online appendix for: how does improvement in...

ONLINE APPENDIX FOR:

How Does Improvement in Commuting Affect Employees? Evidence from a Natural Experiment

Yao Lu, Xinzheng Shi, Jagadeesh Sivadasan, and Zhufeng Xu*This draft: August 22, 2019

Panel A. Bonus

Panel B. Total Income Excluding Bonus

OA Figure 1: Trends of Outcome Variables Using Raw Data

Notes: This figure shows the quarterly comparison between the affected group and the unaffected group. Data are from Jan 1, 2013 to Dec 31, 2016. We eliminate those employees who moved after the subway opened, and we only keep employees who are in the company at least one month before and one month after the opening of the subway. The average bonus (Panel A) and total income excluding bonus (Panel B) are calculated for affected and unaffected groups separately for each quarter. The vertical dashed line indicates the opening of the Subway Line 15. The horizontal lines indicate the means of bonus (Panel A) and total income excluding bonus (Panel B) for affected and unaffected groups before and after the opening of the Subway Line 15, respectively. Horizontal lines indicate the means of bonus (Panel A) and total income excluding bonus (Panel B) for affected and unaffected groups over all quarters before (after) the opening of the Subway 15 if it is to the left (right) of the vertical dashed line

Notes: This figure uses the propensity matched sample. Each point is the coefficient on a quarter dummy interacted with NearSubway, which captures the difference in the Log(BonusT) in the specific year-quarter between the affected workers compared to the control group. The reference quarter is the fourth quarter of 2014 (hence normalized to zero). The error bar shows the 95% confidence interval, based on two-way (at individual and year-month level) clustered standard errors. The vertical dashed line indicates the opening of Subway Line 15.

OA Figure 2: Trends in Differentials for Income Variables in Propensity Score Matched Sample

OA Figure 3: Trends of Bonus Using Raw Data (Matched Sample)

Notes: This figure uses the propensity score matched sample. It shows the quarterly comparison between affected and unaffected groups. Data are from Jan. 1, 2013 to Dec. 31, 2016. The average bonus is calculated for affected and unaffected groups for each quarter. The vertical dashed line indicates the opening of the Subway Line 15. Horizontal lines indicate the means of bonus for affected and unaffected groups over all quarters before (after) the opening of the Subway 15 if it is to the left (right) of the vertical dashed line..

OA Figure 4: Time of Arrival and LeavingPanel A Time of Arrival

Panel B Time of Leaving

Notes: This figure shows the density of the time of arrival and leaving for the two periods (before and after the subway line 15 opening). We collect the daily swiping-in and swiping-out data for the two companies from Jan. 1, 2013 to Dec. 31, 2016. The vertical line represents represents the official work-start time in Panel A and the official work-end time in Panel B.

(1) (2) (3) (4)

Employee Number Observation Employee Number Observation

Total 721 17,718 728 18,196Affected 286 6,976 288 7,152Unaffected 435 10,742 440 11,044Share of Affected 0.397 0.394 0.396 0.393



Panal B: Company One

Panel C: Company Two

Notes: This table presents characteristics of the sample used in the analysis of employee exits.

OA Table 1: Sample Distribution (for Survival Analysis)Variable

At risk from entry or start of panel At risk from start tenure

Panel A: Both Companies

(1) (3) (4)

First Stage Second StageNearSubway × Post Log (Bonus)

Self-reported NearSubway × Post 0.067** 0.632***(0.033) (0.054)

NearSubway × Post 0.106**(0.054)

F Statistic 93.399 Mean of Self-report NearSubway 0.274 0.284 Std. Dev. of Self-report NearSubway 0.446 0.451 Adj. R-Squared 0.718 0.774 0.716N (affected group) 62 62N (unaffected group) 168 160

First Stage Second StageTime Saved × Post Log (Bonus)

Log (Self-Reported TimeSaved + 1) × Post 0.010** 0.649***(0.005) (0.066)

Log (TimeSaved + 1) × Post 0.016**(0.008)

F Statistic 57.389 Mean of Log (Self-report TimeSaved + 1) 1.397 1.449 Std. Dev. of Log (Self-report TimeSaved + 1) 2.718 2.754 Adj. R-Squared 0.718 0.746 0.716N (affected group) 48 48N (unaffected group) 182 174Individual fixed effects Yes Yes YesCompany-Year-Month fixed effects Yes Yes YesObs 9,885 9,529 9,529Notes: Self-reported NearSubway is a dummy defined as 1 for those workers reporting that they use the Subway Line 15, per reponses to our survey of workers. Self-Reported TimeSaved is the time saved in minutes from using the Subway Line 15, per our survey. Accordingly, the sample includes only existing workers in June 2017 who responded to our survey. Two-way (at individual and year-month level) clustered standard errors are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1%, respectively.

