Bài giảng của tôi:
INSTRUMENTS FOR ECONOMIC ANALYSIS
I. Introduction:
The quarterly economic report, jointly prepared by the European Union and the Ministry of Planning and Investment of Vietnam (QER-EU-MPI), is a system of almost existing general economic indicators of Vietnam. It comprehensively reflects the extended reproduction process on 3 dimensions: macro-economy, productive sectors and economic areas. In examining these indicators, we can determine the structure, growth rate, general equilibrium and relations between them. serving for economic analyses and anticipation of possible developments as well as for management needs of leaders at different levels.
In a view of providing information on methodology to users of QER for economic analysis and projection purposes, this chapter briefly presents instruments which have been widely used in the world and adapted to the situation of Vietnam. Those instruments are divided into 2 groups: a group of tables, graphs and charts of statistic and a group of indicators and methods for analyzing and assessing the results and performance of economic development.
II. Tables, graphs and charts of statistic in economic analysis.
Data in QER can be represented in different forms, which make it readily comprehensible and able to reflect the insights of issue to its various users. The most used methods of presenting data in economics are the tables, graphs and charts of statistic. The choice of these instruments depends on the number of time series, nature of data and purposes of its users. For instance, the use of tables may be the most appropriate mean for analyzing in detail changes in prices, exchange rates and domestic markets over a period of last 12 months because it provides not only information on the trend of indicators concerned but also contains specific data which help users calculating additional driving indicators for further analysis. However, if users want to include specific periods of time where peak or abnormal changes of production, market and price have occurred, the use of charts graphs may be the best. In case users want to examine the trend of changes over the time or the linkage between certain indicators, graphs should be in use.
The examples below illustrate principles involved in the construction of tables, graphs and charts. We will present comprehensibly the gross domestic product in agricultural, industrial and service sectors (and also their sub-sectors) under different forms of statistic. The period examined is from 1996 to 1999, data sources are from 1999 statistical yearbook (which have been quoted in QER). Figures are first described in a narrative (text) form, e.g. the agriculture production was respectively 53,577 in 1996 and 55,895 in 1997 and so on. Using tables, graphs and charts to represent the above indicators, we should follow certains principles as follows:
1/ Tables
Data recorded in a narrative form do not allow users to interpret easily the main features of an economic phenomenon. Furthermore, when there are many indicators in use, it is difficult to compare them directly from a narrative form. Thus, the most appropriate means to overcome these difficulties is to set out the data in a tabular form as showed in table 1 and table 2 below:
Table 1: GDP 1996-1999, comparative prices of 1994, by economic sectors, (billion VND)
Sectors
|
1996
|
1997
|
1998
|
1999
|
Agriculture
|
53577
|
55895
|
57866
|
60892
|
Industries
|
67016
|
75474
|
81764
|
88047
|
Services
|
93240
|
99895
|
104966
|
107330
|
Total
|
213833
|
231264
|
244596
|
256269
|
The table 1 is simple, contains only annual data on values added of each economic sector and the whole economy from 1996-1999. It does not allow recognizing abnormal changes as the value of production output expressed by current prices usually increases along the time. The table 2 therefore includes data on sectoral structure so that we can analyze abnormal changes which have been occurred.
Table 2: Values added by sectors, comparative prices of 1994 (billion VND)
Sectors
|
1996
|
1997
|
1998
|
1999
| ||||
billion
VND
|
%
|
billion
VND
|
%
|
billion
VND
|
%
|
billion
VND
|
%
| |
Agriculture, Forestry and Fishery
| ||||||||
- Agriculture,
|
45652
|
21,35
|
47915
|
20,72
|
49639
|
20,29
|
52370
|
20,44
|
- Forestry
|
2448
|
1,14
|
2450
|
1,06
|
2459
|
1,01
|
2536
|
0,99
|
- Fishery
|
5477
|
2,56
|
5530
|
2,39
|
5768
|
2,36
|
5987
|
2,34
|
Total |
53577
|
25,06
|
55895
|
24,17
|
57866
|
23,66
|
60892
|
23,76
|
Industry,
construction
| ||||||||
- Mining
|
11753
|
5,50
|
13304
|
5,75
|
15173
|
6,20
|
17450
|
6,81
|
- Processing
|
34339
|
16,06
|
38743
|
16,75
|
42694
|
17,45
|
45888
|
17,91
|
- Energy..
