# delimit ; clear ; set mem 500m; /* This file generates all Tables and Figures using CEX data for the paper "Conspicuous Consumption and Race" */ ; /* Note: You will need five data files to run this program: 1. merged_file.dta This is the raw CEX data from the NBER web site (See http://www.nber.org/data/ces_cbo.html for raw data and documentations) /* To run this do file, use the This file is the main file used for our analysis 2. race_merge_into_nber_files.dta This is additional CEX data from the raw data available from the BLS. We had to extract additional variables from raw CEX files to merge into the NBER CEX files. These variables included: detailed race codes, the number of surveys each respondent completed, state of residence, number of adults in the household, and other location controls. We thank Mel Stephens for providing us the raw CEX data. 3. cps_income_bystate_main.dta This is file that includes mean income (and standard deviation) for different race-state cells. This data comes from the 1990-2002 CPS. 4. hp_census_data.dta This is a file that includes mean house prices by state from the 1990 and 2000 census. 5. cpi_u.dta This is a file that includes data from the CPI-U so as to deflate the relevant variables. This file will run completely and generate all tables/figures in the paper. All that needs to be done is to change the location of where to find the above 4 files in the use or merge statements throughout the program. */; /* Step 1: Read in and extract the relevant variables from the raw CEX data that we will eventually merge into the NBER CEX files */; ; */ ; use "c:\ErikMain\race_signaling\race_merge_into_nber_files.dta", clear; drop year; gen year = year_first; replace year = 1986 if year == 86 ; replace year = 1987 if year == 87 ; replace year = 1988 if year == 88 ; replace year = 1989 if year == 89 ; replace year = 1990 if year == 90 ; replace year = 1991 if year == 91 ; replace year = 1992 if year == 92 ; replace year = 1993 if year == 93 ; replace year = 1994 if year == 94 ; replace year = 1995 if year == 95 ; gen id_1 = id; keep id_1 as_comp1 as_comp2 state popsize smsastat hispanic black white asian count year; sort id_1 year; save cex_merge.dta, replace; /* Step 2: Use the NBER CEX files and keep only household heads */ ; cd c:\ErikMain\lifecycle_consumption; use merged_file, clear; /* add a date variable so as to merge with the CPI files so we can deflate to real dollars */; gen year=floor(file/10); gen quarter=file-year*10; gen date=yq(year,quarter); format date %tq; sort date; /*create a unique identifier for each CEX household */ egen id=group(newid date); /*define CEX househodl heads */; gen head=relation==1; gen temp=relation==1 if marital==1 & sex==1; egen spouse=max(temp) if marital==1 , by(id); drop temp; replace head=0 if sex==2 & marital ==1 & spouse==1; egen temp=max(age) if head==1, by(id); replace head=0 if agetemp; drop temp; egen tag=tag(head age id sex); replace head=0 if tag==0 & head==1; /* merge with the CPI file */; sort date ; merge date using cpi_u, nokeep; tab _merge; drop _merge; /* keep only heads */ ; #delimit ; keep if head==1 & year >= 1986; /* Step 3: merge in additional CEX data */ ; gen id_1 = newid; sort id_1 year; merge id_1 year using cex_merge, ; tab _merge; keep if _merge == 3; drop _merge; /*generate real expenditure (2005 dollars)*/; foreach var of varlist wages bus farm rents div interest pension socsec ssi unemp workcomp welfare scholar foodstmp gvpremia rrpremia sspremia fedtax statax pproptax othtax nontax foodhome foodout foodwork tobacco alcohol niteclub clothes tailors jewelry toiletry hlthbeau renthome rentothr furnish housuppl elect gas water homefuel telephon servants drugs orthopd doctors hospital nurshome helthins busiserv lifeins autos parts carservs gasoline tolls autoins masstran othtrans airfare books pubs recsport othrec gambling highedu lowedu othedu charity insrefnd intauto intoth rentnpay homevalu homeval2 ohint ohtax ohmaint ohprinc ohlump ohsold ohbuy ohmort1 ohmort2 ohtrans housadd checkng dcheckng saving dsaving securi dsecuri carloan tradein carsold carprinc investb pnpremia sepremia deltaiou owe1 owe1q1 owe5 owe5q1 give receive give2 lumpsums othernet {; gen `var'_real=`var'/cpi_u_4q*194.1; }; /* convert consumption expenditures from annual to quarterly */; /* Note: This step is necessary since the NBER CEX files only report the sum of spending across all quarters the household was in the survey. For households in the survey all four quarters, this is annual expenditure. However, half the survey households participate in the survey for less than 4 quarters. To get average quarterly spending, we divide the NBER CEX average expenditure by the number of quarters the household was in the sample. As seen in our robustness appendix, our results are unchanged if we simply restrict our analysis to the 50% of households who completed all 4 quarters of the survey. */; foreach var of varlist foodhome foodout foodwork tobacco alcohol niteclub clothes tailors jewelry toiletry hlthbeau renthome rentothr furnish housuppl elect gas water homefuel telephon servants drugs orthopd doctors hospital nurshome helthins busiserv lifeins autos parts carservs gasoline tolls autoins masstran othtrans airfare books pubs recsport othrec gambling highedu lowedu othedu charity insrefnd intauto intoth carprinc {; gen `var'_adj =`var'_real/count; }; /*Step 4: Create Demographic Variables and Expenditure Measures by Category Used in the Paper */ ; #delimit ; gen age_4 = 0; /* Creates 4 year age categories */ ; replace age_4 = 22 if age >=22 & age <= 25; replace age_4 = 26 if age >=26 & age <= 29; replace age_4 = 30 if age >=30 & age <= 33; replace age_4 = 34 if age >=34 & age <= 37; replace age_4 = 38 if age >=38 & age <= 41; replace age_4 = 42 if age >=42 & age <= 45; replace age_4 = 46 if age >=46 & age <= 49; replace age_4 = 50 if age >=50 & age <= 53; replace age_4 = 54 if age >=54 & age <= 57; replace age_4 = 58 if age >=58 & age <= 61; replace age_4 = 62 if age >=62 & age <= 65; replace age_4 = 66 if age >=66 & age <= 69; replace age_4 = 70 if age >=70 & age <= 73; #delimit ; gen hhsize = . ; /* Creates