RESOURCE ALLOCATION TO POLITICAL AND ECONOMIC INTELLIGENCE COLLECTION AND PROCESSING (U)

• Document Type: CREST
• Collection: General CIA Records
• Document Number (FOIA) /ESDN (CREST): CIA-RDP84M00713R000200040001-8
• Release Decision: RIPPUB
• Original Classification: S
• Document Page Count: 19
• Document Creation Date: December 22, 2016
• Document Release Date: March 21, 2012
• Sequence Number: 1
• Publication Date: January 1, 2000
• Content Type: REPORT

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RESOURCE ALLOCATION TO POLITICAL AND
ECONOMIC INTELLIGENCE COLLECTION AND PROCESSING
By
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_.
This report presents the analysis and observations resulting
from an investigation of the resources currently allocated by the
Intelligence Community to political and economic intelligence collec-
tion and processing (and, to a lesser extent, production). The work
was performed under contract number
in support of the Program Assessment Office
(PAO) of the Resource Management Staff.
SECRET
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TABLE OF CONTENTS (U)
Section Page
1 STUDY OBJECTIVES . . . . . . . . . . . . . . . . . 1-1
2 STUDY METHODOLOGY . . . . . . . . . . . . . . . . . 2-1
2.1 DESCRIPTION OF THE TASKS . . . . . . . . . . . 2-1
3 POLITICAL AND ECONOMIC INTELLIGENCE:
A PROFILE . . . . . . . . . . . .. . . . . . . . . . 3-1
4 NATIONAL REQUIREMENTS FOR POLITICAL AND
ECONOMIC INTELLIGENCE . . . . . . . . . . . . . . 4-1
4.1 FORMAL REQUIREMENTS REGISTERS . . . . . . . . 4-1
4.2 COLLECTION TASKING . . . . . . . . . . . . . . 4-9
4.3 STATISTICAL ANALYSIS . . . . . . . . . . . . . 4-10
5 RESOURCE DISTRIBUTIONS . . . . . . . . . . . . . . 5-1
5.1 RESOURCE DATA COLLECTION ALTERNATIVES . . . . 5-1
5.2 SUMMARY RESOURCE DISTRIBUTIONS . . . . . . . . 5-5
5.3 COLLECTION SOURCE RESOURCE DISTRIBUTIONS . . . 5-8
5.4 GEOGRAPHIC RESOURCE DISTRIBUTIONS . . . . . .. 5-11
6 DATA BASE STUDIES . . . . . . . . . . . . . . . . . 6-1
6.1 DATA COLLECTION ISSUES . . . . . . . . . . . . 6-1
6.2 P/E RAW DATA BASE INTELLIGENCE . . . . . . . . 6-4
6.3 P/E PRODUCTION . . . . . . . . . . . . . . . . 6-10
7 ANALYST PERSPECTIVE . . . . . . . . . . . . . . . 7-1
7.1 BACKGROUND . . . . . . . . . . . . . . . , . 7-1
7.2 INFORMAL REQUIREMENTS MEASURES . . . . . . . . 7-2
7.3 ANALYST EVALUATIONS OF SOURCE UTILITY . . . . 7-4
7.4 SOURCE CITATIONS IN PRODUCTION REPORTS . . . . 7-11
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TABLE OF CONTENTS
(continued)
Section Page
8 TOPICAL CASE STUDY . . . . . . . . . . . . . . . . 8-1
8.1 SETTING . . . . . . . . . . . . . . . . . . . 8-1
8.2 ANALYST BACKGROUND AND EXPERIENCE . . . . . . 8-2
8.3 JOB CONTENT . . . . . . . . . . . . . . . . . 8-3
8.4 REQUIREMENTS/PRIORITIES . . . . . . . . . . . 8-3
8.5 DATA BASE USAGE . . . . . . . . . . . . . . . 8-7
8.6 SOURCE UTILIZATION . . . . . . . . . . . . . . 8-8
8.7 RESOURCES TO COLLECTION/PROCESSING . . . . . . 8-10
9 INTEGRATION OF STUDY RESULTS . . . . . . . . . . . 9-1
9.1 APPROACH . . . . . . . . . . . . . . . . . . . 9-1
9.2 STATISTICAL TESTS . . . . . . . . . . . . . . 9-3
