovery: Sleeping, Socializing, and Exercising

## tibble [576 x 6] (S3: grouped_df/tbl_df/tbl/data.frame)
##  $ second : num [1:576] 0 0 0 0 0 0 0 0 0 0 ...
##  $ Year   : Factor w/ 16 levels "2003","2004",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ HRMONTH: Factor w/ 12 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ average: num [1:576] 501 504 508 505 502 ...
##  $ sd     : num [1:576] 126 131 129 127 125 ...
##  $ Second : chr [1:576] "One primary job" "One primary job" "One primary job" "One primary job" ...
##  - attr(*, "groups")= tibble [48 x 3] (S3: tbl_df/tbl/data.frame)
##   ..$ second: num [1:48] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ Year  : Factor w/ 16 levels "2003","2004",..: 1 2 3 4 5 6 7 8 9 10 ...
##   ..$ .rows : list<int> [1:48] 
##   .. ..$ : int [1:12] 1 2 3 4 5 6 7 8 9 10 ...
##   .. ..$ : int [1:12] 13 14 15 16 17 18 19 20 21 22 ...
##   .. ..$ : int [1:12] 25 26 27 28 29 30 31 32 33 34 ...
##   .. ..$ : int [1:12] 37 38 39 40 41 42 43 44 45 46 ...
##   .. ..$ : int [1:12] 49 50 51 52 53 54 55 56 57 58 ...
##   .. ..$ : int [1:12] 61 62 63 64 65 66 67 68 69 70 ...
##   .. ..$ : int [1:12] 73 74 75 76 77 78 79 80 81 82 ...
##   .. ..$ : int [1:12] 85 86 87 88 89 90 91 92 93 94 ...
##   .. ..$ : int [1:12] 97 98 99 100 101 102 103 104 105 106 ...
##   .. ..$ : int [1:12] 109 110 111 112 113 114 115 116 117 118 ...
##   .. ..$ : int [1:12] 121 122 123 124 125 126 127 128 129 130 ...
##   .. ..$ : int [1:12] 133 134 135 136 137 138 139 140 141 142 ...
##   .. ..$ : int [1:12] 145 146 147 148 149 150 151 152 153 154 ...
##   .. ..$ : int [1:12] 157 158 159 160 161 162 163 164 165 166 ...
##   .. ..$ : int [1:12] 169 170 171 172 173 174 175 176 177 178 ...
##   .. ..$ : int [1:12] 181 182 183 184 185 186 187 188 189 190 ...
##   .. ..$ : int [1:12] 193 194 195 196 197 198 199 200 201 202 ...
##   .. ..$ : int [1:12] 205 206 207 208 209 210 211 212 213 214 ...
##   .. ..$ : int [1:12] 217 218 219 220 221 222 223 224 225 226 ...
##   .. ..$ : int [1:12] 229 230 231 232 233 234 235 236 237 238 ...
##   .. ..$ : int [1:12] 241 242 243 244 245 246 247 248 249 250 ...
##   .. ..$ : int [1:12] 253 254 255 256 257 258 259 260 261 262 ...
##   .. ..$ : int [1:12] 265 266 267 268 269 270 271 272 273 274 ...
##   .. ..$ : int [1:12] 277 278 279 280 281 282 283 284 285 286 ...
##   .. ..$ : int [1:12] 289 290 291 292 293 294 295 296 297 298 ...
##   .. ..$ : int [1:12] 301 302 303 304 305 306 307 308 309 310 ...
##   .. ..$ : int [1:12] 313 314 315 316 317 318 319 320 321 322 ...
##   .. ..$ : int [1:12] 325 326 327 328 329 330 331 332 333 334 ...
##   .. ..$ : int [1:12] 337 338 339 340 341 342 343 344 345 346 ...
##   .. ..$ : int [1:12] 349 350 351 352 353 354 355 356 357 358 ...
##   .. ..$ : int [1:12] 361 362 363 364 365 366 367 368 369 370 ...
##   .. ..$ : int [1:12] 373 374 375 376 377 378 379 380 381 382 ...
##   .. ..$ : int [1:12] 385 386 387 388 389 390 391 392 393 394 ...
##   .. ..$ : int [1:12] 397 398 399 400 401 402 403 404 405 406 ...
##   .. ..$ : int [1:12] 409 410 411 412 413 414 415 416 417 418 ...
##   .. ..$ : int [1:12] 421 422 423 424 425 426 427 428 429 430 ...
##   .. ..$ : int [1:12] 433 434 435 436 437 438 439 440 441 442 ...
##   .. ..$ : int [1:12] 445 446 447 448 449 450 451 452 453 454 ...
##   .. ..$ : int [1:12] 457 458 459 460 461 462 463 464 465 466 ...
##   .. ..$ : int [1:12] 469 470 471 472 473 474 475 476 477 478 ...
##   .. ..$ : int [1:12] 481 482 483 484 485 486 487 488 489 490 ...
##   .. ..$ : int [1:12] 493 494 495 496 497 498 499 500 501 502 ...
##   .. ..$ : int [1:12] 505 506 507 508 509 510 511 512 513 514 ...
##   .. ..$ : int [1:12] 517 518 519 520 521 522 523 524 525 526 ...
##   .. ..$ : int [1:12] 529 530 531 532 533 534 535 536 537 538 ...
##   .. ..$ : int [1:12] 541 542 543 544 545 546 547 548 549 550 ...
##   .. ..$ : int [1:12] 553 554 555 556 557 558 559 560 561 562 ...
##   .. ..$ : int [1:12] 565 566 567 568 569 570 571 572 573 574 ...
##   .. ..@ ptype: int(0) 
##   ..- attr(*, ".drop")= logi TRUE

