Performance Pay, Gender, and Divorce among Full-Time American Workers

Performance Pay, Gender, and Divorce among Full-Time American Workers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Performance Pay, Gender, and Divorce among Full-Time American Workers Benjamin Adams This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7103256/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Oct, 2025 Read the published version in Review of Economics of the Household → Version 1 posted 9 You are reading this latest preprint version Abstract This work examines the influence of receiving performance pay on the probability that a worker will divorce. Uniquely, it compares two different cohorts of the National Longitudinal Survey of Youth. Probit estimates show that, for men, receiving performance pay decreases the probability of divorce in the older cohort and has no impact in the younger cohort. The impact of performance pay for men in the older cohort is mediated by household income and there is no significant impact of performance pay after accounting for household income. For women, receiving performance pay increases the probability of divorce in the older cohort and has no impact in the younger cohort. In contrast to men, though both household income and wages act as mediators in the relationship between performance pay and divorce, women in the older cohort remain more likely to divorce even after accounting for household income and wages. JEL codes: J12, J33, M52 performance pay divorce gender roles generational differences 1. Introduction Some employers offer payment schemes based on measures of job performance other than time worked. Firms use such “performance pay” schemes to try to attract high performance workers (Curme and Stefanec, 2007 ). These schemes also provide incentives for increased effort from existing workers, increasing their productivity (Lazear, 2000 ) and earnings (Booth and Frank, 1999 ; Green and Heywood, 2016; Heywood and Parent, 2012 ; Heywood and O’Halloran, 2005; Jirjahn and Stephan, 2004 ; Paarsch and Shearer, 2000). While effort and pay are two important consequences of performance pay, additional research, summarized in Bender and Skåtun ( 2022 ), measures the influence of performance pay on health and well-being. This paper contributes to this latter literature by examining the relationship between performance pay and the probability of divorce. Previously, Baktash et al. ( 2024 ) examined a cohort of German workers who were in their 40’s and 50’s from 2004–2016 and found that performance pay for the wife increased the probability of divorce, but performance pay for the husband had no impact. This work builds upon that one by examining two American cohorts of workers, the 1979 and 1997 cohorts of the National Longitudinal Survey of Youth (NSY79 and NLSY97). Respondents in the former were born between 1957 and 1965 and respondents in the latter were born between 1979 and 1985. Both cohorts were observed between ages 23 and 43. Like Baktash et al., in the NLSY79 this work finds that receiving performance pay increases the probability of divorce for women but there is no significant relationship between receiving performance pay and divorce for men. Like Baktash et al., this work finds that, in that older cohort, there is a mediating role of household income and wages. However, in the NLSY97, it finds no significant relationship between receiving performance pay and divorce for women or men. Moreover, household income and wages play a much smaller role in predicting divorce. This change merits attention. Understanding the predictors of divorce in general is crucial, as divorce tends to be associated with decreased physical and mental health, as well as more unsatisfactory economic outcomes, which often fall more heavily on women than on men (Bonnet et al., 2020 ; Bröckel and Andress, 2015; Drewianka and Meder, 2020 ; Zulkarnain and Korenman, 2019). Divorce also negatively impacts the health and future economic well-being of children of the divorced couple (Krein and Beller, 1988 ; McLanahan and Sandefur, 1994 ; Pong et al, 2003; Scharte and Bolte, 2012 ), with spillover effect onto the child’s peers (Lei, 2022 ). Understanding performance pay as a specific predictor of divorce is also crucial. Performance pay is increasingly common in the U.S. (Lemieux et al., 2009 ) and there are a variety of reasons performance pay may increase or decrease the probability of divorce, which are detailed in the next section. Notably, these reasons include more than the easily testable mechanisms of higher wages and longer hours worked, such as increased stress or perceived violations of gender norms. Thus, performance pay acts as a proxy for these other features. Moreover, there is a unique benefit to studying multiple generations of workers. In the U.S., women became more attached to the labor force and the gender earnings ratio rose until about 2000, then labor force participation and the earnings ratio plateaued (Aguiar et al., 2013 ; Aragão, 2023 ; BLS, 2023; Ramey, 2009 ). This may be explained by the relative lack of policies in the U.S. that encourage women to participate in the labor market (Blau and Kahn, 2013 ). These changing patterns in women’s labor market outcomes and in the structure of the family may have been factors in the changing relationship between performance pay and divorce. As the next section makes clear, there are theoretical reasons to think that performance pay might increase the probability of divorce, particularly for women and particularly in the older cohort. The third section discusses the data. The fourth section presents results, showing differences between men and women and between the NLSY79 and the NLSY97. The fifth section demonstrates the robustness of these results. The final section concludes. 2. Theoretical considerations: Divorce and performance pay There exist both general reasons to anticipate that performance pay could lead to divorce and specific reasons why that influence may concentrate among women receiving performance pay. The general reasons include that performance pay implies greater hours and job commitment, greater income risk, and greater tension at home and with family. The specific reasons it may concentrate among women reflect specialization within the marriage and gender identity norms. Becker ( 1973 ) describes marriage as a utility-maximizing choice. If two married individuals believe that they could enjoy greater home and market consumption separately or with some other match, they will divorce. As performance pay increases labor hours worked, it limits time spent in household production (Artz and Heywood, 2022 ; Green and Heywood, 2023 ; DeVaro, 2022 ). It may thus decrease the consumption of household goods for both men and women, decreasing the utility from marriage (Hur et al., 2021 ). Becker et al. ( 1977 ) also note that greater variance in consumption shocks increases the probability of divorce. This is true for both positive and negative consumption shocks. Performance pay increases income risk and generates such shocks (Cornelissen et al, 2011 ; Dohmen and Falk, 2011 ; Heywood, et al., 2017 ; Lazear, 1998 ). Thus, divorce could be more likely under performance pay. Finally, performance pay corresponds to greater stress (Allan et al., 2021 ; Andelic, et al., 2022; Baktash et al., 2022a ; Cadsby et al., 2016 ) and greater alcohol and drug usage (Artz et al., 2020 ; Baktash et al., 2022b ; Dahl and Pierce, 2020 ). This may also strain the marriage and decrease the utility of one or both partners, increasing the probability of divorce. However, there are key gender asymmetries. In Becker’s view, women tend to specialize in household production. As performance pay, on average, increases wages (Booth and Frank, 1999 ; Green and Heywood, 2016; Heywood and Parent, 2012 ; Heywood and O’Halloran, 2005; Jirjahn and Stephan, 2004 ; Paarsch and Shearer, 2000) and labor hours worked (Artz and Heywood, 2022 ; Green and Heywood, 2023 ; DeVaro, 2022 ), it increases the opportunity cost of home production and decreases the amount of time available for home production. Thus, performance pay for women tends to decrease their production of household goods, decreasing marital surplus. By contrast, performance pay for men tends to increase their production of market goods, increasing marital surplus. Moreover, women working in performance pay jobs may violate norms of gender identity and lower utility for one or both spouses. Akerlof and Kranton ( 2000 ) present a model where an individual's utility is greater if they better conform to their gender identity. For men, this identity reflects their role as worker and provider of market goods. For women, this identity traditionally reflects their role as a homemaker. Thus, the utility of women may be reduced by the high labor market attachment associated with performance pay. Artz et al. (2022) show that, holding worker and job characteristics constant, women with a traditional gender identity are more likely to report job burnout. Thus, to the extent that women continue to value traditional gender identity, performance pay may add marital stress and increase the chance of divorce. Performance pay has been shown to change both orientations toward socializing and how leisure time is spent. Hur et al. ( 2021 ) show that women on performance pay tend to be more work oriented and that this results in greater time socializing with work colleagues and greater time at home spent working and thinking about work. This diminishes joint consumption of leisure in the marriage and may be particularly damaging if it also further reduces the expected household production of the wife. Because performance pay corresponds to higher earnings, greater work hours and ties women’s social lives to their work, it may reduce joint surplus and threaten gender identity. By contrast, men earning performance pay may increase joint surplus and reinforce gender identity. They are confirmed in their role as the primary earner. Thus, performance pay may be anticipated to influence divorce especially when earned by women. The asymmetrical impact of performance pay on the probability of divorce should also be most prevalent among older cohorts of workers. Evidence shows that the ratio of wives’ earnings to husbands’ earnings was a much stronger predictor of divorce in the 1960’s and 1970’s than in the 1990’s (Schwartz and Gonalons-Pons, 2016 ). Moreover, Killewald ( 2016 ) found that even though there remains an expectation that husbands act as breadwinners, the idea of wives as homemakers has become less common over time. Thus, the impact of performance pay on divorce should be diminished in the younger cohort, specifically among women. 3. Data and key variable definitions The data comes from the NLSY79 and the NLSY97. Youth aged 14–22 first responded to the NLSY79 in 1979, then those same youth responded every consecutive year through 1994, then every two years. Youth aged 12–18 first responded to the NLSY97 in 1997, then every consecutive year through 2011, then every two years. To ensure the job for which the respondent reported performance pay, wages, and other variables was the respondent’s primary job, the sample was restricted to those who worked more than 30 hours per week at their job. 3.1. Dependent variable In both cohorts, respondents report their marital status each year. Through the observed length of the marriage, regardless of whether the observation is included in the final sample for analysis, the observations were coded with a value of “0”. The first year that they reported being divorced or separated, they were coded with a value of “1”. 1 For the primary analytical sample used in this work, individuals enter the sample while married, leave the sample when the marriage ends, then re-enter if they remarry. In both cohorts, person-year observations were omitted if the respondent had a missing value of one of the explanatory variables in that wave of the data (which are detailed below). However, marriages were tracked for all the years in which the respondent responded to the survey, regardless of any other questions. Thus, though a respondent may enter, leave, and re-enter the sample used for analysis, their marriage is usually observable even when they are outside that sample. Thus, the NLSY provides a mostly complete marriage history. 3.2. Critical explanatory variable The critical explanatory variable is whether the respondent receives performance pay in the same year that they divorce. Respondents in the NLSY79 indicated whether they received performance pay if they were employed in 1988, 1989, 1990, 1996, 1998, and 2000. These were the only years in which respondents indicated whether they received performance pay, so the sample used for analyzing the NLSY79 only includes these years of the data. In these years, respondents in the NLSY79 indicated whether they received performance pay at their current job, then whether this performance pay included piece rates, commissions, bonuses, stock options, tips, or “other” performance pay. They could indicate multiple types. Respondents also reported whether their benefits included a “profit sharing agreement”. For men, the most common types of performance pay were profit sharing (30.91%) and bonuses (19.17%); the least common were tips (0.76%). For women, the most common types of performance pay were profit sharing (29.04%) and bonuses (11.00%); the least common were piece rates (1.63%). Anyone who had at least one type was considered to “receive performance pay”. Respondents in the NLSY97 indicated whether they received performance pay every year in which they were employed. Because the data in the NSLY97 starts at a younger age than the NLSY79, and because the relationship between marital success and other factors changes over the lifecycle, this work restricts the NLSY97 to only those observations between 23 and 43 years of age, the same range as the observations in the NLSY79. Thus, the NLSY97 sample runs from 2003 to 2019. In the years in which they had an employer, respondents in the NLSY97 indicate whether they receive performance pay, then whether they receive incentive pay, commissions, bonuses, tips, or “other” performance pay. In a separate question, respondents report whether their benefits include an “employee stock ownership plan”. For men, the most common types of performance pay were stock options (26.91%) and bonuses (20.49%); the least common was “other” (0.74%). For women, the most common types of performance pay were bonuses (20.70%) and stock options (19.91%); the least common was “other” (1.05%). Neither cohort provides information about how much of the respondent’s earnings come from their base pay versus the part of their pay that depends on performance. Thus, the indicator for performance pay only provides information as to whether the individual received performance pay, and other variables are needed to examine whether this corresponds to higher earnings, more hours worked, etc. Moreover, the NLSY is structured around individual responses rather than family responses. Thus, the NLSY provides only minimal data about the spouse of any respondent. As a result, there is data on whether the respondent receives performance pay but not on whether their spouse earns performance pay. This prohibits exploring the role played by the spouse’s receipt of performance pay (as in Baktash et al., 2024 ). 