Teachers Teaching Teachers: The Role of Networks on

Financial Decisions∗

Gonzalo Maturana† Jordan Nickerson‡

January 2016

This paper studies the role of peers in the transmission of information pertinent to household

financial decisions. Specifically, we examine the effect of peers on the mortgage refinancing decisions

of school teachers in Texas by exploiting commonalities in teacher schedules to identify peer groups.

Using this source of within-campus variation to separate campus-level unobservable shocks, we find

that refinancing activity among a teacher’s peers increases her likelihood of refinancing by 9.5%.

Peers also affect a teacher’s choice of lender. Overall, our findings suggest that a household’s cost

of acquiring and processing financial information is greatly reduced by interacting with peers.

JEL classification: D71, G21, H31, R20

keywords : Household Finance, Peer Effects, Mortgages, Refinancing

∗We are grateful to Sumit Agarwal, Felipe Aldunate, Ian Appel, Francisco Barillas, Tarun Chordia,Slava Fos, Andreas Fuster (discussant), Todd Gormley, Clifton Green, John Griffin, Edith Hotchkiss, DalidaKadyrzhanova, Oguzhan Karakas, Benjamin Keys, Darren Kisgen, Sam Kruger, Mauricio Larrain, KyleMangum, Dmitriy Muravyev, Christopher Parsons, Tomasz Piskorski, Oliver Randall, Jonathan Reuter,Breno Schmidt, Jay Shanken, Martin Schmalz, Phil Strahan, Daniel Weagley, Vincent Yao, as well as theseminar participants at Boston College, Emory University, Georgia State University, Pontificia UniversidadCatolica, the 2014 FRA Early Ideas session, the 2015 All-Georgia Finance Conference, and the 2015 AtlantaFED Real Estate Conference for their helpful comments. We also thank Integra FEC for providing uswith refinancing activity data. Additional results are available in an Online Appendix at http://www.

GonzaloMaturana.com or at http://www.JordanNickerson.com.†Emory University, Goizueta Business School, 1300 Clifton Road, Atlanta, GA 30322; telephone:

(404)727-7497. E-mail: Gonzalo.Maturana@emory.edu‡Boston College, Carroll School of Management, 140 Commonwealth Avenue, Chestnut Hill, MA 02467;

telephone: (617)552-2847. E-mail: Jordan.Nickerson@bc.edu

In recent years, economists have become increasingly interested in how households use finan-

cial instruments to achieve their objectives (Campbell (2006)). Several studies have noted

that households often appear to commit financial mistakes that are inconsistent with rational

models and even seem to contradict practical intuition.1 Thus, understanding the factors

that influence a household’s financial decision-making process is important from a welfare

perspective. One potential explanation for these mistakes is the presence of informational

frictions such as the costly acquisition and processing of pertinent information. Therefore,

this paper studies the exogenous arrival of otherwise costly information and its effect on

household financial decision making.

The seemingly suboptimal mortgage refinancing decisions made by a significant portion

of households is of particular interest in household finance, given the relative importance of

home value in the average household’s wealth.2 Many borrowers refinance at a detrimental

rate, while others wait too long or do not refinance at all (e.g., Agarwal, Driscoll, and Laib-

son (2013); Andersen et al. (2015)). The need to acquire and process information related to

current lending rates and the estimated present value of future savings makes the mortgage

refinancing decision an ideal setting in which to evaluate how information acquisition affects

household financial choices. More specifically, the transmission of information from an indi-

vidual’s peer network provides an interesting laboratory in which we can identify a reduction

in the cost of information and its effect on an individual’s decision-making process.3 Thus,

we study the impact of an individual’s employment network on her mortgage refinancing

decision.

However, studying the information generated by peer networks and their effect on house-

1. For example, many individuals take out excessively expensive loans (Agarwal, Skiba, and Tobacman(2009)) and do not maximize employer-matched 401(k) contributions (Choi, Laibson, and Madrian (2011)).

2. In 2010, home value comprised 43.1% of gross assets for individuals in the U.S., representing the largestportion of household wealth, compared to 28.5% in equities and pension accounts combined (Wolff (2012)).

3. Notably, while peer effects have been explored at length as they relate to the financial decisions ofother agents such as the firm (Leary and Roberts (2014)), financially affluent individuals such as CEOs(Shue (2013)), mutual fund managers (Cohen, Frazzini, and Malloy (2008); Pool, Stoffman, and Yonker(2015)), and sell-side analysts (Cohen, Frazzini, and Malloy (2010)), little is known about the role thatnetworks play in a household’s mortgage refinancing decision.

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hold financial decisions is challenging. First, the identification of peer effects requires that

the selection of peers does not correlate with their subsequent actions. Second, individuals

in the same peer group may be subject to common, unobservable shocks. Any influence

of these shocks on the outcome variable would lead to biased estimates of the peer group’s

effect.

To address these difficulties, we use a novel micro dataset of employment records for all

public school teachers in the state of Texas, which we merge with county deed records to

obtain their refinancing transactions. While teachers have a choice over the school district to

which they apply, they have little or no influence on the campus to which they are assigned

within the school district. In addition, we observe a second layer of quasi-randomization

that exists at the campus level, and stems from class schedule assignments.

This richness of the data permits us to construct a measure of a teacher’s peer group

based on teaching assignments. More specifically, we identify each teacher’s off-periods (i.e.,

periods when the teacher is not in her classroom) and consider two teachers to be in each

other’s peer group if they have at least 40 minutes of overlap in off-periods per day. This

classification allows for the identification of teachers who are more likely to share information.

Furthermore, this formation of peer groups is unlikely to be correlated with refinancing

decisions. The resulting variation in peer groups both across and within campuses allows us

to control for common unobservable shocks to peer groups, such as district-level employment

effects and variations in loan supply exposure at the campus level.4 To further validate our

empirical strategy, we provide evidence consistent with teachers not choosing their schedule

and controlling their off-periods.

We find a strong effect of peers on the refinancing decisions of teachers. A teacher is 16.2%

more likely to perform a mortgage refinance following a one standard deviation increase in

the refinancing activity among teachers who share the same off-period. In addition, the

effect is not driven by variation in loan supply exposure or by another omitted variable at

4. This identification strategy is similar in spirit to that of Mas and Moretti (2009), which exploits theoverlap in work shifts of cashiers in a national supermarket chain.

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the campus-level; it remains equal to 9.5% even after the inclusion of campus-month fixed

effects.5 Furthermore, we find the effect to be stronger when peers share common ethnicity.

The previous identification strategy accounts for variation in loan supply exposure at

the campus level, such as the construction of a new billboard near a campus that advertises

a lender’s superior mortgage terms. Our tests essentially compare the effect of a teacher’s

refinancing activity on other teachers who share a common off-period relative to the effect

on the remaining teachers at the same school who do not share an off-period. However,

it does not account for the existence of an unobservable factor at the off-period level that

affects both the outcome variable and the refinancing decisions of peer groups. To address

this unlikely possibility, we also undertake an instrumental variable (IV) approach. More

specifically, we use the average potential savings (realized upon refinancing) of a teacher’s

peers as an instrument for the peer group’s refinancing activity. Intuitively, after explicitly

controlling for a teacher’s savings, her likelihood of refinancing should not be affected by her

peers’ average savings; therefore, the exclusion restriction should hold. Using this approach,

we confirm the previous findings.

To further validate the effect of a teacher’s peers, we turn to another dimension of the

refinancing decision: the teacher’s choice of lender. It is unlikely that information transferred

from a teacher’s peers is restricted to the prevailing rate while other pertinent information,

such as the lender offering such terms, is not disclosed. To investigate whether individuals

who seek to refinance their loan are more likely to choose the same lender as their peers, we

undertake a nonparametric approach. Specifically, we generate a benchmark set of pseudo-

peer groups that are geographically similar to the true peer groups but fall under the null

hypothesis of no information sharing within the peer group. We find the probability that

a teacher and her peer share a common lender is 8.16%, or 12.9 standard deviations larger

than the expected overlap based on the pseudo-peer groups. This strongly suggests that a

5. This result is robust to alternative constructions of peer refinancing activity and estimated savings, andbecomes stronger when excluding months when teachers are less likely to interact. We also confirm the mainresults using a hazard model to account for the dynamic nature and infrequent occurrences of refinances.

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teacher’s choice of lender is also influenced by her peer group.

After establishing the relevance of peer effects, we test for the heterogeneous effects of

peers across specific individual characteristics. We find evidence that suggests that younger,

less tenured teachers, as well as teachers who live in more expensive homes, incorporate

more information from peers into their refinancing decisions. Finally, we find little evidence

of differences in characteristics between teachers who refinance independently and teachers

who follow their peers.

In addition to the literature on mortgage refinancing, this paper relates to a broader lit-

erature on household finance choices. For example, Grinblatt and Keloharju (2001); Hong,

Kubik, and Stein (2004); and Guiso, Sapienza, and Zingales (2008) study household stock

ownership. Choi, Laibson, and Madrian (2011) study the 401(k) contribution choice of house-

holds. Moreover, Gourinchas and Parker (2002) estimate a structural model of consumption

with labor uncertainty. Related to the effect of peers on household finance choices, Duflo

and Saez (2002) find that peers sharing common characteristics influence retirement plan-

ning choices of university workers. Duflo and Saez (2003) provide further evidence consistent

with peer effects in the same setting using a field experiment. More recently, Bursztyn et al.

(2014), also using a field experiment, separately identify the effect of social learning and

social utility from asset purchases by investors. Beshears et al. (2015) find an oppositional

effect in which individuals decrease their savings when given information about the savings

of their peers. Additionally, Grinblatt, Keloharju, and Ikaheimo (2008) examine the effect

of a neighbor’s actions on the consumption decisions of Finnish households. We add to this

literature by contributing to the understanding of how peers reduce informational frictions

in the context of arguably the most important financial decision faced by a household.6

Finally, this paper also relates to a broader literature that studies peer effects beyond

their effects on financial decisions. Eppel and Romano (1998) study the increase in posi-

6. More broadly, this paper also relates to the classic literature that studies the role that informationacquisition plays in financial decision making (e.g., Grossman and Stiglitz (1980); Ippolito (1989); andEllison and Fudenberg (1995)).

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tive externalities that a high-ability student receives from her peers as the result of tuition

vouchers. Sacerdote (2001) finds a continuation of this peer effect in a post-secondary setting,

where a college student’s class performance and participation in social activities is influenced

by her randomly assigned freshman roommate. More recent work has shed light on the role

of peer effects in fraud among financial advisors (Dimmock, Graham, and Gerken (2015))

and CEOs (Khanna, Kim, and Lu (2015)). Brogaard, Engelberg, and Parsons (2014) find

an increased ability of journal editors in screening their colleagues’ papers. Additionally,

Mas and Moretti (2009) find the introduction of a highly productive worker increases the

productivity of other workers.

