SC968: Panel Data Methods for Sociologists

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SC968 Panel Data Methods for Sociologists
Introducing panel data
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Overview

• Panel data
• What it is
• How to get to know the data
• Change over time
• Tabulating
• Calculating transition probabilities

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What is panel data?

• A data set containing observations on multiple
  phenomena observed at a single point in time is
  called cross-sectional data
• A data set containing observations on a single
  phenomenon observed over multiple time periods is
  called time series data
• Observations on multiple phenomena over multiple
  time periods are panel data
• Cross sectional and time series data are one-
  dimensional, panel data are two-dimensional

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Using panel data in Stata

• Data on n cases, over t time periods, giving a
  total of n t observations
• One record per observation
• i.e. long format
• Stata tools for analyzing panel data begin with
  the prefix xt
• First need to tell Stata that you have panel data
  using xtset

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Complete and incomplete person-wave data
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Telling Stata you have time series data
Unique cross-wave identifier
Time variable
. xtset pid wave panel variable pid
(unbalanced) time variable wave, 1 to
15, but with gaps delta 1 unit
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Cases not observed for every time period
. xtset pid wave panel variable pid
(unbalanced) time variable wave, 1 to
15, but with gaps delta 1 unit
Period between observations in units of the time
variable
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Describing the patterns in panel data
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Examining change over two waves
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Calculating transition probabilities

• The transition probability is the probability of
  transitioning from one state to another

So to calculate by hand,
Cell count
Row total
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Transition probability matrix
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Transition probability matrices in Stata
Mean transition probabilities for all waves t to
t1 when you leave out the if statement
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Change in a categorical variable over timeA
decision tree
empl
0.91
empl
0.03
unemp
0.06
0.90
olf
empl
0.26
unemp
0.03
0.49
empl
unemp
0.25
olf
0.04
empl
0.10
olf
0.03
unemp
0.87
olf
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Change in a continuous variable over time

• Size transition matrix
• Quantile transition matrix
• Mean transition matrix
• Median transition matrix

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Size transition matrix

• Absolute mobility
• e.g. movement in and out of poverty
• Boundaries set exogenously i.e. predetermined
• e.g poverty defined a priori as an income below
  5,000
• Does not depend on distribution under
  investigation
• e.g comparing mobility in 1990s and 2000s
• incorporates both movements of positions of
  individuals and economic growth

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Quantile transition matrix

• Mobility as a relative concept
• Same number of individuals in each class
• Only records movements involving reranking
• Cannot take account of economic growth, for
  example when comparing matrices
• Cannot draw a complete picture if comparing
  mobility in different cohorts/countries/welfare
  regimes

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Mean/median transition matrices

• Both absolute and relative approaches
  incorporated into matrices
• Class boundaries defined as percentages of mean
  or median income of the origin and destination
  distributions
• Example
• 25, 50, 75 of median income
• Note that this is not the same as quartiles

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Example income 1991-1992
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Category boundaries for each method
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Warning!

• Measurement error
• Causes an over-estimation of mobility
• If mothers and babys weight are reported to
  nearest half pound can affect which band the
  observations falls in
• A respondent may describe their marital status as
  separated in year 1 and single in year 2

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Finally..

• Greater challenges to understanding and checking
  panel data
• Transition matrices a good way to summarise
  mobility patterns
• Different methods of constructing matrices lead
  to distinct interpretations
• May need to take account of measurement error
  when modelling change