Sample descriptive statistics - Data source and sample descriptive statistics

4. Data source and sample descriptive statistics

4.2 Sample descriptive statistics

The descriptive statistics for the individual-level are shown in Table 2. According to the distribution of the effective samples, in the previous three years, the percentage of the respondents with an average annual income of 310000-700000 NTD was 49.84%, the percentage of those with an average annual income of 710000 NTD or above was 28.21%, and the percentage of those with an average annual income of less than 300000 NTD was 21.95%. Regarding the proportion of male and female respondents, the male respondents accounted for 59.25% of all respondents, while the female respondents accounted for 40.75%. With regard to marital status, the unmarried accounted for 55.8%, while the married account for 44.2%. In terms of having a child or children aged 6 or below, respondents with a child below 6 accounted for 80.56%, while respondents without a child aged below 6 accounted for 19.44%. Regarding

education level, the respondents with a university degree accounted for 43.89%, followed by the respondents with a high school degree, who accounted for 27.90%.

Regarding the average per week working hours, 41-70 hours was the range given by the most respondents, accounting for 70.53%, followed by 71 hours or above at 18.18%, and then 40 hours at 11.29%. The average age of the respondents was around 35.10, suggesting that most of the respondents are middle-aged. The average working experience of the respondents was around 5.73 years. The descriptive statistics of the branch store level are shown in Table 3. There were a total of 54 branch stores including 10 regular chain stores and 44 franchise stores. There were 13 branch stores located in the downtown area and 41 branch stores located in the suburbs.

Table 2. Individual-level descriptive statistics (N=319) Marital status Unmarried

Married stores, including 10 branch stores of regular chains, accounting for 18.52% of the total.

There were 13 branch stores located downtown, accounting for 24.07% of the total.

Table 3 Branch- level descriptive statistics (N=54)

Variable Categories Times Percentage (% )

Management type Regular chain 10 18.52

Franchise 44 81.48

Location of branch Downtown 13 24.07

Suburbs 41 75.93

5. Empirical results analysis

This study adopted HLM 6.1 version, SPSS19.0 software for estimations, including for the estimation of grand mean centered continuous variables. The analysis and explanations of the estimated results are as follows.

5.1 Null model

Table 4 provides the estimates for the null model. The estimate of ₀₀ is 1.286 with a standard error of 0.158, reaching the 5% significance level. The random effects section shows the decomposition of the variance into its micro-level and branch-level components. The reported ² statistic is 70.550 with 53 degrees of freedom. The results show that the variance at the branch-level is statistically significant, at better than the required 10% level of significance. This suggests that individual income vary across branches. When data are binary, within-group variability is defined by the sampling distribution of the data, typically the Bernoulli distribution. When the logistic model is applied, the level-1 residuals are assumed to follow the standard logistic

distribution, which has a mean of 0 and a variance of ²/33.29. This variance represents the within-group variance for ICC (intra-class correlation coefficient) calculations for dichotomous data, and the ICC can be similarly defined for ordinal outcomes (Snijders and Bosker, 1999). The null model for the intra-class correlation is:

048 . 29 0 . 3 166 . 0

166 . 0 29

. ˆ 3

00 

 

 ICC 

This suggests that 4.8% of the variation in individual income lies between branches.

5.2 Intercepts-as-outcomes model

The intercepts-as-outcomes model estimation results are shown in Table 4. For the impact of GENDER on income level, the estimated coefficient value is 0.294, which is not significant. The findings of many studies suggest that the working income of female workers is less than that of males (Glower and Hendershott, 1988; Crellin et al., 1988; Sirmans and Swicegood, 1997; Jud and Winkler, 1998 and Benjamin, Jud, and Sirmans, 2000). However, Abelson et al. (1990) argued that the working income of female workers is greater than that of males. This study concludes that the probability of a high income is higher among male workers than among female workers, but that the difference between the genders does not reach the level of significance.

The estimated coefficient value of educational level above university is 0.392, which does not reach the level of significance. The coefficient of estimation of

educational level of college is 0.508, which does reach a 10% significance level. This suggests that the agents with an educational level of college do perform better in business than agents with high school or vocational school education.

