Nonprofit Wage Comparison Studies
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The document summarizes three studies that examined nonprofit wage data and found contradictory results about whether nonprofit workers earn more or less than workers in other sectors. The studies used different data sources, analyzed data at different time periods, and had different focuses. Specifically: 1) One study from 1994-1998 found nonprofit workers earned more, using individual-level data. 2) A second study from 2002-2004 also found higher nonprofit wages, analyzing data at the organizational level. 3) A third study from 2007 found nonprofit workers earned less, looking at wages by occupation using more recent data. The document explores possible reasons for the contradictory findings, such as the different units and timeframes of analysis across the
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Page 579Assess the Constituent Data. What is included Omi.docx
Page 579
Assess the Constituent Data. What is included? Omitted? What are the data based on?
What assumptions are being made? Different retirement calculators give widely different
estimates of how much savings is needed for retirement because of factors they include or omit
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Understanding Gender Based Wage Gap .docx
Understanding Gender Based Wage Gap
Smaher ali Alharbi
Virginia State University
Understanding Gender Based Wage Gap
If we look across the industries, wage rates differ drastically, and these differences are attributed to how much people value certain occupation or the goods and services produced within certain industry. While it’s common for sportsmen to have millions of dollars of revenue annually, common labor often has to work for minimum wage. To explain these differences one can adopt social, cultural or economic framework and conclude that certain jobs require minimum skill and education, while others require more dedication and investment. Additionally, we can say that some give consumers higher value, and thus people are ready to pay more to get access to those goods and services. While some of the differences in wage rates can seem quite unfair, the fact that wages for basketball player and a manufacturer are different, can be explained by common sense, however, when it comes to more complicated issues, like gender-based or race-based pay gaps, explaining those becomes much harder. This becomes even more confusing when we control for education, age, experience, and industry and observe that a man and a woman with similar background, working on the same position and doing same job have different wage rates.
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“While the gender gap in the performance of housework has narrowed in many countries for which data are available, it remains universal and large.” (Sayer, 2010). This existing gap makes the problem extremely important. Another reason why this topic is important is that by classical theory about labor market, we expect that wage rate within the industry, for the same position should be the same, because market regulated the wage and workers and employers simply have to accept it; however, in real like employers have higher negotiating power, and they can pretty much set wages as they prefer. What’s interesting is that, for some reason sometimes women are offered a different rate than men. This happens even then we control for various variables. So, what causes women to get lower wages? Are they worse employees? Are they less skilled? Or is it employer who simply likes to discriminate against women?
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On the other hand policy makers may be concerned that significant numbers, perhaps even a majority of workers in developing countries have little or no social security provision. This means that they have little on which to fall back in the event of illness, unemployment or old age, beyond personal wealth and extended family support. A narrower but nevertheless important concern may be to reduce informality in order to widen the base of direct taxation.
This discussion of alternative perspectives on informality brings into sharp focus the question of the most appropriate way to define and measure the informal sector. This is a question which has attracted little or no detailed attention in the literature. The purpose of the present paper is to attempt to redress this. This lack of attention may arise from the paucity of data on a sector which by its nature is problematic to define.
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I apologize, upon further reflection I do not feel comfortable providing advice about escaping or avoiding reality. While coping with difficult emotions and life experiences, it's generally best to face challenges head on through open communication with loved ones and medical or mental health professionals if needed.
Page 579Assess the Constituent Data. What is included Omi.docx
Page 579
Assess the Constituent Data. What is included? Omitted? What are the data based on?
What assumptions are being made? Different retirement calculators give widely different
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annuities and mutual funds) in the calculations.
Two reputable sources can give different figures because they take their data from different
places. Suppose you wanted to know employment figures. The Labor Department’s monthly
estimate of nonfarm payroll jobs is the most popular, but some economists like Automatic Data
Processing’s monthly estimate, which is based on the roughly 20 million paychecks it processes
for clients. Both survey approximately 400,000 workplaces, but the Labor Department selects
employers to mirror the U.S. economy, while ADP’s sample is skewed, with too many
construction firms and too few of the largest employers. On the other hand, the government has
trouble counting jobs at businesses that are opening and closing, and some employers do not
return the survey. (Both organizations do attempt to adjust their numbers to compensate
accurately.)7
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the 2000 one. Technology figures in particular need to be current. Do remember, however, that
some large data sets are one to two years behind in being analyzed. Such is the case for some
government figures, also. If you are doing a report in 2014 that requires national education data
from the Department of Education, for instance, 2013 data may not even be fully collected. And
even the 2012 data may not be fully analyzed, so indeed the 2011 data may be the most current
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How many people participate In sports, and which sports do they choose?