Looking at seasonal patterns (month variable is HRMONTH): Time spent Caring for HH members

Looking at Seasonal Patterns: Time spent in HH Activities

#First, see what class these things are and confirm category variables are factors
str(lale$second)

##  num [1:159512] 0 0 0 NA 0 0 0 0 0 0 ...

str(lale$Year)

##  num [1:159512] 2003 2003 2003 2003 2003 ...

str(lale$age)

##  chr [1:159512] "55 to 64 years" "25 to 54 years" "25 to 54 years" ...

str(lale$race); lale$race <- factor(lale$race)

##  chr [1:159512] "Black" "White" "White" "Black" "White" "White" "White" ...

str(lale$marry); lale$marry <- factor(lale$marry)

##  chr [1:159512] "Married" "Married" "Married" "Married" "Married" ...

str(lale$educ3); lale$educ3 <- factor(lale$educ3)

##  chr [1:159512] "College or higher education" "Some college" "Some college" ...

str(lale$income2); lale$income2 <- factor(lale$income2)

##  chr [1:159512] "$50,000 to $75,000" NA "$75,000 to $99,999" ...

str(lale$tot.hrs)

##  int [1:159512] NA NA NA NA NA NA NA NA NA NA ...

#Reorder the race and income factors
lale$race <- factor(lale$race, levels = c("White", "Black", "Asian","Multiracial", "Native American/Pacific Islander"))
levels(lale$race)

## [1] "White"                            "Black"                           
## [3] "Asian"                            "Multiracial"                     
## [5] "Native American/Pacific Islander"

lale$income2 <- factor(lale$income2, levels = c("$30,000 to $50,000", "Less than $15,000", "$15,000 to $30,000", "$50,000 to $75,000", "$75,000 to $99,999", "$100,000 to $149,999", "$150,000 and over"))
levels(lale$income2)

## [1] "$30,000 to $50,000"   "Less than $15,000"    "$15,000 to $30,000"  
## [4] "$50,000 to $75,000"   "$75,000 to $99,999"   "$100,000 to $149,999"
## [7] "$150,000 and over"

#Check to see if we have people that fall into both single/multiple by IV
xtabs(~lale$second + lale$Year, data = merged)

##            lale$Year
## lale$second  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013
##           0 11776  7740  7351  7235  6940  7210  7070  7046  6584  6478  5946
##           1   757   485   496   480   404   423   468   425   362   376   330
##            lale$Year
## lale$second  2014  2015  2016  2017  2018
##           0  6061  5774  5448  5247  4922
##           1   343   306   305   327   269

xtabs(~lale$second + lale$age, data = merged)

##            lale$age
## lale$second 16 to 24 years 25 to 54 years 55 to 64 years
##           0           8204          82576          18048
##           1            462           5063           1031

xtabs(~lale$second + lale$race, data = merged)

##            lale$race
## lale$second White Black Asian Multiracial Native American/Pacific Islander
##           0 88943 13235  4334        1322                              994
##           1  5480   777   147         107                               45

xtabs(~lale$second + lale$marry, data = merged)

##            lale$marry
## lale$second Married Single Widowed, divorced, or separated
##           0   61820  26737                           20271
##           1    3464   1648                            1444

xtabs(~lale$second + lale$educ3, data = merged)

##            lale$educ3
## lale$second College or higher education High school graduate
##           0                       42526                26066
##           1                        3018                 1141
##            lale$educ3
## lale$second Less than high school Some college
##           0                  8934        31302
##           1                   246         2151

xtabs(~lale$second + lale$income2, data = merged)

