Incentives of Insurance Salespersons from Future Renewal Commissions *

Incentives of Insurance Salespersons from Future Renewal Commissions * Thomas R. Berry-Stölzle 1 David L. Eckles 2 September 2011 * The authors thank Robert E. Hoyt, Jim B. Kau, Henry J. Munneke, and participants of the 2010 Risk Theory Society Seminar for helpful comments and suggestions on this paper. A previous version of this paper was circulated under the title The Effect of Contracting Incentives on Productivity and Compensation of Insurance Salespersons. Please address all correspondence to Thomas R. Berry-Stölzle. 1 Terry College of Business, University of Georgia, 206 Brooks Hall, Athens, GA 30602, Tel.: +1-706-542-5160, Fax: +1-706-542-4295, trbs@terry.uga.edu. 2 Terry College of Business, University of Georgia, 206 Brooks Hall, Athens, GA 30602, Tel.: +1-706-542-3578, Fax: +1-706-542-4295, deckles@terry.uga.edu. 1

Incentives of Insurance Salespersons from Future Renewal Commissions Abstract We provide an economic explanation for the puzzling observation that there are compensation schemes with fixed salary components for property-liability insurance salespersons. With an average policy renewal rate of 89%, selling a policy does not only generate immediate commission, but also results in (expected) future renewal commissions. This link between current performance and future compensation creates implicit incentives. Our empirical analysis documents a strong positive relationship between the fraction of renewals and salespersons output, and between renewals and selection of output based compensation schemes. When controlling for these implicit incentives and self-selection, we do not find a positive effect of output based compensation on sales output. (JEL G22, J3, J22, J00) Key Words: Compensation Schemes, Incentives, Career Concerns, Insurance Salespersons 1

1. Introduction Theory in personnel economics builds on the fundamental concept that workers respond to incentives. Workers who are paid based on their output will ceteris paribus produce more output than workers receiving a fixed salary. This effect is driven by increased effort levels as well as by worker sorting; higher ability workers self-select the output based compensation scheme over a fixed salary to earn a higher compensation (see, e.g., Lazear, 1986, 2000; Brown, 1990, 1992; Shearer, 2004; Pekkarinen and Riddell, 2008). In dynamic models under asymmetric information, however, workers have incentives to hide their ability by holding back production in early periods to avoid more demanding schedules in the future (see, e.g., Freixas, Guesnerie, and Tirole, 1985; Laffont and Tirole, 1988). This so called ratchet effect makes output based compensation schemes inefficient (Gibbons, 1987), unless there is a labor market for older workers. Kanemoto and Mac- Leod (1992) show that competition for older workers permits efficient output based contracts. They further argue that their result may explain why output based compensation schemes are common in certain jobs like sales positions, and less popular in other areas like manufacturing. U.S. property-liability insurance salespersons provide an interesting case. Despite the fact that there is an active labor market for older and experienced salespersons, there is the full spectrum of compensation schemes, ranging from fixed salary, combinations of salary and commission, and compensation entirely based on commission, offered in the industry (see, e.g., Umble, York, and Leverett, 1977; The National Alliance Research Academy, 2004). The goal of this paper is threefold. First, we provide an economic explanation for the puzzling observation that there are compensation schemes with fixed salary components for propertyliability insurance salespersons. Second, we examine the effect of output based compensation schemes on worker output using data from U.S. insurance salespersons. Third, we examine factors determining salespersons choice of a compensation scheme. 2

Salespersons receiving a substantial fraction of their compensation as salary do not have strong direct incentives to deliver high performance. But focusing the discussion on salespersons current compensation structure omits an important source of implicit incentives: career concerns concerns about the effect of current sales performance on future compensation. 1 Most property or liability insurance contracts are one year contracts, and policyholders usually renew their policy with the same salesperson. 2 Therefore, selling a policy does not only generate immediate commission, it also results in (expected) future renewal commissions for the insurance agency. Assuming competition for older experienced salespersons as in Kanemoto and MacLeod (1992), the salesperson working for the agency can expect to get a share of her increased production in form of higher compensation. Thus, salespersons have strong incentives to establish a portfolio of (potential) renewal customers, a so-called book of business. This mechanism creates indirect sales incentives regardless of a salesperson s current compensation scheme and, hence, allows insurance agencies to offer compensation schemes with a fixed salary component as well as fixed salary contracts. If a fixed salary salesperson sells a lot and expands her book of business, she will be able to renegotiate her compensation with the agency owner. Adjustments in compensation are likely to lag behind changes in production. Therefore, for high ability salespersons it is advantageous to be on a pure commission compensation scheme that allows them to profit from improvements in production immediately. 1 Career concerns were first discussed by Fama (1980); he argued that incentive contracts are not necessary for managers because managers are disciplined by the labor market. If a manager performs well she will get high wage offers, if a manager performs poorly she will get low wage offers. Gibbons and Murphy (1992) study optimal incentive contracts when workers have career concerns. Their results emphasize the importance of optimizing total incentives: the sum of explicit contracting incentives and implicit career concern incentives. Chung, Sensoy, Stern, and Weisbach (2010) examine implicit incentives of private equity general partners from expected future fundraising. They quantify the magnitude of these implicit incentives and conclude that, for general partners of private equity funds, implicit incentives are about as large as explicit incentives. 2 Average policy renewal rates for commercial lines policies sold via independent agencies in the U.S. are 89%; average policy renewal rates for personal lines policies sold via independent agencies are 89% as well (The National Alliance Research Academy, 2006, p. 26). 3

