The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
School of Economics
L13500 Economics Dissertation 2015
The Impact of Inheritance on Income Inequality in the
UK
Student: Nicola Mounteney
Supervisor Name: Patrick Marsh
Word Count: 7496
Turnitin Reference no:
This Dissertation is presented in part fulfilment of the requirement for the completion of an
undergraduate degree in the School of Economics, University of Nottingham. The work is the sole
responsibility of the candidate.
I do give permission for my dissertation to be made available to students in future years if selected
as an example of good practice.
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Contents
Introduction ..................................................................................................................... 2
The Characteristics of Inheritance and its Impact on Inequality ......................................... 3
Aggregate Data Investigation ............................................................................................ 7
Results of the Aggregate data ......................................................................................... 13
Survey Investigation ....................................................................................................... 19
Results: Survey Data ....................................................................................................... 24
Discussion ...................................................................................................................... 29
Conclusion...................................................................................................................... 32
Bibliography ................................................................................................................... 33
1
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Introduction
Income inequality is an indicator of how material resources are distributed across society (OECD,
2011) it is the focus of this dissertation for many reasons; it is morally undesirable but also causes
instrumental problems such as conflict and limited co-operation (Wilkinson and Pickett, 2009).
Inequality is thought to cost the UK over £39 billion through reduced life expectancy, poor mental
health and increased imprisonment and crimes (The Equality Trust).
Andrews and Leigh (2009) suggest that economic inequality is fair, provided there are equal
opportunities. However, when limited intergenerational mobility affects a child’s ability to attend a
better school or inherit large sums of money, opportunities may become skewed. These inequalities
may then be perpetuated and intensified by inheritance (Wedgwood, 1929, pg.60-61.). Even if
inheritance’s direct effect on inequality is small, its indirect effects may be significant. It may reduce
other opportunities such as education and earnings potential. It is important to first assess whether
inheritance impacts inequality, and who it may impact, to understand whether further investigation
into indirect effects are necessary. This dissertation will concentrate on the influence that home
ownership, and subsequent inheritance, has on inequality.
With current house prices increasing and mortgage approvals falling, home owners are
benefiting while renters are losing out. If those owning are able to buy due to inheritance, then
opportunities may be unequal. The average price of housing in the UK has risen significantly since
the 1970’s, from £5632 to £250,768 in 2013, with the Joseph Rowntree foundation (2012)
suggesting that home ownership will decline by up to 1.3 million in the next 30 years. A shift into the
rented sector could have significant implications for those whose families are not on the property
ladder, or cannot afford the unreasonably high deposits. Continual population growth increases the
need for housing, and hence prices are consistently rising to unreachable levels particularly for those
in London, where the average price is £514,000 (The Guardian, 2014).
This dissertation will begin by presenting key literature exploring the relationship between
inheritance and income inequality. An econometric investigation into whether a relationship is found
2
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
for the UK will follow. An analysis into which characteristics, if any, improve an individual’s chances
of inheriting will then be undertaken. This will help determine whether inheritance is becoming
more widespread or its benefits are limited. The final section will discuss the results obtained from
the econometric analysis and its implications for the current UK market and the future of home
ownership.
The Characteristics of Inheritance and its Impact on Inequality
Saunders (1990) presented Britain as ‘A Nation of Homeowners’. Using aggregate data and a study
of three towns to compare similarities and differences in home ownership, Saunders discusses
trends motivating the vast growth of housing ownership. Across Britain, a strong desire for home
ownership was found alongside a correlation between occupation and housing tenure, and a
possible pattern of tenure based inequality.
Similarly, Hamnett (1991) studied the growth of housing inheritance in Britain from the late
1960s. He builds on Saunders’ (1990) idea that homeownership leads to increased inheritance and
redistributions of wealth. He discusses changes in the ownership of UK housing suggesting that the
‘tenure structure of Britain has been transformed from a majority of private tenants into two nations
– a majority of owners and a minority of public tenants’ (Hamnett, 1991 pg.509). This rapid change
in the number of owners is unique to the UK. However, owner occupied societies may also see
similar inheritance pathways. Housing structures in other countries, such as Germany’s, are often
based around renting, both private and social. Hamnett and Saunders’ results were found over 25
years ago, their relevance today may thus be questionable. Changes in housing markets, namely as a
result of the 2008 recession, led to a drastic reduction in the levels of mortgage lending affecting
ownership and therefore inheritance.
Thorns (1994) examined housing inheritance’s impact on wealth transfers in three owner
occupied societies; New Zealand, Canada and Britain. He argued the incidence of inheritance later in
life, mostly due to the deaths of parents, decreased the impact on social and wealth status. The UK’s
3
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
high ownership growth was more dramatic in the 1960’s onwards than in other owner occupied
societies. Thorns contends that the long established nature of owner occupation in other countries
as a cause. Two important events spurred increased inheritance in the UK; the post-war expansion
of home ownership and the rapid house price growth in Britain since the 1970s. (Hamnett, Harmer,
& Williams, 1991). Other countries also underwent changes from the 1970’s onwards although this
was largely due to rapid house price inflation rather than tenure changes (Thorns, 1994). As more
people entered the housing market, the probability that an individual would inherit rose quickly in
Britain, from 9% in 1950, to 37% in 1986, whereas, New Zealand started on a higher probability, 30%
likelihood in 1945 rising to 55.8% in 1986(Thorns; Pg. 480).
Some characteristics may be more prominent amongst inheritors. Hamnett (1991) uses
survey data of 3250 adults in the UK to identify such patterns. He found 15% of respondents lived in
households where at least one member had received over $1,000 of inheritance at some point in
their lives, 60% of which included property. He found certain attributes increased the likelihood of
inheritance: age, 59% of inheritors were between 40 and 70; social class, tenure and parental
tenure. Location also plays an important role, 22% of beneficiaries lived in the South East excluding
Greater London, whereas Scotland had only 4% and Yorkshire and Humber, 6%. Hamnett’s work
solely focuses on the UK, thus characteristics may not be universally valid, particularly due to
different housing laws and cultural differences.
Matlock and Vigdor (2006) discuss the implications of increasing incomes of the upper class
on housing affordability for low socio-economic status (SES) households in the US. They use a partial
equilibrium study through a simple housing market representation; consumers split into two
categories, high and low SES. Assuming housing to be a normal good and somewhat inelastic, they
found increasing incomes for high SES households increased costs for low SES, thus reducing their
consumption (Matlock and Vigdor; pg.7). This study assumes housing homogeneity which may not
hold in reality, therefore outcomes may differ in real life. Partial equilibrium does not take into
account the wider economy: increasing incomes of the high SES could be representative of high
4
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
economic growth which in theory would increase opportunities for the low SES. If results are
assumed to hold, the income increases of high SES households should increase inequality and reduce
ownership possibilities for the low SES.
Contrastingly O'Dwyer (2001) finds high SES households in Australia inherited similar
amounts to low SES, suggesting ownership possibilities may not be reduced. She also emphasises the
importance of understanding inheritances’ effect on inequality through wealth distribution. The
debate as to whether inheritance polarises or equalises societies is a controversial one. Hamnett
(1991) promotes that ‘socio-tenurial polarisation’ may be an outcome of the decline in private
renting and growth in both ownership and council renting. High skilled, high paid workers are likely
to own houses and low paid, low skilled workers may become concentrated in a residual council
sector. Others, in contrast, suggest the way in which inheritance is carried out may have equalising
opportunities (Laitner, 1979; Tomes, 1981).
Bowles
and Gintis
(2002)
propose
strong intergenerational
links. They
study
intergenerational transmission of economic status in America and discuss Hertz’s 2002 study, which
found those born in the top decile had a 22.9% chance of attaining it and a 40.7% chance of attaining
the top quintile. They recommend income and wealth as strong predictors of future economic
status. Hertz calculates that the likelihood of sons in the poorest decile reaching the top quintile is
only 1.3% whereas staying in the lowest quintile is 50.7%. They also propose that it is unlikely that
economic status is transferred through the intergenerational transfer of property or financial wealth.
The change in economic status is unlikely to be attributed to the amount of inheritance, which tends
to be insubstantial. Those inheriting large sums are likely to be from high income families anyway,
suggesting class changes are unlikely, restricting people to their born classes. The likelihood of
changing economic decile at the lower end may be aided by the US culture of the ‘American Dream’.
In comparison, the UK’s history is embedded in aristocracy and strict class structures implying UK
results may be more restrictive.
5
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Andrews and Leigh (2009) also address the link between inequality and intergenerational
mobility. They conduct an intergenerational correlation (IGC) study comparing Gini coefficients
across time and intergenerational mobility for 10 countries, finding an 𝑅 2 of 71%. This indicates that
the relationship between intergenerational mobility and inequality in a country is highly correlated;
Gini coefficients should therefore be good predictors of inter-generational mobility. These results
look across multiple countries, hence are more reliable in a broader context.
This literature review demonstrates the strong desire to own homes in the UK. However it
suggests that the ability to buy may be skewed, therefore creating inequality within the country. The
UK is unique in that housing ownership increased rapidly after World War 2 and is now a core part of
society. Current conditions, high demand and therefore rising house prices are making this less
likely, particularly for first time buyers. There is also a correlation between inheritance and
inequality, principally the intergenerational impact of inheritance and its incidence tending to be
with those who are wealthier to start with. The ability to both own and inherit housing may be
unequally distributed and should be a cause of concern for policy makers. A discussion of both the
aggregate trends in inequality and inheritance is undertaken and finally a comparison between
which individuals are most likely to inherit will then be explored.
6
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Aggregate Data Investigation
This investigation is a time series econometric analysis into the impact of inheritance on inequality
using aggregate UK data. Annual data was collated from 1971 to 2012, a period where home
ownership and inequality underwent large changes. A 30 year period was necessary to ensure
variability in the data, with this most recent period being the most relevant. Table 1 displays the
data sources for each variable. The main source of data is the Office for National Statistics (ONS), a
reliable source of information and statistics related to the UK economy, collected and published at
the national, regional and local levels. Every 10 years the ONS is also responsible for collating the
census, which UK households are legally required to fill in truthfully. Results can therefore be used
confidently. The World Bank and OECD data are also used and well-established as quality sources.
Table 1: Description of Variables
Variable
Description
UK Income inequality
𝑰𝒏𝒆𝒒𝒖𝒂𝒍𝒕
measured by the Gini
Coefficient between 0,
complete equality and 1,
complete inequality
The proportion of the UK
𝑬𝒅𝒖𝒄𝒕
each year achieving at least
5 A*-C GCSE’s
𝑯𝒐𝒖𝒔𝒆𝒕
𝑪𝒍𝒂𝒔𝒔𝒕
The average value of homes
in the UK
The percentage of owned
housing in the UK
Units
A scale of 0 -1
1977-2012
%
Pounds
Percentage
𝑰𝒏𝒉𝒆𝒓𝒊𝒕𝒕
The aggregate transfer of
wealth in the UK in £m.