9.3 BASIC INTEGRATED DATA . . . . . . . . . . . . 9-4
9.4 TESTING FOR RANDOMNESS . . . . . . . . . . . . 9-6
9.5 DESCRIPTION OF THE DATA PATTERN . . . . . . . 9-7
9.6 PATTERN DEVIATIONS . . . . . . . . . . . . . . 9-9
9.7 RELATIVE REGIONAL EMPHASIS . . . . . . . . . . 9-11
9.8 INDIVIDUAL METRIC CORRELATIONS . . . . . . . . 9-14
10 CONCLUSIONS . . . . . . . . . . . . . . . . . . 10-1
10.1 GENERAL . . . . . . . . . . . . . . . . . . . 10-1
10.2 DEFINITIONAL CLARIFICATION . . . . . . . . . . 10-1
10.3 FACTUAL FINDINGS . . . . . . . . . . . . . . . 10-2
10.4 SPECULATIVE INTERPRETATIONS . . . . . . . . . 10-7
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TABLE OF CONTENTS
(continued)
Appendices Page
DEFINITIONS . . . . . . . . . . . . . . . . . . . . A-1
A.1 DEFINITIONS . . . . . . . . . . . . . . . . . A-2
A.2 ACRONYMS . . . . . . . . . . . . . . . . . . . A-5
A.3 GEOGRAPHIC REGIONS . . . . . . . . . . . . . . A-7
A.4 DESCRIPTION OF DATA BASES . . . . . . . . . . A-8
B RESOURCE DISTRIBUTIONS . . . . . . . . . . . . . . B-1
C DATA BASE VOLUME . . . . . . . . . . . . . . . . C-1
C.1 AEGIS ACCESSION DATA . . . . . . . . . . . . . C-1
C.2 SOLIS ACCESSION DATA . . . . . . . . . . . C-4
C.3 FBIS ACCESSION DATA . . . . . . . . . . . . . C-5
D PRODUCTION VOLUME . . . . . . . . . . . . . . . . . D-1
E SOURCE UTILIZATION . . . . . . . . . . . . . . . . E-1
E.1 ANALYST SUPPORT TASK FORCE (ASTF) . . . . . . E-1
E,2 SUPPORT FOR THE ANALYSTS' FILE ENVIRONMENT
(SAFE) . . . . . . . . . . . . . . . . . . . . E-5
E.3 PUBLICATIONS SOURCE SURVEY (PSS) . . . . . . . E-7
E.4 DATA DISPLAYS . . . . . . . . . . . . . . . . E-9
F ANALYST QUESTIONNAIRE . . . . . . . . . . . . . . . F-1
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1. STUDY OBJECTIVES (U)
In 1975, Robert M. Macy, in an essay prepared for the
Murphy Commission, suggested that:
a study would be useful to help settle the
sharp differences existing within the U.S. Govern-
ment today on the equitable allocation of intel-
ligence resources. There have been many studies
of the Intelligence Community, but nearly all of
them seem to have been concerned with 'moving the
boxes around on the organization chart', and not
with the allocation of resources or the general
strategy for intelligence . . . .1
Less than a year later, former DIA chief, Lieutenant
General Daniel 0. Graham, noted that:
. political and economic intelligence on a
wide variety of target countries has become
critical to good national decision-making. This
requires new efforts to collect, process, and
analyze politico-economic intelligence . . . .2
Most recently, the Director of Central Intelligence,
Stansfield Turner, cited improving the quality of political and
economic intelligence as the first of three major challenges con-
fronting the Intelligence Community in 1979:
1Robert M. Macy, "Issues on Intelligence Resource Management",
Commission on the Organization of the Government for the Conduct
of Foreign Policy, Vol. 7, Washington: GPO, June 1975, 66-67.
2DanieZ 0. Graham, U.S. InteZZigence at the Crossroads, Washington:
United States Strategic Institute, 1976.