|
3986
|
1,86
|
4572
|
1,98
|
5136
|
2,10
|
5498
|
2,15
|
- Construction
|
16938
|
7,92
|
18855
|
8,15
|
18761
|
7,67
|
19211
|
7,50
|
Total
|
67016
|
31,34
|
75474
|
32,64
|
81764
|
33,43
|
88047
|
34,36
|
Services
| ||||||||
- Distribution
|
36866
|
17,24
|
39422
|
17,05
|
41170
|
16,83
|
41993
|
16,39
|
- Others
|
56374
|
26,36
|
60473
|
26,15
|
63796
|
26,08
|
65337
|
25,50
|
Total
|
93240
|
43,60
|
99895
|
43,20
|
104966
|
42,91
|
107330
|
41,88
|
GDP
|
213833
|
100,00
|
231264
|
100,00
|
244596
|
100,00
|
256269
|
100,00
|
Source: EU - MPI Project 5/2000
General principles for tabulation used by statistics
a/ Any totals required (e.g GDP) should be brought down vertically under their components’ data (e.g sectoral values added) rather than carried across horizontally. The vertical presentation is the easiest to follow. If totals are required for a number of variables, component figures of each variable should also be presented in column, then its total is brought down vertically under the variable’s data column (see table 2). The principe is used in the construction of QER report.
Table 3: GDP by years (previous year =100%)
Total
|
Year
|
Agriculture
|
Industry
|
Services
|
102,84
|
1986
|
102,99
|
110,94
|
97,73
|
103,63
|
1987
|
98,86
|
108,46
|
104,57
|
106,01
|
1988
|
103,65
|
105,00
|
108,77
|
104,68
|
1989
|
107,00
|
97,41
|
107,86
|
105,09
|
1990
|
101,00
|
102,27
|
110,19
|
105,81
|
1991
|
102,18
|
107,71
|
107,38
|
108,70
|
1992
|
106,88
|
112,79
|
107,58
|
108,08
|
1993
|
103,28
|
112,62
|
108,64
|
108,83
|
1994
|
103,37
|
113,39
|
109,56
|
109,54
|
1995
|
104,80
|
113,60
|
109,83
|
109,34
|
1996
|
104,40
|
114,46
|
108,80
|
108,15
|
1997
|
104,33
|
112,62
|
107,14
|
105,76
|
1998
|
103,53
|
108,33
|
105,08
|
104,77
|
1999
|
105,23
|
107,68
|
102,25
|
Source: Year book 1999, Statistic Publishing House, Hanoi 2000
However, these are not hard and fast rules. If a large number of time periods are involved, it may be more convenient to list time and indicators vertically instead horizontally as showed in table 3 above
b/ It is advisable to include data on sectoral structure when they are extremely necessary. As showed in table 2, data on sectoral structure makes the table complicated and difficult to follow, even makes users confused.
c/ Tables should be suitably titled, and units used also need to be indicated in the table (if there are many units in use, e.g billion VND and % in table 2) or on the top of the table. Sources of data should be stated below the table.
Table 2 is more complicated but also contains more information, from it we can make the following remarks:
- Industrial share in the GDP steadily increased in the period 1996-1999 while the share of agriculture and services constantly decreased. However, it is not easy to compare the degree of reducing share in agriculture sector and that in service sector from those data.
- The trend of reducing share in GDP has occurred in all secondary sub-agriculture sectors. According to data in the table, as the degree of share reduction in each sub-sector was almost the same, there were no sharp changes in the structure of values added in agriculture sector. The situation was similarly prevailed in service sector. By contrast, there were considerable changes in the structure of industrial sector: the share of mining industry increased rapidly, followed by the increased shares of electricity, gas, water and processing industries while the share of construction industry in the GDP reduced dramatically after a sharp rise in 1997.
Those remarks of trends are very significant in economic study, at least for the following reasons:
- The trends indicate what the structure and volume of values added will be in the near future, and they therefore facilitate the planning and policy formulation work. The ground for a near future projection is that each economy has certain “inertness” that a new policy, regardless its strength, can not break immediately such a inerness. Big changes only take place after a “time” of implementing the new policy. The evolution in the past can therefore affect the near future and be the ground for near future projection.
Actually, the trend of development or the average development axe of every indicators is considered as a sustainable growth rate. Economic policies should therefore be formulated in a manner that they may help the economy going along with this growth rate, not too fast or too slow. If the economy goes out of its sustainable growth orbit, it takes a certain period of time to drive it into the orbit. The fact of driving the economy back to it orbit is called “adjustment process” and rapidity or slowness of the process is called “speed of ajustment”.