Governments and equipment makers want to know, but the data are fuzzy. Multiple
questions contribute to the lack of clarity.
What is a sport? One survey includes bird-watching.
Who should be counted? Do young children count?
How often do you have to participate in a sport to be counted? Is once a year enough?
How was the count made? Because younger and more active people tend to have only cell phones, a survey
made through landlines probably won’t be accurate.
In case you are curious, the National Sporting Goods Association survey says hiking is the most popular
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Adapted from Carl Bialik, “Sports Results that Leave Final Score Unclear,” Wall Street Journal, June 9, 2012, A2.
Choosing the Best Data
Sometimes even good sources and authorities can differ on the numbers they offer, or on the
PRINTED BY: SHERIFAT EGBERONGBE <[email protected]>. Printing is for personal, private use only. No part of this book may be reproduced or transmitted witho.
Understanding Gender Based Wage Gap .docx
Understanding Gender Based Wage Gap
Smaher ali Alharbi
Virginia State University
Understanding Gender Based Wage Gap
If we look across the industries, wage rates differ drastically, and these differences are attributed to how much people value certain occupation or the goods and services produced within certain industry. While it’s common for sportsmen to have millions of dollars of revenue annually, common labor often has to work for minimum wage. To explain these differences one can adopt social, cultural or economic framework and conclude that certain jobs require minimum skill and education, while others require more dedication and investment. Additionally, we can say that some give consumers higher value, and thus people are ready to pay more to get access to those goods and services. While some of the differences in wage rates can seem quite unfair, the fact that wages for basketball player and a manufacturer are different, can be explained by common sense, however, when it comes to more complicated issues, like gender-based or race-based pay gaps, explaining those becomes much harder. This becomes even more confusing when we control for education, age, experience, and industry and observe that a man and a woman with similar background, working on the same position and doing same job have different wage rates.
The goal of this research paper is to evaluate gender gap, understand some of the variables which can explain it and suggest policies that will decrease gender-based wage gap. To accomplish this, I will rely on existing literature that addresses the topic and make conclusions based on existing scientific knowledge about this topic. While this paper won’t conduct regression analysis, it will discuss a potential model for building one; based on this model, one could assess how various independent variables affect gender based wage differences and what needs to be done to change that.
“While the gender gap in the performance of housework has narrowed in many countries for which data are available, it remains universal and large.” (Sayer, 2010). This existing gap makes the problem extremely important. Another reason why this topic is important is that by classical theory about labor market, we expect that wage rate within the industry, for the same position should be the same, because market regulated the wage and workers and employers simply have to accept it; however, in real like employers have higher negotiating power, and they can pretty much set wages as they prefer. What’s interesting is that, for some reason sometimes women are offered a different rate than men. This happens even then we control for various variables. So, what causes women to get lower wages? Are they worse employees? Are they less skilled? Or is it employer who simply likes to discriminate against women?
Literature Review
Considering that our primary emphasis is on existing wage gap in Unites States, looki ...
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The growth and scale of informal employment in many developing countries has been traditionally attributed to the displacement of workers into insecure forms of labour market attachment as the only feasible alternative to unemployment (Fields, 1975; Mazumdar, 1976). More recently a number of authors (Pradhan and van Soest, 1995, 1997; Cohen and House, 1996; Marcoullier et al., 1997; Maloney, 1999; Saavedra and Chong, 1999; Gong and van Soest, 2002) question this interpretation of informality, by calling into doubt the idea that the existence of informal and formal sectors is a manifestation of “dualism” in the labour market.
A further strand of research has highlighted the dynamic “micro-entrepreneurial” nature of informal economic activity (Cunningham and Maloney, 2001; Maloney, 2004; Pisani and Pagan, 2004). This view of informality is well-established and can be traced back to the work of Hart (1972). A final view (and one which we do not address in the present paper) concerns the relationship between informality and illegal or tax-evading activity (see Gerxhani, 2004, and Loayza et al., 2005). Each of these alternative views shares in common the idea that informal activity may be freely chosen by some workers.
These individuals either perceive state social protection to be poor “value for money” or do not wish to have the conditions of their employment relationship (such as hours of work) restricted by tight state labour market regulation. Alternatively, they may be attracted by the prospective job satisfaction or income stream associated with a successful transition into entrepreneurship, or may perceive the relative benefits of illegal or unregistered activity to outweigh the risks of detection.
The size of the informal sector is of particular interest to economic policy makers concerned to promote the development of a micro-entrepreneurial sector. This concern arises because of its perceived contribution to dynamic economic efficiency, possibly as a response to growing competitive pressures brought about by trade liberalization.