The estimation coefficient of AGE is 0.063 (Odds Ratio = 1.065), reaching the 5%

significance level. This result is not consistent with the findings of Sirmans and Swicegood (1997; 2000), who found that older agents had lower incomes. This suggests that older agents have a higher probability of earning a high income. It further suggests that when serving housing agency clients, older agents may be able to increase their incomes due to their accumulated experiences in the realms of human resources and social interactions. The estimation coefficient of MAR is -0.035 (Odds Ratio = 0.965). According to Herman and Gyllstorm (1977), married workers have higher rates of FWC (family-to-work conflict) than the unmarried. The research findings suggest that marriage has a negative impact on the income without reaching the 10% significance level. Herr and Wolfram (2009) found that the birth of a child has a profound impact on the decision of women to exit the labor market, even among the highly educated. The estimation coefficient of “having children aged under six” is 0.510 (Odds Ratio=1.667). The empirical results are not consistent with the theoretical expectations, without reaching the 10% significance level.

In the respect of average weekly working hours, the estimation coefficient of average working hours (HOURM) is 0.857 (Odds Ratio=2.357), reaching the 5%

significance level. The estimation coefficient of high average working hours (HOURH) is 1.477 (Odds Ratio=4.381), also reaching the 5% significance level. This suggests that the individual incomes of agents with median and high working hours are

significantly higher than those of agents with low average working hours. This finding is consistent with that of Follain, Lutes, and Meier (1987) and Glower and Hendershott (1988) that longer working hours result in better performance. A high number of agents with long working hours also implies that an agency has been in business for a long time.

The coefficient estimate of EXP , ₈₀ , is 0.169 (Odds Ratio=1.185), which reaches the 5% significance level. The coefficient estimate of EXPS, ₉₀ , is -0.005, which reaches the 10% significance level. This suggests that increased work experience can help improve the income of agents; however, the effects of that influence will progressively decrease with the accumulation of work experience. This result is consistent with the conclusions of Glower and Hendershott (1988) and Sirmans and Swicegood (1997) to the effect that experience increases the performance of brokers or salespeople but, beyond a certain point, additional experience is of lesser value.

According to the empirical results, the coefficients of GENDER, MAR, and CHILD do not reach the level of significance, indicating that these variables have no significant impact on individual income. As observed previously in the literature, a variety of influencing factors can, due to different sampling techniques, lead to inconsistent results among different studies. In particular, conflicting results often occur with regard to gender, race, franchise affiliation, and the agent’s age (Sirmans and Swicegood, 2000).

Regarding the cross-level direct impact, the branch-level coefficient estimate of TYPE ,j ₀₁ , is 0.024, and does not reach a 10% significant level. According to empirical studies, the individual incomes of agents working for a regular chain are not significantly higher than those of agents working for franchises. This suggests that

there is no significant difference between the regular chain system and the franchise system in terms of their respective impacts on the probability of high agent income. In recent years, the developmental trends of the brokerage industry in Taiwan suggest that operational systems have considerably changed from the types based on the regular chain system, in which agents receive a high base salary, to types based on the franchise system, in which agents do not receive a base salary. This is evidenced by examples including Yongqing Housing, Pacific Housing, and Dong Sen Housing. Such changing trends can also be confirmed by empirical results. The individual incomes of agents working under the regular chain system are not significantly higher than those of agents working under the franchise system.

The estimation coefficient of branch location is 0.982 (Odds Ratio = 2.674), which reaches a 5% significance level. This result suggests that the probability of better individual incomes among agents working in branches in the downtown area is higher than that among those working in branches in the suburbs, which confirms the findings of previous studies. For example, many studies (Follain et al., 1987); Glower and Hendershott, 1988; and Sirmans and Swicegood, 1997) have suggested that the incomes of agents working in the housing brokerage industry in metropolitan areas are higher than those of agents in outlying areas. Intermediary agency branches closer to downtown areas have more business activity and higher transaction prices, resulting in better income indicators.

In addition, according to the estimation results of the intercepts-as-outcomes model, the u₀_j variance reached a significant level, indicating that the intercepts have random components and that other major branch-level characteristic variables have not been considered.

5.3 Intercepts-and-slopes-as-outcomes

The intercepts-and-slopes-as-outcomes model estimation results are shown in Table 5.