household size dummies */ ; replace hhsize = 1 if famsize >= 0.5 & famsize < 1.5; replace hhsize = 2 if famsize >= 1.5 & famsize < 2.5; replace hhsize = 3 if famsize >= 2.5 & famsize < 3.5; replace hhsize = 4 if famsize >= 3.5 & famsize < 4.5; replace hhsize = 5 if famsize >= 4.5 & famsize < 5.5; replace hhsize = 6 if famsize >= 5.5 & famsize < 6.5; replace hhsize = 7 if famsize >= 6.5 & famsize < 7.5; replace hhsize = 8 if famsize >= 7.5 & famsize < 8.5; replace hhsize = 9 if famsize >= 8.5 & famsize < 9.5; replace hhsize = 10 if famsize >= 9.5 & famsize < 10.5; replace hhsize = 11 if famsize >= 10.5 & famsize < .; #delimit ; gen famsize_1 = hhsize == 1; /* Creates a household size indicator variable */ ; gen famsize_2 = hhsize == 2; gen famsize_3 = hhsize == 3; gen famsize_4 = hhsize == 4; gen famsize_5 = hhsize == 5; gen famsize_6 = hhsize >= 6; gen adults = as_comp1 + as_comp2; /* Genreate total adults in the household */ ; gen adults_1 = adults >= 1 & adults < 1.5; gen adults_2 = adults >= 1.5 & adults < 2.5; gen adults_3 = adults >= 2.5 & adults < 3.5; gen adults_4 = adults >= 3.5 & adults < 4.5; gen adults_5 = adults >= 4.5 & adults < 5.5; gen adults_6 = adults >= 5.5; replace adults_1 = . if adults == .; replace adults_2 = . if adults == .; replace adults_3 = . if adults == .; replace adults_4 = . if adults == .; replace adults_5 = . if adults == .; replace adults_6 = . if adults == .; gen married = marital == 1; /* Generates a marriage dummy */ ; replace married = . if marital == .; gen weight = totwt; /* Renames the CEX sample weight variable */ ; gen ed_a = 0; /* Creates Education Dummies: The Education Code Change in 1996 */ ; gen ed_b = 0; gen ed_c = 0; gen ed_d = 0; replace ed_a = 1 if educatio > 0 & educatio <= 11 ; replace ed_b = 1 if educatio == 12 & year < 1996; replace ed_b = 1 if (educatio == 38 | educatio == 39) & year >= 1996; replace ed_c = 1 if (educatio == 21 | educatio == 22 | educatio == 23) & year < 1996 ; replace ed_c = 1 if (educatio == 40 | educatio == 41 | educatio == 42) & year >= 1996; replace ed_d = 1 if (educatio == 24 | educatio == 31 | educatio == 32) & year < 1996 ; replace ed_d = 1 if (educatio == 43 | educatio == 44 | educatio == 45 | educatio == 46) & year >= 1996 ; replace ed_a = . if educatio == . | educatio == 0 ; replace ed_b = . if educatio == . | educatio == 0 ; replace ed_c = . if educatio == . | educatio == 0 ; replace ed_d = . if educatio == . | educatio == 0 ; gen age_sq = age^2; /* Creates Age Squared */ ; gen male = .; /* Creates a Male Dummy */ ; replace male = 1 if sex == 1; replace male = 0 if sex == 2; gen northeast = region >= 1 & region < 1.5; /* Creates Region Dummies */ ; gen midwest = region >= 1.5 & region < 2.5; gen south = region >= 2.5 & region < 3.5; gen west = region >= 3.5 & region <= 4; replace northeast = . if region == .; replace midwest = . if region == .; replace south = . if region == .; replace west = . if region == .; /* Creates a household income variable */ ; egen income = rsum(wages_real bus_real farm_real rents_real div_real interest_real pension_real socsec_real ssi_real unemp_real workcomp_real welfare_real) ; gen ln_income = ln(income); replace ln_income = 0 if income <= 0; gen income_zero = income <= 0; gen income_sq = income^2; gen income_cube = income^3; gen year_84 = year == 1984 ; /*Create a Vector of Year Dummies */ ; gen year_85 = year == 1985 ; gen year_86 = year == 1986 ; gen year_87 = year == 1987 ; gen year_88 = year == 1988 ; gen year_89 = year == 1989 ; gen year_90 = year == 1990 ; gen year_91 = year == 1991 ; gen year_92 = year == 1992 ; gen year_93 = year == 1993 ; gen year_94 = year == 1994 ; gen year_95 = year == 1995 ; gen year_96 = year == 1996 ; gen year_97 = year == 1997 ; gen year_98 = year == 1998 ; gen year_99 = year == 1999 ; gen year_00 = year == 2000 ; gen year_01 = year == 2001 ; gen year_02 = year == 2002 ; gen year_03 = year == 2003 ; gen urban = blsurbn == 1; /*Creates an Urban Dummy */ ; /* Defines our visible expenditure measures */ ; gen clothes_pcare_adj = clothes_adj + toiletry_adj + hlthbeau_adj + jewelry_adj + tailors_adj; /* Clothing plus personal care */ ; gen ln_clothes_pcare = ln(clothes_pcare_adj); replace ln_clothes_pcare = 0 if clothes_pcare_adj == 0; gen pcare_adj = toiletry_adj + hlthbeau_adj ; /* Personal Care */ ; gen ln_pcare = ln(pcare_adj); replace ln_pcare = 0 if pcare_adj == 0; gen clothing_adj = clothes_adj + jewelry_adj + tailors_adj; /*Clothing */ ; gen ln_clothing = ln(clothing_adj); replace ln_clothing = 0 if clothing_adj == 0; gen totcar_adj = autos_adj + carservs_adj + carprinc_adj + parts_adj; /* Broad Vehicle Measure */ ; gen ln_totcar = ln(totcar_adj); replace ln_totcar = 0 if totcar_adj == 0; gen car_adj = autos_adj; /* Narrow Vehicle Measure */ ; gen ln_car = ln(car_adj); replace ln_car = 0 if car_adj == 0; gen totvis_full = clothes_pcare_adj + totcar_adj; /* Total Visible Expenditure with Broad Vehicle Measure */ ; gen ln_totvis_full = ln(totvis_full); replace ln_totvis_full = 0 if totvis_full == 0; gen totvis = clothes_pcare_adj + car_adj; /* Total Visible Expenditure with Narrow Vehicle Measure */ ; gen ln_totvis = ln(totvis); replace ln_totvis = 0 if totvis == 0; /* Defines all other expenditure measure */; gen food_adj = foodhome_adj + foodout_adj + foodwork_adj; /* Total Food */ ; gen ln_food = ln(food_adj); replace ln_food = 0 if food_adj == 0; gen ln_foodaway = ln(foodout_adj) ; /* Food away from home */ ; replace ln_foodaway = 0 if foodout_adj == 0 ; gen rent_adj = renthome_adj + (homeval2_real) * 3; /* Housing */ ; gen ln_rent = ln(rent_adj); replace ln_rent = 0 if rent_adj == 0; gen health_adj = drugs_adj + orthopd_adj + doctors_adj + hospital_adj + nurshome_adj + helthins_adj; /* Health */; gen ln_health = ln(health_adj); replace ln_health = 0 if health_adj == 0; gen utility_adj = elect_adj + gas_adj + water_adj + homefuel_adj + telephon_adj; /* Utilities */ ; gen ln_utility = ln(utility_adj); replace ln_utility = 0 if utility_adj == 0; gen education_adj = highedu_adj + lowedu_adj + othedu_adj; /* Education */ ; gen ln_education = ln(education_adj); replace ln_education = 0 if education_adj == 0; gen