Millions of
Pounds
𝒊𝒎𝒎𝒊𝒈𝒕
How many people came into
the country each year
The percentage of the UK
population classed as
unemployed
Average life expectancy in
the UK
Thousands of
people
% of the
population
𝑼𝒏𝒆𝒎𝒑𝒍𝒕
𝒍𝒊𝒇𝒆_𝒆𝒙𝒑𝒕𝒕
Source and years
OECD
Years
Department for
Education
Every 5 years from 19712005 and 2009 – 2012
ONS
1971-2013
ONS
Every 10 years from
1971-2011
LSE paper
1971–2008
Data missing 1995, 2000,
2004
ONS
1971-2012
ONS
1971-2013
World Bank
1971-2012
7
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
For all the estimations within this section, Inequal will be the dependent variable, with a specific
focus on whether there is a relationship between the independent variable, Inheritance, and
inequality in the UK. Inequal is measured using the Gini coefficient, a measure of income inequality
on a scale from 0, representing complete equality, to 1, representing complete inequality. No single
data source encompassed the whole period for the Gini Coefficient; the OECD provided the largest
span therefore was chosen. The current economic climate with rising housing prices and low
mortgage approval rates may impair individuals’ abilities to afford housing without help from
parents or inheritance. The relationship is expected to be positive, increased inheritance should
increase inequality. Karagiannaki (2011) argues “the impact of inheritance on wealth accumulation
and wealth inequality is a largely unresolved issue” Consequently an empirical investigation will help
to resolve this issue. It can be difficult to make clear judgements on the impact of inheritance
without knowing how much and to which people incidence occurs, this will be explored in the survey
section.
Table 2 reports summary statistics for each variable. The mean value of the Gini coefficient
for the UK is 0.324 implying it is a fairly equal society. Between 1976 and 1990 there was a steep
upward trend in the coefficient, presenting an increase in income inequality that reached 0.368. This
may be due technological change increasing wage inequality between high and low skilled workers
(IMF Working Paper, 2008). This trend is seen across many OECD countries with low skill jobs being
outsourced to cheaper third world countries. However, as can be seen from Figure 1a, since 1990
there have been fluctuations in inequality that have steadily decreased over time.
Inheritance is the aggregate transferred wealth in the UK between 1971 and 2012. This data
was collected from a London School of Economics’ paper by Sir Anthony Atkinson, a renowned
British economist with an interest in social justice providing data for monitoring rising inequality
across the world. It must be kept in mind throughout the discussion that this aggregated wealth may
include aspects other than housing. The minimum and maximum levels of inheritance show a large
8
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
dispersion over time, particularly with a large standard deviation of 18705.62 in the 1990’s.
Inheritance has been increasing steadily at a much steeper incline since the mid-90s, as Figure 1b
shows. This is intuitive particularly as home ownership increased from approximately 50% in the
1970’s to around 70% in 2012. The median value of inheritance is only £21,246 nevertheless a large
range signifies that some may be inheriting much larger amounts. As both inheritance and the Gini
coefficient rise over similar periods, a concern is that some other external variable is affecting them
both. This may create a spurious result in regressions.
.26
.28
.3
.32
Gini Coefficient
.34
.36
Figure 1a: The value of Inequality across time
1970
1980
1990
Year
2000
2010
40000
0
20000
aggregate transfer of wealth in the UK
60000
Figure 1b: The value of Inheritance across time
1970
1980
1990
Year
2000
2010
9
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
.38
Figure 2: Correlation
between Inequality and
inheritance
.36
Gini
.34
Correlation coefficient:
0.6591
.32
.30
.28
.26
0
10,000
30,000
50,000
70,000
inheritance
To isolate the effect of inheritance, the remaining independent variables are used to control for
factors that may impact inequality. Education is used to control for increased attainment reducing
income inequality, more education increases the likelihood of employment and higher earnings
(Joseph Rowntree Foundation, 2012). House Price indicates economic conditions in a country. When
house prices are high, the economy is believed to be in an economic boom. However, this may also
affect the value of inheritance, higher average house prices should correlate to higher amounts of
inheritance, which may impact the data. Class is used as an indicator of social status in the UK. It is
assumed for this regression that the lower classes are unable to afford housing, the middle and
upper classes will hence be those owning housing. Immig is a proxy for race, it accounts for the
amount of people entering the UK assuming that they are minority, either in nationality or race. A
correlation exists between ethnic minority groups and higher levels of poverty, suggesting that these
groups may experience increased income inequality. Alongside race, immigration also has
implications for inequality related to ethnic discrimination, it “is often viewed as a proximate cause
of the rising wage gap between high and low-skilled workers” (Card, 2009 pg.1). Unempl is the UK
unemployment rate. More unemployment generally correlates to a lower economic climate, which
10
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
in turn increases unemployment further due to conditions in the labour market. This circular
relationship may have implications regarding endogeneity in the model. Finally Life Expect is used to
proxy average health in the country. Increased health in a country improves productivity and
opportunities for individuals and should therefore reduce inequality (Deaton, 2003).
Summary statistics are listed in Table 2. The mean percentage of the UK population owning
housing is 61%. This represents over half of the population expressing the desire and having the
ability to own housing in the UK. The Education mean is low, which may be a result of having the
data set for every five years rather than annually. The difference between the minimum and
maximum result however, is almost 60%, conveying the drastic improvement in the UK’s education
sector.
Variable
Mean
Inequality (Gini)
Education (in Yrs)
Avg. House Price (£)
Inheritance (£1000’s)
Class (%)
Immigration (%)
Unemployment (%)
Life Expectancy (Yrs)
Growth rate GDP
0.324
39.74
90431.7
21246.6
0.614
337976.2
7.186
76.19
0.215
Table 2 – Summary Statistics
Standard
Minimum
Deviation
0.0307
17.78
82543.9
18705.6
0.0764
155993.8
2.373
2.643
0.359
0.266
22.10
5632
2275
0.500
153000
3.700
72.12
-0.375
Maximum
Observations
0.368
81.10
251174
62062
0.690
596000
11.80
81.50
1.325
38
42
43
35
43
42
43
42
43
To test for multicollinearity a correlation matrix, table 3, is used. A strong positive correlation
between inequality and inheritance is found of 0.6591. There is also a strong positive correlation
between Inequality and all the other variables except unemployment and the growth rate of GDP.
This suggests that they should be good control variables as they may account for the movements in
the Gini coefficient. A strong correlation between inheritance and the other control variables also
exists. The exceptions are once more, unemployment and the growth rate of GDP. This may lead to
multicollinearity within the regression which can impact the sensitivity of the results, creating
11
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
difficultly in inference. The Variance Inflation Factors will be monitored during the estimation of the
regressions.
Gini
Education
House Price
Inheritance
% of owned
housing
Immigration
Unemploym
ent rate
Life
Expectancy
Growth rate
of GDP
Table 3a: Correlation Matrix
House Inherit Class
Immig
Gini
Educ
1.0000
0.7088
0.5892
0.6591
0.8300
1.0000
0.9447
0.9672
0.8637
1.0000
0.9876
0.7409
1.0000
0.8007
1.0000
0.6343
0.0538
0.8063
0.9675
0.4327
0.9569
0.9619
0.4327
0.9230
0.9830
0.4333
0.9570
0.7980
0.0418
0.9207
1.0000
0.4823
0.9361
0.0799
0.0732
0.0479
0.0423
0.1337
0.0050
Unem
pl
Life
Expect
Growth
of GDP
1.0000
0.2576
0.2737
1.0000
0.1003
1.0000
To account for possible spurious results as a result of trending variables, the average value of
housing was divided by the amount of GDP, the growth rate of GDP was used instead of GDP and
immigration was divided by the population size, which will be referred to as the immigration rate.
Alongside this, Butterworth filters are introduced into the regressions; the Butterworth filter
separates variables into the cyclical and trending components. These are shown in Appendix 1.
When looking at Table 3b, the correlation matrix for the filtered variables, the correlation between
variables is reduced, thus minimising the likelihood of multicollinearity. There is no concern with
trending for the Gini Coefficient or the unemployment rate; hence these will not be filtered. The
correlation is now 0.1887 between the filtered inheritance and the log of inequality, a weaker
relationship than previously seen although it still demonstrates a positive relationship between the
variables.
Some of the data was not available for each year. Education and Class data were only
available every five or ten years respectively; the available results were averaged across the missing
years. This may limit the data’s variation and needs to be kept in mind in the interpretation. It may
12
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
incorrectly specify the magnitude and directions of the model and thus may lead to unexpected
results.
Table 3b: Correlation Matrix with Filtered Variables
Log Inequality
Inheritance (BW)
GDP (BW)
Immigration
(BW)
Education (BW)
Average House
prices (BW)
% of owned
housing (BW)
Life Expectancy
(BW)
Unemployment
Lnineq
ual
Inherit
_bw
GDP_
bw
Immig_
bw
Educ_
bw
House_
bw
Class_
bw
Life
Expect
_bw
1.0000
0.1887
0.1089
0.0266
1.0000
0.4243
0.1071
1.0000
-0.0276
1.0000
-0.0080
-0.2106
-0.3292
0.1227
1.0000
0.0404
0.3265
0.1835
0.0480
-0.0832
1.0000
-0.0975
-0.3902
-0.8233
0.0225
0.1096
-0.0829
1.0000
-0.0751
-0.3628
-0.3701
-0.2791
0.2131
0.3428
0.3651
1.0000
-0.0592
-0.2280
-0.4287
-0.0467
0.1065
-0.2625
0.3967
0.1114
Unempl
1.0000
Results of the Aggregate data
Preliminary regressions are listed in Table 4. Three regressions were run; a simple OLS regression, an
OLS regression with robust standard errors, to remove heteroskedasticity, and an outlier robust
regression, to account for extreme results influencing the model. These were run in both level and
logarithmic relationships. Using logarithms can linearise relationships, reflecting that a change in the
variable may not have the same impact at each level. This is done to avoid violating the Gauss
Markov assumption of linearity in parameters. For some of the variables it is also more meaningful
to use logs as the percentage relationship can be discussed. However for variables already in
percentages, the interpretation of logs is nonsensical and so for education, class and the
unemployment rate the level form will be interpreted. It should be noted that all interpretations of
the coefficients rely on the Ceteris Paribus clause.