This paper is organized as follows: Section I provides some background on mortgage

refinancing. Section II describes the data sources, the procedure we use to match employment

and county deed records, the sample selection process, and the empirical challenges that this

paper addresses. Section III describes the identification strategy. Section IV presents our

main findings. Section V confirms the main findings using two alternative methodologies.

Section VI concludes.

I. Background on Refinancing

I.A. Institutional Details of Refinancing

Mortgage loans can be used to purchase a new property or to refinance an existing loan

(on a previously purchased property). This paper focuses on mortgage loans that are used

for refinancing. Loans for refinancing are classified into two main groups depending on the

borrower’s objective. In a rate or term refinance, the interest rate and/or the term of the

loan is modified, usually with the goal of lowering the mortgage payment. In contrast, in a

cash-out refinance, the new mortgage amount exceeds the existing mortgage amount, since

the objective of this type of refinance loan is to extract equity from the house. Typically, rate

refinances are motivated by interest rate decreases, while cash-out refinances are motivated

by house price increases.

5

Households purchase their homes at different points in the business cycle. Therefore,

refinancing the interest rate of an existing mortgage can result in significant savings for those

borrowers who financed their purchases in a period of high interest rates. A key determinant

for the amount of savings that follows a rate refinance is the spread between the interest

rate of the existing loan and the current rate. Typically, industry practitioners recommend

refinancing if this spread is sufficiently large such that the present value of savings exceeds

the costs of refinancing.7

However, the present value of savings that results from an interest rate differential is

not the only relevant factor for a rate refinance. Rate refinances also depend heavily on the

expectations of future interest rates. Even if interest rate differentials would result in positive

net savings today, it may be more advantageous for the borrower to wait and refinance later

if interest rates are expected to continue to decline in the near future. Additionally, even if

a borrower wants to refinance, she may not be able to obtain the loan if she is “underwater”

(i.e., if the house price has declined enough since the purchase that the borrower currently

has negative equity in the house).

I.B. Optimal Refinancing

There is a large body of literature that more rigorously studies the mortgage refinancing

decision described above using option valuation models.8 One puzzling result discussed in

this literature is the finding that borrowers seem to refinance their mortgages suboptimally.

Some borrowers appear to leave substantial amounts of money on the table by either waiting

too long to refinance or by simply not refinancing at all, even when the associated benefits

justify doing so (referred to in the literature as an error of omission). Other borrowers seem

7. The costs of refinancing, or closing costs, include application fees, appraisal fees, credit report fees,legal fees, home inspection fees, and origination fees, among others. The most relevant fee is the originationfee, which typically equals around 1% of the loan amount. This is the reason for the widely known rule ofthumb for refinancing recommended by industry experts, which recommends refinancing after interest rateshave declined by at least 200 bp.

8. See, for example, Dunn and McConnell (1981a,b); Stanton (1995); Dunn and Spatt (2005); and Agar-wal, Driscoll, and Laibson (2013).

6

to choose an unfavorable interest rate (referred to in the literature as an error of commission).

For example, Agarwal, Rosen, and Yao (2015b) find that about 59% of the borrowers in their

sample refinanced suboptimally. Likewise, Andersen et al. (2015) document substantial costs

for households due to errors of omission in a large dataset of Danish households.9

II. Data, Sample Selection, & Empirical Challenge

II.A. Data Description

We obtain data from multiple sources regarding teacher-level employment records, home

mortgage actions, and housing characteristics. We briefly describe each data source.

II.A.1. Teacher Employment Records

To begin, we obtain payroll records for all public school employees in Texas from the Texas

Education Agency (TEA). These data cover the period from 1999 to 2011 and represents

1,305 unique school districts. Each district is made up of an average of 7.98 campuses. While

all employees are included in the payroll records, we restrict the sample to include only

teachers. The result is a sample of over 3.6 million teacher-year observations across 585,000

teachers. Each employment record observation consists of the teacher’s name, demographics,

date of birth, education level, tenure, salary, and teaching assignments.

II.A.2. County Deed Records

We obtain property characteristics and residential transaction information from DataQuick’s

Assessor and History files. DataQuick compiles and cleans county deed records, and it is

one of the main real estate data providers in the U.S. The Assessor file contains property

addresses and characteristics. The History file contains detailed information on slightly more

than 11 million property transactions in the state of Texas between 2002 and 2011, which

9. For other recent studies of this refinancing puzzle see Agarwal et al. (2015a); Keys, Pope, and Pope(2014); and Johnson, Meier, and Toubia (2015).

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involve around 4.6 million properties from the Assessor file. Examples of variables available

in the History file include the transaction date, the transaction purpose (i.e., purchase or

refinance), the loan amounts, the interest rate type (i.e., fixed or variable), and the names

of the buyer and the lender.

II.A.3. Registered Voter Rolls

Central to our study is the matching of teachers with their corresponding refinancing

transactions. To aid in the matching process, we obtain the full electoral roll of registered

voters from the Texas Secretary of State. Each record contains the voter’s name, date of

birth, mailing address, and physical address.

II.A.4. Additional Data Sources

Since DataQuick does not provide interest rates, and because house transaction price

coverage is low in Texas, we use two additional datasets. For interest rates, we use the

monthly 30-year fixed rates from Freddie Mac’s Primary Mortgage Market Survey (PMMS).10

Additionally, as a proxy for individual house prices, we use ZIP code-level indices from

Zillow.11

II.B. Matching Description

As mentioned above, in order to evaluate the role of peer effects on the refinancing

decisions of teachers in our sample, we must first map our teacher employment records to

county deed records. We describe each step of the matching procedure below.

10. Freddie Mac surveys lenders weekly and records interest rates on first-lien prime conventional purchasemortgages. Currently, about 125 lenders are included in the survey. Additional information about thissurvey can be found at http://www.freddiemac.com/pmms/abtpmms.htm.

11. Zillow’s indices are based on median home values. A more detailed description of their methodologycan be found at http://www.zillow.com/research/zhvi-methodology-6032.

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II.B.1. Teacher to Voter

While both the TEA payroll records and county deed records provide names, match-

ing teacher records to mortgage transactions using legal names is problematic for multiple

reasons. One particular concern is the matching of teachers who possess common names.

While the distance between the teacher’s campus and each deed holder’s physical address

can help alleviate this concern, it does not fully address the issue. For instance, our sam-

ple contains four occurrences of the name Michael Cunningham listed on active mortgage

records in Denton County alone. In addition, the use of names alone would not now allow us

to link the teachers in our sample to mortgage records in cases in which only the teacher’s

spouse was listed on the deed. For these reasons, we turn to the electoral rolls of registered

voters, which serves as the linchpin in our matching process. The individual’s date of birth

and legal name are included in our teacher employment records. Thus, we can use both

name and date of birth to identify the voting record of every teacher in our sample who is

a registered voter. We are able to match 76.2% of the payroll records to the voter rolls.12

Therefore, the representative nature of our analysis is limited to homeowners who are also

registered voters.

II.B.2. Voter to County Deeds

We then use the resulting voting records, which contain both a physical and mailing

address for the individual, to establish a link between our teacher records and county deed

records. To do this, we require an exact match on the individual’s last name. We also require

an exact match between the address number prefix and the ZIP code for either the physical

address or the mailing address. Finally, we require that the Levenshtein ratio between the

address names exceeds 0.9. Figure 1 provides an illustration of the matching process.

[INSERT FIGURE 1 HERE]

12. Note, the matching rate is likely larger when conditioning on home ownership.

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II.C. Final Sample

After matching registered teacher voters with the county deed records, we obtain slightly

more than 1.2 million teacher-year observations over the period from 1999 to 2011. The

transactions associated with these teachers include purchases, refinances, subdivisions, and

other less common types of transactions. We consider only purchases and refinances. We use

purchase transaction records to identify the time and the terms of the loan when a teacher

buys a house. The purchase date is needed to estimate the existing mortgage’s rate and the

potential savings from refinancing. Thus, for a teacher to remain in the sample, we need to

observe when the teacher purchased the house; that is, the teacher must show a purchase

transaction in the county deed records between 2002 and 2011.13 In addition, because

the refinancing of adjustable-rate mortgages (ARM) could occur due to borrower-specific

reasons, we omit teacher-property observations in which the teacher bought (or refinanced)

the property using an ARM.

Since our identification strategy exploits cross-sectional variation in teachers’ schedules,

we exclude campuses with an off-period shared by 90% or more of all teachers. Finally, to

increase the likelihood of teachers sharing information, we restrict the sample to campuses

with an average peer group of fewer than 50 teachers.14 The final sample consists of 32,465

teacher-year observations. Table 1 describes the final sample, along with the samples cor-

responding to each of the main steps of the matching procedure. Overall, all samples of

teacher-years show similar salaries, tenure, demographics, and education level.

[INSERT TABLE 1 HERE]

13. Consequently, we omit from the sample all teachers who do not show any purchases or teacher-yearobservations before the purchase of the property.

14. Additionally, for the accuracy of our measures, we also require that campuses have at least 70% oftheir teachers registered as voters, on average.

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II.D. Estimation of Savings

To quantify the savings realized upon refinancing, we take the difference between the

present value of a straight annuity discounted at the prevailing interest rate and the estimated

remaining principle balance of the current mortgage. We assume the original mortgage is a

30-year term, and we set the length of the annuity to the minimum of the remainder of the

mortgage’s life and 13 years, which is the average time that buyers remain in their homes

(Emrath (2013)). To calculate the estimated net savings, closing costs of 1.5% of the total

loan amount are considered.15

II.E. Empirical Challenge

We face a twofold empirical challenge in this study. First, studying household financial

decisions is challenging because it is difficult to obtain the necessary data. Ideally, one would

like accurate data that cover a representative sample of the population and follow households

over time (Campbell (2006)). The second challenge is that peer effects studies are difficult

from an identification standpoint. We describe below why our data are advantageous for

addressing these challenges.

