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Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations (2010)

Chapter: Chapter 5 - The Relationship Between Compensation and Turnover

« Previous: Chapter 4 - Model of Factors That Affect Vehicle Operator Recruitment, Retention, and Performance
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Suggested Citation:"Chapter 5 - The Relationship Between Compensation and Turnover." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
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Suggested Citation:"Chapter 5 - The Relationship Between Compensation and Turnover." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
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Suggested Citation:"Chapter 5 - The Relationship Between Compensation and Turnover." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
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Page 61
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Suggested Citation:"Chapter 5 - The Relationship Between Compensation and Turnover." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
×
Page 62
Page 63
Suggested Citation:"Chapter 5 - The Relationship Between Compensation and Turnover." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
×
Page 63
Page 64
Suggested Citation:"Chapter 5 - The Relationship Between Compensation and Turnover." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
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Page 64

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59 Purpose of the Analysis and Data Sources Relatively low levels of compensation are often cited as issues in ADA paratransit vehicle operator recruitment and retention. This was noted in several reports and articles identi- fied in the literature search. It was also one of the key findings of the focus group discussions summarized in Chapter 3. The national survey results summarized in Chapter 4 also provided information that suggested that vehicle operator compensa- tion was an issue. Wages for ADA paratransit vehicle operators were found to be lower than for fixed-route operators. Fringe benefits for ADA paratransit operators were also found to be minimal, especially for services operated by private contrac- tors. An initial tabulation and graphing of starting wage rates versus turnover rates, based on the survey responses, also indi- cated a possible relationship (see Figure 3-8). It would be expected that higher rates of compensation would enable providers to attract more qualified applicants and to be more selective in hiring qualified operators who are likely to stay on the job. High rates of compensation would also be expected to reduce incentives for operators to seek other work. To test this hypothesis, a regression analysis was conducted using data obtained from the national survey. Components of compensation that were requested from respondents in the national survey included the following: • Training wage, • Starting wage, • Maximum wage, • Days of vacation in the first year of employment, • Maximum vacation days per year that can be earned, • Paid holidays per year, • Percent employee contribution required for individual health care coverage, and • Percent employee contribution required for family health care coverage. All of these items were tested for their influence on reten- tion, which was measured using the annual post-training turnover rate (defined as the number of operators terminated after completion of training in the preceding 12 months, either voluntarily or not, divided by the current number of opera- tors). The following three additional variables that could influ- ence retention were also tested: • Whether the provider is a public transit system or a private company under contract to a public transit system; • Completion rate (the percentage of trainees who completed training in the past 12 months); and • Part-time operators as a percentage of all paratransit operators. Labor rates in each service area were used to adjust wages to account for the fact that operators in different parts of the country face very different circumstances in deciding whether to stay with a job driving a paratransit vehicle. For this pur- pose, median hourly wages for Transportation and Material Moving Occupations from the Bureau of Labor Statistics’ May 2008 Metropolitan and Nonmetropolitan Area Occupa- tional Employment and Wage Estimates were used. Some preliminary tabulations of these variables were pre- pared for the Interim Report. Since then, the data have been subjected to rigorous examinations to detect possible mis- understandings in the responses or data entry errors as the respondents typed their answers into the survey website. As needed, respondents were contacted by email to resolve uncer- tainties. Some points of confusion that were found included the following: • Counting taxi drivers as employees; • Responding with full-time equivalents instead of numbers of employees; • Including operators who drive both paratransit and fixed route; C H A P T E R 5 The Relationship Between Compensation and Turnover

