4.4.1.4. Applying Techniques to Specific Quality
Indicators
4.4.1.4.1. Linkage application
The application of the linkage technique to economic
indicators in Rwanda would be considerably enhanced by the disaggregating of
the household sector into income groups. These disaggregated data could then be
used directly by linkage technique. For example, numbers of high, medium, and
low income workers, average and medium income, proportion of workers above some
poverty line can be obtained by using the estimates of the levels of output of
the producing sectors (Agriculture, Industry, and Services) in combination with
the appropriate coefficients showing the type of labours used by each sector.
The appropriate disaggregation of Household for Rwanda seems to be based on
Region and Activity, as stated below:
Households
Rural
Urban
Agricultural
Self-Employed
Non-Agricultural
Self-Employed
Wage-
Earning
Agricultural
Self-Employed
Non-Agricultural
Self-Employed
Wage-
Earning
Applying linkage to community service indicators requires
desegregation of the government sector into specific functional activities.
Indicators can be linked to expenditures on these functional activities:
Examples of this type are: Pupil per teacher, Student by class room, Hospital
and Clinics per unit of population. However, as public service output quality
measurements are improved they can be used in place of the input quality
measurements by linking them to functional expenditures.
Linkage Technique would help Rwanda policymakers and
macro-accountants to disaggregate and to specify labour categories in Rwanda,
the disaggregation may be helpful also when studying the impact of Services
Sector in total economy of Rwanda. Disaggregation of labour categories may be
stated as follow:
1. Unskilled Labour (No-education and primary education)
2. Semi-skilled Labour (Secondary Education and Vocational
Training)
3. Highly Skilled (University Degree)
4.4.1.4.2. Dummy technique application
In Rwanda dummy sector technique can be used for analyses of
land pollution, land degradation, and land use. For the land pollution dummy
sector combines coefficient that measure the amount of solid wastes disposed of
by the using sector into landfills or dumps with coefficients measure the
amount of input used in the land reclaiming or cleaning sector.
To estimate land use, policymakers in Rwanda have to create
two land related sector:
· Space occupied by buildings
· Non building space
With knowledge of the space currently available, the level of
conversion of raw land into sector-usable land in the aggregate and separately
for each producing sector, household, and government can be estimated, given
the final demand specification. Such information can be useful also in showing
the relative amount of land used and available in the provision of housing
services, recreation, commercial enterprises, and transportation facilities.
The linkage technique can allow planers in Rwanda to identify
the producing sector and the kind of final demand sales that significantly
influence the rate of by-product output flow. Such information is relevant
particularly to taxation and regulation policies.
Planers would like to know the expenditure that various
sectors make in seeking to control the by-product flow. Often these
expenditures are not distinguishable from the sectors purchases required for
the sectors goods and services production. The specification technique can
provide some such information and permits calculation of how these expenditures
would change as final demand changes.
Planers would like also to know the feasibility, in terms of
expenditures and resources required, of achieving alternative rates of
by-product output flow; the dummy technique can provide some such information
necessary for Economic Development of Rwanda.
For example by stipulating target levels of allowable
pollution and estimated deliveries to final demand, the dummy technique allows
calculation of the level of resources that must be committed to pollution
suppression activities. Such information is useful particularly for situation
in which the government is seeking to reduce pollution directly to rough its
own programmatic expenditure (Bulmer-Thomas V., 1982: 256-278).
Obviously, Supply and Use framework and Input-Output framework
are not applied in Rwanda due to the lack of information in the following field
even though System of National Accounts is experiencing improvement:
· Intermediate consumption by Product (Agricultural
Products, Industrial Products, Services) and By industry (Agriculture,
Industry, Service).
· Output of Industries (Agriculture, Industry, Service)
by Products (Agricultural Products, Industrial Products, Services).
· Final Uses by Product (Agricultural Products,
Industrial Products, Services) and by Category ( Final Consumption, Gross
capital Formation, exports)
· Import by Products (Agricultural Products, Industrial
Products, Services)
This problem of lack of information constraint the compilation
of Input-Output table from Supply and use Tables in Rwanda in order to perform
the above Techniques.
The first hypothesis «The development of Supply and Use
Tables/Input-output Table has a significant role in the perspective analysis of
economic development of Rwanda» was not verified because those tables are
not applied as such in Rwanda, but if Rwandan Macro-accountants and planer come
on the point of compiling those tables, they will play significant role in the
perspective analysis of economic development of Rwanda as their usefulness have
been stated above. The main root of these problems are technical reasons that
include huge informal and non-monetary sectors (about 65% of the economy in
2006) and data availability among others (Republic of Rwanda, NISR, 2010, GDP
Annual Estimates for 2009 based on 2006 benchmark).
Even though Rwanda is suffering from the lack of information
to compiled Supply and Use Tables and Symmetric Input Output Table, SUT linear
models are applied to provide some macroeconomic indicators such as GDP, Value
Added, Consumption, Investment, Import and export but further analysis from
those figures are not possible because they are totals but not by Industries
(Agriculture, Industry, and Services) and by Products (Agricultural Products,
Industrial Products, Services). Therefore that information is not sufficient as
that provided by SUT.
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