Our analysis is divided into three sections. First, we follow the OLS regression specifications in the salesperson literature and examine what effect the percentage of commission in the total compensation package has on sales output and salespersons income levels. The broad range of compensation schemes for insurance salespersons allows a cross-sectional comparison of their impact on sales output. In the context of these regressions, we also examine the relationship between sales output and the fraction of a salesperson s production that stems from renewals. Second, to control for salesperson s self-selection of compensation schemes, we re-estimate the output and compensation level models together with a model explaining the choice of a compensation system with characteristics of the salesperson as well as the job in a simultaneous equation framework. Third, we employ the Blinder-Oaxaca decomposition (Blinder, 1973; Oaxaca, 1973) and Jann s (2008) significance test for the resulting differential, to examine which part of the output difference between fixed salary and pure commission salespersons can be explained by differences in productivity characteristics, such as education and work experience. The explained part of the output difference can be attributed to a firm s ability to hire more productive workers. The unexplained part of the output difference results from the firm s salespersons producing more because of incentive effects. Our empirical analysis provides four main results: First, we find a strong positive relationship between the fraction of renewals and salespersons output; and this finding is robust with respect to self-selection of compensation schemes. Second, we find that more established salespersons with a higher percentage of renewals are more likely to choose a pure commission compensation scheme. Third, when controlling for self-selection of compensation schemes, we do not find a positive effect of output based compensation on sales output. Fourth, salespersons compensation is mainly determined by their sales output. Overall, these findings support the view that insurance 4

salespersons have strong incentives to build a book of business, and that these indirect sales incentives allow insurance agencies to offer compensation schemes with a fixed salary component. Our research contributes to numerous strands of the literature. First, we provide new empirical evidence to the idea pioneered by Fama (1980) that career concerns can be an important source of incentives inside firms. Second, we contribute to the insurance literature by studying the performance impact of compensation schemes for insurance salespersons for the first time. 3 Third, we extend the marketing literature by using a simultaneous equation model to address the endogeneity of compensation scheme choice in analyzing the relationship between compensation schemes and sales performance. The remainder of this article is organized as follows. The next section introduces the institutional environment we examine, i.e. independent insurance agencies. The third section provides the conceptual background and explains the development of the hypotheses. The data and methodology are discussed in the fourth section of the article including a detailed description of the measures used in the empirical analysis. The fifth section presents the empirical results, and the final section concludes. 2. Independent Insurance Agencies In the U.S., insurance products are sold via two main distribution channels. 4 Some insurers, referred to as direct writers, employ their own sales force to primarily (if not exclusively) sell their products. Other insurers utilize independent agencies to distribute their insurance products. Unlike direct writers, independent agencies typically represent multiple insurers. Another important cha- 3 The existing literature on insurance sales focuses on the choice of a distribution channel from the point of view of the insurance company (see, e.g., Won-Joong, Mayers, and Smith, 1996). Since all types of distribution systems have their specific advantages and disadvantages, the fit between the distribution system and insurer and market characteristics is important (Zweifel and Ghermi, 1990; Barrese, Doerpinghaus, and Nelson, 1995; Berger, Cummins, and Weiss, 1997; Regan, 1997; Regan and Tzeng, 1999; Brockett, Cooper, Golden, Rousseau, and Yuying, 2005; Trigo-Gamarra, 2008). 4 For a more detailed discussion of insurers distribution channels in the U.S. see, e.g., Regan and Tennyson (1996, 2000). 5

racteristic of independent agencies is that they own their book of business. This means that an insurance company is not allowed to contact policyholders directly to renew their policies or to sell them additional products; all communication between the insurer and the policyholder has to go through the agency. While some of the independent agencies consist of only one self-employed agent, most agencies are corporations with multiple employees. Employees whose primary task it is to sell insurance are called producers. Producers of this type make up approximately 51% of insurance sales agents (Bureau of Labor Statistics, 2009). Whenever a producer sells an insurance policy of one of the insurers represented by the agency, the agency receives commission from the corresponding insurance company. Sales commissions are the most important source of revenues for independent agencies as they account for 87% of agency revenues (The National Alliance Research Academy, 2006). Therefore, the amount of sales commission generated by a producer directly measures the producer s sales production, and this production measure is directly observable by the agency owners. However, the fact that insurance companies compensate independent agencies on a commission basis does not imply that independent agencies compensate all their producers on a pure commission basis. In fact, agencies offer the full spectrum of commission based compensation schemes, ranging from a fixed salary (no commissions) to compensation entirely based on commission (one hundred percent commissions). Umble, York, and Leverett (1977) surveyed independent insurance agencies in the state of Georgia regarding producers hired in a three year window. They report that 54% of the 239 newly hired producers in their sample were paid only a salary, 29% received a combination of salary and commission, and 17% were paid on a commission only basis. More recently, the National Alliance Research Academy (2004) surveyed producers working in independent agencies across the United States. They distinguish between producers mainly selling 6

