Education through Globalization
International and Partisan Effects on Governments’ Skills Policies
Abstract
The past three decades have seen an unprecedented up-skilling of the populations of
advanced industrial countries. The average public expenditure in the OECD on education
increased from 3.1% in 1960 to 5.6% by 2000. Tertiary education enrolment rates have
tripled during this time. And despite an increased reliance on tuition fees, governments
have continued to increase the level of public investment in the provision of human
capital – this effect is particularly strong in very open economies. This paper addresses
two questions that are provoked by this phenomenon. Firstly, why do more open
economies invest more heavily in human capital and what are the political dynamics
underpinning this decision? Secondly, is this effect independent of the behavior of a
given state’s trading partners, or is there a diffusion effect, where states’ human capital
policies become interdependent? This paper grapples with the puzzle of why global
integration appears to accompany human capital provision by positing a model based
around the effects of open markets on skill premia. This dynamic is then combined with a
political economy model of skill provision to show why open states favor heavy public
investment in human capital, contrary to the typical neoliberal effects of globalization.
The model is then adapted to demonstrate how partisan preferences operate within this
context. Finally, the model is expanded to a multi-state model and the interaction
between states’ human capital policies is explored. Three sets of statistical tests are
performed: on the effect of openness on human capital provision; on the effects of
partisanship; and on the degree of international convergence in human capital investment.
Introduction:
Globalization has become the straw man sans pareil of political economy. Over the past
decade, debate has raged over whether globalization forces convergence to a neoliberal
model of state retreat from economic affairs or whether states can place a bulwark against
the forces of economic integration and continue to implement nationally specific
economic policies. In these exchanges globalization has become largely synonymous
with an unreconstructed neoliberalism – increased economic integration is supposed to
lead to a monotonic decline in state economic capacity. The convergence / divergence
debate is largely over the extent to which states can or cannot dilute these pressures. Even
those analyses which do present a more nuanced evaluation of globalization by
presenting it as an opportunity rather than a constraint – in particular, the Varieties of
Capitalism literature which presumes that globalization leads states to strengthen their
own particular institutional comparative advantage – tend to avoid the claim that
globalization might lead to a uniform increase in state management of economic affairs.
However, over the past few decades we have seen globalization accompany an
unprecedented up-skilling of the populations of all states, both rich and poor, largely
driven by activist state human capital policy. Skill provision has both deepened – there
has been a larger amount of human capital investment per skilled worker – and widened –
a greater proportion of the population have become skilled. This pattern is demonstrated
through a series of figures in Section Two detailing the significant increase in human
capital provision worldwide. The increase in human capital investment has been
particularly conspicuous in highly open states like Ireland, Sweden and Singapore. While
many of these states have trended toward lower government spending, public spending
on human capital formation has risen on aggregate. What explains the apparent
connection between economic integration and public investment in human capital? In
Section Three, this paper presents a model that suggests that globalization reduces the
wage elasticity of skill supply, thus permitting the expansion of skilling without a
commensurate decline in skilled wages. A skilled median voter is therefore incentivized
to increase government funding of human capital since they share in the upside of
investment (the externalities produced by human capital and the increased tax base
produced by aggregate growth), while avoiding the downside (the negative effects on
skilled wages of skill supply). Tests of the model’s implications – that trade openness,
lower tariffs, and openness to foreign investment are associated with higher government
spending on human capital – are conducted in using a 140 country dataset from 1960 to
2002. The political model is expanded upon in Section Four, where a basic partisan
model is tested statistically and then adapted through the creation of a coalitional model
which emphasizes the difference between absolute and relative expenditure on education.
The effect of partisanship on relative expenditure on education is then tested statistically.
The model presented in the first part of the paper focuses only on the effect of openness
to the anonymous global market on human capital and on partisan differences within the
state. It thus examined states independently – examining their responses to exogenous,
market-wide international shocks or domestic distributional preferences. In reality,
however, the decisions of states to invest in human capital might be affected by the
decisions of other states. In other words, an alternative explanation of the trend toward
higher human capital investment can be developed. In this alternative account, states
compete on international markets to have relative advantage in human capital
endowments. Thus, the human capital investment decision is interdependent rather than
an independent response to the global market as a whole. A two state model is developed
in Section Five to demonstrate this alternative mechanism and this model is then tested in
by examining whether there is competitive diffusion of human capital investment. It is
shown that there is considerable evidence of convergence to an international equilibrium
level of human capital investment both in the short run and in the long run.
2. Human Capital Investment Since 1960
The international increase in the proportion of national income states have devoted to
investment in education and human capital since 1960 has been dramatic. From an
average of 2.5% in 1960, today the average expenditure on public education across 146
states is nearly 5% of GDP (see Figure One). Moreover this figure is not just an artifact
of the population explosion – in fact, educational expenditure per individual under 15 has
expanded almost linearly since 1960 – a case of human capital deepening as well as
widening (see Figure Two). On the whole, this expansion has in fact come at the cost of
other forms of government expenditure because the proportion of educational expenditure
to other state expenditure (i.e. the relative expenditure) has also increased during the last
few decades (see Figure Three), albeit with less constancy than the simpler absolute
measures of human capital investment. This pattern holds both for the poorer countries
and the already skilled OECD states (see Figure Four).
Paralleling this increase in human capital expenditure there has been a similar rise in the
average level of openness internationally, whether in the case of exports and imports (see
Figure Five) or in terms of the reduction of duties (see Figure Six). Particularly intriguing
is the mirroring of the dip in openness during the 1980s with that in average educational
expenditure during the same period. This correspondence supports the assertion that the
connection between openness and human capital investment is not merely a one-way
trend indistinguishable from the timeline but a multi-directional influence. Increases in
human capital investment are not given – and it appears from these simple figures that
protectionism is a serious threat to them.
Figure One – Mean Expenditure on Public Education / GDP – All states
5
meanpubed
4
3
2
1960
1970
1980
year
1990
2000
Figure Two – Mean of (Public Education/GDP) / Population Under 15 – All states
20
meanedin
15
10
5
1960
1970
1980
year
1990
2000
Figure Three – Mean (Expenditure on Education/Government Expenditure) – All
.3
meanpege
.28
.26
.24
.22
1960
1970
1980
year
1990
2000
Figure Four – Mean of (Public Education/GDP) / Population Under 15 – OECD
30
meanedin
25
20
15
10
1960
1970
1980
year
1990
2000
Figure Five - Mean ((Exports+Imports )/ GDP) – All states
90
meanopen
80
70
60
50
1960
1970
1980
year
1990
2000
Figure Six – Mean of Duties as a Percentage of Tax Revenue – All States
meanduties