Table 4 demonstrates a positive relationship between inheritance and inequality when taken
in logs, which is significant across all regressions. This shows that for a 1% increase in inheritance,
13
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
inequality will increase by 0.3%. This may be because the likelihood of inheriting is highest among
the rich and therefore creates discrepancies between classes. Conversely, when examining the
regressions run in levels, the opposite is found. A one pound increase in the aggregate transfer of
wealth leads to a decrease in inequality. This may reflect that the value of inheritance is more
widespread, benefiting the majority rather than a select few. Alternatively, this difference may be
due to the original relationship being non-linear thus the log relationship shall be focused on.
Table 4: Preliminary Regressions
OLS
Regression
Levels
Robust
Standard
Errors
Outlier
Robust
OLS Regression
Logs
Robust Standard
Errors
Outlier robust
Inherit
-4.97e-06***
(1.83e-06)
-4.97e-06*
(2.56e-06)
-3.49e-06*
(1.82e-06)
0.314291***
(0.0962585)
0.372674***
(0.0745859)
0.320877***
(0.1114997)
Educ
-.002868**
(0.001412)
0.0475656
(0.156566)
-0.002868*
(0.001407)
0.0475656
(0.187152)
-0.00359**
(0.00140)
0.1794247
(0.155319)
0.4399286**
(0.1878026)
0.4078371
(0.3014726)
0.3558758*
(0.1911935)
0.0363964
(0.255612)
0.4483814*
(0.2175385)
0.3617139
(0.3492066)
Unempl
-.005825**
(0.002226)
-.005825***
(0.002055)
-0.0053778
(0.002208)
-0.0550804
(0.0554901)
1.897379***
(0.5192451)
-0.04939
(0.0642762)
Life
Expect
House/
GDP
0.049005***
(0.010874)
9.14e-07
(6.01e-07)
0.049005***
(0.0128321)
9.14e-07
(6.82e-07)
0.0425***
(0.010788)
5.95e-07
(5.96e-07)
-12.03243***
(3.296663)
0.1324243
(0.1257877)
-9.83146***
(3.136245)
0.0455427
(0.101203)
-12.22131***
(3.818644)
0.1297465
(0.1457044)
Growth
rate
GDP
0.0017403
(0.010943)
0.0017403
(0.0069426)
0.0033595
(0.010856)
0.02216
(0.0131197)
0.0174817*
(0.0088926)
0.0240751
(0.015197)
Immig
rate
3.734406
(7.589862)
0.8100
(Adjusted)
3.734406
(7.095086)
0.8606
3.165513
(7.529423)
-
-0.35370***
(0.1010467)
0.86
(Adjusted)
0.3524036***
(0.0943866)
0.9384
-0.345739***
(0.117046)
-
Class
𝑹𝟐
Standard errors are in Parentheses
*** significant at the 1% level; **significant at the 5% level; *significant at the 10% level
Intuitively, there is a negative relationship between education and inequality. A 1% increase in those
with 5 A*-C GCSE’s will lead to a 0.002 percent decrease in inequality. A higher proportion of the
country with a basic level of education will improve earnings potential and should reduce the
likelihood of income inequality. Contrastingly, the results for the unemployment rate displays a
negative relationship, a 1% increase in the unemployed population will decrease inequality by 0.5%.
This is counterintuitive, the more people who are unemployed should have further disadvantages in
14
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
terms of monetary inequality. There may be some connection between the unemployment rate and
unemployment benefits that make being unemployed more advantageous. Life Expectancy similarly
has a negative relationship, a one year increase in life expectancy will lead to a 0.49 decrease in the
Gini Coefficient, implying that health has a large impact on inequality. Improved health benefits
productivity and earnings potential and this result therefore is expected. There is a link between the
life expectancy of an individual and the education that they receive, which may lead them to make
more informed health decisions. An interaction term, health*educ will be added in the subsequent
regression to examine this relationship. Immigration has a postive effect on the Gini Coefficent, a 1%
increase in the immigration rate will lead to a 0.37 increase in the Gini Coefficient. Immigration can
cause increased income inequality through labour market discrimination and therefore reduced
income for such groups. It may conversely, be that such groups are less skilled or educated therefore
need to undertake jobs with a lower wage. An interaction term to see whether there is a relationship
between the level of immigration and the unemployment rate, immig*unempl will also be tested.
Further regressions were run to incorporate additional variables to control for serial
correlation and functional form misspecification. The results of these are listed in the table below.
The interaction terms, Immig*Unempl and Educ*Health, a lag of inequality and a regression using
the butterworth filters to control for trending are included. All regressions reject the null of serial
correlation however only the final is able to reject the null of functional form misspecification. They
all still have a high R squared even when using the Butterworth smoothing filter on GDP, House,
Immigration, Education and Life Expectancy.
15
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Table 5: Further Tests of the Data
Robust model
Robust model
Robust model
with lagged
with interaction
with interaction
inequality
term
term
Log Inheritance
0.210599***
(0.0602619)
0.2536885***
(0.0686791)
0.2162871***
(0.0618061)
Education (level)
0.0095315***
(0.003186)
-0.521117
(0.3267471)
0.0094519**
(0 .0034911)
-0.6128095
(0.3589392)
0.0098776
(0.0033916)**
-0.5274897
(0.3312545)
Unemployment
(level)
0.005136
(0.0056982)
-0.0089724
(0.0158688)
0.0048032
(0 .0057478)
Log Life Expectancy
-5.377674**
(2.101842)
-5.864989
2.088336
-5.732744**
(2.336568)
Log House/ GDP
-0.0523612
(0.0918816)
0.0054777
(0.0109785)
-31.30096*
(14.92033)
-0.0740099
(0.089984)
0.0062063
(0.0114842)
-51.04817
(25.99318)*
-0.0441944
(0 .0996676)
0.0065242
(0.0115841)
-32.34374*
(15.66084)
0.8029359***
(0.1794709)
0.7249134 ***
(0.2156382)
0.7903456***
(0.1789016)
Class (level)
Log Growth rate of
GDP
Immigration rate
(level)
Lag log Inequality
immig*unempl
(level)
Robust Model
with
Butterworth
Filters
0.0244041*
(0.012059)
0.0072408*
(0.0039538)
0.8028058***
(0.0769253)
6.12e-08
(5.68e-08)
health*educ
(level)
-0.0073303
(0.0197474)
House (Butterworth
Filter)
Education
(Butterworth Filter)
6.90e-07
(1.32e-06)
0.3586238
(0 .4661468)
-3.67e-07**
(1.71e-07)
0.0052531**
(0.0021409)
Life Expectancy
(Butterworth Filter)
-0.0737813
(0.0471862)
GDP (Butterworth
Filter)
Immigration
(Butterworth Filter)
R-squared
(adjusted)
0.9276
(0.8914)
0.9484
(0.9116)
0.9448
(0.9054)
Durbin Watson Test
for serial
correlation
Test for functional
form
misspecification
2.334035
2.328612
2.309418
0.9469
(0.9252)
2.840529
F(3, 12) = 5.28
Prob > F =
0.0150
F(3, 11) = 3.73
Prob > F =
0.0453
F(3, 11) = 4.76
Prob > F =
0.0230
F(3, 19) =1.81
Prob > F =
0.1798
Standard errors are in Parentheses
*** significant at the 1% level; **significant at the 5% level; *significant at the 10% level
16
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
The interaction terms were insignificant therefore won’t be included. The lag of inequality removed
the serial correlation and was significant at the 1% level. Upon further analysis all regressions apart
from the last displayed signs of high multicollinearity, as expected, therefore the filtered results will
be the focus. Using the previous results, Equation 1 is estimated. The subscript t represents each
year in the sample the variable is investigated. The 𝜀 denotes the residuals each year for the
regression. These were found to follow a normal distribution.
Equation 1:
𝑳𝒏𝒊𝒏𝒆𝒒𝒖𝒂𝒍𝒕 = 𝜷𝟎 + 𝜷𝟏 𝑳𝒏𝑰𝒏𝒉𝒆𝒓𝒊𝒕𝒕 + 𝜷𝟐 𝑮𝑫𝑷𝒃𝒘 𝒕 + 𝜷𝟑 𝑰𝒎𝒎𝒊𝒈𝒃𝒘 𝒕 + 𝜷𝟒 𝒆𝒅𝒖𝒄𝒃𝒘 𝒕 +
𝜷𝟓 𝒄𝒍𝒂𝒔𝒔𝒃𝒘 𝒕 + 𝜷𝟔 𝒍𝒊𝒇𝒆𝒆𝒙𝒑𝒆𝒄𝒕𝒃𝒘 𝒕 + 𝜷𝟕 𝒖𝒏𝒆𝒎𝒑𝒍𝒕 + 𝜷𝟖 𝒍𝒂𝒈𝒊𝒏𝒆𝒒𝒖𝒂𝒍𝒕 + 𝜺𝒕
The results, shown in Table 6, are similar to the previous regressions. The significant variables, with p
values close to 0, include lninherit, educ_bw, unempl and the Lag of Inequality. All the other
variables were insignificant.
Table 6: Final Time Series Regression
Log Inequal
Coefficient
(S.E)
Log
Inherit/
Inherit
(BW)
0.0235*
(0.0126)
Outlier
Robust
Coefficient
(S.E)
0.0179*
(0.0087)
Robust
model with
Inheritance
Filter
-0.0645
(0.09139
83)
GDP
(BW)
Immig
(BW)
Educ
(BW)
Class
(BW)
Life
Expect
(BW)
Unempl
Lag Log
Inequality
𝑹𝟐
0.0235
*
(0.012
6)
0.1768
(0.309
5)
-2.36e07
(2.52e07)
-2.43e07
(1.74e07)
0.0069
**
(0.002
5)
0.0048
**
(0.001
7)
-0.0630
(0.4733)
0.0069*
*
(0.0028
)
0.0051*
*
(0.0019
)
0.8308***
(0.0882)
0.937
5
0.8686***
(0.0611)
0.946
0
0.2864
(0.514
3237)
4.44e0
7**
(1.72e07)
0.0047
**
(0.002)
0.3750
(0.396
5)
0.8179
***
(0.274
7)
0.5347
(0.366
2)
0.9477***
(0.0461)
0.939
0
-0.0549
(0.0327)
-0.0801*
(0.0409)
0.0038
(0.0028
)
Standard errors are in Parentheses
*** significant at the 1% level; **significant at the 5% level; *significant at the 10% level
These results are in the same directions as the preliminary results, and of a similar magnitude.
Education is the only result out of line with expectations, a 1% increase in the level of educational
17
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
attainment in the relevant population will increase inequality by 0.69%. It would be expected that
the higher the percentage of the UK with 5 A*-C GCSE’s will reduce inequality. Further educational
attainment such as university, may be of additional interest.
