are: the atmosphere at that particular job, some sort of utility gained from working, some benefits package associated with that job, etc. Economists studying this phenomenon have been focusing their research on proving that the reasons hypothesized correlate with the lower wages actually observed in the for profit and non-profit industries. The dominant literature states that individuals take lower wages because of particular phenomenon called the donative labor hypothesis. From the very basic overview above, there are three objectives in this paper. First is to show the existence of wage differentials. The second objective is to investigate the reasons why individuals would take lower wages at a non-profit and try to show that these reasons exist empirically. Thirdly, to prove or disprove the postulate that the donative labor hypothesis is not as important as other factors when explaining wage differentials. To facilitate achieving these goals, this paper will begin by laying out the economic theory behind wages. Then it will move to looking at the current research on the topic of wage differentials, which will be followed by an econometric model. Lastly, to conclude there will be a discussion of the results. Labor Theory Firstly, there needs to be a discussion about economic labor theory, as it provides most of the backbone to this paper. Significantly, standard labor theory assumes that all jobs are the same. Each individual worker is indifferent to working between one firm and any another, assuming wages are the same. Although this is nice for simple modeling, we want to go a bit deeper. Thus we must amend our model. What will happen when a
Sample of 1990 census showed wage differentials between the for-profit and non-profit industries. The areas which had the most concentrated differentials were in the managerial and professional industries. does do an excellent job of breaking down the workers by race, occupation, and industry. Laura Leete (2001) Five Percent Public Use Microdata Sample of 1990 census Negative differentials in legal services, broadcasting, publishing, entertainment, elementary and secondary schools, job training firms, vocational training firms, and research facilities could be explained by the donative labor hypothesis. While the paper rejects an economy wide donatives labor explanation for all Breaks it down by industry, sector, and whole economy. Yet, does not have any sort of proxy to measure some of the intrinsic utility gained from employment (i.e. benefits, working environment, etc.) J. Ruhm & Carey Borkoski (2003) 1994-1998 Current Population Survey Outgoing Rotation Groups. Specifically 25-55 year olds. Disproves the dontative labor hypothesis, postulating that the lower non-profit wages are due to most of these firms situated in low paying industries. Further the transition from non-profit to for profit and vise-versa there are very little changes to earnings. This paper does an excellent job at showing not only industry break-downs over time but also tracking worker movement between industries. What is lacking is any sort of inclusion of intrinsic and hard to observe factors of the job. Lester M. Salamon & S. Wojeiech Sokolowski (2005) The Quarterly Census of Employment and Wages Program 1995-2003 data Non-profit wages were generally lower. Although, in industries where non-profits compete with for industries, the wages in are larger. The authors do not make any mention of comparing capital attributes of the workers, nor the jobs in this study. Haruko Noguchi & Satoshi Shimizutani (2005) Statistical Survey on Nursing Home Employee 2000 data. Non-profit wage premium in Japan’s long-term care market. Certain factors such as worker’s age contribute to said premium. Tried to filter out unobservable qualities of each job via Heckman's two-stage approach. Included things like benefits and whether the individuals received healthcare. Matthias Benz (2005) American National Longitudinal Found that employees are more satisfied with their work, and that working for a This data incorporates all of the possible standard human capital explanations for wage
Study of Youth years 1994, 1996, 1998, and 2000. Also British Household Panel Survey from non-profit was the strongest statistically significant variable. Leading the authors to postulate that non-profit firms attract altruistic people differentials. What is lacking is anyway to tell which industries were involved in these surveys or if the effects varied between industries. Thomas Cornille, Ronald Mullis, Ann Mullis, and Michael Shriner (2005) Surveyed 558 different faith based, non-profit, and for profit childcare centers in one south eastern state. Primarily used survey questions to investigate intrinsic attributes of childcare centers. These survey questions covered benefits, education, and work environment. They found that there was no difference in satisfaction between for profit and workers. Further, Cornille et al. found that they both received similar working conditions This data only looks at education and none of the other standard factors when used to describe wage differentials. As illustrated in Table 1, the seven most recent articles on wage differentials have made no attempt to use any data that is before the economic crisis. In addition, it becomes evident that most of the variables used in these studies do not include any sort of measurement of non-standard human capital variables. Thus this paper attempts to lend to the debate about wage differentials by using an updated data source and include some variables to try and measure the other intrinsic utility producing aspects of employment. Data profit
Turning to Table 8 we see that all of the variables are statistically significant at the .001 level. Though what is troubling is that the insurance variables, all of the industry variables, and race are not negative as was initially predicted. Table 7: Non-Profit Wages Predictiveness R2 Adjusted R2 Durbin-Watson 0.404 0.404 1.972 Table 8: Non-Profit Wages Variable Mean Standard Deviation Coefficient Significant at level VIF NP Wages 14108.8145 33754.11855 Age 44.64 14.829 68.063 0.001 1.027 Insurance .72 .448 4125.61 0.001 1.093 Race .24 .429 743.558 0.001 1.034 Ed 18.89 2.977 1319.29 0.001 1.107 Sex .77 .419 -7910.485 0.001 1.051 NP Art .088 .09342 24000.084 0.001 1.009 NP Childcare .0286 .16673 15755.395 0.001 1.02 NP Community Service .0069 .08269 22106.665 0.001 1.006 NP Hospitals .1665 .37256 51854.385 0.001 1.114 NP Individual Service .0433 .20362 25911.583 0.001 1.028 NP Nursing .0255 .15762 28884.931 0.001 1.016 NP Publishing .0016 .03985 39836.949 0.001 1.002 NP Religious .0931 .20062 19516.108 0.001 1.119 We can see from table 8 that the Durbin-Watson statistic is 1.972, which signals no autocorrelation. Similarly, the check for multicollinearity shows from the VIF column on table 7 that none of the variables are exhibiting multicollinearity.
Education -18.917 0.001 1.080 Education 18.668 0.001 1.081 NP Status 53461.41 0.001 1.049 Table 15c: Nursing Industry Coefficients Variables of Model FP Nursing Wage Coefficient Significance level VIF Variables of Model NP Nursing Wage Coefficient Significance level VIF Age 10.345 0.001 1.005 Age 4.518 0.001 1.006 Race 724.275 0.001 1.023 Race 12.057 0.654 1.023 Sex 1048.791 0.001 1.009 Sex -105.383 0.001 1.010 Insurance 749.628 0.001 1.069 Insurance 426.723 0.001 1.069 Education -141.766 0.001 1.080 Education 61.139 0.001 1.082 NP Status 53461.41 0.001 1.049 Table 15d: Individual Service Industry Coefficients Variables of Model FP Individual Service Coefficient Significance level VIF Variables of Model NP Individual Service Coefficient Significance level VIF Age 12.817 0.001 1.005 Age 7.329 0.001 1.005 Race 830.698 0.001 1.023 Race 54.789 0.108 1.023 Sex 34.558 0.527 1.009 Sex -187.596 0.001 1.009 Insurance 483.545 0.001 1.069 Insurance 712.93 0.001 1.069 Education 101.781 0.001 1.080 Education 99.879 0.001 1.082 NP Status 53461.41 0.001 1.049 Table 15e: Community Service Industry Coefficients Variables of Model FP Community Service Coefficient level VIF Variables of Model NP Service Coefficient level VIF Age 0.293 0.337 Age -0.574 0.115 1.006