25
20
15
1970
1980
1990
year
2000
3. The Openness Model – Why Do Open Economies Invest in Education?
The previous section demonstrated that there has been a secular increase cross-nationally
in public investment in education and that this has accompanied the opening of the
international economy. Moreover, this trend is most pronounced in highly open
economies like Ireland, Korea, and Sweden. What explains this pattern? This section
presents a model suggesting that the key effect of globalization on the provision of
education is through its reduction of the factor supply elasticity of returns to skill. Put
simply, in an open economy returns to factors are determined on global markets rather
than purely through the domestic level of skill supply and demand. In a closed economy,
an increase in the supply of skills will, all else equal, reduce the returns to skilled
individuals since wage rates are determined by a purely domestic labor market
equilibrium. However, if a country opens its borders to trade, the price of goods, and thus
the returns to the factors that went into producing these goods, is determined by
international supply and demand. Therefore, in an open economy, changes in domestic
supply of skills have a negligible effect on returns to skill.1
So far, however, this is an apolitical statement. It may well be the case that openness
reduces the supply elasticity of skilled wages but how does this impact a government’s
decision over how much to invest in public education? To model this decision we need to
1
This mechanism may seem less intuitive for workers in the sheltered sector whose wages are not directly
determined by international market forces. However, there is an indirect effect on these wages since the
sheltered sector may become more or less attractive to workers depending on its relative wage compared to
the trading sector. Thus, if we assume a flexible labor market where workers can move between the
sheltered and traded sectors, the same effect of reduced factor supply elasticity should hold.
examine how the utility of individual citizens is affected by public investment in human
capital and then to show how political institutions translate these preferences into policy.
Let us assume a closed state with a population normalized to 1, which is split into a
proportion S of skilled workers and a proportion (1-S) of unskilled workers. In a closed
economy wage rates will be determined by the relative supply of each factor (i.e. skilled
vs. unskilled).2 The wage equations for unskilled and skilled workers are as follows:
ws   s  bS  x
wu   u  aS
Wages are a function of σ: the basic rate of return at S = 0, the skill supply response
parameters: a and b (which determine the slopes of the wage functions), and x: a skill
premium or demand-side shock affecting only skilled workers. Skilled workers are
subscripted with s, unskilled workers with u. To move between being skilled and
unskilled requires an investment of c: the cost of skilling, where c =kS – thus the cost of
skilling an unskilled worker increases linearly as more of the population becomes
skilled.3 Because up-skilling the entire population becomes increasingly expensive, an
equilibrium policy will tend to leave some of the population unskilled. From an aggregate
perspective it only becomes viable to upskill an individual if the difference in present and
future wages is greater than the cost of upskilling. Thus, we can also show that at the
margin: ws  wu  c  kS . Figure Seven shows this effect:
Since this model does not suppose any firms, it does not deal with skill demand – nonetheless, if demand
is incorporated into the model it does not change any of the key conclusions over supply.
3
This linear model of costing is consistent with typical results on skilling: for example training a skill
Level 1 worker to skill Level 2 in the UK costs £2400, whereas training a Level 0 worker to Level 2 costs
£3600.
2
Figure Seven – The Basic Factor Supply Model
σs
kS
ws
kS*
wu
σu
0
S*
1
The basic version of the factor supply model, graphed above, demonstrates the effects of
skill supply - S  [0,1] - on the wage rates and the cost of skilling. As the proportion rises
of individuals in the economy who are skilled, three effects are noticeable: firstly, skilled
wages decline (at rate b); secondly, unskilled wages increase (at rate a); and thirdly, the
cost of skilling the marginal unskilled person increases. Note that the cost of skilling at
S* is equal to the gap between skilled and unskilled wages. Even though the size of this
skill premium declines as S > S*, the increased cost of skilling means that the
equilibrium at S* continues to hold. Notice also that in this basic model x=0: there is no
skill-biased demand shock. How does a demand side shock affect this model? Figure
Eight demonstrates the effect of this shock.
Figure Eight - The Effect of a Skill-Biased Demand Shock
x
σs
kS
kS**
ws
wu
σu
0
S*
S**
1
As can be seen, a skill-biased demand shock will lead to an increase in the equilibrium
rate of skilling from S* to S**. Although the cost of upskilling the marginal individual is
now higher (i.e. kS** > kS*), the extra gap in wage rates (x) makes such an investment
sustainable. However, we have not yet addresses the key issue at hand, the effects of
opening the economy on the skill supply equilibrium. When the economy is opened let us
assume that b, the response of skilled wages to domestic skill supply, drops to zero. For
the moment we hold a constant.4 As Figure Nine below shows, this leads to a flattening
of the skilled wage response curve, an increase in the skilled / unskilled wage gap for any
level of S, and thus, a higher equilibrium rate of S.
We might assume that unskilled workers can work only in the sheltered sector – perhaps in personal
services – and that skilled workers always earn more than unskilled workers. This means there will be no
inter-sectoral mobility and hence the supply elasticity of unskilled wages is not affected by the supply
elasticity of skilled wages.
4
Figure Nine - The Effect of Opening the Economy
x
σs
kS***
kS
ws
wu
σu
0
S*
S**
S***
1
In this open scenario, the demand side shock has been accompanied by a supply side
shock produced by opening the economy. Now the skilled wage rate is unaffected by the
level of S, the domestic skill supply. The wage premium increases, as does the wage for
skilled workers, and so too does the equilibrium rate of investment in human capital.
Note also, that unskilled wages are actually higher in this open model than in the closed
model because a is strictly positive in the current formulation – thus, although inequality
increases so too does the income of the unskilled. This mirrors the folk wisdom that a
globalizing world is leading to both inequality and a reduction in poverty. It also jibes
with Alan Manning’s assertion that regions with a greater proportion of highly skilled
workers tend to have higher demand for unskilled workers (usually in personal services),
which all else equal should increase the wage rate for the unskilled. However, there are
strong alternative arguments suggesting that unskilled wages are being forced downward
by competition over labour costs from abroad which would lead us to the opposite
scenario, where a=0. As can be seen from analyzing the figures above, when a
asymptotes toward zero, the increase in wage inequality is not accompanied by a
countervailing improvement in the wags of the unskilled. This pattern may explain why
the unskilled workforce has moved from the traded to the sheltered sector over the past
few decades in advanced industrial nations, whereas the skilled workforce remains
involved with the traded sector. In the scenario where a = 0, it can be seen that the
equilibrium rate of S is determined by S  ws  wu  k .
So far we have seen how openness to the world economy permits a higher equilibrium
rate of investment in human capital. However, it is important to see precisely how this
affects individual preferences so as to examine the interests of the median voter, whose
preferences will set policy.5 In order to accomplish this we now turn to a model of
individual preferences. Let us assume that all individuals, who live for two periods
(periods zero and one), are taxed at a flat rate t of their income and that this tax goes only
to providing human capital in period one. This can be expressed as:
t ws S 0   wu 1  S 0  
S1
 cS dS
S0
Thus the tax revenue from the skilled workers (S0) and from the unskilled workers (1-S0)
equals the total cost of moving from the proportion of skilled in round zero - S0 - to that
in round one - S1. In a case of linear marginal costs (where, e.g., c=2kS) this becomes:

t ws S 0   wu 1  S 0   k S12  S 02

This section examines only the median voter’s preferences and thus presents an institution-free model of
policy. The following section examines in greater detail the effect of partisan coalitions on equilibrium
human capital policy. The models are kept separate to aid interpretability but in combination their separate
effects continue to hold.
5
This leads to the following rather complex expression for S1:


1
t
2
S i   ws S 0  wu (1  S 0 )  S 02  where
k

1
S1
1 1
2
  ws S 0  wu (1  S 0 )  S 02   S1'
t
2  tk



It can be seen that the level of S1 is increasing in the tax rate, the wage rates and in the
previous level of human capital S0 but is decreasing in the marginal cost of upskilling.
Now that we know how the round one rate of human capital is produced we need to
examine the preferences of individual over this rate (and implicitly over the rate of
taxation which is the sole endogenous determinant of S1). To do so, we must examine the
utility functions of individual citizens. The two-period utility of any individual is:
U i  1  t wi 0  g (S 0 )   wi1  g (S1 )
Thus utility is a function of net income in round zero plus the benefits obtained from the
round zero stock of skills g(S0), and the discounted benefits from round one income and
round one stock of skills. We assume that the stock of skills in the economy produces
externalities through the concave function g(S) and that these externalities benefit all
individuals – there is a strong justification for this assumption throughout the labor
economics literature, normally thought of us arising through skill complementarities in
the production process. We could think about this more simply as merely the variegated
benefits individuals obtain from living in an educated society. Since S0 and k are fixed,
the only key variable of interest is the level of taxation, t, which will determine round
zero investment in round one human capital. This can be simply expressed as:
U i
g S1 
 w
  wi 0    i1 
t
t 
 t
That is, utility is affected by the foregone round zero wage costs of taxation and the
round two effects on wage rates and on skill supply externalities.
We cannot however, deduce an optimal rate of taxation, and hence human capital
investment from this equation alone, since we need to know the effects of taxation on
round one wages which will vary between three types of individual. 6 These three types
are: firstly, the person who is already skilled in round zero (s); secondly, the person who
is unskilled in round zero and remains unskilled in round one (u); and thirdly, the person
who is unskilled in round zero but becomes unskilled in round one (c). We can combine
types u and c into one equation for all unskilled workers by incorporating the probability
p of becoming skilled. The effects of taxation on utility for skilled and unskilled
individuals are as follows:
 p
U u
S 

 S  g S1 
 wu 0    ws1  wu1   p  b 1   1  p  a 1  
t
t 
t 

 t 
 t
U s
S
g S1 

  ws 0    b 1 
t
t
t 

S1
0
t
g ( S1) )
t
0
p
0
t
These complex-looking equations are actually relatively easy to interpret. For an
unskilled individual in round zero the effects of taxation are as follows: there is a
negative effect because of foregone round zero income; then there are four discounted
effects in round one. Firstly, there is the increase in probability of becoming skilled
multiplied by the increase in wages that one obtains from becoming skilled. Secondly,
there is the negative effect of becoming skilled in round one, only to have the skilled
6
These are the same three types of individual as in the partisan model in the next section, although the
model used is somewhat altered.
wage return reduce because of increase skill supply. Thirdly, there is a (1-p) chance of
remaining unskilled with the surprising benefit that emerges from increased wages due to
a smaller stock of unskilled workers. Thus, there exists the paradoxical possibility that
unskilled workers may want investment in human capital so that they can remain
unskilled!7 Finally, there is the increased benefit of externalities from human capital. For
skilled individuals the results are somewhat simpler. Since they cannot change type they
are only affected by the reduction in round zero income, the negative effects of increased
skill supply on their earnings in round one and the positive effects of increased
externalities. Skilled workers, unsurprisingly, derive less utility from taxation than
unskilled workers in this formulation, although the change in their utility need not be
negative because of the externalities produced by investment in human capital.
The effect of opening the economy is dramatic. If b=0, the above equations become:
 p
U u
 S  g S1 
 wu 0    ws1  wu1   1  p  a 1  
t
t 
 t 
 t
U s
 g S1 
  ws 0   