For the impact of GENDER on income level, the estimated coefficient value is 0.388, which is not significant. The findings of many previous studies suggest that the working incomes of female workers are lower than those of male workers (Glower and Hendershott, 1988; Crellin et al., 1988; Sirmans and Swicegood, 1997; Jud and Winkler, 1998; and Benjamin, Jud and Sirmans, 2000). However, Abelson et al. (1990) argued that the working incomes of female workers are greater than those of males.

This study concludes that the probability of high income among male workers is higher than that among female workers, but that the difference does not reach the level of significance.

The estimation coefficient of educational level above university is 0.303, which does not reach the level of significance. The coefficient of estimation of educational level of college is 0.427, which also does not reach a 10% significance level. This suggests that the agents with an educational level of college or an educational level above university do not perform better in business than agents with high school or vocational school educations.

The estimation coefficient of AGE is 0.067 (Odds Ratio = 1.070), reaching the 5%

significance level. This result is not consistent with the findings of Sirmans and Swicegood (1997; 2000), who found that older agents had lower incomes. This suggests that older agents have a higher probability of high incomes. The estimation coefficient of MAR is -0.041(Odds Ratio = 0.960), which also does not reach a 10%

significance level. This result is not consistent with the findings of Abelson et al.

(1990). They used the labor capital model to test the impact of marital status on performance, finding that married workers exhibit better performance. The estimation

coefficient of “having children aged under six” is 0.401 (Odds Ratio=1.493). Goldin (2006) argued that children were the most important factor related to out-of-work spells for women and a clear non-linearity exists in the impact of successive numbers of children. One child increased total time not at work by just 0.36 years on average, two children by 1.41 years, and three (or more) by 2.84 years. This result suggests that agents with children under the age of six have a greater probability of high income.

The empirical results are not consistent with the theoretical expectations, without reaching the 10% significance level.

Regarding the weekly average working hours, the estimation coefficient of the median average working hours (HOURM) is 0.969 (Odds Ratio=2.639), having reached the 5% significance level. The estimation coefficient of the high average working hours (HOURH) is 1.846 (Odds Ratio=4.81), having reached the 5%

significance level. This suggests that the individual incomes of agents with median and high working hours are significantly higher than those of agents with low average working hours. This indicates that working in the housing agency industry imposes a time cost. In addition to the familiarity with specific laws and regulations, it requires more efforts in development, marketing sources of clients and case sources to get higher income. The results are consist with the findings of Glower and Hendershott (1988), Crellin, Frew, and Jud (1988), Abelson, Kacmar, and Jackofsky (1990), and Sirmans and Swicegood (1997) regarding working hours and income.

The coefficient estimate of EXP,₈₀ , is 0.121 (Odds Ratio=1.129), which reaches the 10% significance level. The coefficient estimate of EXPS, ₉₀ , is -0.004, which does not reach the 10% significance level. This suggests that increased work experience can help improve the income of agents; however, the effects of that influence will not progressively decrease with the accumulation of work experience.

This result is partly consistent with the conclusions of Glower and Hendershott (1988) and Sirmans and Swicegood (1997) to the effect that experience increases the performance of brokers or salespeople but, beyond a certain point, additional experience is of lesser value.

According to the empirical results, the coefficients of GENDER, UNI, COLLEGE, MAR, CHILD, and EXPS do not reach the level of significance, indicating that these

variables have no significant impact on individual income. As observed previously in the literature, a variety of influencing factors can, due to different sampling techniques, lead to inconsistent results among different studies. In particular, conflicting results often occur with regard to gender, race, franchise affiliation, and the agent’s age (Sirmans and Swicegood, 2000).

Concerning the cross-level direct impact, the branch-level coefficient estimate of TYPE ,j ₀₁ , is 4.121, and does not reach the 10% significance level. According to empirical studies, the individual incomes of agents working for a regular chain are not significantly higher than those of agents working for franchises. This suggests that there is no significant difference between the regular chain system and the franchise system in terms of their respective impacts on the probability of high agent income. In recent years, the developmental trends of the brokerage industry in Taiwan suggest that operational systems have considerably changed from the types based on the regular chain system, in which agents receive a high base salary, to types based on the franchise system, in which agents do not receive a base salary. This is evidenced by examples including Yongqing Housing, Pacific Housing, and Dong Sen Housing. Such changing trends can also be confirmed by empirical results. The individual incomes of agents working under the regular chain system are not significantly higher than those of agents working under the franchise system.