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Our experience . . . underscores the importance
of sensitivity to broad trends and underlying
social, political, and economic forces that will
shape the international environment in the 1980's.
How well equipped are we today to detect these
kinds of social and political changes, changes
that could threaten U.S. interests. . . .3
In recognition of the increasing importance of political
and economic (hereafter, P/E) intelligence to U.S. security interests,
this study was commissioned to identify and, where possible, measure
current P/E intelligence activities within the Community. In order
to accomplish this, the study first compiled data that should be of
interest to Community resource managers concerned with:
? the links between political and economic
intelligence requirements and collection
and processing resource allocations;
? the value of available information sources
to P/E analysts; and,
? the current patterns of human and material
resource allocations to P/E intelligence
collection and processing.
duplicating those studies, and instead has concentrated on the collec-
tion and processing end of the intelligence cycle.
Any analysis of resource allocations to intelligence
activities - particularly one that attempts to draw inferences about
the adequacy of such allocations - ideally should assess the
quality of finished intelligence products. However, a number of
recent studies have focused on finished intelligence production and
consumer satisfaction; consequently, this effort has sought to avoid
3"The Intelligence Community in 1979", Annual Report of the Director
of_Central Intelligence to the Congress, 25 Jan. 1980, 1,TS/Codeword.
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To support the objectives of the study, specific indicators
from which reasonable inferences could be drawn were identified.
These include:
? the national requirements emphasis on P/E
subjects, as reflected in the DCID 1/2,
NITS, and collection tasking summaries;`'
? the size and scope of various P/E intelli-
gence activities, as reflected in CIRIS,
CBJB, and ZBB documents;
? the types and volume of intelligence in-
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formation on P/E subjects, including that
from namon-NFIP entities, as reflected in
automated data bases-
? analysts' assessments of the utility of
both information sources and data bases,
as reflected in surveys and other avail-
able statistics; and
? the costs of collecting and processing
P/E intelligence, as reflected in the NFIP.
The intangible, qualitative factors that tend to skew
intelligence resource allocation decisions in particular directions,
though not totally ignored, do not figure prominently in this
analysis. Furthermore, the lack of an accepted conceptual framework
or model of the Intelligence Community prohibits the derivation of
algorithms that could relate various Community functions. The
surrogate indicators adopted for this effort, therefore, are both
imperfect and imprecise. Where problems with the data revealed by
the indicators have hampered the analysis of P/E resource allocation,
those problems have been discussed in the study.
4Definitions and explanations of these and other acronyms are
contained in Appendix A.
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Despite the lack of precise indicators and data, however,
the trends discernible in the data, and an analysis of those trends,
should be of value to those concerned with enhancing political and
economic intelligence. By identifying the type, size,and contents
of automated P/E data bases, for example, gaps, redundancies, and
disor r ions in these data bases may be revealed. Similarly,
isolation of the resources expended on collection and processing of
P/E intelligence could lead to identification of redundancies and
imbalances in the allocation of those resources. Finally, by
studying resource allocations relative to national requirements,
relationships between changes in national requirements and changes
in allocations might be uncovered.
This study is intended to be descriptive rather than
prescriptive. Its goal is to integrate the data from the indicators
mentioned into a description of P/E collection and processing that
will help Community resource managers and decision-makers identify
disproportions or discrepancies in the relationships between national
requirements, and resource allocations. Additionally, where usable
and meaningful data have permitted, political and economic intelli-
gence has been compared with other forms of intelligence, such as
military or S&T, to determine the relative emphasis placed on the
former.
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2. 'STUDY METHODOLOGY (U)
2.1 DESCRIPTION OF THE TASKS
Despite the absence of an accepted model of the processes
and functions of the Intelligence Community, most Community members
agree to a simplified model in which a variety of national intelli-
gence requirements - both formal and informal - result in demands for
data, which in turn induce collection and processing activities.