t
 t 
If unskilled wages are also set internationally, i.e a=0, these transform into:
U u
g S1 
 p
  wu 0    ws1  wu1  
t
t 
 t
U s
 g S1 
  ws 0   

t
 t 
This outcome is magnified if we make the probability of becoming skilled dependent on an individual’s
place in the skill distribution. Since the least skilled worker has the smallest chance of being upskilled they
will only favor human capital investment if they benefit from the increased scarcity of unskilled labot,
which can only hold if a>0.
7
The key result of these manipulations is that the effect of taxation on the utility of skilled
individuals is strictly greater in an open economy than in a closed economy. The effect of
human capital provision on the utility of unskilled individuals will also be strictly greater
in an open economy provided:
b
(1  p)
a
p
Thus, for unskilled individuals to prefer human capital investment in an open economy, it
must be that they stand to gain more from investment in an open economy (i.e. the effect
of b – the supply effect in a closed economy - being reduced to zero) than from
investment in a closed economy (where their wages increase by a for an incremental
increase in the provision of human capital). This trade-off is weighted by the relative
probability of becoming skilled p: as the probability increases, the likelihood of
preferring an open economy also increases. It should also be noted that if p were
permitted to vary across individuals, those with low probabilities of being upskilled
would be tend to prefer human capital investment to occur in a closed economy than in an
open economy. Moreover, in those situations where b=0 but a>0, that is in Manning’s
scenario (see above), unskilled workers receive a lower expected utility from human
capital investment than they do in a fully open economy because even if they fail to
become upskilled their wages will improve – they are thus in a bizarre win-win situation.
To find a simple political equilibrium we need to derive the preferences over taxation and
human capital policy of the median voter. The exact formulation will depend on whether
the median voter is skilled or unskilled: we want to know, in particular, whether for a
given level of openness unskilled median voters prefer a higher equilibrium rate of
investment in human capital than skilled voters. In fact, this assertion does hold. The
utility equations from earlier demonstrate this comparison;
 p
U u
S 