##            lale$income2
## lale$second $30,000 to $50,000 Less than $15,000 $15,000 to $30,000
##           0              20897              5101              14832
##           1               1368               266                928
##            lale$income2
## lale$second $50,000 to $75,000 $75,000 to $99,999 $100,000 to $149,999
##           0              22052              17009                12887
##           1               1435               1081                  707
##            lale$income2
## lale$second $150,000 and over
##           0              9475
##           1               449

xtabs(~lale$second + lale$tot.hrs, data = merged)

##            lale$tot.hrs
## lale$second   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17
##           1   2   7   5   9  12  11   3  14  10  29   9  28  23  17  30  28  16
##            lale$tot.hrs
## lale$second  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34
##           1  35  17  62  28  49  35  53  63  38  32  55  29 114  27  69  48  58
##            lale$tot.hrs
## lale$second  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51
##           1 118  91  53  79  45 272  68 144  97 123 198 146 110 203  60 412  56
##            lale$tot.hrs
## lale$second  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68
##           1 210  72  87 268 148  67  84  43 414  39  59  48  69 141  43  27  48
##            lale$tot.hrs
## lale$second  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85
##           1  15 183  12  47  15  21  53  17  14  14   3 122   5  10   4  15  16
##            lale$tot.hrs
## lale$second  86  87  88  90  91  92  93  94  95  96  97  98  99 100 104 108 109
##           1   4   2   5  22   2   9   1   4  11   7   1   2   1  19   1   3   1
##            lale$tot.hrs
## lale$second 110 111 112 115 116 118 120 130
##           1   8   1   1   1   1   1   4   1

logit <- glm(second ~ Year + gender + age + race + marry + educ3 + income2, data = lale, family = "binomial") # logistic regression
summary(logit) #here's the summary table

## 
## Call:
## glm(formula = second ~ Year + gender + age + race + marry + educ3 + 
##     income2, family = "binomial", data = lale)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.5312  -0.3827  -0.3381  -0.2874   2.9598  
## 
## Coefficients:
##                                       Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                          24.308117   5.810322   4.184 2.87e-05 ***
## Year                                 -0.013283   0.002893  -4.591 4.41e-06 ***
## genderWomen                           0.015898   0.026584   0.598  0.54980    
## age25 to 54 years                    -0.089541   0.056835  -1.575  0.11515    
## age55 to 64 years                    -0.165675   0.065613  -2.525  0.01157 *  
## raceBlack                            -0.066199   0.041586  -1.592  0.11142    
## raceAsian                            -0.659115   0.087988  -7.491 6.84e-14 ***
## raceMultiracial                       0.236353   0.104378   2.264  0.02355 *  
## raceNative American/Pacific Islander -0.237016   0.153826  -1.541  0.12336    
## marrySingle                           0.065713   0.036116   1.820  0.06883 .  
## marryWidowed, divorced, or separated  0.218587   0.035742   6.116 9.61e-10 ***
## educ3High school graduate            -0.652994   0.039239 -16.641  < 2e-16 ***
## educ3Less than high school           -1.171472   0.073533 -15.931  < 2e-16 ***
## educ3Some college                    -0.169742   0.031622  -5.368 7.97e-08 ***
## income2Less than $15,000             -0.106511   0.069795  -1.526  0.12700    
## income2$15,000 to $30,000             0.042984   0.044465   0.967  0.33370    
## income2$50,000 to $75,000            -0.059184   0.039722  -1.490  0.13624    
## income2$75,000 to $99,999            -0.136755   0.043898  -3.115  0.00184 ** 
## income2$100,000 to $149,999          -0.286607   0.050516  -5.674 1.40e-08 ***
## income2$150,000 and over             -0.450071   0.059520  -7.562 3.98e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 47719  on 108486  degrees of freedom
## Residual deviance: 47010  on 108467  degrees of freedom
##   (51025 observations deleted due to missingness)
## AIC: 47050
## 
## Number of Fisher Scoring iterations: 6

exp(coef(logit)) #these are supposed to be the odds ratios ????