The possibility of extreme results impacting the regression is combatted using an outlier
robust regression. The results are in the same direction as that of the first regression however in
some cases are to a slightly different magnitude, GDP is no longer significant and class is now -0.819
rather than -0.375 signifying there may be a much larger effect than suggested. A 1% increase in the
percentage of owned housing in the UK will decrease the inequality by 81%. This is surprisingly large,
however may be because the more people who own might suggest a higher level of income within
the country, therefore decrease inequality. All other results are in line with the former regression.
For the sake of this dissertation it is not a major influence on the findings, as it is the direction that is
of most interest.
When filtering the Inheritance variable to account for its trending component, significance is
lost. Immigration and Life Expectancy are now significant, suggesting they may have more of an
influence than first thought. This could be that although there is a relationship between inheritance
and inequality it may be that its effects are indirect and exacerbate other inequalities, as
Wedgewood (1929) suggested.
The output in Table 6 shows high adjusted 𝑅 2 values suggesting a very high level of
explanatory power. Trending and non stationarity were combatted through the use of the
Butterworth filter on GDP, education, immigration and house prices, life expectancy and in the final
regression inheritance. Other tests of the model also do not imply problems in the model. The
Durbin-Watson test value is greater than 2 for each regression suggesting that there is no serial
correlation across the model. The Ramsey RESET test for functional form misspecification was
carried out rejecting the null of no omitted variables at the 10% level. There is also no
multicollinearity problem in the model with VIF values all under 10. These results are shown in
Appendix 2. Therefore it may be assumed that the model is a good fit.
18
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
It becomes clear that there is a strong relationship between inheritance and inequality
although it may be that its influence is indirect through other variables. There may be implications
for future generations particularly if inheritance is restricted to certain groups of individuals. An
investigation on whether certain characteristics make it more likely to inherit is now discussed.
Survey Investigation
A survey was designed to collect responses from a range of people regarding the characteristics that
may increase the likelihood of inheritance. This survey is intended to be complimentary to the
aggregate data. A preliminary sample survey was sent out to 30 participants to observe any
structural issues and collect feedback. The survey was then refined and released through an online
platform to improve the overall reach. 184 responses were collected and recorded anonymously.
Participants were asked 16 questions about their personal characteristics, about whether they, or a
family member, had ever inherited alongside a question about their opinion on the current housing
market.
A range of ages were surveyed however a large proportion, 68.5% of respondents, were 1625 which may skew results. Nevertheless at least 2% of responses came from every other age
bracket. A larger sample size would be needed in the future to get a more consistent number of
responses against all age groups. The survey technique may not have been the most appropriate for
older age brackets as they are less likely to use social media.
Hamnett (1991;1994) provides a framework for the types of questions asked in the survey.
The paper suggests that inheritance is more prevalent amongst homeowners, higher social classes,
and Southern Britain. Therefore these areas were investigated. The questions were centred around
attributes of the individual and their parents, such as job types and educational levels. Its also
helpful to see if there is a link between the characteristics of the individuals and their parents.
1
In order to estimate the regression the survey results were converted into numerical format.
1
The survey and results are shown in Appendix 3
19
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
This involved creating a ranking system for each category, shown in appendix 4. Inheritance, Gender
and Race are binary variables: they equal 1 if the subject inherited, was female or white, and 0
otherwise. The race variable is a dummy to illustrate the difference between the majority race of
white, and any minority races in the UK. Month of birth is ranked from 1 to 12 starting in September,
this acts as a control for the level of educational attainment, each month younger the child is, the
worse they perform (IFS ,2010). Research suggests that Inheritance is postively correlated with age
(Hamnett, 1991; Munro, 1988) Age is ranked as the median of the age category for example; 21-25 is
23. The value for the 61+ category has been calculated using the life expectancy in the UK, 81.5, as
an upper-bound (World Bank, 2012) and averaged to 71. 45% of those who inherited in this survey
were over 40, fewer than expected. This result may be the outcome of having a sample majority of
21-25 year olds, which encompassed a high level of inheritance, thereby skewing the results.
Instead, this may be due to the ageing population2, where grandchildren are receiving inheritance
rather than children. A report by the National Centre for Social Research (2008) suggests that
although all age groups are likely to inherit, those of an older age are likely to inherit more.
However, as suggested by Thorns (1994) this may not have a substantial effect on opportunities due
to being later in life.
Education has been converted into the approximate number of school years attended for
each qualification, having no qualifications equals 9 years, GCSE’s equals 11 years etc. Parental
education may play an important role, perhaps more so than the individual’s education. Higher
educational attainment is often associated with higher earnings potential, increasing parent’s
abilities to own homes and thus pass it on as inheritance. Table 7 shows that the highest number of
beneficiaries had parents who had obtained a postgraduate degree; this was then surprisingly
followed by those with no qualifications at all. This is an interesting result as there seems to be no
clear pattern. It may be that the beneficiaries’ parents with no qualifications opened up their own
2
A population in which the number of elderly (65+) is increasing relative to the number of 20-64 year olds (Population
Europe).
20
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
business or were able to promote quickly through firms due to having many years of experience.
Previously it was not necessary to have a degree except in certain occupations such as doctors; for
other high paying occupations it was not required. Conversely it has become increasingly necessary
to have a degree qualification for many jobs, having a degree may therefore have a smaller earnings
effect now.
Beneficiaries
Total Sample
%
Table 7: Parental Education and the incidence of inheritance
No
GCSE’s
AS/A Levels
Undergraduat
qualifications
e
8
4
5
7
23
35
30
58
35%
11.4%
16%
12%
Postgrad
uate
9
37
24%
Job and Parental Job have been ranked according to an occupational social class system, 1 being
unemployed up to 8, encompassing professional jobs such as accountants and doctors. Table 8
shows that there is quite an even distribution of the incidence across all groups. The high percentage
for 1 can be ignored as this is due to a limited sample of unemployed workers. 4 and 5, skilled
agriculture and administrative both have a high incidence of inheritance and 8, professional workers
also has a high incidence, conveying that there may be some increased probability in such jobs.
Beneficiaries
Total
Sample
%
Restricted %
1
1
3
33%
11.7%
Table 8: Incidence of inheritance and job type
2
3
4
5
6
11
0
1
6
2
103
2
3
14
11
7
2
14
8
10
34
10.6%
14%
29.4%
0%
33%
42.9%
27.4%
18%
Table 9 displays that most beneficiaries had parents in either administrative(4) or skilled
agricultural(5) jobs. If we restrict each to either being 1-4 or 5-8 it shows that for the beneficiaries,
‘lower class jobs’ have an 11.7% incidence, whereas ‘high class jobs’ have a 27.4% incidence of
inheritance. This suggests that those in ‘higher class jobs’ are also more likely to inherit, even more
so when students are excluded. Contrastingly, for parental jobs, the incidence is in the opposite
direction. This may be due to skilled agriculture and manual workers previously having relatively
21
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
higher wages whereas now there is a strong wage differential between those in professional jobs
and those in manual labour, as previously discussed.
Job Rank
Beneficiaries
Total
Sample
%
1
3
17
17.6
22.9%
Table 9: Incidence of Inheritance of parents job
2
3
4
5
6
0
5
6
5
0
0
32
12
22
15
7
8
46
8
15
99
0
17.3%
15%
15.6%
50%
22.7%
15.4%
0%
Region has been ranked according to distance from London; London being 1 and any regions that are
directly in contact with London ranked as 2, for each extra region needed to travel to increases the
value by 1, Scotland is the furthest away with a score of 6. There is thought to be a higher level of
wealth in the South of England, 15.5% of such households having a value of total wealth greater than
£967,000; enough to belong to the wealthiest tenth of the UK (ONS, 2012). Table 10 shows this
prediction is true although there is a high incidence across the country.
Table 10 : Incidence of inheritance by location
Beneficiaries Total
%
Sample
London
1
18
5%
South East
14
55
25.4%
South West
2
13
15.3%
East
6
41
14.6%
Midlands
West
4
35
11.4%
Midlands
North East
0
4
0
North West
1
2
50%
Yorkshire
3
10
30%
and the
Humber
Scotland
1
3
33.3%
Wales
1
1
100%
Northern
0
0
0%
Ireland
House Type has been ranked from council housing, 1, to owned with no mortgage, 4, also increasing
in social status. Table 11 shows 26 beneficiaries were owner occupiers, 4 having a mortgage,
compared to 11 renters, displaying a much higher likelihood of inheriting for owners. Nonetheless
22
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
privately rented also has quite a high incidence, perhaps due to renters planning on buying later in
life. That being said, 6 out of 11 renters were aged 50 or above which might suggest otherwise. The
high incidence of inheritance amongst these groups suggests that a higher social class are more
likely to inherit.
beneficiaries
Total sample
%
Table 11: Incidence of inheritance across housing tenure
Owner (No
Owner(Mortgage)
All owners
Privately
Mortgage)
rented
18
4
22
11
70
61
131
51
26%
6.5%
17%
21%
Council
Rented
0
2
0%
These results suggest that the incidence of inheritance is most likely for owner occupiers, with a high
class job, with a particularly high incidence within manual and skilled agricultural jobs and living in
the South East of England. Summary statistics are shown in appendix 5 however key trends have
been discussed. These results are expected to be reproduced in the econometric analysis.
23
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Results: Survey Data
The survey was used to test the influences of individual characteristics on the likelihood of
inheritance. The independent variable is binary and so takes the form of 0, no inheritance or 1,
having inherited. There is therefore little variation in the dependent variable reducing the power of
OLS estimation; instead a Probit Model is used. The Probit model uses maximum likelihood
estimation to predict what value of 𝛽 will maximise the likelihood that Y= 1.
3
Equation 2 was
estimated.
Equation 2:
𝑰𝒏𝒉𝒆𝒓𝒊𝒕𝒂𝒏𝒄𝒆 =
𝜷𝟎 + 𝜷𝟏 𝒂𝒈𝒆 + 𝜷𝟐 𝑩𝒊𝒓𝒕𝒉𝑴𝒐𝒏𝒕𝒉 + 𝜷𝟑 𝒆𝒅𝒖𝒄 + 𝜷𝟒 𝒉𝒐𝒖𝒔𝒆𝒕𝒚𝒑𝒆+ 𝜷𝟓 𝒋𝒐𝒃 + 𝜷𝟔 𝒑𝒂𝒓𝒆𝒏𝒕𝒂𝒍𝒆𝒅𝒖𝒄 +
𝜷𝟕 𝒓𝒆𝒈𝒊𝒐𝒏 + + 𝜷𝟕 𝒓𝒆𝒈𝒊𝒐𝒏 + 𝜶𝟏 𝒇𝒂𝒎𝒊𝒍𝒚𝒊𝒏𝒉𝒆𝒓𝒊𝒕 + 𝜶𝟐 𝒈𝒆𝒏𝒅𝒆𝒓 + 𝜶𝟑 𝒑𝒂𝒓𝒆𝒏𝒕𝒂𝒍𝒊𝒏𝒉𝒆𝒓𝒊𝒕 + 𝜶𝟒 𝒓𝒂𝒄𝒆 +
𝜷𝟏𝟐 𝒔𝒊𝒃𝒍𝒊𝒏𝒈𝒔 + 𝜺
In order to interpret coefficients it is necessary to calculate the marginal effects of the variables. This
is shown in equation 4, using the z distribution, the effect of a one unit change in the Independent
variable (IV) on the probability that Y = 1 given the other IV’s variables are constant.