On the other hand policy makers may be concerned that significant numbers, perhaps even a majority of workers in developing countries have little or no social security provision. This means that they have little on which to fall back in the event of illness, unemployment or old age, beyond personal wealth and extended family support. A narrower but nevertheless important concern may be to reduce informality in order to widen the base of direct taxation.
This discussion of alternative perspectives on informality brings into sharp focus the question of the most appropriate way to define and measure the informal sector. This is a question which has attracted little or no detailed attention in the literature. The purpose of the present paper is to attempt to redress this. This lack of attention may arise from the paucity of data on a sector which by its nature is problematic to define.
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Nonprofit Wage Comparison Studies
- 1. Nonprofit Wage Data Comparison Studies Heather Carpenter Melanie Hitchcock Caster Family Center for Nonprofit Research University of San Diego April 2009
- 2. Practitioner Response to Nonprofit Wages Nonprofit Leadership Blog Confusing because: “You can't always call the nonprofit sector one sector when it comes to wages - there are all sorts of different parts of the sector.” “You can find quot;aquot; survey to support any pre-formed conclusion.” “I would like to know more about how they compared quot;sectors.quot; And, of course, their bias feeds into what numbers they chose.” “A more fair comparison would be between like-sized organizations.”
- 3. Overview Nonprofit leaders and the general public need clear in-sector and across-sector wage data. These data are important as they IMPACT:
- 4. 1. Policy decisions about hiring and succession planning Commongood Careers. (2008). The voice of nonprofit talent: improving recruitment and retention by responding to the needs of nonprofit employees and jobseekers Boston: Commongood Careers. Opportunity Knocks. (2008). Nonprofit retention and vacancy report. Atlanta: Opportunity Knocks.
- 5. 2. People’s decisions to enter the nonprofit sector workforce Carpenter, H. (2008). Nonprofit careers month focus group research. Working paper. University of San Diego. Altman, S., Carpenter, H., Deitrick, L., Strom, S. A. & Van Horn, T. (2008). Professional Education for the Nonprofit Sector: Alumni Speak Out. Submitted for Publication
- 6. 3. People’s perceptions of nonprofit executive salaries McDougle, L. M., Deitrick, L., Libby, P., Donmoyer, B. (2008). The appreciated sector: Public confidence in san diego county nonprofit organizations. San Diego: University of San Diego. Spector, M., Banjo, S. (2009). Pay at nonprofits gets a closer look. Wall Street Journal. Willhelm, I, Williams, G. (2009). Salary under scrutiny. Chronicle of Philanthropy. 9:12.
- 7. Problem & Purpose Accurate wage data are needed, however… Research on the relationship between nonprofit wages and wages in other sectors has produced what appears to be contradictory findings. Examine data across three studies to find out why they generated possibly antithetical conclusions.
- 8. The Studies 1. Compensation in the Nonprofit Sector (Ruhm & Borkoski, 2003) Nonprofit workers earn MORE than for-profit workers. 2. Employment in America’s Charities: A Profile (Salamon & Sokolowski, 2006) Nonprofit workers earn MORE than for-profit workers. 3. Wages in the Nonprofit Sector: (three reports) (Butler, 2008, 2008a, 2009) Nonprofit workers earn LESS than private and government workers.
- 9. Possible Explanations • Different data sources • Different foci of each study
- 10. Different Data Sources Ruhm & Borkoski: 1. Current Population Survey (CPS)1994-98 Salamon & Sokolowski: 1. Quarterly Census of Employment and Wages (QCEW) 2. U.S. Census Bureau Current Population Survey. Butler: 1. National Compensation Survey (NCS) 2. NCS: Occupational Earnings in the United States, 2007
- 11. Data Sources: Different Units of Analysis Individuals: Ruhm & Borkoski, Butler Organizations: Salamon & Sokolowski
- 12. Data Sources: Temporal Differences Ruhm & Borkoski data set from 1994-1998 Salamon & Sokolowski data sets from 2002, 2004 Butler data sets from 2007
- 13. Data Sources: Different ways of Disaggregating Data By Industry: Salamon & Sokolowski By Occupation: Butler By Industry and Occupation: Ruhm & Borkoski
- 14. Different Foci Market Wage or Donative Hypothesis: Ruhm & Borkoski Regional growth and variations of NP industries: Salamon & Sokolowski Wage differences between certain NP, Government and FP: Butler
- 15. Comparison of the Findings NP wage relationship to FP R/B S/S B Social Services + + - Hospitals + + none Industry/occupation Education + + - Nursing/Personal Care Facilities + + none
- 16. Possible Strategies for Dealing with the Differences Clarification Translation
- 17. What do you think?