II.E.1. Data Accuracy and Representativeness

Mortgage-related decisions are arguably the most important financial decisions for the av-

erage household, since their house is their most valuable asset. As opposed to other datasets

of household investments in which the information is self-reported, county deed datasets are

more accurate and complete because, by law, all house transactions are required to be reg-

15. Estimated lending rates used in the calculation are based on the PMMS 30-year fixed rates at thetime of the last and the current transactions. Note that the closing costs of some mortgage refinances areincorporated into the lending rate. While we cannot see for which observations this is the case, we willsubtract the estimated closing costs from our estimates. If both products are fairly priced, the resulting netsavings should be equivalent. Earlier versions of this paper considered a fixed closing cost of $5,000, and theresults remained unchanged.

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istered with the county.16 In addition, county records provide a history of transactions for

each property, allowing us to construct a data panel of refinances for each matched teacher,

rather than just a cross-section. Finally, Texas teachers represent a large group of house-

holds, providing thorough coverage across different characteristics such as age, ethnicity, and

wealth.17

II.E.2. Identifying Peer Effects

Studying peer effects is challenging for multiple reasons. First, it is difficult to identify

links between individuals merely because they share a common environment. Second, indi-

viduals tend to self-select themselves into peer groups. Third, individuals within the same

peer group are subject to common shocks. Our empirical setting is useful to address these

challenges in identifying peer effects. Teacher employment records allow us to identify sub-

sets of teachers who are likely to interact regularly and thus are likely to share information

about their financial decisions. Additionally, the Texas public school setting is advantageous

because teachers apply for jobs at the district level, but they have little or no choice regard-

ing which campus they are assigned to. Furthermore, our identification strategy involves

a second layer of quasi-randomization that occurs at the campus level, as discussed in the

following section. This mitigates concerns about individuals self-selecting into peer groups

(i.e., teacher networks are less likely to be formed endogenously). Furthermore, in contrast

to other settings where participation is voluntary, employment records include all the teach-

ers in the system, thus eliminating concerns about sample bias. Finally, the richness of

our micro-level data allows for the exploitation of variation across and within campuses to

account for common shocks to the peer group. Next, we describe our identification strategy.

16. Although the information requirements vary across states, it is a crime to purposely report falseinformation to a county clerk’s office.

17. While teachers’ salaries do not vary considerably and are correlated with tenure, there is variation inhouse value across our sample, which can proxy for family income.

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III. Identification Strategy

We want to study whether an individual’s refinancing decision is influenced by the refi-

nancing activity of his or her peer group. While the use of within-district variation helps to

address endogeneity concerns related to common unobservable employment shocks that may

influence both a teacher and her peer group’s decision to refinance, some concerns remain. Of

particular concern is the potential variation in campus-level loan supply exposure, resulting

in the clustering of mortgage refinancing at the campus level. For example, the construc-

tion of a new billboard that advertises a lender’s superior mortgage terms near a campus

would provide an alternative information channel which could be mistakenly attributed to

a teacher’s peers. Therefore, to disentangle the possibility of campus-level variation in loan

supply exposure from the role of an individual’s peers, we exploit a source of within-campus

variation when constructing the peer groups: common off-periods.

While the teachers within a school campus share a common set of buildings, this does not

ensure that all teacher-pairs within the campus interact daily. Rather, it is much more likely

for an individual to acquire and incorporate information from a peer with whom they have

frequent contact. While two teachers may be employed at the same campus, their amount

of interaction is largely dictated by the teacher’s class schedule. More specifically, a given

school day is typically divided into multiple class periods. While the teachers in our sample

are assigned a class during the majority of the class periods, each teacher is also allotted one

or more periods which are designated as off-periods. These non-teaching periods are used to

prepare lectures, grade assignments, use office resources such as the school’s copier, and visit

the teacher’s lounge. More importantly, off-periods do not typically occur at a uniform time

for all teachers at a given campus. Therefore, the extent to which two teachers interact daily,

and thus share information, is significantly influenced by the amount of overlap between the

teacher’s off-periods.18

18. We find anecdotal support for this claim based on multiple conversations with public school teacherswho acknowledge that they interact primarily with other teachers while in the teacher’s lounge and teacherworkroom during their off-period.

13

Fortunately, our employment records include detailed information on each teacher’s class

assignments, which includes the beginning and ending times for each class. From this infor-

mation, we are able to recover the off-periods for each teacher in our sample. Using this data

on teacher off-periods, we then construct a more refined measure of a teacher’s peer group.

For each pair-wise combination of teachers within a campus, we first identify all overlapping

off-period segments and then compute the number of minutes of overlap for each segment.

We exclude any segments which overlap by less than 10 minutes, as it is unlikely that teach-

ers are able to interact in a meaningful way in such a brief amount of time before returning

to their class assignments. We define a teacher’s peer group as any teacher with an average

of 40 minutes or more per school day of commonality in off-periods.19

Figure 2 illustrates the class assignment schedule for three teachers and our classification

of each teacher’s peer group. The colored blocks denote a teacher’s off-periods, while the

hatched regions indicate an overlap in off-periods with another teacher. The figure indicates

that both the Bob–Kim and Kim–Ava pairs share 45 minutes of common off-periods per

day, while there are only 15 minutes of common off-periods for the Bob–Ava pair. This

figure illustrates the asymmetric nature of each teacher’s peer group. For instance, while

Bob’s peer group consists only of Kim, Kim’s peer group is made up of both Bob and Ava.

We now turn to the importance of peer effects on a teacher’s refinancing decision using this

measure of one’s peers.

[INSERT FIGURE 2 HERE]

19. In Texas, school districts have discretion over the format of the class schedule. While some districtshave a standardized set of class periods across all days, other districts utilize an alternating, or block, schedule.It is possible for two teachers to share an off-period block during some days of the week but have no overlapon other days. Thus, for each teacher-pair, we compute the average number of overlapping minutes perschool day. Finally, some campuses have a uniform campus-wide off-period. So, as mentioned in SectionII.C, we exclude any campus-year in which more than 90% of teachers have at least 40 minutes of off-periodoverlap per day.

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IV. Peer Effects on the Refinancing Decision

First, we exploit novel microdata of teacher class schedules to better understand the

transmission of information across individuals. Second, we validate the empirical strategy,

which relies on the assumption of quasi-random schedules. Third, we study the possibility of

commonality in lenders across peers. Fourth, we study the cross-sectional intensity of peer

effects. Finally, we study additional features of peer refinancing.

IV.A. Peer Effects Matter

If informational frictions do play a role in the suboptimal refinancing behavior of house-

holds, and if we assume that teachers share information about their refinancing decisions

with their peers, then there should be a higher probability that a teacher will refinance

her house after a refinance occurs in her peer group. Conversely, there should be a lower

probability of a teacher refinancing when no one in her peer group has recently refinanced.

Figure 3 plots the relation between the probability of a teacher refinancing her existing

mortgage and her estimated net savings. Consistent with the intuition above, we separate

teachers into two groups. The first group (indicated by the black line with hollow markers)

consists of teacher-month observations in which no individual in the teacher’s peer group

performed a rate refinance in the trailing 3-month period.20 The second group (indicated by

the red and blue line with solid markers) consists of the remaining teacher-month observa-

tions in which at least one individual in the teacher’s peer group refinanced within the last

three months. As we are interested in individuals for whom refinancing is a feasible option,

we do not consider teachers whom we estimate to be underwater (i.e., having negative equity

in their home).21

20. We do not take into account refinances that appear to be cash-outs. More specifically, based on thePMMS 30-year fixed rate at the time of the last transaction, and assuming a 30-year loan, we estimate themonthly unpaid principal balance of each mortgage in our sample. We ignore all transactions associatedwith the teacher’s property if the loan amount of a teacher’s refinance is more than 1.05 times the remainingprincipal balance of the existing loan, and if the estimated net savings are negative.

21. For this, we estimate house price fluctuations based on Zillow’s ZIP code-level indices.

15

The figure shows a positive relation between the probability of refinancing and net savings

for both groups. However, the monthly probability of a teacher choosing an interest rate that

will result in negative savings is non-negligible (about 0.3%). The probability of refinancing

conditional on a peer refinancing is greater than the probability for the peer non-refinancing

group for all net saving bins. For example, for an estimated net savings of $5,000, the

likelihood of an individual refinancing increases from 0.383% to 0.965% (about 2.5 times)

when the number of peers who recently refinanced a mortgage increases from zero to one

or more. This difference tends to increase with net savings. Interestingly, although peer

refinancing has a more important effect on individuals who benefit from refinancing (i.e.,

those who have positive net savings), peer effects also appear to increase the probability

of refinancing for some teachers with negative net savings. We revisit this issue in Section

IV.E. Overall, these facts strongly support the hypothesis that peer effects are a significant

determinant of the probability of refinancing among Texas teachers.

[INSERT FIGURE 3 HERE]

We now turn to a more formal framework to better evaluate the economic and statistical

significance of peer effects while controlling for other determinants of refinancing activity in

addition to savings. We estimate OLS regressions in which the dependent variable is a 0/1

indicator for refinancing. Recall that the peer group for each observation contains all other

teachers who have at least 40 minutes of overlap in off-periods per school day. Thus, the

primary variable of interest, Peer Refinances, equals the number of teachers with overlapping

off-periods who have undertaken a mortgage refinance in the previous 3-month period, scaled

by the size of the peer group.22 We standardize Peer Refinances for ease of interpretation,

and we report effects in basis points.

The first regression in Table 2, which controls for other determinants of refinancing such

as (a) the estimated savings, (b) a 0/1 indicator for whether the teacher is underwater, and

22. We obtain quantitatively similar results when scaling by the average peer group size across all teachersin a given campus-year.

16

(c) the underwater amount (in percent); confirms the results in Figure 3. To address the

possible concern that refinancing activity is being driven by variation in local loan supply

exposure or local economic conditions, the first specification includes Metropolitan Statis-

tical Area (MSA)-Year fixed effects. The coefficient of 11.088 (t-stat=4.82) indicates that

a one standard deviation increase in the percentage of an individual’s peer group with re-

cent refinancing activity is associated with an 11.1 bp increase in the individual’s monthly

probability of refinancing. To add additional economic perspective, the unconditional prob-

ability of a teacher refinancing her mortgage in a given month is 68.5 bp in the same sample.

Therefore, a one standard deviation increase in Peer Refinances increases the probability of

refinancing by 16.2% of its unconditional value.