OA Table 2: Impact of the Opening of Line 15 on Bonus -- Robustness to using Self-reported Data

2SLSLog (BonusT)

Panel A: Using Self-Reported Use of Subway

Panel B: Using Self-Reported Time Saved

Log (BonusT)2SLS

(1) (2) (3) (4)Log(HousePrice)

NearSubway × Post 0.031 0.144*** 0.145***(0.025) (0.054) (0.054)

Log(HousePrice) -0.027 -0.053(0.103) (0.101)

Adj. R-Squared 0.913 0.752 0.744 0.752Obs 3,640 3,640 3,640 3,640

NearSubway × Post 0.019 0.108** 0.108**(0.021) (0.050) (0.050)

Log(HousePrice) -0.013 -0.024(0.085) (0.083)

Adj. R-Squared 0.918 0.752 0.748 0.752Obs 4,970 4,970 4,970 4,970

NearSubway × Post 0.014 0.082* 0.082*(0.021) (0.046) (0.046)

Log(HousePrice) -0.027 -0.033(0.074) (0.073)

Adj. R-Squared 0.914 0.761 0.759 0.761Obs 5,743 5,743 5,743 5,743

Individual fixed effects Yes Yes Yes YesCompany-Year-Month fixed effects Yes Yes Yes YesNotes: We collect data on (used) home sales from Lianjia (https://bj.lianjia.com), and aggregate the data to the community-year-month level. We impute missing values with community quarterly or yearly means. The unit of house price is RMB/m2. Distance cutoffs are defined based on the distance from employee's home to the nearest community for which home price data are available. In Panel A, B and C, we only keep the employees with distance equal to or lower than 200, 400 and 800 meters, respectively. Two-way (at individual and year-month level) clustered standard errors are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1% respectively

OA Table 3: Conditioning on Local Housing Price

Log(BonusT)Panel A : Distance ≤ 200m

Panel B : Distance ≤ 400m

Panel C : Distance ≤ 800m

(1) (2) (3) (4) (5) (6) (7) (8) (9)

NearSubway × Post 0.044* 0.043+ 0.047* 0.046* 0.094*** 0.092***

(0.027) (0.026) (0.025) (0.025) (0.033) (0.033)Log(Rent) -0.270* -0.264* -0.146 -0.130 -0.224 -0.191

(0.145) (0.153) (0.107) (0.107) (0.157) (0.164)

Individual fixed effects Yes Yes Yes Yes Yes Yes Yes Yes YesCompany-Year-Month fixed effects Yes Yes Yes Yes Yes Yes Yes Yes YesAdj. R-Squared 0.724 0.724 0.725 0.684 0.683 0.684 0.722 0.719 0.722Obs 14,657 14,657 14,657 11,081 11,081 11,081 6,673 6,673 6,673

Notes: We collect the average rent price (RMB/month/m2) for each district-month in Beijing from China Real Estate Association (http://www.creprice.cn). Two-way (at individual and year-month level) clustered standard errors are reported in parentheses. +, *, **, and *** denote statistical significance at 15%, 10%, 5%, and 1%, respectively.