3.3. Other explanatory variables The analysis includes a variety of explanatory variables, recognizing that many factors play a role in divorce. Thus, this analysis includes an indicator for whether the respondent is white, respondent age and its square, age at the time of marriage and its square, number of children in the household, and region of residence. This work measures educational attainment using highest grade completed, which ranges through 20 years of education (8 years of postsecondary education). This work also includes the worker’s length of tenure at their job and its square, as well as broad industry and occupation codes. All explanatory variables were measured in the same year as marital status and performance pay receipt. The inclusion of these variables is resembles the work of Baktash et al. ( 2024 ). Of particular importance are hourly wages and household income. Both were measured in the same year as performance pay and were deflated by CPI of that year. This work then uses the natural log of those real values. A few explanatory variables are not as intuitive. For example, the NLSY also provides Armed Forces Qualification Test (AFQT) scores as a measure of cognitive ability. These were measured in 1980 in the NLSY79 and 1997 for the NLSY97, so most respondents have valid test scores. These are included because more able workers are more likely to receive performance pay (Cornelissen et al., 2011 ; Dohmen and Falk, 2011 ; Curme and Stefanec, 2007 ) and greater cognitive ability may be associated with being less likely to divorce (Blazys, 2009 ). Following the work of Curme and Stefanec ( 2007 ), this work normalizes AFQT scores by the age at which respondents took the test. Moreover, the previous section discusses the role of income risk in divorce, noting that performance pay increases income risk. To account for varying attitudes towards that income risk, this work includes a measure of risk tolerance, which comes from a hypothetical income gamble. This work also includes, for the NLSY79 only, a measure of the respondents’ attitudes towards women. This measure is formed by asking respondents the extent to which they agree, on a 4-point Likert scale, to 8 statements. These include, for example, “a woman’s place is in the home, not in the office or shop”. Responses are then aggregated, with a larger score indicating a more traditional view of women. Finally, while the NLSY does not provide much information about the job conditions of workers’ spouses, it does provide the spouse’s annual income. Like workers’ wages and household income, this was deflated by CPI of the survey year. The natural log of spouse’s real annual income was then used for analysis. Means and standard deviations of all variables are presented in Table 1 , broken down by sex and by cohort. These are calculated for the analytical sample, not the full NLSY. Person-year observations are pooled. The base probability of divorce is slightly higher for women and higher in the older cohort. In the older cohort, 5.63% of all observations of men are divorced, whereas 7.56% of all observations of women are divorced. In the younger cohort, 3.59% of all observations of men are divorced, whereas 4.06% of all observations of women are divorced. These values are not unreasonable; according to Buck et al. ( 2024 ), writing for the U.S. Census Bureau, 7.1 out of 1,000 Americans divorced in 2022. Table 1 Means and standard deviations of key variables. Men, NLSY79 Women, NLSY79 Men, NLSY97 Women, NLSY97 Mean Mean Mean Mean (SD) (SD) (SD) (SD) Divorce, all marriages = 0 if the individual is 0.0563 0.0756 0.0359 0.0406 married, 1 if they became divorced in this period (0.2306) (0.2644) (0.1860) (0.1973) Divorce, first marriage only = same as above, only for 0.0479 0.0659 0.0347 0.0387 the first marriage ever (0.2136) (0.2481) (0.1831) (0.1929) Length of marriage = number of years since start of 10.24 9.838 10.24 10.66 marriage, measured at time t (4.509) (5.943) (4.455) (4.628) Performance pay = 1 if the individual 0.4661 0.3982 0.4560 0.4605 received performance pay, else 0 (0.4989) (0.4896) (0.4991) (0.4985) White = 1 if white, else 0 0.6177 0.5930 0.6599 0.6348 (0.4860) (0.4913) (0.4738) (0.4815) Age = age in years 36.60 36.68 30.36 29.90 (2.821) (2.829) (4.331) (4.340) Age at marriage = age, in the first year the respondent 27.21 26.69 25.61 25.02 reported being married (5.038) (5.791) (4.015) (4.131) Education = highest grade completed 13.31 13.38 14.00 14.80 (2.700) (2.501) (2.820) (2.792) Children in HH = number of children in the household 1.747 1.615 1.403 1.389 (1.215) (1.133) (1.251) (1.271) Risk tolerance = based on three hypothetical income 0.6185 0.5197 0.5523 0.3910 gambles; larger corresponds to more risk tolerant (0.5953) (0.5787) (0.5602) (0.4990) AFQT score = score on Armed Forces Qualification 0.2640 0.1486 0.2174 0.2961 Test, standardized by age (1.023) (0.9558) (0.9946) (0.9246) Hours worked = average number of hours worked each 45.83 40.44 43.47 41.03 week (9.220) (6.255) (8.799) (7.535) Log HH income = the log of household income, 10.39 10.35 10.37 10.43 deflated by CPI (0.7525) (0.7329) (0.6783) (0.6446) Log real wage = log of wage at primary job, deflated 2.376 2.062 2.933 2.598 by CPI (0.6266) (0.5993) (0.7374) (0.8324) Log spouse’s income = log of spouse’s annual income, 9.220 9.838 9.336 9.691 deflated by CPI (1.010) (0.7131) (0.9093) (0.7644) Attitude towards women = sum of 8 questions about 10.16 8.288 NA NA attitudes towards women, each rated on a 4-point Likert scale; scores range from 0 to 24 with larger = more traditional attitude (3.307) (3.385) Broad occupation codes = 11 broad categories in the NLSY79, 13 in the NLSY97 Broad industry codes = 12 broad categories in the NLSY79, 13 in the NLSY97 Observations 4,632 3,506 6,327 5,520 Performance pay receipt is similar across men and women in both cohorts. In the NLSY79, 46.61% of observations of men and 39.82% of observations of women receive performance pay. In the NLSY97, 45.60% of observations of men and 46.05% of observations of women receive performance pay. Highest grade completed is around 13–14, or the first or second year of postsecondary education. The average real wage for men in the NLSY79 is $ 10.76/hour; for women it is $ 7.86/hour. The average real wage for men in the NLSY97 is $ 18.78/hour; for women it is $ 13.44/hour. 4. Initial Results Table 2 presents the results of a probit model, estimated for the NLSY79 on a pooled sample, as in Baktash et al. ( 2024 ). Columns 1–3 examine men and columns 4–6 examine women. Columns 1 and 4 begin with a base specification that includes whether the individual is white, age and its square, age at marriage and its square, education, the number of children in the household, risk tolerance, AFQT scores, hours worked, tenure, and tenure squared. Columns 2 and 5 add the log of household income to the set of regressors in columns 1 and 4. Columns 3 and 6 add the log of the worker’s real wage to the set of regressors in columns 2 and 5. Table 2 Relationship between performance pay and divorce: NLSY79. Probit estimation, focusing on HH income and wages. Men Men Men Women Women Women (1) (2) (3) (1) (2) (3) Performance -0.0107 ** -0.0055 -0.0056 0.0208 ** 0.0239 *** 0.0193 *** pay (0.0049) (0.0044) (0.0044) (0.0094) (0.0072) (0.0069) White -0.0136 *** -0.0124 ** -0.0124 ** -0.0143 -0.0075 -0.0063 (0.0052) (0.0049) (0.0049) (0.0096) (0.0077) (0.0072) Age 0.0468 *** 0.0416 ** 0.0416 ** 0.0228 0.0268 0.0230 (0.0176) (0.0165) (0.0165) (0.0282) (0.0213) (0.0200) Age 2 -0.0006 *** -0.0006 ** -0.0006 ** -0.0003 -0.0004 -0.0003 (0.0002) (0.0002) (0.0002) (0.0004) (0.0003) (0.0003) Marital age 0.0144 *** 0.0137 *** 0.0137 *** 0.0163 ** 0.0118 ** 0.0122 ** (0.0051) (0.0047) (0.0047) (0.0072) (0.0054) (0.0050) Marital age 2 -0.0003 *** -0.0003 *** -0.0003 *** -0.0003 ** -0.0002 ** -0.0002 *** (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) Education -0.0030 *** -0.0011 -0.0012 -0.0052 ** 0.0017 0.0006 (0.0011) (0.0011) (0.0011) (0.0022) (0.0018) (0.0018) Children in HH -0.0351 *** -0.0315 *** -0.0316 *** -0.0057 -0.0053 * -0.0052 * (0.0024) (0.0023) (0.0023) (0.0039) (0.0029) (0.0028) Risk tolerance -0.0004 0.0008 0.0008 0.0063 0.0055 0.0052 (0.0037) (0.0034) (0.0035) (0.0073) (0.0052) (0.0050) AFQT score 0.0027 0.0046 0.0045 -0.0096 0.0032 0.0001 (0.0032) (0.0030) (0.0030) (0.0059) (0.0049) (0.0047) Hours worked 0.0001 0.0003 0.0003 0.0000 0.0005 0.0005 (0.0002) (0.0002) (0.0002) (0.0006) (0.0005) (0.0005) Tenure -0.0022 * -0.0010 -0.0010 -0.0037 0.0036 * 0.0034 * (0.0013) (0.0012) (0.0012) (0.0026) (0.0021) (0.0020) Tenure 2 0.0001 0.0000 0.0000 0.0000 -0.0002 * -0.0002 * (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) Log HH -- -0.0228 *** -0.0230 *** -- -0.0841 *** -0.0881 *** income (0.0042) (0.0044) (0.0074) (0.0074) Log real wage -- -- 0.0012 -- -- 0.0394 *** (0.0044) (0.0105) Region codes Included Included Included Included Included Included Broad industry codes Included Included Included Included Included Included Broad occupation codes Included Included Included Included Included Included Constant -15.7608 *** -12.5052 ** -12.5155 ** -5.6150 -0.1098 0.2714 (5.1012) (5.3000) (5.2960) (3.9823) (4.6008) (4.6249) Observations 4,632 4,632 4,632 3,506 3,506 3,506 Pseudo-R 2 0.1763 0.2163 0.2164 0.0341 0.2441 0.2635 Standard errors in parentheses, clustered by individual. Reporting average marginal impact. * p < 0.10, ** p < 0.05, *** p < 0.01 In column 1, men who receive performance pay are approximately 1.07% less likely to divorce, on average. This drops to an insignificant 0.55% in column 2 and remains at 0.56% in column 3. Non-white men and men with fewer children are more likely to divorce. There is a concave relationship between both age and age at marriage and divorce. As theory would predict, men who have more household income are less likely to divorce. However, there is no significant relationship between wages and divorce. In column 4, women who receive performance pay are approximately 2.08% more likely to divorce, on average. This becomes 2.93% in column 5 and falls to 1.93% in column 3. Women with more children and greater tenure at their job are weakly less likely to divorce. As with men, there is a concave relationship between age at marriage and divorce, though not between age and divorce. Like men, women with higher household income are less likely to divorce. However, women with higher wages are more likely to divorce. These results are consistent with the theoretical considerations detailed earlier. Table 2 presents the results of a probit model, estimated for the NLSY97 on a pooled sample. Columns 1–3 examine men and columns 4–6 examine women. The specifications across columns are the same as in Table 2 . Table 3 Relationship between performance pay and divorce: NLSY97. Probit estimation, focusing on HH income and wages. Men Men Men Women Women Women (1) (2) (3) (1) (2) (3) Performance -0.0051 -0.0052 -0.0046 0.0042 0.0051 0.0048 pay (0.0037) (0.0038) (0.0038) (0.0048) (0.0048) (0.0047) White 0.0022 0.0022 0.0028 0.0128 ** 0.0135 ** 0.0138 ** (0.0042) (0.0042) (0.0042) (0.0056) (0.0056) (0.0056) Age 0.0104 ** 0.0104 ** 0.0110 ** 0.0093 * 0.0107 ** 0.0103 * (0.0045) (0.0045) (0.0045) (0.0055) (0.0054) (0.0055) Age 2 -0.0001 -0.0001 -0.0001 -0.0001 -0.0001 -0.0001 (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) Marital age -0.0049 -0.0049 -0.0051 -0.0089 -0.0087 -0.0088 (0.0050) (0.0050) (0.0050) (0.0055) (0.0055) (0.0055) Marital age 2 0.0000 0.0000 0.0000 0.0001 0.0001 0.0001 (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) Education -0.0028 *** -0.0029 *** -0.0028 *** -0.0030 ** -0.0025 ** -0.0026 ** (0.0009) (0.0009) (0.0009) (0.0012) (0.0012) (0.0012) Children in HH -0.0205 *** -0.0205 *** -0.0199 *** -0.0039 * -0.0041 * -0.0039 * (0.0020) (0.0020) (0.0020) (0.0022) (0.0022) (0.0022) Risk tolerance -0.0001 -0.0002 0.0000 0.0013 0.0021 0.0022 (0.0035) (0.0035) (0.0035) (0.0048) (0.0048) (0.0048) AFQT score -0.0030 -0.0031 -0.0028 -0.0063 * -0.0058 * -0.0060 * (0.0024) (0.0024) (0.0024) (0.0032) (0.0032) (0.0032) Hours worked -0.0002 -0.0002 -0.0003 0.0002 0.0002 0.0003 (0.0003) (0.0003) (0.0003) (0.0003) (0.0003) (0.0003) Tenure -0.0025 * -0.0026 * -0.0020 -0.0048 *** -0.0043 ** -0.0047 ** (0.0014) (0.0014) (0.0014) (0.0018) (0.0018) (0.0019) Tenure 2 0.0001 0.0001 0.0000 0.0001 0.0001 0.0001 (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) Log HH income -- 0.0014 0.0058 -- -0.0094 ** -0.0115 ** (0.0030) (0.0037) (0.0040) (0.0050) Log real wage -- -- -0.0073 ** -- -- 0.0038 (0.0035) (0.0041) Region codes Included Included Included Included Included Included Broad industry codes Included Included Included Included Included Included Broad occupation codes Included Included Included Included Included Included Constant -2.2959 -2.5024 -3.0563 * -1.4981 -0.6846 -0.4127 (1.6027) (1.6568) (1.7108) (1.1492) (1.2119) (1.2790) Observations 6,632 6,632 6,632 6,023 6,023 6,023 Pseudo-R 2 0.1039 0.1041 0.1065 0.0536 0.0569 0.0574 Standard errors in parentheses, clustered by individual. Reporting average marginal impact. * p < 0.10, ** p < 0.05, *** p < 0.01 There is no significant relationship between performance pay and divorce among men or women in the NLSY97. Moreover, men and women generally look more similar than in the NLSY79. Both men and women who are older, less educated, and have fewer children are more likely to divorce. White women and women with less tenure at their job are more likely to divorce. There is no significant relationship between household income and divorce among men, but men with higher wages are less likely to divorce. Conversely, women with greater household income are less likely to divorce, but there is no significant relationship between wages and divorce among women. For both men and women, the impact of household income is much smaller than in the NLSY79, and higher wages are more beneficial to the marriage. The changing impact of wages and household income on the probability of divorce suggests a fundamental change in either work or marriage. Both the theoretical considerations and Table 2 suggest that household income and wages may be important mediators in the relationship between performance and divorce in the NLSY79, though there is no relationship to mediate in the NLSY97. Thus, Table 4 presents the results of a seemingly-unrelated regression. Column 1 presents the results for men and column 2 for women. In the first panel, divorce is a function of performance pay, household income, wages, and the full set of regressors from Tables 2 and 3 . In the second panel, household income is a function of performance pay and the set of other regressors. In the third panel, wages are a function of performance pay and the set of other regressors. Table 4 Seemingly-unrelated regression. NLSY79 NLSY79 Men Women Outcome: Divorce Performance pay -0.0046 0.0320 *** (0.0071) (0.0100) Log HH income -0.0594 *** -0.1668 *** (0.0083) (0.0130) Log real wage 0.0100 0.0597 *** (0.0078) (0.0111) Regressors in base specification Included Included Constant -0.9131 ** 0.8782 (0.4339) (0.5352) R 2 0.0945 0.1604 Outcome: Log HH income Performance pay 0.1703 *** 0.1058 *** (0.0223) (0.0266) Regressors in base specification Included Included Constant 8.3367 *** 8.3638 *** (1.2301) (1.6696) R 2 0.2786 0.2679 Outcome: Log real wage Performance pay 0.1547 *** 0.1376 *** (0.0191) (0.0208) Regressors in base specification Included Included Constant 0.9286 0.3209 (1.0113) (0.9786) R 2 0.3265 0.3094 Observations 4,632 3,506 Standard errors in parentheses, clustered by individual. Reporting average marginal impact. * p < 0.10, ** p < 0.05, *** p < 0.01 The results in the first panel generally resemble the results of the probit. The point estimates differ slightly because the seemingly-unrelated regression utilizes a linear probability model. Nonetheless, there remains no significant relationship between performance pay or wages and divorce for men. A 100% increase in household income is associated with a decrease of approximately 5.94 percentage points in the probability of divorce. Performance pay receipt is associated with an increase of approximately 17.03 percentage points in real household income. Performance pay receipt is associated with an increase of approximately 15.47 percentage