𝝏𝑷(𝒚𝒊 =𝟏|𝒙𝒊 )
𝝏𝒙𝒌
𝝏𝑭(𝒛𝒊 )
={
𝝏𝒛
}𝜷𝒌
Equation 4.
A Robust Probit Regression was run in order to account for heteroskedasticity. The Marginal effects
of this are shown in Table 12. The variables Age, Education, Region, Parental inheritance and Family
Inheritance are all statistically significant at the 5% level. These results were expected to be
significant; however are not all in the expected direction. This is discussed below. These results will
again only hold under the Ceteris Paribus clause.
3
Derivation of the Probit model is listed in Appendix 6
24
The Impact of Inheritance on Income Inequality in the UK
Table 12: Robust Probit Regression, Marginal Effects
Dy/dx
Standard Error
Gender
0.0547045
(0.0454094)
Age
Birth Month
Race
Education
Parental
Education
Siblings
Job
Highest Parent’s
Job
Region
House Type
Parental
Inheritance
Family
Inheritance
Nicola Mounteney
z
1.20
P > |z|
0.228
0.0082314***
0.0070575
0.0356919
-0.0781169**
0.0119038
(0.001918)
(0.007037)
(0.0981963)
(0.0309974)
(0.0085897)
4.29
1.00
0.36
-2.52
1.39
0.000
0.316
0.716
0.012
0.166
0.0227526
0.0001739
0.0174161
(0.024049)
(0.0089869)
(0.0130764)
1.02
0.02
1.33
0.310
0.985
0.183
0.03801**
0.0197348
0.0897156**
(0.0194313)
(0.0268588)
(0.045807)
1.96
0.73
1.96
0.050
0.462
0.050
0.02564807***
(0.0365221)
7.02
0.000
For every extra 5 years of life expectancy the probability of inheriting increases by 0.82% this is
intuitive as inheritance generally occurs later in life due to the deaths of parents or grandparents.
This has been increasing recently due to the ageing population.
Unexpectedly, for each extra year of schooling there is a reduction in the likelihood of
inheritance of 7.8%. This may be due to the fact that education is a measure of the person who
inherits and may not be their final level of education. Alternatively, the results may be skewed by
small samples implying a higher incidence for those with lower qualifications. Parental Education,
which was insignificant, may play a more important role in inheritance than the individuals. This is
discussed more later.
For each region further away from London, inheritance increases by 3.8%. This is also
surprising. London has the wealthiest individuals and therefore should have the most likelihood of
owning housing. This may be because the price of housing in London is so high that housing is
unaffordable, reducing the probability of inheritance. There is also large income dispersion within
London Boroughs, some of which are amongst the poorest areas in the UK. Moving away from
25
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
London, the price of housing reduces dramatically making it is easier to buy and therefore inherit for
those of a similar wealth.
As expected, having parental inheritance increases the likelihood of inheritance by 8.9%. If
parents have inherited, then there will be money or housing available to pass onto children. If
someone in your family has inherited the likelihood of inheriting increases by 25%. This may be
because if other family members have inherited it may imply that you are in a higher social class
where inheriting is more frequent. These high values emphasise the pattern of staying within
classes, family members are likely to be in the same economic group and so members are likely to
stay in the same quintile if not decile as their family.
The McFadden 𝑅 2 is 0.4385 this measure is usually lower than the normal 𝑅 2, as the
regression is predicting probabilities it is harder to say with certainty whether a binary event will
2
happen or not. As the value of 𝑅𝑀𝑐𝐹
increases, the explanatory power of the model also increases.
The sensitivity and specificity of the model was calculated and found to correctly predict 48.48% of
correctly predicted y=1 observations for sensitivity and 92.91% of correctly predicted y=0
observations for specificity. This suggests that the data was more able to explain those not inheriting
than inheriting which may be the result of a small sample of beneficiaries. More investigation is
therefore needed. Overall the score of 84.48% was given suggesting that the percentage of correctly
predicted observations is quite high. The area under the ROC curve showed a result of 0.93 with
results around 0.8 generally indicating acceptable discrimination for the model. Performing the
Pearson χ2 goodness of fit test, it suggests the model fits well Prob>chi2=0.6431 however as the
number of observations are close to the number of covariate patterns the applicability of the chi
squared analysis is questioned. However, separating the data into 10 groups there is still evidence
that the model fits well with a Prob>chi2=0.7573. These are shown in Appendix 7.
Restrictions of the model, reported in table 13, were tested to check for robustness, to
observe whether characteristics change across categories such as gender, age and location.
Interestingly, across all models there is no variable that is consistently statistically significant.
26
The Impact of Inheritance on Income Inequality in the UK
Variable
Gender
Age
Birth
Month
Race
Parental
Educ
Siblings
Job
Highest
Job
Region
House
Type
Parental
Inheritanc
e
Family
Inheritanc
e
Educ (id)
𝑹𝟐 /
Pseudo 𝑹𝟐
Nicola Mounteney
Table 13: Alternative Regressions, Survey Data
Standard
Gender
Gender
Age > 40
Age < 40
Probit,
Dummy
Dummy
Robust
=1
=0 Male
Female
0.547045
0
0
0.368421
-.0533478
5***
0.0082314 0.012695 0.003011 0.006996 0.013349**
***
4***
4
8
*
0.0070575 0.005005 0.009436 0.05226*
-.0058261
4
6
0.0356919 -.1016152
0
0
.2801359**
*
0.0119038 0.129485 0.012352 -.0147416 0.018994**
8
0.227526
-.0087501 0.010025 .0552525 0.0153795
9
0.0001739 -0.23116* 0.010911 0.024929
-.0012006
8
8
0.0174161 0.010756 0.027300 0.050351
-.007415
3*
4
0.03801** 0.074037 0.116219 0.088374 0.0299217
***
8
**
0.0197348 -0.131003 0.099001 0.088374 0.0576644
4***
8
**
0.0897156 0.108740 0.031427 -.1538456 0.3002522
**
7**
7
***
Region < 3
0.2017529**
0.0088027**
*
0.0335386**
*
0.1173237
0.0484331**
*
-.0012247
0.0170651
0.0608368**
*
0.3180266**
*
0.0088935
0.0179718
0.2564807
***
0.276746
1***
0.248350
5***
0.085997
8
0.2386796
***
0.2275761**
*
.0781169**
*
0.4377
.0933751
**
0.5013
.0689532
**
0.5143
-.0058396
-.0127824
.2224504***
0.2270
0.8555
0.4971
For females, age, job, region, education, parental inheritance and family inheritance are all
statistically significant. Results suggest that a better job would reduce the likelihood of inheritance
by 23%. This is not in line with expectations. It may be the case that most females in the sample had
lower class jobs. This may be true as some women may not work, or may work in an easier job due
to having children. When they have inherited, which as shown is often later in life, they may have
lower class jobs, causing the increased likelihood of inheriting. It would be interesting here to look at
27
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
the jobs of partners to see whether this was in line with the male results. For males, the highest job
of parents, housing type, education and family inheritance were statistically significant. Males are
significantly more likely to inherit as housing tenure increases, an increase from one category to
another, such as rented to a mortgaged house, there will be a 9.9% increase in the probability of
inheriting. When the region is restricted to less than 3, within 2 regions distance of London, its
impact on inheritance increases drastically. The probability of inheriting increases from 3.8% in the
standard model to a 32% increased likelihood when restricted.
Age, education, region, parental and family inheritance are significant suggesting these
factors may contribute to the likelihood of inheritance with other variables being significant when
restricting the model to certain groups.
28
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Discussion
Hamnett (1991) argued housing inheritance incidence would become more widespread due to
increased ownership possibilities. Yet housing market instability and ‘increasing’ costs of housing
have made it harder to buy, particularly for low income individuals and first time buyers. Estimates
suggest housing tenure in the UK will change dramatically; 1.5 million more young people aged 1830 will be pushed towards the private rented sector (Joseph Rowntree Foundation, 2012).
There is a strong desire to own housing in the UK which may not subside even if housing is
unaffordable. Those already on the property ladder or inheriting properties will be at an advantage
over those whose parents are renting or are in the council housing sector. If there is a causational
link between inequality and the incidence of housing inheritance then the current market conditions
may cause an increase in the inequality of the country. Fewer people owning housing may lead to
fewer people inheriting. This may continue in a vicious cycle until only the very wealthy can afford
housing unless the individual or their family members have previously invested in housing.
During the survey an open-ended question on the current housing market condition was
posed. 86 out of 184 responded that the current market worried them; many said housing was too
expensive and are concerned about their ability to afford deposits. It will be interesting to consider
house prices and the level of ownership in the future and determine whether there is a relationship
between the two and if this correlates to changes in inequality.
The econometric analysis emphasises two key correlations; the likelihood of inheritance may
depend on certain characteristics and, that the aggregate transfer of wealth impacts inequality. This
could have implications for members of society who are not likely to inherit. As certain
characteristics make it more likely to inherit, the likelihood of inequality between those with and
without such characteristics increases. These include age, education, region and family inheritance.
Two of these are of most interest, Education, as this is a choice by individuals, and family inheritance
including parents, suggesting there is a level of within family inheritance, as expected.
29
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
The econometric analysis indicated that those who had less education had inherited. In the
initial data Table 14, the highest number of beneficiaries had an undergraduate degree. This may not
have been significant due to the large sample size meaning the total incidence was lower. There was
also one individual with no qualifications who had inherited, skewing the sample towards having a
negative effect. If the level of education does improve chances of inheritance then there may be a
need to influence education decisions for those more disadvantaged. Educational attainment
correlates to improved incomes later in life and therefore leads to better outcomes in all aspects
(The Department for BIS, 2013). It may be that individuals in the higher social classes are more likely
to have higher education and so cannot be influenced by policy makers. In the aggregate
investigation, there was also a link between educational attainment and inequality. These results
however turned out to be counter intuitive, making this an interesting topic for further research.
Table 14: Incidence of Inheritance by educational attainment
No
GCSE’s
AS/A Undergraduate Postgraduate
qualifications
Levels
Beneficiaries
1
6
3
19
4
Total Sample
1
15
21
124
21
Incidence (%)
100%
40%
14%
15%
19%
Surprisingly, parental education, which as previously suggested would most likely play a larger role in
whether the individual could inherit, was insignificant. If the parents had a higher level of education
it would be assumed they also have a higher paying job and thereby increased ability to own a
home. Parental education also has an impact on their children’s education, for a one year increase in
the education of parents there will be a 0.09 year increase in the education of the individual,
suggesting that parental education may play a larger role than results suggests4.