 S  g S1 
 wu 0    ws1  wu1   p  b 1   1  p  a 1  
t
t 
t 

 t 
 t
U s
S
g S1 

  ws 0    b 1 
t
t
t 

These imply that the effect of taxation on the utility of an unskilled individual will exceed
that of a skilled individual if:
ws 0
 p
 S 
 wu 0     ws1  wu1   1  p (b  a) 1   0
 t 
 t
This inequality will always hold provided skilled wages always exceed unskilled wages
and provided that a and b are always strictly non-negative. Thus, we have shown that an
unskilled individual always prefers higher investment in human capital than a high skilled
individual if taxes purely fund human capital.8 Hence, an unskilled median voter will
demand higher rates of investment in human capital than will a skilled median voter: a
relatively intuitive outcome. A simple median voter analysis would lead to the conclusion
that the less skilled is the median voter, the higher will be public investment in skilling.
However, whether or not the median voter is skilled, a higher level of openness will also
lead to a higher equilibrium rate of investment in human capital. We now test the latter
prediction over openness but the political argument is picked up and expanded in the
Section Four.
8
However, as we shall see presently in the discussion of partisan preferences, if taxes can be used for
current redistribution as well as for the provision of human capital , this outcome may not hold.
Table One examines the effects of two measures of openness, the log of exports plus
imports over GDP – LOG(OPENNESS) – which is the classic measure for openness used
in the wider political economy literature, and the log of duties as a percent of total tax
revenue – LOG(DUTIES) - which is a more explicitly political measure of openness
since it is a direct policy choice rather than an outcome like LOG(OPENNESS). The
dependent variable used – EDINTEN – is the percentage of GDP spent on public
education divided by the percentage of the population under the age of 15. This measure
gives a useful proxy for educational intensity, since it prevents spuriously defining
countries with extremely high birth-rates as necessarily high investors in human capital.
In fact, EDINTEN adjusts human capital investment for the ‘challenge’ of the size of the
presently schooled cohort and creates a measure that estimates human capital per student.
It has a mean value of 14.10 and a standard deviation of 8.97. Moreover, EDINTEN is
very similar to the measure used by Carles Boix in his important study of partisan effects
on human capital provision (see Section Four) and thus has some support from the extant
literature.9 The dataset used derives from the World Bank Development Indicators and
includes 146 countries between 1960 and 2002, with a maximum sample size of 1,670
cases in the regression. I use a time series panel regression with random effects and
panel-robust standard errors. A one period lag is used to deal with autocorrelation and
control variables for non-educational government expenditure (GOVEX), GDP,
population, and a dummy for OECD states, are included.
9
Boix, Carles, 1997. Boix uses an index which divides public education / GDP by the percentage of people
under 21. The results are unlikely to be particularly different, however, because cohort effects between 15
and 21 are likely to be similar to those between 1 and 15.
The results obtain neatly match the prediction of the model above and the stylized facts
seen in Section Two that show openness and public educational expenditure track one
another closely. In particular, Model C shows that an incremental increase in the log of
openness increases EDINTEN by over two points – moving from the least to the most
open state in the sample increases EDINTEN by around one standard deviation. Duties
have a countervailing effect but similarly strong effect – moving from the country with
the highest duties to the lowest duties increases EDINTEN by 1.5 standard deviations.
Table One – Effects of Openness and Duties on Educational Intensity
MODEL A
MODEL B
MODEL C
EDINTEN (T-1)
.759
(.012)
.130
(.013)
.125
(.012)
LOG (OPENNESS)
.692
(.245)
-
2.121
(.35)
LOG (DUTIES)
-
GOVEX/GDP
.137
(.022)
GDPUS
2.04 E(-13)
1.89 E(-13)
-4.09 E(-13)
3.61 E(-13)
-2.34 E(-13)
3.56 E(-13)
POP
1.23 E(-10)
8.87 E(-10)
-5.55 E(-10)
2.25 E(-9)
4.53 E(-10)
2.21 E(-9)
OECD
2.663
(.336)
5.545
(.863)
5.756
(.847)
CONSTANT
-2.01
(.976)
8.22
(.743)
.263
(1.621)
N / GROUPS
1670 / 146
1173 / 120
1159 / 119
R SQ
.831
-1.548
(.141)
-1.519
(.140)
.404
(.031)
.386
(.031)
.711
.733
Dependent Variable is Educational Intensity = 100 * (% of GDP spent on education / % population under
15). Cross-sectional time-series regressions with random effects. Dataset is 146 states from 1960 to 2002.
Coefficients with p<0.05 in bold, coefficients with p<0.10 in italics.
Table Two checks Table One for robustness by incorporating a time trend variable.
Because there has been a secular trend toward higher levels of investment in human
capital over the last forty years, it is important to make sure that this increase is not
explained by the passing of years alone. Adding time reduces the magnitude of the
coefficients on the openness variables somewhat but they retain significance and a fairly
powerful effect on human capital investment.
Table Two – Effects of Openness and Duties on Educational Intensity
with added Time Trend
MODEL A
MODEL B
MODEL C
EDINTEN (T-1)
.756
(.012)
.108
(.012)
.107
(.012)
LOG (OPENNESS)
.589
(.250)
-
1.090
(.356)
LOG (DUTIES)
-
GOVEX/GDP
.143
(.022)
GDPUS
1.68 E(-13)
1.89 E(-13)
-8.92 E(-13)
3.55 E(-13)
-7.61 E(-13)
3.54 E(-13)
POP
3.25 E(-11)
8.88 E(-10)
-2.90 E(-9)
2.26 E(-9)
-2.20 E(-9)
2.24 E(-9)
OECD
2.751
(.338)
7.542
(.881)
7.511
(.870)
YEAR
.047
(.022)
.174
(.0161)
.161
(.017)
CONSTANT
-3.07
(1.093)
N / GROUPS
1670 / 146
R SQ
.932
-1.048
(.143)
-1.089
(.144)
.425
(.030)
.414
(.030)
2.386
(.963)
-1.564
(1.596)
1173 / 120
1159 / 119
.940
.940
Dependent Variable is Educational Intensity = 100 * (% of GDP spent on education / % population under
15). Cross-sectional time-series regressions with random effects. Includes time variable. Dataset is 146
states from 1960 to 2002. Coefficients with p<0.05 in bold, coefficients with p<0.10 in italics.
Finally, I examine the effect of Hiscox and Kastner’s gravity-model-based openness
measures to see if their different operationalization has the same effect as the World Bank
variables used so far. Hiscox and Kastner construct two variables, BCFE and ACFE
(basic / amended country-year fixed effects), which represent the “percentage reduction
in imports in each country year that is due to the deviation of trade policy from the ‘free
trade’ benchmark of the Netherlands in 1964”. These results are obtained by using a
gravity model to estimate potential trade and then examining the difference between
estimates and actual levels of trade and then comparing these deviations to the base case
of Holland 1964. Higher rates of BCFE and ACFE mean a higher level of protection.
These two variables are extremely useful since they proxy for all forms of protection,
whether direct or indirect, that reduce trade from its ‘natural’ level. Table Three shows
the effect of incorporating these variables and their logs into the earlier regressions.
Again, the expected results obtain – the coefficient on BCFE and ACFE is always
negative and statistically significant and the introduction of a time trend does not effect
these conclusions. Moving from the minimum to the maximum level of BCFE, for
example, reduces EDINTEN by around half a standard deviation.
Table Three – Effects of Hiscox-Kastner Measures on Educational Intensity
MODEL A
MODEL B
MODEL C
MODEL D
EDINTEN(T-1)
0.979
(.012)
0.978
(.012)
0.980
(0.012)
0.979
(0.012)
BCFE
-0.031
(.010)
-
-
-
ACFE
-
-0.036
(.011)
-
-
LOG(BCFE)
-
-
-0.634
(0.229)
-
LOG(ACFE)
-
-
-
-0.703
(.232)
GOVEX
0.014
(.020)
0.011
(.02)
0.016
(0.020)
0.014
(.020)
GDPUS
-4.91E-14
(1.15E-13)
-5.000E-14
(1.15E-13)
-1.28E-14
(1.13E-13)
-1.46E-14
(1.13E-13)
POP
3.75E-10
(5.49E-10)
4.060E-10
(5.45E-10)
2.52E-11
(5.11E-10)
5.71E-11
(5.10E-10)
YEAR
-0.076
(.012)
-0.074
(.012)
-0.077
(0.012)
-0.076
(0.012)
CONSTANT
151.691
(24.080)
147.866
(24.268)
156.402
(23.816)
153.236
(23.929)
N / GROUPS
861 / 72
861 / 72
861 / 72
861 / 72
R SQ
.942
.942
.942
.942
Dependent Variable is Educational Intensity = 100 * (% of GDP spent on education / % of population
under 15). Cross-sectional time-series regressions with random effects. Dataset includes all 72 states
covered by Hiscox-Kastner dataset (it excludes old Soviet bloc) and runs from 1960 to 1992. Coefficients
with p<0.05 in bold, coefficients with p<0.10 in italics.
4. Partisan Preferences and the Provision of Human Capital
The previous two sections examined the effect of openness on human capital, noting that
integration with global markets reduces the factor supply elasticity of wages and thus
leads to a higher equilibrium rate of human capital investment. This prediction finds
strong support in empirical analysis and helps to explain why we have seen a secular
trendacross nearly every state to higher investment in human capital during a period of
increased globalization.10 However, this only answers part of our question about why
human capital policy varies. The last sections hinted at a political dynamic, that a less
skilled median voter would desire a higher rate of human capital investment. This
assertion does indeed find a fair amount of support in the data. Carles Boix’s work
suggests that partisan preferences mattered significantly in explaining variation in human
capital policy from 1970 to 1989 in the OECD. By expanding the time period
significantly and using a full time-series panel dataset from 1960 to 2002 of the OECD,
the following analysis confirms Boix’s suggestion that partisan control of government
had a major effect on levels of human capital investment.
In Table Four I use as my dependent variable the percentage of GDP spent on public
education. This is the standard international measure for the level of investment in human
capital but it is, of course, somewhat limited and a number of caveats should be noted.
Firstly, this figure is not demographically adjusted and thus could be determined partly