points in real wages. The total indirect impact of performance pay via household income can be found by multiplying the impact of performance pay on household income with the impact of household income on divorce. This is approximately − 1.01 percentage points, which is significant. The total indirect of performance pay via wages is approximately 0.15 percentage points, which is not significant. Women who earn performance pay see an increase of approximately 3.20 percentage points in the probability they divorce. A 100% increase in household income is associated with a decrease of approximately 16.68 percentage points in the probability of divorce. A 100% increase in wages is associated with an increase of approximately 5.97 percentage points in the probability of divorce. Performance pay receipt is associated with an increase of approximately 10.58 percentage points in real household income. Performance pay receipt is associated with an increase of approximately 13.76 percentage points in real wages. The total indirect impact of performance pay via household income is approximately − 1.76 percentage points, which is significant. The total indirect impact of performance pay via wages is approximately 0.82 percentage points, which is also significant. Thus, while household income is an important mediator for both men and women, wages are only an important mediator for women. Moreover, there remains a significant direct impact of performance pay on women’s probability of divorce. Another theorized mechanism by which performance pay may impact men and women differently is via normative gender roles. To see if the impact of performance pay on women’s marriages in the NLSY79 is due to expectations about gender roles, this work estimates another probit, this time including respondents’ attitudes towards women and the earnings of respondents’ spouses. These results are presented in Table 5 . Table 5 Relationship between performance pay and divorce, conditional upon gendered attitudes and spouse’s income. Probit estimation, reporting average marginal impacts. NLSY79 NLSY79 Men Women Performance pay -0.0045 0.0160 *** (0.0040) (0.0053) Hours worked 0.0004 ** 0.0008 *** (0.0002) (0.0003) Log HH income -0.0358 *** -0.0841 *** (0.0075) (0.0085) Log real wage 0.0092 * 0.0350 *** (0.0051) (0.0078) Attitudes towards women -0.0004 0.0005 (0.0006) (0.0007) Log spouse’s income 0.0079 ** 0.0244 *** (0.0036) (0.0078) Regressors in base specification Included Included Constant -10.2527 -1.0577 (7.1820) (8.0720) Observations 3,157 2,950 Pseudo-R 2 0.2854 0.3668 Standard errors in parentheses, clustered by individual. Reporting average marginal impacts. * p < 0.10, ** p < 0.05, *** p < 0.01 The results in Table 5 do not substantially differ from those in Table 2 . Once again, there is no relationship between performance pay and divorce for men in the NLSY79, but there is a strong, positive relationship for women. Attitude towards women is not a significant predictor of divorce. On average, among men, a 100% increase in their spouse’s income is associated with an increase of approximately 0.79 percentage points in the probability of divorce. On average, among women, a 100% increase in their spouse’s income is associated with an increase of approximately 2.44 percentage points in the probability of divorce. Thus, it does not appear that normative attitudes towards gender drive the positive relationship between performance pay and divorce that is present among women in the NLSY79. 5. Robustness checks There are a variety of limitations to the model thus far. First, a random-effects model may be more appropriate, as Baktash et al. ( 2024 ) only prefer the pooled estimates based on a Breusch-Pagan LM test. There may also be heterogeneity across types of performance pay. It is also possible that the impact of performance pay (and other factors) may accumulate over time. Moreover, it may be a mistake to include individuals in their second or third marriage in the sample. Finally, there may be concern that workers non-randomly select into performance pay. This section attempts to address these concerns. All analyses use the full specification from Tables 2 and 3 that includes household income and wages. 5.1. Random-effects model Both this work and Baktash et al. ( 2024 ) use a pooled probit estimation, despite access to a longitudinal dataset. Baktash et al. reject the random-effects probit based on an empirical test, so the model should not be ruled out a priori . Thus, Table 6 presents the results of a random-effects probit. The first column presents results for men in the NLSY79, the second for women in the NLSY79, the third for men in the NLSY97, and the fourth for women in the NLSY97. Table 6 Random-effects probit estimation, reporting average marginal impacts. NLSY79 NLSY79 NLSY97 NLSY97 Men Women Men Women Performance pay -0.0064 0.0194 *** -0.0044 0.0052 (0.0046) (0.0071) (0.0043) (0.0051) Hours worked 0.0003 0.0005 -0.0004 0.0003 (0.0002) (0.0005) (0.0003) (0.0003) Log HH income -0.0240 *** -0.0884 *** 0.0068 -0.0119 ** (0.0048) (0.0082) (0.0042) (0.0053) Log real wage -0.0001 0.0394 *** -0.0079 ** 0.0044 (0.0048) (0.0105) (0.0039) (0.0043) Regressors in base specification Included Included Included Included Constant -17.1922 ** 0.2020 -4.1404 * -0.7201 (8.3639) (4.6343) (2.3517) (1.4648) \(\:\rho\:\) 0.4169 0.0262 0.3989 *** 0.2005 *** (0.2725) (0.2133) (0.0799) (0.0727) Observations 4,632 3,506 6,632 6,023 Pseudo-R 2 0.2225 0.2705 0.1382 0.1020 \(\:{\chi\:}^{2}\) 3.4671 0.0206 21.0574 7.8517 Standard errors in parentheses, clustered by individual. Reporting average marginal impact. * p < 0.10, ** p < 0.05, *** p < 0.01 Notably, for both men and women in the NLSY79, the parameter \(\:\rho\:\) is not significant, so there is insufficient evidence to reject the null hypothesis of no individual-specific random effects. The null can be rejected for both men and women in the NLSY97, but the results are, in any case, very similar to the pooled estimates across all four columns. Thus, there is insufficient evidence to prefer the random-effects probit over the pooled probit. 2 5.2. Different types of performance pay To address concerns about heterogeneity in types of performance pay, the analysis in Tables 2 and 3 was repeated but the single indicator of performance pay was replaced with indicators for the different types of performance pay. In the NLSY79, these types are profit sharing, piece rates, commissions, bonuses, stock options, tips, and “other” performance pay. In the NLSY97, these types are commissions, bonuses, stock options, tips, “other” performance pay, and incentive pay. However, no one in the NLSY97 who received “other” performance pay was observed divorcing. While very little was significant, the signs of the estimates mostly match the general measure. Men in the NLSY79 who receive commissions are weakly less likely to divorce. Women in the NLSY79 who receive profit sharing or bonuses are weakly more likely to divorce. There is no significant relationship between any of the types and divorce for men in the NLSY97. Women in the NLSY97 who receive commissions are weakly more likely to divorce. Because so little was significant, this analysis was omitted but will be provided upon request. 5.3. Simultaneity of performance pay and divorce There may be concern that performance pay and divorce are measured in the same period. The impact of performance pay, or any other characteristics, may lag as stress accumulates over time. Thus, another robustness check utilizes explanatory variables measured in the current period and divorce either in this period or the next. This analysis is presented in Table 7 . While the standard errors of the estimates are larger, the point estimates are consistent with Tables 2 and 3 . There remains a positive relationship for women in the NLSY79. There is no significant relationship for men in either cohort or women in the NLSY97. Table 7 Relationship between performance pay and divorce in this period or next. Probit estimation, reporting average marginal impacts. NLSY79 NLSY79 NLSY97 NLSY97 Men Women Men Women Performance pay -0.0050 0.0209 * -0.0057 0.0107 (0.0093) (0.0127) (0.0077) (0.0085) Hours worked 0.0008 * 0.0012 -0.0004 0.0002 (0.0005) (0.0009) (0.0004) (0.0005) Log HH income -0.0497 *** -0.1349 *** 0.0116 * -0.0162 ** (0.0079) (0.0133) (0.0065) (0.0078) Log real wage 0.0001 0.0573 *** -0.0213 *** 0.0054 (0.0091) (0.0173) (0.0061) (0.0063) Regressors in base specification Included Included Included Included Constant -6.3337 * -0.9956 -3.3260 ** -0.8161 (3.8503) (3.7793) (1.5401) (1.3306) Observations 4,632 3,506 6,632 6,042 Pseudo-R 2 0.1117 0.1353 0.0642 0.0511 Standard errors in parentheses, clustered by individual. Reporting average marginal impact. * p < 0.10, ** p < 0.05, *** p < 0.01 Additionally, to address the cumulative impact of performance pay over multiple years, a Cox proportional hazards model was estimated. In this model, the likelihood of a marriage “surviving” is a function of the history of explanatory variables and, critically, the history of receiving performance pay. Ideally, this would be done with both cohorts. However, the NLSY79 consists of two groups of three adjacent waves with a substantial time gap between the groups. Rather than incorporate truncation in short three wave panels, the analysis was run on the longer panel in the NLSY97. These results, available upon request, do not substantially differ from earlier estimates. There is no significant relationship for men or women, as in previous tables examining the NLSY97. 5.3. First marriages It is not uncommon to distinguish between first marriages and subsequent marriages. To ensure the results presented were not due to workers in marriages other than their first, the analysis was repeated but only for those workers who report being in their first marriage ever. The results are presented in Table 8 . Table 8 Relationship between performance pay and divorce among first marriages only. Probit estimation, reporting average marginal impacts. NLSY79 NLSY79 NLSY97 NLSY97 Men Women Men Women Performance pay -0.0035 0.0207 *** -0.0048 0.0059 (0.0042) (0.0077) (0.0039) (0.0050) Hours worked 0.0004 ** 0.0008 * -0.0004 0.0003 (0.0002) (0.0004) (0.0002) (0.0003) Log HH income -0.0156 *** -0.0758 *** 0.0058 -0.0085 (0.0037) (0.0084) (0.0043) (0.0052) Log real wage -0.0025 0.0341 *** -0.0054 0.0031 (0.0039) (0.0117) (0.0037) (0.0041) Regressors in base specification Included Included Included Included Constant -17.5346 *** 1.5511 -4.9605 ** 0.4231 (6.3632) (5.5897) (2.1406) (2.0368) Observations 3,735 2,520 5,933 5,041 Pseudo-R 2 0.2262 0.2648 0.1044 0.0654 Standard errors in parentheses, clustered by individual. Reporting average marginal impact. * p < 0.10, ** p < 0.05, *** p < 0.01 These strongly resemble those in Tables 2 and 3 . In Table 2 , the average marginal impact of performance pay on divorce was approximately − 0.56 percentage points for men in the NLSY79 and 1.93 percentage points for women in the NLSY79. In Table 8 , it is approximately − 0.35 percentage points for men and 2.07 percentage points for women. In Table 3 , the average marginal impact of performance pay on divorce was approximately − 0.46 percentage points for men in the NLSY97 and 0.48 percentage points for women in the NLSY97. In Table 8 , it is approximately − 0.48 percentage points for men and 0.59 percentage points for women. 5.4. Selection into performance pay Finally, concern over omitted variable bias has led past work to utilize an instrumental variables analysis to account for the endogeneity of performance pay receipt (Andelic et al., 2023 ; Baktash et al., 2022a , 2022b ; Baktash et al., 2024 ; Cornelissen et al., 2011 ; Fisman and Svensson, 2007 ; Lai and Ng, 2014 ; Lee, 2004 ; Machin and Wadhwani, 1991 ; Woessman and West 2006). Like previous work, this analysis uses the share of workers in an occupation who receive performance pay as the instrument. However, this instrument does not pass the weak instrument test, so the results can not be clearly interpreted. Those results will be provided upon request. 6. Conclusion In summary, this work generally follows the same methods as Baktash et al. ( 2024 ) but finds somewhat different results. In the NLSY79, as in the German data, there is no significant relationship between receiving performance pay and the probability of divorce for men after accounting for wages and household income, but women who receive performance pay are more likely to divorce. Moreover, this work confirms that wages are an important mediator of the relationship between performance pay and divorce for women, but they do not completely mediate the relationship. In the NLSY97, however, there is no significant relationship between receiving performance pay and the probability of divorce for men or women. Moreover, there is no strong evidence that wages or household income play mediating roles. Future work should examine why this relationship no longer holds. The results presented suggest that this change is not due to changes in beliefs about the role of women in the household, as these are not responsible for the relationship between performance pay and divorce found in women in the older cohort. Unfortunately, this work is limited in that it only examines one partner in the marriage. The results for the NLSY79 strongly resemble Baktash et al. ( 2024 ), who did use both partners, so this does not seem to be a critical limitation. Nonetheless, future research should examine recent U.S. data where both partners are observed and thus determine if there is a difference between one partner receiving performance pay and both partners receiving performance pay. This work is also limited in that it examines workers in the earlier half of their working lives. Thus, this work does not examine the phenomenon of divorce among older workers and retirees. While the cohort tracked in the NLSY79 is at the age where such divorce could be studied, the NLSY79 does not contain data on whether these older workers receive performance pay. Future research could thus examine if the results identified in this work are present in older workers. Declarations Author Contribution B. A. is the sole author of this work. Acknowledgement I am grateful to Dr. John Heywood for his guidance and support. I also want to thank Dr. Scott Drewianka, Dr. Scott Adams, and Dr. Matt McGinty for their valuable suggestions. Data Availability The data used comes from the National Longitudinal Surveys of Youth of 1979 and 1997. All data used is publicly available at https://www.nlsinfo.org/investigator/ Benjamin C. Adams declares that no funds, grants, or other support were received during the preparation of this manuscript. Benjamin C. Adams has no relevant financial or non-financial interests to disclose. Benjamin C. Adams declares that no funding was received for this work. Benjamin C. Adams is the sole author of this work. References Aguiar, M., Hurst, E., and Karabarbounis, L. (2013). Time Use During the Great Recession. The American Economic Review, 103 (5), 1665 – 1696. https://doi.org/10.1257/aer.103.5.1664 Akerlof, G.A. and Kranton, R.E. (2000). Economics and Identity. 