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AN ANALYSIS OF THE PERFORMANCE IN EXTERNAL TRADE
STATISTICS OF UGANDA, USING AN AUTOREGRESSIVE
MODEL (ARM) (1980- 2012)
Eldard Ssebbaale Mukasa (PhD)
2
Dedication
Dedicated to my family, mama and children for their contributions towards my
welfare while handling this great task, which has been highly demanding
3
Acknowledgement
I wish to highly acknowledge the valuable efforts of my academic advisors at
Atlantic International University, and the academic department of the school of
Economics and Management, for their assistance to this noble job.
I further extend my sincere gratitude to all staff of the AIU, in the department of
management and Economics and staff of the school and my fellow students in
the struggle.
Further more I wish to extend my sincere appreciation to all staff members of the
institutions where I collected the data. I was able to visit during the research
period. These include but not limited to; Uganda Bureau Of Statistics (UBOS),
Bank of Uganda (BOU), Ministry of Finance and Economic Development,
Migrations offices , Major and Minor entry and exit point and from Uganda,
Domestic and International Airlines.
4
Acronyms:
ACP
African, Caribbean and Pacific
ASBEA
Association of Savings Banks in East Africa
COMESA
Common Market for Eastern and Southern
Africa
EAC
East African Community
EU
European Union
GATT
Genera Agreements of Tariffs and Trade
GDP
Gross Domestic Product
ITC
International Trade Corporation
MTS
Multilateral Trade System
PTA
Preferential Trade Area
SITC
Standard International Trade Classifications
UNCTAD
United Nations Conference on Trade and
Development
UNDP
United Nations Development Programme
WTO
World Trade Organization
5
Contents
Dedication ........................................................................................................................... 3
Acknowledgement .............................................................................................................. 4
Acronyms:........................................................................................................................... 5
ABSTRACT........................................................................................................................ 9
CHAPTER ONE ............................................................................................................... 11
1.0 Introduction:................................................................................................................ 11
1.1 Back ground of the study ............................................................................................ 11
1.1.1 Uganda’s liberalization of trade policy:................................................................... 12
1.2 Problem Statement:................................................................................................. 13
1.3 Purpose of the study:................................................................................................... 13
1.4 Specific Objectives ................................................................................................... 13
1.5 Scope of the research .................................................................................................. 14
1.5.1 Contextual Scope ..................................................................................................... 14
1.5.2 Geographical Scope ................................................................................................. 14
1.5.3 Time Scope .............................................................................................................. 14
1.6 The theoretical framework or what is known on the subject:................................... 14
1.7 Choice of Variables/Conceptual frame work......................................................... 15
CHAPTER TWO .............................................................................................................. 17
2.0 ELEMENTS OF PROCEDURE............................................................................ 17
2.1 Research Design..................................................................................................... 17
2.2 Population Description................................................................................................ 17
2.3 Data Collection Methods ............................................................................................ 17
2.3.1 Extraction from published records........................................................................... 17
2.4 Sample survey............................................................................................................. 18
2.5 Data Quality Control................................................................................................... 18
2.6 Measurements/Scales.................................................................................................. 18
2.7 Data Analysis and Sampling Strategies ...................................................................... 18
2.7.1 Descriptive analysis ................................................................................................. 19
2.7.2 The Auto Regressive Model (ARM)...................................................................... 19
2.7.2.1 What is AR Model? An Autoregressive model .................................................... 19
2.7.2.2 Why AR Model:.................................................................................................. 20
2.7.2.3 AR Model specification ...................................................................................... 20
2.7.4 Diagnostic tests ........................................................................................................ 21
2.7.5 Other Models ........................................................................................................... 21
2.8 Hypothesis tested ...................................................................................................... 21
1.8 Activities that were carried out included .................................................................... 22
LITERATURE REVIEW ................................................................................................. 23
3.0 Introduction:............................................................................................................. 23
6
3.1 Methods data collection and compilation of countries statistics ........................... 23
3.1.1 The Multilateral Trade System ................................................................................ 24
3.2 Treatment of certain types of special commodities ............................................... 26
3.3 Tools of analysis of the foreign trade .................................................................... 26
3.4 Reporting methods of external trade statistics ....................................................... 27
3.4.1 Errors and estimations.............................................................................................. 27
3.5 ARM Model ........................................................................................................... 31
CHAPTER FOUR:............................................................................................................ 33
4.0 DETAILED ANALYSIS AND INTERPRETATIONS ........................................ 33
4.1
ACTUAL PROCESSES ....................................................................................... 33
4.1.2 Trends of Uganda’s Exports and Imports ................................................................ 33
4.3 Uganda’s Exports and Imports (Quarterly – 1980- 2001) ..................................... 35
4.4 Uganda’s exports, and imports, by type (visible and invisible):............................ 36
4.4.1 Uganda’s External trade data by International Standards of Industrial Classifications
(ISIC) and by Standard International Trade Classifications (S.I.T.C)............................ 37
4.4.2 Trade systems Uganda uses and indicators of discrepancies and discrepancy
measures for world trade................................................................................................... 38
4.4.3 Uganda’s external trade data compilation methods as matched against the
international set standards................................................................................................. 38
4.4.4 Uganda’s treatment of certain special types of commodities............................ 38
4.4.5 Commodity classification groups and discrepancies in reporting...................... 39
Invisible Trade in Uganda................................................................................................. 39
4.5 Determining the Industrial Classification .............................................................. 39
4.5.1 Industrial Classifications.......................................................................................... 40
4.5.2 Non Standard Industry Classifications............................................................... 41
4.5.3 Indicators on consistency:........................................................................................ 41
4.5.4 Sources of discrepancies .......................................................................................... 42
4.6 Uganda’s external trade data compilation methods as matched against the
international set standards................................................................................................. 42
4.6.2 Uganda’s Status regarding Statistical Territories of the World ........................ 57
CHAPTER FIVE .............................................................................................................. 61
5.0 OVER ALL OUT COMES AND RESULTS........................................................... 61
5.1 The Actual Results................................................................................................... 61
5.2 Interpretations of Results .......................................................................................... 63
5.3 Link to Real Life...................................................................................................... 63
This situation in relation to real life, there a situation which needs to be addressed ny the
nationals as regards performances. ................................................................................... 63
CHAPTER SIX................................................................................................................. 64
6.0 ANALYSIS............................................................................................................ 64
6.1 ISOLATED ANALYSIS .......................................................................................... 64
From the data in Appendix 1, we plotted the data and the output came out like this....... 64
This graph shows a down word noicy trend over time. .................................................... 65
.6.1.1 1AR Model specification ........................................................................................ 65
6.2 Diagnostic tests ....................................................................................................... 70
6.2.1 The AIC tests to confirm the Order of the models................................................... 71
6.2 Testing the hypothesis............................................................................................ 71
7
6.2.1 Difference between trade data trends between 1980 to 2012............................. 71
6.3 Testing for reliability of the design and approaches using cronbatch’s alpha
coefficient ......................................................................................................................... 73
6.4 Comparative Analysis............................................................................................ 73
CHAPTER SEVEN: ......................................................................................................... 76
7.0 CONCLUSION GENERAL DISCUSSIONS AND RECOMMENDATIONS .... 76
7.1 Conclusions............................................................................................................ 76
7.2 Discussions ............................................................................................................ 77
7.3 Recommendations.................................................................................................. 79
APPENDICES .................................................................................................................. 85
Appendix 1: Performance of Uganda in BoP, data collection methods, Compilation and
Compliances (1980 – 2012) .............................................................................................. 86
Appendix 3: International Merchandise Trade Statistics National Compilation and
Reporting Practices: UGANDA’S STATUS IN RELATION TO UNITED NATIONS
CHECK LIST QUESTIONNAIRE ................................................................................. 90
APPENDIX 4: Focus group discussion Guide with Key Informants at the Borders Points
........................................................................................................................................... 94
APPENDIX: 5 Definitions of Concepts and Terms: ........................................................ 95
8
ABSTRACT
The thesis examines the analysis of external trade statistics of Uganda, takes an
assessment of the data collection methods of the external trade of Uganda, the
study further examines the compilation methods of the data collected concerning
external trade statistics and then critically considers reporting and presentation
styles of the external trade statistics of Uganda.
The scope of the study covers the period between 1980 up to and including 2012
calendar years
The thesis employed all the three pronounced approaches in research, that is
qualitative, quantitative and a mixed approach. The methods employed in data
collection included but was not limited to: Interviews (Structured and
unstructured), Observations especially the border points of the country, focused
group discussions with senior and operational official in the key organizations
and institutions and others
The major tools of data collections included mainly questionnaires, check lists,
interview guides; Focused group discussion guides observation guides and
others.
The data was analyzed using simple and advanced statistical packages like, MS
EXCEL, SPSS, STATA and SAS.
Multivariate analysis was conducted and the ARIMA Model was used as the key
analysis model because of the nature of the data, given the variations
experienced over the period because of the political waves experienced by the
country
over
the
period
of
study
Time series plots were done and lagged data analysis conducted. Furthermore
because of the noisy nature of the data, smoothening methods were use which
included among other the differentiations of initial data to allow the applications of
the ARIMA 2 Model.
9
The study revealed that external trade statistics still has quite a lot of gaps
despite the major innovations as carried out by UBOS, BOU, URA and other
players in the market,
The current account and capital account of the country are still unstable over the
period save the last 15 years of the period of study
Taking the hypothesis tested it was concluded that the three variables which
were talked of influencing performances was rendered quite significant in
influencing the performances of balance of payments at 0.5% level of
significances.
Testing the design and approaches reliabilities using Conbrach’s coefficient od
reliability, it was found to be 0.99 which indicated good reliability of the tools of
analysis.
10
CHAPTER ONE
1.0 Introduction:
The researcher carried out a statistical analysis of the external trade statistics of
Uganda for the period 1980 – 2012. Specifically the researcher reviewed the
methods employed in the collection and compilation of Uganda’s external trade
statistics and reporting practices, where he established the strengths,
weaknesses, gaps, and/or omissions and variations in the statistics.
The researcher further examined the treatment of certain types of special
commodities such as Gold, Gifts and borderline items, bank notes, fish catch, aid
in kind, re-imports, and re-exports with in Uganda.
The researchers assessed the appropriateness of Uganda’s tools of analysis of
the foreign trade data over the period of study; and evaluated the reporting
methods, techniques and carried out a trend analysis of the performances of
external trade statistics over the period of study using an ARM model.
1.1 Back ground of the study
Uganda got independence from Britain on October 9, 1962. Agriculture was the
dominant activity, but the expanding manufacturing sector appeared capable of
increasing its contribution to GDP, especially through the production of foodstuffs
and textiles. Some valuable minerals, notably copper, had been discovered, and
waterpower resources were substantial.
The colonial economic policy and thus its trade policy resulted into four
characteristic features for the Ugandan economy: (i) it was concentrated in a few
export crops mainly coffee, cotton and tea; (ii) subsistence indigenous farmers
produced most of the export crops that were traded internationally but as a result
of the licensing systems, processing like cotton ginning, value addition and
trading was conducted by non-Africans; (iii) the colonial administration taxed
African export crop growers to obtain revenue to run the colonial administration
and other government purposes, thus the indigenous export crop producers only
obtained a small proportion of receipts from crop exports; and (iv) the sale of
export crops was concentrated in government run monopolies in form of
marketing boards for instance the Coffee Marketing Board that procured and
exported coffee produce.
11
The key legacy of the colonial economic policy making in Uganda that continued
to influence the evolution of the structure of Ugandan economy is that the
colonial administration focused on Uganda producing raw materials for export to
Britain and imported finished goods from Britain. It also did not encourage
development of indigenous skills to engage in crop processing, value addition,
manufacturing activities through provision of appropriate education. This is
because the early administration did not prioritize provision of social services like
education to the indigenous people except those provided by mission societies,
despite the revenue collections from taxes. (World Bank Report 2001).
As Acemoglu et al (2001) note on different colonization policies around the
world, British policy in Uganda could be viewed as one which promoted
establishing Uganda as an “extractive state” thus, did not introduce much of the
institutions for protection of private property, nor provide checks and balances
against government expropriation.
However, it evidently supported the establishment of the physical infrastructure
mainly railway to transport export crops to Kisumu on Lake Victoria and all the
way to Mombasa port. Among the many limitations identified was the deficiency
of the foreign trade statistics within the African region (UNDP 1993).
1.1.1 Uganda’s liberalization of trade policy:
Uganda’s liberalization policy introduced in 1990 has had a market effect on
economic performance. Between 1990/91 and 1995/96, export volumes grew at
an annualized rate of 17%, import volumes by 12% and private investment (at
cost prices) by 18%. A similar trend has been experienced in subsequent
periods (Background to the Budgets- various issues). Uganda's risk rating has
improved by 2.5 points per annum, the rate achieved by Mauritius after
liberalization in early 1980’s (Oxfam Various Issues).
However, Uganda remained still rated as highly risky, while its reforms and its
growth rate placed it at the top of African league table; its rating places it near the
bottom. Uganda’s trade policy is attuned to promoting economic growth through
export sector diversification, attracting investment and improving productivity and
efficiency in international trade that will also improve the balance of payments
position. 1 For the last 7 years, per capita has been one of the most rapid
growing2 Uganda Government is one of the leading exponents of liberalization on
the African continent. Uganda, however, has to co-ordinate and enforces its
trade in order to have it liberalized.
Collier (1999), in his review of trade policy in Uganda argues that, though
Uganda’s achievements in liberalization are impressive, there is fear among
potential investors of a policy reversal, which is the single most important
obstacle to investment3.
1
2
3
BOU, Jan. 30, 2003
World Development reports (1990)
World Bank, 1994.
12
Measures supportive of trade activities include those intended to remove
uncertainties within the overall trade environment and to instill price and
economic stability necessary for efficient allocation of resources while helping to
boost confidence in the role of international trade in Uganda. Since the trade
policy review in 1995 and subsequently 2001, the Government of Uganda has
continued to implement policies consistent with free trade through the
liberalization of exchange systems and marketing of inputs and products,
elimination of trade –distorting biases, and reduction of undesirable trade
barriers.
1.2 Problem Statement:
International merchandise trade statistics worldwide compilation and reporting
practices have indicated that, trade statistics has a number of weaknesses,
which have down played the role of external trade statistics and use in different
economies (UNDP 2001).
However, generally the quality of data collection has improved overtime in such
areas like household survey data. Despite of this improvement, recent studies
have revealed that in a cross country data set for developing and transitional
economies, private consumption per capita from the national accounts, deviates
on average from the mean household income or expenditure based on the
national sample house hold surveys. Also growth rates were observed to differ
systematically (Martin Ravallion, 2001)
The researcher therefore seeks to focus on the external trade statistics handling
because the overall improvement in data collection parse has not been extended
to the external trade statistics compilation and reporting by various economies of
the world, Uganda inclusive/
1.3 Purpose of the study:
The study was to analyze the performance of Uganda’s external trade for the
period 1980 to 2012 using the ARM Model. The study will further consider
methods of data collection, compiling and reporting techniques and styles of as
compared to the compliance standards of internationally recommended practices
with International merchandise trade statistics worldwide.
1.4 Specific Objectives
More specifically, the study was to:
i.
Review the methods employed in the collection and compilation of
Uganda’s external trade data and establish the possible likely strengths
and weaknesses if any. like gaps, or omissions and variations
13
ii.
iii.
iv.
Examine the treatment of certain types of commodities such as Gold, Gifts
and borderline items, bank notes, fish catch, aid in kind, re-imports, and
re-exports with in Uganda.
Review the appropriateness of Uganda’s tools of analysis of the foreign
trade data over the period of study; and
Assess a critically the reporting methods, techniques and performances of
external trade statistics in terms of trends
1.5 Scope of the research
1.5.1 Contextual Scope
The study examined, assessed and analyzed the methods of data collection,
compilation techniques, reporting methods and trends of external trade data for
Uganda as an economy and per the international standards. The following
variables will be tested at 0.05 level of significance
i.
Data collection methods
ii.
Data compilation techniques
iii. Reporting formats and levels
iv.
Trends of performances ( 1980 – 2012)
1.5.2 Geographical Scope
The research will look at Uganda as an economy looking at the trade statistics
1.5.3 Time Scope
The period of study will for the years 1980 to 2012 trade data.
This study is intended to take close to 10 months from June 2014 through April
2015.
1.6 The theoretical framework or what is known on the subject:
The following topics were analyzed in details in developing the thesis
ƒ External trade data performance for the period 1980 to 2012
ƒ Trends of external trade data of Uganda over the period of study
ƒ External trade data collection methods across borders, with attention to
informal external trade data
ƒ External trade data compilation methods, over the period of study
ƒ External trade statistics reporting techniques and styles, over the period
ƒ International standards and practices of the key parameters in external
trade statistics over the period
ƒ Reviewing existing and used data collection and compilation methods,
modifying, developing data collection methods and analysis tools
appropriate to external trade data that can be used to model and analysis
Uganda’s external trade handling
ƒ Developing a frame work of data collection structures that can be
14
ƒ
ƒ
ƒ
employed to capture all the external trade data for Uganda
Developing specific tools for researching and measuring data analysis of
Uganda’s external trade.