commercial property-liability insurance products and producers mainly focusing on personal lines. They report that 12% of the commercial lines producers in their sample are paid a salary only, 49% are paid with a mixed compensation scheme, and 39% are paid commission only. 5 We use this variation in compensation structures across producers working for independent insurance agencies to analyze the impact of different compensation structures on the producers sales output. Note that we only focus on property-liability insurance sales in our discussion because contractual incentives in life insurance sales are substantially different from contractual incentives in property-liability insurance sales. 6 However, this is not very restrictive since life and health insurance commissions account for only about 10% of revenue of independent agencies (The National Alliance Research Academy, 2006). Most property or liability insurance contracts are one year contracts. On average, 89% of policyholders renew their existing policy at the end of the contract term. 7 Thus, selling a policy does not only generate immediate commission for the agency, it also results in (expected) future renewal commissions. Insurance companies usually pay independent agencies the same percentage commission for renewals as for new business (Regan and Tennyson, 2000); otherwise, independent agencies would have strong incentives to switch their clients to another insurance company after the first policy term. Producers working for an agency, however, usually receive a slightly lower renewal commission rate than new business commission rate from their agency. Standard commission rates for a commercial lines producer on a pure commission compensation scheme are 40% for 5 In the same sample, compensation for personal lines producers slightly differs with 19% paid a salary only, 54% paid with a mixed pay scheme, and 27% paid commission only. 6 In contrast to property-liability insurance salespersons, the vast majority of life insurance salespersons work on a pure commission compensation scheme. Commission schemes usually pay large first year commissions for new business, but hardly any commissions in later years of a policy, or for renewals (Regan and Tennyson, 2000). The turnover rate for life insurance agents is 26% per year, and the average four year retention rate for new agents is only 18% (Hoesly, 1996). 7 Average policy renewal rates for commercial lines policies sold via independent agencies in the U.S. are 89%; average policy renewal rates for personal lines policies sold via independent agencies are 89% as well (The National Alliance Research Academy, 2006, p. 26). 7

new business and 35% for renewals. 8 This means that the producer receives 40% (35%) of the commission which the agency receives from the insurance company for each policy sold (renewed). Commission rates are lower if a producer s compensation package also includes a fixed salary component. 9 Table 1 provides an example highlighting the importance of expected renewal commissions for commercial lines producers. Assuming that the producer sells a policy that generates $2,500 in commission for the agency, and assuming a 40% commission rate, the producer earns $1,000 on this sale. 10 Panel B of Table 1 calculates the present value of expected renewal commissions the producer will receive from this sold policy. The ten year evaluation framework, the two percent inflation rate and the ten percent discount rate we use are based on Trieschmann, Davis, and Leverett s (1975) valuation model for property-liability insurance agencies. The calculation assumes an 89% renewal rate and a renewal commission rate of 35%. Column 7 presents the ratio of the present value of expected renewal commissions to the first year commission the producer receives. If the producer stays in business for 10 years, the present value of expected renewal commissions is 3.5-times as big as the first-year commission on the policy sold. If the producer quits after 2 years, the present value of expected renewal commissions is still larger than the first-year commission as the ratio is 1.32. Since retention rates of producers are relatively high in independent agencies, expected renewal commissions play an important role for these salespersons. 11 8 The median share of new business commission that a commission-only producer in our sample can keep is 40%; the median share of renewal commission that a commission-only producer in our sample can keep is 35%. The data is described in Section 4. 9 The median share of new business commission that a mixed pay producer in our sample can keep is 33%; the median share of renewal commission that a mixed pay producer in our sample can keep is 30%. The data is described in Section 4. 10 The median commission per policy agencies in our sample receive is $2,500. The data is described in Section 4. 11 Umble, York, and Leverett (1977) surveyed independent insurance agencies to calculate the retention rate of producers. They report that 85% of producers hired two to five years prior to the survey were still with the agency. This 85% retention rate for property-liability producers contrasts with the 18% average four year retention rate for new life insurance agents reported by Hoesly (1996). 8

