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Analytical Support to Peacekeeping Operations in Bosnia Barbara Borniolini 2 zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
I” Theater Army Area Coininand
Slide 1: Today’s brief covers the Measures of Effectiveness Program conducted by the HQ SFOR Asscssnlcnl Cell in Sarajevo during the period Nov 96 - Jun 97. Slide 2: I’ll quickly discuss both the purpose of our program and the methodology---how we do what we do. Then I’ll describe the different Measures of Effectiveness (MOE) that we examine and sliow results froin a selccted number of these MOEs. zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
I n
niy concluding slides, zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
1’11 tie together results froin tlie various Measures to draw sonie
general trends. Slide 3: The purpose of our prograin is to provide input to tlie staff on how the situation in Bosnia is changing. What we are perhaps doing a little differently than other sections, is trying to t‘ake qualitative data and turn it into quantitative information. The larger objective is to determine trends in various areas--the MOEs--and froin tliosc trends draw conclusions about what is happening in this country- where there is progress and where progress is
- lacking. This slide shows the things we hope to provide with this type of analysis. The first, and most
straightfonvard, is to identify problem areas--potential flash point locations or areas where progress is slower than
- desired. This inay also alert us to sources of instability witliin B-H. The other objective is sonietliing that our
program contributes to along with other staff sections. Slide 4: Tlie Measures of Effectiveness program follows a program on “Norniality Indicators” conducted by ll~c ARRC Operations Analysis Branch during IFOR. Tlie differcnce in their program was tliat they exaniincd priinarily “basic level” needs, to determine if life for tlie citizens of Bosnia was improving. Our prograin esainincs issues relevant for the longer tenn stability of the country. We’ve esaniined the ARRC data and colllpi~rcd
- ur
measures with theirs. However, because the normality indicators are different, or were measured diflcrcntly. iii most cases the data is not directly comparable. Slide zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
5 :
This slide depicts our methodology. We obtain reports from both SFOR and tlie International
- Organizations. Inforination froin the reports is entcred into a database in tlic form of individual events wliicli ilrC
categorized into one or more MOEs. The data is then reduced to provide a “snapshot” of the situation in B-H for a given MOE and a given time. Reducing the data on a monthly basis then enablcs us to dcterniine trcnds and monitor changes. Tlie data I’ll be showing you today includes five niontlis wortli of reports, and we are
.ius1 now
beginning to see definitive trends. Slide 6: We currently track 21 MOEs. They are groupcd under tlirce upper level areas: Human Rights/ Military/ Security; Political Stability/ Rule of Law; and Economic/ Social. Tlie MOEs that we have the best datil for--:ind therefore can track in the most quantitative fashion--arc those in tlie Huinan Rights/ Military/ Security group. Here, tlie events are easily identifiable and we liavc well dcfincd scoring critcria for the “goodncss” or “badncss” of an event. In contrast, tlic MOEs under Economic/Social, particularly tlic Inrrastnicture relatcd MOEs sucli ;IS Electricity, Health Care, Education, and Employment, are the niost difficult to track and analyze in tcrins of trciids. One reason is that changes in these areas are gcnerally slow. It is the middlc group of MOEs tliat is wortli soine
- attention. The MOEs rclated to Institution Building arc very important for the rcgeneralion and long tcrni stubilily
- f this country. However, progress in thesc areas is very difficult to quantify and slow.
Slide 7: This slide shows that our data conies from a variety of sources in a variety of forniats. Tlierc is sonic
- verlap between the sources wliicli helps us validate our inputs. Tlic otlicr point on this slide is that sonic or our
sources have requested anonyinity, and we respect t hat. Slide 8: To turn qualitative data into quantitative inforination. when wc put an cvenl into the dalabasc. wc scorc it using a special “Bosnian” traffic light system. We attcinpt to define scoring for each MOE as precisely zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
as possiblc,
so that the score an event receives is not dcpendent on the analyst who inputs tlic data. As 1 nieiitioiicd bcforc. this is easier for some MOEs than for others. Here arc the scoring criteria for Frccdoin of Moveinent. Slide 9: Tlie map sliow incidents reported in Mar and Apr. O w recognizes first of all, that niany olllic iiicidcrits are clustered in certain areas. We now look at llic change i n this data over a sevcii niontli pcriod. Wc cannot