Using action research approach, identify and follow the main external
trade data collection methods and tools of analysis for Uganda ‘s external
trade data
Using structured questionnaires, with existing data collection methods,
identify the key aspects of data required to compile the external trade
statistics for Uganda.
1.7
Choice of Variables/Conceptual frame work
The variables which were considered were identified and analysed and as the
structures indicate, the independent variables were critical in this study. The
dependent variables were influenced by the independent and extraneous
variables as summarized here below.
Dependent Variables
Independent Variables
Data collection methods
External trade performance,
Statistics reporting techniques and
styles
Data Compilation methods
International standards and
practices
Extraneous Variables
Macro economic factors
Political factors
Level of expertise
Environmental factors
Others factors
.
.
1.8 Issues debated in the study
The researcher investigated with intentions of answering the following question:
ƒ
What methods used to collect external trade data of Uganda across
borders?
15
ƒ
ƒ
ƒ
ƒ
ƒ
What explanations lie under the trends of external trade data of Uganda
under the period of study
What compilation methods have been used to compile external trade
statistics of Uganda over time?
What reporting techniques and styles are used to report external trade
statistics of Uganda over time?
What are the international standards used in collecting, compilation,
reporting of external trade statistics over countries and over time?
What is the trend of External trade statistics for Uganda over the period
1980 – 2012?
16
CHAPTER TWO
2.0
ELEMENTS OF PROCEDURE
2.1
Research Design
The research took a Correlational and experimental design with mixed approach
which included both. Qualitative and quantitative approaches.
A descriptive summary was made explaining the kinds of compilations for
Uganda in relation to reporting practices of international merchandise trade
statistics. A time series Autoregressive model and other possible econometric
models were employed to analyse the data.
2.2 Population Description
The study considered international approved practices using a sample of 148
economies’ responses answers as a standard for the pre set questions and
related Uganda’s practices and reporting methods to the same.
2.3 Data Collection Methods
A number of data collection techniques were employed, using different sources
of information. The details of methods are summarized here below.
2.3.1 Extraction from published records
Published statistics was extracted from previous publications; websites and
surveys carried out. The following places and Institutions were visited/ contacted
for data collection and information gathering:
i.
Uganda Bureau of Statistics (UBOS)
Uganda Bureau of Statistics was used as major a source of data in the study,
basing on the publications, survey reports and discussions with the officials on
the data collected and the common problems encountered in data collections and
publications.
ii.
Bank of Uganda (BOU)
Bank of Uganda produces periodical reports through the research department
provided a very good database on the subject under study. The various reports
were used and discussions held with staff to share their major experiences in the
data collected and compiled.
iii. Ministry of Finance
Ministry of Finance and planning compiles many reports annually, which are
relevant to the study. Various reports were accessed reviewed and used as
17
major source of secondary data. Officials from the ministry were also very useful
in discussions on the different aspects of the data collected
iv.
immigration offices
Migration offices in Uganda capture a lot of information about the people entering
and leaving the country. The migrants usually carry baggage whose contents are
in most cases declared/ examined while entering and leaving the country. The
kind of information gathered was a very good input in the study.
v.
Major Entry and Exit points of Uganda
Uganda, borders with Sudan, Kenya, Tanzania, Rwanda and the Democratic
Republic of Congo. There are more than one-entry and exit points to these
countries and from Uganda. However little is published of the kind of goods
entering and leaving the country. These entry/exit points have been visited and
both formal and informal interviews administered with people responsible for the
entry/exit points.
vi.
Domestic and International air lines’ offices in Uganda
Uganda’s flight statistics has been gathered from the different airlines with a
purpose of establishing the kind and nature of goods leaving and entering the
country. A little more of the problems encountered have been discussed with the
officials concerned.
2.4 Sample survey
Selected questions from the extracted questionnaire as recommended by the
United Nations was used and administered
International Merchandise trade statistics catch questions were used as scales of
measures for Uganda’s compilation methods and reporting practices.
2.5 Data Quality Control
Considering international reporting practices, data collected was administered
under tests of reliability and validity of instruments used including the used
questionnaire.
2.6 Measurements/Scales
The selected recommended procedures and compilation/reporting practices shall
be used as scales of measurements for the rest of sampled data. The UN
recommendations for the 52 questions shall be used as scale for the
measurement of Uganda’s practices.
2.7 Data Analysis and Sampling Strategies
Uganda’s data on Balance of trade, exports, and imports, by type (visible and
invisible) over the period 1980- 2012 shall be analyzed.
The study analyzed and modeled Uganda’s external trade data over the study
period
18
Identify appropriate strategies for data compilation and reporting
2.7.1 Descriptive analysis
The following descriptive analyses was carried out
i.
Uganda’s exports, and imports, by type (visible and invisible),
indicating the countries with which Uganda trades and in which
products.
ii.
Uganda’s External trade data by International Standards of
Industrial Classifications (ISIC) and by Standard International Trade
Classifications (S.I.T.C)
iii. Trade systems Uganda uses and indicators of discrepancies and
discrepancy measures for world trade.
iv.
Uganda’s external trade data compilation methods as matched
against the international set standards. Uganda’s treatment of
certain types of commodities like Gold, Gifts and borderline items,
bank notes, fish catch, aid in kind, re-imports, re-exports
v. Commodity classification groups and discrepancies in reporting
The results of the descriptive analysis were quantified, fed into the AR
Model where applicable and used for the hypothesis testing later where
necessary
2.7.2 The Auto Regressive Model (ARM)
The AR Model is one of the methods used in the data analysis, in relation
to the topic under study.
2.7.2.1 What is AR Model? An Autoregressive model
The notation AR (p) refers to an autoregressive model of order p
Thus, an AR (p) model is written as
Where
are the parameters of the model, c is a constant and İt is an error term
The constant term is omitted by many authors for simplicity
19
Example: An AR(1) model is given by
An autoregressive model is essentially an infinite impulse response filter
with some additional interpretation placed on it .Some constraints are
necessary on the values of the parameters of this model in order that the
model remains stationary
2.7.2.2 Why AR Model:
The external trade data available over the period (1980- 2012), when
plotted on a quarterly basis, did not show a stationery trend, but are rather
noisy. However, the series assume a linear trend after differentiation.
Implying an Auto Regressive Model and other econometric models were
appropriate to analyse the data over the period.
The Autoregressive model was adopted because by the nature of the
external trade data, the current levels of the series are said to depend on
the recent history of the series.
In this regard, the model was adopted to assess whether, the gaps and
deviations in the external trade data for Uganda are related to the recent
historical reasons of compilation methods and reporting practices.
The model revealed the magnitude of the relationship and an appropriate
order of the model was to address the aspect.
2.7.2.3 AR Model specification
The AR Model specified depending on the behaviour of the quarterly data during
20
the analysis and after performing the following tests:
a.
b.
c.
d.
e.
Plotting the time series and testing for stationary
Differentiating the series if not stationary
Plotting the ACF of the series
Plotting the PACF of the series
Determine the significant values at lag x.
2.7.4 Diagnostic tests
The following diagnostic tests were carried out
i.
The AIC tests to confirm the Order of the models
ii.
The CHISQUAQRE test at 95% confidence interval
These tests were carried out to determine whether the fitted model/models were
the best for the series.
2.7.5 Other Models
In addition to the AR model, more other econometric models were used to
assess the data, analyse it present the results, interpret the results in an
understandable manner and draw useful conclusions out the results. Analysis of
variance between exports and imports, GDP for the period of study was also
analysed
2.8 Hypothesis tested
i. Ho : There is no significant difference between the foreign trade data
trends over time at 5% level of significance
Ha: There is a significant difference between the foreign trade data
trends over time at 5% level of significance
ii. Ho: There is no significant difference between the data collection and
compilations methods of external trade statistics of Uganda and that of
international standards over the years at 5% level of significance
Ha: There is a significant difference between the data collection and
compilations methods and reporting techniques of external trade statistics
of Uganda that of international standards over the years at 5% level of
significance
iii. Ho: There is no significant difference between reporting techniques of
external trade statistics of Uganda and that of international standards
over the years at 5% level of significance
Ha: There is a significant difference between the reporting techniques of
external trade statistics of Uganda that of international standards over the
years at 5% level of significance
21
1.8 Activities that were carried out included
a) Literature review ( Extraction from published records)
b) Interviews of Cross border points officials possible to provide data both
qualitative and quantitative data about the external trade statistics
c) Interviews of officials from UBOS, BOU, URA,
d) FGD – With border trade officials, traders, leaders
e) Analysis of cross sectional data
f) Analysis of trends of exports and imports using foreign trade as an
experimental unit with many variables ( thus Multivariate analysis model
application)
g) The ARMA Model was used in most cases
22
CAHPTER THREE
LITERATURE REVIEW
3.0 Introduction:
This Chapter looks at the related literatures by other scholars and gives an over
view of the kinds of works so far have been done.
This chapter furthers gives opinions and views of what others have experienced
and reported about the variables under the study. The chapter looks at the
reviews taking objective by objective and variable by variable.
International trade worldwide has grown rapidly since the end of the Second
World War. The total number of goods exported by all countries each year has
grown at an average rate of 6 percent per year since 1950.
In 2000, the
countries of the world traded goods and services worth a total of $7.6 trillion
(WTO 2001, 9). To put this number in perspective, on average, about one out of
every four dollars of income in each country in the world is either earned by
exporting goods and services to foreign markets or is spent on goods and
services produced in foreign countries.
3.1 Methods data collection and compilation of countries statistics
The main sources of data and metadata are Euro stat/OECD and the IMF. Some
data are obtained from CARICOM and national sources (Belarus, Russian
Federation and China, Hong Kong SAR). At this point the focus is on
functionalities of the database. Therefore, no special efforts were made to get
into the database the most current statistics. The data are distinguished by
source which makes it possible to compare data for the same country, same
service category and same year among different sources. The IMF data covers
over 150 countries and areas for the years 1948 to 2003. These data represent
exports to/imports from the World and are broken down by service categories.
Two kinds of Euro stat/OECD data have been loaded. The first kind covers
exports to/imports from the World for 1970 – 1998 and is broken down by
EBOPS category. The second kind
covers 1999, 2000 and 2012 and is
23
broken down by EBOPS category and by partner country.
Services are classified according to the Extended Balance of Payments Services
Classification (EBOPS), EBOPS memorandum items and some additional BOP
components, namely: compensation of employees, workers’ remittances,
migrant’s transfers and direct investment. The Task Force was formed at the
request of the General Agreement on Tariffs and Trade (GATT) and UNCTAD to
the Statistical Commission in 1994. The objectives of the Task Force were to
elaborate the statistical requirements of the General Agreement on Trade in
Services (GATS).
3.1.1 The Multilateral Trade System
The multilateral trade system is an international political system. International
trade institutions stand at the center of this system. Throughout most of the
postwar period, the General Agreement on Tariffs and Trade (GATT) was the
principle international trade institution. In 1994, the GATT was folded into a new
international institution called the World Trade Organization (WTO). In contrast to
the GATT, which was a treaty, the WTO is an international organization that
enjoys the same legal status as other international organizations such as the
United Nations, the World Bank and the International Monetary Fund. Based in
Geneva, Switzerland, the WTO is a relatively small international organization.
Although 141 countries belong to the WTO, it has a staff of only 500 people and
a budget in the year 2000 of only about $225 million.
The GATT continues to provide many of the rules governing international trade
relations. The creation of the WTO, therefore, represented an organizational
change, but it did not produce a new set of international trade rules.
.
Despite the levels , quality and quantities of international figures, different
authorities and scholars regarding the external trade statistics of different
economies and their partners worldwide have raised different views on the
subject, some of which can be taken for granted whereas others need
investigations
to
ascertain
the
realities underlying their existences.
24
Trade data are never complete. Smuggling and non-reporting represent a serious
problem in a number of countries.
Trade statistics - as any source of information - are not free of mistakes
(a)
and omissions.
(b)
Most countries include re-exports in their export and import statistics.
(c)
According to international conventions for reporting trade statistics, the
export value refers to total Or Contract value, which, in many cases,
may differ significantly from local value added.
In view of the above shortcomings, strategic market research urged that
trade statistics should never be the sole source of insight but need to be
complemented by other sources, and in particular cross-checked by product
specialists and industry insiders. Overall, ITC's experience suggests that
trade statistics represent a very useful source of information and a valid point
of departure for strategic market research, if analyzed with a healthy mix of
skepticism and pragmatism vis-à-vis their strength and shortcomings.
In
order to tackle the issue of "unreliability" or inconsistency of trade statistics,
ITC identified two useful sources of information that complement each other:
♦ Technical notes on trade data
♦ Indicators on reliability and consistency of trade statistics
While indicators on consistency show to what extent a country's trade data is
consistent with it's
Partner
customs
declarations
(hence
providing
an
assessment
of
discrepancies), technical notes on Trade data try to explain why trade data
reported by one country may be not reliable or consistent with other sources
(including mirror estimates). Both sources of information try to highlight the
products. In addition, there are several countries with potential problems,
regarding the analysis or the estimation of trade flows.
25
3.2 Treatment of certain types of special commodities
As opposed to most economic data, like production or consumption, there are
usually two records for merchandise trade data, since transactions are both
recorded by the customs offices in the exporting
In addition, to the importing countries, it is informative to analyze the
discrepancies between a country's export statistics and the corresponding import
statistics of its partner country (mirror estimate). An approximate match of trade
statistics and their mirror statistics is a good sign of data reliability. Import figures
should be slightly higher than export figures, as they include freight and
insurance costs, although these costs obviously vary between products. An
average difference of about 10% between import and export figures is the norm
3.3 Tools of analysis of the foreign trade
The Uganda government’s fiscal plans will play an important role in determining
the course of interest rates for the east African country, its central bank governor
said in an interview. Capital inflows from foreign aid, coupled with remittances by
Ugandans working abroad, have caused the central bank to issue government
securities to prevent inflationary effects and offset currency strength, Emmanuel
Tumusiime-Mutebile, Bank of Uganda governor, said. "That has kept interest
rates very high," he told Reuters on the sidelines of the Bank for International
Settlements meetings held here over the weekend. Uganda’s official interest rate
was 15.82 per cent at the end of May. The policy has proven unpopular
domestically, but he said it has contributed to sustainable growth by helping
exports and keeping inflation in recent years around the government’s 5 per cent
target, down from triple digits previously. If the government continues to rely
heavily on foreign aid to finance investment and development, these capital
inflows will need to be sterilized through central bank selling of government
securities, Tumusiime-Mutebile said. "Government expenditure policies are an
important factor for our monetary policy," he said. Foreign donors funded 46
percent of Uganda’s 2004/2005 budget. Government spending is expected to
26
rise 10.9 per cent in the coming fiscal year to 3,749 billion shillings. The central
bank governor welcomed the prospect of cancellation of multi-lateral foreign
debt, planned by the Group of Eight, which holds a summit in July. Debt
forgiveness could halve Uganda’s roughly $4 billion in external debt, equivalent
to about 60 per cent of its GDP.
3.4 Reporting methods of external trade statistics
Capital inflows keep rates high in Uganda (The East African Standard
Paper Tuesday June 28, 2005)
"It will create fiscal space for the government to spend more money on
education, health and infrastructure. Whether or not it leads to inflation depends
on whether the government fails to reduce its absorption of foreign grants," he
said. Monetary policy is steady right now but less reliance on foreign aid would
give the central bank more maneuverability, Tumusiime-Mutebile said. —
Reuters
3.4.1 Errors and estimations
The statistical territory in the General Trade System is broader than in the
Special Trade System, since it includes both warehouses, commercial and
industrial free zones, whereas in the strict version of the Special Trade System,
the statistical territory is limited to the free circulation area of the country.
Around two thirds of the countries of the world use the General Trade System.
In this context, it is often difficult to assess the origin and the final destination
for goods that transit through one or even more countries. For example, many
goods transit through Hong-Kong, Panama, Dubai (Emirates) or the
Netherlands. Consequently, the Netherlands appear in the statistical databases
as an exporter of bananas to other EU countries, while it is clear that there is no
local production. Another famous case is Hong Kong, which functions as a
major “international marketing centre” for China, re-exporting Chinese
production with an average margin of around 30%. Chinese producers are
often not aware of the final destination of the products.
27
Uganda's export sector is growing at a rate of 9 per cent annually and is now
worth $655 million, the Uganda Export Promotion Board (UEPB) has said.