In the remainder of the paper we will use the terms producer and insurance salesperson interchangeably to refer to such salespersons working for independent insurance agencies. 3. Conceptual Background and Hypotheses Development Theory predicts that profit maximizing firms select compensation schemes for their workforce by comparing the respective costs and benefits of these schemes (Hart and Holmström, 1987; Journal of Labor Economics, 1987). Benefits of an output based compensation scheme in static models under asymmetric information include incentives to exert more effort (Holmström, 1979) and the attraction of higher ability workers (see, e.g., Lazear, 1986, 2000; Brown, 1990, 1992; Shearer, 2004; Pekkarinen and Riddell, 2008). When workers of different abilities are faced with a choice between an output based compensation scheme and a fixed salary, higher ability workers prefer and, hence, select an output based compensation scheme that allows them to earn more than the fixed salary. In dynamic models under asymmetric information, however, output based compensation schemes may be inefficient. A worker s performance provides information about the worker s unobservable ability. In a long-term employment relationship, a firm can use the information revealed by observing the worker s output to set performance standards in the future. Anticipating this behavior, a worker has incentives to hide her ability by holding back production in early periods to avoid more demanding schedules in the future (see, e.g., Freixas, Guesnerie, and Tirole, 1985; Laffont and Tirole, 1988). This is referred to as the ratchet effect. Two important characteristics of models explaining the ratchet effect are that firms cannot commit to long-term contracts and that workers have only limited (if any) employment alternatives in later periods (see, e.g., Gibbons, 1987). Kanemoto and MacLeod (1992) show that competition for older workers permits efficient output based contracts. Their model includes a job market for older workers with output based 9

compensation contracts. This model implies that workers employment alternatives in later periods depend on the ability of the workers. Kanemoto and MacLeod s result holds even if workers make some relationship-specific investments that create positive mobility costs. Since there is an active job market for older and experienced property-liability insurance producers in the U.S., we expect a pure commission contract between an agency and a producer to be an efficient compensation scheme. Pure commission compensation schemes provide direct sales incentives for producers. However, in his analysis of managers, Fama (1980) argues that incentive contracts are not necessary because workers are disciplined by the labor market. If a worker performs well she will get high wage offers, if she performs poorly she will get low wage offers. Therefore, concerns about the effect of current performance on future compensation create an important source of implicit incentives for workers. Formal models of such career concerns are usually based on a learning framework (see, e.g., Gibbons and Murphy, 1992; Chung, Sensoy, Stern, and Weisbach, 2010); the labor market uses a worker s current performance to update its beliefs about the worker s ability and then bases future wages on these updated beliefs. In the case of property-liability insurance salespersons, one can argue that the current size of their book of business as well as the fraction of their sales generated by renewal business are observable signals of their ability. Thus, a learning model can be used to explain the existence of career concerns incentives of insurance salespersons. A producer s employability in the external labor market changes as agencies update their beliefs about the producers ability. With respect to the internal labor market within the producer s current agency, however, expected future renewal commissions create a direct link between current and future performance. Since about 89% of policyholders renew their existing policy at the end of the contract term, selling a policy does not only generate immediate commission, it also results in (expected) future renewal commissions for the insurance agency. Assuming competition for older ex- 10

perienced salespersons as in Kanemoto and MacLeod (1992), the salesperson working for the agency can expect to get a share of her increased production in form of higher compensation. Thus, salespersons have strong incentives to establish a portfolio of (potential) renewal customers, a socalled book of business. This mechanism creates indirect sales incentives regardless of a salesperson s current compensation scheme. Using the fraction of a salesperson s sales production from renewals as a measure of ability for building a book of business, we can formulate the following two testable hypotheses: (H1) Salespersons with a higher fraction of sales production from renewals are, on average, more productive than salespersons with a lower fraction of production from renewals, and (H2) more productive salespersons earn, on average, a higher compensation than less productive salespersons. In a learning model where the labor market uses a worker s current performance to update its beliefs about the worker s ability, information in earlier time periods is more valuable to the market than information in later periods (Chung, Sensoy, Stern, and Weisbach, 2010). Thus, the sensitivity of future compensation to the fraction of sales production from renewals should decrease in the producer s years of experience. Furthermore, there is a limit on how many accounts a producer can handle. If a producer already has a large book of business then the producer is busy taking care of all renewals and has little time left to sell policies to new clients. Assuming that first, all producers are working towards building a book of business and that second, it takes a number of years to establish a book of business, we can formulate the following testable implication: (H3) The sensitivity of future sales performance to the fraction of sales production from renewals should decrease in the producer s years of experience. Overall, the existence of implicit sales incentives from expected future renewal commission allows insurance agencies to offer compensation schemes with a salary component as well as fixed 11