OA Table 4: Conditioning on Local Rent

Log (BonusT)Full Sample Non-Manager Marketing

(1) (2) (3) (4)

Excludes all movers before subway opened, and excludes workers that joined after date:

December 2014 July 2014 Jan 2014 July 2013

NearSubway × Post 0.046* 0.047* 0.051* 0.051+

(0.027) (0.028) (0.030) (0.031)

Individual fixed effects Yes Yes Yes YesCompany-Year-Month fixed effects Yes Yes Yes YesAdj. R-Squared 0.726 0.723 0.718 0.716Obs 13,947 13,471 12,382 11,511

N (affected group) 135 125 106 99N (unaffected group) 223 210 189 169Mean of dep. var. 9.287 9.294 9.308 9.319Std. Dev. of dep. var. 0.388 0.390 0.394 0.400Mean of NearSubway 0.383 0.381 0.371 0.380Std. dev. of NearSubway 0.486 0.486 0.483 0.485

OA Table 5: Excluding Movers before Subway Opening

Notes: We drop employees moving before the opening of subway line 15. We also exclude workers that join the firm before the opening of the subway, for alternative windows with start date indicate at the top of each column. Two-way (at individual and year-month level) clustered standard errors are reported in parentheses. +, *, **, and *** denote statistical significance at 15%, 10%, 5%, and 1%, respectively.

Log (BonusT)

(1) (2) (3)

Full Sample Non-Managers MarketingPerformanceScore × NearSubway -0.001 0.000 0.000

(0.001) (0.001) (0.001)PerformanceScore 0.005*** 0.004*** 0.004***

(0.001) (0.001) (0.001)

Individual fixed effects Yes Yes YesYear-Month fixed effects Yes Yes YesAdj. R-Squared 0.786 0.743 0.711Obs 5084 3928 2036

OA Table 6: Checking for Differential Pay-Performance Sensitivity

Log (BonusT)

Notes: Data on performance scores was obtained from Company One for 2015-2016. Two-way (at individual and year-month level) clustered standard errors are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1%, respectively.

(1) (2)

Drop Subway Line 13 from Control Group

Drop Employees Taking Subway Line 5 in the Affected Group

NearSubway × Post 0.108** 0.052*(0.043) (0.029)

Adj. R-Squared 0.723 0.725N (affected group) 144 94N (unaffected group) 64 233Obs 8361 12883

Mean of dep. var. 9.302 9.296Std. Dev. of dep. var. 0.390 0.390Mean of NearSubway 0.689 0.298Std. dev. of NearSubway 0.463 0.457

Individual fixed effects Yes YesCompany-Year-Month fixed effects Yes Yes

OA Table 7: Excluding Employees that Take the Subway Line 13 from the Unaffected Group, and Taking Line 5 in Affected

Dependent variable

Notes: Subway Line 13 has a station 1.1 kms away, and may have experienced lower crowding after opening of Subway Line 15. The transfer station between Subway Line 5 and Subway Line 15 only opened in December 2015, so affected employees who connect using Subway Line 5 may not have had access prior to that date. Two-way (at individual and year-month level) clustered standard errors are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1%, respectively.

Log (BonusT)

(1) (2) (3)

Dropping employees with minimum paired distance of: 250m 500m 750m

NearSubway × Post 0.046* 0.051* 0.055*(0.027) (0.028) (0.029)

Individual fixed effects Yes Yes YesCompany-Year-Month fixed effects Yes Yes YesAdj. R-Squared 0.726 0.722 0.723 N (affected group) 138 130 122N (unaffected group) 228 220 210Obs 14,293 13,621 12,855Notes: We exclude affected (unaffected) employees with unaffected (affected) employees living in the neighborhood within 250, 500 or 750 meters. Two-way clustered standard errors (at individual and year-month level) are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1%, respectively.

OA Table 8: Excluding Employees Living Very Close to Different Affected Status Employees

Log (BonusT)

(1) (2) (3) (4) (5)

Baseline Level 1% Winsorizing 5% Winsorizing

NearSubway × Post 636.328* 630.731* 593.201* 0.154** 0.054*(376.378) (362.476) (329.185) (0.073) (0.030)

Individual fixed effects Yes Yes Yes Yes YesCompany-Year-Month fixed effects Yes Yes Yes Yes YesAdj. R-Squared 0.674 0.707 0.724 0.711 0.712Obs 14807 14807 14807 11837 14807Mean for Bonus 4944.790 4944.790 4944.790 4944.790 4944.790 Mean of dep. var. 4,944.790 4,909.660 4,793.939 8.367 191.989Std. Dev. of dep. var. 5,525.948 5,306.679 4,916.041 0.901 0.425Effect as % of SD of dep. var. 11.52% 11.89% 12.07% 17.09% 12.70%Implied level effect 636.328 630.731 593.201 823.262 608.624 % effect relative to level mean 12.87% 12.76% 12.00% 16.65% 12.31%

100 * Box-Cox Transformation

(Bonus)

Notes: In column (4) we use log of the bonus, so that all observations with zero or negative bonus is dropped. In column 5 we use the Box-Cox transformation that allows for negative values. Two-way (at individual and year-month level) clustered standard errors are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1%, respectively.