by the relative size of the cohort presently in schooling. Thus, as in the openness
10
It also helped explain why human capital investment went through a dip in the 1980s, at the same time as
most measures of openness.
regressions, I also use a measure adjusted for the percentage of the population under 15 –
this variable EDINTEN is used in Table Five, and is similar to Boix’s measure of
educational intensity. It should be noted that these measures fail to pick up the
composition of educational expenditure, which may have serious partisan repercussions
if, for example, left-wing parties have different preferences over the funding of tertiary
education or other such distinctions. Nonetheless, using the aggregate measures does
have the distinct advantage of being directly comparable with Boix’s established work
and makes clearer comparisons between absolute and relative expenditure, discussed in
the next section.
The independent variables used are taken from the Huber-Stephens dataset and are
constructed as follows.11 Each variable (LEFT, LEFT-CENTER, CENTER, RIGHTCENTER, RIGHT) is created by taking the percentage of votes cast in the most recent
election for each party. Huber and Stephens divide parties into left, center, and right, and
the latter two groupings into secular, Christian (Protestant), and Catholic parties. In may
analysis I combine these religious and secular groupings. I create the left-center and
right-center variables simply by adding the cumulative votes of all left parties and center
parties, or all right parties and center parties. I use votes since the political model in the
following section is built on individual preferences, hence it seems apt to use the proxy
closest to the aggregation of voter’s preferences. However, political institutions
inevitably channel and filter preferences so that the outcomes in terms of political power
do not always match voters’ aggregated preferences. In order to check the robustness of
the partisan argument I also conducted these regressions using the proportion of seats in
11
Huber, Ragin, Stephens, 1997.
parliament held by these parties – the results (not shown) are very similar in terms of the
magnitude and statistical significance of coefficients. Table Four and Five have very
similar qualitiative implications: in both tables left-wing parties / coalitions prefer higher
absolute spending on education and right-wing parties / coalitions prefer lower levels of
spending. The key difference between the tables is that Table Five has slightly more
significant results because it controls for demographic factors – only center parties fail to
have a significant impact on expenditure on public education in Table Five.
In terms of interpreting the coefficients, Table Four, is easier to analyze. An increase of
ten percent in the amount of votes received by center and left parties leads to an increase
of just under one percent point in the proportion of GDP spent on education. Conversely,
an increase of ten percent in the amount of voters received by right-wing parties leads to
a reduction of just over half a percent point in PUBED. Notice that in both the tables, the
percentage of votes that go to center parties alone has no statistically significant effect on
government expenditure on public education – these groups may represent a median voter
who already has their favored policy equilibrium in place, and hence favors the status
quo. On the whole, the partisan argument developed partly in the openness model, and
corroborated by Boix’s research finds ample support in statistical analysis.
Table Four – Effects of Partisanship on Public Education / GDP
MODEL A
MODEL B
MODEL C
MODEL D
MODEL E
PUBED (N-1)
.801
(.034)
.796
(.033)
0.786
(.034)
0.799
(.035)
0.798
(.034)
LEFT
0.039
(.027)
-
-
-
-
-
.084
(.306)
-
-
-
-
-
.019
(.023)
-
-
-
-
-
-.027
(.029)
-
-
-
-
-
-.068
(.032)
LEFTCENTER
CENTER
CENTERRIGHT
RIGHT
GDPCAP
GOVEX
CONSTANT
-1.28E-03
(4.59E-04)
-1.22E-03
4.49E-04)
-1.13E-03
(4.57E-04)
-1.27E-03
(4.66E-04)
-1.27E-03
(4.53E-04)
-.458
(1.283)
-.814
(1.239)
.332
(1.199)
-.270
(1.308)
-0.675
(1.258)
143.087
(21.801)
113.142
(25.036)
146.529
(21.87)
171.508
(30.371)
185.693
(26.444)
N / GROUPS
295/19
295/19
295/19
295/19
295/19
R SQ
.725
.730
.723
.723
.727
Dependent variable is public expenditure on education as a % of GDP. Cross-sectional time-series
regressions with random effects. Cases are limited to the 19 members of the OECD covered by the Huber
Stephens dataset and range from 1960 to 1994. Coefficients with p<0.05 in bold, coefficients with p<0.10
in italics.
Table Five – Effects of Partisanship on Educational Intensity
MODEL A
MODEL B
MODEL C
MODEL D
MODEL E
EDINTEN
(N-1)
.822
(.032)
.816
(.032)
.813
(.033)
0.820
(.032)
0.821
(.032)
LEFT
.027
(.013)
-
-
-
-
-
.052
(.014)
-
-
-
-
-
.009
(.011)
-
-
-
-
-
-.021
(.013)
-
-
-
-
-
-.043
(.015)
LEFTCENTER
CENTER
CENTERRIGHT
RIGHT
GDPCAP
3.04E-06
(2.58E-05)
GOVEX
.053
(.067)
.037
(.066)
.0987
(.065)
.058
(.069)
.040
(.067)
CONSTANT
3.816
(.931)
1.909
(1.097)
3.895
(.983)
5.983
(1.458)
6.518
(1.219)
N / GROUPS
295/19
295/19
295/19
295/19
295/19
.831
.836
.829
.830
.727
R SQ
9.63E-06
2.51E-05)
1.42E-05
(2.58E-05)
2.01E-06
(2.62E-05)
4.24E-06
(2.54E-05)
Dependent variable is Educational Intensity = 100 * (% of GDP spent on education / % of population under
15). Cross-sectional time-series regressions with random effects. Cases are limited to the 19 members of
the OECD covered by the Huber Stephens dataset and range from 1960 to 1994. Coefficients with p<0.05
in bold, coefficients with p<0.10 in italics.
However, this analysis of partisan effects on investment in education does not tell the
whole story of how political parties influence human capital policy. While the resurgence
of the right in the 1980s was accompanied by a slight dip in educational expenditure, on
the whole, right-wing parties are not the foes of human capital policy that the above
analysis paints them to be. Examining aggregate figures on human capital expenditure as
a percentage of GDP misses a critical nuance in how parties develop human capital
policies. Left wing parties in government tend to raise the overall level of aggregate
government expenditure while maintaining the same proportion of such expenditure that
goes to education – this transpires as an increase in education but the actual composition
of government expenditure between education and other spending remains constant.
Conversely, right-wing governments often attempt to trim overall government
expenditure but often let educational expenditure avoid the fiscal shears. Right-wing
governments avoid cutting human capital investment partly because it has a vociferous
political base but also partly because it is a relatively more efficient and meritocratic form
of redistribution than most government expenditure. The following model diverges from
the previous analysis by examining how policies over both the overall tax rate and the
composition of expenditure (between human capital investment and redistribution) are set
and the kinds of political coalitions that can emerge over different policy mixes.12
Let us assume again that individuals live for two periods (periods zero and one) and that
their population is normalized to one. As before individuals differ in their cost of
upskilling but unlike the previous analysis we now permit skill to vary along a continuum
12
In particular it diverges from the model in the openness section in that we now assume that skills do not
carry over from period to period unless new investment is made. The role of externalities is also dropped in
the partisan analysis.
as well as distinguishing between the skilled and the unskilled. Each individual has an
innate skill level si, which can range from 0 to 1 and is constant over both periods. A gap
still exists however between skilled and unskilled workers – when a worker receives
upskilling through public investment in human capital they receive an extra lump sum
skill σi (which equals zero if a person is unskilled and equals σ if they are skilled). This
acquired skill is not necessarily constant over both periods – round one acquired skills
can only be produced if there is investment in human capital. Income is a function of total
innate and acquired skill.
y i  si   i
As noted costs are linearly proportional to innate skill:
ci  1  s i
Thus, the most innately skilled person in the economy has a cost of zero for being
upskilled, whereas the least skilled person has a cost of one.13 Taxes (τ) are imposed on
income in period zero and the revenues are split between a lump sum redistribution in
round zero and investment in human capital which is reaped in round one according to a
weighting parameter β, which varies between zero and one and designates the proportion
of tax revenue devoted to human capital investment. The lump sum redistribution is
denoted g, and the amount of human capital redistribution is denoted h. Notice that this
model is distinct from the previous openness model, in which taxation was used only to
provide human capital. The issue of composition between pure redistribution and
investment in human capital will be much more critical here. The total tax revenue is
obtained by summing individual taxes across the population for round zero.
13
Because the cost function is linear we can multiply this cost through by a constant if necessary. The most
obvious constant to use is σ, the acquired skill.
T   y 0    y i 0 dy i 0   y i 0 dy i 0
The lump sum period zero redistribution g and the round one investment of h are
determined by the tax rate τ and the composition parameter β and average income in
period zero.
g  (1   ) y0
h   y 0
We also need to examine how h is spent. Whereas each individual receives g as a lump
sum, h is used to skill up people for round one. No individual can carry over σ to round
one so h is used to pay the cost of providing this acquired skill. Since individuals with the
highest innate skill level si =1 are cheapest to upskill, they receive skilling first. Skilling
continues throughout the economy until the sum of costs equals the tax revenues devoted
to human capital investment, which occurs at the marginal person with skill level s*.
1

