TABLE OF CONTENTS Chapter Topics Page Economics Syllabus INTRODUCTION TO ECONOMICS 1 Data Response Skills 1 - 8 2 Essay and Case Study Skills 9 - 28 SCARCITY, CHOICE AND OPPORTUNITY COST 3 Nature and Scope 29 - 34 RESOURCE ALLOCATION IN COMPETITIVE MARKETS 4 Demand and Supply 35 - 68 5 Elasticity of Demand and Supply 69 - 118 KEY ECONOMICS INDICATORS 6 National Income Accounting 119 - 134 7 An Introduction to Macroeconomics 135 - 138 HOW THE MACROECONOMY WORKS 8 Keynesian Theory [H2 Only] 139 - 156 9 Government Policy Tools 157 - 172 10 Economic Problems: Inflation 173 - 214 11 Economic Problems: Unemployment 215 - 244 12 Economic Problems: Economic Growth 245 – 274 Nanyang Junior College 2011 JC1 H1 & H2 Economics © 0 2011 JC1 H1 & H2 ECONOMICS Introduction to Economics: 1. Data Response Skills NYJC 1.1 Numbers (a) Absolute and Relative Absolute – refers to the actual size/value of a certain variable and what happens to the actual size/value over a period of time. Example: Pei Ling‘s Mathematics marks rose by 5 from 25, while Wei Xiong‘s marks rose by 8 from 80. Relative – refers to the size / value of one variable with regard to some aggregate or other variable and how this position changes over a period of time. Example: Pei Ling‘s marks rose by 20%, while Wei Xiong‘s rose by 10%. NOTE: Data can show a larger absolute change but a smaller relative change. In ―absolute‖ terms Wei Xiong showed a larger improvement, but in ―relative‖ terms Pei Ling‘s improvement was more significant. (b) Index Numbers Index Numbers (or indices) are a common tool that economists use to measure the relative changes of variables over time. It is especially useful when the absolute or ‗raw‘ data becomes very unwieldy or when absolute comparisons are not necessary. Example: We can ‗index‘ the changes of the waist size of a few individuals over time, so that more valid or ‗fair‘ comparisons can be made. Table 1 Waist (cm) January March June Person A 40 44 48 Person B 60 63 69 Person C 80 86 94 Table 2 Waist (Jan =100) January March June Person A 100 110 120 Person B 100 105 115 Person C 100 107.5 117.5 Whose waist grew the most? Person A Nanyang Junior College 2011 JC1 H1 & H2 Economics © 1 NOTE: Indices are used to measure changes in many variables. They are most frequently applied to measure changes in prices of groups of things. The most common being the Consumer Price Index (CPI), which measures the changes in the prices of a selection of consumer goods. Measuring the change in the CPI can give us an indication of how fast consumer prices are changing. If the changes are increasing over a period of time, then we have an indicator for the rate of inflation. Although Indices are a quick and fair way of measuring changes (and rates of change), they are not useful when making comparisons in absolute terms. Figure 1 Index of House Prices by house type Using the data presented in figure 1 answer the following questions: Calculate the approximate change in the prices of each of the two housing types from the first quarter 1996 as compared to the first quarter 2006. Resale HDB flats= 102.8 -110 x 100 Private homes= 122.1 – 170 x 100 110 170 = -6.5% = -28.2% The data does not state what the ‗base‘ period is. In your opinion which period do you think is the base period? Why? First quarter 1999 was the base year because the index was 100. In Q1 1999, the index for Private House prices and that of Resale HDB flats is the same. Can it be concluded that Private Houses and Resale HDB flats were sold, on average, at the same price? Explain your answer. No. The index is the same but the actual prices are not be the same. We can only conclude that prices are the same unless we are given the actual data which states so. Nanyang Junior College 2011 JC1 H1 & H2 Economics © 2 1.2 Graphs Graphs represent a quantity as a distance on a line. The main point of a graph is to enable us to visualise the relationship between 2 variables. Two types of relationship can be observed: Positive – when the 2 variables change in the same direction. Negative – when the 2 variables change in the opposite direction. When looking at graphs you should consider the following patterns: Variables that move in the same direction. Variables that move in the opposite direction. Variables that have a maximum or a minimum Variables which are unrelated. Types of Graphs: (a) Scatter Diagrams This type of diagrams (graph) is used to reveal whether a relationship exists between two variables. Figure 2 Inflation Each of the 10 dots in the diagram shows the combination of the inflation rate and the unemployment rate for a ‘98 ‘97 single economy in a particular year. ‘99 The 10 dots imply that we have