Referral and Job Performance: Evidence from the Ghana Colonial Army

Since Granovetter.s (1974) seminal work, it is widely recognized that job referral plays an important role in the way the labor market works. There are many di¤erent types of referrals .e.g., by relatives, teachers, or previous employers. One kind of referral that has attracted the attention of economists is referral by current employees. This form of referral is thought to play two possible roles: the transmission of information that is relevant to the hiring process; and the exchange of favors between employer, referee, and new recruit. In the latter case, referral is a source of inefficiency and inequity since it distorts the recruitment process to favour friends and relatives (Barr and Oduro 2002). In the former case, referral instead plays an efficiency enhancing role: it raises the quality of the match, either by providing employers with better information about workers (Saloner 1985), or by providing workers with better information about job characteristics (Simon and Warner 1992, Mortensen and Vishwanath 1994) .Montgomery (1991) provides an elegant formalization of employee referral. In his model, referral by employees is valuable because the unobserved quality of a new worker is positively correlated with the revealed quality of the current employee providing the reference. If the current employee has proved to be of high quality, anyone referred by this employee is also more likely to be of high quality. Underlying this assumption is the idea that social ties are characterized by homophyly, and hence that characteristics of socially proximate individuals are correlated (Jackson 2008). As Montgomery shows, this assumption is sufficient to induce employers to rely on referral from high quality employees. It does, however, supposes that referees truthfully report the information at their disposal. Whether this is the case in practice is unclear.

Work Stress

Work stress is defined as the harmful physical and emotional responses that occur when job requirements do not match the worker’s capabilities, resources, and needs (National Institute of Occupational Safety and Health 1999). It is recognized world-wide as a major challenge to individual mental and physical health, and organizational health (ILO 1986). Stressed workers are also more likely to be unhealthy, poorly motivated, less productive and less safe at work. And their organizations are less likely to succeed in a competitive market. By some estimates work-related stress costs the national economy a staggering amount in sick pay, lost productivity, health care and litigation costs (Palmer et al. 2004). Work stress can come from a variety of sources and affect people in different ways. Although the link between psycho-social aspects of the job and the health and well-being of workers has been well documented (Dollard and Metzer 1999), limited work has been done on the effects of distinct stressors on job performance. As well, various protective factors can prevent or reduce the effects of work stress, and little research has been done toward understanding these mitigating individual and organizational factors. One important source of work stress is job strain. According to the demand/control model (Karasek 1979), job strain is determined by the interactions between psychological demands and decision latitude (see Work stress). The first dimension, the psychological demands on the worker, relate to pace and intensity,skills required, and the ability to keep up with colleagues. The second dimension relates to the degree of creativity versus repetition, as well as the extent of freedom and responsibility to decide what to do and when to do it (Lindström 2005). Four work environments can then be derived: high-strain jobs, active jobs, low-strain (relaxed) jobs, and passive jobs (see Psychological demand/decision latitude model). Though simple identification of low- and high-strain jobs may be important, the distinction between job control and psychological demands must be retained because each category can have different effects on workers and their organizations. For instance, when job control is high and psychological demands are also high, learning and growth are the predicted behavioural outcomes. Much of the energy aroused by job challenges can be translated into direct action—effective problem solving—with little residual strain. The growth and learning stimuli are conducive to high productivity. On the other hand, low demand and low control lead to a very unmotivating job setting, which results in gradual loss of previously acquired skills (Karasek 1998).