s*
1
(1  si )dsi  ci dsi  h   y0
s*
The sum of individual upskilling costs is relatively simple to calculate:
1
h
si 
s*
h
1 2
si
2
1
1  s *2
2
Note, that as s* gets higher, the marginal person who becomes upskilled in round one has
a higher innate skill – thus a higher s* means a smaller proportion of the population
becomes skilled and less money has been spent on human capital investment:
h
 s *  1  0 s *  1
s *


We can now analyze the two period utility function of individuals in this set up.
U i  1   yi 0  g    yi1 
U i  1   (si   i 0 )  1    y0   si   i1 
We can now analyze the effects of taxation τ and the composition parameter β on
individuals’ two period utility.
U i
  
 si   i 0   1    y0    i1 

  
In the case of the impact of taxation on utility, we can see three effects. Firstly, there is
the foregone round zero income, which is higher if the individual has higher innate skill
and if they have the acquired skill. Secondly, there is the positive gain from the lump sum
redistribution, which is weighted by (1- β). Finally, there is the discounted effect of
taxation on receiving the acquired skill in round one. This final expression can be
redefined as:
 i1
 i1 h
h

  y0
where

h 

This converts the utility function into:
U i
  
 si 0   i 0   1    y0   y0   i1 

 h 
All that is left is to examine the effect of increased h (investment in human capital) on the
acquired skill in round one. This has a simple interpretation – if there is enough
investment for an individual to be above the skill threshold s*, they will obtain the
acquired skill. Otherwise, they will not receive the acquired skill.
if si  s *
if si  s *
 i1
0
h
 i1

h
Thus, a key determinant of whether increased taxes will lead to increased utility is
whether individuals remain skilled in both rounds, unskilled in both rounds, or change
states. We ignore the possibility that skilled individuals in round zero could become
unskilled in round one since this could only emerge with a very low discount factor. This
leaves us with three groups: the always skilled (s), the always unskilled (u), and those
who change from being unskilled to skilled (c). We can examine the separate effects of
taxation on the utility of each of these groups and then order them according to their tax
preferences:
Us
 1    y0  si   0     y0  1 