The 2004 UEPB annual report was released at the 6th President's Export
Award 2004 ceremonies in Kampala recently. It shows that the country's
export sector has continued to grow since 2000, when it was worth $401
million. The total export value of goods in 2003 was $522.5 million.
The non-traditional sector contributed 63 per cent to total export earnings last
year, compared with 62 per cent in 2003. Fish exports increased from $87.4
million in 2003 to $103 million last year. The report says that traditional
exports contributed 37 per cent, down from 39 per cent the previous year. In
real terms however, the sector experienced an increase. Cotton and coffee
exports grew from $17.7 million and $100 million in 2003 to $42.7 million and
$124 million respectively last year.
COMESA and the EU remained the
favored destinations for exports, accounting for 27 per cent and 28 per cent
respectively. Over the 2003/4 period, exports to Europe and the Middle East
grew by 4 per cent and 3 per cent respectively. In the COMESA market,
manufactured, processed and semi-processed products were the main traded
items. These included building materials, edible oils, plastics, processed fruit
juices, batteries, soap and cosmetics among others. The region has 350
million consumers with an annual import consumption of $32 billion.
Manufactured exports exhibit positive growth trends and this is so in the
regional markets, the report says. Compared with 2003, UEPB notes an
increase of about 5 per cent in the contribution of the manufacturing sector to
total exports. Ugandan exports have continued to make gains in the US
market under the AGOA initiative, doubling from $7.7 million in 2002 to $15
million in last year.
28
Service Exports in Uganda
The Uganda Services Exporters' Association (USEA) is a voluntary
nonprofit Non Governmental Organization registered and incorporated in
August 1998 as a Company Limited by Guarantee.
With fifteen founder
members, the Association was formed following a resolution at an
UNCTAD/WTO/ITC sponsored Workshop organized by the Uganda Export
Promotion Board (UEPB) between June 4th and 6th 1997 in Kampala on the
theme “Increasing Service Exports under GATS" for stakeholders in the
services industry. The Association has a mission to evolve into a strong
voice for service providers and exporters to government and be a link with
service industry coalitions in other countries.
The objectives of USEA include but not limited to;
› To increase the visibility of Uganda's service industry and therefore
improve its profile. So that service exports are seen as being as important
as merchandise exports to the country's development.
› Foster the improvement of the quality of Ugandan services as both
domestic inputs and as exports. In addition, establish a vision of excellent
customer service to which all service providers must aspire.
› To encourage and increase access to and the use of information
technology as the new arena for successful service exports.
› Establish an accurate database of trade statistics on the services market
for domestic and international use to become a local focal point for service
market information on export opportunities.
› Evolve into a strong lobby to influence Government pursue policies and
reforms that will encourage the growth of the service sector especially
exports.
› Facilitate strategic alliances among local service firms and between local
and foreign firms.
The General Agreement on Trade in Services (GATS) will increase the rate
29
of growth in this sector even further and completely liberalize the global
service
market
environment.
As a result of this agreement, the International Trade Centre (ITC) forecasts
that in the next five years the trade in services especially in the developing
and transitional economies will contribute up to a third of all their exports.
This will create unprecedented opportunities for Ugandan service firms
abroad but also open Uganda's domestic market to Foreign Service
providers. (USEA WEBSITE, http://www.servicexport.com/uganda )
Challenges and Constraints
The challenges are still numerous from the country and individual
enterprises point of view in Uganda. Some of the challenges include;
• The need to attract technical programmes to implement the various
components of the services export strategy
• Low awareness and dissemination activities to promote trade opportunities
in the sector.
• The need to strengthen policy dialogue between Government, the private
sectors and professionals on services exports.
• Inadequate expertise in services export competences in the country
• Limited marketing programme specific to the services sector.
As a strategy to enable the country increase its foreign exchange earnings,
tackle poverty and attain the UN Millennium Development Goals by the year
2015, Exporting services offers a valuable contribution to this development
agenda not only for Uganda but the entire developing economies. ITC has
already acknowledged that developing countries are already exporting
services and significant opportunities are in south-south trade of which
developing countries account for 68% of service export markets. Trade has
been accepted as an engine for economic growth and poverty reduction in
Uganda. Uganda is now committed to put the export of services in the
priorities of it trade development programmes. The major obstacle is the
limited
resources.
Attracting
technical
30
assistance
programmes
of
development partners is urgently needed to realize the benefits of this trade
and in developing the sector for exports.
3.5 ARM Model
An autoregressive model is essentially an infinite impulse response filter with
some additional interpretation placed on it .Some constraints are necessary on
the values of the parameters of this model in order that the model remains
stationary
According to Agung Harjaya Buana( 2010) A time series model may either
follow..
Static Models
• A time series model where only contemporaneous explanatory
variables affect the dependent variable
• Relate a time series variable to other time series variables
• The effect is assumed to operate within a same period
Or
Dynamic Models
• A time series model where the lagged value of explanatory
variables and/or dependent variable affect the present value of
dependent variable
DŝŶĚŵĂƉ
ŝƐƚƌŝďƵƚĞĚͲ>ĂŐ
DŽĚĞů
K>^
ůŵŽŶ
ƉƌŽĂĐŚ ;W>Ϳ
>'
ƵƚŽƌĞŐƌĞƐƐŝǀĞ
DŽĚĞů
<ŽLJĐŬ
ƉƉƌŽĂĐŚ
/ŶƐƚƌƵŵĞŶƚĂů
sĂƌŝĂďůĞ
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ĂƵƐĂůŝƚLJdĞƐƚ
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31
ĚĂƉƚŝǀĞ
džƉĞĐƚĂƚŝŽŶƐ
WD
From the trade statistics of Uganda, the time series assume a dynamic
model due to the irregularities of the data collection practices, the
reporting methods and compilation techniques which are then detailed in
the data collected from the field and published statistics.
The model therefore takes the form:
Yt is our external trade performance, X1 = data collection methods, X2 =
The compilation methods and X3 = the reporting techniques in relation to
the international standards.
The AR Models however, may have three major pronounced problems
which are vital to note:
• If Yt, has a trend, then Yt-1, Yt-2 …..Have trends leading to the
problems associated with regression trended series.
• Because the independent variables are actually trivial values of the
dependent variables, there will be the problem of multi co linearity.
• Yt = B0 + B1t + B2Yt-1 + B3Yt-2 + B4 X
• The order of the model has to be selected
Yt = B0 + B1Y t-1 + B2Yt-2 +… + BPYt-p + Et
If one regressed trended series, the error term (Et), will be correlated
resulting in the problem of Autocorrelation. If therefore Yt, is trended, the
regression on Yt-1, Yt-2…, can easily give misleading results. The
Regression model of the form:
Yt = f (Yt-1, Yt-2, Yt-3 ….), lagged , the model: Yt = B0 + B1 Yt-1 + B2 Yt-2 +
….+ BP Yt-p + Et
,
is referred to as regressive where p is the number of
lagged terms in the order of the model.
In the context of the study, following the research design, a descriptive
summary of the kinds of compilations made shall be referred to the
various methods used in various countries world-wide in the compilation
and reporting practices of international merchandise trade statistics.
A time series analysis has been identified as Auto Regressive (AR) and
other econometric models to be used to analyse the external trade data
over the period of study.
32
CHAPTER FOUR:
4.0
DETAILED ANALYSIS AND INTERPRETATIONS
This chapter gives a detailed presentation and analysis of the data of the study.
The chapter makes observations, analysis in details and gives interpretations of
the different analysis carried out. Gives the out puts and further interpretations of
the research findings
4.1
ACTUAL PROCESSES
From the data collected qualitatively and quantitatively is evident that Uganda
like many other countries compile that data collected from the different sources
following the international standards. However with quite a number of gaps which
has rendered the trade data giving a slightly not true picture of the exports versus
imports. The major weakness include among others
4.1.2 Trends of Uganda’s Exports and Imports
The trends of exports and imports and dynamic in a sense that they have kept on
changing over time due to such factors like, political instability during the 1970s
to middle 1980s. This can be observed in the graphs here below.
In the first part of the period of study, the figures were quite low, but then kept on
changing over time.
However the Balance of Payments of Uganda has not been favorable throughout
the period, because of the economic and political dynamics within the country.
Looking the different variables considered in the analysis as summarized in
Appendix 1, we note that imports have always exceeded exports over the period,
which has kept the country operating a deficit Balance of Payments position.
It was observe that, to some extent, though not the major reason for poor
performances, there are a number of factors which we ought to remember for
attention if improvements are to be realized in this sector. These include among
others; the data collection methods of merchandise leaving and entering the
33
country, the compilation methods, and reporting practices.
The graph here below gives us an over view of what was realized on the ground
during the study.
Graph 1: Trends of Uganda’s performances in trade statistics (1980- 2012)
Uganda imports more than exporting for the period under study. This may be
partly attributed to the nature of the economy being subsistence than industrial.
34
4.3
Uganda’s Exports and Imports (Quarterly – 1980- 2001)
On the other hand considering the quarterly data for the
Considering the data in details we take a look at the quarterly data for the period
1980 to 2001 for purposes of analysis. This data plotted gives noisy scenarios,
which implies that in order to analyze this data we have to stabilize it and
carefully analyze it. The time series don not show a clear trend which therefore
calls an ARM for making it smoother?
Graph 4. Uganda’s Exports and Imports (Quarterly – 1980- 2001
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Q
Exports (US $Millions)
Imports (US $' Millions)
Looking at the quarterly data above it is revealed that the time series are noisy.
Therefore we need to smoothen them and carry out a clear analysis. This calls
for the application of the AR Model to systematically analyze the data. This will
call for lagging the Yt variable to enable us get a better model for the time series
over the period under study..
4.4 Descriptive analysis
The descriptive analysis section represents the qualitative analysis of the
research thesis and gives an insight of what has been found out in relation to the
set out objectives of the study using the spelt out methodology and approaches.
Below are some of the items and areas of descriptive analyses that have been
carried out
Uganda’s performance in real terms is described in the graph below with the
trend always in the negative direction here below
35
Describing the performance over the period can be summarized as in the table
here below
Performance of External trade 1980 to 2012
Mean
Standard Error
Median
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence Level(95.0%)
The overall performance over the period is in deficit.
-131.5909091
27.23809698
-51.5
156.4709545
24483.1596
0.405806487
-1.231532493
559.7
-548.2
11.5
-4342.5
33
55.48218771
Graph: 5 An overview of the performances, Exports, Imports ( 1980 – 2012)
4.4 Uganda’s exports, and imports, by type (visible and invisible):
Uganda like many other economies
of the world trades in goods and
36
services. This therefore implies that the country imports and exports visible and
invisible goods and services
Because of the nature of trade going on between countries and economies the
trade terms pose a challenge to many other countries because of the handling of
both visible and invisible trade, imports and exports.
Uganda exports mostly agricultural products say coffee, tea as the traditional
crops and also and other non traditional crops as indicated in the sample table
here below
Looking at extract of the Uganda’s Exports, and imports ( 2003- 2004)
We observe that agricultural exports dominate, but not sufficient to balance the
equation
Table 4: Major Exports of Uganda ( 2003 – 2004)
03 2003/04Items
2003
2004
Tourism
172
201
Fish & fish products
111
118
Coffee
105
114
Transportation
39
47
Cotton
17
43
Tea
29
39
Tobacco
40
36
Flowers
17
27
Financial services
8
23
Maize
8
19
Source: BOU 2005
4.4.1 Uganda’s External trade data by International Standards of Industrial
Classifications (ISIC) and by Standard International Trade Classifications
37
(S.I.T.C)
Uganda complies by over 80% to the international reporting systems as
indicated in the index 4
4.4.2 Trade systems Uganda uses and indicators of discrepancies and
discrepancy measures for world trade.
Uganda as per the international reporting and data compilation system only 52
questions were complied with the international standards as indicated in
appendix 5
Uganda’s external trade data compilation methods as matched
against the international set standards
Uganda compiles her trade statistics following international standard by taking at
4.4.3
64% compliance as per the questionnaires administered with the different
respondents.
4.4.4
Uganda’s treatment of certain special types of commodities
Non Monetary Gold: Uganda includes Non Monetary Gold in trade statistics.
This practice also complies with the UN standard criteria.
Monetary Gold: Uganda includes Monetary Gold in trade statistics. This practice
does not comply with the UN standard criteria.
Gifts from abroad and Borderline items: Uganda includes gifts from abroad
and borderline items in the trade statistics.
However, some items depending on how they are brought into the country may
be omitted and in a way one could refer to this as illegal trade if discovered.
Bank notes: To Uganda, securities, banknotes and coins in circulation, no
answer was given (N/A), however, UN standards indicate that this is not included
in trade statistics.
Fish catch and salvage landed from foreign vessels in national ports
This is applicable to Uganda’s statistics reporting though not applicable to UN
standards. However some of this may be incomplete.
Aid in kind: Uganda includes Aid in Kind in the trade statistics as part of foreign
38
aid. This practice complies with the UN standard criteria.
Re-imports: Re- imports are not included. However, omissions are possible and
could be captured.
Re-exports: Re - exports are not included. However, omissions are possible
and could be captured.
Postal items: Uganda does not include postal items in the international trade
statistics. This however, contradicts with the UN standards as per the criteria.
Military equipment and goods: Uganda does not include military equipment
and goods in the international trade statistics. However this contradicts with the
UN standards as per the criteria.
4.4.5 Commodity classification groups and discrepancies in reporting
The results of the descriptive analysis have been used in the different Models
where applicable and applied for the hypothesis testing later where it has been
found necessary and ideal
Visible trade is the part of International trade that involves the transfer of goods
or tangible objects. Visible trade is comprised of visible imports and visible
exports. The exporter is defined as the supplier of the products. The net total of a
country's visible imports and visible exports is called the visible balance of trade.
This forms part of the country’s balance of trade.
Invisible Trade in Uganda
Invisible trade is the part of
International trade that does not involve the
transfer of goods or tangible objects. It is mostly generated by the overseas
activities of tertiary industries.( service sector industries)
Invisible trade is comprised of invisible imports and invisible exports. Since
nothing tangible is transferred, the importer is defined as the person, group or
country that receives the service. The exporter is defined as the supplier of the
service. The net total of a country's invisible imports and invisible exports is
called the invisible balance of trade. This forms part of the country’s balance of
trade
4.5
Determining the Industrial
Classification
39
The easiest way to identify the industry classification of the industry being
researched is to use a directory. Directories frequently list basic company
information including the industrial classification code assigned to the company.
By using a directory to look up a company that operates within the desired
industry, directory listings easily identify industrial classifications. Because
companies operate in complex ways, most are assigned more than one industrial
classification code. The first code listed is the primary code while all others are
considered secondary codes.
If a company name is not available as representative or typical of an industry, it
may be necessary to consult an industrial classification manual for more
information. Classification manuals, regardless of print or online format, detail the
characteristics of the industry and keywords associated with the classification.
With this information, it is possible to assign an industrial classification to a
desired industry. Classification manuals are available for both SIC and NAICS.
4.5.1 Industrial Classifications
Both SIC and NAICS industrial classification codes rely on a cascading format.
Each is a series of numbers where the first two numbers signify the most general
industrial classification, the next two numbers signify a more specific designation,
and so forth. The second set of numbers "cascades" from the first set of numbers
in the classification code.
SIC - The SIC system uses a 4-digit cascading code. The first two digits indicate
a general industrial sector while the next two digits specify a more focused
industry segment.
NAICS - The NAICS utilizes a more flexible 6-digit cascading system. The first
two digits represent the industry sector, the third digit represents a sub-sector,
the fourth digit represents a industry group level, the fifth digit represents an
international industry level, and the sixth digit represents a national industry
40
detail.
4.5.2
Non Standard Industry Classifications
Not all industry information sources use the SIC or NAICS codes. These nonstandardized industrial categories present a challenge to the business
researcher. Non-standard industrial classifications are highly varied and require
methodical translation (where possible). The desired industry is subsumed within
another category making it difficult to interpret the information. Frequently, nonstandardized classifications are too general and incorporate many standard
classifications. In these cases, the researcher must rely on detailed tables and
charts to excerpt information as available.
Industry classifications assemble companies into common designations that
reflect shared markets and products. Since the 1930's, industries have been
classified according to the four-digit Standard Industrial Classification (SIC)
codes used by the U.S. government in census collection. In 1997, the Office of
Management and Budget (OMB) adopted a new six-digit classification system
called the North American Industry Classification System (NAICS). The old SIC
system did not reflect a fundamental change in industrial markets towards
service. The new NAICS addresses these changes and provides more than 350
new industries and 9 new industry sectors.