- The trends also indicate changes which require special attention and interpretation so that appropriate policies may be proposed to respond. For example, attention should be paid to the question why the share of services in GDP reduce so rapid and constantly in 1996-1999 while in the period 1989-1995, the share of this sector had steadily increased and the world practice showed that the share of service sector had a tendency of increase in economic growth. Similarly, why the structure of agriculture sector was unchanged while the economic development usually entails the increase of aqua and forestry sectors? In industrial sector, we should be concerned by the sharp increase of mining sector, which means the development based on the sell of natural resources while the share of processing and infrastructure construction industries (energy, water supply, construction etc.) had a very modest increase, even decrease. The above concerns will lead to more rooted-causes of the problem (such as the competitiveness, investment structure, changes of product demand) and the proposal of policy to improve the situation.
If we continue with more detailed tables which containing data on the changes of production output of main products in those sectors, we may find out causes of the above development and structural changes.
2/ Charts.
The purpose of charts and graphs is to select relevant features from data under consideration and present them in pictorial forms so that visual comparison can be made. The main types of charts include: bar charts, component bar charts, percentage component bar charts, pie charts, multiple bar chart etc. Those main types of charts and their derived forms have been standardized in EVIEWS and EXCEL softwares. Users simply enter basic data and the software will produce a chart of results.
2.a. Bar charts
Bar charts are used for comparing the sizes of figures. The height of each bar representing the size of corresponding figure. The width of the bars and the distance between each bar does not bear any significance in the chart. The chart 1 below illustrates the economic growth rates of Vietnam in the period 1986-1999
Chart 1: GDP growth from 1986-1999 (by %)
In the above chart, both vertical and horizontal axes are labeled: the vertical axe presents the growth rates by percentage (%), the horizontal axe presents yearly time. The chart indicates the trends of growth over the years, the years of peak rate and lowest rate. Comparison of growth rates between different years can also be made from the chart.
2.b. Component bar chart:
Chart 2 below is an illustration of a component bar chart. In this, the bars are divided into sections to show the breakdown of totals into component figures. The height of each section represents the seize of the corresponding component. The sections should be suitably coloured or shaded for easy distinction. In this connection, it should be noted that adjacent sections should be distinguished by shadings of different intensities (black, white, grey...) rather the than by the use of hatching lines which differ only in direction (horizontal, vertical etc.) as the eyes can appreciate intensity differences more easily.
Chart 2: The GDP by sectors, comparative prices of 1994, billion VND
Besides component bar charts, percentage or structural component bar charts are also in use to represent the structure of each component so that comparison of structural changes over the period can be made. In this kind of chart, the total height of each bar is 100% and the varying heights of sections in the bar show the percentages of the corresponding component figures in the total ( See chart 3)
Chart 3: Component of GDP over the period 1996-1999 based on comparative prices of 1994, by %
c) Pie chart
Pie chart can be used instead of percentage component bar charts to show relative seizes of figures. In this case, the angle of each slice of the pie is proportional to the size of the corresponding component figure. The calculation of these angles differs from percentage calculation only in that 360° instead of 100% has to be shared between the components. For instance, the GDP of 1996 can be represented as follows:
- Angle representing the share of agriculture: 53577 / 213833 * 360° = 90,2°
- Angle representing the share of industry: 67016 / 213833 * 360° = 112,8°
- Angle representing the share of services: 93240 / 213833 * 360° = 157,0°
Chart 4 is an example of pie chart.
Chart 4: GDP in 1996, by %
In provinces where computers are not available, compact and scale are needed. By experience, pie charts are more attractive to users than percentage component bar charts. Furthermore, if there are more than 4 or 5 component figures, their relative sizes can be more readily appreciated from a pie chart than from a percentage component bar chart. However, if only a few components are involved, the eye can more easily compare the heights on a component bar chart than the angles of a pie chart.
d) Multiple bar charts
Multiple bar charts are a very effective means of comparing the sizes of component figures. Each bar of the chart can represent the size of corresponding component figure (see figure 5). However, as remarkable in the figure 5, the main disadvantage of multiple bar chart is that the sizes of total figures are not shown (e.g the Figure 5 does not show the annual GDP).
Chart 5: Value added by sector, billion VND, comperative prices 1994
e) Multi-dimensional charts
The above captured charts are relatively simple, represented on a plane. In some cases, data can be represented in a multi-dimensional chart which is more attractive and eye-catching. However, the disadvantage of a multi-dimensional charts is that they are too complicated then comparison between components figures are difficult to be made.