However, other factors beyond local economic conditions may influence an individual’s

decision to refinance her mortgage. For instance, the positive correlation between the re-

financing activity of a teacher and her peer group could be the result of the exposure of

both sets of individuals to common employment risk shocks. Fortunately, the risk of unem-

ployment is the result of economic hardship at the school district level. Thus, the second

specification in Table 2 includes school district-year fixed effects. The coefficient on Peer

Refinances is 10.171 (t-stat=4.41). However, the previous specifications suffer from the

shortcoming that the measured effect of Peer Refinances could be driven by campus-level

loan supply exposure (e.g., the construction of a new billboard that advertises a lender’s

superior mortgage terms near a campus). Fortunately, the within-campus variation in our

definition of Peer Refinances based on off-periods allows us to separate campus-level effects

from the effects of peers. For this reason, the third specification includes campus-year fixed

effects. The results remain largely unchanged, with a coefficient of 9.204.

While these results are consistent with a teacher’s refinancing decision being influenced

by her peers, even with campus-year fixed effects, we cannot rule out the possibility that

the teachers within a campus are exposed to a common shock that dissipates within the

school year. To address this concern, we include campus-month fixed effects in the fourth

17

specification. Thus, we are relying only on the variation in Peer Refinances generated from

teachers within the same campus at the same point in time, and only differ in the set of

coworkers with common off-periods. Following the inclusion of these effects, the coefficient

of Peer Refinances decreases to 6.42 bp. Finally, we include controls for employment and

individual characteristics. The effect remains virtually unchanged (i.e., a coefficient of 6.495),

which indicates that a one standard deviation change in the refinancing activity of a teacher’s

peers increases the probability that the teacher refinances by 9.48% of the unconditional

probability of refinancing.23 To provide additional economic content, an increase of $10,000

in net savings to an individual from refinancing is associated with a 56 bp increase in the

likelihood of refinancing. Therefore, a one standard deviation increase in Peer Refinances

has an effect equivalent to approximately a $1,150 increase in the net savings realized upon

refinancing.24 Overall, the regression analysis shows that peer effects have an economically

significant effect on the probability of refinancing among Texas teachers.

[INSERT TABLE 2 HERE]

IV.B. Self-Selection of Off-Periods

By exploiting overlap in teacher off-periods, we are able to disentangle the role of peers

from other confounding factors, such as campus-level variation in supply effects. Ultimately,

the identification of peer effects in this empirical setting relies on the exogenous formation of a

teacher’s peer group. Specifically, this requires that the assignment of a teacher’s schedule is

uncorrelated with other factors that affect the probability of refinancing. A plausible scenario

that violates this assumption would be the self-selection of teachers into peer groups based on

teacher discretion in the formation process of class schedules. However, conversations with

school officials provide anecdotal evidence that is inconsistent with this view; specifically, the

23. Additionally, Table IA.2 shows that the previous results are similar when Peer Refinances takes intoaccount rate refinances in the previous 2-month period, or when we use an alternative measure of savings,assuming a 30-year life for the loan.

24. More specifically, 6.49556.30 · $10, 000 = $1, 153.

18

fact that teacher assignments are determined by a scheduling algorithm designed to optimize

the scheduling needs of students and the menu of classes available. However, the possibility

remains that optimizing class assignments results in peer groups that share a set of common

characteristics.

To address this concern, we undertake a nonparametric approach to contrast the com-

monality of characteristics among the observed peer groups with that of the counterfactual,

in which peer groups are randomly formed. Specifically, we first compute the probability

that an individual and another teacher from her peer group share a common characteristic,

such as being a registered voter. We then generate a pseudo-peer group for each teacher by

randomly drawing peers from the pool of all teachers at the campus in that year. Each peer

group is constructed to be of equal size with the teacher’s true peer group. Finally, using

the pseudo-peer groups, we compute the probability of sharing a common characteristic. We

repeat this process 10,000 times to generate the distribution of commonality under the of

randomly selected peer groups.

[INSERT FIGURE 4 HERE]

Figure 4 reports the simulation results across multiple individual characteristics. Panel

A reports the likelihood of a teacher and her peer either having a common voter registration

status, having the same self-reported ethnicity, both possessing the same degree level, or

being of the same gender. Across the four estimates, the observed probability of sharing a

common characteristic falls within the bounds of the distribution under the null hypothesis.

The likelihood of two teachers in the same peer group sharing a common gender appears to

differ most substantially from the null, with slightly more than 3% of the pseudo-peer groups

having a greater likelihood of sharing a common gender. However, the difference between

the observed probability and the mean under the null is 0.23%, which suggests that the

difference is not economically important. Panel B repeats the previous analysis on contin-

uous individual characteristics such as annual salary. Here, we examine the mean absolute

difference between a teacher’s characteristic (e.g., her salary) and that of her peer group.

19

If teachers self-select into peer groups of similar pay, we would expect the mean absolute

difference in pay to be less than that of a randomly generated set of peer groups. The figure

indicates that the differences in tenure at the current school, years of teaching experience,

and ages tend to be less than that of a randomly generated peer group. However, the dif-

ferences are economically insignificant with the largest divergence (Tenure) representing a

difference of 0.6 months. Additionally, the absolute difference in annual salary appears to

be larger than that of the randomly generated peer groups. One possible explanation for

this result is the choice of some teachers to supplant an off-period with additional duties in

exchange for increased pay. This would result in a decreased likelihood of two such teachers

sharing an off-period relative to sharing an off-period with another teacher who did not take

on additional duties, thus earning less pay.

Additionally, we test the predictive power of a teacher’s peer group in forecasting observ-

able characteristics using OLS regressions. Internet Appendix Table IA.3 presents the results

when regressing a teacher’s characteristic on the average of her peer group, Peer Group Av-

erage. Alternatively, the possibility exists that clustering occurs in teacher characteristics

due to geographic or institutional effects. Most notably, teacher pay is likely correlated with

a school district’s tax base, resulting in district-level variation in teacher salaries. Further-

more, self-selection of teachers into student age groups could result in variation in teacher

gender across campuses within a district, such as differences between elementary school and

high school campuses. Thus, to control for this possibility, we also include the average of all

teachers who are within the campus, but outside the teacher’s peer group, Campus Aver-

age. Standard errors are clustered at the campus level. These results reinforce the previous

analysis, as Peer Group Average is not economically important determinant for any of the

characteristics tested. Overall, these findings yield no evidence of significant clustering along

teacher characteristics within off-period groups, which supports the use of off-periods in the

main identification strategy of this paper.

20

IV.C. Do Peers Share the Same Lender?

The previous findings are consistent with an individual’s peer group influencing his or

her actions. However, if this effect is the result of information transmission within the peer

network, then the information passed between agents need not be confined to the prevailing

lending rate in the market and to the potential savings associated with refinancing.

The primary determinant in an individual’s refinancing decision is the available lending

rate at which he or she can secure a new loan. While the time-series of mortgage rates

are certainly correlated across lenders, it is likely that there is a large amount of cross-

sectional variation in lending rates.25 Thus, the effort required to solicit rates from multiple

lenders may provide an additional friction that decreases the amount of refinancing activity.

However, this friction may be reduced if the information gained from a teacher’s peers

includes the local lender that currently offers the most beneficial terms. To examine this

possibility, we now turn to the commonality of lender choice within peer groups.

Specifically, if upon refinancing, the information passed from a teacher to a peer who

also refinanced included the lender that currently offers a superior rate, we would expect

the likelihood of both teachers using the same lender to be greater than random chance.

Therefore, for each observation included in Table 2 in which we observe a refinance, we

first select all teachers who share a common off-period and who also performed a mortgage

refinance over the trailing 3-month period. Using this peer group, we then compute the lender

overlap, which is the percentage of refinancing peers who use the same lender as our original

teacher. We find that the average of lender overlap (which we refer to as common lender % )

across our sample is 8.16%.26 However, it is possible that the refinancing activity of a given

lender is either temporally or geographically concentrated. Such concentration would result

in a value of common lender % that is greater than the probability of two randomly selected

25. Scharfstein and Sunderam (2013) show that the sensitivity of local lending rates to MBS yields isrelated to the concentration of mortgage lenders in the area.

26. Note that this is conditional on having at least one peer who refinances over the trailing 3-monthperiod.

21

refinances sharing a common lender. Thus, we need an appropriate counterfactual to serve

as a benchmark for our results in order to evaluate its economic significance. To do this, we

generate pseudo-peer groups under the null hypothesis that there is no information sharing

between individuals, and we then compute common lender % under this null. To address

the concern of lender concentration across time or geographic region, we define the candidate

pool of potential peers for each observation as all teacher refinances over the trailing 3-month

period within the same MSA but outside the true peer group. From this candidate pool, we

randomly select a peer group that is the same size as the peer group in the nonrandomized

sample. We repeat this randomized sampling procedure for 10,000 iterations to approximate

the distribution of common lender % under the null of no information sharing within the

peer group.

Panel A of Figure 5 reports the histogram of the results from the pseudo-peer group

simulations. The figure indicates that, under the null of no information dissemination within

peer groups, the expected value of common lender % is 3.05%. In addition, the maximum

value across the 10,000 iterations performed is 4.76%, or 8.6 standard deviations less than

the value of 8.16%, which we find when computing common lender % using the true peer

groups.

[INSERT FIGURE 5 HERE]

The previous panel suggests that in addition to the refinancing decision, a teacher’s

choice of lender is also influenced by his or her peer group. However, the possibility re-

mains that the abnormally large percentage of common lenders within peer groups is driven

by geographically concentrated lender activity within an MSA. Alternatively, such a result

could be explained by strategic partnerships between lenders and individual school districts.

Ideally, these concerns would be addressed by further restricting the candidate pool from

which pseudo-peer groups are drawn, so that it includes only teachers from the same school

district or even from the same campus. Unfortunately, such constraints are too restrictive;

they result in an insufficient number of candidates for some observations when a candidate

22

is required to have performed a mortgage refinance in the trailing 3-month period. Thus, we

repeat our analysis after relaxing the latter constraint.

Specifically, for each observation in our sample in which we observe a refinance, we

define lender overlap as the percentage of teachers who share a common off-period and

whose existing lender matches that of the teacher performing the refinance. Additionally,

when constructing the pseudo-peer group, we consider only teachers who work at the same

campus but do not share a common off-period. Thus, our focus is now on the percentage of

common lenders among the stock of all mortgages rather than in the flow of new refinances.

Panel B of Figure 5 reports the histogram of common lender % under the pseudo-peer

groups. The figure indicates that the expected value of lender overlap when drawing peers

from all teachers who do not share a common off-period is 1.71% compared to 2.57% when

using the true peer groups. Additionally, in only 0.24% of the iterations does the value of

common lender % in the randomized groups exceed the value when considering teachers

with a common off-period.

IV.D. Sample Splits

We now turn to cross-sectional variation in the effect of peers on a teacher’s refinancing

decision. We segment the sample examined in Table 2 into mutually exclusive subsets based

on tenure, age, pay, education level, and the house price index level of the teacher’s ZIP code.