4.5.3 Indicators on consistency:
As opposed to most economic data, like production or consumption, there are
usually two records for merchandise trade data, since transactions are both
recorded by the customs offices in the exporting. In addition, to the importing
countries, it is informative to analyze the discrepancies between a country's
export statistics and the corresponding import statistics of its partner country
(mirror estimate). An approximate match of trade statistics and their mirror
statistics is a good sign of data reliability. Import figures should be slightly higher
than export figures, as they include freight and insurance costs, although these
costs
obviously
vary
between
products. An average difference of
41
about
10% between import and export figures is the norm.
4.5.4 Sources of discrepancies
Discrepancies can occur for many reasons, according to WTO (1999). These
include: :
Coverage and time of recording, of which, Goods to be included or not
(returned goods, vessels, emergency aid, military goods), Classification as goods
or services (e.g. software), Statistical threshold values (e.g. intra-EU trade),
Confidentiality (usually at the 6 digit level of the HS), Simplification, Time lag in
compilation (the time lag between the shipment and the arrival in the country of
destination), Reference period (July-June or January-December) , Illegal and
unrecorded trade (ex: cut flowers in Uganda)
Uganda’s external trade data compilation methods as matched
against the international set standards.
From the selected questions and following the answers from Uganda (Appendix 3),
4.6
it has been observed and therefore concluded that:
(a)
22 Answers comply with UN recommendations which accounts for (42.3%)
(b)
5 Answers do not comply with UN recommendations which accounts for
(9.6%)
(c)
25 Questions have no answer. (48.1%)
Taking a look at the comparison of reporting practices, in terms of groups, section
and divisions, we observed and summarized here below.
Comparative Table (1)
GB/T4754-2002
ISIC/Rev3
Difference
Section
20
17
3
Division
95
60
35
Group
396
157
239
Class
913
292
621
42
2. Comparison of inner construction between GB/T4754-2002 and ISIC
The following Comparative Table (2) shows the Comparison of inner construction
between
GB/T4754-2002
43
and
ISIC.
44
Production
and
19
2
4
J Finance
K Real estate
L Tenancy and business services
11
1
I Accommodation and Restaurants
4
16
23
27
4
16
Transport,
storage
activities
3
5
1
1
1
and 5
K Real estate, renting and business
J Financial intermediation
communications
I
H Hotels and restaurants
4
H Wholesale and retail trade
7
2
software
7
and household goods
93
motor vehicles, motorcycles and
of
2
18
1
2
23
5
1
2
Division
G Wholesale and retail trade; repair 3
F Construction
E Electricity, gas and water supply
and
14
37
11
10
personal
10
24
7
7
G Information transfer, computer services
3
4
F Traffic, transport, storage and post
of
9
distribution
E Construction
electricity, gas and water
D
3
D Manufacturing
C Manufacturing
482
33
C Mining and quarrying
169
30
B Mining
15
6
fishing
A Agriculture, hunting and forestry
B Fishing
38
and
18
Section
A Agriculture, Forestry, Animal husbandry 5
ISIC/Rev3
Group Class
Section
Division
GB/T4754-2002
Comparative Table (2)
4
3
17
5
10
2
17
5
2
61
10
1
6
5
8
31
12
17
2
29
5
4
127
12
1
9
Group Clas
45
5
P Education
Public
management
˄Total˅ 20
T International organizations
organization
S
and
R Culture, sports and entertainment
welfare
social
95
Q Sanitation, social security and social 1
5
O Resident services and other services
396
1
12
22
11
3
environment and public establishment
913
1
24
29
17
13
16
˄Total˅ 17
bodies
Q Extra-territorial organizations and
persons
P Private households with employed
personal service activities
O Other community, social and
N Health and social work
M Education
5
12
N Management of water conservancy, 1
2
L Public administration and defense;
geologic perambulation
18
compulsory social security
8
and
M Scientific research, technical service 3
60
1
1
4
1
157
1
1
9
3
292
1
1
22
6
Though GB/T4754-2002 has the same level of industrial classification with ISIC/Rev3 and also
sets up simple corresponding relation on the level of classes, their specific industrial
classification is so different on the level of sections, divisions and groups. Because
Manufactory is the dominant industry in China that is the main force to pull the national
economy increasing, the differences of industrial classification mainly exist in it that can be
seen clearly from Comparative Table (2) above. Specifics as follow: In GB/T4754-2002
Manufactory has 30 divisions, 169 groups and 482 classes, but in ISIC/Rev3 it is respectively
less 7 divisions, 108 groups and 355 classes. It mainly includes difference in classification of
dominant products of Agriculture, Forestry, Animal husbandry and fishing, Traffic, transport,
storage and post, Wholesale and retail trade, Public management and social organization and
so on.
Current industrial classification in Chinese national accounts is still not enough specific
because current statistical standards especially on Services and data collection are not
enough perfect. On the other hand, because now GB/T4754-2002 is still a transitional standard
that was implemented for government general statistics and administration statistics only from
2003 and data collection according to new standards is so difficult to satisfy the demand of
national accounts, current industrial classification in national accounts still adopt the old
GB/T4754-1994. All of these cause the differences between GB/T4754-2002 and ISIC/Rev3.
At present, China is developing the first national economic census in 2004 and researching the
methods of GDP accounts. National economic census will supply specific data resource of
industrial classification for national economic activities. It is good basic condition to strengthen
the link between industrial classification in Chinese national accounts and ISIC/Rev3.
Remarks:
National (NSC) and international statistical classifications (ISC) are mutually dependent.
o According to the standard statistical classifications basic principles and practices 4 the
basic rule obeyed in establishment and revision of “Industrial classification for national
economic activities (GB/T4754-2002)”, was that “the industrial classification had to be
based on current situation of national industrial development”, obeyed and acknowledged
homogenous principle of economic activities and linked with ISIC/Rev3 which was issued
4
Statistical Commission , Thirtieth session, New York, 1-5 March 1999 items 8 of the provisional agenda
46
by United Nations in 1989. Also emphasized the consistency in principle and methods and
paid attention to the switchover between GB/T4754-2002 and ISIC/Rev3.
o Important to note from the revisions conducted by chine, was :
ƒ
Stating the goals and problems of the exercise
ƒ
Identifying the actors involved in the developments and use of classificationsproducers and users of statistics
ƒ
Identifying the injunctions which follow from legislations and government policies
ƒ
Describing how the structures and details of the classifications are used when
producing and presenting statistics
ƒ
Understanding the of statistics produced with the classifications
ƒ
Establishing monitoring mechanisms for proper feedback from classification users
about problems in use
ƒ
Maintaining a time table to draft, update and revise the classifications
ƒ
Coordinating the process with work on other classifications and
ƒ
Setting standards for dissemination of the classifications and related updates and
revisions.
4. Comparative Studies of OECD countries: Econometric Models of Foreign Trade in
OECD Countries (By Guisan, M.Carmen and Cancelo, M.Teresa)
This section presents some extracted examples of econometric models, which take into
account both supply and demand sides as determinants of real Exports, including the
important relationships existing between industrial development and external trade. The
models focus on the positive role human capital plays in reducing external debt by fostering
the evolution of exports and allowing the increase of imports necessary for industrial
development. The analysis is performed with data of 25 OECD countries during the period
1960-97.
a.
Evolution of Exports in 25 OECD countries, 1960-97.
Trade has an important role in development as it is deeply related to industrial development,
so countries with low levels of industry by inhabitant usually have low levels of national and
foreign trade per capita, and very often they have problems of deficit in their balances of
payments which lead to increasing external debt and difficulties in promoting development.
Foreign trade is particularly important for small countries, or for countries with low levels of
production of raw materials. They need to sell goods and services to foreign countries in order
47
to finance some intermediate goods and services necessary for their production, which are not
produced in the country and have to be bought in international markets The positive impact of
imports on economic development does not usually receive enough attention in many reports
on economic policies for less developed countries. In our view it deserves more attention, as
the empirical evidence shows that industry is essential for development, with positive impact
on other sectors such as building and services. Industrial development, on the other hand, is
usually deeply related to both national and international trade. Large countries usually have a
high level of national trade so they can acquire most of their intermediate inputs and machinery
by producing them themselves, but small countries usually need to have a high level of foreign
trade in order to improve their industrial development.
Table 2: Real exports by inhabitant (, thousands of Dollars at 1990 prices and exchange
rates)
Country
1960
1975
1985
1997
Australia
0.825
1.659
2.339
4.844
Austria
1.169
3.423
6.360
11.08
Belgium
2.511
7.085
10.27
17.50
Canada
1.263
2.681
4.548
8.531
Denmark
2.314
4.771
7.448
11.87
Finland
1.607
3.171
5.728
10.64
France
0.853
2.538
3.835
6.722
Germany
1.208
3.020
5.084
6.882
Greece
0.127
0.607
0.970
1.711
Iceland
3.125
4.954
8.174
9.779
Ireland
0.829
2.373
4.932
17.07
Italy
0.550
1.913
3.036
5.906
Japan
0.190
1.074
2.266
3.559
Luxembourg
7.707
13.87
20.49
32.13
Mexico
0.138
0.225
0.481
1.276
Netherlands
2.352
6.013
8.173
13.75
New Zealand
1.426
2.190
3.102
4.547
Norway
2.466
5.233
8.526
16.16
48
Portugal
0.342
0.761
1.468
3.467
Spain
0.266
0.969
1.806
4.372
Sweden
2.043
4.675
7.087
12.87
Switzerland
3.706
6.985
10.73
13.77
Turkey
0.061
0.118
0.274
0.704
UK
1.290
2.465
3.435
5.877
USA
0.577
1.217
1.412
3.736
EU-15
1.045
2.664
4.139
7.091
OECD-25
0.754
1.805
2.689
4.853
Source: OECD National Accounts Statistics
The real value of exports has increased in the OECD countries, not only because of the
increase of real Gross Domestic Product, GDP, but also because of the increase of the ratio
Exports/GDP. In all 25 OECD countries the ratio between Exports and GDP has evolved from
9.11% in 1960 to 13.57% in 1975, 16.73% in 1985 and 24.69% in 1997. The European Union
has higher levels of exports by inhabitant and higher ratios between exports and GDP due to
the high level of intra-EU trade. Big countries, like the USA, have similar levels of internal trade
inside its territory but this of course counts as national trade and not as foreign trade. Mexico
has experienced a high degree of openness to foreign trade, but its levels of exports by
inhabitant are already very low. This country, as well as most Latin American countries, needs
to increase foreign trade to foster industrial development.
The degree of openness of a country to foreign trade can be measured in absolute terms, by
the value of exports by inhabitant, and it can also be measured in relative terms, by means of
the ratio between Exports and Gross Domestic Product. Generally the degrees of absolute
openness depends positively on the degree of development of the country and negatively on
the size of the country, as big countries have usually more opportunities for internal trade than
smaller ones. The degree of relative openness to foreign trade depends on several factors:
1) The size of the country, with generally a greater need for openness in small countries.
(Guisan, M.C. and Cancelo, M. T. Econometric Models of Foreign Trade in OECD Countries)
2) The increase of industry, with generally a greater need for openness in countries which are
increasing their level of industrialization.
49
3) The distance to other markets, with more trade when the distance is small and/or the cost of
transport is low.
4) Trade barriers, trade increases when these barriers are lowered or disappear.
5) The degree of development. In countries with very low levels of GDP by inhabitant even low
levels of exports by inhabitant imply a relatively high ratio.
If GDP increases faster than
Exports, by inhabitant the ratio between Exports and GDP can diminish with development.
The degree of openness has been measured by the ratio between real exports and real GDP
at 1990 prices and exchange rates. We consider the following groups according to the value of
Exports/GDP in 1997.
Group 1. - Countries with ratio higher than 0.40: Luxembourg, Ireland, Belgium, Netherlands,
Austria, Norway and Sweden.
Group 2. - Countries with ratio between 0.34 and 0.40: New Zealand, United Kingdom, Spain,
Germany, France and Italy.
Group 3. - Countries with ratio between 0.26 and 0.33: Portugal, Switzerland, Canada,
Denmark, Mexico and Iceland.
Group 4. - Countries with ratio below 0.25: Australia, Turkey, Greece, USA and Japan
We can see the important increase in all cases, although we should note that the high level of
EU15 is the sum of fifteen countries belonging to the European Union and includes not only
extra-EU foreign trade but also intra-EU foreign trade. If we analyzed only foreign trade
between the European Union and other areas, excluding intra-EU trade, we would see that
foreign trade of the EU has a value by inhabitant very similar to that of the USA, because only
about one half of EU total foreign trade is extra-EU. Many theoretical and empirical studies
have noted the important positive role of exports in economic growth, although many of them
only emphasizes its impact on the demand side. Actually, the impact on the supply side is also
very important, as imports also have in many cases a positive role in economic growth. The
increase in imports usually contributes to the improvement of industry, building and services,
making available some intermediate goods which are needed to expand production in these
sectors. This happens because many imported goods and services are complementary to
internal production, and their positive impact on internal production usually overrides some
negative effects due to substitution effects of other imported goods.
In Cancelo, Guisan and Frias (2012) we present a combined model for real Value-Added of
Manufacturing in OECD countries, where the final effect of the increase of one unity both in
imports and exports signifies a positive increase of industrial development. The important
50
positive impact of industry on other sectors explains that an increase of one unity in real ValueAdded of Industry can increase the real Value-Added of Services by 0.8, as shown in the
cross-country world model presented in Guisan, Aguayo and Exposito(2012). Similar
conclusions have been reached by several authors with different approaches, although some
studies on openness and on the role of foreign trade are inconclusive.(Guisan, M.C. and
Cancelo, M. T. Econometric Models of Foreign Trade in OECD Countries)
Econometric Model for Exports in OECD countries 1960-97
We have seen that there are important differences in exports by inhabitant, both in the time
series of a country or in a cross-section of countries. In this section we present an econometric
model that explains the real value of exports as a function of some factors from the demand
and the supply sides, estimated with a pool of 925 observations of 25 OECD countries in the
period 1961-97.
Usually econometric models of exports focus on the demand side, with two main explanatory
variables: the level of external demand, with a positive effect on exports, and the relative price
of the country in relation with the external market. Our approach includes other relevant
variables such as the level of GDP of the country as a variable that represents the supply side
and has a positive effect, the level of private consumption of the country, as a variable that
represents internal demand and has a negative effect on exports, and a variable related with
the educational level of the population, as a measure of the changes in quality of production
and organization.
The variables included in Model 1 are the following:
•
EXP90it= Exports of goods and services of country i in year t, in billions of dollars at the
price levels and exchange rates of 1990.
•
DEXTit = External Demand, measured by the sum of the real value of GDP in the other
24 OECD countries in year t, in B$90.
•
GDP90it = Internal Supply, measured by the real value of GDP in country i and year t,
in B$90. Guisan, M.C. and Cancelo, M. T. Econometric Models of Foreign Trade in
OECD Countries
•
IPRit = Index of Prices Ratio, measured by the ratio between the external index of
prices of exports of each country and the external index of exports of the USA.
51
•
TYRit/TYRUt = ratio between the average years of schooling of adult population of each
country in comparison with the corresponding value of this variable in the USA. This
variable is an indicator of relative quality of production and socio-economic organization.
The Index of prices ratio is measured by the ratio between the index of external prices
of exports of each country, IPEXX, and that variable in the USA:
(1) IPRit =IPEXXit/IPEXXUt
The index of external prices of exports of a country is the ratio between the index of internal
prices of exports, IPINX, and the index of the exchange rate, the index of the exchange rate
being the ratio between the exchange rate in year t, ERit, and the same variable in the base
year. The base year is 1990 in this case.
(2) IPEXXit = IPINXit / IERit, where IERit = ERit/ERio
As the exchange rate is in units of currency in each country by US$, the variable IER is equal
to unity for the USA and in that country the index of external prices is equal to the index of
internal prices. Relation (2) has an important role in explaining the international variations of
the exchange rate, as shown in several models such as those analyzed in Guisan (2003).
Model 1 is a dynamic log-linear model, expressed in the form of a mixed dynamic model,
including among the explanatory variables, besides the lagged value of the dependent
variable, the increases in the natural logarithms of DEXT, GDP90, and IPR, as Applied
Econometrics and International Development. AEEADE. Vol. 2-2 (2002) well as the indicator of
changes in quality, measured by the educational distance in relation with the USA.
Model 1
Mixed dynamic model for log (EXP 90)
Dependent Variable: LOG (EXP 90?)
Method : Pooled Least Squares
Sample (adjusted): 1961 1997
Included observations: 37 after adjusting end points
Number of cross sections used: 25
Total panel (balanced) observations: 925
White Heteroskedasticity- Consistent Standard Errors & Covariance
Variable
Coefficient
Std. Error
t- Statistic
1.021635
0.121969
8.376172
Prob
D (LOG (DEXT?))
52
0.0000
D (LOG (GDP90?))
1.442267
0.184443
7.819566
-0.983835
0.199605
-4.928901
-0.091943
0.025175
-3.652152
0.322314
0.094902
3.396301
0.000742
1351.375
0.0000
D (LOG (C90?))
