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Chapter 2  Positive and Normative Economic statements  What do economists do? o Economic theories, hypotheses and models o Ceteris paribus  Building and testing economic theories o Why and how economists use theories? o Interaction between theories and empirical observations  Economic data: o Types of data  Cross sectional  Time series  Longitudinal o Scattered diagrams o Index numbers (price indexes)  Single Price Index  Consumer Price Index (CPI)  Brief math review

 Positive and Normative Statements Positive statement (Objective statements) o Positive economics is a branch of economics that tries to explain and describe economic phenomenon. It usually focuses on studying the cause-and-effect relationship behavior between different economic factors. In other words, it studies what the condition of the economy actually is. o Positive statements do not involve value judgments. They are statements about matters of fact.  A positive statement is about what actually is, was, or will be. o Examples:  It is 21 degrees today.  Higher interest rates will reduce house prices.  The inflation rate is three percent.  As prices increase, demand for goods decreases.  There is a negative relationship between inflation rates and unemployment.  A fall in income would lead to an increase in demand for potatoes.

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If the government increases the tax on alcohol, this will lead to a fall in wine consumption. Normative Statement (Subjective statements) o Normative economics is a branch of economics that incorporates value judgments and makes recommendations on what the outcome of the economy should be. In other words, this branch studies what ought to be rather than what actually is. o Normative statements depend on value judgements and cannot be evaluated solely by a recourse to facts.  A normative statement is about what ought to be. o Examples:  Weather is nice today.  You should get minimum B- to be qualified for ABC program.  The government should double the tax rate to achieve equity.  Health issues are more important than literacy in economic growth.  The government should increase the minimum wage to decrease poverty. 

 What do economists do? -

Observe and study economic behaviour, experience and events  Example: what is the effect of interest rates on house prices

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What Are Theories? – A theory is an abstraction from reality. – A theory consists of: Ø variables Ø assumptions Ø predictions

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Use economic theories that can explain economic behaviour  The theory says: Interest rates determine the cost of buying a house  Make a hypothesis: changes in interest rates cause changes in house prices

 Testing Theories •

A theory is tested by confronting its predictions with evidence. 2

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If a theory is in conflict with facts, it will usually be amended to make it consistent with those facts, or it will be discarded to be replaced by a superior theory.

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The scientific approach is central to the study of economics.

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Use economic models to test a hypothesis or theory, explain economic behaviour and predict policy outcomes. To do this, some tools and techniques are needed, such as:

o Statistical Analysis o Algebra o Verbal explanation o Statistical Analysis o Data: Actual recorded values of variables  Variables take on different values. Variables here are house prices and interest rates  Collect house price and interest rate data o Diagrams: Observe the data. Present the model in graphical form  Increasing house prices and falling interest rates at the same time  E.g Scattered plot o Regressions and estimations

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House Price

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Interest Rate

o Algebra 

Construct a model in a mathematical form

o Verbal explanation: Explain your work and your result  House prices are inversely related to interest rates

o Ceteris paribus -

It is a Latin phrase meaning “other things being equal”, "other things held constant" or "all else unchanged". There are many factors in how you make a decision. Ceteris paribus says that we should change only one factor at a time to isolate how that one factor affects our behaviour.

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Correlation versus Causation •

Positive correlation means only that X and Y move together, in the same direction.

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Negative correlation means that X and Y move in opposite directions. Ø A finding that X and Y are correlated is not direct evidence of a causal relationship.

Ø Most economic predictions involve causality. Establishing causality usually requires advanced statistical techniques.

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Causation explicitly applies to cases where action A Causation explicitly applies to cases where action A causes outcome B. On the other hand, correlation is simply a relationship. Action A relates to Action B—but one event doesn't necessarily cause the other event to happen.

Forms of Data A single economic variable, such as unemployment, national income, or the average price of a house, can come in two basic forms:  Time-series data  Cross-sectional data

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Time series data: Different values of a variable over time  

Average house prices in Canada from 2000 to 2007 Interest rates in Canada from 2000 to 2007

Year 2000 2001 2002 2003 2004 2005 2005 2007

House Interest price rate 8.34 250,000 7.58 260,000 6.62 275,000 6.26 300,000 5.78 310,000 5.61 389,000 5.65 420,000 4.81 467,000

House Price 500000 450000 400000 350000 300000 250000 200000 1999

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Cross-sectional data: Sample of observations of subjects (individuals, firms, countries) at a specific point of time 

eg, average house prices in St. John’s, Halifax, Fredericton and Montreal in June 2020

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 

Eg, GPA of students in ECON1010-03 in 2020 eg, Individual companies’ income in 2000 in Canada Income in June 2020 House City Price ($) A 368000 B 594000 C 950000 D 734000 E 180,000 F 1000000

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Longitudinal data: Track the same sample at different points in time.  

GPA Jef Rebecca Marc Zoë

Health Survey Data: Track the health condition of a specific sample of patients over time In education data: Track a specific sample of students’ life condition over a long period of time

1995 A A+ BC+

1996 A+ A+ B+ D

1997 AAC+ C-

1998 B B+ C B-

1999 B+ B+ BB+

2000 A AB A

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 Index Numbers

– An index number is a measure of some variable, conventionally expressed relative to a base period, which is assigned the value 100.

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Absolute value∈given year ∗100 Absolute value∈base year Representing data in a simpler way Interpretation of the data is easier with reference to base year Value of Index∈given period❑=

Example: Year 2000 2001 2002 2003 2004 2005 2005 2007

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House Price$ 250,000 260,000 275,000 300,000 310,000 389,000 420,000 467,000

House Price Index (HPI) (Base year 2001) (250,000/260,000)*100 100 (275,000/260,000)*100 (300,000/260,000)*100 (310,000/260,000)*100 (389,000/260,000)*100 (420,000/260,000)*100 (467,000/260,000)*100

HPI (Base year 2001) 96.2 100.0 105.8 115.4 119.2 149.6 161.5 179.6

Base year index is always 100 More Complex Index Numbers: CPI (Chapter 19)

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 Brief math review When one variable, X, is related to another variable, Y, in such a way that to every value of X there is only one possible value of Y, we say that Y is a function of X. A function can be expressed:  in a verbal statement  in a numerical schedule (a table)  in a mathematical equation  in a graph Example: When W (wage income) is zero, consumption is $800 a year. For every extra $1 of wage income the person will increase consumption by 0.8($1) or 80 cents. C= $800 + 0.8W

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When two variables move together, the variables are positively related. When two variables move in opposite directions, the variables are negatively related. If the graphs of these relationships are straight lines, the variables are linearly related to each other. A function that is not graphed as a straight line is a non-linear function.

The Slope of a Straight Line 9

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Let X be the variable measured on the horizontal axis and Y be the variable measured on the vertical axis. The slope of a straight line is calculated as  Y/  X.

Suppose we have the following linear equation: Y =a+bX

Y and X are variables. a and b are parameters. Y: dependent variable X: independent or explanatory variable a: intercept (X = 0  Y = a) ∆ Y Y 2−Y 1 = =b ) b: slope ( ∆ X X 2− X 1

Suppose a>0, b>0:

Suppose a>0, b...