car_serv_adj = carservs_adj + carprinc_adj + parts_adj; /* Car Services */ ; gen ln_car_serv = ln(car_serv_adj); replace ln_car_serv = 0 if car_serv_adj == 0; gen transport_other_adj = gasoline_adj + tolls_adj + autoins_adj + masstran_adj + othtrans_adj ; /* Other Transportation */ ; gen ln_transport_other = ln(transport_other_adj); replace ln_transport_other = 0 if transport_other_adj == 0; gen alc_tob_adj = tobacco_adj + alcohol_adj + niteclub_adj ; /* Alcohol and Tobacco */ ; gen ln_alc_tob = ln(alc_tob_adj); replace ln_alc_tob = 0 if alc_tob_adj == 0; gen ent_serv_adj = othrec_adj + airfare_adj + books_adj + pubs_adj ; /* Entertainment Services */ ; gen ln_ent_serv = ln(ent_serv_adj); replace ln_ent_serv = 0 if ent_serv_adj == 0; gen other_adj = busiserv_adj + lifeins_adj + gambling_adj + charity_adj + housuppl_adj + servants_adj + rentothr_adj; /* Other */ ; gen ln_other = ln(other_adj); replace ln_other = 0 if other_adj == 0; gen ent_dur_adj = recsport_adj; /* Entertainment Durables */ ; gen ln_ent_dur = ln(ent_dur_adj); replace ln_ent_dur = 0 if ent_dur_adj == 0; gen ln_furnish = ln(furnish_adj); /* Household Furnishings */ ; replace ln_furnish = 0 if furnish_adj == 0; /* generate total expenditures */ ; #delimit ; gen totexp = totvis + rent_adj + food_adj + utility_adj + car_serv_adj + transport_other_adj + ent_serv_adj + health_adj + furnish_adj + education_adj + ent_dur_adj + alc_tob_adj + other_adj ; gen ln_totexp = ln(totexp); replace ln_totexp = 0 if totexp == 0; gen totexp_sq = totexp^2; gen totexp_cube = totexp^3; /* Generate Sub Aggregates To Explore Budget Constraint */ ; #delimit ; gen staples = rent_adj + food_adj + utility_adj ; gen ln_staples = ln(staples); replace ln_staples = 0 if staples == 0 ; gen investments = health_adj + education_adj; gen ln_investments = ln(investments); replace ln_investments = 0 if investments == 0 ; gen fun = ent_serv_adj + ent_dur_adj + alc_tob_adj; gen ln_fun = ln(fun); replace ln_fun = 0 if fun == 0 ; gen travel = transport_other_adj + car_serv_adj ; gen ln_travel = ln(travel); replace ln_travel = 0 if travel == 0 ; gen remaining = other_adj + furnish_adj ; gen ln_remaining = ln(remaining); replace ln_remaining = 0 if remaining == 0 ; /* generate expenditure share and fraction positive shares */ ; gen vis_share = totvis/totexp; gen rent_share = rent_adj/totexp; gen food_share = food_adj/totexp; gen utility_share = utility_adj/totexp; gen transport_other_share = transport_other_adj/totexp; gen car_serv_share = car_serv_adj/totexp; gen ent_serv_share = ent_serv_adj/totexp; gen health_share = health_adj/totexp; gen furnish_share = furnish_adj/totexp; gen education_share = education_adj/totexp; gen ent_dur_share = ent_dur_adj/totexp; gen alc_tob_share = alc_tob_adj/totexp; gen other_share = other_adj/totexp; gen totvis_pos = totvis > 0; gen rent_pos = rent_adj > 0; gen food_pos = food_adj > 0; gen utility_pos = utility_adj > 0; gen transport_other_pos = transport_other_adj > 0; gen car_serv_pos = car_serv_adj > 0; gen ent_serv_pos = ent_serv_adj > 0; gen health_pos = health_adj > 0; gen furnish_pos = furnish_adj > 0; gen education_pos = education_adj > 0; gen ent_dur_pos = ent_dur_adj > 0; gen alc_tob_pos = alc_tob_adj > 0; gen other_pos = other_adj > 0; /* Generate other controls */ ; gen liquid_wealth = checkng + saving + securi ; /* Liquid Wealth */ ; gen liquid_pos = liquid_wealth > 0 & liquid_wealth < . ; gen ln_liquid = ln(liquid_wealth) ; replace ln_liquid = 0 if liquid_wealth == 0 ; gen debt = owe1; /* Debt */ ; gen debt_pos = debt > 0 & debt < .; gen ln_debt = ln(debt); replace ln_debt = 0 if debt == 0 ; /* Restricting the sample to people with a clear race identifier */ ; keep if (black == 1 | black == 0) & ( white == 1 | white ==0 ) & (hispanic == 1 | hispanic == 0) & (asian == 1 | asian == 0); /* -------------------- generate state dummies ------------------------------ */ ; #delimit ; gen state_1 = 0 ; gen state_2 = 0 ; gen state_4 = 0 ; gen state_5 = 0 ; gen state_6 = 0 ; gen state_8 = 0 ; gen state_9 = 0 ; gen state_10 = 0 ; gen state_11 = 0 ; gen state_12 = 0 ; gen state_13 = 0 ; gen state_15 = 0 ; gen state_16 = 0 ; gen state_17 = 0 ; gen state_18 = 0 ; gen state_19 = 0 ; gen state_20 = 0 ; gen state_21 = 0 ; gen state_22 = 0 ; gen state_23 = 0 ; gen state_24 = 0 ; gen state_25 = 0 ; gen state_26 = 0 ; gen state_27 = 0 ; gen state_28 = 0 ; gen state_29 = 0 ; gen state_31 = 0 ; gen state_32 = 0 ; gen state_33 = 0 ; gen state_34 = 0 ; gen state_35 = 0 ; gen state_36 = 0 ; gen state_37 = 0 ; gen state_39 = 0 ; gen state_40 = 0 ; gen state_41 = 0 ; gen state_42 = 0 ; gen state_45 = 0 ; gen state_46 = 0 ; gen state_47 = 0 ; gen state_48 = 0 ; gen state_50 = 0 ; gen state_51 = 0 ; gen state_53 = 0 ; gen state_54 = 0 ; gen state_55 = 0 ; gen state_44 = 0 ; gen state_38 = 0 ; gen state_30 = 0 ; gen state_56 = 0 ; gen state_49 = 0 ; replace state_1 = 1 if state == 1 ; replace state_2 = 1 if state == 2 ; replace state_4 = 1 if state == 4 ; replace state_5 = 1 if state == 5 ; replace state_6 = 1 if state == 6 ; replace state_8 = 1 if state == 8 ; replace state_9 = 1 if state == 9 ; replace state_10 = 1 if state == 10 ; replace state_11 = 1 if state == 11 ; replace state_12 = 1 if state == 12 ; replace state_13 = 1 if state == 13 ; replace state_15 = 1 if state == 15 ; replace state_16 = 1 if state == 16 ; replace state_17 = 1 if state == 17 ; replace state_18 = 1 if state == 18 ; replace state_19 = 1 if state == 19 ; replace state_20 = 1 if state == 20 ; replace state_21 = 1 if state == 21 ; replace state_22 = 1 if state == 22 ; replace state_23 = 1 if state == 23 ; replace state_24 = 1 if state == 24 ; replace state_25 = 1 if state == 25 ; replace state_26 = 1 if state == 26 ; replace state_27 = 1 if state == 27 ; replace state_28 = 1 if state == 28 ; replace state_29 = 1 if state == 29 ; replace state_31 = 1 if state == 31 ; replace state_32 = 1 if state == 32 ; replace state_33 = 1 if state == 33 ; replace state_34 = 1 if state == 34 ; replace state_35 = 1 if state == 35 ; replace state_36 = 1 if state == 36 ; replace state_37 = 1 if state == 37 ; replace state_39 = 1 if state == 39 ; replace state_40 = 1 if state == 40 ; replace state_41 = 1 if state == 41 ; replace state_42 = 1 if state == 42 ; replace state_45 = 1 if state == 45 ; replace state_46 = 1 if state == 46 ; replace state_47 = 1 if state == 47 ; replace state_48 = 1 if state == 48 ; replace state_50 = 1 if state == 50 ; replace state_51 = 1 if state == 51 ; replace state_53 = 1 if state == 53 ; replace state_54 = 1 if state == 54 ; replace state_55 = 1 if state == 55 ; replace state_44 = 1 if state == 44 ; replace state_38 = 1 if state == 38 ; replace state_30 = 1 if state == 30 ; replace state_56 = 1 if state == 56 ; replace state_49 = 1 if state == 49 ; /* Step 5 - Merge in Mean Male Labor Income By Race and State Using CPS Data */ ; /* All data can be found in "cps_incmoe_bystate_main and the corresponding CPS programs on the website the generates these results */ ; sort state ; merge state using c:\ErikMain\race_signaling\cps_income_bystate_main ; tab _merge ; drop _merge ; /* Create logs of state mean income by race/state and create one race/state variable for income, standard deviation and coefficient of variation */; #delimit ; gen ln_st_inc_mean_wm = ln(st_inc_wm); /* create log of state male labor income for white men */ ; gen ln_st_inc_mean_bm = ln(st_inc_bm); /* create log of state male labor income for black men */ ; gen ln_st_inc_mean_hm = ln(st_inc_hm); /* create log of state male labor income for hispanic men */ ; gen ln_st_inc_mean_m = ln(st_inc_m); /* create log of state male labor income for all men */ ; gen ln_st_inc_meanbyrace = .; /* create one variable that is log state male labor income by race/state */ ; replace ln_st_inc_meanbyrace = ln_st_inc_mean_wm if white == 1; replace ln_st_inc_meanbyrace = ln_st_inc_mean_bm if black == 1; replace ln_st_inc_meanbyrace = ln_st_inc_mean_hm if hispanic == 1; #delimit ; gen ln_st_sdinc_mean_wm = ln(st_sdinc_wm); /* create log of state standard deviation of male income for white men */ ; gen ln_st_sdinc_mean_bm = ln(st_sdinc_bm); /* create log of state standard deviation of male income for black men */ ; gen ln_st_sdinc_mean_hm = ln(st_sdinc_hm); /* create log of state standard deviation of male income for hispanic men */ ; gen ln_st_sdinc_mean_m = ln(st_sdinc_m); /* create log of state standard deviation of male income for all men */ ; gen ln_st_sdinc_meanbyrace = . ; /* create one variable that is log of standard deviation of state male labor income by race/state */ ; replace ln_st_sdinc_meanbyrace = ln_st_sdinc_mean_wm if white == 1; replace ln_st_sdinc_meanbyrace = ln_st_sdinc_mean_bm if black == 1; replace ln_st_sdinc_meanbyrace = ln_st_sdinc_mean_hm if hispanic == 1; #delimit ; gen coeffvar_wm = st_sdinc_wm/st_inc_wm ; gen coeffvar_bm = st_sdinc_bm/st_inc_bm ; gen coeffvar_hm = st_sdinc_hm/st_inc_hm ; gen coeffvar_m = st_sdinc_m/st_inc_m ; gen coeffvar_byrace = .; replace coeffvar_byrace = coeffvar_wm if white == 1; replace coeffvar_byrace = coeffvar_bm if black == 1; replace coeffvar_byrace = coeffvar_hm if hispanic == 1; /* Step 6 - Merge in Housing Price Data from the 2000 Census By State */ ; /* Note: See file hp_census_data on web page */ ; sort state ; merge state using c:\ErikMain\race_signaling\hp_census_data ; tab _merge; drop _merge ; gen ln_hp_state = ln(hp_state_1990) if year <= 1994; /*gen log of state housing price: set to log of house price from 1990 census if survey year < 1994 */; replace ln_hp_state = ln(hp_state_2000) if year >= 1995 ; /*gen log of state housing price: set to log of house price from 2000 census if survey year >= 1995*/; /* Create some remaining variables for tables: annual total expenditure and total spending on non-visible goods, non-housing goods */ ; gen totexp_annual = totexp * 4 ; /* annual total expenditure */ ; label var totexp_annual "Total Expenditure (Annual)"; gen totexp_novis_norent = totexp - totvis - rent_adj ; /* Total expenditure less visible goods less housing */ ; gen ln_totexp_novis_norent = ln(totexp_novis_norent) ; replace occup = 0 if occup == . ; replace indust = 0 if indust == . ; /* Save data after creating all the variables needed for the empirical work */ ; save c:\ErikMain\race_signaling\nber_cex_main_tables_figures.dta, replace ; # delimit ; clear ; set mem 500m; use c:\ErikMain\race_signaling\nber_cex_main_tables_figures.dta, clear ; /* --------- Genarate all the CEX Tables and Figures for the Paper --------------- */ ; /* ------------------ Appendix Table A1 ------------------------------ */ ; #delimit ; sum totexp totvis rent_adj food_adj utility_adj car_serv_adj transport_other_adj ent_serv_adj health_adj furnish_adj education_adj ent_dur_adj alc_tob_adj other_adj [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= ., ; #delimit ; sum totexp totvis rent_adj food_adj utility_adj car_serv_adj transport_other_adj ent_serv_adj health_adj furnish_adj education_adj ent_dur_adj alc_tob_adj other_adj [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1, ; #delimit ; sum totexp totvis rent_adj food_adj utility_adj car_serv_adj transport_other_adj ent_serv_adj health_adj furnish_adj education_adj ent_dur_adj alc_tob_adj other_adj [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & black == 1, ; #delimit ; sum totexp totvis rent_adj food_adj utility_adj car_serv_adj transport_other_adj ent_serv_adj health_adj furnish_adj education_adj ent_dur_adj alc_tob_adj other_adj [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & hispanic == 1, ; #delimit ; sum totvis_pos rent_pos food_pos utility_pos car_serv_pos transport_other_pos ent_serv_pos health_pos furnish_pos education_pos ent_dur_pos alc_tob_pos other_pos [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= ., ; #delimit ; sum totvis_pos rent_pos food_pos utility_pos car_serv_pos transport_other_pos ent_serv_pos health_pos furnish_pos education_pos ent_dur_pos alc_tob_pos other_pos [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1, ; #delimit ; sum totvis_pos rent_pos food_pos utility_pos car_serv_pos transport_other_pos ent_serv_pos health_pos furnish_pos education_pos ent_dur_pos alc_tob_pos other_pos [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & black == 1, ; #delimit ; sum totvis_pos rent_pos food_pos