For reasons of convenience, timeliness, or accuracy, the formal links
between these various functions are frequently circumvented, as when,
for example, an analyst needing additional data bypasses the formal
tasking mechanism and informally queries the collector most likely
to have access to the needed information. The six basic tasks of
the study were derived from the following simple model of the intelli-
gence process:
Foreign U.S. U.S
Events & - Security Security
Issues Interests Policy
INTELLIGENCE ANALYSIS, PRODUCTION
REQUIREMENTS & PRESENTATION
Q
Observation COLL'ION?PROCESSING
(2)TASKING
Figure 2-1. Intelligence Process-Study
Task Relationship*
*Circled numbers depict the study task(s) corresponding
to each element of the process.
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TASK 1: Definitions and Profiles - In order to understand the
content, data flows, and data requirements of P/E intelligence from
collection through analysis, the study first reviewed existing reports
and documentation on the subject. These included (1) standard defi-
nitions from such sources as the Community's Glossary of Intelligence
Terms and Definitions and the major data bases used in the study -
AEGIS, SOLIS, and HIMS - as well as (2) more detailed profiles of
Community activities from the Analyst Support Task Force and SAFE
surveys. Collectively, these sources provided clarity concerning
the accepted composition of the major elements of the study -
political intelligence, economic intelligence, collection, and pro-
cessing; they highlighted commonalities and differences across
agencies in the classification of geographic regions; and they
clarified the roles and characteristics of the analyst as an inter-
vening variable between the acquisition and consumption of intelli-
gence information. It is important to note that, because the study
was fundamentally descriptive in nature, no attempt was made to
modify existing definitional distinctions, even where discrepancies
were noted; rather, emphasis was placed on achieving approximate
comparability wherever possible.
TASK 2: Requirements and Priorities - To assess the relative level
of emphasis on P/E intelligence, the study next developed a set of
requirements metrics, based on information contained in the National
Intelligence Topics (NITS), DCI Directive 1/2 (DCID 1/2), and the
collection tasking summaries produced by the Collection Tasking Staff
(CTS). The intent of this effort was not to measure the extent to
which national requirements and priorities are being satisfied, but
instead to identify, as a vehicle for subsequent comparisons, where
established formal requirements would seem to suggest that Community
emphasis be placed. A major difficulty of attempting to manipulate
established sets of priorities mathematically, however, is that they
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 consist of ordinal numerical scales that have no intrinsic quantita-
tive value. Beyond the basic determination of which subjects and
geographic regions are more or less important than others, it is
essentially impossible to determine by how much. Consequently, this
study employed statistical approach specifically designed to treat
ordinal rankings; these statistics sought to determine the randomness
of the data, the nature of the overall data pattern, any pattern
deviations, and individual cross-dimensional correlations.
TASK 3: Volume of Data - In order to describe the product of the
collection and processing functions of the Intelligence Community and
thereby permit the identification of gaps and redundancies in those
activities, Task 3 measured the quantity of data available to analysts,
as reflected in various Community automated data bases.5 This parti-
cular effort sought not to measure the flow of data from particular
sources or collectors, to measure losses of data, nor to identify
criteria for data retention. Likewise, it did not seek to assess
data quality, a subject treated in Task 5. Rather, the intent was
to capitalize upon the best available data, in terms of both format
and accessibility, and determine the volume of reporting on parti-
cular subjects and geographic regions, measured as the flow of data
into and out of extant data bases. Though providing a recognizably
less than perfect measure, this metric was adjudged to be a suitable
representation of the "actual" emphases of intelligence collection/
processing and production that could be compared with the "desired"
emphases reflected in national requirements.
5These included CIA's AEGIS, DIA's RIMS, and the FBIS data bases.
Other data bases that might have been used for the study, but were
not, include NSA's SOLIS and NPIC's NDS.
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TASK 4: Resource Allocations to Collection and Processing - Funda-
mental to the objectives of the study was the identification and
analysis of current patterns of resource allocations to each collection
and processing program by subject area and geographic region. Task 4
sought to identify these current patterns but did not attempt to
measure the effectiveness of either resource allocations or actual
expenditures. Although resource data from the annual CBJB (Congres-
sional Budget Justification Books) and ZBB (Zero-Based Budget)
submissions were investigated, the format of these data precluded their
effective use in the present context. Specifically, the fact that
the fiscal displays in those documents typically are not organized
geographically, in many instances do not reflect substantive foci,
and emphasize systems and organizations served as a major disadvantage.