salary contracts. As Gibbons and Murphy (1992) point out, optimal incentive contracts in the presence of career concerns should be based on total incentives: the sum of explicit incentives from the compensation contracts and implicit incentives from career concerns. Since career concern incentives are especially pronounced at earlier career stages, a fixed salary may be the optimal compensation structure for relatively new salespersons. Gibbons and Murphy (1992) argue that explicit incentives from the compensation scheme should be strongest for older workers reaching retirement because career concerns are less important for those workers. However, the case of older experienced property-liability insurance salespersons with an established book of business is slightly different. Because of the established book of business, the largest fraction of the salesperson s total sales production will come from renewals which are predictable. Even if the salesperson reduces her sales effort completely, existing customers will still renew with a probability of 89%. 12 Thus, there is a lower bound on the salesperson s production that allows agencies to offer a fixed salary contract. However, whether salespersons prefer a fixed salary or a pure commission compensation scheme is another issue. In Kanemoto and MacLeod s (1992) model with competition for older experienced salespersons, a salesperson who sells a lot and expands her book of business can expect to get a share of her increased production in form of higher compensation. If the salesperson is on a pure commission compensation scheme, compensation directly increases with sales. However, if the salesperson is on fixed salary she will have to renegotiate her compensation with the agency owner, and because of this negotiation process, adjustments in compensation are likely to lag behind changes in production. Thus, for high ability salespersons it is advantageous to be on a pure commission compensation scheme that allows them to profit from improvements in production immediately. Using the fraction of a salesperson s sales production from renewals as a measure of ability for building and 12 Average policy renewal rates for commercial lines policies sold via independent agencies in the U.S. are 89%; average policy renewal rates for personal lines policies sold via independent agencies are 89% as well (The National Alliance Research Academy, 2006, p. 26). 12

maintaining a book of business, we can postulate the following hypothesis: (H4) Salespersons with a higher fraction of sales production from renewals are, on average, more likely to choose a pure commission compensation scheme than salespersons with a lower fraction of production from renewals. 4. Data and Methodology 4.1 Data Our data comes from the third survey of producer compensation, collected by the National Alliance Research Academy in 2003. We use the commercial lines producer portion of their original dataset. 13 Producers are classified as commercial lines producers if they derive 60% or more of their total sales volume from commercial lines business. The data provides descriptive information on sales production, producer compensation, as well as producer and agency characteristics. There are 307 commercial lines producers in the dataset and approximately two-thirds answered all of the survey questions required for our study. Therefore, we are left with 196 producers in our sample. 14 Forty-three percent of these producers are paid only by commissions, while 14% of the producers are paid only a fixed salary. The remaining 43% are compensated with both salary and commissions. Ultimately, 63% of the average producer s income comes from commissions. 15 13 We would like to thank the National Alliance Research Academy for providing us access to an anonymous version of the original survey responses of the commercial lines producers. A descriptive analysis of the survey was published by the National Alliance Research Academy in 2004 under the title Producer Profile: Compensation, Production, and Responsibilities. 14 The distribution of compensation schemes among the 197 producers with usable data is similar to the distribution of compensation schemes among all 307 commercial lines respondents. 15 There is very little information available for the population of insurance salespersons in the U.S. Thus, it is difficult to test for sample selection bias. A 2006 report by Research and Markets (http://www.researchandmarkets.com) provides a few basic descriptive statistics of the U.S. insurance agency market. Comparing these statistics to our sample indicates that our sample is rather representative of the U.S. insurance agency market. Research and Markets reports that the average agency consists of 5 employees. Though the average size of our sample is larger (19.2), the median size in our sample is 6 employees. Additionally, the report by Research and Markets shows the average revenue per employee to be approximately $200,000. Again, the average revenue for the employees in our sample is slightly higher ($299,963), though the median is very near the average reported by Research and Markets ($205,000). 13

4.2 Methodology We analyze the effects of explicit incentives from output based compensation schemes and implicit incentives from expected future renewal commissions on salespersons production in four steps. First, we regress sales output on the percentage of commission in the total compensation package and on the percentage of renewals. Second, we regress salespersons income levels on sales output as well as the percentage of commission in the total compensation package. Third, to control for salespersons self-selection of compensation schemes, we re-estimate the output and compensation regression models together with a model explaining the choice of a compensation system in a simultaneous equation framework. Fourth, we employ the Blinder-Oaxaca decomposition to decompose the output difference between fixed salary and pure commission salespersons into a component explained by salespersons characteristics and an unexplained component. 4.2.1 Productivity Regression Models Our analysis of the impact of output based compensation schemes and renewals on salespersons production is based on an ordinary least squares (OLS) regression model. The specification of the model is as follows: LogProduction = α + β % Commission + β % Renewals + β X + ε (1) i 1 i 2 i 3 i i where LogProduction i is the natural logarithm of the commissions paid by insurance companies to the agency for sales generated by producer i in one year, %Commission i is the fraction of outputbased compensation or commission producer i receives from the agency as a percent of the producer s total compensation, %Renewals i is the fraction of producer i s sales production from renewals 14