Secondly, inheritance is likely to occur within families. Thorns (1994) suggested that
inheritance should not impact on the wealth of the individual mainly due to the timing of the
phenomenon. Conversely if the inheritance is received at a younger age, such as from a grandparent,
then they may have better opportunities than peers who have not inherited. It is clear that there is
4
Regression in Appendix 8
30
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
a correlation between inheritances within families which may disadvantage those with no previous
inheritance. This may be of interest to policy makers who may be able to help those with no history
of inheritance and a low ability to buy. Current government programs aim to help everyone join the
housing market. The ‘Help to Buy Scheme’ works by allowing a lower deposit of only 5%. This will
help more people to get onto the property ladder. These schemes will also increase the level of
inheritance in the housing market. In turn, if the results from the regressions hold will reduce
inequality in a country as ownership becomes more evenly distributed and more accessible.
The link between inequality and inheritance primarily falls under which characteristics
inheritance occurs. A higher class job and more education both of which tend to lead to increased
incomes and therefore ability to own homes, infer that inheritance is a consequence of ability to
earn a higher income. Further research into the factors inhibiting individuals from gaining a higher
income may need to be studied.
31
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Conclusion
This dissertation set out to investigate the impact of inheritance on inequality. The last major
investigation into the characteristics of inheritance was Hamnett (1991) therefore a more up to date
set of results has been provided. There have also been few investigations into the effect housing
inheritance has on inequality which has also been discussed. It is clear that there are key
characteristics that influence the likelihood of inheritance therefore biasing opportunities to such
people. If inheritance improves individual’s opportunities then it has the potential to have a more
severe impact on inequality. Several characteristics influence the likelihood of inheritance; age,
education, region and the inheritance of family members. Family inheritance, in particular that of
parents, is a key determinant of whether individuals will inherit or not. This suggests an
intergenerational impact on inequality as suggested by Bowles and Gintis(2002). Improving individuals’
abilities to afford housing should reduce the distributional issues associated with the incidence of
housing inheritance and reduce the skewedness of intergenerational inheritance away from only the
middle and upper classes. For future investigation a larger survey sample is needed, with a focus on
the amount inherited. This would help see whether the inheritance is equally distributed or whether
certain individuals inherit much larger amounts. It will also be of interest to see whether the current
market has any impact on the characteristics seen here and whether more people will rent rather than
buy.
32
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Bibliography
Andrews, D., & Leigh, A. (2009). More Inequality, Less Social Mobility. Applied Economics Letters,
16(15), 1489 - 1492.
Andrews, D., & Sánchez, A. C. (2011). The Evolution of Homeownership Rates in Selected OECD
Countries:Demographic and Public Policy Influences. OECD Journal: Economic Studies, 207 243.
Atkinson, A. B. (2013, November). Wealth and Inheritance in Britain from 1896 to the Present. Centre
for Analysis of Social Exclusion.
Bolton, P. (2012, November 27). Education: Historical statistics. Retrieved January 2015, from
www.parliament.uk/briefing-papers/sn04252.pdf
Bowles, S., & Gintis, H. (2002). The Inheritance of Inequality. Journal of Economic Perspectives, 16(3), 330.
Card, D. (2009). Immigration and INequality. Cambridge: National Bureau of Economic Research.
Davidson, R., & MacKinnon, J. (1982). Convenient Specification Tests for Logit and Probit Models.
Ontario, Canada: Queen’s Economics Department.
De Nardi, M. (2004). Wealth Inequality and Intergenerational Links. Review of Economic Studies, 71(3),
743-768.
Deaton, A. (2003). Health, Income and Inequality. The National Bureau of Economic Research.
Hamnett, C. (1991, October). A nation of Inheritors? Housing Inheritance, Wealth and Inequality in
Britain. Journal of Social Policy, 20(4), 509- 536.
Hamnett, C. (2009). A Nation of Inheritors. Journal of Social Policy , 530.
Hamnett, C. (94). Housing Inheritance and Inquality: A response to Watt. The Journal of Social Policy, 3,
413-422.
Hamnett, C., Harmer, M., & Williams, P. (1991). Safe as Houses; Housing Inheritance in Britain. London:
Paul Chapman Publishing Ltd.
Hertz, T. (2002). Intergenerational Economic Mobility of Black and White Familiesin the United States.
Heshmati, A. (2004). The World Distribution of Income and Income Inequality. Bonn, Germany: Institute
for the Study of Labour .
Hubert, F. (1998). Private rented housing in Germany. Journal of Housing and the Built Environment,
13(3), 205 - 232.
IFS. (2010). When You Are Born Matters: The Impact of Date of Birth on Educational Outcomes in
England. London: Economic and Social Research Council.
IMF Working Paper. (2008). Rising Income Inequality: Technology, or Trade and Financial Globalization?
33
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Joseph Rowntree Foundation. (2012). Can Improving UK Skilll Levels Reduce Poverty and Income
Inequality in the UK? York: Joseph Rowntree Foundation.
Joseph Rowntree Foundation. (2012). Housing Options and Solutions for young people in 2020.
Joseph Rowntree Foundation. (2012). Housing Options and Solutions for young people in 2020.
Karagiannaki, E. (2011). The impact of inheritance on the distribution of wealth: Evidence from the UK.
London: Centre for Analysis of Social Exclusion, LSE.
Laitner, J. (1979). Household bequest behaviour and the national distribution of Wealth. Review of
Economic Studies, 46(3), 467-483.
Matlock, J., & Vigdor, J. (2006). Do Rising Tides Lift all prices? Journal of Housing Economics, 17(3), 212224.
National Life Tables, United Kingdom 2011-2013. (2014, September 25). Retrieved January 01, 2015,
from ONS Statistical Bulletin : http://ons.gov.uk/ons/dcp171778_377972.pdf
O'Dwyer, L. A. (2001). The Impact of Housing Inheritance on the Distribution of Wealth in Australia.
Australian Journal of Political Science, Volume 36, 36(1), 83-100.
OECD. (2011). Society at a Glance: OECD Social Indicators.
OECD (2015), Income inequality (indicator). doi: 10.1787/459aa7f1-en (Accessed on 31 January 2015)
ONS. (2012, January 20). Mortality in the United Kingdom, 2010. Retrieved from
http://www.ons.gov.uk/ons/rel/mortality-ageing/mortality-in-the-united-kingdom/mortalityin-the-united-kingdom--2010/mortality-in-the-uk-2010.html
ONS. (2012). South East has biggest share of the wealthiest households. Retrieved 03 29, 2015, from
http://www.ons.gov.uk/ons/dcp171776_289407.pdf
ONS. (2013, April 19). A Century of Home Ownership and Renting in England and Wales (full story).
Retrieved January 01, 2015, from ONS: http://www.ons.gov.uk/ons/rel/census/2011-censusanalysis/a-century-of-home-ownership-and-renting-in-england-and-wales/short-story-onhousing.html
ONS. (2013, August 23). Long-term profile of Gross Domestic Product (GDP) in the UK. Retrieved
January 01, 2015, from ONS: http://www.ons.gov.uk/ons/rel/elmr/explaining-economicstatistics/long-term-profile-of-gdp-in-the-uk/sty-long-term-profile-of-gdp.html
ONS. (2014, November). House Price Index. Retrieved January 01, 2015, from Office for National
Statistics: http://www.ons.gov.uk/ons/datasets-and
tables/index.html?pageSize=50&sortBy=none&sortDirection=none&newquery=house+prices&
content-type=Reference+table&content-type=Dataset ONS data
ONS. (2014, February 27). ONS estimates of Long-Term International Migration in the year ending
September 2013. Retrieved January 01, 2015, from ONS:
http://www.ons.gov.uk/ons/rel/migration1/migration-statistics-quarterly-report/february34
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
2014/sty-ltim.html
Phillips, M. (2014, 01 23). Most Germans don’t buy their homes, they rent. Here’s why. Retrieved 11 23,
2014, from Quartz: http://qz.com/167887/germany-has-one-of-the-worlds-lowesthomeownership-rates/
Platt, L., Simpson, L., & Akinwale, B. (2005). Stability and change in Ethnic Groups in England and
Wales. ONS.
Population Europe. (n.d.). Retrieved 03 18, 2015, from https://www.populationeurope.eu/Library/Glossary.aspx
Saunders, P. (1990). A Nation of Homeowners. Unwin Hyman Ltd.
Solon, G. (2002). Cross Country Differences in Intergenerational Earnings Mobility. Journal of Economic
Perspectives, 16(3), 59-66.
The Department for Business Innovation and Skills. (2013). The Benefits of Higher Education
Participation for Individuals and Society: key findings and reports "The Quadrants".
The Equality Trust. (n.d.). The Cost of Inequality. Retrieved 04 03, 2015, from
http://www.equalitytrust.org.uk/sites/default/files/The%20Cost%20of%20Inequality%20%20%20full%20report.pdf
The Guardian . (2011, March 2011). Brits buy homes, the Germans rent – which of us has got it right?
Retrieved November 26 , 2014, from The Guardian :
http://www.theguardian.com/money/2011/mar/19/brits-buy-germans-rent
The Guardian. (2014, September 14). UK house prices hit new record as London average breaks
£500,000. Retrieved April 20, 2015, from The Guardian :
http://www.theguardian.com/business/2014/sep/16/house-prices-record-bubble-interestrates-uk
Thorns, D. C. (1994). The Role of Housing Inheritance in selected owner occupied societies (Britain,
New Zealand, Canada). Housing Studies, 9(4), 473-492.
Tomes, N. (1981). The Family, Inheritance and the Intergenerational Transmission of Inequality. Journal
of the Political Economy, 5(89), 928-958.
Van Der Heijden, H. (2002). Social Rented Housing in Western Europe: Developments and Expectations.
Urban Studies, 39(2), 327- 340.
Van Ham, M., Williamson, L., Feijten, P., & Boyle, P. (2013). Right to buy … time to move? investigating
the moving behaviour of right to buy owners in the UK. Journal of the House and the Built
Environment, 28(1), 129–146.