The estimation coefficient of branch location is 1.100 (Odds Ratio = 3.003), which reaches the 5% significance level. This result suggests that the probability of better individual incomes among agents working in branches in the downtown area is higher than that among those working in branches in the suburbs, which confirms the findings of previous studies. For example, many authors (Follain et al., 1987); Glower and Hendershott, 1988; and Sirmans and Swicegood, 1997) have suggested that the incomes of agents working in the housing brokerage industry in metropolitan areas are higher than those of agents in outlying areas. Intermediary agency branches closer to downtown areas have more business activity and higher transaction prices, resulting in better income indicators. In addition, according to the estimation results of the intercepts-and-slopes-as-outcomes model, the u₀_j variance did not reach a significant level, indicating that the intercepts do not have random components.

Regarding moderating effects, the results indicated that management type can significantly moderate the marginal effect of UNI on individual income. This indicates that the incomes of those with a university and above degree are higher than the incomes of those with a high school or below degree. Moreover, this gap is higher in the case of the regular chain system than the franchise store system. In addition, the management type can significantly moderate the marginal effect of age on individual income. When the age is older, the expected individual income is higher, and the marginal effect of the regular chain system is lower than that of the franchise system.

Management type can significantly and negatively moderate the marginal impact of working hours on individual income. This suggests that the individual incomes of those with median and high working hours are higher than those of agents with lower average working hours. However, the gap is smaller in the case of the regular chain system and the franchise system. The management type can also significantly and

positively moderate the marginal impact of working experience on individual income;

that is, the positive impact of working experience on individual income is more significant in the case of the regular chain system than the franchise system. Moreover, the marginal effect of working experience on individual income will decrease with increasing working experience. The speed at which this decrease occurs is faster in the case of the regular chain system as compared to the franchise system.

Finally, this study used the single level ordinal logit model to estimate the parameters. The estimation results suggest that the variable estimation coefficient and standard errors have no significant difference with intercepts-and-slopes-as-outcomes.

This implies the robustness of the research estimation results.

Table 4. Empirical results of the null model and the intercepts-as-outcomes model

Model Null model

Intercepts-as-outcomes model

Coefficient (se) Odds Ratio Coefficient (se) Odds Ratio

00 1.286

HOURM₇₀ 0.857

* p<0.10, ** p<0.05. In the part of fixed effects, ( ) is the robust error; in the random effects part, ( ) is the ² value, the null model’s degree of freedom is 53, and the intercepts-as-outcomes model’s degree of freedom is 51.

Table 5. Empirical results of the intercepts-and-slopes-as- outcomes

Model Intercepts and

slopes-as- outcomes

Ordinal logit model

Coefficient (se) Odds Ratio Coefficient (se) Odds Ratio

00 ^-3.094

AGE₄₀ 0.067

* p<0.10, ** p<0.05. In the part of fixed effects, ( ) is the standard error; in the random effects part, ( ) is the ² value and the model’s degree of freedom is 51.

6. Conclusion and suggestions 6.1 Conclusion

This study discussed the impact of individual-level characteristic variables including gender, education level, age, marital status, having a child aged below 6 or not, working hours, working experience, and branch-store-level characteristic variables such as management type and branch store location, on the individual incomes of real estate salespersons. According to the null model estimation results, individual incomes differ significantly among branch stores. Specifically, 4.8% of the variation in individual income is caused by differences between branch stores. The intercepts-as-outcomes model was applied in analysis, and showed that gender had a positive impact on individual income without reaching the significance level. Also, real estate salespersons with a college degree had significantly higher individual incomes than salespersons with a high school education. Age has a positive and significant impact on individual income, while marital status has a negative impact on individual income without reaching the level of significance. Having a child aged below 6 has a positive impact without reaching the significance level. Regarding the variable of average weekly working hours, the individual incomes of salespersons with long working hours are significantly higher than those of salespersons with short working hours. Working experience has a positive and significant impact on individual income, suggesting that age, working experience, and resources can help increase income. The working experience square coefficient is negative at the 5% significance level, suggesting that increased working experience can help increase income. However, accumulating work experience has a diminishing effect. In the respect of branch stores,

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