as a percent of the producer s total sales production, X is a vector of control variables, and ε is a random error term. 16 To control for differences in the producers marginal productivity of sales effort (see, e.g., Basu, Lal, Srinivasan, and Staelin, 1985), we include YearsWithAgency and InsuranceExperience variables in the regression model. YearsWithAgency measures the number of years the producer has worked for the current agency. We expect YearsWithAgency to be positively related to sales productivity. The InsuranceExperience variable measures the years of experience in insurance sales. In addition, we use a number of variables to control for agency specific characteristics. In some agencies, producers have to take on administrative duties as well. Therefore, we include %TimeSelling as a variable in the model; this variable measures the percent of the producer s work week allocated towards selling. Alternatively, producers might also receive sales support from the agency. To control for differences in sales support, the model includes Support as a variable which captures the number of full-time-equivalent support staff assisting the producer. The size of the agency can also influence sales output due to economies of scale and name recognition. Therefore, we include AgencySize as a variable in the model; this variable measures the number of full-time producers employed by an agency. To control for a possible difference between rural and urban areas, we include LargeCity as a variable in the model. This dummy variable is coded as 1 if the agency is located in a city with more than 2,000,000 inhabitants and 0 otherwise. To examine whether the sensitivity of sales performance on the fraction of sales production from renewals decreases in the producer s years of experience, we estimate the following extension of Equation (1): 16 The %Renewals variable should not create an endogeneity problem for the following two reasons: First, by construction, the fraction of renewals is (mainly) determined by a salesperson s past (or lagged) sales production. Second and most importantly, the %Renewals variable is not significantly correlated with the error term in any of the regression models. We examine the correlation coefficients and find that they are close to zero and not significant at the 10-percent significance level. As Baum (2006) points out, from a statistical perspective, a variable in a regression model is endogenous and may cause inconsistent estimates if the variable is correlated with the disturbance term. It is this definition of endogeneity that matters for empirical work (Baum, 2006, p. 185). 15

LogProduction = α + β % Commission + β % Renewals i 1 i 2 i + β % Renewals YearsWithAgency + β X + ε 3 i i 4 i i (2) with the additional interaction term % Renewalsi YearsWithAgencyi, and all other variables as specified in Equation (1). A negative and significant estimate for β 3 provides support for Hypothesis H3. 4.2.2 Compensation Regression Model To establish the existence of career concern incentives for salespersons, we not only need to show that the fraction of renewals has a positive impact on sales production, but we also need to show that sales production has a positive impact on producers compensation. Thus, we estimate the following OLS regression model: LogTotalCompensation = α + β LogProduction + β X + ε (3) i 1 i 2 i i where LogTotalCompensation i is the natural logarithm of the producer s total annual compensation, LogProduction i is the natural logarithm of the commissions paid by insurance companies to the agency for sales generated by producer i in one year, X is a vector of control variables, and ε is a random error term. The set of control variables includes %Commission as a variable to control for the impact of output-based compensation schemes on producer compensation (Parent, 1999, 2009; Aggarwal and Samwick, 2006). Since producers opportunity cost of time depends on the value of their human capital, we include YearsWithAgency, InsuranceExperience and BachelorsDegree variables in the model to control for differences in human capital investments (Coughlan and Narasimhan, 1992). BachelorsDegree is a dummy variable equal to one if the highest level of education attained is a bachelor s degree. To capture differences in additional compensation-like benefits, we include Pension and TravelExpenses variables; these two variables are dummy variables equal to one if the 16

agency provides pension benefits, and if the agency provides reimbursement for travel expenses, respectively. To control for differences in agency location, we again include the LargeCity variable. We also include FirstJob as a dummy variable which is equal to one if the current position is the producer s first job, and zero otherwise. We expect this variable to be negatively related to the producer s total compensation because of the lack of negotiating power when looking for an entry level position. To ensure that our results are not distorted by producers self-selection of a compensation scheme, we also estimate a simultaneous equation model consisting of Equations (1) and (3) and a third equation explaining the choice of a compensation system. 4.2.3 Simultaneous Equation Model Our analysis of factors determining the choice of a compensation system is based on the model specified in Equation (4). We estimate Equation (4) together with Equations (1) and (3) with three-stage least squares (3SLS). This approach allows us to check whether the results from the productivity and earnings regressions are distorted by potential endogeneity resulting from the fact that the choice of a compensation system is usually not random. The choice of the compensation scheme is modeled as follows: = α + β + β + ε (4) % Commissioni 1Abilityi 2X i i where % Commission is the fraction of output-based compensation producer i receives as a percent i of total compensation, Ability is a vector of variables measuring producers ability, X is a vector of control variables, and ε is a random error term. Variables measuring a producer s ability to sell include the fraction of a producer s sales production from renewals, %Renewals, the number of years the producer works for the current 17

agency YearsWithAgency, the years of experience in insurance sales InsuranceExperience as well as the BachelorsDegree dummy variable. Since administrative support staff in an agency usually receives a fixed salary, we expect producers who take on administrative duties to get a substantial fraction of their compensation as fixed salary as well. On the other hand, producers who allocate their complete work week towards selling may be more likely to be on an output based compensation scheme. Thus, we include %TimeSelling as control variable in the model. Travel expenses borne by the agency shift risk from the individual producer to the agency. Every sales call is risky because there is a change that this call may be non-productive. An agency bearing the producer s expenses increases the certainty equivalent value of the producer s compensation package. Coughlan and Narasimhan (1992) argue that agencies bearing producers expenses can, hence, offer compensation packages with a larger fraction of risky output-based compensation. On the other hand, one cannot expect employees receiving a fixed salary to pay for travel expenses. Therefore, an alternative hypothesis is that agencies bearing producers expenses should be more likely to offer fixed salary compensation. To control for any effect of expense reimbursement schemes on compensation packages, we include TravelExpenses as a variable in the model. If a producer does not find a suitable fit in her current agency, the producer might start looking for a new job that better suits her ability. When selecting a new job, one of the criteria that plays a role in the producer s decision is the job s compensation structure. To capture differences in producers selection of compensation schemes when leaving their previous agency because of a lack of fit, we include OtherGoals as a variable in the model. OtherGoals is a dummy variable coded as one if the producer left the previous job because the previous agency did not meet the producer s 18