0.0000
D (LOG (IPR?))
0.0003
D (LOG(TYR?/TYRU))
0.0007
LOG (EXP90?(-1))
1.003198
0.0000
R-squared
Var.
0.998803
Mean dependent
3.517324
Adjusted R-squared
0.998797
S.D. dependent var.
1.473892
S.E of regression
0.051125
Sum squared resid
2.402023
Log likeli hood
1440.967
F-statistic
153409.3
Durbin –Watson stat
1.773522
0.000000
53
Prob (F-statistic)
The model performs very well: the goodness of fit is high, all the coefficients are significantly
different from zero and have the adequate signs, and lagged value of the explained variable
has a coefficient near one.
The hypothesis of homogeneity of parameters between individual countries is tested in Guisan
(2003) with satisfactory results, and the model shows good forecasting accuracy as we shall
see in the next section, where we compare this model with other interesting models of foreign
trade, in relation with predictive capacity.
Econometric Models for Manufacturing Exports
We consider two econometric models for manufacturing exports in OECD countries with a
sample of 11 countries in the period 1975-90, using the indices of prices elaborated by
Cancelo (1996) and published in Cancelo and Guisan (2012).
Model 2 relates the exponential rate of yearly growth of real exports of sector 10
(Manufacturing), which is equal to the difference between log of XR10 and log of its lagged
value XR10L=XR10(-1), with the following explanatory variables:
Log(Q10/Q10L), exponential rate of growth of Q10, where Q10 is the real value of
Manufacturing Value-Added in billions of dollars at the price levels and exchange rates of
1990.
Log (PRI10/PRI10L), exponential rate of growth of prices, where PRI10 is the International
Price Ratio of sector 10 calculated and results presented:
54
Model 2:
LS estimation of the exponential rate of growth of XR10
LS // Dependent Variable is LOG (XR10/XR10R)
Sample: 1975 1990
Included observations: 165
Variable
Coefficient
Std Error
t-Statistic
Prob
LOG(Q10/Q10R)
0.654030
0.086386
7.571028
0.0000
LOG (PR110/PR110R)
-0.264720
0.068575
-3.860275
0.0002
LOG (DEXT/DEXTR)
0.659224
0.117188
5.625375
0.0000
LOG (NE3/NE3R)
0.779364
0.145619
5.352092
0.0000
R-squared
0.993762
Adjusted R-squared
0.993653
S.E. of regression
0.056073
Sum squared resid
0.540796
Mean dependent var
S.D. dependent var
Akanke info
Schwarz criterion
0.053791
0.072345
-5.739743
-5.667686
Log likelihood
259.3642
F-statistic
39.76954
Durbin-Watson stat
1.521495
Prob(F-statistic)
0.000000
Model 3 presents the relation between the log of XR10 and the same explanatory variables of
model 2, which are not expressed in exponential rates but in levels. Model 3 also includes an
intercept and the lagged value of the explained variable.
although Model 2 shows better forecasting accuracy.
55
Both models present good results,
Model: 3. LS //estimation for log XR10.
LS // Dependent Variable is LOG(XR10), Sample: 1975 1990, Included observations: 176
Variable
Coefficient
Std. Error
t-Statistic
Prob
C
-0.333233
0.119509
-2.788343
0.020026
0.007859
2.548168
0.037321
0.017021
2.192600
-0.247913
0.050998 -
4.861189
0.048028
0.016794
2.859828
0.960375
0.012945
74.19058
0.0059
LOG(Q10)
0.0117
LOG(DEXT)
0.0297
LOG(PRI10)
0.0000
LOG(NE3)
0.0048
LOG(XR10R)
0.0000
R-squared
0.995573
Mean dependent var
0.995442
S.D. dependent var
0.067137
Akaike info criterion
4.057490
Adjusted R-squared
0.994489
S.E. of regression
-5.368530
Sum squared resid
0.766264
Schwarz criterion
-5.260445
Log likelihood
228.6975
F-statistic
7645.590
Durbin-Watson stat
1.615829
Prob(F-statistic)
0.00000
.
Table 3 presents the forecasting accuracy of Model 1, based on the individual regressions for
each country, in comparison with Models 2 and 3. Although Model 1 is a simplified version of
56
this approach to exports equation, with less detailed information than model 2 and 3, we can
see that in spite of its higher simplicity Model 1 performs almost equally to Model 2 regarding
the root of mean square forecasting error, in percentage of the true mean of the explained
variable, %RMSE, and both models perform better than model 3.
Table 3. Forecasting accuracy for real exports 1991-1992
Model
Dep. Variable
%RMSE
1
EXP90
4.996
2
XR10
3.430
3
XR10
7.386
The variables utilized in these models are presented in Cancelo and Guisan(2012) and in
Guisan(2003) where other Interesting relations concerning the relationship between exports,
imports, external trade prices and exchange rates are analyzed.
The main conclusions point
to the comparison between a simplified version of exports equation, Model 1, which, under
general circumstances, has goodness of fit and an accuracy of predictions as good as the
more elaborated version, Model 2, and better than the more elaborate version Model 3. In
comparison with other approaches to Exports equation, based mainly on demand side, our
models have the added feature of taking into account both demand and supply sides, and they
take into account the important role of education, not only directly in the equation, by means of
the variable Tyr in Model 1 and NE3 in Models 2 and 3, but also indirectly. The indirect effect
of education on Exports is highly positive and comes through the variables GDP in Model 1
and Q10 in Models 2 and 3. Both variables are highly sensitive to the educational level of
population, as it is shown in Cancelo, Guisan and Frias (2012).
Another conclusion is that the mixed dynamic specification of Model 1 and the first difference
specification of Model 2, show better results than the equation in levels of Model 3.
4.6.2
Uganda’s Status regarding Statistical Territories of the World
The United Nations Statistical Commission in collaboration with the Task Force on
International Trade Statistics in an effort to stream line the basis for revising the Customs
Areas of the World , developed a questionnaire and was on May 11, 1999 was sent out to
57
countries. By May 2000, 128 countries had responded. The expected answers included Yes,
No, and N/A.
According to the response Uganda’s status was among those who some times answered Yes,
No and sometimes did not give an answer (N/A)
58
59
YES
UG
Military bases
Territorial enclaves of your country in other countries
4.6
4.62
Other (please specify)
4.54
Embassies
Research installations
4.61
Military bases
Territorial enclaves of other countries in your country
4.5
4.53
Customs warehouses
4.4
4.52
Commercial free zones
4.3
Embassies
Industrial free zones
4.2
4.51
Premises where inward processing is carried out
or leaving these elements in your foreign trade statistics
following elements, i.e. do you make records of goods entering
Do you include in the statistical territory of your country the
customs territory?
NO
NO
YES
NO
NO
NO
Does the statistical territory of your country coincide with the YES
geographical territory?
Does the statistical territory of your country coincide with the YES
economic territory?
Does the statistical territory of your country coincide with the
Question
4.1
4
3
2
1
s NO
Question
20.8
24.0
53.6
26.4
53.6
57.8
40.0
41.6
38.4
26.4
52.8
29.6
Exc
Incl
80.8%
68.0%
68.0%
Others
8.8
5.6
7.2
28.8
30.4
19.2
NA
60
Territorial enclaves of international organizations in your country NO
Territorial waters, and continental shelf lying in international NO
4.7
4.8
Is your country a member of the customs union?
5
of the union and not available separately?
Is foreign trade of the country included in foreign trade statistics
excludes inter trade with the member states?
Does the data cover only trade with third countries and
with the member states?
Do the data cover both trade with third countries and inter trade
NO
NO
60.8
43.0
58.4
26.4
24.0
16.0
24 of the 45% cases
76% of the 45% cases
45%
14.4
33.0
27.2
Some countries did not give responses to the questions thus failure of totaling to 100% in some instances
5.3
5.2
5.1
Other territorial elements?
4.10
If Yes
Off shore territories, possessions, dependencies, islands, etc
4.9
exploit fuels or minerals below the sea bed
claims to have , jurisdiction in respect of the right to fish or to
the country enjoys exclusive rights or over which it has or
NO
Other (please specify)
4.64
waters over which
Research installations
4.63
CHAPTER FIVE
5.0 OVER ALL OUT COMES AND RESULTS
This chapter looks at the discussion of findings of the study as outlined in chapter four
above. These findings are discussed in line with the set objectives in chapter one
looking at one by one.
5.1
The Actual Results
Uganda’s trade performances is realized by looking at the exports and imports differences
thus giving us the Balance of Payments calculated by considering the data
The graph here below illustrates the situation
Mean
Standard Error
Median
Standard
Deviation
Sample Variance
Kurtosis
Skewness
Range
-131.5909091
27.23809698
-51.5
156.4709545
24483.1596
0.405806487
-1.231532493
559.7
61
Minimum
Maximum
Count
Confidence Level
(95.0%)
-548.2
11.5
33
55.48218771
Regression output of the performance, data collection methods, compilation methods, for
the period (1980 to 2012)
From the out put we realize a model
The model of performance is given by:
Yt
= 0 - 0, 009 X1+ 0.09X2+1.18x3+e
This implies that the performance is serious affected by the factors under considerations
From chapter four of this report a number of observations are made and therefore followed
by dynamic assumptions.
62
5.2 Interpretations of Results
From the data available, Uganda methodologies of data collection of external trade cover 84%
of the total trade because of the weak system of data collection. Compilation methods
compliance is by 64% and standards are met by 80%.
Under these observations, the following assumptions are adopted while analyzing the data.
(a)
Performance is influenced by data collection methods by 16% in terms of accuracy and
consistence
(b) Compliance to international standards is influenced and therefore a fall short of 20% is
observed.
(c)
On the other hand, compilation methods are with less effectiveness by 36%
The model suggests that there is little improvement in terms of performances about the
parameters considered under the study.
5.3
Link to Real Life
This situation in relation to real life, there a situation which needs to be addressed ny the
nationals as regards performances.
Much as the methods of data collection of the trade statistics with in the country exists, there is
only about 80% coverage which needs to improve to closer to 100% if the situation is to
improve.
On the other hand, we realize that much as the international practices are in place, the country
is sometimes reluctant to follow the laid down guidelines because 64% is so low to make it to
the required standards
This situation may be attributed to the weak laws or/and enforcements of laws regarding the
practices within the country.
On the other hand following the focus group discussions with the officials at the border points,
wit was observed that much of nontraditional exports are not really much tracked.
We find that also through postal services a lot of merchandise goes out and comes in the
country. This already is major hindrances in real practice.
63
CHAPTER SIX
6.0
ANALYSIS
This chapter looks at the detailed analysis of tje study findings to guide us draw informed
decisions basing on valid and realistic findings.
Taking the data individually,
From the statistics indicated above we shall test the hypothesis below
1.
Ho :
There is no significant difference between the foreign trade data trends over time at 5% level of
significance
Ha: There is a significant difference between the foreign trade data trends over time
at 5% level of significance
2.
Ho: There is no significant difference between the data collection and compilations methods of external
trade statistics of Uganda and that of international standards over the years at 5% level of significance
Ha: There is a significant difference between the data collection and compilations
methods and reporting techniques of external trade statistics of Uganda that of
international standards over the years at 5% level of significance
3. Ho: There is no significant difference between reporting techniques of external trade
statistics of Uganda and that of international standards over the years at 5% level of
significance
Ha: There is a significant difference between the reporting techniques of external trade
statistics of Uganda that of international standards over the years at 5% level of
significance
6.1 ISOLATED ANALYSIS
From the data in Appendix 1, we plotted the data and the output came out like this.
This at a glance shows how noisy the statistics is. This calls for further treatment to enable analysis and
coming up with realistic out comes and conclusions.
Graph 6.1: Performances in terms of exports, imports and BoP in general
64
This graph shows a down word noicy trend over time.
We therefore looked at the quarterly data over the period 1980 to 2001 for purposes of analysis.
\
Here below we have the data further analysed from Appendix 2.
From the data an analysis was carried out and hypotheses tested to decide on the state of affairs.
.6.1.1 1AR Model specification
The AR Model was specified depending on the behaviour of the quarterly data during
the analysis and after performing the following tests: We take the quarterly values, and
plot the results on a graph.
The graph in the section below is based on the quarterly
data that was considered.
6.1.2 Plotting the time series and testing for stationary
This plotting is only for one variable ( performance) with intentions of examining the
nature of the results and taking a further step in the analysis
Graph 6.2 Performances data quarterly 1980 -2001
65
100
0
-100
-200
-300
1980
1986 q4
1990 q41
year
11996 q4
2001q4
The time series look noisy and there is need to smoothen the data.
In the scenario above, tests of regression on times series were carried out
and discussed here below.
ARIMA regression
Sample: 19680q1 - 2001q4
Wald chi2 (2)
=
number of obs
=
86
54.09
Log likelihood = -446.9349
Prob > chi2
=
0.0000
-----------------------------------------------------------------------------D2.
|
OPG
Performance |
Coef. Std. Err.
z P>|z| [95% Conf. Interval]
-------------+---------------------------------------------------------------Performance |
_cons | -.1308461 3.034951 -0.04 0.966 -6.079241 5.817549
-------------+---------------------------------------------------------------66
ARMA
|
ar |
L1. | -.4824014 .0659299 -7.32 0.000 -.6116217 -.3531811
L2. | -.2342645 .0759165 -3.09 0.002 -.383058 -.0854709
-------------+---------------------------------------------------------------/sigma | 43.65668 1.991951 21.92 0.000 39.75252 47.56083
------------------------------------------------------------------------------
Cumulative period gram white-noise test
Bartlett's (B) statistic =
Prob > B
=
4.8175
0.0000
From the test there is evidence of the noise. But with the treatment the model of lag2,
gives better results.
The model would look like:
Yt-2 = - 0.1308461 - 0.234X1+e , with a std dev of 43.65 and z- value of -3.09
This looks better and adoptable, and therefore the model appropriate.
67
Cumulative periodogram for performance
.
.
.
.
.
.
Cumulative Periodogram White-Noise Test
.
.
Bartlett's (B) statistic =
.
.
Frequency
.
.Prob > B = .
There is evidence from the graph that, the data set was noisy but has been,
smoothened using a better approach. .
68
.
6.1.3
Removing noise from the series if not stationary
From the graph above the ARM Model equation would look like the straight line which
gives a true picture of the model above. The time series seizes to be noisy and gives
the true trend of the output. This in this case shows a down ward trend of the economy’s
performance.
69
Plotting the ACF of the series
6.1.4
There is need to further look at more treatment of the series to come up with
another explanation.
Here below we consider the polynomial plot derived from the time series.
-
-
performance
-
Local polynomial smooth
q
q
q
years
q
q
kernel = epanechnikov, degree = , bandwidth = .
6.2 Diagnostic tests
The following diagnostic tests were carried out
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of performance
chi2(1)
=
1.95
Prob > chi2 = 0.1623
70
With the test above, and given the outcome, the model requires further analysis,
because there is no major problem in the results realized.
6.2.1 The AIC tests to confirm the Order of the models
DF-GLS for performance
DF-GLS tau
[lags]
Number of obs =
1% Critical
Test Statistic
Value
83
5% Critical
Value
10% Critical
Value
-----------------------------------------------------------------------------4
-3.062
-3.626
-2.982
-2.695
3
-3.015
-3.626
-3.006
-2.718
2
-3.393
-3.626
-3.029
-2.738
1
-3.486
-3.626
-3.048
-2.755
Opt Lag (Ng-Perron seq t) = 1 with RMSE 34.66831
Min SC = 7.19813 at lag 1 with RMSE 34.66831
Min MAIC = 7.44734 at lag 1 with RMSE 34.6
From the out put above, it implies that the model is fairly good.
6.2 Testing the hypothesis
This section takes a look at the individual variables together with the performance.
6.2.1 Difference between trade data trends between 1980 to 2012
This analysis will take one independent variable regressed with the dependent variable.
71
1.
Ho : There is no significant difference between the foreign trade data trends over time
at 5% level of significance
Ha: There is a significant difference between the foreign trade data trends over time
at 5% level of significance
2.
Ho: There is no significant difference between the data collection and compilations methods of external
trade statistics of Uganda and that of international standards over the years at 5% level of significance
Ha: There is a significant difference between the data collection and compilations
methods and reporting techniques of external trade statistics of Uganda that of
international standards over the years at 5% level of significance
3. Ho: There is no significant difference between reporting techniques of external trade
statistics of Uganda and that of international standards over the years at 5% level of
significance
Ha: There is a significant difference between the reporting techniques of external trade
statistics of Uganda that of international standards over the years at 5% level of
significance
The output from excel and Stat analysis have been used and here below we observe
and test the hypothesis.
Source |
SS
df
MS
Number of obs =
-------------+-----------------------------Model | 783356.723
Residual | .039985631
F( 3,
3 261118.908
Total | 783356.763
Prob > F
29 .001378815
-------------+------------------------------
29) =
33
.