utility_pos car_serv_pos transport_other_pos ent_serv_pos health_pos furnish_pos education_pos ent_dur_pos alc_tob_pos other_pos [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & hispanic == 1, ; #delimit ; sum vis_share rent_share food_share utility_share car_serv_share transport_other_share ent_serv_share health_share furnish_share education_share ent_dur_share alc_tob_share other_share [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= ., ; #delimit ; sum vis_share rent_share food_share utility_share car_serv_share transport_other_share ent_serv_share health_share furnish_share education_share ent_dur_share alc_tob_share other_share [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1, ; #delimit ; sum vis_share rent_share food_share utility_share car_serv_share transport_other_share ent_serv_share health_share furnish_share education_share ent_dur_share alc_tob_share other_share [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & black == 1, ; #delimit ; sum vis_share rent_share food_share utility_share car_serv_share transport_other_share ent_serv_share health_share furnish_share education_share ent_dur_share alc_tob_share other_share [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & hispanic == 1, ; /* ---- Table 1 (means) ------ */; #delimit ; set more on; sum age ed_a ed_b ed_c ed_d married famsize adults income_zero totexp [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . ; #delimit ; sum income [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & income > 0 ; #delimit ; sum age ed_a ed_b ed_c ed_d married famsize adults income_zero totexp [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1 ; #delimit ; sum income [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & income > 0 & white == 1 ; #delimit ; sum age ed_a ed_b ed_c ed_d married famsize adults income_zero totexp [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & black == 1 ; #delimit ; sum income [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & income > 0 & black == 1 ; #delimit ; sum age ed_a ed_b ed_c ed_d married famsize adults income_zero totexp [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & hispanic == 1 ; #delimit ; sum income [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & income > 0 & hispanic == 1 ; /* ---- Table 2 - ---- */ ; #delimit ; set more on ; xi:ivreg ln_totvis black hispanic [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= ., cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= ., cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic ln_totexp [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= ., cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= ., cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= ., cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= ., cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp ln_totexp_sq = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= ., cluster(state) ; /* Robustness Specifications */ ; /* robustness appendix table R5 - alternate specifications */; #delimit ; set more on; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & income > 0 , cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & srepstat == 1 , cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & totexp > 5800 , cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 24 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & fullyr == 1, cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt ln_hp_state [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & empstat == 1, cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & year >= 1996 , cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt i.popsize [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & year >= 1996 , cluster(state) ; /* Robustness Table R4 - Robustness Appendix */; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & (male == 1 & married == 0), cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & (male == 0 & married == 0), cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & (married == 1), cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & (ed_a == 1), cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & (ed_b == 1), cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & (ed_c == 1), cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & (ed_d == 1), cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 35 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 35 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 50 & age < 69 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; /* Table 3 */ ; #delimit ; xi:ivreg ln_clothing black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_pcare black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_car black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_totcar black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_clothing black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & car_adj > 0 , cluster(state) ; #delimit ; xi:ivreg ln_pcare black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & car_adj > 0 , cluster(state) ; #delimit ; xi:ivreg ln_car black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & car_adj > 0 , cluster(state) ; #delimit ; xi:ivreg ln_totcar black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & car_adj > 0 , cluster(state) ; #delimit ; xi:ivreg ln_totcar black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & num_autos > 0 , cluster(state) ; /* Table 4 */ ; #delimit ; xi:ivreg ln_rent black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_utility black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_food black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_transport_other black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_ent_serv black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; /* tobit regressions for categories that had non-trival amounts of zero spending */ ; #delimit ; xi: ivtobit ln_furnish black hispanic (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , ll(0) robust ; mfx compute, predict(ys(0,.)) varlist(black hispanic) nose ; #delimit ; xi: ivtobit ln_education black hispanic (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , ll(0) robust ; #delimit ; mfx compute, predict(ys(0,.)) varlist(black hispanic) nose ; #delimit ; xi: ivtobit ln_ent_dur black hispanic (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , ll(0) robust ; mfx compute, predict(ys(0,.)) varlist(black hispanic) nose ; #delimit ; xi: ivtobit ln_health black hispanic (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , ll(0) robust ; mfx compute, predict(ys(0,.)) varlist(black hispanic) nose ; #delimit ; xi: ivtobit ln_alc_tob black hispanic (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , ll(0) robust ; mfx compute, predict(ys(0,.)) varlist(black hispanic) nose ; /* Analysis of Food Away from Home for Robustness Appendix */ ; #delimit ; xi:ivreg ln_foodaway black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; /* Analysis of Visible difference Over Time for Robustness Appendix */ ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & (year >= 1990 & year <= 1993) & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & (year >= 1999 & year <= 2002) & midwest ~= . , cluster(state) ; /* Table 5 - From the PSID data - See PSID Files on Web Page */ ; /* Table 6 - Whites Only */ ; #delimit ; set more on ; #delimit ; xi:ivreg ln_totvis ln_st_inc_meanbyrace (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1, cluster(state) ; #delimit ; xi:ivreg ln_totvis ln_st_inc_meanbyrace coeffvar_byrace (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1, cluster(state) ; #delimit ; xi:ivreg ln_totvis ln_st_inc_meanbyrace coeffvar_byrace (ln_totexp ln_rent = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust ln_hp_state ) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1, cluster(state) ; #delimit ; xi:ivreg ln_food ln_st_inc_meanbyrace coeffvar_byrace (ln_totexp ln_rent = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust ln_hp_state ) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1, cluster(state) ; #delimit ; xi:ivreg ln_totexp_novis_norent ln_st_inc_meanbyrace coeffvar_byrace (ln_totexp ln_rent = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust ln_hp_state ) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1, cluster(state) ; /* Table 7 - Blacks and Hispanics Only */ ; #delimit ; xi: ivreg ln_totvis ln_st_inc_meanbyrace (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust ) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & (black == 1 | hispanic == 1), cluster(state) ; #delimit ; xi: ivreg ln_totvis ln_st_inc_meanbyrace coeffvar_byrace (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & (black == 1 | hispanic == 1), cluster(state) ; #delimit ; xi: ivreg ln_totvis ln_st_inc_meanbyrace coeffvar_byrace (ln_totexp ln_rent = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust ln_hp_state ) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & (black == 1 | hispanic == 1), cluster(state) ; #delimit ; xi: ivreg ln_totvis ln_st_inc_meanbyrace coeffvar_byrace ln_st_inc_mean_m (ln_totexp ln_rent = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust ln_hp_state ) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & (black == 1 | hispanic == 1), cluster(state) ; test ln_st_inc_meanbyrace = ln_st_inc_mean_m ; #delimit ; xi: ivreg ln_food ln_st_inc_meanbyrace coeffvar_byrace (ln_totexp ln_rent = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust ln_hp_state ) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & (black == 1 | hispanic == 1), cluster(state) ; #delimit ; xi: ivreg ln_totexp_novis_norent ln_st_inc_meanbyrace coeffvar_byrace (ln_totexp ln_rent = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust ln_hp_state ) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & (black == 1 | hispanic == 1), cluster(state) ; /* Table 8 */; #delimit ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; xi:ivreg ln_totvis black hispanic (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt state_2 state_5 state_4 state_6 state_8 state_9 state_10 state_11 state_12 state_13 state_15 state_16 state_17 state_18 state_19 state_20 state_21 state_22 state_23 state_24 state_25 state_26 state_27 state_28 state_29 state_31 state_32 state_33 state_34 state_35 state_36 state_37 state_39 state_40 state_41 state_42 state_45 state_46 state_47 state_48 state_50 state_51 state_53 state_54 state_55 state_44 state_38 state_30 state_56 state_49 [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic ln_st_inc_meanbyrace (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic ln_st_inc_meanbyrace (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt state_2 state_5 state_4 state_6 state_8 state_9 state_10 state_11 state_12 state_13 state_15 state_16 state_17 state_18 state_19 state_20 state_21 state_22 state_23 state_24 state_25 state_26 state_27 state_28 state_29 state_31 state_32 state_33 state_34 state_35 state_36 state_37 state_39 state_40 state_41 state_42 state_45 state_46 state_47 state_48 state_50 state_51 state_53 state_54 state_55 state_44 state_38 state_30 state_56 state_49 [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_totvis black hispanic ln_st_inc_meanbyrace coeffvar_byrace (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt state_2 state_5 state_4 state_6 state_8 state_9 state_10 state_11 state_12 state_13 state_15 state_16 state_17 state_18 state_19 state_20 state_21 state_22 state_23 state_24 state_25 state_26 state_27 state_28 state_29 state_31 state_32 state_33 state_34 state_35 state_36 state_37 state_39 state_40 state_41 state_42 state_45 state_46 state_47 state_48 state_50 state_51 state_53 state_54 state_55 state_44 state_38 state_30 state_56 state_49 [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; * ----- Figure 1 - Engel Curve Estimation ---- */; #delimit ; gen ln_totexp_sq = ln(totexp)^2 ; gen ln_totexp_cube = ln(totexp)^3; #delimit ; xi:ivreg ln_totvis (ln_totexp ln_totexp_sq = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1 & totexp > 1300 & totexp < 26200 , ; predict ln_totvis_hat_w if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1 , ; #delimit ; xi:ivreg ln_totvis (ln_totexp ln_totexp_sq = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & black == 1 & totexp > 1300 & totexp < 26200, ; predict ln_totvis_hat_b