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Lazear, E.P. (2000). Performance Pay and Productivity. American Economic Review, 90 (5), 1346 – 1361. https://www.jstor.org/stable/2677854 Lee, S. (2004). A Re-examination of Public Sector Wage Differentials in the United States: Evidence from the NLSY with Geocode. Industrial Relations: A Journal of Economy and Society, 43 (2), 448 – 472. https://doi.org/10.1111/j.0019-8676.2004.00338.x Lei, Z. (2022). Short-run and Long-run Effects of Peers from Disrupted Families. Journal of Population Economics, 35 (2022), 1007 – 1036. https://doi.org/10.1007/s00148-021-00839-0 Lemieux, T., MacLeod, W.B., and Parent, D. (2009). Performance Pay and Wage Inequality. The Quarterly Journal of Economics, 124 (1), 1 – 49. https://doi.org/10.1162/qjec.2009.124.1.1 Machin, S. and Wadhwani, S. (1991). The Effects of Unions on Organizational Change and Employment. The Economic Journal, 101 (407), 835 – 854. https://doi.org/10.2307/2233858 McLanahan, S. and Sandefur, G. (1994). Growing Up with a Single Parent: What Hurts, What Helps. Harvard University Press. Cambridge, MA. Ortiz-Ospina, E., and Roser, M. (2020). Marriages and divorces . Our World in Data. https://ourworldindata.org/marriages-and-divorces Paarsch, H.J. and Shearer, B. (2001). Piece Rates, Fixed Wages, and Incentive Effects: Statistical Evidence from Payroll Records. International Economic Review, 41 (1), 59 – 92. https://doi.org/10.1111/1468-2354.00055 Parent, D. (1999). Methods of pay and earnings: A longitudinal analysis. Industrial and Labor Relations, 1 (1999), 71 – 86. https://doi.org/10.2307/2696162 Pong, S., Dronkers, J., and Hampden-Thompson, G. Family Policies and Children’s School Achievement in Single- Versus Two-Parent Families. Journal of Marriage and Family, 65 (3), 681 – 699. https://doi.org/10.1111/j.1741-3737.2003.00681.x Ramey, V.A. (2009). Time Spent in Home Production in the Twentieth-Century United States: New Estimates from Old Data. The Journal of Economic History, 69 (1), 1 – 47. https://doi.org/10.1017/S0022050709000333 Scharte, M. and Bolte, G. (2012). Increased health risks of children with single mothers: the impact of socio-economic and environmental factors. European Journal of Public Health, 23 (3), 469 – 475. https://doi.org/10.1093/eurpub/cks062 Schwartz, C.R. and Gonalons-Pons, P. (2016). Trends in Relative Earnings and Marital Dissolution: Are Wives Who Outearn Their Husbands Still More Likely to Divorce?. The Russell Sage Foundation Journal of the Social Sciences, 2 (4), 218 – 236. https://doi.org/10.7758/RSF.2016.2.4.08 U.S. Bureau of Labor Statistics. (2023, September 1). Labor Force Participation Rate - Women [LNS11300002], retrieved from FRED, Federal Reserve Bank of St. Louis, September 15, 2023. https://fred.stlouisfed.org/series/LNS11300002 U.S. Bureau of Labor Statistics. (2023, July 18). Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over [LES1252881600Q], retrieved from FRED, Federal Reserve Bank of St. Louis, September 15, 2023. https://fred.stlouisfed.org/series/LES1252881600Q Woessmann, L. and West, M. (2006). Class Size Effects in School Systems around the World: Evidence from Between-Grade Variation in TIMMS. European Economic Review, 50 (3), 695 – 736. https://doi.org/10.1016/j.euroecorev.2004.11.005 Zulkarnain, A. and Korenman, S. (2018). Divorce and health in middle and older ages. Review of Economics of the Household, 17 (2019), 1081 – 1106. https://doi.org/10.1007/s11150-018-9435-z Footnotes Few individuals are counted as “separated” in the sample. Simply dropping the separated does not substantially impact any results. It should also be noted that, as in Baktash et al. ( 2024 ), many individuals in the sample are never observed divorcing, so fixed-effects estimation is ruled out a priori. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Oct, 2025 Read the published version in Review of Economics of the Household → Version 1 posted Editorial decision: Revision requested 29 Jul, 2025 Reviews received at journal 29 Jul, 2025 Reviews received at journal 27 Jul, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviewers invited by journal 23 Jul, 2025 Editor assigned by journal 17 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 11 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7103256","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490358223,"identity":"b5d162cf-2420-471d-9eb0-99a653257a91","order_by":0,"name":"Benjamin Adams","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACAzBi+FffD+EzE63lAOPMBmZStWw4QKwWc/bDGz/d+HOH2fhG/tENDBXWiQ2EtFj2pBVL57Y9YzO7kcx2g+FMOmEtBgdyDKRzG5h5wFoY2w4ToeX8G+PfOX+YJYxngLT8I0bLjRwz6Ry2wwYGEiAtDURosZzxrMw6ty0tQeLMY7MbCcfSjQlqMedP3nw7549NAn974rMbH2qsZQlqQQUJpCkfBaNgFIyCUYALAADL0kMmC2G1GgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Tennessee at Martin","correspondingAuthor":true,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Adams","suffix":""}],"badges":[],"createdAt":"2025-07-11 16:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7103256/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7103256/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11150-025-09813-1","type":"published","date":"2025-10-03T15:58:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92884494,"identity":"a90866dc-e55c-4c13-93bd-756c895e17d2","added_by":"auto","created_at":"2025-10-06 16:13:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1448566,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7103256/v1/de5c2faa-7989-4920-bc00-e0c40bfcb83c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance Pay, Gender, and Divorce among Full-Time American Workers","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSome employers offer payment schemes based on measures of job performance other than time worked. Firms use such \u0026ldquo;performance pay\u0026rdquo; schemes to try to attract high performance workers (Curme and Stefanec, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These schemes also provide incentives for increased effort from existing workers, increasing their productivity (Lazear, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and earnings (Booth and Frank, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Green and Heywood, 2016; Heywood and Parent, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Heywood and O\u0026rsquo;Halloran, 2005; Jirjahn and Stephan, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Paarsch and Shearer, 2000). While effort and pay are two important consequences of performance pay, additional research, summarized in Bender and Sk\u0026aring;tun (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), measures the influence of performance pay on health and well-being. This paper contributes to this latter literature by examining the relationship between performance pay and the probability of divorce.\u003c/p\u003e\u003cp\u003ePreviously, Baktash et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) examined a cohort of German workers who were in their 40\u0026rsquo;s and 50\u0026rsquo;s from 2004\u0026ndash;2016 and found that performance pay for the wife increased the probability of divorce, but performance pay for the husband had no impact. This work builds upon that one by examining two American cohorts of workers, the 1979 and 1997 cohorts of the National Longitudinal Survey of Youth (NSY79 and NLSY97). Respondents in the former were born between 1957 and 1965 and respondents in the latter were born between 1979 and 1985. Both cohorts were observed between ages 23 and 43.\u003c/p\u003e\u003cp\u003eLike Baktash et al., in the NLSY79 this work finds that receiving performance pay increases the probability of divorce for women but there is no significant relationship between receiving performance pay and divorce for men. Like Baktash et al., this work finds that, in that older cohort, there is a mediating role of household income and wages. However, in the NLSY97, it finds no significant relationship between receiving performance pay and divorce for women or men. Moreover, household income and wages play a much smaller role in predicting divorce.\u003c/p\u003e\u003cp\u003eThis change merits attention. Understanding the predictors of divorce in general is crucial, as divorce tends to be associated with decreased physical and mental health, as well as more unsatisfactory economic outcomes, which often fall more heavily on women than on men (Bonnet et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Br\u0026ouml;ckel and Andress, 2015; Drewianka and Meder, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zulkarnain and Korenman, 2019). Divorce also negatively impacts the health and future economic well-being of children of the divorced couple (Krein and Beller, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; McLanahan and Sandefur, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Pong et al, 2003; Scharte and Bolte, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), with spillover effect onto the child\u0026rsquo;s peers (Lei, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnderstanding performance pay as a specific predictor of divorce is also crucial. Performance pay is increasingly common in the U.S. (Lemieux et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and there are a variety of reasons performance pay may increase or decrease the probability of divorce, which are detailed in the next section. Notably, these reasons include more than the easily testable mechanisms of higher wages and longer hours worked, such as increased stress or perceived violations of gender norms. Thus, performance pay acts as a proxy for these other features.\u003c/p\u003e\u003cp\u003eMoreover, there is a unique benefit to studying multiple generations of workers. In the U.S., women became more attached to the labor force and the gender earnings ratio rose until about 2000, then labor force participation and the earnings ratio plateaued (Aguiar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Arag\u0026atilde;o, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; BLS, 2023; Ramey, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This may be explained by the relative lack of policies in the U.S. that encourage women to participate in the labor market (Blau and Kahn, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These changing patterns in women\u0026rsquo;s labor market outcomes and in the structure of the family may have been factors in the changing relationship between performance pay and divorce.\u003c/p\u003e\u003cp\u003eAs the next section makes clear, there are theoretical reasons to think that performance pay might increase the probability of divorce, particularly for women and particularly in the older cohort. The third section discusses the data. The fourth section presents results, showing differences between men and women and between the NLSY79 and the NLSY97. The fifth section demonstrates the robustness of these results. The final section concludes.\u003c/p\u003e"},{"header":"2. Theoretical considerations: Divorce and performance pay","content":"\u003cp\u003eThere exist both general reasons to anticipate that performance pay could lead to divorce and specific reasons why that influence may concentrate among women receiving performance pay. The general reasons include that performance pay implies greater hours and job commitment, greater income risk, and greater tension at home and with family. The specific reasons it may concentrate among women reflect specialization within the marriage and gender identity norms.\u003c/p\u003e\u003cp\u003eBecker (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1973\u003c/span\u003e) describes marriage as a utility-maximizing choice. If two married individuals believe that they could enjoy greater home and market consumption separately or with some other match, they will divorce. As performance pay increases labor hours worked, it limits time spent in household production (Artz and Heywood, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Green and Heywood, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; DeVaro, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It may thus decrease the consumption of household goods for both men and women, decreasing the utility from marriage (Hur et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBecker et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) also note that greater variance in consumption shocks increases the probability of divorce. This is true for both positive and negative consumption shocks. Performance pay increases income risk and generates such shocks (Cornelissen et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Dohmen and Falk, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Heywood, et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lazear, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Thus, divorce could be more likely under performance pay.\u003c/p\u003e\u003cp\u003eFinally, performance pay corresponds to greater stress (Allan et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Andelic, et al., 2022; Baktash et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Cadsby et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and greater alcohol and drug usage (Artz et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Baktash et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Dahl and Pierce, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This may also strain the marriage and decrease the utility of one or both partners, increasing the probability of divorce.\u003c/p\u003e\u003cp\u003eHowever, there are key gender asymmetries. In Becker\u0026rsquo;s view, women tend to specialize in household production. As performance pay, on average, increases wages (Booth and Frank, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Green and Heywood, 2016; Heywood and Parent, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Heywood and O\u0026rsquo;Halloran, 2005; Jirjahn and Stephan, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Paarsch and Shearer, 2000) and labor hours worked (Artz and Heywood, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Green and Heywood, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; DeVaro, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), it increases the opportunity cost of home production and decreases the amount of time available for home production. Thus, performance pay for women tends to decrease their production of household goods, decreasing marital surplus. By contrast, performance pay for men tends to increase their production of market goods, increasing marital surplus.\u003c/p\u003e\u003cp\u003eMoreover, women working in performance pay jobs may violate norms of gender identity and lower utility for one or both spouses. Akerlof and Kranton (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) present a model where an individual's utility is greater if they better conform to their gender identity. For men, this identity reflects their role as worker and provider of market goods. For women, this identity traditionally reflects their role as a homemaker. Thus, the utility of women may be reduced by the high labor market attachment associated with performance pay. Artz et al. (2022) show that, holding worker and job characteristics constant, women with a traditional gender identity are more likely to report job burnout. Thus, to the extent that women continue to value traditional gender identity, performance pay may add marital stress and increase the chance of divorce.\u003c/p\u003e\u003cp\u003ePerformance pay has been shown to change both orientations toward socializing and how leisure time is spent. Hur et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) show that women on performance pay tend to be more work oriented and that this results in greater time socializing with work colleagues and greater time at home spent working and thinking about work. This diminishes joint consumption of leisure in the marriage and may be particularly damaging if it also further reduces the expected household production of the wife.\u003c/p\u003e\u003cp\u003eBecause performance pay corresponds to higher earnings, greater work hours and ties women\u0026rsquo;s social lives to their work, it may reduce joint surplus and threaten gender identity. By contrast, men earning performance pay may increase joint surplus and reinforce gender identity. They are confirmed in their role as the primary earner. Thus, performance pay may be anticipated to influence divorce especially when earned by women.