= 0.0000
R-squared
= 1.0000
Adj R-squared = 1.0000
32 24479.8988
Root MSE
= .03713
----------------------------------------------------------------------------------------performanceexpimp |
Coef.
Std. Err.
t P>|t|
[95% Conf. Interval]
------------------------+---------------------------------------------------------------datacollectionmethods84 | .5422509 .1802067
compilationmethods64 | .0432305 .2662922
compliancewithis80 | .6460153 .1826526
_cons |
3.01 0.005
0.16 0.872 -.5013982 .5878591
3.54 0.001
-.0202219 .0095042
72
.1736868 .9108151
-2.13
.2724488 1.019582
0.042 -.0396601 -.0007837
----------------------------------------------------------------------------------------From above we note that in the three cases the values p( Probability) is greater than the
level of significance which is 0.05 ( 5%)
This implies that in the three cases we reject HOs and Accept Has
In the real life experience we can say that there in significant improvements in data
collection methods, compilation practices and reporting mechanisms.
However, taken individually the components under study gave inconsistent results.
But on the other hand taking the value of R2
We say that the performances of the trade terms are to a greater extent explained by the
three factors highly.
6.3 Testing for reliability of the design and approaches using cronbatch’s alpha
coefficient
The cronbatch’s alpha coefficient between, performance of the Balance of Payments, data collection methods
compliancewithis80 compliance with international standards over the period yielded the following results.
Average interterm covariance:
Number of items in the scale:
Scale reliability coefficient:
18866.67
3
0.9952
The scale is high compared to the range which is always between 0.7 and 0.9
We therefore can say that the reliability of findings using the design and approach was very
good.
6.4 Comparative Analysis
Comparison between Industrial classification in Chinese national accounts
and International standard industrial classification for all economic activities
(ISIC) during the 8th OECD – NBS Workshop on National Accounts (6-10
December 2004- OECD Headquarters, Paris)
Industrial classification for national economic activities was first issued and
implemented as Chinese national standard in 1984.As national economy develop
rapidly and new activities come forth continuously, great changes of industrial
construction have took place. Two editions of industrial classification for national
economic activities are “Industrial classification and codes for national economic
activities (GB/T4754-1994)” and “Industrial classification for national economic
activities (GB/T4754-2002)”. The later will be implemented for national
73
macro scopical administrations such as plan, statistics, finance, tax, and industrial
and commerce administration from 2003.
Basic construction of “Industrial classification for national economic activities
(GB/T4754-2002)”
“Industrial classification for national economic activities (GB/T4754-2002)” has four
levels. It includes 20 sections, 95 divisions, 396 groups and 913 classes. Specifics as
follow:
74
75
CHAPTER SEVEN:
7.0
CONCLUSION GENERAL DISCUSSIONS AND RECOMMENDATIONS
This chapter gives an over view of the entire research and considers the objectives of the
study, looks at the outcomes and makes an analysis which results into conclusions and
recommendations based on the study findings and observations.
The study had a broad purpose of analyzing the performance of Uganda’s external trade for
the period 1980 to 2012 using the ARM Mode on the variables identified in section 1.7 of
chapter one.
The study further considered the methods of data collection, compiling and reporting
techniques and styles of as compared to the compliance standards of internationally
recommended practices with International merchandise trade statistics worldwide.
More specifically, the study was to review the methods employed in the collection and
compilation of Uganda’s external trade data and establish the possible likely strengths and
weaknesses if any like gaps, or omissions and variations. The study further examined the
treatment of certain types of commodities such as Gold, Gifts and borderline items, bank
notes, fish catch, aid in kind, re-imports, and re-exports with in Uganda.
In addition, a detailed review of the appropriateness of Uganda’s tools of analysis of the
foreign trade data over the period of study; and more so the study assessed critically the
reporting methods, techniques and performances of external trade statistics in terms of trends
7.1 Conclusions
The study findings revealed that Uganda’s performances in external trade was not in good
direction because, looking at the model which was developed using regression analysis, a
general declining trend in terms of balance of payments was observed and noted in the model
Yt = 23.6 + 0.09 +e . The model was constructed using the difference between Uganda’s
exports versus imports. This resulting figure was purely negative.
The study revealed that, regarding data collection practices especially on special products,
Uganda can effectively and efficiently collect about 80% of the merchandise entering and
leaving the country with accuracy. This leaves a very big gap of 20% for all trade data not well
recorded and sometime missed and/or omitted.
On the other hand, regarding compilations of international trade statistics of Uganda, about
36% of the practices are not complied with. This evidenced from the 156 questions that were
administered to different respondents from different sources of the data collected during the
research.
Considering the reporting standards internationally Uganda has tried over the period but
compliances were rated at just fair according to international standards as summarized in the
paragraph here below. During the discussions and using the questionnaire administered with
respondents,22 responses complied with UN recommendations which accounted for (42.3%),
while 5 responses did not comply with UN recommendations which accounts for (9.6%) and
76
25 Questions did not have answers. (48.1%).
Despite of this situation, Uganda is not exonerated from performing divergently from
internationally accepted standards as per records of external trade statistics.
7.2 Discussions
Taking each aspect considered separately and one at a go we generate the following
discussions for this report.. Taking a few examples from the repot findings a number of items
that generate discussions are here below.
According to Uganda, reporting practices, Non Monetary Gold is included in the trade statistics
of the country. This practice also complies with the UN standard criteria.
This makes Uganda be ranked among the many countries which comply with this criteria.
But looked at from an economic perspective the country ought to report this righly because it
does not pose any threat from any direction. Without reporting this, there would be conflict of
interest especially as per the IMF and WTO criterion.
Uganda includes Monetary Gold in trade statistics. This practice does not comply with the UN
standard criteria. Why then is this so?
In any case Uganda being land locked would loose nothing because it mines gold and
therefore this attracts many traders coming to deal in this product.
But then would the economy be affected if not reported?
The answer to this remains yes, because this would lower the market rating of the economy,
therefore the need to report right away.
Uganda includes gifts from abroad and borderline items in the trade statistics.
Quite
interesting though it is obvious that many gift received may not be recorded because if one got
a say wrist watch from a friend whom he visited abroad, he would come back putting the watch
on. This automatically would be omitted in the records unless declared at the immigration
which is not he practice by many of us today.
However, such items depending on how they are brought into the country may be omitted and
in a way one could refer to this as illegal trade if discovered.
Bank notes: To Uganda, securities, banknotes and coins in circulation, no answer was given
however; UN standards indicate that this is not included in trade statistics. But even then, is it
possible to track and know how much money is within the borders of a country?
77
I don’t believe so.
Fish catch and salvage landed from foreign vessels in national ports
This is applicable to Uganda’s statistics reporting though not applicable to UN standards.
However some of this may be incomplete. How would one know which fish was caught within
the waters of Uganda and not Kenya, Tanzania, say for a lake like Victoria? This for sure
leaves the reader with many questions but many of them of uncertainty and accuracy of
reporting
Aid in kind: Uganda includes Aid in Kind in the trade statistics as part of foreign aid. This
practice complies with the UN standard criteria. But the sometimes these goods may be
brought in through the borders sometimes un declared.
Re-imports: Re-imports are not included. However, omissions are possible and could be
captured. With the tax collections today and the quest for revenue by regulators in Uganda
today, it quite possible and even easy to recapture this data more than once.
Re-exports: Re - exports are not included. However, omissions are possible and could be
captured. Like for the case of re imports those is another possible duplications
Postal items: Uganda does not include postal items in the international trade statistics. This
however, contradicts with the UN standards as per the criteria.
This being the case if an
individual received valuables say jewelry through postal services, courier services say on a
weekly basis how much would be left out if over 1000 people received the same items?
Military equipment and goods: Uganda does not include military equipment and goods in the
international trade statistics. However this contradicts with the UN standards as per the criteria.
This is an obvious item for security reasons and confidentiality by the government. But then for
accuracy of records and declaration of terms of trade, these items ought to be declared and
recorded for that matter.
Considering the 43% compliancy from above, and 48% no answers to questions, means that
Uganda still has a lot to be done to comply.
78
From the data analysis the ARM Model identified is of Order 2 which is stated as
Yt-2 = - 0.1308461 - 0.234X1+e , with a std dev of 43.65 and z- value of -3.09
This implies that there was need to further analyze the data.
Taking the design and approach test of reliability, it is evident that, the design was good
because the value is 0.99 which is greater than 0.7 to 0.9 which is the range of an appropriate
coefficient.
A further look at the hypothesis revealed that, there is no significant improvements in the
performance of the BoP, because of the three factors under considerations which are data
collection methods used, the data compilation approaches and the reporting practices.
7.3 Recommendations
Following the performance indicators as discussed in section 7.2 above and looking at the
level of scores, I strongly want to recommend that Uganda takes keen interest in the
international reporting systems.
Looking at re-imports not being included in reporting, given the likely, omissions are possible
and could be captured. The country should set up a system to cater for this.
.
From the whole study there is need to adhere to the international reporting practices
and improve on the three parameters of the study which include among others, the data
collection methodology especially at the immigration points and more so the border
points.
79
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1.
APPENDICES
85
86
87
Appendix 2: Quarterly data for the trade statistics ( 1980 – 2001)
88
89
Appendix 3: International Merchandise Trade Statistics National Compilation and
Reporting Practices: UGANDA’S STATUS IN RELATION TO UNITED
NATIONS CHECK LIST QUESTIONNAIRE
1. Do you include in trade statistics non-monetary gold?
Yes
2. Do you include in trade statistics trade on government account?
Yes
3. Do you include in trade statistics military goods?
Yes
4. Do you include in trade statistics electricity, gas and water?
Yes
5. Do you include in trade statistics postal items?
Yes
6. Do you include in trade statistics goods used as carriers of information and
software (not to order)?
7. Do you include in trade statistics trade in marine vessels and aircraft that
engage in international traffic?
8. Do you include in trade statistics mobile equipment (i.e. drilling rigs) operating
in international waters?
9. Do you include in trade statistics goods delivered to and from offshore
installations located in the economic territory of the compiling country?
10. Do you include in trade statistics trade in products mined from the seabed in
90
Yes
Yes
Yes
Yes
Yes
international waters?
11. Do you include in trade statistics bunkers, stores, ballast and Dunn age
supplied to foreign vessels or aircraft in the national territory?
12. Do you include in trade statistics bunkers, stores, ballast and Dunn age sold
from foreign vessels or aircraft in the national territory?
13. Do you include in trade statistics fish and salvage landed from foreign vessels
in national ports?
14. Do you include in trade statistics fish and salvage acquired by national
vessels on the high seas from foreign vessels?
15. Do you include in trade statistics goods for processing?
Yes
Yes
Yes
Yes
Yes
16. Do you include in trade statistics goods on financial lease (one year or more)? Yes
17. Do you include in trade statistics illegal trade?
No
18. Do you include in trade statistics barter trade?
Yes
19. Do you include in trade statistics foreign aid?
Yes
20. Do you include in trade statistics local border trade?
Yes
21. Do you include in trade statistics goods consigned by a government to its
armed forces and diplomatic representatives abroad?
22. Do you include in trade statistics monetary gold?
23. Do you include in trade statistics issued securities, banknotes and coins in
circulation?
24. Do you include in trade statistics bunkers, stores, ballast and Dunn age
acquired abroad for national vessels or aircraft?
25. Do you include in trade statistics fish and salvage sold abroad from national
vessels?
26. Do you include in trade statistics fish and salvage sold to foreign vessels from
national vessels?
27. Do you include in trade statistics goods on operational lease (less than one
year)?
No
No
No
No
No
No
No
28. Do you include in trade statistics improvement and repair trade?
No
29. Do you include in trade statistics goods on temporary admission?
No
91
30. Is transit trade (i.e. goods simply being transported through the territory)
excluded from the import and export statistics?
41. Do you use the general trade system?
Yes
Yes
49. If you use the special trade system, are separate data available for imports
into premises for customs warehousing?
50. If you use the special trade system, are separate data available for exports
from premises for customs warehousing?
51. If you use the special trade system, are separate data available for exports
from premises for inward processing?
52. If you use the special trade system, are separate data available for imports
into premises for inward processing?
Yes
Yes
Yes
Yes
53. Is the statistical value of imported goods a CIF-type value?
Yes
54. Is the statistical value of exported goods an FOB-type value?
Yes
57. Are data separately available for freight and insurance costs?
Yes
58. Do you classify imports by country of origin or production?
Yes
59. Do you classify imports by country of consignment?
Yes
62. Do you classify exports by country of last known destination?
Yes
92. Do you use the Harmonized Commodity Description and Coding System
(HS)?
Yes
93. Do you use the Standard International Trade Classification (SITC)?
Yes
95. For coding commodities in the basic transactions, do you use HS?
Yes
106. Do you use customs declarations as a source?
Yes
107. If you use customs declarations as a source, do you use other sources as
well?
108. Is the date used for recording external trade statistics the date goods
enter/leave the economic territory?
112. Is conversion of foreign currencies into national currency based on the
prevailing market or on the official rate?
117. Is the exchange rate used for currency conversion that which is in effect at
the time of exportation or importation?
92
Yes
Yes
Yes
Yes
142. Do you collect quantity data?
Yes
143. Do you use a standard unit of weight for quantity measurement of all
commodities where applicable?
148. Do you use units of weight on a net basis (e.g. excluding packing)?
Yes
Yes
149. Do you use units of weight on a gross basis?
150. Do you use for ships, number of ships?
151. Do you use for ships, gross registered tons?
152. Do you use for ships, dead-weight tons?
153. Do you use for ships, other quantity units?
154. Do you use for aircraft, number of planes?
155. Do you use for aircraft their carrying capacity in terms of number of seats and/or
kilograms?
156. Do you use for aircraft other quantity units?
Source: International Merchandise Trade Statistics: Concepts and Definitions, United
Nations publications, Sales number: 98.XVII.16)
52 of the 156 questions relate to recommendations made in International Merchandise
Trade Statistics: Concepts and Definitions
93
APPENDIX 4: Focus group discussion Guide with Key Informants at the Borders Points
1. Can you share with me your experiences at the border regarding the tracking of
exports and imports at this board
2. \What are the major challenges that you face
3. What do think can be done to address these challenges in the future
4. In your opinion what do you consider to be future plans of addressing some of
these challenges
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APPENDIX: 5 Definitions of Concepts and Terms:
Aggregation/Disaggregation:
Aggregation is the combination of related categories,
usually within a common branch of a hierarchy, to provide information at a broader level to
that at which detailed observations are taken. Dis aggregation is the breakdown of
observations, usually within a common branch of a hierarchy, to a more detailed level to
that at which detailed observations are taken. With standard hierarchical classifications,
statistics for related categories can be grouped or collated (aggregated) to provide a
broader picture, or categories can be split (disaggregated) when finer details are required
and made possible by the codes given to the primary observations.
Balance of Trade: =(Export + Re-export)–(Import + Re-import)
External Trade refers to the goods that add to or subtract from material resources in a
country as a result of their movement into or out of the country
Balance of Payments Manual 5 (BPM5): The manual describes the methodology for
measuring the economic transactions of an economy with the rest of the world. The
International Monetary Fund is the custodian of BPM5.
Balance Of Payments: The balance of payments is a statement that provides a record of
economic transactions of a particular country with the rest of the world. Despite the
connotation, the balance of payments is not concerned with payments, as that term is
generally understood, but economic transactions. A transaction itself is defined as an
economic flow that reflects the creation, transformation, exchange, transfer, or extinction of
economic value and involves changes in ownership of goods and/or financial assets, the
provision of services, or the provision of labor and capital. The BOP covers a wide range of
economic transactions which include: ҏTransactions in goods, services, income and current
transfers (which are shown in the current account); and ҏ Capital transactions, such as
capital transfers, and financial transactions involving claims on and liabilities to
nonresidents (these categories of transactions are shown in the capital and financial
account).
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Best Practices about Classifications: Refers to the approach or procedure recognized
as most efficient and effective in producing a desired result. Best practice is based on the
experience of experts in particular fields and is usually promulgated through the agreement
and endorsement of experts and expert groups. In the development and revision of
international and national classifications, best practice would generally involve the
application of practices and procedures promulgated by international and national
organizations responsible for classification development in their own particular fields. These
practices may well include cost benefit analyses weighing the applicability of final
classifications against of their terms of reference, the application of agreed classification
principles, an agreed methodology for incorporating local requirements (i.e. an evaluation
of the requirements of the society/economy where the classification is to be applied) where
they differ from existing standards and the selection of suitable recognized classification
characteristics to produce good classifications. The result should optimize the incorporation
of these principles in a product that is achievable within budgetary and other constraints.
