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Applied Economics - Lecture notes 1
Module: Applied Economics (B003)
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University: University College London
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Introduction to Data, Economic Modelling and Econometrics
Economic models help us to understand economic phenomena and forecast changes. Models suggest a relationship. We use data and statistical tools to
test these relationships and their magnitude. Tools used in econometrics are statistical in nature.
What data do applied economists work with?
• Datasets are collections of realisations of random variables
-Datasets usually include several variables X, Y , Z ...
-We have value for these variables for each of the N observations in the dataset
-Each observation is a realisation of the random variable
-(X1, X2;..... XN ), (Y1, Y2, ....YN ), (Z1,Z2,...ZN )
!
• Data differ by their unit of observation (or level):
-Individual person, household, firm
-Aggregated at geographical areas, e.g. countries !
Data used in applied economics
•Time series data
-Same unit observed at different points in time
-Good for investigating effects of variables which vary over time e.g. stock prices, inflation. Ordered chronologically
-Used in applications of macro-economic models e.g. UK economic growth between 1979 and 2012
•Cross sectional data
-Data from units observed in the same time. Doesn’t have to be exact e.g. different week of same year. Ordering doesn’t matter
-Good for investigating relationships between variables which vary between individuals at any point in time
-E.g. productivity and output of firms in the UK at one point in time, wages and education of workers, incomes
•Combination of cross-sectional and time series data
-If these are linked by observational unit, they are called panel or longitudinal data. Consists of a time series for each cross-sectional member in the
data
-Example: longitudinal surveys that follow the same cohort of individuals from childhood through old age (family environment, schooling, wages,
retirement etc.)
-If not linked, then it is repeated cross sections.e.g. 1985 survey of households for income, savings etc. 1990 new survey of different households
with same questions.
-Main difference is that in a cross section, the same units e.g individuals, firms are followed over a given time period.
-So for houses sold in 1993 and 1995, it is not panel as houses sold are different
-Good for investigating life-cycle phenomena and evaluating gov policy
Sources of data used in applied economics
•Most popular source of data used in economics is survey data
-Collected on a sample of the population of interest but samples not always representative of the population
-Typically rely on surveys collected by a third party (e.g. government) but becoming popular to collect primary data
•Economists also increasingly use administrative or register based data
-Data are on the entire population rather than a sample
-Administrative data are not collected for the purpose of research, but for statistical or accounting purposes
-Advantages: Very large number of observations, often very precise measurements
-Disadvantages: Small number of variables, often very confidential
•Empirical analysis uses data to test theory or estimate a relationship. We may construct a formal economic model which consists of math equations that
describe relationships. We then turn it into an econometric model by specifying the form of the function and how to deal with variables that are too hard to
observe. We then collect data on the variables, estimate parameters and then test hypothesis. Other times, we can create an informal model using
intuition.
•We try to determine whether a variable has a causal effect on another variable. But be careful with correlation and causation. We use ceteris paribus to
determine causal effect.
Economic Theoretical Model
•Production function: h is human capital, total labour input is hL, A is measure of productivity, α is capital’s share of income. 0<α<1
•Per-worker production function: output = productivity x factors of production
•We want to estimate the income ratio: ratio of output = ratio of productivity x ratio of factors of production
•How to measure y, k, h? Where to find the data? Empirical proxy for α? Larger the ratio, greater the income inequality.
•y: GDP per capita, k: physical capital per worker, h: number of years of schooling. Income difference due to productivity and factor differences.
•We transform y into equation relating growth rates so growth rate of output = growth rate of productivity + growth rate of factors of production
-where (^) means the growth rate of that variable
•We can rearrange this for A^ since y, k and h can be measured. This is called growth accounting. A^ is the Solow residual
•Correlation is only a statement of numerical facts; it says nothing about cause and effects.Correlation doesn’t equal causality.
•Consider a positive correlation between X and Y.
-X causes Y. X affects Y – causation is running from X to Y. e.g. rain and umbrella
-Causation can go either way (reverse causality). e.g. health and income
-There is no direct causal relationship between X and Y. But some third variable, Z, causes both X and Y. In this case, Z is called an omitted variable
e.g. Data description: readers of tabloids are more hostile to immigration. Data interpretation: we want to know the extent to which tabloid reading means
you are more hostile to immigration. We create a theory of the causal process A=f(N) i.e. attitudes to immigration are a function of newspaper readership.
Considerations
•Representativeness of the sample: has the sample been drawn in a way such that it doesn’t induce any unwanted correlation between the variables
•Quality of the data: do variables in the data match up to theoretical concepts in the model? Is the supposed explanatory variable imprecisely measured?
•Data generating process and Direction of Causality: could the same data have been generated by a totally different model? Is there reverse causality i.e.
attitudes determine newspaper so N=f(A). if so, we are misinterpreting the association
•Assume that other influences on attitudes are not associated with newspaper readership else we would be mistakenly attributing their influence to that of
newspaper readership.
•Sometimes these influences are observed but other times are unobserved i.e. are omitted variables e.g. education influences.
-Broadsheet readers better educated so less hostile.
•Confounding variables- explain the correlation between the variables observed
•Measurement error
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