\u003c/p\u003e\u003cp\u003eThe asymmetrical impact of performance pay on the probability of divorce should also be most prevalent among older cohorts of workers. Evidence shows that the ratio of wives\u0026rsquo; earnings to husbands\u0026rsquo; earnings was a much stronger predictor of divorce in the 1960\u0026rsquo;s and 1970\u0026rsquo;s than in the 1990\u0026rsquo;s (Schwartz and Gonalons-Pons, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, Killewald (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that even though there remains an expectation that husbands act as breadwinners, the idea of wives as homemakers has become less common over time. Thus, the impact of performance pay on divorce should be diminished in the younger cohort, specifically among women.\u003c/p\u003e"},{"header":"3. Data and key variable definitions","content":"\u003cp\u003eThe data comes from the NLSY79 and the NLSY97. Youth aged 14\u0026ndash;22 first responded to the NLSY79 in 1979, then those same youth responded every consecutive year through 1994, then every two years. Youth aged 12\u0026ndash;18 first responded to the NLSY97 in 1997, then every consecutive year through 2011, then every two years. To ensure the job for which the respondent reported performance pay, wages, and other variables was the respondent\u0026rsquo;s primary job, the sample was restricted to those who worked more than 30 hours per week at their job.\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.1. Dependent variable\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eIn both cohorts, respondents report their marital status each year. Through the observed length of the marriage, regardless of whether the observation is included in the final sample for analysis, the observations were coded with a value of \u0026ldquo;0\u0026rdquo;. The first year that they reported being divorced or separated, they were coded with a value of \u0026ldquo;1\u0026rdquo;.\u003csup\u003e1\u003c/sup\u003e For the primary analytical sample used in this work, individuals enter the sample while married, leave the sample when the marriage ends, then re-enter if they remarry.\u003c/p\u003e\u003cp\u003eIn both cohorts, person-year observations were omitted if the respondent had a missing value of one of the explanatory variables in that wave of the data (which are detailed below). However, marriages were tracked for all the years in which the respondent responded to the survey, regardless of any other questions. Thus, though a respondent may enter, leave, and re-enter the sample used for analysis, their marriage is usually observable even when they are outside that sample. Thus, the NLSY provides a mostly complete marriage history.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Critical explanatory variable\u003c/h2\u003e\u003cp\u003eThe critical explanatory variable is whether the respondent receives performance pay in the same year that they divorce. Respondents in the NLSY79 indicated whether they received performance pay if they were employed in 1988, 1989, 1990, 1996, 1998, and 2000. These were the only years in which respondents indicated whether they received performance pay, so the sample used for analyzing the NLSY79 only includes these years of the data.\u003c/p\u003e\u003cp\u003eIn these years, respondents in the NLSY79 indicated whether they received performance pay at their current job, then whether this performance pay included piece rates, commissions, bonuses, stock options, tips, or \u0026ldquo;other\u0026rdquo; performance pay. They could indicate multiple types. Respondents also reported whether their benefits included a \u0026ldquo;profit sharing agreement\u0026rdquo;. For men, the most common types of performance pay were profit sharing (30.91%) and bonuses (19.17%); the least common were tips (0.76%). For women, the most common types of performance pay were profit sharing (29.04%) and bonuses (11.00%); the least common were piece rates (1.63%). Anyone who had at least one type was considered to \u0026ldquo;receive performance pay\u0026rdquo;.\u003c/p\u003e\u003cp\u003eRespondents in the NLSY97 indicated whether they received performance pay every year in which they were employed. Because the data in the NSLY97 starts at a younger age than the NLSY79, and because the relationship between marital success and other factors changes over the lifecycle, this work restricts the NLSY97 to only those observations between 23 and 43 years of age, the same range as the observations in the NLSY79. Thus, the NLSY97 sample runs from 2003 to 2019.\u003c/p\u003e\u003cp\u003eIn the years in which they had an employer, respondents in the NLSY97 indicate whether they receive performance pay, then whether they receive incentive pay, commissions, bonuses, tips, or \u0026ldquo;other\u0026rdquo; performance pay. In a separate question, respondents report whether their benefits include an \u0026ldquo;employee stock ownership plan\u0026rdquo;. For men, the most common types of performance pay were stock options (26.91%) and bonuses (20.49%); the least common was \u0026ldquo;other\u0026rdquo; (0.74%). For women, the most common types of performance pay were bonuses (20.70%) and stock options (19.91%); the least common was \u0026ldquo;other\u0026rdquo; (1.05%).\u003c/p\u003e\u003cp\u003eNeither cohort provides information about how much of the respondent\u0026rsquo;s earnings come from their base pay versus the part of their pay that depends on performance. Thus, the indicator for performance pay only provides information as to whether the individual received performance pay, and other variables are needed to examine whether this corresponds to higher earnings, more hours worked, etc.\u003c/p\u003e\u003cp\u003eMoreover, the NLSY is structured around individual responses rather than family responses. Thus, the NLSY provides only minimal data about the spouse of any respondent. As a result, there is data on whether the respondent receives performance pay but not on whether their spouse earns performance pay. This prohibits exploring the role played by the spouse\u0026rsquo;s receipt of performance pay (as in Baktash et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Other explanatory variables\u003c/h2\u003e\u003cp\u003eThe analysis includes a variety of explanatory variables, recognizing that many factors play a role in divorce. Thus, this analysis includes an indicator for whether the respondent is white, respondent age and its square, age at the time of marriage and its square, number of children in the household, and region of residence. This work measures educational attainment using highest grade completed, which ranges through 20 years of education (8 years of postsecondary education). This work also includes the worker\u0026rsquo;s length of tenure at their job and its square, as well as broad industry and occupation codes. All explanatory variables were measured in the same year as marital status and performance pay receipt. The inclusion of these variables is resembles the work of Baktash et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOf particular importance are hourly wages and household income. Both were measured in the same year as performance pay and were deflated by CPI of that year. This work then uses the natural log of those real values.\u003c/p\u003e\u003cp\u003eA few explanatory variables are not as intuitive. For example, the NLSY also provides Armed Forces Qualification Test (AFQT) scores as a measure of cognitive ability. These were measured in 1980 in the NLSY79 and 1997 for the NLSY97, so most respondents have valid test scores. These are included because more able workers are more likely to receive performance pay (Cornelissen et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Dohmen and Falk, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Curme and Stefanec, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and greater cognitive ability may be associated with being less likely to divorce (Blazys, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Following the work of Curme and Stefanec (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), this work normalizes AFQT scores by the age at which respondents took the test.\u003c/p\u003e\u003cp\u003eMoreover, the previous section discusses the role of income risk in divorce, noting that performance pay increases income risk. To account for varying attitudes towards that income risk, this work includes a measure of risk tolerance, which comes from a hypothetical income gamble.\u003c/p\u003e\u003cp\u003eThis work also includes, for the NLSY79 only, a measure of the respondents\u0026rsquo; attitudes towards women. This measure is formed by asking respondents the extent to which they agree, on a 4-point Likert scale, to 8 statements. These include, for example, \u0026ldquo;a woman\u0026rsquo;s place is in the home, not in the office or shop\u0026rdquo;. Responses are then aggregated, with a larger score indicating a more traditional view of women.\u003c/p\u003e\u003cp\u003eFinally, while the NLSY does not provide much information about the job conditions of workers\u0026rsquo; spouses, it does provide the spouse\u0026rsquo;s annual income. Like workers\u0026rsquo; wages and household income, this was deflated by CPI of the survey year. The natural log of spouse\u0026rsquo;s real annual income was then used for analysis.\u003c/p\u003e\u003cp\u003eMeans and standard deviations of all variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, broken down by sex and by cohort. These are calculated for the analytical sample, not the full NLSY. Person-year observations are pooled. The base probability of divorce is slightly higher for women and higher in the older cohort. In the older cohort, 5.63% of all observations of men are divorced, whereas 7.56% of all observations of women are divorced. In the younger cohort, 3.59% of all observations of men are divorced, whereas 4.06% of all observations of women are divorced. These values are not unreasonable; according to Buck et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), writing for the U.S. Census Bureau, 7.1 out of 1,000 Americans divorced in 2022.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeans and standard deviations of key variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMen, NLSY79\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWomen, NLSY79\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMen, NLSY97\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWomen, NLSY97\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(SD)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorce, all marriages\u0026thinsp;=\u0026thinsp;0 if the individual is\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0406\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emarried, 1 if they became divorced in this period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.2306)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.2644)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.1860)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.1973)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorce, first marriage only\u0026thinsp;=\u0026thinsp;same as above, only for\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0387\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ethe first marriage ever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.2136)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.2481)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.1831)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.1929)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength of marriage\u0026thinsp;=\u0026thinsp;number of years since start of\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emarriage, measured at time \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4.509)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(5.943)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4.455)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4.628)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance pay\u0026thinsp;=\u0026thinsp;1 if the individual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4605\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ereceived performance pay, else 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.4989)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.4896)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.4991)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.4985)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u0026thinsp;=\u0026thinsp;1 if white, else 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6348\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.4860)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.4913)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.4738)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.4815)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u0026thinsp;=\u0026thinsp;age in years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.821)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.829)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4.331)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4.340)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at marriage\u0026thinsp;=\u0026thinsp;age, in the first year the respondent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ereported being married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(5.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(5.791)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4.131)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u0026thinsp;=\u0026thinsp;highest grade completed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.700)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.501)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.820)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.792)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildren in HH\u0026thinsp;=\u0026thinsp;number of children in the household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.215)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.133)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.251)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.271)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk tolerance\u0026thinsp;=\u0026thinsp;based on three hypothetical income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3910\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egambles; larger corresponds to more risk tolerant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.5953)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.5787)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.5602)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.4990)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFQT score\u0026thinsp;=\u0026thinsp;score on Armed Forces Qualification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2961\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest, standardized by age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.9558)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.9946)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.9246)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHours worked\u0026thinsp;=\u0026thinsp;average number of hours worked each\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eweek\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(9.220)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.255)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(8.799)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(7.535)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog HH income\u0026thinsp;=\u0026thinsp;the log of household income,\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edeflated by CPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.7525)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.7329)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.6783)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.6446)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog real wage\u0026thinsp;=\u0026thinsp;log of wage at primary job, deflated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.598\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eby CPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.6266)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.5993)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.7374)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.8324)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog spouse\u0026rsquo;s income\u0026thinsp;=\u0026thinsp;log of spouse\u0026rsquo;s annual income,\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.691\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edeflated by CPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.7131)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.9093)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.7644)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttitude towards women\u0026thinsp;=\u0026thinsp;sum of 8 questions about\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eattitudes towards women, each rated on a 4-point Likert scale; scores range from 0 to 24 with larger\u0026thinsp;=\u0026thinsp;more traditional attitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.307)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.385)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBroad occupation codes\u0026thinsp;=\u0026thinsp;11 broad categories in the\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLSY79, 13 in the NLSY97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBroad industry codes\u0026thinsp;=\u0026thinsp;12 broad categories in the\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLSY79, 13 in the NLSY97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5,520\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePerformance pay receipt is similar across men and women in both cohorts. In the NLSY79, 46.61% of observations of men and 39.82% of observations of women receive performance pay. In the NLSY97, 45.60% of observations of men and 46.05% of observations of women receive performance pay. Highest grade completed is around 13\u0026ndash;14, or the first or second year of postsecondary education. The average real wage for men in the NLSY79 is \u003cspan\u003e$\u003c/span\u003e10.76/hour; for women it is \u003cspan\u003e$\u003c/span\u003e7.86/hour. The average real wage for men in the NLSY97 is \u003cspan\u003e$\u003c/span\u003e18.78/hour; for women it is \u003cspan\u003e$\u003c/span\u003e13.44/hour.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Initial Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of a probit model, estimated for the NLSY79 on a pooled sample, as in Baktash et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Columns 1\u0026ndash;3 examine men and columns 4\u0026ndash;6 examine women. Columns 1 and 4 begin with a base specification that includes whether the individual is white, age and its square, age at marriage and its square, education, the number of children in the household, risk tolerance, AFQT scores, hours worked, tenure, and tenure squared. Columns 2 and 5 add the log of household income to the set of regressors in columns 1 and 4. Columns 3 and 6 add the log of the worker\u0026rsquo;s real wage to the set of regressors in columns 2 and 5.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between performance pay and divorce: NLSY79. Probit estimation, focusing on HH income and wages.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0107\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0208\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0239\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0193\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0094)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0072)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0069)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0136\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0124\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0124\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0052)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0096)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0072)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0468\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0416\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0416\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0230\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0176)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0282)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0213)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0200)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0006\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0144\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0137\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0137\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0163\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0118\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0122\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0072)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0050)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital age\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0003\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0003\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0003\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0002\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0030\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0052\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0018)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildren in HH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0351\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0315\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0316\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0053\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0052\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0029)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0028)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk tolerance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0073)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0052)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0050)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFQT score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0032)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0047)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHours worked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTenure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0022\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0036\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0034\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTenure\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog HH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0228\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0230\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0841\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0881\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eincome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0074)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0074)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog real wage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0394\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0105)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion codes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBroad industry codes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBroad occupation codes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-15.7608\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-12.5052\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-12.5155\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.6150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.1098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2714\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(5.1012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(5.3000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(5.2960)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3.9823)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(4.6008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(4.6249)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4,632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3,506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3,506\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2635\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStandard errors in parentheses, clustered by individual. Reporting average marginal impact.\u003c/p\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003cp\u003eIn column 1, men who receive performance pay are approximately 1.07% less likely to divorce, on average. This drops to an insignificant 0.55% in column 2 and remains at 0.56% in column 3. Non-white men and men with fewer children are more likely to divorce. There is a concave relationship between both age and age at marriage and divorce. As theory would predict, men who have more household income are less likely to divorce. However, there is no significant relationship between wages and divorce.\u003c/p\u003e\u003cp\u003eIn column 4, women who receive performance pay are approximately 2.08% more likely to divorce, on average. This becomes 2.93% in column 5 and falls to 1.93% in column 3. Women with more children and greater tenure at their job are weakly less likely to divorce. As with men, there is a concave relationship between age at marriage and divorce, though not between age and divorce. Like men, women with higher household income are less likely to divorce. However, women with higher wages are more likely to divorce. These results are consistent with the theoretical considerations detailed earlier.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of a probit model, estimated for the NLSY97 on a pooled sample. Columns 1\u0026ndash;3 examine men and columns 4\u0026ndash;6 examine women. The specifications across columns are the same as in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between performance pay and divorce: NLSY97. Probit estimation, focusing on HH income and wages.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0047)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0128\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0135\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0138\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0056)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0104\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0104\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0110\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0093\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0107\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0103\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0055)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0055)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital age\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0029\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0030\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0025\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0026\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0012)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildren in HH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0205\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0205\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0199\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0039\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0041\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0039\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0022)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk tolerance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0048)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFQT score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0063\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0058\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0060\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0032)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0032)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0032)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHours worked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTenure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0025\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0026\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0048\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0043\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0047\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTenure\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog HH income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0094\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0115\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0050)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog real wage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0073\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0041)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion codes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBroad industry codes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBroad occupation codes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.2959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.5024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.0563\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.4981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.6846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.4127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.6027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.6568)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.7108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.1492)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.2119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.2790)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6,023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6,023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0574\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStandard errors in parentheses, clustered by individual. Reporting average marginal impact.\u003c/p\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003cp\u003eThere is no significant relationship between performance pay and divorce among men or women in the NLSY97. Moreover, men and women generally look more similar than in the NLSY79. Both men and women who are older, less educated, and have fewer children are more likely to divorce. White women and women with less tenure at their job are more likely to divorce.\u003c/p\u003e\u003cp\u003eThere is no significant relationship between household income and divorce among men, but men with higher wages are less likely to divorce. Conversely, women with greater household income are less likely to divorce, but there is no significant relationship between wages and divorce among women. For both men and women, the impact of household income is much smaller than in the NLSY79, and higher wages are more beneficial to the marriage. The changing impact of wages and household income on the probability of divorce suggests a fundamental change in either work or marriage.