Bogus Goods: Replicated legitimate goods
Bonded Goods: Imported goods stored in a bonded warehouse, usually after the owners
of the goods have deposited a bond guaranteeing that the duty will be paid when and if the
goods are withdrawn for domestic sale.
Boundary: Represents the limit of a known or recognizable quantity, area or scope. Each
classification has its own boundary, as do its constituent categories, such as activities,
commodities, occupations etc. Whilst it is possible for the boundaries of individual
classifications to overlap, there should be no overlap within individual classifications.
Building blocks (elementary items):
Are the most elementary items of a statistical
classification, i.e. the most detailed code for a variable. They may be used alone or in
combination to describe a category in one or more classifications, or to compare
classifications. A prime example is the Harmonized Commodity Description and Coding
System (HCDCS or HS), the categories of which are used not only for the construction of
country specific tariff and trade classifications, but also as the building blocks of the
Standard International Trade Classification (SITC Rev. 3) and the goods component of the
Central Product Classification (CPC). The SITC and the CPC regroup individual HS
96
categories to meet differing statistical needs. Another example is the General Industrial
Classification of Economic Activities within the European Community (NACE) which can be
combined to reconstruct higher levels of ISIC.
Capital Account: That portion of a country's balance of payments that includes the inward
and outward flow of money for investment and international grants and loans (public and
private). This comprises both capital transfers and the acquisition and disposal of nonproduced, non-financial assets e.g. trademarks, patents, copyrights, leasing agreements
among other items. Capital transfers’ entries are required where there is no quid pro quo to
offset transfer of ownership of fixed assets, or transfer of funds linked to fixed assets (e.g.
aid to finance capital works), or the debt forgiveness of debt. It should also include the
counterpart to the transfer of net wealth by migrants, referred to as migrants’ transfers if
information on these is available. The entries in Uganda’s capital account are basically
counter entries as a result of debt forgiveness.
Capital Goods:
These are Industrial products or other goods that are used in the creation of additional
wealth, such as machine tools. Capital goods are sometimes called intermediate goods
because they only indirectly satisfy human wants, and sometimes producer goods,
because they are used to produce other goods.
Category: Is the generic term for items at any level within a classification, typically
tabulation categories, sections, subsections, divisions, subdivisions, groups, subgroups,
classes and subclasses. Classification categories are usually identified by codes
(alphabetical or numerical) which provide both a unique identifier for each category and
denote their place within the hierarchy. They contain elements which are subsets of the
classification to which they belong, such as activities, products, types of occupations, types
of education, etc.
Class: Is a title/name used in classifications to depict a particular level within a hierarchy
(e.g. Section, Division, Group, Class). It usually refers to the one of the lower levels of a
classification, often the lowest (e.g. in ISIC Rev. 3 the lowest level - 4 digit - is referred to as
97
the class, while in the CPC the class level is the second lowest level). Its use is not
mandatory.
Classification: Is a set of discrete, exhaustive and mutually exclusive observations which
can be assigned to one or more variables to be measured in the collation and/or
presentation of data. The terms 'classification' and 'nomenclature' are often used
interchangeably, despite the definition of a 'nomenclature' being narrower than that of a
'classification'.
The structure of a classification can be either hierarchical or flat.
Hierarchical classifications range from the broadest level (e.g. division) to the detailed level
(e.g. class). Flat classifications (e.g. sex classification) are not hierarchical.
The
characteristics of a good classification are - the categories are exhaustive and mutually
exclusive (i.e. each member of a population can only be allocated to one category without
duplication or omission); - the classification is comparable to other related (national or
international) standard classifications. - the categories are stable i.e. they are not changed
too frequently, or without proper review, justification and documentation; - the categories
are well described with a title in a standard format and backed up by explanatory notes,
coding indexes, coders and correspondence tables to related classifications (including
earlier versions of the same classification); - the categories are well balanced within the
limits set by the principles for the classification (i.e. not too many or too few categories).
This is usually established by applying significance criteria (e.g. size limits on variables
such as employment, turnover, etc.) - the categories reflect realities of the field (e.g. the
society or economy) to which they relate (e.g. in an industry classification, the categories
should reflect the total picture of industrial activities of the country); and - the classification
is backed up by availability of instructions, manuals, coding indexes, handbooks and
training.
Classification structure: Refers to how the categories of a classification are arranged,
grouped and sub-divided. The categories of a classification can be arranged in either a
hierarchical or flat structure. In flat classifications the categories are arranged at a single
level. Hierarchical classifications have several levels corresponding to different degrees of
resolution (detail) in the measurement (specification) of the variable being observed.
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Classification unit: Is the basic unit to be classified in the classification (e.g. in an activity
classification this would be the establishment or enterprise, in an occupational classification
it will be the job). Refer also to Observation Unit.
Code, code letters and numbers: Normally consists of one or more alphabetic, numeric
or alpha/numeric characters assigned to a descriptor in a classification. Each code is
unique to a property within a classification. If the property changes, then the code should
also be changed. Codes can be linked to other codes with common characters, especially
in hierarchical classifications. For example, in ISIC Rev. 3, Technical and vocational
secondary education has a class code 8022, which is linked to Group 802 Secondary
education and to Division 80 Education. Statistical compilation, storage and retrieval is
facilitated by the use of codes with their descriptors.
Coding: Refers to the transformation of a textual information about an observation into a
code which identifies the correct category (value) for that observation.
Commercial counterfeiting:
The production or marketing of goods with the intent of
defrauding the purchaser by falsely implies, directly or indirectly, that a known and
reputable manufacturer produces the goods. Counterfeit goods are usually distinguished
from bogus goods in that in addition to replicating the legitimate good, they bear a forged
trademark. The Uruguay Round accord includes TRIPS provisions to discourage
counterfeiting
Correspondence table: Is a tool for the linking of classifications a correspondence table
systematically explains where, and to what extent, the categories in one classification may
be found in other classifications, or in earlier versions of the same classification.
Methodologically, correspondence tables (also referred to as tables) describe the way in
which the value sets of classifications are related, by describing how the units classified to
the groups defined for a classification would be classified in other classifications.
Consumer Goods: Those directly satisfy human desires (as opposed to capital goods).
An automobile used for pleasure is considered a consumer good. An automobile used by
businessperson to deliver wares is considered a capital good.
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Coverage: Specifies the population from which observations for a particular topic can be
drawn.
An understanding of coverage is required to facilitate the comparison of data.
Coverage issues are often explained through the use of tables showing linkages (e.g. part
or full correspondence); and can also be used to explain the ratio of coverage. The rules
and conventions of coverage are largely determined by concept definitions, scope rules,
and information requirements and, in the case of statistical collections and classifications,
collection and counting units and the collection methodology.
Coverage ratio: Measures the extent to which observations designated as primary to a
particular category are undertaken by units primarily involved with the observations related
to that category. In industry statistics, the coverage ratio is the output of goods and services
characteristic of a particular industry in proportion to the total output of the same goods and
services by the economy as a whole).
Cross reference:
classification
or
Is the linking, tracing or comparing of concepts/categories in one
between
classifications.
This
could
be
done
by
specifying
inclusions/exclusions, footnotes or descriptors in an annotation. Cross referencing draws
users' attention to related concepts/categories, inclusions/exclusions etc. in the same or
other related classifications.
Current Account: That portion of a country's balance of payments that records current (as
opposed to capital) transactions, including visible trade (exports and imports), invisible
trade (income and expenditures for services), profits earned from foreign operations,
interest and transfer Payments
Current Transfers: Transfers represent offsets to the provision of resources between
residents and nonresidents with no quid pro quo in economic value. Current transfers are
distinguished from capital transfers, which should be included in the capital account.
Current transfers consist of all transfers that directly affect the level of disposable income
and consumption. In the case of Uganda’s balance of payments, current transfers include
grants in form of both cash and kind. Transfers are classified according to the sector of the
compiling economy and are divided into two main categories: general government and
other sectors. Other sectors’ transfers are divided into workers remittances and other
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transfers, which includes transfers to Non Governmental Organizations (NGOs),
international aid agencies etc.
Customs territory is "the territory in which the customs law of a state applies in full."
Customs warehousing "means the customs procedure under which imported goods are
stored under customs control in a designated place (a customs warehouse) without
payment of import duties and taxes...Storage in customs warehouses should [also] be
allowed for goods which are entitled to repayment of import duties and taxes when
exported...[and] goods that have previously been dealt with under another customs
procedure...[This] makes it possible for the customs authorities to grant discharge of such
other customs procedure or to repay the import duties and taxes, as the case may be,
before the goods are actually re-exported." 13 "Warehoused goods...[are] allowed to
undergo usual forms of handling to improve their packaging or marketable quality or to
prepare them for shipment, such as breaking bulk, grouping of packages, sorting and
grading and repacking...it is not intended to authorize any change in the essential character
of the goods themselves."
A customs union is an entity formed by a customs territory, replacing two or more
territories and having in its ultimate state the following characteristics:- a common customs
tariff and a common or harmonized customs legislation for the application of that tariff; the absence of any customs duties and charges having equivalent effect in trade between
the countries forming the customs union in products originating entirely in those countries
or in products of other countries in respect of which import formalities have been complied
with and customs duties and charges having equivalent effect have been levied or
guaranteed and if they have not benefited from a total or partial drawback of such duties
and charges; - the elimination of restrictive regulations of commerce within the customs
union”.
Custodian of a classification: Refers to an institution or statistical area which has
responsibility
for
development,
maintenance,
implementation,
promulgation
and
interpretation of classifications. Collaboration among custodians is essential for
harmonization of classifications. Classifications are often constructed by, or on behalf of,
those responsible for policy implementation. In such cases, the administrative agency will
101
normally be the custodian, sometimes in cooperation with the statistical agency. For
example, Customs agencies are often the custodians of tariff classifications, even though
such classifications are also used for statistical purposes.
Derived classifications: Are based upon reference classifications. Derived classifications
may be prepared either by adopting the reference classification structure and categories,
providing additional detail beyond that provided by the reference classification or they may
be prepared through rearrangement or aggregation of items from one or more reference
classifications. Derived classifications are often tailored for use at the national or multinational level (e.g. NACE).
Description/Descriptor: Is normally a one line statement/heading/index entry of a
category in a classification, designed to convey its content
Division:
Is a title/name used in classifications to depict a particular level within a
hierarchy (e.g. Section, Division, Group, and Class). It usually refers to one of the upper
levels of a classification (e.g. in ISIC Rev. 3 the highest level - 1 digit - is referred to as the
division, while in the CPC the division level is the second highest level). Its use is not
mandatory.
Economic entity: Refers to a legal or social entity, or a group of entities, that engage(s) in
economic activities and transactions in its/their own right, such as corporations, non-profit
institutions or government units. An economic entity has legal, administrative, or fiduciary
arrangements, organizational structures or other parties having the capacity to efficiently
allocate resources in order to achieve objectives. Economic entities are often used as a
specific classification unit or a statistical unit.
Economic territory of a country consists “of the geographic territory administered by a
government within which persons, goods and capital circulate freely" and includes:
Airspace, territorial waters, and continental shelf lying in international waters over which the
country enjoys exclusive rights or over which it has, or claims to have, jurisdiction in respect
of the right to fish or to exploit fuels or minerals below the seabed; Territorial enclaves in
the rest of the world (clearly demarcated areas of land which are located in other countries
and which are used by the government which owns or rents them for diplomatic, military,
102
scientific or other purposes - embassies, consulates, military bases, scientific stations,
information or immigration offices, aid agencies, etc. - with the formal political agreement of
the government of the country in which they are physically located);
Any free zones, or
bonded warehouses or factories operated by offshore enterprises under customs control
(these form part of the economic territory of the country in which they are physically
located)."
In the case of maritime countries their economic territory "includes any islands
belonging to that country which are subject to exactly the same fiscal and monetary
authorities as the mainland, so that goods and persons may move freely to and from such
islands without any kind of customs or immigration formalities”. The economic territory of a
country does not include the territorial enclaves used by foreign governments or
international organizations which are physically located within the geographical boundaries
of that country.
Elements; Enterprise unit: An institutional unit or the smallest combination of institutional
units that encloses and directly or indirectly controls all necessary functions to carry out its
production activities.
EXPORT:
Export Statistics compiled by Federal Bureau of Statistics (FBS) reflects
physical exit of goods out of the country‘s custom boundary irrespective of money
consideration. However, the following are excluded from export statistics
Family (of classifications): The family of international economic and social classifications
is comprised of those classifications that have been internationally approved as guidelines
by the United Nations Statistical Commission or other competent inter-governmental board
on such matters as economics, demographics, labor, health, education, social welfare,
geography, environment and tourism.
Field:
Generally refers to the area or sphere of operation, observation, activity etc. In
classifications, field could describe the scope of individual classifications or their constituent
groups, categories or items at a given level.
Fiscal year (FY) : Uganda's fiscal year runs from July 1 to June 30. Fiscal year dates of
reference correspond to the year in which the period ends. For example, fiscal year 1990
began July 1, 1989 and ended June 30, 1990.
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Foreign Trade Index: Laspeyers formula in its original form is used in the computation of
trade indices that is as under
Unit
Value
Index
™Pn
=
™Po
Quantum
Index
™Qo
Where:
x
Qo
x
™Qn
=
x
x
x
100
Qo
Po
x
100
Po
Pn --> refers to the price (Unit Value) of each item during the current period.
Po --> refers to the price (Unit Value) of each item during the base period.
Qn --> refers to the quantity data (Volume) of each item during the current period.
Qo --> refers to the quantity data (Volume) of each item during the base period.
Framework: A framework is a multi dimensional classification system that seeks to bring in a
range of elements. A framework could include a combination of classifications, code lists
and/or data items modules, and generally metadata.
Source: ABS, Standard Procedure for Creation of Alternate Industry Views and Frameworks,
1999 the term framework can also be used to describe the skeleton of classification from
which a detailed classification is developed. Such a framework encompasses the concepts to
be embedded in a classification (e.g. product and activity) and provides the structure for the
classification. At a broader level, the term framework may be used to describe a family of
related classifications, such as those produced by the UN.
Franchising (from the French for free) is a method of doing business wherein a franchisor
licenses trade marks and tried and proven methods of doing business to a franchisee in
exchange for a recurring payment, and usually a percentage piece of gross sales or gross
profits as well as the annual fees. Various tangibles and intangibles such as national or
international advertising , training and other support services are commonly made available
by the entity licensing the 'chain store' or franchise outlet (commonly shortened to the one
word: franchise), and may indeed be required by the franchisor, which generally requires
audited books, and may subject the franchisee or the outlet to periodic and surprise spot
checks. Failure of such tests typically involve non-renewal or cancellation of franchise rights
Free zones. "The term 'free zone' means a part of the territory of a State where any goods
introduced are generally regarded, insofar as import duty and taxes are concerned, as being
104
outside the customs territory and are not subject to the usual customs control." "A distinction
may be made between commercial and industrial free zones. In commercial free zones the
permitted operations are generally limited to those necessary for the preservation of the
goods and the usual forms of handling to improve their packaging or marketable quality or to
prepare them for shipment. In industrial free zones processing operations are authorized."
"By specifying that the goods are not subject to the usual customs control, the definition
draws attention to the fact that the customs control exercised over goods placed in free zones
is more flexible than that applicable to goods stored in customs warehouses, for example, or
admitted under the temporary admission for inward processing procedure. Whereas in
exercising the usual customs control the customs authorities have at their disposal a whole
series of specific measures designed to ensure compliance with the laws and regulations
which they are responsible for enforcing, in the case of free zones they normally have
recourse to general surveillance measures only. Thus, premises situated in free zones are
not usually subject to permanent customs surveillance. The control measures applied to
goods during their stay in the free zone are generally reduced to an absolute minimum and
are principally concerned with the relevant documentation." "In some countries [a free zone]
is also known under various other names, such as 'free port', 'free warehouse'
Goods: Under the goods are exports and imports of goods. Goods include most movable
goods that change ownership between Ugandan residents and non-residents. In Uganda’s
BOP, we have separate lines to show general merchandise, goods for processing, repairs on
goods, goods procured in ports by carriers and non monetary gold. Due to the level of
development of the BOP statistics and trade in Uganda some line items such as goods for
processing and repairs on goods are still blank due to lack of data.
Gross domestic product (GDP): This is a measure of the total value of goods and services
produced by a domestic national economy during a given period, usually one-year. GDP is
obtained by adding the value contributed by each sector of the economy in the form of profits,
compensation to employees, and depreciation (consumption of capital). Only domestic
production is included, not income arising from investments and possessions owned abroad,
hence the use of the word "domestic" to distinguish GDP from gross national product (q.v.).
Real GDP is the value of GDP when inflation has been taken into account. In this book,
subsistence production is included and consists of the imputed value of production by the
105
farm family for its own use and the imputed rental value of owner-occupied dwellings. In
countries lacking sophisticated data-gathering techniques, such as Uganda, the total value of
GDP is often estimated.
