Dades objectives

My primary data source is rich proprietary data from a large US for-profit freestanding home health provider firm operating 106 autonomous offices in 18 states, which contain firms’ labor configuration and patients’ outcomes in each office at an unusual level of detail.4 Since each office autonomously decides scheduling and staffing and is run as a profit center, it is regarded as a separate firm in the empirical analysis.5 The data cover years 2012–2015.

My data contain the entire home health visit records in each office showing all the interac- tions between a patient and individual providers who served her. I match these visit-level data with the human resources data containing the history of employment arrangements for each provider. Thus, I can construct weekly panel data for firms showing demand and labor supply characteristics at the firm-week level. On the demand side, I can observe the

4_{These 18 states are Arizona, Colorado, Connecticut, Delaware, Florida, Hawaii, Massachusetts, Mary-}

land, North Carolina, New Jersey, New Mexico, Ohio, Oklahoma, Pennsylvania, Rhode Island, Texas, Virgina, and Vermont.

5_{This large set of independently run offices alleviates some concern about the generalizability of our}

results to other HHAs even if they all belong to one company. During 2013, compared to a national sample of freestanding agencies, home health offices in our sample tend to be larger, have a lower share of visits provided for skilled nursing and instead have a higher share of visits provided for therapy, and have a lower share of episodes provided to dual-eligible Medicare or Medicaid beneficiaries, which seem to be more

number of ongoing episodes as well as construct the degree of demand volatility. An episode is defined as a 60-day period of receiving home health services, as described in SectionA.1. On the labor demand side, I can measure the total number of active nurses in each work arrangement and firms’ labor mix by computing the percentage of active nurses in each work arrangement. I can also compute the employee turnover rate by using HR data that contain employment start and termination dates for each worker. I define the turnover rate as the ratio of the number of nurses who terminate employment to the total number of nurses for each week.

Furthermore, my data provide referral sources for each patient, allowing me to track how many referrals come from each referral source, such as a hospital, for each firm-week cell. Using the referral source data, I compute various measures of reputation—defined as the establishment of strong referral network—as described in detail in Section 2.5.

I use firm-level data which provide each firm’s address and start and end of business dates where the end of business date is available only for closed firms. Using the address infor- mation, I obtain the county of each firm by merging with external CMS data, such as the Provider of Services files and Medicare Cost Reports data. I also construct each firm’s age (in years) using its start of business date information.

Finally, I use Medicare claims data I obtained from a private consulting company for se- lective years to obtain data on hospitals’ referral patterns and market-level demand. First, I use 2012–2014 Medicare hospital claims data which identify the number of patients who are being discharged from each hospital and being referred to home health care. I then construct the total number of referrals to home health from each hospital in each week, and link the information with my patient referral data. Thus, I can measure the share of home health referrals coming from each hospital to each firm in my data every week. This referral share is then used to construct one of the reputation measures, as described in Section 2.5. Second, I use 2013–2014 Medicare home health claims data which provide the number of home health episodes in each Medicare-certified home health agency on each week. This

count is aggregated to the county-week level so that I can create the county-week level demand volatility and market concentration index. These two variables are included as additional covariates in robustness check specifications.

For the main analysis, I construct firm-week level data where a week is chosen as the time unit of analysis because firms hire workers on a weekly basis. I use a period of weeks spanning from April 23, 2012 to November 9, 2015, the last week for which I have personnel data for the entire week.6 Importantly, I restrict to the sample of “new in town” firms that had no previously existing branches in the firm’s county, the unit of local market.7 Since the firms in my data belong to one company, firms may get more brand recognition in the markets with previously existing branches. Furthermore, I restrict to firms that have been around for at most 8 years to keep the sample reasonably well balanced and avoid having too small sample size per firm.8 Thus, my final sample includes 50 “new in town” firms in 15 states and contains 7,233 observations. 21 firms can be observed since their start of business.

2.5. Evolution of Firm’s Demand Volatility, Reputation and Labor Mix: Descriptive