World Bank. (2012). Data World Bank Data. Retrieved 03 07, 2015, from World Bank:
http://data.worldbank.org/indicator/SP.DYN.LE00.IN
35
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Appendix 1 – Graphs to show Butterworth time series filters of variables
0
5000
-5000
-10000
0
-15000
50000
100000
150000
200000
House cyclical component from bw filter
10000
250000
Butterworth Filter on House Price
1970
1980
1990
Year
2000
1970
2010
1980
1990
Year
2000
2010
1990
Year
2000
2010
1970
60000
40000
0
20000
-20000
1980
1990
Year
2000
2010
-40000
100000
200000
300000
400000
500000
Immigration cyclical component from bw filter
600000
Butterworth Filter on Immigration
1970
1980
0
.02
-.02
-.04
.5
-.06
.55
.6
.65
Class cyclical component from bw filter
.7
.04
Butterworth Filter % of owned housing
1970
1980
1990
Year
2000
2010
1970
1980
1990
Year
2000
2010
36
.02
0
1970
1980
1990
Year
2000
2010
1970
1980
1990
Year
2000
2010
1990
Year
2000
2010
1990
Year
2000
2010
-0.50
0.00
0.50
1980
growth rate GDP
1970
1.00
0.60
1.50
0.80
-.04
-.02
1.40
1.60
lngdp cyclical component from bw filter
Butterworth Filter on GDP
GDP
1.00
1.20
Nicola Mounteney
.04
The Impact of Inheritance on Income Inequality in the UK
0
-5
-10
20
40
60
Educ cyclical component from bw filter
80
5
Butterworth Filter on Education
1970
1980
1990
Year
2000
2010
1970
1980
37
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
.1
.05
0
-.05
-.1
8
9
lnInherit
10
lnInherit cyclical component from bw filter
11
.15
Butterworth Filter on Inheritance
1970
1980
1990
Year
2000
1970
2010
1980
1990
Year
2000
2010
Appendix 2: Equation 1
Linear regression
Number of obs =
F( 8,
23) =
Prob > F
=
R-squared
=
Root MSE
=
lninequal
Coef.
lnInherit
gdp_bw
immig_bw
educ_bw
class_bw
lifeexpect_bw
Unempl
laglninequal
_cons
.0234723
.4027657
-3.41e-07
.005103
-.563095
-.0590154
.0067755
.8073484
-.4940233
Robust
Std. Err.
.0112031
.5146686
1.70e-07
.0020553
.3916099
.0429982
.0034152
.0765389
.2083726
t
2.10
0.78
-2.01
2.48
-1.44
-1.37
1.98
10.55
-2.37
P>|t|
0.047
0.442
0.056
0.021
0.164
0.183
0.059
0.000
0.027
32
74.07
0.0000
0.9460
.02582
[95% Conf. Interval]
.000297
-.6619075
-6.93e-07
.0008513
-1.373202
-.1479639
-.0002894
.6490155
-.9250748
.0466475
1.467439
1.01e-08
.0093547
.2470118
.0299331
.0138404
.9656813
-.0629718
Post Estimation tests for equation 1
Test for heteroskedasticity
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of lninequal
chi2(1)
Prob > chi2
=
=
0.25
0.6142
Test for Serial Correlation
38
The Impact of Inheritance on Income Inequality in the UK
Number of gaps in sample:
Durbin-Watson d-statistic(
Nicola Mounteney
3
9,
29) =
2.401264
Test for Functional Form Misspecification
Ramsey RESET test using powers of the fitted values of lninequal
Ho: model has no omitted variables
F(3, 17) =
2.11
Prob > F =
0.1364
Test for Multicollinearity
Variable
VIF
1/VIF
lnInherit
gdp_bw
laglninequal
class_bw
Unempl
educ_bw
lifeexpect~w
immig_bw
4.73
4.49
3.85
3.56
2.05
1.62
1.58
1.38
0.211598
0.222922
0.259503
0.281050
0.486947
0.618784
0.632885
0.722921
Mean VIF
2.91
10
5
0
Density
15
20
Figure (X) Residuals for the Time series regression
-.04
-.02
0
.02
.04
Residuals
39
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
.3
.2
.1
0
Density
.4
.5
Checking for Outliers
-3
-2
-1
Standardized residuals
0
1
Post Estimation with filtered Inheritance
Linear regression
Number of obs =
F( 8,
23) =
Prob > F
=
R-squared
=
Root MSE
=
lninequal
Coef.
inherit_bw
gdp_bw
immig_bw
educ_bw
class_bw
lifeexpect_bw
Unempl
laglninequal
_cons
-.0645907
.2864587
-4.44e-07
.0047178
-.5347329
-.0800771
.0037911
.9477426
-.0800973
Robust
Std. Err.
.0913983
.5143237
1.72e-07
.0022446
.3662613
.0408731
.0028202
.0460681
.0584797
t
-0.71
0.56
-2.59
2.10
-1.46
-1.96
1.34
20.57
-1.37
P>|t|
0.487
0.583
0.016
0.047
0.158
0.062
0.192
0.000
0.184
32
71.95
0.0000
0.9390
.02744
[95% Conf. Interval]
-.2536626
-.777501
-7.99e-07
.0000745
-1.292402
-.1646297
-.0020429
.8524435
-.2010718
.1244811
1.350418
-8.94e-08
.0093611
.2229364
.0044754
.009625
1.043042
.0408772
40
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Test for Multicollinearity
Variable
VIF
1/VIF
gdp_bw
class_bw
Unempl
lifeexpect~w
inherit_bw
educ_bw
immig_bw
laglninequal
4.31
3.62
1.56
1.45
1.35
1.33
1.18
1.17
0.231885
0.276230
0.639806
0.690745
0.738018
0.752148
0.850581
0.854868
Mean VIF
2.00
Test for Serial Correlation
Durbin-Watson d-statistic(
9,
32) =
2.750308
Test for Functional Form Misspecification
Ramsey RESET test using powers of the fitted values of lninequal
Ho: model has no omitted variables
F(3, 20) =
3.05
Prob > F =
0.0525
41
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Appendix 3: Survey and Results
Figure 1: Survey
This survey will be completely anonymous and the answers gained will be used in my dissertation to
determine whether there are certain characteristics that are more likely to influence inheritance.
You do not have to answer any questions you are not comfortable with.
1. What is your Gender?
Male
Female
2. What is your age?
16-20
21-25
26-30
31-35
36-40
41-45
46-50
51-55
56-60
60+
3. What is your month of birth?
Jan
Feb
March
April
May
June
July
August
Sept
Oct
Nov
Dec
4. What is your race?
White (British/ Irish/ Scottish/ Welsh/ Northern Irish)
White Irish
White other
Black/ African/ Caribbean/ Black British
Asian (Indian, Pakistani, Bangladeshi, Chinese etc.)
Other ethnic group (i.e. Arabic)
5. What is your highest level of Education? (please put equivalent/most relevant)
None
GCSE’s
AS/A-levels
Undergraduate
Postgraduate
other ……………….
6. What is your parent’s highest level of Education? (please put equivalent/most relevant)
None
GCSE’s
AS/A-levels
Undergraduate
Postgraduate
other ……………….
7. How many siblings do you have?
0
1
2
3
4
5+
8. What best describes your job?
Unemployed
Student
Manual
Professional (Doctor, Lawyer, Accountant etc.)
Managerial
Administrative
Sales/ Services
42
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Skilled agriculture/ forestry/ Fishery
Other…………………………………………………………
9. What best describes your parent’s/guardian’s jobs?
P1
P2
Unemployed
Student
Manual
Professional (Doctor, Lawyer, Accountant etc.)
Managerial
Administrative
Sales/ Services
Skilled agriculture/ forestry/ Fishery
Other…………………………………………………………
10. Where do you live? (Region)
North west
North East
Yorkshire and the Humber
Northern Ireland
East Midlands
London
West Midlands
Wales
South East
Scotland
South west
Ireland
11. What type of house do you live in? (if you are at university please state your parent’s house)
Owned (Mortgaged)
Owned (no mortgage)
Privately Rented
Council Rented
12. Have you ever received housing inheritance or inheritance from the sale of a house?
Yes
No
13. Have either/ both your parents received housing inheritance or inheritance from the sale of
a house?
Yes
No
14. Has anyone else in your family received Housing inheritance or money from the sale of a
house?
Yes
No
15. If Yes, please state who
Siblings
Husband/ Wife
Children
Other (please state)…………………………..
Guardian
16. How do you feel about the current housing market? If you don't already own
one are you worried about owning a house in the future?
……………………………………………………………………………………………………………………………
43
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
……………………………………………………………………………………………………………………………
…………………………………………………………………………………………………………………………..
Table 1: Survey Results
What is your Gender
Answer Options
Male
Female
Response
Percent
Response
Count
38.4%
61.6%
71
114
answered question
185
What is your age?
Answer Options
16-20
21-25
26-30
31-35
36-40
41-45
46-50
51-55
55-60
61+
Response
Percent
Response
Count
26.1%
42.4%
2.2%
4.9%
2.7%
2.7%
4.9%
5.4%
4.3%
4.3%
48
78
4
9
5
5
9
10
8
8
answered question
184
What is your month of birth?
Answer Options
January
February
March
April
May
June
July
August
September
October
November
December
Response
Percent
Response
Count
8.7%
8.7%
8.7%
7.6%
7.6%
10.3%
8.2%
7.6%
9.8%
8.7%
6.5%
7.6%
16
16
16
14
14
19
15
14
18
16
12
14
answered question
184
What is your Race?
Answer Options
White (English/ Irish/ Scottish/ Welsh)
White Other
Black British/ African/ Caribbean
Asian (Indian/ Pakistani/ Bangladeshi/ Chinese etc)
Other ethnic group (e.g. Arabic etc.)
Response
Percent
Response
Count
85.3%
6.0%
0.0%
7.6%
1.1%
157
11
0
14
2
44
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
answered question
184
What is your highest level of education (please tick the equivalent/ most relevant)
Answer Options
No qualifications
GCSE's (High school, i.e. leaving school at 16 years)
AS/ A levels (leaving school at 18)
Undergraduate (please tick if currently a student)
PostGraduate (Please tick if currently a student)
Response
Percent
Response
Count
0.5%
8.2%
11.5%
68.3%
11.5%
1
15
21
125
21
answered question
183
What is your parents highest level of education (please tick the equivalent/ most
relevant)
Answer Options
No qualifications
GCSE's (High school, i.e. leaving school at 16 years)
AS/ A levels (leaving school at 18)
Undergraduate (please tick if currently a student)
PostGraduate (Please tick if currently a student)
Response
Percent
Response
Count
12.5%
19.0%
16.3%
32.1%
20.1%
23
35
30
59
37
answered question
184
How many siblings do you have?
Answer Options
0
1
2
3
4
5+
Response
Percent
Response
Count
8.6%
44.3%
31.9%
12.4%
1.1%
1.6%
16
82
59
23
2
3
answered question
185
What best describes your job? If retired please put the job you did when working
Answer Options
Unemployed
Student
Manual
Professional (Doctor, Lawyer, Accountant etc.)