\u003c/p\u003e\u003cp\u003eBoth the theoretical considerations and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e suggest that household income and wages may be important mediators in the relationship between performance and divorce in the NLSY79, though there is no relationship to mediate in the NLSY97. Thus, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of a seemingly-unrelated regression. Column 1 presents the results for men and column 2 for women. In the first panel, divorce is a function of performance pay, household income, wages, and the full set of regressors from Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the second panel, household income is a function of performance pay and the set of other regressors. In the third panel, wages are a function of performance pay and the set of other regressors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSeemingly-unrelated regression.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNLSY79\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNLSY79\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOutcome: Divorce\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0320\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog HH income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0594\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1668\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0130)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog real wage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0597\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0078)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0111)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegressors in base specification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.9131\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8782\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.4339)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.5352)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1604\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOutcome: Log HH income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1703\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1058\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0223)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0266)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegressors in base specification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.3367\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.3638\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.2301)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.6696)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2679\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOutcome: Log real wage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1547\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1376\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0191)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0208)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegressors in base specification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3209\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.0113)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.9786)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,506\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStandard errors in parentheses, clustered by individual. Reporting average marginal impact.\u003c/p\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003cp\u003eThe results in the first panel generally resemble the results of the probit. The point estimates differ slightly because the seemingly-unrelated regression utilizes a linear probability model. Nonetheless, there remains no significant relationship between performance pay or wages and divorce for men. A 100% increase in household income is associated with a decrease of approximately 5.94 percentage points in the probability of divorce. Performance pay receipt is associated with an increase of approximately 17.03 percentage points in real household income. Performance pay receipt is associated with an increase of approximately 15.47 percentage points in real wages.\u003c/p\u003e\u003cp\u003eThe total indirect impact of performance pay via household income can be found by multiplying the impact of performance pay on household income with the impact of household income on divorce. This is approximately \u0026minus;\u0026thinsp;1.01 percentage points, which is significant. The total indirect of performance pay via wages is approximately 0.15 percentage points, which is not significant.\u003c/p\u003e\u003cp\u003eWomen who earn performance pay see an increase of approximately 3.20 percentage points in the probability they divorce. A 100% increase in household income is associated with a decrease of approximately 16.68 percentage points in the probability of divorce. A 100% increase in wages is associated with an increase of approximately 5.97 percentage points in the probability of divorce. Performance pay receipt is associated with an increase of approximately 10.58 percentage points in real household income. Performance pay receipt is associated with an increase of approximately 13.76 percentage points in real wages.\u003c/p\u003e\u003cp\u003eThe total indirect impact of performance pay via household income is approximately \u0026minus;\u0026thinsp;1.76 percentage points, which is significant. The total indirect impact of performance pay via wages is approximately 0.82 percentage points, which is also significant. Thus, while household income is an important mediator for both men and women, wages are only an important mediator for women. Moreover, there remains a significant direct impact of performance pay on women\u0026rsquo;s probability of divorce.\u003c/p\u003e\u003cp\u003eAnother theorized mechanism by which performance pay may impact men and women differently is via normative gender roles. To see if the impact of performance pay on women\u0026rsquo;s marriages in the NLSY79 is due to expectations about gender roles, this work estimates another probit, this time including respondents\u0026rsquo; attitudes towards women and the earnings of respondents\u0026rsquo; spouses. These results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between performance pay and divorce, conditional upon gendered attitudes and spouse\u0026rsquo;s income. Probit estimation, reporting average marginal impacts.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNLSY79\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNLSY79\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0160\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0053)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHours worked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0004\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0008\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog HH income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0358\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0841\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0075)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0085)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog real wage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0092\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0350\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0078)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttitudes towards women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog spouse\u0026rsquo;s income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0079\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0244\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0078)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegressors in base specification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-10.2527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.0577\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(7.1820)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(8.0720)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,950\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3668\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStandard errors in parentheses, clustered by individual. Reporting average marginal impacts.\u003c/p\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e do not substantially differ from those in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Once again, there is no relationship between performance pay and divorce for men in the NLSY79, but there is a strong, positive relationship for women. Attitude towards women is not a significant predictor of divorce. On average, among men, a 100% increase in their spouse\u0026rsquo;s income is associated with an increase of approximately 0.79 percentage points in the probability of divorce. On average, among women, a 100% increase in their spouse\u0026rsquo;s income is associated with an increase of approximately 2.44 percentage points in the probability of divorce. Thus, it does not appear that normative attitudes towards gender drive the positive relationship between performance pay and divorce that is present among women in the NLSY79.\u003c/p\u003e"},{"header":"5. Robustness checks","content":"\u003cp\u003eThere are a variety of limitations to the model thus far. First, a random-effects model may be more appropriate, as Baktash et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) only prefer the pooled estimates based on a Breusch-Pagan LM test. There may also be heterogeneity across types of performance pay. It is also possible that the impact of performance pay (and other factors) may accumulate over time. Moreover, it may be a mistake to include individuals in their second or third marriage in the sample. Finally, there may be concern that workers non-randomly select into performance pay. This section attempts to address these concerns. All analyses use the full specification from Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e that includes household income and wages.\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Random-effects model\u003c/h2\u003e\u003cp\u003eBoth this work and Baktash et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) use a pooled probit estimation, despite access to a longitudinal dataset. Baktash et al. reject the random-effects probit based on an empirical test, so the model should not be ruled out \u003cem\u003ea priori\u003c/em\u003e. Thus, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the results of a random-effects probit. The first column presents results for men in the NLSY79, the second for women in the NLSY79, the third for men in the NLSY97, and the fourth for women in the NLSY97.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRandom-effects probit estimation, reporting average marginal impacts.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNLSY79\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNLSY79\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNLSY97\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNLSY97\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0194\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0051)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHours worked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog HH income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0240\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0884\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0119\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0053)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog real wage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0394\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0079\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0105)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0043)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegressors in base specification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-17.1922\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.1404\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.7201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(8.3639)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.6343)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.3517)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.4648)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3989\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2005\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.2725)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.2133)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0799)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0727)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.4671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.0574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.8517\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStandard errors in parentheses, clustered by individual. Reporting average marginal impact.\u003c/p\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003cp\u003eNotably, for both men and women in the NLSY79, the parameter \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:\\)\u003c/span\u003e\u003c/span\u003e is not significant, so there is insufficient evidence to reject the null hypothesis of no individual-specific random effects. The null can be rejected for both men and women in the NLSY97, but the results are, in any case, very similar to the pooled estimates across all four columns. Thus, there is insufficient evidence to prefer the random-effects probit over the pooled probit.\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Different types of performance pay\u003c/h2\u003e\u003cp\u003eTo address concerns about heterogeneity in types of performance pay, the analysis in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e was repeated but the single indicator of performance pay was replaced with indicators for the different types of performance pay. In the NLSY79, these types are profit sharing, piece rates, commissions, bonuses, stock options, tips, and \u0026ldquo;other\u0026rdquo; performance pay. In the NLSY97, these types are commissions, bonuses, stock options, tips, \u0026ldquo;other\u0026rdquo; performance pay, and incentive pay. However, no one in the NLSY97 who received \u0026ldquo;other\u0026rdquo; performance pay was observed divorcing.\u003c/p\u003e\u003cp\u003eWhile very little was significant, the signs of the estimates mostly match the general measure. Men in the NLSY79 who receive commissions are weakly less likely to divorce. Women in the NLSY79 who receive profit sharing or bonuses are weakly more likely to divorce. There is no significant relationship between any of the types and divorce for men in the NLSY97. Women in the NLSY97 who receive commissions are weakly more likely to divorce. Because so little was significant, this analysis was omitted but will be provided upon request.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Simultaneity of performance pay and divorce\u003c/h2\u003e\u003cp\u003eThere may be concern that performance pay and divorce are measured in the same period. The impact of performance pay, or any other characteristics, may lag as stress accumulates over time. Thus, another robustness check utilizes explanatory variables measured in the current period and divorce either in this period or the next. This analysis is presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. While the standard errors of the estimates are larger, the point estimates are consistent with Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There remains a positive relationship for women in the NLSY79. There is no significant relationship for men in either cohort or women in the NLSY97.