Consequently, it was felt that, by virtue of format and accessibility,
the Intelligence Community Staff's CIRIS data base was most appropriate
for the purposes of the study. CIRIS provides the requisite breakouts
across subject areas, functions, and countries necessary to provide
the comparisons of importance here.
TASK 5: Utility of Data - Analysts' evaluations of the various
sources of data available to them provide the most useful measure of
the value of intelligence data collected and processed by the
Community. This study used two primary sources for this purpose:'
(1) the Publications Source Survey (PSS) of NFAC's Requirements
Evaluation Staff, and (2) the results of the Analyst Support Task
Force (ASTF) survey sponsored by the Information Resources Office
and Information Handling Committee of the Resource Management Staf
PSS contains evaluations of the reference/source material used by
NFAC analysts in the preparation of (1) the National Intelligence
Daily (NID) and (2) Intelligence Memoranda (IMs) and Intelligence
Reports (IRs). Each intelligence product is evaluated by its author
in terms of the contribution (key, supplemental, incidental) to that
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report of each of sixteen collection sources. PSS contains data only
on NFAC products and does not address all of those (e.g., National
Intelligence Estimates and Interagency Memoranda are omitted). The
ASTF survey, in contrast, interviewed 122 analysts across agencies
and asked them to evaluate each of eighteen collection sources ac-
cording to their general utility (essential, important, marginal,
none). Together, these two sources of data (PSS and the ASTF survey)
provide a useful basis for comparing the utility of various collection
sources with the level of resources expended on them.
TASK 6: Case Study - To supplement and cross-check the results of
earlier tasks, an in-depth case study of the recent Nicaragua crisis
was undertaken in Task 6. The task involved the investigation of
relevant documentation and interviews with political and economic
analysts concerning the major dimensions of the overall study:
requirements/priorities, data base usage, source utilization, and
resource allocations. A total of thirteen analysts from CIA/NFAC
(3- OPA, 2- OER), State/INR (3), NSA (3), and DIA Current Intelligence
(2) were interviewed to elicit their views and thereby provide con-
firmatory findings or uncover anomalies not otherwise surfaced in the
remainder of the study.
2.2 DATA AGGREGATIONS AND RELATIONSHIPS
An early and critical decision involved the selection of an
appropriate level of detail for the data to be analyzed; for example,
should data be organized by geographic region or by country? Would
organization by specific collection platform or generic collection
category be preferable? The study initially determined that the
maximum amount of detail provided by the available records and
surveys should be used. Efforts quickly revealed, however, the
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infeasibility of organizing data by specific collection program
because of the lack of data comparability across agencies, the lack
of visibility of specific questions of interest to this study, and
the time delays required to reformat available data. Discussions
with personnel familiar with the data further revealed that data
broken down by regions would be less sensitive to double-counting
than data arrayed by specific country, because many sources are
attributed to more than one country. Similarly, the subject content
of the data usually can be identified as political and/or economic,
but not as more specific subsets of those disciplines.
The level of detail and organization of the data eventually
used in the study, though far from ideal, were dictated by the
'` available sources of resource and management information. A major
problem continues to be the lack of standardization of such infor-
mation within the Community. Not even the definition of geographic
regions, for example, is consistent across data bases. This study
so selected the AEGIS geographic categorization scheme as a common
denominator and applied it to resource information data bases. The
source categorization scheme used by PSS was selected to represent
the various types of collection.
The various levels of management information detail -
subject (or discipline) content, geographic region, and collection
type (or source) - were then related to each other and to the chosen
indicators - requirements, resources, volumes of data and production,
and analyst evaluations - according to the following matrix:
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Table 2-1. Data Relationships
STUDY DIMENSION
REQUIREMENTS
RESOURCE
COLLECTION
PRODUCTION
ALLOCATION
VOLUME
VOLUME
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More sophisticated and complete analysis would have been
possible had the Community's resource management data been better
organized, readily available, and adequate. Hopefully, the findings
of this report will prove interesting and useful enough that managers
will be persuaded to improve their resource management data bases so
that future studies may be more valuable.
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