We then run the previous analysis again. The results are presented in Table 3. To control

for unobservable factors, we include campus-month fixed effects in each model specification

considered. The regressions begin to suggest that less tenured, younger teachers, and teachers

who live in more expensive homes seem to incorporate more information from peers into their

refinancing decisions. However, these results should be interpreted with caution, as F -tests

indicate that none of the coefficient pairs differ statistically from one another.

[INSERT TABLE 3 HERE]

23

IV.E. Negative Savings

Up to this point, we have taken the broad view that the actions of the teacher’s peer group

contribute information (e.g., the potential benefits of refinancing, prevailing market rates for

refinanced mortgages). While the incorporation of additional information is traditionally

associated with more efficient outcomes, Figure 3 suggests that this is not always the case

for the teachers in our sample. Although the effects of peer refinancing are, on average, higher

for teachers whom we estimate will benefit from refinancing, the figure also shows that peer

refinancing increases the average refinancing probability of teachers with estimated negative

net savings. This suggests that the effect of a teacher’s peers could result in an information

cascade in which the knowledge that a peer recently refinanced could lead a teacher to falsely

conclude that she should also refinance her mortgage.

We now examine the effect of a teacher’s peer group within this subsample. Table 4 shows

the effect of peers for the subsample of observations in which the estimated net savings of

refinancing are negative. Without the inclusion of campus-month fixed effects, the effect

of a one standard deviation increase in the refinancing activity of a teacher’s peers on her

probability of refinancing ranges from 6.19 bp (Specification 1) to 5.34 bp (Specification 2)

across the specifications considered, while being statistically significant in each. While the

point estimates are substantially lower than those from Table 2, the results in this panel are

consistent with a teacher simply mimicking the behavior of peers and refinancing regardless of

the benefits and costs. However, the coefficient of Peer Refinances is statistically insignificant

with the inclusion of campus-month fixed effects in the final two specifications.

The results in Table 4 need to be interpreted with caution. While this paper focuses

on mortgage refinances that are initiated to capture savings on interest payments, mortgage

refinances also serve as a means for a homeowner to extract home equity, thereby increasing

their household leverage. Thus, this result is also consistent with an individual’s choice to

increase their household leverage being affected by the actions of her peers. Given the lack of

some loan details in the data, we are forced to make simplifying assumptions when estimating

24

the potential savings of a mortgage refinance, which could result in the misassignment of

some refinances into the wrong subsample based on net savings.

[INSERT TABLE 4 HERE]

IV.F. Conditional Effects of Peers

The results presented thus far have demonstrated the impact of a teacher’s peer group

on her refinancing behavior. Table 5 extends this analysis by examining how the magnitude

of this effect changes when exploiting institutional features of the public school system and

the homogeneity of peer networks. The underlying mechanism within our setting is the

communication of information between teachers during their off-periods. However, public

schools in Texas do not operate for the entire year. Instead, there is a 2-month break during

the summer. During this time, it is less likely that teachers interact, possibly attenuating

the estimated effect of a teacher’s peer group. Thus, in Panel A of Table 5, we exclude the

first two months of the school year, since a teacher is less likely to interact with peers in the

preceeding 3-month period. Following this refinement, we find an increase in the coefficient

of Peer Refinances. For instance, in the final specification, the point estimate increases by

20% to 7.828 bp, relative to the coefficient of 6.495 bp reported in Table 2.

Panel B of Table 5, in turn, examines how the effect of a teacher’s peers varies with

the degree of commonality in characteristics. Thus, we segment Peer Refinances into two

mutually exclusive groups. The first group contains peers with the same ethnicity as the

teacher, while the second group contains peers who differ in ethnicity. The results indicate

that refinancing among a teacher’s peers is associated with an increase in the teacher’s

probability of refinancing, regardless of shared or differing ethnicities when controlling for

local economic and employment effects (Specifications 1–3). However, following the inclusion

of campus-month fixed effects in the final two specifications, only the refinancing activity of

peers who share a common ethnicity remains as a statistically significant factor in a teacher’s

25

refinancing behavior.27

IV.G. First Movers and Followers

Another question of interest is whether the characteristics of teachers who refinance

independently of their peers differ from those of teachers who follow their peers. We explore

this question in Table IA.6, and find that the first movers, or “triggers,” are quite similar

to followers in terms of tenure, age, gender, education level, whether they teach a technical

class, the size of their loans, and the amount of savings when refinancing. The only dimension

in which the two types differ statistically is pay. First movers earn $853 more per year than

followers, on average, a difference equivalent to 1.8% of the mean salary in our sample.28

V. Evidence from Alternative Methods

One assumption that underlies the previous tests is that the actions of the teacher’s peer

group are exogenous with respect to her refinancing decision. To address this concern, we

begin this section by undertaking an instrumental variable approach. Next, we conduct

a survival analysis to account for the dynamic nature and the infrequent occurrences of

refinances.

V.A. Instrumented Refinancing among Peers

To address concerns that an unobservable factor is influencing both the outcome variable

and the refinancing decisions of peer groups, we exploit within-campus variation in peer

groups. However, the use of off-periods only alleviates this endogeneity concern if the unob-

servable variable exists at the campus level, such as a billboard placed near a school campus

which advertises a lender’s superior mortgage rates. In contrast, we would be unable to iden-

27. However, F -tests fail to reject the null of no difference between the two coefficients. Internet AppendixTable IA.4 reports additional tests that examine alternative constructions of commonality based on age andgender.

28. Finally, the lack of sufficient events prevents a reliable examination of possible learning by followers.

26

tify the true effect if the variation in the unobservable variable exists at the off-period level.

One example of such an unobservable variable, which we find to be particularly entertaining,

is the possibility that bank loan officers visit campuses to discuss refinancing benefits with

teachers. However, due to some unforeseen circumstance, the loan officers are only able to

speak to teachers during one off-period before leaving the campus.

Also, by definition, the first teacher within each peer group to refinance does so without

being influenced by any individual in her peer group. The decision of this first mover

or “trigger” to refinance could be influenced by the peer group of the teacher’s spouse,

the teacher’s social network formed outside of her work environment, or another source of

information such as media advertisements. Regardless, we do not observe the reason for this

first action. Thus, up to this point, we have taken the view that this initial refinance is

exogenous in nature, a view which some may find unsatisfactory.

Thus, we next seek an instrumental variable that is able to generate exogenous variation

in the refinancing decision of a teacher’s peers to justify this assumption of exogeneity. While

this paper focuses on the transmission of information within peer groups, the role that peer

effects play on an individual’s refinancing decision is likely secondary when compared to

the effect of potential savings as a result of refinancing. Intuitively, while an individual

may gather information regarding the importance of refinancing and the prevailing rates

in the market, such information is of no use to a teacher who does not currently benefit

from refinancing. Thus, a natural choice for an instrumental variable is the variation in

the potential savings among a teacher’s peers to predict the refinancing activity of this peer

group. In doing so, we are explicitly assuming (when the teacher’s savings are also included

in the regression) that the average savings of a teacher’s peer group only affects the teacher’s

refinancing decision through the refinancing activity of her peers.

Specifically, we compute net savings for each individual in a teacher’s peer group and

take the average across all the teacher’s peers to instrument for Peer Refinances, yielding

Peer Refinances. It is possible that an unobservable characteristic exists that is correlated

27

among the teachers of a peer group and results in correlated refinancing choices in the group.

However, the estimated quantity Peer Refinances utilizes only the information contained

in the savings of a teacher’s peers and not this unobservable factor.

We now turn to the empirical evidence when instrumenting for the refinancing activity

of a teacher’s peer group using the peer groups’ average savings. Table 6 reports the results

from the second stage of 2SLS regressions for the full sample.29 As before, the variable Peer

Refinances has been scaled by its standard deviation. The results are quite striking. When

we include MSA-year fixed effects in the first specification to control for geographic effects,

the coefficient indicates that a one standard deviation increase in the refinancing activity of

a teacher’s peers increases her probability of refinancing by 36.31 bp (t-stat=6.20). Thus,

the effect of a one standard-deviation increase in Peer Refinances is equivalent to slightly

more than a $7,590 increase in a teacher’s savings. The effect remains relatively stable

with the inclusion of district-year fixed effects (Specification 2), campus-year fixed effects

(Specification 3), and teacher demographics (Specification 4).30

[INSERT TABLE 6 HERE]

At first glance, these results appear to be quite substantial, especially when benchmarked

against the coefficient estimates of Table 2. For instance, when viewed relative to the effect

of Savings, the point estimate for the effect of a one standard deviation increase in Peer

Refinances in the first specification is roughly 3.35 times the estimated effect from the pre-

vious table, which did not include an instrument variable.31 Alternatively, the coefficient of

36.31 in the first specification represents an increase of 54.2% relative to the mean monthly

probability of refinancing, which is 67.0 bp in this sample. However, recall that the in-

strumental variable predicts the refinancing activity of a teacher’s peer group based on the

29. The first stage of each regression is reported in Internet Appendix Table IA.5. The partial F -statisticfor the instrument variable ranges from 23.04 to 24.01, indicating that there should not be concern about aweak instrument problem.

30. Note that Table 6 differs from previous tables and does not include campus-month fixed effects, astheir inclusion results in a weak instrument.

31. The relative effect for the IV estimate is $7,590, while the effect in Table 2 is $2,265.

28

group’s potential savings. Thus, the estimated value of Peer Refinances is at its largest when

a teacher’s peers would benefit the most from refinancing, which is when the teacher would

plausibly be the most eager to discuss her savings with her peers. In contrast, the results

from the previous subsection include all peer refinancing activity that we cannot explicitly

classify as a cash-out refinance. It is unlikely that a teacher who initiates a cash-out refi-

nance would discuss the benefits of an interest-rate refinance with peers. Thus, the inclusion

of peer refinances initiated for a reason other than the savings benefits would result in a

downward attenuation of the estimated effect.

V.B. Hazard Model Results

While the previous tables present our main findings, they do so in the context of a

linear probability model. Doing so provides the advantage of being able to control for

unobservable variables using fixed effects, sometimes at a highly granular level, such as the

campus-month level, while still presenting the results within a standard framework. However,

for complementarity, given the relatively rare occurrence of mortgage refinances and the

dynamics of this decision faced by households, we seek to augment our current findings

with results from a survival analysis framework. Thus, Table 7 presents the results of Cox

Proportional Hazard model estimations, analogous to the results of Table 2.32 Reported are

hazard ratios with standard errors clustered at the MSA-year level.