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between performance pay and divorce in this period or next. Probit estimation, reporting average marginal impacts.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNLSY79\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNLSY79\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNLSY97\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNLSY97\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0209\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0093)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0085)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHours worked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0008\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog HH income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0497\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1349\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0116\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0162\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0133)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0065)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0078)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog real wage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0573\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0213\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0091)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0173)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0063)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegressors in base specification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.3337\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.9956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.3260\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.8161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.8503)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.7793)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.5401)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.3306)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStandard errors in parentheses, clustered by individual. Reporting average marginal impact.\u003c/p\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003cp\u003eAdditionally, to address the cumulative impact of performance pay over multiple years, a Cox proportional hazards model was estimated. In this model, the likelihood of a marriage \u0026ldquo;surviving\u0026rdquo; is a function of the history of explanatory variables and, critically, the history of receiving performance pay. Ideally, this would be done with both cohorts. However, the NLSY79 consists of two groups of three adjacent waves with a substantial time gap between the groups. Rather than incorporate truncation in short three wave panels, the analysis was run on the longer panel in the NLSY97. These results, available upon request, do not substantially differ from earlier estimates. There is no significant relationship for men or women, as in previous tables examining the NLSY97.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e5.3. First marriages\u003c/h2\u003e\u003cp\u003eIt is not uncommon to distinguish between first marriages and subsequent marriages. To ensure the results presented were not due to workers in marriages other than their first, the analysis was repeated but only for those workers who report being in their first marriage ever. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between performance pay and divorce among first marriages only. Probit estimation, reporting average marginal impacts.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNLSY79\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNLSY79\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNLSY97\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNLSY97\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0207\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0050)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHours worked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0004\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0008\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog HH income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0156\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0758\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0084)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0052)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog real wage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0341\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0117)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0041)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegressors in base specification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-17.5346\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.9605\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(6.3632)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(5.5897)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.1406)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.0368)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5,041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0654\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStandard errors in parentheses, clustered by individual. Reporting average marginal impact.\u003c/p\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003cp\u003eThese strongly resemble those in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the average marginal impact of performance pay on divorce was approximately \u0026minus;\u0026thinsp;0.56 percentage points for men in the NLSY79 and 1.93 percentage points for women in the NLSY79. In Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, it is approximately \u0026minus;\u0026thinsp;0.35 percentage points for men and 2.07 percentage points for women. In Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the average marginal impact of performance pay on divorce was approximately \u0026minus;\u0026thinsp;0.46 percentage points for men in the NLSY97 and 0.48 percentage points for women in the NLSY97. In Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, it is approximately \u0026minus;\u0026thinsp;0.48 percentage points for men and 0.59 percentage points for women.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e5.4. Selection into performance pay\u003c/h2\u003e\u003cp\u003eFinally, concern over omitted variable bias has led past work to utilize an instrumental variables analysis to account for the endogeneity of performance pay receipt (Andelic et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Baktash et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Baktash et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cornelissen et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fisman and Svensson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lai and Ng, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lee, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Machin and Wadhwani, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Woessman and West 2006). Like previous work, this analysis uses the share of workers in an occupation who receive performance pay as the instrument. However, this instrument does not pass the weak instrument test, so the results can not be clearly interpreted. Those results will be provided upon request.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn summary, this work generally follows the same methods as Baktash et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) but finds somewhat different results. In the NLSY79, as in the German data, there is no significant relationship between receiving performance pay and the probability of divorce for men after accounting for wages and household income, but women who receive performance pay are more likely to divorce. Moreover, this work confirms that wages are an important mediator of the relationship between performance pay and divorce for women, but they do not completely mediate the relationship.\u003c/p\u003e\u003cp\u003eIn the NLSY97, however, there is no significant relationship between receiving performance pay and the probability of divorce for men or women. Moreover, there is no strong evidence that wages or household income play mediating roles. Future work should examine why this relationship no longer holds. The results presented suggest that this change is not due to changes in beliefs about the role of women in the household, as these are not responsible for the relationship between performance pay and divorce found in women in the older cohort.\u003c/p\u003e\u003cp\u003eUnfortunately, this work is limited in that it only examines one partner in the marriage. The results for the NLSY79 strongly resemble Baktash et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who did use both partners, so this does not seem to be a critical limitation. Nonetheless, future research should examine recent U.S. data where both partners are observed and thus determine if there is a difference between one partner receiving performance pay and both partners receiving performance pay.\u003c/p\u003e\u003cp\u003eThis work is also limited in that it examines workers in the earlier half of their working lives. Thus, this work does not examine the phenomenon of divorce among older workers and retirees. While the cohort tracked in the NLSY79 is at the age where such divorce could be studied, the NLSY79 does not contain data on whether these older workers receive performance pay. Future research could thus examine if the results identified in this work are present in older workers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eB. A. is the sole author of this work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eI am grateful to Dr. John Heywood for his guidance and support. I also want to thank Dr. Scott Drewianka, Dr. Scott Adams, and Dr. Matt McGinty for their valuable suggestions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used comes from the National Longitudinal Surveys of Youth of 1979 and 1997. All data used is publicly available at https://www.nlsinfo.org/investigator/\u003c/p\u003e\u003cp\u003eBenjamin C. Adams declares that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003eBenjamin C. Adams has no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eBenjamin C. Adams declares that no funding was received for this work.\u003c/p\u003e\n\u003cp\u003eBenjamin C. Adams is the sole author of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAguiar, M., Hurst, E., and Karabarbounis, L. (2013). Time Use During the Great Recession. \u003cem\u003eThe American Economic Review, 103\u003c/em\u003e(5), 1665 \u0026ndash; 1696. https://doi.org/10.1257/aer.103.5.1664 \u003c/li\u003e\n\u003cli\u003eAkerlof, G.A. and Kranton, R.E. (2000). Economics and Identity. \u003cem\u003eQuarterly Journal of Economics 115\u003c/em\u003e(3), 715 \u0026ndash; 753. https://doi.org/10.1162/003355300554881 \u003c/li\u003e\n\u003cli\u003eAllan, J.L., Andelic, N., Bender, K.A., Powell, D., Stoffel, S., and Theodossiou, I. (2021). 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Class Size Effects in School Systems around the World: Evidence from Between-Grade Variation in TIMMS. \u003cem\u003eEuropean Economic Review, 50\u003c/em\u003e(3), 695 \u0026ndash; 736. https://doi.org/10.1016/j.euroecorev.2004.11.005 \u003c/li\u003e\n\u003cli\u003eZulkarnain, A. and Korenman, S. (2018). Divorce and health in middle and older ages. \u003cem\u003eReview of Economics of the Household, 17\u003c/em\u003e(2019), 1081 \u0026ndash; 1106. https://doi.org/10.1007/s11150-018-9435-z \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Few individuals are counted as \u0026ldquo;separated\u0026rdquo; in the sample. Simply dropping the separated does not substantially impact any results.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e It should also be noted that, as in Baktash et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), many individuals in the sample are never observed divorcing, so fixed-effects estimation is ruled out a priori.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"review-of-economics-of-the-household","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"reho","sideBox":"Learn more about [Review of Economics of the Household](http://link.springer.com/journal/11150)","snPcode":"11150","submissionUrl":"https://submission.nature.com/new-submission/11150/3","title":"Review of Economics of the Household","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"performance pay, divorce, gender roles, generational differences","lastPublishedDoi":"10.21203/rs.3.rs-7103256/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7103256/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis work examines the influence of receiving performance pay on the probability that a worker will divorce. Uniquely, it compares two different cohorts of the National Longitudinal Survey of Youth. Probit estimates show that, for men, receiving performance pay decreases the probability of divorce in the older cohort and has no impact in the younger cohort. The impact of performance pay for men in the older cohort is mediated by household income and there is no significant impact of performance pay after accounting for household income. For women, receiving performance pay increases the probability of divorce in the older cohort and has no impact in the younger cohort. In contrast to men, though both household income and wages act as mediators in the relationship between performance pay and divorce, women in the older cohort remain more likely to divorce even after accounting for household income and wages.\u003c/p\u003e\n\u003cp\u003eJEL codes: J12, J33, M52\u003c/p\u003e","manuscriptTitle":"Performance Pay, Gender, and Divorce among Full-Time American Workers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-27 09:20:47","doi":"10.21203/rs.3.rs-7103256/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-29T18:41:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T16:18:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T11:33:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2918225172069858404738269839853712091","date":"2025-07-24T13:27:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44074938030544050773071962281171342246","date":"2025-07-24T11:13:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-24T02:56:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-17T09:06:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-17T09:05:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Review of Economics of the Household","date":"2025-07-11T15:53:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"review-of-economics-of-the-household","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"reho","sideBox":"Learn more about [Review of Economics of the Household](http://link.springer.com/journal/11150)","snPcode":"11150","submissionUrl":"https://submission.nature.com/new-submission/11150/3","title":"Review of Economics of the Household","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d73b8b9c-ac6b-4292-9ff5-0528bb713303","owner":[],"postedDate":"July 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T16:10:38+00:00","versionOfRecord":{"articleIdentity":"rs-7103256","link":"https://doi.org/10.1007/s11150-025-09813-1","journal":{"identity":"review-of-economics-of-the-household","isVorOnly":false,"title":"Review of Economics of the Household"},"publishedOn":"2025-10-03 15:58:08","publishedOnDateReadable":"October 3rd, 2025"},"versionCreatedAt":"2025-07-27 09:20:47","video":"","vorDoi":"10.1007/s11150-025-09813-1","vorDoiUrl":"https://doi.org/10.1007/s11150-025-09813-1","workflowStages":[]},"version":"v1","identity":"rs-7103256","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7103256","identity":"rs-7103256","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}