INDUSTRY CLASSIFICATION A-1 APPENDIX A INDUSTRY CLASSIFICATION Industry Classification Codes for Detailed Industry (4 digit) (Starting January 2014) These categories are aggregated into 52 detailed...

1 answer below »
Follow the directions in the R-file "ch09-earnings-inference.R" script, Need to contextualize gender difference in earnings by age first then contextualize a different group by previous citizenship status. And compare the two, he said something about subsetting data too. After compare and contrast the two observations.


INDUSTRY CLASSIFICATION A-1 APPENDIX A INDUSTRY CLASSIFICATION Industry Classification Codes for Detailed Industry (4 digit) (Starting January 2014) These categories are aggregated into 52 detailed groups and 14 major groups (see pages 10-12 of this attachment). The codes in the right hand column are the NAICS equivalent. These codes correspond to Items PEIO1ICD and PEIO2ICD, in positions 856-859 and 864-867 of the Basic CPS record layout in all months, except March. In the March, these codes correspond to PEIOIND and INDUSTRY, in positions 168-171 and 292-295 of the Person record. CENSUS NAICS CODE DESCRIPTION CODE Agriculture, Forestry, Fishing, and Hunting 0170 Crop production 111 0180 Animal production 112 0190 Forestry except logging 1131, 1132 0270 Logging 1133 0280 Fishing, hunting, and trapping 114 0290 Support activities for agriculture and forestry 115 Mining 0370 Oil and gas extraction 211 0380 Coal mining 2121 0390 Metal ore mining 2122 0470 Nonmetallic mineral mining and quarrying and not specified type of mining Part of 21 0490 Support activities for mining 213 Utilities 0570 Electric power generation, transmission and distribution Pt. 2211 0580 Natural gas distribution Pt. 2212 0590 Electric and gas, and other combinations Pts. 2211, 2212 0670 Water, steam, air-conditioning, and irrigation systems 22131, 22133 0680 Sewage treatment facilities 22132 0690 Not specified utilities Part of 22 CODE DESCRIPTION INDUSTRY CODE A-2 INDUSTRY CLASSIFICATION Construction 0770 ** Construction 23 (Includes the cleaning of buildings and dwellings is incidental during construction and immediately after construction) Manufacturing Nondurable Goods manufacturing 1070 Animal food, grain and oilseed milling 3111, 3112 1080 Sugar and confectionery products 3113 1090 Fruit and vegetable preserving and specialty food manufacturing 3114 1170 Dairy product manufacturing 3115 1180 Animal slaughtering and processing 3116 1190 Retail bakeries 311811 1270 Bakeries, except retail 3118 exc. 311811 1280 Seafood and other miscellaneous foods, n.e.c. 3117, 3119 1290 Not specified food industries Part of 311 1370 Beverage manufacturing 3121 1390 Tobacco manufacturing 3122 1470 Fiber, yarn, and thread mills 3131 1480 Fabric mills, except knitting 3132 exc. 31324 1490 Textile and fabric finishing and coating mills 3133 1570 Carpet and rug mills 31411 1590 Textile product mills, except carpets and rugs 314 exc. 31411 1670 Knitting mills 31324, 3151 1680 Cut and sew apparel manufacturing 3152 1690 Apparel accessories and other apparel manufacturing 3159 1770 Footwear manufacturing 3162 1790 Leather tanning and products, except footwear manufacturing 3161, 3169 1870 Pulp, paper, and paperboard mills 3221 1880 Paperboard containers and boxes 32221 1890 Miscellaneous paper and pulp products 32222, 32223, 32229 1990 Printing and related support activities 3231 2070 Petroleum refining 32411 2090 Miscellaneous petroleum and coal products 32419 2170 Resin, synthetic rubber and fibers, and filaments manufacturing 3252 2180 Agricultural chemical manufacturing 3253 2190 Pharmaceutical and medicine manufacturing 3254 2270 Paint, coating, and adhesive manufacturing B46 3255 2280 Soap, cleaning compound, and cosmetics manufacturing 3256 2290 Industrial and miscellaneous chemicals 3251, 3259 2370 Plastics product manufacturing 3261 2380 Tire manufacturing 32621 2390 Rubber products, except tires, manufacturing 32622, 32629 CODE DESCRIPTION INDUSTRY CODE INDUSTRY CLASSIFICATION A-3 Durable Goods Manufacturing 2470 Pottery, ceramics, and related products manufacturing 32711 2480 Structural clay product manufacturing 32712 2490 Glass and glass product manufacturing 3272 2570 Cement, concrete, lime, and gypsum product manufacturing 3273, 3274 2590 Miscellaneous nonmetallic mineral product manufacturing 3279 2670 Iron and steel mills and steel product manufacturing 3311, 3312 2680 Aluminum production