Social Impact Assessment vs. Organization Evaluation

Posted on May 31, 2012 by

0


Freudenburg, William R. 1986. “Social Impact Assessment.” Annual Review of Sociology 12 (January 1): 451–478 by Brian J. Delas Armas.

Social Impact Assessment (SIA) measures a broad range of effects likely to be experienced by a broad range of social groups as as a result of a course of action.  It differs from evaluation research in three main aspects:  1)  SIA tends to focus on the consequences of technological developments  2) SIA focuses on the unintended consequences of developments  3)  SIA is a tool used in planning rather than a tool used after the fact (Fraudenberg 1986:  pp.452-453).

According to Freudenberg, Social Impact Assessment (SIA) is a form of assessment that rose from society’s increased concern with environmental degradation during the 1970s and demand for scientific investigations (p.452).  Adequacy in conducting an SIA was initially measured in terms of whether or not it followed procedures established by a group of experts.  This led to a proliferation of how-to manuals (p.456).  The method grew most when it adapted social scientific procedures of surveys, demographic projections, and field research techniques (p.457).  SIA has become synonymous with applied social science and the political process (p.464).

Despite a presidential administration adverse to SIA, the 1980s oversaw what Freudenberg saw as one of the most productive periods for SIA.  SIA had begun to be synthesized and written about in terms of the actual effects of the projects.  Freudenberg attributed this result to two reasons:  1)  researchers found time to do detailed analyses.  2)  researchers met at conferences in which practitioners were able to compare and contrast their practices.

The findings of SIA have been assessed to be neither as bad as critics say, nor as good as proponents say.  Impact findings are not as bad.  Fraudenberg finds that rural communities tend to favor industrial development (p.461). The impact findings may not have been as good as people say.  For example, the local benefits of employment have typically been overestimated.   Local employment tends to fail for three reasons:  1)  local workers tend to be concentrated in the less-skilled categories  2)  new jobs often do not go to local unemployed, underemployed minorities.  3)  they do not increase the propensity of local youth to stay in their communities (p.462).

SIA in the US has typically sided with development and has typically been viewed as “conservative (p.466).” Freudenberg notes two factors towards this orientation:  1)  the selective availability of data and 2)  analysts’ tendency to focus on certain questions while ignoring others.  SIA practitioners often conclude that the decent and humane thing to do is to help communities cope with developments destined to go ahead as opposed to helping developers learning ways to adapt local practices (p.466)  Arguments such as the “soaring costs of living” as a result of a development or, that the environment is “stressful” for the elderly have proven to be difficult to find in practice (p.463).

Freudenberg mentions issues that come up in the practice of SIA.  First, he mentions that SIAs need to explore its potential to contribute to non-project projects.  For example, this might include the presence of toxic wastes in communities.  In that case, SIA would explore impacts ranging from the long-term but localized problems (p.471).  Secondly, SIA provides practitioners the opportunity to contribute information on the decision-making process itself.  By virtue of membership in “interdisciplinary teams”, SIA practitioners may be able to see the faults of expert judgement (p.472).  Thirdly, SIA practitioners should devote attention to the fallibility of their own judgments.  Rather than projecting specific impacts, SIAs should plan for uncertainty and attempt to maintain flexibility to respond to a large number of outcomes (p. 474).

Fraudenberg concludes with suggestions for SIA that would move the method forward:  1)  Focusing on sociological variables.  Such a focus would contrast to allowing analysis to be guided by data availability, political pressures, or lists of potential impacts.   2)  Focusing on what dependent variables should be studied. For example, “Quality of life” would be an example of an indicator that needs to be further defined.  3)  How a project impacts a particular population.  Instead of a broadly defined community, the SIA could focus on understanding how it impacts a sub-population like women or the Latino population.  4)  emphasizing a theory of the middle range, or reaching a level of abstraction that allows conclusions from one setting to be applied to another (pp.469-471).  However, Fraudenberg warns against reaching a level of abstraction too high where no direction can be determined.