Figure 6: Changes of values added of sectors by years
3/ Graphs
Graphs are largely used in economic papers as they are readily explanatory the evolution of indicators as well as the relationships between them over time. Generally, graphs are often preferred than charts if it is required to show the way in which data changes over time. There are many types of graphs, the choice depends on the number of indicators, their nature and the purposes of users. Below is the presentation of some graphs that are in most use. More types of graphs can be found in statistic materials.
a) Graph representing relationship between 2 indicators
Graphs usually present the relationship between a number of indicators, also called variables. For instance, to show the relation between unemployment rates and inflation (the Phillip curve), we can use the vertical axe to present the variable on unemployment rates and the horizontal axe for inflation rates. The base point (where vertical axe and horizontal axe cut each other) is 0 for both indicators.
The construction of the graph is as follows: if a inflation rate of 10% is corresponding with an unemployment rate of 5%, we can find the point (5;10) on the graph by drawing 2 lines: the first line starts at the point (10;0) and goes in parallel with the horizontal axe, the second line starts at the point (0;5) and goes in parallel with the vertical axe. The point where the two line meet is (5,10). By doing the same with other sets of figures, we can get the graph representing the relationship between unemployment rates and inflation rates over the time. Graph 1 is an example.
Graph 1: Relationship between unemployment and inflation 1992-2000 (%)
In the above example, urban unemployment rates are a depending variable and inflation rates are a independent variable. Conventionally, depending variables are presented on the horizontal axe while independent variables are presented on vertical axe. In this type of graph, the element of time is ignored.
b) Time graphs
Some graphs represent the evolutions of variables over the time. In this case, time is a dependent variable, marking for example by years (e.g 1996, 1997, 1998 etc.) or by quarters (q1.1999, q2.1999, q3.1999, q4.1999, q1.2000). This type of graphs, called time graphs, is largely use in economics.
In time graphs, we can construct different points in correspondent with every time variables, e.g. 1996, 1997... We can also link those points together by a line or curb for easier observation. Actually, lines are often used to link points. Graph 2 is an example.
Graph 2: Evolution of inflation and unemployment over years (by %)
There are various forms to represent time graphs. We can dot points of graphs to highlight the values of variables, or even indicate the value of variables at every points on the graph. We can also combine time graphs with charts, for example, unemployment rates can be represented under the form of a chart while inflation rates represented by a line. Users can look up other forms of time graphs in EVIEWS and EXCEL softwares.
The main difereent between graphs and charts is that the horizontal axe in graphs is continuous, e.g if the horizontal axe represent the inflation rates, it will be valued from 0 to infinite (+ or -), or if it represent the time, its value will marked along the time. That is the reason why some data in charts (e.g chart 4) can not be represented in graphs. For example, if the annual data from 1996 to 1999 in the charts 2 and 3 are replaced by the data on sectoral structures of 4 economic sectors, they can not thus be represented by graphs.
c) Graphs with double vertical axis differently scaled.
There are cases that we have to compare the development of many variables with so great differences in values that normal graph (with 1 vertical axe and 1 horizontal axe) can not represent them ( for example some lines may fell nearly on the horizontal axe as their values are too small. In such cases, we need to use graphs with double vertical axis differently scaled. Graph 3 is an example.
Graph 3. Changes of GDP growth versus portion of savings on GDP, period 1996-1999 (by %)
In the graph 3, the base value of the left vertical axe is 0 while that of the right vertical axe is 27.8%. The scale on left vertical axe is divided by every 1% while that on the right vertical axe is 0.1%. The graph shows that the portion of savings on GDP has increased during 1996-1999 while the GPD growth rates decreased considerably. Furthermore, saving rate in 1999 was higher the rates of 1996 and 1997 but the GDP growth rate of 1999 was much lower those of 1996 and 1997. We therefore need to analyze causes of this phenomenon as it indicated that the performance of the economy has been reducing.
d) Graphs using intersectional areas
In some cases, we can use intersection graphs to compare the differences between two variables, e.g inflation and loan rates, deposit rates, titles, living levels among areas etc.
Graphs 4: Compare interest rate and inflation rate, supposed data, %
In graph 4, the difference between deposit rates and inflation rates has a tendency of increase. We know that this difference is actually the real deposit rate, Thus the graph indicate that the more money we depose in the bank, the more benefits we have. So we can forecast an increase of deposit money growth rate in the near futur.
Không có nhận xét nào:
Đăng nhận xét