• Duplicate responses (for example, responses from two people at the same agency); and • Providing the employer contribution to health care instead of the employee contribution. Operators who drive both paratransit and fixed-route ser- vice could have much lower turnover than operators who drive only paratransit. Unfortunately, too few systems use this mode of operations to test this hypothesis, but these responses were included for analysis of other variables. After elimination of systems that provided unusable data, 57 systems remained that were usable for regression analysis. Of these some did not provide answers to all the questions, so the number of cases varies somewhat, depending on the com- bination of variables tested. Exploratory Analysis As a preliminary exploration, correlations between turnover and candidate variables were calculated. Only four variables have statistically significant correlations with turnover, as shown in Table 5-1. The negative values mean that increasing values of these variables correspond to lower rates of turnover. All of these correlations are significantly different from zero with at least 95% confidence. The absence of a number of variables is some- what surprising. Notably, adjusted labor rates turn out to be weakly correlated with turnover. The maximum wage level and the percentage of health coverage cost paid by the employee are somewhat more strongly correlated with turnover but not at a statistically significant level. Public providers have significantly lower turnover rates than contract providers. As shown in Table 5-2, contract providers averaged a 32% turnover, compared to 16% at public providers. This suggests an exploration of whether the difference is due to better wages and benefits at public providers or to some aspect of public agency operation other than compensation. Table 5-2 shows how the various measures of compensation compare between contract and public providers. In general, public operators provide better compensation on average, especially as regards wages and health care coverage. The per- centage of part-time operators is somewhat lower at contract operators, but not by a statistically significant amount. There is also very little difference in the total amount of paid time off that employees can receive at contract and public providers. Regression Analysis Variables can often interact in surprising ways. For this reason, even variables that did not directly have significant 60 Variable Correlation* Percent part-time -0.25 Public or contract provider -0.37 Starting wage (unadjusted) -0.28 Training completion rate -0.35 *A correlation of -1.0 would mean a perfect correlation between the variable shown and the turnover rate, while a correlation of 0.0 would mean no correlation at all. Table 5-1. Correlations with turnover rate. Variable Contract Providers Public Providers Difference (Public – Contract) Statistical Significance Turnover 32% 16% -16% 99% Percent Part-time 14% 18% 4% Not significant Training Wage $8.55 $9.62 $1.07 90% Completion Rate 63% 80% 17% 95% Starting Wage $10.46 $12.05 $1.59 99% Maximum Wage $14.18 $16.61 $2.42 99% Starting Wage - Adjusted $10.30 $12.41 $2.11 99% Maximum Wage - Adjusted $13.85 $17.10 $3.25 99% Starting PTO for full-time operators** 11.2 14.4 3.2 96% Maximum PTO for full-time operators* 21.9 22.8 0.9 Not significant Employer contribution to health coverage (full-time operators)*** 60% 81% 21% 99% * Numbers and percentages may vary slightly from national survey results presented in Chapter 3 since the regression analysis was based on a subset of systems with complete survey data and on revised data obtained during follow-up contacts. ** Paid Time Off (PTO) is calculated as paid vacation plus paid holidays. *** Typically, employers pay a lower share for family coverage than individual coverage. For simplicity, the average of the two is used in this analysis. If a provider reported not offering individual or family health coverage, the employer contribution was set at 0%. Table 5-2. Differences between contract and public providers.*

correlations with turnover could have an influence after con- trolling for some other variable. Therefore, most of the vari- ables in Table 5-2 are included in the regression analysis. One variable that was excluded was the training completion rate. Although completion rate is correlated with turnover, examination of the data suggests that a high completion rate may be as much a result of low turnover as a cause. Many of the systems with high completion rates had very small num- bers of trainees, which would be expected with low rates of turnover. As a hypothesis, a low turnover rate results in the provider needing to recruit only a few new operators, which means that the provider can be very selective, which would tend to produce a high training completion rate. The regression analysis found that only the same three variables in Table 5-1 (excluding completion rate) con- tributed significantly to any model of turnover rate. Two models of interest are the following: Model 1 Turnover = 0.685 − (0.035 × Starting Wage) − (0.101 × Public) (t statistic) (−2.11) (−1.77) (significance) (0.040) (0.083) R Squared = 0.21 Model 2 Turnover = 0.864 − (0.051 × Starting Wage) − (0.244 × Percent Part-Time) (t statistic) (−3.44) (−1.71) (significance) (0.001) (0.092) R Squared = 0.20 The variable “Public” takes the value 1 for public providers and 0 for contract providers. In both models, the variables other than starting wage are significant with less than 95% confidence but are nearly significant and can be included on the basis of strong theoretical justification and the small sample size. No other combination of these three variables results in a model with significant or even nearly significant coefficients. Both models use 57 valid cases. The two models demonstrate that there is a strong connec- tion between wages and turnover, with higher wages connect- ing with lower turnover. The equations imply that an increase of $1.00 in wages corresponds, on average, to a drop of 3.5% (Model 1) to 5.1% (Model 2) in turnover rate. Although, the R Squared values show that, even in combination with other variables, wages only account for 20% to 21% of the variation in turnover rates. In addition, the models show that (1) by controlling wages, public providers have turnover rates that are 10% lower than private providers on average and (2) lower percentages of part-time operators are connected with lower turnover rates. A difference of 10% in the percentage of part- time operators corresponds, on average, with a difference of 2.4% in turnover rate. Graphical Analysis and Discussion Starting Wage, Provider Type, and Turnover Figure 5-1 shows in graphical form the relationship repre- sented by regression Model 1. The public providers (black squares) have a lower turnover than the contract providers (hollow diamonds), though there is considerable overlap 61 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% $7 $8 $9 $10 $11 $12 $13 $14 $15 $16 A n n u al T u rn o ve r Starting Hourly Wage Contract Public Trendline-Contract Trendline-Public Figure 5-1. Starting wage, public/private contract providers, and turnover.