##                          (Intercept)                                 Year 
##                         3.604799e+10                         9.868047e-01 
##                          genderWomen                    age25 to 54 years 
##                         1.016025e+00                         9.143506e-01 
##                    age55 to 64 years                            raceBlack 
##                         8.473217e-01                         9.359446e-01 
##                            raceAsian                      raceMultiracial 
##                         5.173088e-01                         1.266621e+00 
## raceNative American/Pacific Islander                          marrySingle 
##                         7.889783e-01                         1.067920e+00 
## marryWidowed, divorced, or separated            educ3High school graduate 
##                         1.244318e+00                         5.204852e-01 
##           educ3Less than high school                    educ3Some college 
##                         3.099104e-01                         8.438823e-01 
##             income2Less than $15,000            income2$15,000 to $30,000 
##                         8.989649e-01                         1.043921e+00 
##            income2$50,000 to $75,000            income2$75,000 to $99,999 
##                         9.425331e-01                         8.721838e-01 
##          income2$100,000 to $149,999             income2$150,000 and over 
##                         7.508070e-01                         6.375832e-01

exp(cbind(OR = coef(logit), confint.default(logit))) #95% confidence intervals of standard errors

##                                                OR        2.5 %       97.5 %
## (Intercept)                          3.604799e+10 4.084381e+05 3.181528e+15
## Year                                 9.868047e-01 9.812247e-01 9.924164e-01
## genderWomen                          1.016025e+00 9.644432e-01 1.070367e+00
## age25 to 54 years                    9.143506e-01 8.179647e-01 1.022094e+00
## age55 to 64 years                    8.473217e-01 7.450719e-01 9.636037e-01
## raceBlack                            9.359446e-01 8.626840e-01 1.015426e+00
## raceAsian                            5.173088e-01 4.353658e-01 6.146748e-01
## raceMultiracial                      1.266621e+00 1.032285e+00 1.554153e+00
## raceNative American/Pacific Islander 7.889783e-01 5.836170e-01 1.066602e+00
## marrySingle                          1.067920e+00 9.949402e-01 1.146252e+00
## marryWidowed, divorced, or separated 1.244318e+00 1.160133e+00 1.334611e+00
## educ3High school graduate            5.204852e-01 4.819564e-01 5.620940e-01
## educ3Less than high school           3.099104e-01 2.683149e-01 3.579542e-01
## educ3Some college                    8.438823e-01 7.931683e-01 8.978388e-01
## income2Less than $15,000             8.989649e-01 7.840305e-01 1.030748e+00
## income2$15,000 to $30,000            1.043921e+00 9.567953e-01 1.138980e+00
## income2$50,000 to $75,000            9.425331e-01 8.719369e-01 1.018845e+00
## income2$75,000 to $99,999            8.721838e-01 8.002805e-01 9.505475e-01
## income2$100,000 to $149,999          7.508070e-01 6.800312e-01 8.289489e-01
## income2$150,000 and over             6.375832e-01 5.673787e-01 7.164743e-01

## APA Table for Output

library(gtsummary)
tbl_regression(logit, exponentiate = TRUE, label = list(Year ~ "Year", gender ~ "Gender", age ~ "Age", race ~ "Race", marry ~ "Marital Status", educ3 ~ "Education level", income2 ~ "Income"))

Characteristic	OR¹	95% CI¹	p-value
Year	0.99	0.98, 0.99	<0.001
Gender
Men	—	—
Women	1.02	0.96, 1.07	0.5
Age
16 to 24 years	—	—
25 to 54 years	0.91	0.82, 1.02	0.12
55 to 64 years	0.85	0.75, 0.96	0.012
Race
White	—	—
Black	0.94	0.86, 1.01	0.11
Asian	0.52	0.43, 0.61	<0.001
Multiracial	1.27	1.03, 1.55	0.024
Native American/Pacific Islander	0.79	0.58, 1.05	0.12
Marital Status
Married	—	—
Single	1.07	0.99, 1.15	0.069
Widowed, divorced, or separated	1.24	1.16, 1.33	<0.001
Education level
College or higher education	—	—
High school graduate	0.52	0.48, 0.56	<0.001
Less than high school	0.31	0.27, 0.36	<0.001
Some college	0.84	0.79, 0.90	<0.001
Income
$30,000 to $50,000	—	—
Less than $15,000	0.90	0.78, 1.03	0.13
$15,000 to $30,000	1.04	0.96, 1.14	0.3
$50,000 to $75,000	0.94	0.87, 1.02	0.14
$75,000 to $99,999	0.87	0.80, 0.95	0.002
$100,000 to $149,999	0.75	0.68, 0.83	<0.001
$150,000 and over	0.64	0.57, 0.72	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

The odds ratios would be the odds of X depending on whether someone is a multiple job holder or not. The Confidence intervals should NOT include 1, if they do then they are not significant!

#References

Lale, Etienne. 2015. “Multiple Jobholding over the Past Two Decades : Monthly Labor Review: U.S. Bureau of Labor Statistics.” 2015. https://www.bls.gov/opub/mlr/2015/article/multiple-jobholding-over-the-past-two-decades.htm.

SIOP 2020 Careers Projects

Kulas

Most recently updated Thursday January 21 2021 2:10:50 PM

SIOP 2020 Lale extension

Lale