[INSERT TABLE 7 HERE]

In the first specification, the coefficient of 1.041 (z -stat=4.03) indicates that a one stan-

dard deviation increase in the percentage of an individual’s peer group with recent refinancing

activity is associated with a 4.1% increase in the hazard rate of an individual refinancing.

In contrast to the previous analysis, the hazard model does not easily accommodate high

32. Recall that the peer group for each observation contains all other teachers who have at least 40 minutesof overlap in off-periods per school day. Thus, the primary variable of interest, Peer Refinances, equals thenumber of teachers with overlapping off-periods who have undertaken a mortgage refinance in the previous3-month period, scaled by the size of the peer group. Similar to Table 2, we standardize Peer Refinances forease of interpretation.

29

density fixed effects. Therefore, as a control for the effect of local loan supply exposure or

economic conditions, we include as an additional variable the percentage of all mortgages

that were refinanced in the teacher’s MSA-year. The results remain relatively unchanged

when controlling for the possibility of employment effects or differences in local supply expo-

sure with the inclusion of variables measuring the percentage of refinancing activity in the

district-year and campus-year, respectively.

In the fourth specification, the coefficient increases to 1.102 (z -stat=5.29) with the in-

clusion of a measure of refinancing activity constructed at the campus-month level. This

specification indicates that a one standard deviation change in the refinancing activity of a

teacher’s peers increases the hazard rate of the teacher’s refinancing behavior by 10.2%. To

provide some economic content, note that a $10,000 increase in the effective net savings upon

refinancing increases the hazard rate by 63.3%. Thus, a one standard deviation increase in

the refinancing activity of a teacher’s peers is roughly equivalent to a $1,600 increase in the

savings realized upon refinancing. The effect remains unchanged in the final specification

with the inclusion of controls for employment and individual characteristics, while increasing

the statistical significance slightly to a z -stat of 5.52. Overall, the results presented in Table

7 confirm the previous findings that a teacher’s peer group is an important determinant of

her refinancing behavior.

VI. Conclusion

Using a comprehensive data set of employment records of Texas teachers, we exploit the

quasi-random assignment of teacher class schedules to identify the effect of peers on an im-

portant household financial decision. We find that a one standard deviation increase in the

amount of recent mortgage refinances by a teacher’s peers increases her likelihood of refinanc-

ing by 9.5%. Our setting allows us to control for other determinants of refinancing, teacher

characteristics, and even unobservable campus-level shocks. The results are confirmed using

an IV estimation approach and robust to the consideration of a hazard model. We find that

30

a teacher’s choice of lender upon refinancing is also influenced by her peer group, a finding

that is consistent with information being transmitted within her peer network. Additionally,

we find evidence that suggests that younger, less tenured teachers, as well as teachers who

live in more expensive homes, incorporate more information from peers into their refinancing

decisions. Finally, we find little evidence of differences in characteristics between teachers

who refinance independently and teachers who follow their peers.

Overall, our results support the hypothesis that informational frictions can be detrimental

to household financial decisions, and that the effects of peers reduce these frictions. Sub-

stantial savings for households could be achieved by educating homeowners on the benefits

of refinancing and by making interest rate information widely available.

31

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34

TeacherEmployment

Records

Voting Records

NameDOB

Gender

Registered TeacherVoters

County DeedRecords

AddressLast Name

Teacher-Transactions

+ Employment information+ Demographic information

+ Residential transactions+ Property characteristics

Phase 1

Phase 2

FIGURE 1. Data Matching Procedure

This figure illustrates the process used to map teacher employment records to county deed records. First,the TEA payroll records are matched with the electoral records of registered voters based on name, date ofbirth, and gender, in order to obtain teachers’ addresses (Phase 1). Then, we use these addresses and lastnames to match the resulting teacher voter records with county deed records (Phase 2). We require an exactmatch between the address number prefix, ZIP code, and individual’s last name; we also require that theLevenshtein ratio between the address name listed on the county and voting records exceeds 0.9.

35

9:00am 10:00am 11:00am 12:00pm 1:00pm 2:00pm 3:00pm

Ava

Kim

Bob

Ava Kim Bob

4515

4545

15 45

-

45

15

45

-

45

15

45

-

Teacher SchedulesMinutes Overlap

Time

FIGURE 2. Off-Periods Classification

This figure illustrates an example of the class assignment schedule for three teachers and our classificationof each teacher’s peer group using overlapping off-periods. The colored blocks denote a teacher’s off-periods,while the hatched regions indicate an overlap in off-periods with another teacher. Both the Bob-Kim andKim-Ava pairs share 45 minutes of common off-periods per day, while there are only 15 minutes of commonoff-periods for the Bob-Ava pair. This figure illustrates the asymmetric nature of each teacher’s peer group:While Bob’s peer group consists of only Kim, Kim’s peer group is made up of both Bob and Ava.

36

0.5

11.

52

2.5

mon

thly

pro

babi

lity

of re

finan

cing

(%)

−10 −5 0 5 10 15 20net savings ($1,000)

FIGURE 3. Net Savings and Refinancing Probability

This figure shows the relation between the probability of an individual refinancing her mortgage and herestimated net savings upon refinancing when using overlapping off-periods to identify peer groups. Teachersare separated into two groups. The first group (indicated by the black line with hollow markers) consists ofteacher-month observations in which no individual in the teacher’s peer group performed a mortgage refinancein the trailing 3-month period. The second group (indicated by the red and blue line with solid markers)consists of the remaining teacher-month observations, in which at least one individual in the teacher’s peergroup refinanced within the last three months. Teachers whom we estimate to be underwater (i.e., havenegative equity in the house) are not included.

37

Panel A: % Peers with Common Characteristic

65.25 65.50 65.75 66.00 66.25Voter

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5Density

74.75 75.00 75.25 75.50 75.75Ethnicity

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

69.25 69.50 69.75 70.00 70.25Degree

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Density

64.50 64.75 65.00 65.25 65.50 65.75Gender

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

FIGURE 4. Off-Periods Selection SimulationThis figure illustrates the degree of commonality for individual characteristics among peer groups relativeto pseudo-peer groups. The observed degree of commonality in our peer groups are indicated by the dashedvertical blue line, while the histogram and kernel density estimate report the distribution of commonality fromrandomly generated peer groups. Panel A reports the probability of having the same voter registration status(Voter), self-reported ethnicity (Ethnicity), degree level (Degree), and gender (Gender). Panel B reportsthe mean absolute difference between a teacher and her peer group with respect to annual compensation(Pay), years of tenure at the current school (Tenure), total years of teaching experience (Experience), andcurrent age as of September 1 of the current school year (Age). Pseudo-peer groups are formed by randomlydrawing peers from the pool of all other individuals within a teacher’s campus but outside her peer group.

38

Panel B: Mean Absolute Difference with Peers

7575 7600 7625 7650 7675 7700 7725Pay

0.000

0.005

0.010

0.015

0.020

0.025

0.030Density

7.60 7.65 7.70 7.75Tenure

0

5

10

15

20

25

9.95 10.00 10.05 10.10 10.15Experience

0

5

10

15

20

Density

12.05 12.10 12.15 12.20 12.25Age

0

5

10

15

20

39

Panel A

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5Probability of Common Lender (%)

0.0

0.2

0.4

0.6

0.8

1.0

1.2Density

FIGURE 5. Common Lenders SimulationThis figure illustrates the probability of a teacher and her peer sharing a common lender relative to pseudo-peer groups. Panel A reports the probability of a common lender between a refinancing teacher and herpeer, conditional on the peer also refinancing in the prior 3-month period (indicated by the dashed blueline). The figure also reports the distribution of probabilities for pseudo-peer groups. Pseudo-peer groupsare formed by constructing a pool of candidate peers by selecting all individuals who refinance within theprior 3-month period and who are within a teacher’s MSA but not in her peer group. Then, candidates arerandomly selected from the pool to match the true size of the teacher’s peer group. Panel B reports theprobability of a refinancing teacher having the same lender as her peer. Pseudo-peer groups are formed bydrawing candidates from the pool of all other individuals within a teacher’s campus but outside of her peergroup.

40

Panel B

0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75Probability of Common Lender (%)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6Density

41

TABLE 1–Summary Statistics

All Teachers Registered Voters Teacher-Transactions Final SampleMean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Employment:Pay 43998.67 (9558.94) 44339.12 (9678.51) 45330.83 (9730.22) 47813.23 (8245.75)Tenure 7.65 (8.05) 8.23 (8.20) 7.70 (7.79) 6.19 (6.86)

Ethnicity:Caucasian 0.694 (0.461) 0.705 (0.456) 0.700 (0.458) 0.742 (0.438)African American 0.090 (0.287) 0.089 (0.285) 0.100 (0.300) 0.115 (0.320)Asian 0.010 (0.100) 0.007 (0.084) 0.009 (0.094) 0.013 (0.111)Hispanic 0.199 (0.400) 0.193 (0.395) 0.185 (0.388) 0.127 (0.333)Other 0.006 (0.079) 0.006 (0.075) 0.006 (0.079) 0.003 (0.053)

Other Characteristics:Bachelor’s Degree 0.764 (0.425) 0.757 (0.429) 0.742 (0.438) 0.728 (0.445)Advanced Degree 0.227 (0.419) 0.234 (0.423) 0.250 (0.433) 0.264 (0.441)Age 42.49 (11.19) 43.31 (10.97) 42.72 (10.88) 41.40 (10.51)Female 0.772 (0.420) 0.771 (0.420) 0.771 (0.420) 0.656 (0.475)

N 3,672,937 2,798,802 1,258,810 32,465

This table describes the different samples that correspond to each of the main steps of the matching procedure and the sample selection process. Morespecifically, the table provides the mean and standard deviations of multiple employment and demographic variables of 1) all teachers in the TEApayroll records, 2) teachers who are registered voters, 3) teachers who were matched to county deed transaction records, and 4) teachers in the finalsample. The units of observation are teacher-years.