between the two groups. Within each group there is a loose association between wages and turnover. Most of the higher wage providers have a very low turnover, but there are many lower wage systems with moderate rates of turnover. In the case of the public providers, the trend is dominated by a few providers that have zero or near-zero turnover and high wages. The results suggest that there is some aspect of public ser- vice provision that produces lower turnover other than sim- ply higher wage rates. For example, operators working for public providers have more job security because they do not face the uncertainty connected with changes in contract providers and also because public providers may have more procedures in place that require a clear cause of termination and attempts to remedy the poor performance before an operator can be terminated. Operators working for public providers probably have better pension benefits which would provide an incentive for an operator to stay with the job. Since public operators have access to capital funds to build facilities and are less driven to maximize profit or cut costs, working conditions for vehicle operators at public providers are probably better in most cases than at private contractors. Figure 5-2 shows the relationship represented by regression Model 2. The highest wage group (asterisks) has the lowest turnover, the lowest wage group (hollow diamonds) has the highest turnover, and the medium-wage group (black squares) is in the middle. As with the public/private distinction, there is a great deal of overlap in turnover rates among the wage cat- egories. Within the middle- and low-wage groups, the associ- ation between the percentage of part-time operators and turnover found by the regression model is apparent though loose. In the case of the high-wage group, there is no clear asso- ciation between turnover and the percentage of part-time operators. The above analysis is based on the actual wages reported by survey respondents. In an effort to control for differences in labor costs in each area, the research team adjusted these actual wages to reflect typical wages in each area. As noted above, these adjustments were made using information from the Bureau of Labor Statistics’ May 2008 Metropolitan and Nonmetropolitan Area Occupational Employment and Wage Estimates. Median hourly wages for Transportation and Materials Moving Occupations was used to make the adjustments. Figure 5-3 shows that adjusting starting wages to account for different prevailing wage rates among areas does not improve the association of wages with turnover. Interestingly, using the adjusted wages does not improve the association with turnover; instead, it obscures the association. Health Care Coverage, Provider Type, and Turnover Figure 5-4 shows the relationship between health care coverage and turnover. There does appear to be a tendency for systems that pay a higher percentage of health care to have lower turnover on average. Regression analysis finds that the relationship is almost statistically significant (with 91% confidence) despite the obviously wide scatter of obser- vations. Since public systems have both lower turnover and higher employer contributions to health care, it is possible that some of the apparent influence of paid health benefits 62 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 0% 20% 40% 60% 80% 100% A n n u al T u rn o ve r Percent Part-Time Less than $10 per hour $10 up to $12 per hour $12 per hour or more Trendline - L ess than $10 Trendline - $10 up to $12 Trendline - $12 or more Figure 5-2. Starting wage, part-time percentage, and turnover.

could be related to employment by a public operator. Fig- ure 5-5 shows the relationship between paid health benefits and turnover after controlling for public versus contract operation. While there is still some apparent relationship, it is much reduced and not statistically significant at all. The influence of health coverage could be partly obscured by difficulties in the data collection process. The questionnaire asked for employee contribution to health care coverage, but examination of responses indicated that some systems mis- understood the question and reported employer contribution instead. Wherever possible, these errors were corrected, but some cases could not be confirmed and others could have gone undetected. (In the analysis, the employer contribution has been used and calculated as 100% minus the employee contribution.) Summary of Findings The analysis of national survey data does show a strong connection between wages and turnover. The models suggest an average reduction in turnover of between 3.5% and 5.1% for every $1.00 increase in starting wage. 63 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% $7 $9 $11 $13 $15 $17 $19 $21 An nu al T ur n o ve r Starting Adjusted Hourly Wage Contract Public Trendline - Contract Trendline - Public 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 0% 20% 40% 60% 80% 100% A nn ua l T ur no ve r Employer Contribution to Health Care All Systems Trendline - Contract Figure 5-3. Adjusted starting wage, public/private providers, and turnover. Figure 5-4. Health coverage and turnover.

While there is a strong connection, differences in starting wages appear to only explain 20% to 21% of the variation in turnover rates. Clearly, while pay rates are important, there are many other factors that affect turnover. Of the other factors tested in this analysis, the percentage of part-time operators and the type of entity appear to be significant. The analysis suggests that, on average, turnover is lowered by about 3.5% for every 10% reduction in the percentage of part-time operators employed. Employment by a public entity also appears to affect turnover. Control- ling for wages, public entities appear to have turnover that is 10% lower than private companies providing ADA para- transit service. The analysis found that turnover is also impacted some- what by the percent of employer contribution to health care coverage. This relationship was not as significant as expected, however. While this analysis begins to explore the relationship between compensation and turnover, more analysis is needed. Research is needed to identify and quantify the other factors that account for differences in turnover. The underlying reasons why pub- lic entities experience lower turnover also needs further study. Given that the qualitative information suggests that health care coverage is more significant than this initial analysis indicates, more research is needed to document the impacts of health care coverage on turnover. 64 0% 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 0% 20% 40% 60% 80% 100% A nn ua l T ur no ve r Employer Contribution to Health Care Contract Public Trendline - Contract Trendline - Public Figure 5-5. Health coverage, public/contract providers, and turnover.

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TRB’s Transit Cooperative Research Program (TCRP) Report 142: Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations provides guidance for understanding the relationships that influence and enhance operator recruitment, retention, and performance in Americans with Disabilities Act (ADA) complementary paratransit services.

Appendixes to TCRP Report 142 were published electronically as TCRP Web-Only Document 50: Survey Instrument, Productivity Charts, and Interview Protocol for Case Studies for TCRP Report 142.

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