data ‘01 from 10 different years. ‘96 ‘05 ‘95 ‘04 ‘02 Unemployment 0 ‘03 Does the scatter diagram (fig 1) reveal a relationship between inflation and unemployment? Yes. From the line of best fit, we can see a negative relationship between inflation and unemployment. The line is downward sloping depicting an inverse relationship between inflation and unemployment. Nanyang Junior College 2011 JC1 H1 & H2 Economics © 3 (b) Time-Series Graphs & Data These types of data chart the changes in a variable over time. It is used to examine patterns or trends or changes in trends. Time-series data involve the identification of trends and the recognition of changes or exceptions to the trends. Rates of change often can also be inferred and calculated. Figure 3 Describe the trend of the quarterly changes in the ―Real GDP‖ (Fig 3). Generally increasing over the years except for the quarter leading to June 99 and a slowdown in the increase for the quarter leading to Dec 99. Upward trend with lowest negative growth in Jun 98 and highest growth in Sep 99. Table 3 Singapore GDP per capita (Current Market Price) Year 1997 1998 1999 2000 2001 2002 2003 2004 2005 S$ 37520 35161 35440 39784 37130 37976 38599 42852 44666 Calculate the percentage increase in GDP per capita from 1997 to 2005 (Table 3). % increase in GDP per capita= [(44666-37520)/37520] * 100%= 19.05% Nanyang Junior College 2011 JC1 H1 & H2 Economics © 4 (c) Cross-sectional data This type of data shows how figures are ‗built‘ up. They are useful to show the components of any single figure. Cross-sectional data presented in table form usually use „indentation‟ to show the different levels. Table 4 Gross Domestic Product by Industry S$ million GDP at Current Market Prices (2005) 194,359.8 Goods Producing Industries 62,207.1 Manufacturing 52,127.5 Construction 7,044.3 Utilities 2,844.2 Other Goods Industries 191.1 Service Producing Industries 121,901.1 Wholesale and Retail Trade 28,838.1 Hotels & Restaurants 3,637.9 Transport & Communications 23,142.6 Financial Services 20,906.9 Business Services 24,584.2 Other Service Industries 20,791.4 Ownership of Dwellings 6,890.4 Less: FISIM1 7,673.5 Gross Value Added at Basic Prices 183,325.1 Add: Taxes on Products 11,034.7 1Refers to Financial Intermediation Services Indirectly Measured In table 4: {GDP at Current Market Prices (2005)} = {Goods Producing Industries} + {Service Producing Industries} + {Ownership of Dwellings} – {FISIM} + {Taxes on Products} i.e.:- 194,359.8 = (62,207.1) + (121,901.1) + (6,890.4) – (7,673.5) + (11,034.7) Identify the components of the item ‗Goods Producing Industries‖ and the item ―Service Producing Industries‖. Components of the item ‗Goods Producing Industries‖ = Manufacturing, Construction, Utilities and Other Goods Industries. Components of the Item ‗Service Producing Industries‘= Wholesales and Retail Trade, Hotels & Restaurants, Transport & Communications, Financial Services, Business Services and Other Service Industries. The cross-sectional data in Table 5 are in terms of ―average‖ figures. Thus, although the item ―All households‖ is made up of ―Private Houses‖, ―Private Flats‖ and ―Public Flats‖; the numbers are not a simple summation. For this reason no indentation is used in the table. Nanyang Junior College 2011 JC1 H1 & H2 Economics © 5 Table 5 Average Monthly Household Expenditure by Type of House in 2003 (S$) All Households 3,244 Private Houses 6,958 Private Flats 5,846 Public Flats 2,804 Figure 4 shows another way cross-sectional data is often presented, using a pie chart. This mode of presentation has the benefit of showing the relative contribution of each component visually. E.g from Fig. 4, we can visually identify ―Capital Goods, not Autos‖ as the largest contributor to Exported Goods and Services in 2006. Figure 4 Nanyang Junior College 2011 JC1 H1 & H2 Economics © 6 (d) Pooled data This refers to various data presented in the same figure or table. This is a very useful form of presenting data as many things can be inferred. Figure 5: Real Economic Growth (Singapore) Fig. 5 shows the Real Economic Growth for the whole of the Singapore economy from 1995 to 2005. At the same time the growth rates for two key industry sectors, the Financial Services and the Manufacturing sectors are also shown. NOTE: A falling growth rate still means positive growth. A typical question on this type of data would be: Describe the trend of the Real Economic Growth for the Singapore from 1995 to 2005 and account for the changes in this trend. (4m) The Real Economic Growth for the Singapore economy shows a steady positive growth trend of between 3% to 7% annual growth.

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