expectations or because she did not meet the previous agency s expectations. 17 Since high ability salespersons should be able to be successful in various environments, fit is likely to be more of an issue for lower ability salespersons. Thus, we expect OtherGoals to be negatively related to the fraction of output-based compensation in the total compensation package. 4.2.4 Blinder-Oaxaca Decomposition To further examine differences in sales output between the two groups of fixed salary and pure commission salespersons, we employ the Blinder-Oaxaca decomposition method (Blinder, 1973; Oaxaca, 1973). For two groups of observations, an outcome variable and a set of predictor variables, the Blinder-Oaxaca decomposition gives an answer to the question how much of the difference in the two group means of the outcome variable can be explained by group differences in the predictors? Originally, this method was used by Blinder (1973) and Oaxaca (1973) to study wage discrimination between male and female workers. The part of the wage differential that could not be explained by group differences in productivity characteristics was interpreted as a measure of discrimination. In our analysis, we focus on what fraction of the output difference between fixed salary and pure commission salespersons can be explained by differences in productivity characteristics, such as work experience. To correct for salespersons self-selection of compensation schemes, we deduct the selection effects from the overall output difference first, and then examine the corrected output difference (Reimers, 1983). The part of the corrected output difference that cannot be explained by different endowments of fixed salary and pure commission salespersons can be interpreted as a pure incentive effect: A pure commission environment ceteris paribus in- 17 The variable OtherGoals is generated from survey questions asked regarding the rationale for a producer leaving her last job. Respondents are asked why they left their previous job and are given eight choices in addition to an other option. If the producer responded with Inadequate dollar compensation, Inadequate benefits, Poorly managed agency, Inadequate support staff, Low Personal Production or Long Work Hours, OtherGoals is set to one. It should be noted that if the producer is in her first job, OtherGoals is zero. Therefore, OtherGoals should be interpreted as the effect that experienced producers goals have on their total compensation. 19

duces producers to exert more effort and, hence, increases output. Thus, the Blinder-Oaxaca decomposition allows us to test whether there is an incentive effect in addition to a sorting effect, i.e. more productive workers self-select into positions offering a pure commission compensation scheme. We perform the decomposition for the two groups of commission-only and salary-only producers and for the outcome variable LogProduction. More precisely, we estimate Equation (1) without the %Commission variable separately for the two groups of commission-only and salaryonly producers, including a correction term for self-selection. 18 We derive the self-selection correction term based on a probit estimation of Equation (4) using a CommissionOnlyDummy as the dependent variable (Maddala, 1983, pp. 120-121). 19 As suggested by Reimers (1983), we then take the output difference after deducting the selection effects and apply the standard decomposition formulas. We use the twofold decomposition procedure with coefficients from a pooled regression over both groups as the reference case as outlined in Neumark (1988) and Jann (2008) and correct standard errors for sampling variances. 20,21 18 Let the index c denote the group of commission-only producers and the index s denote the group of salary-only producers. Since E( ε ) = 0, the mean outcome difference between the two groups is E( LogProduction ) E( LogProduction ) = ˆ β E( X ) ˆ β E( X ) + ˆ δ E( h ) ˆ δ E( h ) c s c c s s c c s s where ˆ δ Eh ( ) ˆ δ Eh ( ) corrects for self-selection effects, and the hazard h is calculated as described in Footnote 15. c c s s i 19 In a first step, probit estimates are obtained of the equation Pr( CommissionOnlyDummy = 1 X ) = Φ ( γ X ), i i i where X is the vector of independent variables from Equation (4), and Φ is the cumulative distribution function of the standard normal distribution. From these estimates, the correction term or hazard, h i, for each observation i is then calculated as: φγ ( ˆ X )/ Φ ( ˆ γ X ), if CommissionOnlyDummy = 1 i i i h = i φγ ( ˆ X )/(1 Φ ( ˆ γ X )), if CommissionOnlyDummy = 0 i i i where φ is the standard normal density function. 20 The twofold decomposition procedure assumes that there is a nondiscriminatory coefficient vector that should be used * as a basis for determining the contribution of the differences in the predictors. We can use the coefficients ˆβ from a pooled regression over both groups as such a nondiscriminatory coefficient vector. The outcome difference between the two groups can then be written as: ˆ * ˆ ˆ* ˆ* E( LogProduction ) E( LogProduction ) = β ( E( X ) E( X )) + { E( X )( β β ) + E( X )( β ˆ β )} c s c s c c s 20 s