OA Table 9: Impacts of the Opening of Line 15 on Employee Compensation

Bonus

Log(Bonus)

(1) (2) (3) (4) (5) (6)

NearSubway × Post 0.045* 0.116 0.045* 0.239*** 0.088*** 0.164**

(0.027) (0.087) (0.025) (0.091) (0.032) (0.076)NearSubwayc × Post 0.085** 0.100** 0.122** 0.172*** 0.091** 0.108**

(0.041) (0.048) (0.049) (0.058) (0.042) (0.054)NearSubway × NearSubwayc × Post -0.074 -0.203** -0.081

(0.092) (0.093) (0.085)NearSubway × NearSubwayc -0.069 -0.042 -0.122

(0.067) (0.081) (0.075)NearSubwayc 0.032 0.049 0.031 0.041 0.033 0.062

(0.034) (0.040) (0.040) (0.046) (0.038) (0.045)

Individual fixed effects Yes Yes Yes Yes Yes YesCompany-Year-Month fixed effects Yes Yes Yes Yes Yes YesAdj. R-Squared 0.724 0.725 0.687 0.689 0.725 0.728N (affected group) 144 144 112 112 56 56N (control group) 233 233 177 177 109 109Obs 14,807 14,807 11,166 11,166 6,701 6,701

Mean of dep. var. 9.296 9.296 9.228 9.228 9.271 9.271Std. Dev. of dep. var. 0.390 0.390 0.329 0.329 0.369 0.369Mean of NearSubway 0.389 0.389 0.393 0.393 0.350 0.350Std. dev. of NearSubway 0.488 0.488 0.489 0.489 0.477 0.477Mean of NearSubwayc 0.914 0.914 0.929 0.929 0.855 0.855Std. dev. of NearSubwayc 0.280 0.280 0.258 0.258 0.352 0.352Notes: NearSubwayC is equal to one if at least one collleague's fastest public transport route contains the Subway 15 in the same department and in the same month, and zero otherwise. Two-way (at individual and year-month level) clustered standard errors are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1%, respectively.

OA Table 10: Spillover Effects to Subordinates and Coworkers

Log(BonusT)Full Sample Non-Managers Marketing

(1) (2) (3)Full Sample Non-Manager Marketing

NearSubway × Post 0.060* 0.055* 0.113***(0.033) (0.031) (0.041)

Individual fixed effects Yes Yes YesCompany-Year-Month fixed effects Yes Yes YesAdj. R-Squared 0.700 0.662 0.653Obs 10,628 8,060 4,442

N (affected group) 104 82 41N (control group) 130 98 55Mean of dep. var. 9.300 9.230 9.221Std. dev. of dep. var. 0.386 0.324 0.323Mean of NearSubway 0.438 0.446 0.416Std. dev. of NearSubway 0.496 0.497 0.493Change on level variable 712.532 594.550 1212.442Effect as % at mean of dep. var. 14.33% 16.34% 34.96%Effect as % of SD of dep. var. 15.56% 15.22% 31.69%

Log (BonusT)

OA Table 11: Effect for Sample of Workers Who Survive to End of Sample Period

Notes: The sample consists of workers existing in the company before the opening of the Subway Line 15 (Dec 2014) and surving till the end of the sample period (Dec 2016). Two-way clustered (at individual and year-month level) standard errors are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1%, respectively.

Data Appendix: Absenteeism (Attendance/Time at Work) dataWe have three sources to construct the attendance data. The first source is administrative finger print based check-in/out data for Company 2, which records daily first swiping-in and last swiping-out times for some employees. We understand from the Company that where available, this is the most reliable data among the three sources, because it is harder to fake, is used specifically for tracking work attendance, and is monitored by the human resources department. This data is not available for all employees, as some employees use other information systems to log in their arrival and departure times. The reason for the existence of multiple systems to track attendance is that the current company structure evolved from mergers and reorganizations of several separate companies.