if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & black == 1 , ; #delimit ; label variable ln_totvis_hat_b "Black"; label variable ln_totvis_hat_w "White"; #delimit ; #delimit ; sort totexp; twoway (line ln_totvis_hat_b ln_totexp) (line ln_totvis_hat_w ln_totexp, lpattern(_-)) if totexp > 1300 & totexp < 26200 & age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , xti(Log Quarterly Total Expenditure) yti(Log Quarterly Visible Expenditures) name(graph_main_engel, replace) ; graph save graph_main_engel, replace; ; * ----- Figure 1 - Kernel Density Plots of Engel Curves ---- */; #delimit ; xi: reg ln_totexp income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust [aw=weight] if age >= 18 & age < 50 & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1 & ln_totexp > 7.6 & ln_totexp < 9.8 , ; predict ln_totexp_white ; #delimit ; xi: reg ln_totexp income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust [aw=weight] if age >= 18 & age < 50 & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & black == 1 & ln_totexp > 7.6 & ln_totexp < 9.8 , ; predict ln_totexp_black; #delimit ; sort ln_totexp_white ln_totexp_black; lowess ln_totvis ln_totexp_white if ln_totexp_white > 8.2 & ln_totexp_white < 9.8 & age >= 18 & age < 50 & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1 & ln_totvis >= 4 & ln_totvis <= 9, lpattern(-) addplot(lowess ln_totvis ln_totexp_black if ln_totexp_black > 8.2 & ln_totexp_black < 9.8 & age >= 18 & age < 50 & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & black == 1 & ln_totvis >= 4 & ln_totvis <= 9) ti(" ") note(" ") xti(Log Quarterly Total Expenditure) yti(Log Quarterly Visible Expenditures) mc(none) ylab(4(1)9) xlab(8.0(.25)10.0) legend(order(2 3) label(2 "White" ) label(3 "Black")) ; graph save graph_main_engel, replace; ; /* Figure 2*/; kdensity totexp_annual [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 40000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1, addplot(kdensity totexp_annual [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 40000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & black == 1) legend(label(1 "White" ) label(2 "Black")) xlab(0(20000)160000) lp(dash) ti(" ") sub(" ") note(" ") cap(" ") ; #delimit ; sum totexp_annual [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 40000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & black == 1, detail; sum totexp_annual [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 40000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1, detail; /* Figure 3 */ ; ; #delimit ; xi:ivreg ln_totvis (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban liquid_pos ln_liquid debt_pos ln_debt state_2 state_4 state_5 state_6 state_8 state_9 state_10 state_11 state_12 state_13 state_15 state_16 state_17 state_18 state_19 state_20 state_21 state_22 state_23 state_24 state_25 state_26 state_27 state_28 state_29 state_31 state_32 state_33 state_34 state_35 state_36 state_37 state_39 state_40 state_41 state_42 state_45 state_46 state_47 state_48 state_50 state_51 state_53 state_54 state_55 state_44 state_38 state_30 state_56 state_49 [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; /* Figure 3 - ALT */ ; #delimit ; gen b_state_1 = black * state_1; gen b_state_2 = black * state_2; gen b_state_4 = black * state_4; gen b_state_5 = black * state_5; gen b_state_6 = black * state_6; gen b_state_8 = black * state_8; gen b_state_9 = black * state_9; gen b_state_10 = black * state_10; gen b_state_11 = black * state_11; gen b_state_12 = black * state_12; gen b_state_13 = black * state_13; gen b_state_15 = black * state_15; gen b_state_16 = black * state_16; gen b_state_17 = black * state_17; gen b_state_18 = black * state_18; gen b_state_19 = black * state_19; gen b_state_20 = black * state_20; gen b_state_21 = black * state_21; gen b_state_22 = black * state_22; gen b_state_23 = black * state_23; gen b_state_24 = black * state_24; gen b_state_25 = black * state_25; gen b_state_26 = black * state_26; gen b_state_27 = black * state_27; gen b_state_28 = black * state_28; gen b_state_29 = black * state_29; gen b_state_31 = black * state_31; gen b_state_32 = black * state_32; gen b_state_33 = black * state_33; gen b_state_34 = black * state_34; gen b_state_35 = black * state_35; gen b_state_36 = black * state_36; gen b_state_37 = black * state_37; gen b_state_39 = black * state_39; gen b_state_40 = black * state_40; gen b_state_41 = black * state_41; gen b_state_42 = black * state_42; gen b_state_45 = black * state_45; gen b_state_46 = black * state_46; gen b_state_47 = black * state_47; gen b_state_48 = black * state_48; gen b_state_50 = black * state_50; gen b_state_51 = black * state_51; gen b_state_53 = black * state_53; gen b_state_54 = black * state_54; gen b_state_55 = black * state_55; gen b_state_44 = black * state_44; gen b_state_38 = black * state_38; gen b_state_30 = black * state_30; gen b_state_56 = black * state_56; gen b_state_49 = black * state_49; #delimit ; gen h_state_1 = hispanic * state_1; gen h_state_2 = hispanic * state_2; gen h_state_4 = hispanic * state_4; gen h_state_5 = hispanic * state_5; gen h_state_6 = hispanic * state_6; gen h_state_8 = hispanic * state_8; gen h_state_9 = hispanic * state_9; gen h_state_10 = hispanic * state_10; gen h_state_11 = hispanic * state_11; gen h_state_12 = hispanic * state_12; gen h_state_13 = hispanic * state_13; gen h_state_15 = hispanic * state_15; gen h_state_16 = hispanic * state_16; gen h_state_17 = hispanic * state_17; gen h_state_18 = hispanic * state_18; gen h_state_19 = hispanic * state_19; gen h_state_20 = hispanic * state_20; gen h_state_21 = hispanic * state_21; gen h_state_22 = hispanic * state_22; gen h_state_23 = hispanic * state_23; gen h_state_24 = hispanic * state_24; gen h_state_25 = hispanic * state_25; gen h_state_26 = hispanic * state_26; gen h_state_27 = hispanic * state_27; gen h_state_28 = hispanic * state_28; gen h_state_29 = hispanic * state_29; gen h_state_31 = hispanic * state_31; gen h_state_32 = hispanic * state_32; gen h_state_33 = hispanic * state_33; gen h_state_34 = hispanic * state_34; gen h_state_35 = hispanic * state_35; gen h_state_36 = hispanic * state_36; gen h_state_37 = hispanic * state_37; gen h_state_39 = hispanic * state_39; gen h_state_40 = hispanic * state_40; gen h_state_41 = hispanic * state_41; gen h_state_42 = hispanic * state_42; gen h_state_45 = hispanic * state_45; gen h_state_46 = hispanic * state_46; gen h_state_47 = hispanic * state_47; gen h_state_48 = hispanic * state_48; gen h_state_50 = hispanic * state_50; gen h_state_51 = hispanic * state_51; gen h_state_53 = hispanic * state_53; gen h_state_54 = hispanic * state_54; gen h_state_55 = hispanic * state_55; gen h_state_44 = hispanic * state_44; gen h_state_38 = hispanic * state_38; gen h_state_30 = hispanic * state_30; gen h_state_56 = hispanic * state_56; gen h_state_49 = hispanic * state_49; #delimit ; xi:ivreg ln_totvis (ln_totexp = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban liquid_pos ln_liquid debt_pos ln_debt state_2 state_4 state_5 state_6 state_8 state_9 state_10 state_11 state_12 state_13 state_15 state_16 state_17 state_18 state_19 state_20 state_21 state_22 state_23 state_24 state_25 state_26 state_27 state_28 state_29 state_31 state_32 state_33 state_34 state_35 state_36 state_37 state_39 state_40 state_41 state_42 state_45 state_46 state_47 state_48 state_50 state_51 state_53 state_54 state_55 state_44 state_38 state_30 state_56 state_49 b_state_1 b_state_2 b_state_4 b_state_5 b_state_6 b_state_8 b_state_9 b_state_10 b_state_11 b_state_12 b_state_13 b_state_15 b_state_16 b_state_17 b_state_18 b_state_19 b_state_20 b_state_21 b_state_22 b_state_23 b_state_24 b_state_25 b_state_26 b_state_27 b_state_28 b_state_29 b_state_31 b_state_32 b_state_33 b_state_34 b_state_35 b_state_36 b_state_37 b_state_39 b_state_40 b_state_41 b_state_42 b_state_45 b_state_46 b_state_47 b_state_48 b_state_49 b_state_50 b_state_51 b_state_53 b_state_54 b_state_55 b_state_44 b_state_38 b_state_30 b_state_56 b_state_49 h_state_1 h_state_2 h_state_4 h_state_5 h_state_6 h_state_8 h_state_9 h_state_10 h_state_11 h_state_12 h_state_13 h_state_15 h_state_16 h_state_17 h_state_18 h_state_19 h_state_20 h_state_21 h_state_22 h_state_23 h_state_24 h_state_25 h_state_26 h_state_27 h_state_28 h_state_29 h_state_31 h_state_32 h_state_33 h_state_34 h_state_35 h_state_36 h_state_37 h_state_39 h_state_40 h_state_41 h_state_42 h_state_45 h_state_46 h_state_47 h_state_48 h_state_49 h_state_50 h_state_51 h_state_53 h_state_54 h_state_55 h_state_44 h_state_38 h_state_30 h_state_56 h_state_49 [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; /* Regressions for Figures R1a-R1b */ ; #delimit ; xi:ivreg ln_totvis (ln_totexp totexp totexp_sq totexp_cube = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban state_2 state_5 state_4 state_6 state_8 state_9 state_10 state_11 state_12 state_13 state_15 state_16 state_17 state_18 state_19 state_20 state_21 state_22 state_23 state_24 state_25 state_26 state_27 state_28 state_29 state_31 state_32 state_33 state_34 state_35 state_36 state_37 state_39 state_40 state_41 state_42 state_45 state_46 state_47 state_48 state_50 state_51 state_53 state_54 state_55 state_44 state_38 state_30 state_56 state_49 i.region [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1, cluster(state) ; #delimit xi:ivreg ln_totvis (ln_totexp totexp totexp_sq totexp_cube = income_zero ln_income income income_sq ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban state_2 state_5 state_4 state_6 state_8 state_9 state_10 state_11 state_12 state_13 state_15 state_16 state_17 state_18 state_19 state_20 state_21 state_22 state_23 state_24 state_25 state_26 state_27 state_28 state_29 state_31 state_32 state_33 state_34 state_35 state_36 state_37 state_39 state_40 state_41 state_42 state_45 state_46 state_47 state_48 state_50 state_51 state_53 state_54 state_55 state_44 state_38 state_30 state_56 state_49 i.region ln_hp_state [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1, cluster(state) ; /* Budget Accounting */ ; #delimit ; gen remaining_pos = remaining > 0 & remaining < . ; gen travel_pos = travel > 0 & travel < . ; gen fun_pos = fun > 0 & fun < . ; gen investments_pos = investments > 0 & investments < . ; gen staples_pos = staples > 0 & staples < . ; #delimit ; sum travel fun investments staples remaining rent_adj food_adj utility_adj remaining_pos travel_pos fun_pos investments_pos staples_pos [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . & white == 1,; #delimit ; xi:ivreg ln_rent black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_rent black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt ln_totvis [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_staples black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_staples black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt ln_totvis [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_fun black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_fun black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt ln_totvis [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_travel black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_travel black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt ln_totvis [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_remaining black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_remaining black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt ln_totvis [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_investment black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ; #delimit ; xi:ivreg ln_investment black hispanic (ln_totexp = income_zero ln_income income income_sq income_cube ed_b ed_c ed_d i.occup i.indust) year_87 year_88 year_89 year_90 year_91 year_92 year_93 year_94 year_95 year_96 year_97 year_98 year_99 year_00 year_01 year_02 age age_sq married male famsize_2 famsize_3 famsize_4 famsize_5 famsize_6 adults_2 adults_3 adults_4 adults_5 adults_6 urban midwest south west liquid_pos ln_liquid debt_pos ln_debt ln_totvis [aw=weight] if age >= 18 & age < 50 & (black == 1 | white == 1 | hispanic == 1) & totexp < 100000 & ln_st_inc_meanbyrace ~= . & ln_st_inc_mean_bm ~= . & (ed_a == 1 | ed_b == 1 | ed_c == 1 | ed_d == 1) & year < 2003 & midwest ~= . , cluster(state) ;