INTERPRETATION OF MODELS OF FUNDAMENTAL ECOLOGICAL NICHES AND SPECIES’ DISTRIBUTIONAL AREAS

The fact that, at certain scales, climatic and physical factors affect profoundly the distributions of species has been known for a very long time. In the last two decades, mathematical techniques designed to estimate the geographic extent of the “fundamental ecological niche” (FN), or subsets of it, defined mostly in coarse-scale climatic dimensions [the “bioclimatic envelope” or climatic niche (Pearson & Dawson 2003)], have seen increasing use. Although of interest in itself regarding the evolutionary ecology of species, estimating the FN is often taken as an intermediate step towards estimating the geographic distribution of the species. The FN has been estimated in two ways: (1) direct measurement or physical modeling of responses of individuals to temperature, humidity, and other physical parameters, and inferring from them fitness values of different combinations of physical variables. Then, using GIS technology, geographic regions of positive fitness can be displayed (Porter et al. 2000; Porter et al. 2002). This line of work has been referred to as the ‘mechanistic approach’ to niche modeling (Guisan & Zimmermann 2000). (2) Niches may be reconstructed by relating data on species’ occurrences with data sets summarizing climatic, topographic, edaphic, and other ‘ecological’ dimensions (in the form of GIS layers); combinations of environmental variables most closely associated with observed presences of species can then be identified and projected onto landscapes to identify appropriate regions, as above. The inferential steps in this manifestation of niche modeling have been achieved using diverse algorithms, including range rules (Nix 1986), DOMAIN (Carpenter  et al. 1993), FloraMap (Jones & Gladkov 1999), multiple regression and other generalized linear and additive models (Guisan & Zimmermann 2000), neural networks (Pearson  et al. 2002), and genetic algorithms (Stockwell 1999; Stockwell & Noble 1992; Stockwell & Peters 1999), and several others. All, in essence, extrapolate from associations between point occurrences and environmental data sets to identify areas of predicted presence on the map. These areas are (in some sense determined by the algorithm) similar ecologically to those where the species is known to occur, and this procedure can be termed as the ‘correlative approach’ to ecological niche modeling. Whether the result is interpreted as the species’ distribution, the spatial extent of its fundamental niche, or some other phraseology, it is important to remember that (strictly speaking) these extrapolation algorithms only find regions that “resemble,” in terms of the layers provided, those where occurrence points are located. The rest of the process is interpretation. Often (but not always), tools used to perform these niche estimations require information about absence of species. At certain spatio-temporal scales, and for certain taxa, this information may be available, perhaps in the form of well-surveyed sites at which the species was not detected. Alternatively, and at cost of restrictive assumptions, “absence data” may be generated in the form of pseudoabsences—areas without definite information regarding the species’ occurrence, but assumed to be lacking the species for the purpose of providing statistical contrasts upon which analyses may be based. The mechanistic approach, being based on direct measures of physiological variables, ignores biotic interactions, and indeed has little hope of taking them into account. The correlative approach, in contrast, is based on observations that already  include effects of biotic interactions on distributions of species—here, of course, the challenge is removing the effects of those same interactions. The two approaches thus estimate quite-different phenomena, and should be interpreted carefully before being used interchangeably in applications. In what follows, we clarify the logic behind the correlative approach, and review its key limitations—our purpose is to provide a more formal discussion of what is being modeled in ‘ecological niche modeling.’ Pearson and Dawson (2003) discussed characteristics and limitations of these two methods as regards estimating niches and predicting distributions under scenarios of climate change.

REFERENCES
Argaez, J., A. Christen, M. Nakamura, and J. Soberón. In press. Prediction of high potential areas of habitat for monitored species. Ecological Statistics.

Brown, J. H. 1995. Macroecology. University of Chicago Press, Chicago.

Carpenter, G., A. N. Gillison, and J. Winter. 1993. DOMAIN: A flexible modeling procedure for mapping potential distributions of animals and plants. Biodiversity and Conservation 2:667-680.

Chase, J. M. and M. A. Liebold. 2003. Ecological Niches. Linking Classical and Contemporary Approaches. University of Chicago Press, Chicago.

Etterson, J. R., and R. G. Shaw. 2001. Constraint to adaptive evolution in response to global warming. Science 294:151-153.


Fielding, A. H., and J. F. Bell. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24:38-49.

Gaston, K. 2003. The Structure and Dynamics of Geographic Ranges. Oxford University Press, Oxford.