and processing 3313 2690 Nonferrous metal, except aluminum, production and processing 3314 2770 Foundries 3315 2780 Metal forgings and stampings 3321 2790 Cutlery and hand tool manufacturing 3322 2870 Structural metals, and tank and shipping container manufacturing 3323, 3324 2880 Machine shops; turned product; screw, nut and bolt manufacturing 3327 2890 Coating, engraving, heat treating and allied activities 3328 2970 Ordnance 332992 to 332995 2980 Miscellaneous fabricated metal products manufacturing 3325, 3326, 3329 exc. 332992, 332993, 332994, 332995 2990 Not specified metal industries Part of 331 and 332 3070 Agricultural implement manufacturing 33311 3080 Construction, mining and oil field machinery manufacturing 33312, 33313 3095 Commercial and service industry machinery manufacturing 3333 3170 Metalworking machinery manufacturing 3335 3180 Engines, turbines, and power transmission equipment manufacturing 3336 3190 Machinery manufacturing, n.e.c. Part of 333 3365 Computer and peripheral equipment manufacturing 3341 3370 Communications, audio, and video equipment manufacturing 3342, 3343 3380 Navigational, measuring, electromedical, and control instruments manufacturing 3345 3390 Electronic component and product manufacturing, n.e.c. 3344, 3346 3470 Household appliance manufacturing 3352 3490 Electrical lighting, equipment, and supplies manufacturing, n.e.c. 3351, 3353, 3359 3570 Motor vehicles and motor vehicle equipment manufacturing 3361, 3362, 3363 3580 Aircraft and parts manufacturing 336411 to 336413 3590 Aerospace products and parts manufacturing 336414, 336415, 336419 3670 Railroad rolling stock manufacturing 3365 3680 Ship and boat building 3366 3690 Other transportation equipment manufacturing 3369 CODE DESCRIPTION INDUSTRY CODE A-4 INDUSTRY CLASSIFICATION 3770 Sawmills and wood preservation 3211 3780 Veneer, plywood, and engineered wood products 3212 3790 Prefabricated wood buildings and mobile homes 321991, 321992 3875 Miscellaneous wood products 3219 exc. 321991, 321992 3895 Furniture and related product manufacturing 337 3960 Medical equipment and supplies manufacturing 3391 3970 Toys, amusement, and sporting goods manufacturing 33992, 33993 3980 Miscellaneous manufacturing, n.e.c. 3399 exc. 33992, 33993 3990 Not specified manufacturing industries Part of 31, 32, 33 Wholesale Trade Durable Goods Wholesale 4070 Motor vehicles, parts and supplies, merchant wholesalers 4231 4080 Furniture and home furnishing, merchant wholesalers 4232 4090 Lumber and other construction materials, merchant wholesalers 4233 4170 Professional and commercial equipment and supplies, merchant wholesalers 4234 4180 Metals and minerals, except petroleum, merchant wholesalers 4235 4195 Electrical goods, merchant wholesalers 4236 4265 Hardware, plumbing and heating equipment, and supplies, merchant wholesalers 4237 4270 Machinery, equipment, and supplies, merchant wholesalers 4238 4280 Recyclable material, merchant wholesalers 42393 4290 Miscellaneous durable goods, merchant wholesalers 4239 exc. 42393 Nondurable Goods Wholesale 4370 Paper and paper products, merchant wholesalers 4241 4380 Drugs, sundries, and chemical and allied products, merchant wholesalers 4242, 4246 4390 Apparel, fabrics, and notions, merchant wholesalers 4243 4470 Groceries and related products, merchant wholesalers 4244 4480 Farm product raw materials, merchant wholesalers 4245 4490 Petroleum and petroleum products, merchant wholesalers 4247 4560 Alcoholic beverages, merchant wholesalers 4248 4570 Farm supplies, merchant wholesalers 42491 4580 Miscellaneous nondurable goods, merchant wholesalers 4249 exc. 42491 4585 Wholesale electronic markets, agents and brokers 4251 4590 Not specified wholesale trade Part of 42 CODE DESCRIPTION INDUSTRY CODE INDUSTRY CLASSIFICATION A-5 Retail Trade 4670 Automobile dealers 4411 4680 Other motor vehicle dealers 4412 4690 Auto parts, accessories, and tire stores 4413 4770 Furniture and home furnishings stores 442 4780 Household appliance stores 443111 4795 Radio, TV, and computer stores 443112, 44312 4870 Building material and supplies dealers 4441 exc. 44413 4880 Hardware stores 44413 4890 Lawn and garden equipment and supplies stores 4442 4970 Grocery stores 4451 4980 Specialty food stores 4452 4990 Beer, wine, and liquor stores 4453 5070
Answered 2 days AfterDec 06, 2021

Answer To: INDUSTRY CLASSIFICATION A-1 APPENDIX A INDUSTRY CLASSIFICATION Industry Classification Codes for...

