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From Handmaidens to POSH Humanitarians: The Case for Making Human Capabilities the Business of I-O Psychology

Published online by Cambridge University Press:  13 June 2017

Alexander Gloss*
Affiliation:
Department of Psychology, North Carolina State University
Stuart C. Carr
Affiliation:
School of Psychology, Massey University
Walter Reichman
Affiliation:
OrgVitality
Inusah Abdul-Nasiru
Affiliation:
Department of Psychology, University of Ghana
W. Trevor Oestereich
Affiliation:
North Carolina State University
*
Correspondence concerning this article should be addressed to Alexander Gloss, Department of Psychology, North Carolina State University, 640 Poe Hall, Campus Box 7650, Raleigh, NC 27695-7650. E-mail: alexandergloss@gmail.com
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Abstract

Industrial-organizational (I-O) psychology has begun to shed its reputation as a handmaiden to corporate and managerial interests, in part, through its engagement with humanitarian concerns. However, as highlighted by recent commentary, I-O psychology still has a decidedly POSH perspective on the world; that is, it has focused on Professionals who hold Official jobs in a formal economy and who enjoy relative Safety from discrimination while also living in High-income countries. This POSH perspective reflects an underlying bias away from people living in multidimensional poverty. We empirically illustrate some of the connections between a POSH perspective and poverty by reviewing 100 years of research in I-O psychology, and then we make a case for why a neglect of people living in poverty undermines the discipline's science, its practice, and its humanist charge. As moral justification for greater engagement with humanitarian concerns and as a guide to navigate the difficult ethical quandaries involved in doing so, we suggest that I-O psychologists should consider the capability approach. We discuss the concept of human capabilities, relate it to I-O psychology, and demonstrate its utility in the form of three hypothetical scenarios. Perhaps our most controversial claim is that there is a moral imperative for I-O psychology to overrepresent people living in the deepest forms of poverty in both its science and practice.

Information

Type
Focal Article
Copyright
Copyright © Society for Industrial and Organizational Psychology 2017 
Figure 0

Figure 1. Example of the relationship of a professional bias to a bias away from poverty. Population figures are in millions of people. Figures feature countries represented according to (1) the percentage of men or women receiving a secondary education (ages 25–65); (2) whether the country's representation in I-O literature is greater (well-represented) or smaller (underrepresented) than the country's share of the world's population (if a country was not represented at all, it is categorized as unrepresented); and (3) the size of the country's population, which is reflected in the radius of the bubble reflecting that country. Next to each figure, the total country count (N-country), the total population covered (N-population), group country count (n-country), group population covered (n-population), and group country-level mean on the variable in question (M-country) are displayed. For more information about the methodology underlying these calculations, see the Appendix. All statistics are from UNDP (2015b).

Figure 1

Figure 2. Example of the relationship of a bias toward official work in a formal economy with a bias away from poverty. Population figures are in millions of people. Figures feature countries represented according to (1) the percentage of the population in vulnerable employment (including own-account workers and unpaid family workers) or the percentage of 5–14-year-olds employed more than 1 hour per day; (2) whether the country's representation in I-O literature is greater (well-represented) or smaller (underrepresented) than the country's share of the world's population (if a country was not represented at all, it is categorized as unrepresented); and (3) the size of the country's population, which is reflected in the radius of the bubble reflecting that country. Next to each figure, the total country count (N-country), the total population covered (N-population), group country count (n-country), group population covered (n-population), and group country-level mean on the variable in question (M-country) are displayed. For more information about the methodology underlying these calculations, see the Appendix. All statistics are from UNDP (2015b).

Figure 2

Figure 3. Example of the relationship of a bias toward groups that are relatively safe from discrimination with a bias away from poverty. Population figures are in millions of people. Figures feature countries represented according to (1) standardized values of life expectancy inequality within country (higher value = greater inequality) or standardized values of gender inequality within country based on health, workforce participation, and empowerment (higher value = greater inequality); (2) whether the country's representation in I-O literature is greater (well-represented) or smaller (underrepresented) than the country's share of the world's population (if a country was not represented at all, it is categorized as unrepresented); and (3) the size of the country's population, which is reflected in the radius of the bubble reflecting that country. Next to each figure, the total country count (N-country), the total population covered (N-population), group country count (n-country), group population covered (n-population), and group country-level mean on the variable in question (M-country) are displayed. For more information about the methodology underlying these calculations, see the Appendix. All statistics are from UNDP (2015b).

Figure 3

Figure 4. Example of the relationship of a bias toward high-income countries with a bias away from poverty. Population figures are in millions of people. Figures feature countries represented according to (1) the percentage of the workforce qualified as working poor (< $2/day) or average life expectancy (in years); (2) whether the country's representation in I-O literature is greater (well-represented) or smaller (underrepresented) than the country's share of the world's population (if a country was not represented at all, it is categorized as unrepresented); and (3) the size of the country's population, which is reflected in the radius of the bubble reflecting that country. Next to each figure, the total country count (N-country), the total population covered (N-population), group country count (n-country), group population covered (n-population), group country-level mean (M-country), and group population-weighted mean (M-population) are displayed. For more information about the methodology underlying these calculations, see the Appendix. All statistics are from UNDP (2015b).

Figure 4

Figure 5. Example of the consequences of a POSH bias as it relates to economic growth. Population figures are in millions of people. Figures feature countries represented according to (1) growth in gross domestic product (GDP) in 2014 (UNDP, 2015b); (2) whether the country's representation in I-O literature is greater (well-represented) or smaller (underrepresented) than the country's share of the world's population (if a country was not represented at all, it is categorized as unrepresented; and (3) the size of the country's total GDP, which is reflected in the radius of the bubble reflecting that country. Next to each figure, the total country count (N-country), the total population covered (N-population), group country count (n-country), group population covered (n-population), group country-level mean (M-country), and group population-weighted mean (M-population) are displayed. For more information about the methodology underlying these calculations, see the Appendix. All statistics except those noted above are from UNDP (2015b).

Figure 5

Table 1. Fundamental Sets of Capabilities

Figure 6

Table 2. Example of a Capability Set for Potential Use in I-O Psychology Research and Practice

Figure 7

Table A1. Representation Levels of Countries