42

TABLE 2–Peer Effects on the Decision to Refinance

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

Peer Refinances 11.088*** 10.171*** 9.204*** 6.422* 6.495**(4.82) (4.41) (3.64) (1.96) (1.99)

Savings ($, ×10,000) 48.961*** 49.299*** 53.049*** 56.306*** 56.299***(6.47) (6.48) (6.75) (5.46) (5.45)

1(Underwater) -7.661 -9.099 -7.893 -5.428 -4.744(-1.10) (-1.16) (-0.93) (-0.57) (-0.50)

Percent Underwater -9.846 5.737 15.127 -33.014 -58.476(-0.12) (0.05) (0.12) (-0.24) (-0.42)

Teacher Characteristics N N N N YMSA-Year FE Y N N N NDistrict-Year FE N Y N N NCampus-Year FE N N Y N NCampus-Month FE N N N Y Y

N 303,467 303,467 303,467 303,467 303,467R2 0.024 0.032 0.055 0.242 0.242

This table shows OLS regressions in which the dependent variable is a 0/1 indicator of refinances, and themain variable of interest is Peer Refinances, a variable that captures the number of peers having undertakena mortgage refinance in the previous 3-month period, scaled by the size of the peer group. Reported arethe effects of a one standard deviation change in Peer Refinances. The regressions also include controlsfor other determinants of refinancing such as the estimated savings, a 0/1 indicator of whether the teacheris underwater, and the underwater amount (in percent). Depending on the specification, MetropolitanStatistical Area (MSA)-year fixed effects, district-year fixed effects, campus-year fixed effects, campus-monthfixed effects, or teacher characteristics such as gender, ethnicity, highest academic degree obtained, and salaryare also included. Reported t-statistics in parentheses are heteroskedasticity-robust and clustered by MSA-year. ***p<0.01, **p<0.05, *p<0.1.

43

TABLE 3–Cross-Sectional Splits

Tenure Age Pay Grad Degree House PriceAbove Below Above Below Above Below Yes No Above Below

Peer Refinances 2.393 9.806** 6.930 11.895* 5.215 7.770* 4.778 7.048 7.770* 11.365*(0.30) (2.49) (1.29) (1.86) (0.82) (1.70) (0.54) (1.61) (1.70) (1.75)

Savings ($, ×10,000) 51.743*** 57.376*** 49.276*** 61.566*** 55.728*** 50.529*** 54.713*** 53.659*** 50.529*** 52.548***(4.45) (4.96) (4.00) (5.36) (4.83) (4.81) (6.63) (4.62) (4.81) (5.45)

1(Underwater) -12.960 -11.054 -8.449 -1.435 -10.439 11.759 -1.156 -4.906 11.759 -8.380(-1.00) (-0.73) (-0.62) (-0.12) (-0.95) (0.49) (-0.08) (-0.44) (0.49) (-0.79)

Percent Underwater 63.303 -90.848 -100.055 -135.588 2.840 -182.053 -275.092 79.780 -182.053 35.420(0.41) (-0.35) (-0.53) (-0.60) (0.03) (-0.56) (-1.03) (0.42) (-0.56) (0.14)

Teacher Characteristics Y Y Y Y Y Y Y Y Y YCampus-Month FE Y Y Y Y Y Y Y Y Y Y

N 131,964 171,503 139,921 163,546 143,597 159,870 79,536 223,931 143,268 160,199R2 0.409 0.347 0.394 0.368 0.370 0.392 0.507 0.289 0.376 0.387

This table shows OLS regressions in which the dependent variable is a 0/1 indicator of refinances, while the main variable of interest is Peer Refinances,a variable that captures the number of peers having undertaken a mortgage refinance in the previous 3-month period, scaled by the size of the peergroup. The regressions also include controls for other determinants of refinancing such as the estimated savings, a 0/1 indicator of whether theteacher is underwater, and the underwater amount (in percent). Campus-month fixed effects and teacher characteristics such as gender, ethnicity,highest academic degree obtained, and salary are also included. The sample has been segmented into two mutually exclusive groups based on whetherthe teacher is above or below the median in terms of tenure, age, pay, or house price value or based on whether the teacher holds a graduatedegree (Yes) or does not (No). Reported are the effects of a one standard deviation change in Peer Refinances, and t-statistics in parentheses areheteroskedasticity-robust and clustered by MSA-year. ***p<0.01, **p<0.05, *p<0.1.

44

TABLE 4–Negative Net Savings

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

Peer Refinances 6.189*** 5.338*** 5.827*** 4.525 4.670(3.21) (2.70) (2.73) (1.27) (1.31)

Savings ($, ×10,000) 3.100 1.159 -1.544 6.565 6.080(0.62) (0.24) (-0.26) (0.66) (0.61)

1(Underwater) -4.370 -5.495 -5.717 -0.767 -0.656(-0.58) (-0.71) (-0.63) (-0.08) (-0.06)

Percent Underwater 109.134 149.026 87.799 -33.722 -51.563(0.71) (1.03) (0.58) (-0.19) (-0.29)

Teacher Characteristics N N N N YMSA-Year FE Y N N N NDistrict-Year FE N Y N N NCampus-Year FE N N Y N NCampus-Month FE N N N Y Y

N 172,756 172,756 172,756 172,756 172,756R2 0.013 0.023 0.055 0.299 0.299

This table shows OLS regressions in which the dependent variable is a 0/1 indicator of refinances, and themain variable of interest is Peer Refinances, a variable that captures the number of peers having undertaken amortgage refinance in the previous 3-month period, scaled by the size of the peer group. The main differencewith Table 2 is the inclusion of only observations in which estimated net savings are negative. Reportedare the effects of a one standard deviation change in Peer Refinances, and t-statistics in parentheses areheteroskedasticity-robust and clustered by MSA-year. ***p<0.01, **p<0.05, *p<0.1.

45

TABLE 5–Peer Effects on the Decision to Refinance: Additional Tests

Panel A: Excluding September and October(1) (2) (3) (4) (5)

Peer Refinances 11.985*** 11.122*** 9.597*** 7.740** 7.828**(4.91) (4.25) (3.28) (2.17) (2.19)

Savings ($, ×10,000) 48.407*** 48.597*** 52.215*** 56.491*** 56.448***(6.63) (6.64) (6.73) (6.00) (6.00)

1(Underwater) -8.973 -9.931 -9.722 -6.959 -6.166(-1.19) (-1.17) (-1.07) (-0.68) (-0.61)

Percent Underwater -29.107 -29.750 4.188 -56.815 -87.348(-0.31) (-0.23) (0.03) (-0.35) (-0.53)

Teacher Characteristics N N N N YMSA-Year FE Y N N N NDistrict-Year FE N Y N N NCampus-Year FE N N Y N NCampus-Month FE N N N Y Y

N 255,808 255,808 255,808 255,808 255,808R2 0.025 0.034 0.061 0.237 0.237

Panel B: Refinement of Peer Groups Based on Common Ethnicity(1) (2) (3) (4) (5)

Peer Refinances 10.218*** 9.484*** 8.483*** 7.058** 7.288**common ethnicity (4.42) (4.13) (3.18) (2.00) (2.08)

Peer Refinances 6.851*** 6.166*** 5.715*** 1.399 1.054different ethnicity (3.12) (3.00) (2.85) (0.40) (0.30)

Savings ($, ×10,000) 49.044*** 49.464*** 53.021*** 55.956*** 55.906***(6.48) (6.48) (6.67) (5.42) (5.41)

1(Underwater) -7.803 -9.165 -8.588 -5.403 -4.754(-1.09) (-1.15) (-1.01) (-0.54) (-0.47)

Percent Underwater -34.850 -24.934 7.458 -54.166 -80.997(-0.41) (-0.22) (0.06) (-0.35) (-0.53)

Teacher Characteristics N N N N YMSA-Year FE Y N N N NDistrict-Year FE N Y N N NCampus-Year FE N N Y N NCampus-Month FE N N N Y Y

N 298,397 298,397 298,397 298,397 298,397R2 0.024 0.033 0.056 0.242 0.242

This table shows OLS regressions in which the dependent variable is a 0/1 indicator of refinances, and themain variable of interest is Peer Refinances, a variable that captures the number of peers having undertakena mortgage refinance in the previous 3-month period, scaled by the size of the peer group. In Panel A, themain difference with Table 2 is the exclusion of September and October from each year of the sample. PanelB, in turn, segments Peer Refinances into two mutually exclusive groups. The first group contains peers withthe same ethnicity as the individual, while the second group contains peers who differ in ethnicity. Reportedare the effects of a one standard deviation change in Peer Refinances, and t-statistics in parentheses areheteroskedasticity-robust and clustered by MSA-year. ***p<0.01, **p<0.05, *p<0.1.

TABLE 6–Instrumental Variable Estimation

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

Peer Refinances 36.309*** 37.323*** 42.474*** 42.518***(6.20) (5.73) (6.56) (6.59)

Savings ($, ×10,000) 47.817*** 48.513*** 51.894*** 51.477***(7.77) (7.69) (7.45) (7.38)

1(Underwater) -4.676 -4.367 -5.184 -5.489(-0.69) (-0.59) (-0.69) (-0.75)

Percent Underwater -25.072 -8.416 37.119 17.932(-0.39) (-0.09) (0.27) (0.13)

Teacher Characteristics N N N YMSA-Year FE Y N N NDistrict-Year FE N Y N NCampus-Year FE N N Y Y

N 245,200 245,167 244,937 244,937F -statistic 24.01 23.62 23.04 23.23

This table shows the second stage of 2-stage least squares regressions in which the dependent variable is a0/1 indicator of refinances, and the main variable of interest is the estimated quantity Peer Refinances, avariable that captures the number of peers having undertaken a mortgage refinance in the previous 3-monthperiod, scaled by the size of the peer group. In the first stage, we use the average net savings conditional onrefinancing of a teacher’s peer group as an exogenous instrument to estimate Peer Refinances. The first-stagepartial F -statistics of the IV is reported, while the full first stage of each specification is reported in InternetAppendix Table IA.5. Reported are the effects of a one standard deviation change in Peer Refinances, andz -statistics in parentheses are heteroskedasticity-robust and clustered by MSA-year. ***p<0.01, **p<0.05,*p<0.1.