Gross National Product (GNP): This is the total market value of all-final goods and services
produced by an economy during a year. GNP is obtained by adding the gross domestic
product (q.v.) and the income received from abroad by residents and then subtracting
payments remitted abroad to non-residents. Real GNP is the value of GNP when inflation has
been taken into account.
Grouping/De-grouping
In a hierarchical or tree structure classification, categories are
grouped ranging from broad to detailed levels for each set. The categories within each set can
be grouped (aggregated) or de-grouped (disaggregated). For example, a multi level
hierarchical classification would be structured such that the sum of the detail of each level
equates to the level above. In this way, observations can be taken at the level of detail of
interest of particular purposes. Observations at the lower levels can be summed to provide
observations at more aggregated levels (grouped) and, with appropriate manipulations,
observations at higher levels can be inferred at lower levels (de-grouped).
Harmonization: Classification harmonization involves the alignment, wherever possible, of
the underlying concepts and definitions of both similar and disparate classifications to produce
classifications which can related to the maximum extent possible within the constraints of the
requirements of individual classifications.
Harmonization is the process of combining or
comparing data for purposes of analysis, either through the use of similar standard definitions
and classifications, or through a complex set of explanations on how to achieve comparisons
across standards and classifications. In the harmonization of classifications, building blocks
for common groupings and regroupings of items from different structures of the classifications
are identified. The process is facilitated by reducing or eliminating minor differences among the
classifications Harmonization of classifications requires continuous co-ordination and
exchange of information between the custodians of the relevant classifications on a regular
basis. Without such exchange, different interpretations of similar concepts and categories will
occur.
In the harmonization process the classifications could be described as reference,
derived or related classifications. Source: United Nations Statistics Division, Fourth Meeting of
106
the Expert Group on International Economic and Social Classifications, New York, 2-4
November (1998): A Statement of Best Practices
Harmonized Commodity Description and Coding System (HS) The Harmonized System
(HS) is developed by the World Customs Organization (WCO), (formerly the Customs Cooperation Council (CCC). It has been adopted by over 100 countries for customs tariffs, export
statistics and import statistics.
Hierarchy: Refers to the classification structure where a classification is arranged in levels of
detail from the broadest to the most detailed level. Each level of the classification is defined in
terms of the categories at the next lower level of the classification.
Homogeneity (homogeneous):
One of the characteristics of a good classification is
reasonably high homogeneity for its categories. Homogeneity is the measure of the degree to
which categories consist of components with similar characteristics and is achieved by
systematic grouping and stratifying members of the population being classified. Homogeneity
ratios are defined on a mathematical basis to minimize the variance within a classification.
IMPORT: Import Statistics reflects physical entry of goods into the country‘s custom boundar
irrespective of money consideration.
Income: This account covers income earned by Ugandan residents from non-residents and
vice versa. It covers compensation of employees and investment income. Compensation of
employees shown in Uganda’s balance of payments comprises salaries earned by expatriates
on short term assignments in Uganda. Currently, our balance of payments does not capture
the wages and salaries earned by Ugandans working abroad. Investment income comprises
income earned from the provision of financial capital and income payments for financial
capitareceived from abroad.
Index: A listing, usually alphabetical, providing pointers to the location within classifications of
the observations contained therein. This may be achieved by references to page numbers,
paragraph numbers or classification codes. Indexes often contain terminology not expressly
used within classifications (synonyms) and may contain cross-references to related
observations.
107
Invisible Trade: Items such as freight, insurance and financial services that are included in a
country's balance of payments accounts (in the "current" account), even though they are not
recorded as physically visible exports and imports.
International Monetary Fund (IMF) :
Established along with the World Bank (q.v.) in 1945, the IMF is a specialized agency affiliated
with the United Nations; it is responsible for stabilizing international exchange rates and
payments. The main business of the IMF is the provision of loans to its members (including
industrialized and developing countries) when they experience balance-of-payments
difficulties. These loans frequently carry conditions that require substantial internal economic
adjustments by the recipients, most of which are developing countries.
Item: Refers to an article or a unit included in enumeration. In classifications, the term item
generally applies to a classification category.
Legal entities: Are entities created for purposes of production, mainly corporations and nonprofit institutions (NPIs), or government units, including social security funds. They are capable
of owning goods and assets, incurring liabilities and engaging in economic activities and
transactions with other units in their own right
Level: Level denotes the position within the hierarchy of a category or a group of categories.
Life cycle: Refers to the creation, changes and death of a given classification. A classification
can be revised due to a number of factors e.g. changes in industries, changes in international
standard classifications, etc. Such changes may include the aggregation of disaggregation of
items, changes in terminology, additions and/or deletions etc. These changes will result in
either a revised version of the existing classification (where changes are essentially in the
detail), or a replacement version (where the changes are substantial, involving structural
changes, etc). The life span of a classification is dependent upon a number of factors, most
importantly the rate of change of the observations it describes and time series (stability)
requirements.
Linkages: Refers to mapping or linking one classification to another. That is each individual
group in one classification should be linked with the most appropriate corresponding group(s)
108
in the other. This allows for better management of classifications in a coordinated way, and for
the transfer from using one classification to using the other. The first step when establishing
linkages should always be to give to the most detailed groups of one classification the code of
the most detailed appropriate group in the other. This then allows, when needed, the groups of
one classification to be subsequently aggregated to most of the relevant aggregated groups of
the other.
Local unit: Is defined as an enterprise, or part of an enterprise, which engages in productive
activity at or from one location. The definition has only one dimension in that it does not refer to
the kind of activity that is carried out. Location may be interpreted according to the purposenarrowly, such as specific address, or more broadly, such as within province, state, country,
etc. Local units are also used as Statistical Units
Maintenance: Refers to an institution or statistical area which has or has been given the
responsibility for maintaining and/or updating or revising the classification.
Network:
Applies to a chain of interconnected persons, things, operations etc. In
classifications, networking could result in reference, derived or related classifications.
Exchange of information and knowledge across classifications would be facilitated and
implemented if national classifications could be presented as part of the web sites of statistical
offices and an international cyber platform on the Internet is used for all the major international
classification debates.
Nomenclature: Systematic naming of things or a system of names or terms for things. In
classifications, nomenclature involves a systematic naming of categories or items. The terms
"nomenclature" and "classification" are often used interchangeably, despite the definition of a
"classification" being broader than that of a "nomenclature". A nomenclature is essentially a
convention for describing observations, whereas a classification structures and codifies the
observations as well.
Normalized heading and codes: This refers to standardization of headings/titles and their
codes in the classifications. For example, the titles and use of n.e.c. (not elsewhere classified)
categories and the codes given to them, as well as the code ending in zero should normally be
standardized, e.g. by using the title of the aggregate group when naming its n.e.c. group, and
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by using 9 as the last digit for n.e.c. categories and 0 to be equivalent with coding to the higher
level, e.g. because the information needed for a more detailed code is not available.
Not elsewhere classified (n.e.c.), not elsewhere included (n.e.i.) or not elsewhere
specified (n.e.s.) residual category: Applies to a subset of a category (e.g. class, group,
etc.) which represents those members of the category that do not belong to any of the other,
separately identified, categories. (Note that it should not be used as a 'dump' code for
observations for which there is insufficient information to assign a detailed code. Such units
should be given a code ending in zero to indicate the appropriate aggregate group.) The
significance of the observations (e.g. income, employment etc.) for this category should be
relatively low compared to those of the other categories in the same more aggregated group of
the hierarchical set.
Paris Club : The informal name for a consortium of Western creditor countries that have made
loans or have guaranteed export credits to developing nations and that meet in Paris to
discuss borrowers' ability to repay debts. The organization has no formal or institutional
existence and no fixed membership. The French treasury runs its secretariat, and it has a
close relationship with the World Bank (q.v.), the International Monetary Fund (q.v.), and the
United Nations Conference on Trade and Development (UNCTAD).
Partial correlation/correspondence: Occurs where a category of one classification can be
coded to two or more categories of other classifications.
Population: It is the total membership or population or 'universe' of a defined class of people,
objects, or events.
There are two types of population viz. target population and survey
population. A target population is the population outlined in the survey objects about which
information is to be sought and a survey population is the population from which information
can be obtained in the survey. The target population is also known as the scope of the survey
and the survey population is also known as the coverage of the survey. For administrative
records the corresponding populations are: the 'target population' as defined by the relevant
legislation and regulations, and the actual 'client population'.
Primary unit: Is a unit which always can take one and only one value for the variable for
which the classification represents a value set. (e.g. the primary unit to be classified by
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'occupation' is the 'job'. To classify a 'person' by 'occupation' one first needs to establish a link
between the person and an appropriate 'job').
Process:
A systematic series of actions directed to some end. It is a usually a set of
continuous actions or operations undertaken in a defined manner. Process, when applied to
classification development and maintenance, is the means by which the concepts and
methodology supporting and underlying classifications are incorporated within classifications,
thereby promoting classification best practice
Re-exports: Are goods that are temporarily imported into a country and then exported again?
The temporary nature of the importing usually means that the importer or agent is permitted to
reclaim some, or all, of the import duty and VAT paid on the goods. Re-exporting may be
necessary for several reasons, including the following:
When goods require further processing, for example, when an unfinished factory product
requires additional manufacturing work.
When goods are in need of repair work.
When goods are in transit within the European Union.
When imported the buyer has rejected goods. In all these circumstances, there are special
customs control procedures with which the importer must comply, including specific
warehousing regulations.
Reference classifications: Are those economic and social classifications that are a product
of international agreements approved by the United Nations Statistical Commission or another
competent inter-government board, such as that of the International Lab our Organization
(ILO), the International Monetary Fund (IMF), the United Nations Educational, Scientific and
Cultural Organization (UNESCO), World Health Organization (WHO), or the World Customs
Organization (WCO) depending upon the subject matter area. Thus reference classifications
have achieved broad acceptance and official agreement and are approved and recommended
as guidelines for the preparation of classifications. They may be used as models for the
development or revision of other classifications, both with respect to the structure and with
respect to the character and definition of the categories
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.Re-Imports: Goods exported and returned to the consignor country without any modification
or change in the original shape or form is termed as re-import.
Related categories: Are those categories which have some form of elementary relationship.
Such related categories can be meaningfully aggregated to give a broad picture or
disaggregated when finer details are required. Related categories often have commonality in
their codes, due to their common starting point (e.g. an international standard), although
relationships can be applied between classifications with different structures and coding
systems provided the concepts embedded within the observations under consideration are
consistent.
Related classifications: Are classifications which encompass the same or similar
observations within different structures and/or to different levels of detail. They often occur as
part of a family of classifications, sometimes with a common starting point, such as an
international standard classification
Revised classification: Refers to a classification which replaces the previous classification. A
change in a classification does not necessary result in a change of the name of the
classification but can be distinguished by use of a version number (e.g. ISIC Rev. 2 was
replaced by ISIC Rev. 3).
A revised classification will normally represent a rethinking of
conceptual basis, similarity criteria and/or scope, and should be distinguished from an updated classification
Services: Comprise services provided between Ugandan residents and non-residents. The
services account is broken into the various components shown in the table 1. A quick glance at
Uganda’s BOP reveals that imports of services exceed exports by far.
Similarity criteria:
Refers to the criteria used to define categories in hierarchical
classifications (e.g. the grouping of elementary building blocks). In ISCO-88 the main similarity
criteria are the skill level and skill specialization needed to carry out the tasks and duties of the
jobs. Skill level is the main criterion to delineate the most aggregate categories, while skill
specialization is used to delineate the more detailed categories within the aggregate
categories.
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Special drawing right(s) ( SDRs )
Monetary unit(s) of the International Monetary Fund (IMF-- q.v.) based on a basket of
international currencies consisting of the United States dollar, the German deutsche mark, the
Japanese yen, the British pound sterling, and the French franc.
Specialization ratio:
Aids in the assessment of the homogeneity of categories within a
classification. Specialization ratios measure the extent to which observations contained within
a category are representative of the population of those observations as a whole (e.g. in
industry statistics, the specialisation ratio is the output by an industry of goods and services
characteristic to that industry in proportion to its total output).
Standard classifications:
Are those that follow prescribed rules and are generally
recommended and accepted. They aim to ensure that information is classified consistently
regardless of the collection, source, point of time etc.
Standard Classification of Goods
The conceptual basis of the SCG relies on mixed
principles such as component material, use and function, stage of fabrication and to a certain
extent, industrial origin. The concepts are inherited from the Harmonized Commodity and
Description and Coding System (HS), the international classification designed primarily for
customs identification and trade statistics. The SCG uses the 6-digit coding structure of the HS
as a base and then adds 3 digits to identify the country’s requirements for statistical purposes.
Standard Statistical Units: A Statistical unit is the unit of observation or measurement for
which data are collected or derived. or listed
Statistical classification:
Refers to a classification constructed for the collection and
presentation of numerical facts systematically collected (i.e. statistics). The usefulness of a
statistical classification is enhanced if based on or representing a standard classification.
Statistical territory of a country or area is "the territory with respect to which data are being
collected"; that is, goods which enter or leave the statistical territory are to be recorded in the
external trade statistics.
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Structure (tree):
Provides the means for identifying relationships, usually hierarchical,
between categories. A hierarchical classification is based on a tree structure where each set of
its detailed categories are subsets of categories at the level about the one in which they
contained.
Structural Links: Are correspondence links where opportunities for (direct) correspondence
between the categories of different classifications are difficult or not possible to establish,
owing to significant structural differences in the defined value sets that do not allow for
common correspondence at a similar hierarchical level in the structure. In some
circumstances, an approximate or truncated correspondence may be made by aggregating
subclasses of one classification to different structural levels of the other classification.
Temporary admission (of goods) for inward processing "...means the customs procedure
under which certain goods can be brought into a customs territory conditionally relieved from
payment of import duties and taxes; such goods must be intended for re-exportation within a
specific period after having undergone manufacturing, processing or repair...Operations
allowed under the temporary admission for inward processing procedure may be carried out in
premises designated as warehouses for inward processing..."
Terminology (classification): Refers to the system of terms commonly used or adapted for
use in a classification. Wording or terminology, which may have broader meanings within the
wider community, may have specific meaning within the context of given classifications. For
example, 'industry' and 'homogeneity ratios' have unique definitions in the context of industry
classifications.
Terms of trade: It shows the average price of a country’s aggregate exports in relation to the
average
Terms
price
of
Trade
=
of
Index
of
Unit
its
Values
of
imports.
Exports
x
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Index of Unit Value of Imports
Transition economy is an economy which is changing from a planned economy to a free
market Transition economies undergo economic liberalization, letting market forces set prices
and lowering trade barriers, macroeconomic stabilization, where immediate high inflation is
brought under control, and restructuring and privatization, in order to create a financial sector
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and move from public to private ownership of resources. These changes often may lead to
increased inequality of incomes and wealth, dramatic inflation and a fall of GDP.
Uganda shilling:
USh; basic unit of currency divided into 100 cents. The Uganda shilling
was introduced in 1966 and was tied to the United States dollar until 1975, when its value was
tied to the special drawing right (SDR; q.v.) of the IMF (q.v.). In 1986 the Uganda shilling was
officially valued at US$1 = USh1450. A new Uganda shilling was introduced in May 1987. It
involved an effective devaluation of 76 percent, was given an official value equal to 100 old
shillings, and had an international exchange rate of US$1 = USh60. Successive devaluation in
1988, 1989, and 1990 reduced the official dollar value to US$1 = USh510 by late 1990.
Units (classified): Refer to entities, respondents to a survey or things used for purpose of
calculation or measurement. Their statistics are collected, tabulated and published. They
include, among others, businesses, government institutions, individual organizations,
institutions, persons, groups, geographical areas and events. They form the population from
which data can be collected or upon which observations can be made.
Valuation:
Exports are valued on FOB basis while imports on CIF basis, as declared by
exporters/importers and accepted by the customs.
Validity period: Refers to the time period that any document, classification, etc. could be
applicable or used. In classification, usually there is an overlapping time period where the old
classification could still be used before being superseded by the revised edition.
Variable: Is a characteristic of a unit being observed that may assume more than one of a set
of values to which a numerical measure or a category from a classification can be assigned
(e.g. income, age, weight, etc. and 'occupation', 'industry', 'disease' etc).
Visible Trade: Imports, exports and re-exports of merchandise
World Bank: International name used to designate a group of three affiliated international
institutions: the International Bank for Reconstruction and Development (IBRD), the
International Development Association (IDA), and the International Finance Corporation (IFC).
The IBRD, established in 1945, has as its primary purpose the provision of loans to developing
countries for productive projects. The IDA, a legally separate loan fund administered by the
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staff of the IBRD, was set up in 1960 to furnish credits to the poorest developing
countries on much easier terms than those of conventional IBRD loans. The IFC founded
in 1956.
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