Mohd answered on Dec 09 2021
122 Votes
C9
C9
-
12/7/2021
# Import libraries
library(tidyverse)
library(lspline)
library(cowplot)
library(huxtable)
library(stargazer)
library(modelsummary)
library(readr)
library(magrittr)
library(dplyr)
library(ggplot2)
library(rmarkdown)
library(skimr)
Topic 4
Chapter 09
CH09A Estimating gender and age differences in earnings
using the cp
s-earnings dataset
——————————————————————————————————
SET UP
It is advised to start a new session for every case study
CLEAR MEMORY
rm(list=ls())
rm(list=ls())
#import data
library(readr)
data_all <- read_csv("~/data/morg-2014-emp.csv")
set working directory
#SELECT OCCUPATION # keep only two occupation types: Market research analysts and marketing specialists #and Computer and Mathematical Occupations
data_all <- data_all %>%
mutate(sample=ifelse(occ2012==0735,1,ifelse(data_all$occ2012>=1005 & data_all$occ2012<=1240,2,0)))
data_all<- data_all %>% filter(sample==1 | sample==2)
tabulate(data_all$sample)
## [1] 281 4740
#gen female variable
#gen wage variables
data_all <- data_all %>% mutate(female=as.numeric(sex==2)) %>%
mutate(w=earnwke/uhours) %>%
mutate(lnw=log(w)) %>%
mutate(agesq=age^2)
#SET SAMPLE - Choose one of the occupations!
#Market research analysts and marketing specialists -1
#Computer and Mathematical Occupations-2
i=1
subdata <- data_all %>% filter(sample==i)
write_csv(subdata, "earnings_inference.csv")
#DISTRIBUTION OF EARNINGS #######################
subdata %>% dplyr::select(earnwke,uhours,w) %>% summary()
## earnwke uhours w
## Min. : 40 Min. : 5.00 Min. : 7.25
## 1st Qu.: 700 1st Qu.:40.00 1st Qu.:17.79
## Median :1096 Median :40.00 Median :25.95
## Mean :1206 Mean :40.15 Mean :29.06
## 3rd Qu.:1538 3rd Qu.:40.00 3rd Qu.:37.02
## Max. :2885 Max. :80.00 Max. :84.60
subdata %>% filter(w>=1) %>% dplyr::select(earnwke,uhours,w) %>% summary()
## earnwke uhours w
## Min. : 40 Min. : 5.00 Min. : 7.25
## 1st Qu.: 700 1st Qu.:40.00 1st Qu.:17.79
## Median :1096 Median :40.00 Median :25.95
## Mean :1206 Mean :40.15 Mean :29.06
## 3rd Qu.:1538 3rd Qu.:40.00 3rd Qu.:37.02
## Max. :2885 Max. :80.00 Max. :84.60
tabulate(subdata$female)
## [1] 172
table(subdata$occ2012,subdata$female)
##
## 0 1
## 735 109 172
#linear regressions ##############################
First, look at them one by one
female binary
# plain SE
reg1<-lm(lnw~female,subdata)
summary(reg1)
##
## Call:
## lm(formula = lnw ~ female, data = subdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.23545 -0.36310 0.02556 0.35946 1.23611
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.31489 0.04804 69.004<2e-16 ***
## female -0.11306 0.06140 -1.841 0.0666 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5015 on 279 degrees of freedom
## Multiple R-squared: 0.01201, Adjusted R-squared: 0.008465
## F-statistic: 3.39 on 1 and 279 DF, p-value: 0.06664
# with robust SE (Stata) IT AS SOME PROBLEM DON'T RUN!
#install.packages("estimatr")
The model with lnw as dependent variable and female as independent variable was developed. we have F(1,279)=3.39 | P value >0.05...
SOLUTION.PDF

Answer To This Question Is Available To Download

Related Questions & Answers

More Questions »

Submit New Assignment

Copy and Paste Your Assignment Here