Managerial
Administrative
Sales/ Services
Skilled Agriculture/ Forestry/ Fishery
Other (please specify)
Response
Percent
Response
Count
0.0%
55.1%
1.1%
16.2%
7.6%
7.6%
4.9%
1.1%
6.5%
0
102
2
30
14
14
9
2
12
answered question
skipped question
185
0
What best describes your parents jobs? (please tick all relevant) If Retired please put
job when working
Answer
Options
Unemployed
Student
Response Percent
Response Count
3.8%
0.0%
7
0
45
The Impact of Inheritance on Income Inequality in the UK
Manual
Professional
(Doctor,
Lawyer,
Accountant
etc.)
Managerial
Administrative
Sales/
Services
Skilled
Agriculture/
Forestry/
Fishery
Other (please
specify)
Nicola Mounteney
16.8%
31
42.7%
79
27.0%
14.1%
50
26
8.1%
15
7.0%
13
14.6%
27
answered question
185
Where do you live?
Answer Options
North West
North East
East Midlands
West Midlands
South East
South West
Yorkshire and the Humber
London
Scotland
Ireland
Wales
Northern Ireland
Response
Percent
Response
Count
1.1%
2.2%
22.5%
19.2%
30.2%
7.1%
5.5%
9.9%
1.6%
0.0%
0.5%
0.0%
2
4
41
35
55
13
10
18
3
0
1
0
answered question
182
What type of house do you live in?
Answer Options
Owned (Mortgaged)
Owned (no mortgage)
Privately Rented
Council Rented
Response
Percent
Response
Count
37.8%
33.5%
27.6%
1.1%
70
62
51
2
185
answered question
Have you ever received housing inheritance or money from the sale of a house (i.e.
money from a relative who has passed away and owned their house?)
Answer Options
Yes
No
Response
Percent
Response
Count
18.0%
82.0%
33
150
answered question
183
Have your parents ever received housing inheritance or money from the sale of a
house (i.e. money from a relative who has passed away and owned their house?)
46
The Impact of Inheritance on Income Inequality in the UK
Answer Options
Yes
No
Nicola Mounteney
Response
Percent
Response
Count
36.8%
63.2%
67
115
answered question
182
Has any of your family (other than your parents) ever received housing inheritance or
money from the sale of a house (i.e. money from a relative who has passed away
and owned their house?)
Answer Options
Yes
No
Response
Percent
Response
Count
22.4%
77.6%
41
142
answered question
183
If Yes to question 14, Please state who?
Answer Options
Siblings
Spouse (Husband/ Wife)
Children
Guardian
Other (please specify)
Response
Percent
Response
Count
45.0%
17.5%
5.0%
2.5%
30.0%
18
7
2
1
12
answered question
40
Appendix 4: Survey converted into numerical form
Variable
Gender
Month of Birth
Age
Variable
Female
Male
September
October
November
December
January
February
March
April
May
June
July
August
16-20
21-25
26-30
31-35
36-40
41-45
46-50
51-55
56-60
Number
1
0
1
2
3
4
5
6
7
8
9
10
11
12
18
23
28
33
38
43
48
53
58
47
The Impact of Inheritance on Income Inequality in the UK
61+
Race
White
Not white
No Qualification
GCSE
A-Levels
Undergraduate
Postgrad
Education
Job/ parental Job
Region
House type
Inheritance/ Parental/ Family
Inheritance
Professional (Doctor, Lawyer,
Accountant etc.) inc. Self Employed
Manual
Managerial
Unemployed
Skilled agriculture etc.
Administrative
Sales/ Services
Student
London (Distance from London)
South East
South West
East
East Midlands
West Midlands
Wales
Yorkshire and the Humber
North West
North East
Scotland
Owned (No Mortgage)
Owned (Mortgage)
Privately Rented
Council Rented
Inheritance
No inheritance
Nicola Mounteney
71 (half way between 60 and 80
(life expectancy)
1
0
9
11
13
16
18
8
3
7
1
4
5
6
2
1
2
3
3
3
4
4
4
5
6
4
3
2
1
1
0
Appendix 5:
Gender
Age
Birth Month
Race
Parental Education
Siblings
Table 4: Summary Statistics of the Survey Data
Mean
Standard
Minimum
Maximum
Deviation
0.614
0.488
0
1
29.71
14.77
18
71
6.475
3.497
1
12
0.918
0.274
0
1
14.08
3.095
9
18
1.576
0.966
0
5
Observations
184
184
183
184
183
184
48
The Impact of Inheritance on Income Inequality in the UK
Job rank
Average Parent Job
Region
House Type
Inheritance
Parental
Inheritance
Family Inheritance
Education
P1Job
P2Job
Highest Job
Nicola Mounteney
3.984
3.791
2.665
3.033
0.180
0.368
2.527
1.609
0.948
0.809
0.386
0.484
1
0.500
1
1
0
0
8
7.500
6
4
1
1
184
184
182
184
183
182
0.225
2.846
5.070
6.881
6.424
0.419
0.750
2.320
1.803
2.053
0
1
1
1
1
1
5
8
8
8
182
182
115
118
184
Appendix 6: Derivation/ Probit Model
The Probit Model is estimated using maximum likelihood estimation. It estimates the following
equation (X)
𝐏(𝐘 = 𝟏| 𝐗) = ɸ(𝐗’𝜷)
𝒙′𝜷
𝟐
𝑭(𝒙′ 𝜷) = ɸ(𝐗’𝜷) = ∫−∞ 𝒆−𝟎.𝟓𝒖 𝒅𝒖
(Equation 2)
This shows that the probability that Y is equal to 1, that individuals inherit, given the vector of
characteristics contributing to the outcome of Y is equal to the Cumulative Distribution Function of
the normal distribution, ɸ, and the vector of characteristics X and β, a column vector of parameters
to be estimated. (Davidson & MacKinnon, 1982)
X is increasing in Y and carries the characteristics that if F(-∞ ) = 0 and F(∞)= 1
The interpretation of 𝜷𝒋 can be derived5
E(Y|X) = P(y=1|x)
(Equation 3)
This states that the Expected value of Y given the Vector X of dependent variables is equal to the
probability that the individual inherits, Y =1 given the x’s.
From this it follows that if 𝑥𝑗 goes up by 1 unit then the z value will increase by 𝛽𝑗 ceteris paribus. In
order to interpret coefficients it is necessary to calculate the marginal effects of the variables. This is
shown in equation 4. The differential of inheritance with respect to each individual variable is taken
where 𝑧𝑖 = 𝑥𝑖 ′𝛽. This is the probability of inheriting
𝜕𝑃(𝑦𝑖 =1|𝑥𝑖 )
𝜕𝑥𝑘
5
𝜕𝐹(𝑧𝑖 )
={
𝜕𝑧
}𝛽𝑘
Equation 4
See appendix (number) for derivation of equation (X)
49
The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
McFadden R squared – pseudo r squared
̂)
𝐿(𝛽
𝜌2 = 1 − 𝐿(𝛽̅)
(X)
̅ is 0.
L(𝛽̅) is the restricted log likelihood where β̂ is the maximum likelihood estimator and β
Appendix 7 : Equation 2
Average marginal effects
Model VCE
: Robust
Number of obs
=
174
Expression
: Pr(Inheritance), predict()
dy/dx w.r.t. : Gender Age BirthMonth Race id ParentalEduc Siblings Job
Highestjob Region HouseType ParentalInheritance
Familyinheritance
dy/dx
Gender
Age
BirthMonth
Race
id
ParentalEduc
Siblings
Job
Highestjob
Region
HouseType
ParentalIn~e
Familyinhe~e
.0547045
.0082314
.0070575
.0356919
-.0781169
.0119038
.0227526
.0001739
.0174161
.03801
.0197348
.0897156
.2564807
Delta-method
Std. Err.
.0454094
.001918
.007037
.0981963
.0309974
.0085897
.0224049
.0089869
.0130764
.0194313
.0268588
.045807
.0365221
z
1.20
4.29
1.00
0.36
-2.52
1.39
1.02
0.02
1.33
1.96
0.73
1.96
7.02
P>|z|
0.228
0.000
0.316
0.716
0.012
0.166
0.310
0.985
0.183
0.050
0.462
0.050
0.000
[95% Conf. Interval]
-.0342962
.0044721
-.0067348
-.1567694
-.1388708
-.0049316
-.0211603
-.0174402
-.0082131
-.0000747
-.0329075
-.0000645
.1848986
.1437053
.0119907
.0208497
.2281532
-.0173631
.0287393
.0666654
.0177879
.0430453
.0760948
.0723772
.1794957
.3280627
Post estimation tests for equation 2
Probit model for Inheritance, goodness-of-fit test
number of observations
number of covariate patterns
Pearson chi2(156)
Prob > chi2
=
=
=
=
174
170
148.96
0.6431
50
Nicola Mounteney
0.00
0.25
0.50
0.75
1.00
The Impact of Inheritance on Income Inequality in the UK
0.00
0.25
0.50
Probability cutoff
Sensitivity
0.75
1.00
Specificity
Probit model for Inheritance
number of observations =
area under ROC curve
=
174
0.9312
51
Nicola Mounteney
0.50
0.25
0.00
Sensitivity
0.75
1.00
The Impact of Inheritance on Income Inequality in the UK
0.00
0.25
0.50
1 - Specificity
0.75
1.00
Area under ROC curve = 0.9312
Probit model for Inheritance
True
Classified
D
~D
Total
+
-
16
17
10
131
26
148
Total
33
141
174
Classified + if predicted Pr(D) >= .5
True D defined as Inheritance != 0
Sensitivity
Specificity
Positive predictive value
Negative predictive value
Pr( +| D)
Pr( -|~D)
Pr( D| +)
Pr(~D| -)
48.48%
92.91%
61.54%
88.51%
False
False
False
False
Pr( +|~D)
Pr( -| D)
Pr(~D| +)
Pr( D| -)
7.09%
51.52%
38.46%
11.49%
+
+
-
rate
rate
rate
rate
for
for
for
for
true ~D
true D
classified +
classified -
Correctly classified
84.48%
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The Impact of Inheritance on Income Inequality in the UK
Nicola Mounteney
Appendix 8: Regression Education against parental education (id – education)
Source
SS
df
MS
Model
Residual
14.1852338
87.5070739
1
180
14.1852338
.48615041
Total
101.692308
181
.561835954
id
Coef.
ParentalEduc
_cons
.0906182
1.57252
Std. Err.
.0167758
.2413805
t
5.40
6.51
Number of obs
F( 1,
180)
Prob > F
R-squared
Adj R-squared
Root MSE
=
=
=
=
=
=
182
29.18
0.0000
0.1395
0.1347
.69724
P>|t|
[95% Conf. Interval]
0.000
0.000
.0575157
1.09622
.1237207
2.04882
53