47

TABLE 7–Cox Proportional Hazard Model

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

Peer Refinances 1.041*** 1.045*** 1.054*** 1.102*** 1.102***(4.03) (4.19) (5.69) (5.29) (5.52)

Savings ($, ×10,000) 1.550*** 1.586*** 1.624*** 1.623*** 1.633***(16.71) (12.12) (12.47) (12.77) (12.46)

1(Underwater) 0.961 0.899 0.890 0.916 0.927(-0.53) (-1.00) (-1.02) (-0.79) (-0.67)

Percent Underwater 0.699 1.241 1.318 1.086 0.668(-0.41) (0.24) (0.30) (0.09) (-0.42)

Teacher Characteristics N N N N YMSA-Year Refi Control Y N N N NDistrict-Year Refi Control N Y N N NCampus-Year Refi Control N N Y N NCampus-Month Refi Control N N N Y Y

N 252,706 252,703 252,660 241,131 241,131

This table shows hazard ratios of Cox Proportional Hazard model regressions in which the dependent variableis a 0/1 indicator of refinances, and the main variable of interest is Peer Refinances, a variable that capturesthe number of peers having undertaken a mortgage refinance in the previous 3-month period, scaled by thesize of the peer group. The regressions also include controls for other determinants of refinancing such asthe estimated savings, a 0/1 indicator of whether the teacher is underwater, and the underwater amount (inpercent). Depending on the specification, a Metropolitan Statistical Area (MSA)-year refinancing activitycontrol, a district-year refinancing activity control, a campus-year refinancing activity control, a campus-month refinancing activity control, or teacher characteristics such as gender, ethnicity, highest academicdegree obtained, and salary are also included. Reported are the effects of a one standard deviation changein Peer Refinances, and z -statistics in parentheses are heteroskedasticity-robust and clustered by MSA-year.***p<0.01, **p<0.05, *p<0.1.

48

Teachers Teaching Teachers: The Role of Networks on Financial Decisions

Gonzalo Maturana and Jordan Nickerson

Internet Appendix

Additional Tables and Figures

Table IA.1–Correlation Matrix of Teacher Characteristics

Pay Tenure Grad Degree Age Female

Pay 1Tenure 0.52 1Grad Degree 0.29 0.17 1Age 0.47 0.48 0.24 1Female -0.16 0.04 -0.02 -0.0001 1

This table shows the correlation coefficients among teacher characteristics.

1

Table IA.2–Robustness for Table 2

Panel A: Alternative measure of Peer Refinances(1) (2) (3) (4) (5)

Peer Refinances 11.854*** 10.851*** 9.492*** 6.554** 6.618**(5.55) (5.08) (3.97) (2.05) (2.08)

Savings ($, ×10,000) 48.903*** 49.243*** 53.010*** 56.310*** 56.307***(6.46) (6.46) (6.74) (5.45) (5.45)

1(Underwater) -7.638 -9.089 -7.887 -5.400 -4.712(-1.10) (-1.16) (-0.93) (-0.57) (-0.50)

Percent Underwater -10.605 5.204 14.607 -33.544 -58.994(-0.13) (0.05) (0.12) (-0.24) (-0.42)

Teacher Characteristics N N N N YMSA-Year FE Y N N N NDistrict-Year FE N Y N N NCampus-Year FE N N Y N NCampus-Month FE N N N Y Y

N 303,467 303,467 303,467 303,467 303,467R2 0.024 0.032 0.055 0.242 0.242

Panel B: Alternative measure of Savings(1) (2) (3) (4) (5)

Peer Refinances 11.075*** 10.153*** 9.164*** 6.281* 6.343*(4.81) (4.39) (3.62) (1.93) (1.96)

Savings ($, ×10,000) 44.621*** 45.032*** 48.668*** 54.139*** 54.126***(6.66) (6.70) (7.00) (5.41) (5.40)

1(Underwater) -9.967 -11.383 -10.167 -8.621 -7.888(-1.45) (-1.46) (-1.21) (-0.93) (-0.85)

Percent Underwater -18.839 -7.812 -3.728 -47.085 -72.377(-0.23) (-0.07) (-0.03) (-0.33) (-0.51)

Teacher Characteristics N N N N YMSA-Year FE Y N N N NDistrict-Year FE N Y N N NCampus-Year FE N N Y N NCampus-Month FE N N N Y Y

N 303,467 303,467 303,467 303,467 303,467R2 0.024 0.032 0.055 0.242 0.242

This table shows OLS regressions in which the dependent variable is a 0/1 indicator of refinances, and themain variable of interest is Peer Refinances, a variable that captures the number of peers having undertakena mortgage refinance in the previous 3-month period, scaled by the size of the peer group. In Panel A, themain difference with Table 2 is the definition of Peer Refinances, which only considers refinances done bypeers in the last two months (instead of three months). Panel B, in turn, uses an alternative measure ofsavings which assumes a term of 30 years. Reported are the effects of a one standard deviation change inPeer Refinances, and t-statistics in parentheses are heteroskedasticity-robust and clustered by MSA-year.***p<0.01, **p<0.05, *p<0.1.

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Table IA.3–Teacher Characteristics and Group Characteristics

Employment Characteristics DemographicsAge Pay Grad Degree Female White Black Asian Hispanic(1) (2) (3) (4) (5) (6) (7) (8)

Peer Group Average 0.030*** 0.042*** 0.006 0.075*** 0.026** 0.009 -0.001 0.011(3.31) (3.95) (0.66) (8.10) (2.36) (0.63) (-0.11) (0.87)

Campus Average 0.424*** 0.830*** 0.407*** 0.469*** 0.863*** 0.891*** 0.139*** 0.878***(26.35) (86.22) (20.64) (30.65) (71.43) (56.50) (4.19) (57.23)

N 187,369 187,369 187,369 187,369 187,369 187,369 187,369 187,369R2 0.024 0.254 0.017 0.038 0.287 0.286 0.001 0.303

This table shows OLS regressions of a teacher’s characteristic on the average of her peer group (Peer Group Average) and the average of all teacherswithin the campus but outside the teacher’s peer group (Campus Average), using all teachers in the TEA records. Reported t-statistics in parenthesesare heteroskedasticity-robust and clustered by campus. ***p<0.01, **p<0.05, *p<0.1.

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Table IA.4–Alternative Refinements of Peer Groups

Panel A: Common Age(1) (2) (3) (4) (5)

Peer Refinances 7.245*** 6.336*** 4.987** 3.779 3.842common age group (3.75) (3.32) (2.39) (1.41) (1.42)

Peer Refinances 9.251*** 8.940*** 8.983*** 6.132 6.202different age group (4.93) (4.58) (4.05) (1.57) (1.59)

Savings ($, ×10,000) 49.060*** 49.470*** 53.023*** 55.955*** 55.912***(6.47) (6.48) (6.67) (5.41) (5.41)

1(Underwater) -7.799 -9.174 -8.613 -5.382 -4.734(-1.09) (-1.15) (-1.01) (-0.54) (-0.47)

Percent Underwater -36.040 -25.252 7.299 -54.718 -81.225(-0.42) (-0.22) (0.06) (-0.36) (-0.53)

Teacher Characteristics N N N N YMSA-Year FE Y N N N NDistrict-Year FE N Y N N NCampus-Year FE N N Y N NCampus-Month FE N N N Y Y

N 298,397 298,397 298,397 298,397 298,397R2 0.024 0.033 0.056 0.242 0.242

Panel B: Common Gender(1) (2) (3) (4) (5)

Peer Refinances 8.508*** 7.939*** 6.515*** 4.487 4.562common gender (4.33) (3.87) (2.77) (1.37) (1.40)

Peer Refinances 9.737*** 8.743*** 9.141*** 6.964 7.041different gender (3.44) (3.06) (3.01) (1.60) (1.60)

Savings ($, ×10,000) 49.065*** 49.475*** 53.030*** 55.958*** 55.910***(6.48) (6.48) (6.67) (5.42) (5.41)

1(Underwater) -7.788 -9.162 -8.577 -5.358 -4.714(-1.09) (-1.15) (-1.01) (-0.53) (-0.47)

Percent Underwater -36.140 -25.328 6.800 -55.000 -81.479(-0.42) (-0.22) (0.05) (-0.36) (-0.53)

Teacher Characteristics N N N N YMSA-Year FE Y N N N NDistrict-Year FE N Y N N NCampus-Year FE N N Y N NCampus-Month FE N N N Y Y

N 298,397 298,397 298,397 298,397 298,397R2 0.024 0.033 0.056 0.242 0.242

This table shows OLS regressions in which the dependent variable is a 0/1 indicator of refinances, and themain variable of interest is Peer Refinances, a variable that captures the number of peers having undertaken amortgage refinance in the previous 3-month period, scaled by the size of the peer group. The main differencewith Table 5 is the segmentation of Peer Refinances into two mutually exclusive groups. The first groupcontains peers with the same characteristic as the individual, while the second group contains peers whodiffer in the characteristic. In Panel A, peers are defined as belonging in the same age group if the absolutedifference in ages is less than 10 years. Panel B, in turn, segments the peer group based on having the samegender as the individual. Reported are the effects of a one standard deviation change in Peer Refinances, andt-statistics in parentheses are heteroskedasticity-robust and clustered by MSA-year. ***p<0.01, **p<0.05,*p<0.1.

Table IA.5–First Stage Regressions

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

Avg. Peer Savings 0.385*** 0.381*** 0.400*** 0.401***(4.90) (4.86) (4.80) (4.82)

Savings ($, ×10,000) -0.074*** -0.070** -0.034 -0.027(-2.82) (-2.60) (-0.90) (-0.71)

1(Underwater) -0.044 -0.057 -0.071 -0.054(-0.64) (-0.83) (-1.16) (-0.87)

Percent Underwater -0.490 -0.016 -0.114 -0.071(-0.50) (-0.02) (-0.14) (-0.09)

Teacher Characteristics N N N YMSA-Year FE Y N N NDistrict-Year FE N Y N NCampus-Year FE N N Y Y

N 245,201 245,201 245,201 245,201R2 0.032 0.080 0.211 0.211

This table reports the first stage of the 2SLS IV regressions of Table 6. The dependent variable, PeerRefinances, instrumented for in the second stage, is the number of a teacher’s peers who have undertakena mortgage refinance in the previous 3-month period, scaled by the size of the teacher’s peer group. Weuse the average net savings conditional on refinancing of a teacher’s peer group, Avg. Peer Savings, as anexogenous instrument to estimate Peer Refinances. The variables Avg. Peer Savings and Peer Refinanceshave been standardized. Reported t-statistics in parentheses are heteroskedasticity-robust and clustered byMSA-year. ***p<0.01, **p<0.05, *p<0.1.

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Table IA.6–First Mover and Follower Characteristics

Trigger Follower Difference t-statistic

Unpaid Balance ($) 138,700.9 138,190.5 510.4 0.13Savings ($) 6,087.8 6,600.1 -512.3 -1.07Pay ($) 47,807.3 46,954.8 852.5 2.08Tenure 6.17 6.33 -0.167 -0.48Technical Class 0.26 0.27 -0.001 -0.06Grad Degree 0.27 0.26 0.008 0.37Age 41.64 41.33 0.307 0.61Female 0.67 0.63 0.041 1.72

This table compares the characteristics of teachers that refinance independently of their peers with those ofteachers who follow their peers.

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