5. Results Table 2 contains summary statistics for our dataset. The producers in our sample generate an average of almost $300,000 in revenue for the agency and on average earn approximately $119,000 in annual income. The agencies in the sample vary quite a bit in size. The smallest agency has only 1 producer while the largest agency has 500 producers; the median is 6 producers per agency. Approximately 20% of the agencies are in large cities with more than 2,000,000 inhabitants, and the remaining 80% of the agencies are in more rural areas. With respect to the producers themselves, approximately half of the respondents are in their first job as a producer, and the average producer has approximately 14 years of experience in the industry. Graph 1 of Figure 1 presents kernel density estimates of the LogProduction variable for the three compensation schemes salary-only, mixed pay and commission-only, separately. For salaryonly producers, the peak of the estimated density function as well as the center of the distribution are further left than for the group of producers receiving mixed pay, and the peak and center of the distribution for mixed pay producers is further left than for commission-only producers. These findings provide some preliminary evidence of a positive association between output-based compensation schemes and production levels. Graph 2 of Figure 1 presents kernel density estimates of the LogTotalCompensation variable for the three compensation schemes salary-only, mixed pay and commission-only, separately. Similar to the findings for the LogProduction variable, we observe that the peak and the center of the estimated density function for salary-only producers are to the left of the function s peak and center for mixed pay producers, and the peak and the center of the density function for mixed pay produc- * where ˆ β ( E( X ) E( X )) is the part of the outcome difference that is explained by differences in the predictor variable ( endowment effect), and { E( X )( ˆ β ˆ ) ( )( ˆ ˆ ) } c s * * c c β + E X β β s s is the unexplained part of the outcome differential. 21 Following Jann s (2008, p. 458) suggestion, we also include a group dummy in the pooled regression model as an additional covariate. The group dummy variable is coded as 1 if the producer belongs to the salary-only group, and 0 otherwise. 21

ers are to the left of the function s peak and center for commission-only producers. These findings indicate a positive association between output-based compensation schemes and earnings. In addition, the distributions of the LogTotalCompensation variable look similar to the distributions of the LogProduction variable indicating a positive relationship between sales production and salespersons compensation. Table 3 contains summary statistics for the three groups of salary-only producers, mixed pay producers, and commission-only producers, separately. We can see that salary-only producers have on average more experience (17 years vs. 15 years), a less established client base (%Renewals = 63% vs. 75%), and that a higher fraction of salary-only producers gets sales reimbursement for travel expenses (89% vs. 54%). 5.1 Productivity Regression Models 5.1.1 OLS Results The OLS estimation results from Equation (1) are presented in column 1 of Table 4. The regression coefficient of the %Commission variable is positive and significant, indicating a positive relationship between the fraction of output-based compensation in the compensation package and a producer s sales production. Most importantly, the regression coefficient of the %Renewals variable is positive and significant. This result supports Hypothesis H1 which states that salespersons with a higher fraction of sales production from renewals are more productive than salespersons with a less established book of business. In addition, two of the control variables have a significant impact on LogProduction. The coefficient of the Support variable is positive, indicating that the availability of support staff increases a producer s sales production. The coefficient of the LargeCity variable is also positive, indicating that average sales production is higher in large cities with over two million inhabitants. 22

As a robustness check, Column 2 of Table 3 presents the OLS estimation results from an alternative specification of Equation (1); the %Commission variable is replaced by the CommissionOnlyDummy variable which is coded as 1 of the producer is on a commission only compensation scheme, and 0 otherwise. The results of this alternative specification are consistent with the ones from the baseline model. Columns 3, 4 and 5 of Table 4 show the OLS estimation results from Equations (1) for the subsamples of commission-only producers, mixed-pay producers and salary-only producers, respectively. For all three subsamples, the regression coefficient of the %Renewals variable is positive and significant, indicating that an established client base is positively associated with sales production for all three types of compensation schemes. These results provide additional support for Hypothesis H1. Column 1 in Panel A of Table 5 presents the OLS estimation results from Equation (2). The regression coefficient of the %Commission variable is positive and significant, indicating a positive relationship between the fraction of output-based compensation in the compensation package and a producer s total compensation. The regression coefficient of the %Renewals variable is positive and significant, and the coefficient of the % Renewalsi YearsWithAgencyi interaction term is negative and significant. These findings indicate that establishing a book of business of (potential) renewal customers increases sales production, but at a decreasing rate. Thus, these results support Hypothesis H3. To check the robustness of our results with respect to Hypothesis H3, we estimate a number of additional models. Column 2 in Panel A of Table 5 presents the OLS results from an alternative specification of Equation (2); the %Commission variable is replaced by the CommissionOnlyDummy variable. The results in Column 2 are consistent with the ones from the original model presented in Column 1. Columns 3, 4 and 5 in Panel A of Table 5 show the OLS estimation results 23