The second data source is swipe data, available from both companies, which records swiping behavior of each employee for entry into the office floor of each company, from January, 2013 to October, 2016. Per the Companies, this swiping behavior is retained mainly for security-related concerns, and not for tracking attendance. Therefore, each employee is not required to swipe in -- if several employees enter or exit the office floor in a group, only one of the employees needs to swipe for opening the door.

In the last few months of 2016, the building updated its access control system, and every employee was required to swipe in or out on the ground floor. This is the third data source, and provides data on employee swiping behavior on the ground floor in November and December 2016. Even for this source, some exceptions may still exist. For instance, an employee may forget her access card, but the guard may allow her in if the employee is familiar with the guard. Because both track swiping in and out data, we combine the second and third sources together. We take two steps to clean this swipe data. First, we drop all swiping records from midnight to 6:00AM (0:00~6:00AM). Such records are rare, and more importantly, it is unclear if a swipe in this time frame relates to an early entry or a very late (overnight from previous day) exit from the office. Second, we drop the observations that have swiping-in time later than 17:30PM (because this is after normal office hours) or swiping-out time earlier than 9:00AM (because this is before normal office hours) or when we get estimated attendance time is equal to zero (which can happen if the system erroneously records one swipe as two coincident swipes). We then get the times in minutes from midnight of first swiping-in (denoted as swiping-in time or time of arrival) and last swiping-out (denoted as swiping-out time or time of leaving) for each employee on every day, and calculate the attendance time as the duration between swiping-in time and swiping-out time.

Finally, we combine all three sources together, as they track the same variables. Because the first source is more reliable, we use the information in the first source where available. We drop employee-day observations if they have missing variables, and for weekends and holidays. Finally, we calculate the average swiping-in time, swiping-out time, and attendance time, and aggregate this to the employee-month level.

(1) (2) (3) (4) (5) (6)

Full Sample Non-Manager Marketing Full Sample Non-Manager Marketing

NearSubway × Post 0.047 -0.071 0.180 0.080*** 0.096*** 0.082**

(0.120) (0.132) (0.150) (0.030) (0.029) (0.036)

Individual fixed effects Yes Yes Yes Yes Yes YesCompany-Year-Month fixed effects Yes Yes Yes Yes Yes YesN (Affected group) 116 86 51 116 86 51N (Unaffected group) 171 124 94 171 124 94Mean of dep. var. 4.850 4.928 4.815 9.244 9.223 9.279Std. Dev. of dep. var. 1.848 1.866 1.726 0.424 0.388 0.454Adj. R-Squared 0.747 0.777 0.752 0.733 0.682 0.725Obs 4,794 3,373 2,632 11,757 8,541 5,945

OA Table 12: Effects on Attendance Time per Workday

Log(Attendance Time per Workday) Log (Bonus) for attendance sample

Notes: We collect the daily swiping-in and swiping-out data for the two companies, and calculate the attendance time as the difference between the time of the first swiping-in and the last swiping-out. Attendance time per workday is defined as the total monthly attendance time in the workdays divided by the number of workdays in the month. Columns (4)-(6) use the same sample of employees in Columns (1)-(3) respectively but cover more months. Two-way (at individual and year-month level) clustered standard errors are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1%, respectively.

(1) (2) (3) (4) (5) (6)

Full Sample Non-Manager Marketing Full Sample Non-Manager MarketingNearSubway × Post -0.006 -0.002 -0.004 0.007 0.003 0.009

(0.010) (0.010) (0.014) (0.010) (0.011) (0.014)

Individual fixed effects Yes Yes Yes Yes Yes YesCompany-Year-Month fixed effects Yes Yes Yes Yes Yes YesN (Affected group) 115 85 51 115 85 51N (Unaffected group) 164 118 91 164 118 91Mean of dep. var. 6.323 6.324 6.320 6.904 6.912 6.902Std. Dev. of dep. var. 0.127 0.125 0.132 0.117 0.112 0.117Adj. R-Squared 0.557 0.58 0.586 0.527 0.52 0.52Obs 4,486 3,151 2,497 4,486 3,151 2,497

OA Table 13: Effects on Time of Arrival and Leaving

Log(Time of Arrival) Log(Time of Leaving)

Note: We collect the daily swiping-in and swiping-out data for the two companies. The time of arrival is the daily average minutes between 0am and the first time of swiping-in and the leaving time is the the daily average minutes between 0am and the last time of swiping-out, over working days in each month. Two-way (at individual and year-month level) clustered standard errors are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1%, respectively.

