From Truth to Justice: How Does Amnesty Factor In? A Comparative Analysis of South Africa and Sierra Leone's Truth and Reconciliation Commissions

Truth and Reconciliation Commissions (TRC) have emerged in the last few decades as a mechanism for a state to overcome widespread, grave, human rights violations. There are numerous approaches to a TRC all with an ultimate goal: that formerly warring factions, perpetrators, witnesses, and victims can move forward as a united people. I propose that the provision of amnesty is critical to the success of a TRC. I hypothesize that the form of amnesty chosen (i.e. blanket v. conditional amnesty) determines the revelation of truth and realization of justice, which in turn dictates whether a TRC can achieve reconciliation. To test this hypothesis, I use two case studies: South Africa, which has utilized conditional amnesty, and Sierra Leone which has employed blanket amnesty. I create a model for measuring reconciliation. I can then look at the implications of both types of amnesty and assess which, in the end, is more effective. My overarching conclusion is that the provision of conditional amnesty is more effective than blanket amnesty in achieving reconciliation. Ultimately, I hope that this conclusion can be generalized to other TRCs.

A Bootstrap-Regression Procedure to Capture Unit Specific Effects in Data Envelopment Analysis

The Data Envelopment Analysis (DEA) efficiency score obtained for an individual firm is a point estimate without any confidence interval around it. In recent years, researchers have resorted to bootstrapping in order to generate empirical distributions of efficiency scores. This procedure assumes that all firms have the same probability of getting an efficiency score from any specified interval within the [0,1] range. We propose a bootstrap procedure that empirically generates the conditional distribution of efficiency for each individual firm given systematic factors that influence its efficiency. Instead of resampling directly from the pooled DEA scores, we first regress these scores on a set of explanatory variables not included at the DEA stage and bootstrap the residuals from this regression. These pseudo-efficiency scores incorporate the systematic effects of unit-specific factors along with the contribution of the randomly drawn residual. Data from the U.S. airline industry are utilized in an empirical application.

Input Aggregation in Models of Data Envelopment Analysis: A Statistical Test with an Application to Indian Manufacturing

A problem frequently encountered in Data Envelopment Analysis (DEA) is that the total number of inputs and outputs included tend to be too many relative to the sample size. One way to counter this problem is to combine several inputs (or outputs) into (meaningful) aggregate variables reducing thereby the dimension of the input (or output) vector. A direct effect of input aggregation is to reduce the number of constraints. This, in its turn, alters the optimal value of the objective function. In this paper, we show how a statistical test proposed by Banker (1993) may be applied to test the validity of a specific way of aggregating several inputs. An empirical application using data from Indian manufacturing for the year 2002-03 is included as an example of the proposed test.