(1) (2) (3) (4)Obs. Mean Std. Dev. Median

Log(House Price) (yuan/m2, Distance ≤ 200m)

Logarithm of the average resale house price at the community-level, where the employee's house is assigned to the nearest community with available house price data if the distance is equal to or less than 200m 3,640 10.802 0.333 10.776





Log(Rent) Logarithm of the average rent price of the district 14,657 4.064 0.294 4.224Log (Late for Work) Logarithm of one plus the number of late arrivals in the month 14,742 0.062 0.266 0.000

Log (Leave Early) Logarithm of one plus the number of times employee left early without suitable reason in the month 14,742 0.004 0.068 0.000

Log(Sick Leave) Logarithm of one plus the days for sick leaves in the month 14,742 0.024 0.195 0.000Log (Personal Leave) Logarithm of one plus the days leave for personal reasons in the month 14,741 0.057 0.301 0.000Log (Maternity Leave) Logarithm of one plus the days for maternity leave in the month 14,742 0.012 0.189 0.000Log(Funeral Leave) Logarithm of one plus the days leave for attending funerals in the month 14,742 0.000 0.006 0.000Log(Marriage Leave) Logarithm of one plus the days for marriage leave in the month 14,742 0.001 0.030 0.000Log(Attendance Time per Workday)

Logarithm of one plus the monthly average over working days of stay in minutes from first swipe in to last swipe out 4,794 4.850 1.848 5.671

Log(Time of Arrival) Logarithm of the time in minutes from midnight to first swipe into the building (E.g., A 9:00AM swipe-in corresponds to Time of Arrival of 360) 4,486 6.323 0.127 6.294

Log(Time of Leaving) Logarithm of the time in minutes from midnight to last swipe out the building (E.g., A 17:30PM swipe-out corresponds to Time of leaving of 1050) 4,486 6.904 0.117 6.944

Variable

OA Table 14: Summary statistics on additional variables used in supplementary analysis

Definition

(1) (2) (3) (4) (5) (6)

NearSubwayi × Postt 0.160 0.298 0.381* 1.461 -0.017 -0.562(0.185) (0.786) (0.214) (0.938) (0.233) (1.205)

NearSubwayi -0.273 -0.426 -0.512(0.291) (0.347) (0.431)

Postt 0.024 0.019 0.220*(0.102) (0.110) (0.117)

Male Dummy -0.108 -0.119 -0.382(0.248) (0.292) (0.374)

Experience -0.003** -0.002 -0.003(0.001) (0.001) (0.002)

Experience2 0.000*** 0.000 0.000*(0.000) (0.000) (0.000)

Education 0.011 0.013 -0.100(0.074) (0.098) (0.140)

Number of YoungChild 0.402 0.293 0.397(0.284) (0.356) (0.408)

Married Dummy 0.735 0.890* 0.242(0.462) (0.509) (0.697)

Threshold 1 -0.673 -3.056 -0.650 -3.689 -3.415 -1.936Threshold 2 1.015 2.454 1.272 2.750 -1.597 5.068Individual fixed effects No Yes No Yes No YesCompany-Year-Month fixed effects No Yes No Yes No YesPseudo R2 0.032 0.740 0.030 0.759 0.026 0.789Obs 8,974 8,998 6,744 6,768 4,414 4,414Notes: Employees rated worklife balance for every year on a 3 point scale, reordered so higher score indicates better work-life balance. The mean (sd) of the reported work-life balance is 2.013 (0.786). Ordered logit regressions are estimated. Standard errors clustered at the individual level are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1%, respectively.

Dependent variable

OA Table 15: Effect on Self-reported Recall of Employee Work-Life Balance

Work-Life Balance (3 Good 2 Moderate 1 Bad)Full Sample Non-Manager Marketing

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