Uu
 1    y0  si

Uc
 1    y0  si    y0  1 

As can be seen, these marginal utilities of taxation can be partially ordered:
Us Uc  Uu


  
In order to show that people unskilled in both rounds receive a higher marginal utility of
taxation we need to assert the following assumption:
sc  su    y0 1  
Uc Uu



Note that this can only hold for all unskilled individuals if we have a discontinuous
distribution of innate skills. Henceforth, we shall assume that our three groups have
uniform innate skills within their group but differ substantially from the level of innate
skills in the other groups: or, ss > sc > su. With this assumption we can rank order their
preferences over taxation.
But what of the composition of taxes? Here a dramatic reversal takes place. Firstly, let us
examine the generic effect of increasing β on utility.
  
U i
  y0    i1 

  
U i
  
  y0   y0  i1 

 h 
These equations are similar to the marginal effects of taxation on utility but with a key
difference. Now period zero marginal utility is strictly negative for all types. Conversely,
round one marginal utility depends once more on the marginal effect of increased human
capital supply on acquired skills in round one. Thus the three types, once more, have
differing marginal utilities of tax composition:
Us
  y0   y0   y0   1

Uu
  y0

Us
  y0   y0   y0   1

Us Uc Uu





Thus, in the case, the rank ordering is reversed. Skilled individuals have a higher
marginal utility from β than unskilled individuals. In this formulation the marginal
utilities of skilled and changing individuals from β are identical. However, this does not
necessarily imply that changing individuals derive positive utility from β. They may
simply prefer to tax and claim the round zero redistribution if their discount rates are low
or if the round one acquired skill has a small value. Note also that if we make the
acquired skill dependent on innate skill, skilled individuals always gain a higher marginal
utility from β than do changing individuals. Since this assumption seems likely to hold,
given what we know about skill complementarities for the highly skilled, we can assume
that there is a clear ranking over the marginal utility of β (skilled > changing > unskilled).
It is also apparent that increasing β always has a negative effect for those who remain
unskilled. Instead, the unskilled would always prefer high rates of tax with low β.
Conversely, as can be seen above, skilled individuals prefer lower rates of tax with higher
rates of β. The preferences of the changing type over taxation versus composition are
much more complex since they depend critically on parameter values for the discount
rate and the acquired skill.14 Although, the exact tradeoff between taxation levels and
composition may be indeterminate for the changing group, this does not prevent us from
analyzing the coalitional politics that emerge in deciding over the two policy instruments
because changing group is always in the middle of both other groups in terms of their
preference rankings.
14
This can be seen from the cross-products for taxation and composition which both equal:
y0 1  1
In order to examine how coalitions over policy mixes emerge, I construct below a twodimensional policy space with the indifference curves of each group. If we assume that
the groups bargain over policy mixes and that any coalition must split the difference
between their ideal points15 we can derive two policy mix equilibria. Note, that the figure
assumes that S, C, and U all have spherical indifference curves (S and U’s curves are not
explicitly drawn).
S
β
SC Coalition
C
UC Coalition
τ
U
The figure above shows how two possible coalitions might emerge between the ideal
points of S, C, and U. The first coalition, between S and C, forms around a policy mix of
low taxation but high β. The second coalition, between U and C, forms around a policy
mix of high taxation but low β. Note that these two coalitions could potentially lead to
the same amount of aggregate expenditure on education – hence the necessity of
This is a strong political assumption – there are good reasons why the median group might always be
able to push any coalition towards its ideal point but if we impose institutional conditions on bargaining –
for example, reputation or by giving the median member of the coalition agenda setting power - policy
mixes will end up splitting the difference between the ideal points of the two members of the coalition.
15
distinguishing between aggregate expenditure and the relative composition of overall
government expenditure. Of course, we could obtain the results from Tables Four and
Five if we assumed nonspherical indifference curves. In particular, if both S and U have
vertically stretched curves, that is, if both care more relatively about taxation than tax
composition, then the SC coalition will lead to lower aggregate expenditure on education
than the UC coalition.
Table Six examines the results from the model above, testing the hypothesis that centerright coalitions spend more as a proportion of overall government expenditure on
education than do center-left coalitions. Whereas Tables Four and Five examined
absolute educational expenditure, Table Six analyzes relative expenditure. As in the
previous two tables, the sample used is of the OECD from 1960 to 1994, and again relies
on the Huber-Stephens dataset. However, Table Six shows a striking difference from the
previous analysis. When we examine the effect of partisanship on relative expenditure on
education we see the opposite effect from its effect on absolute expenditure. In Table Six,
right-wing control is positively associated with relative expenditure on education,
whereas left-wing control is negatively associated with this measure. Again, the
proportion of votes / seats held by centrist parties has no clear effect on the dependent
variable. Thus, the coalitional model shown above receives some solid support from this
analysis. The model predicted that because skilled individuals prefer a higher value of β
than unskilled individuals, an SC coalition (proxied for by the center-right measure)
should prefer higher relative expenditure on education than a UC coalition (proxied for
by the center-left coalition) – a result mirrored by the data analysis.
Table Six – Effects of Partisanship on
Public Expenditure on Education / Total Government Expenditure
MODEL A
MODEL B
PUBED /
GOVEX(N-1)
.794
(.031)
.803
(.030)
LEFT
-.024
(.012)
-
LEFTCENTER
-
CENTER
MODEL D
MODEL E
.790
(.032)
.802
(.030)
-
-
-
-.026
(.013)
-
-
-
-
-
.002
(.010)
-
-
CENTERRIGHT
-
-
-
.026
(.012)
-
RIGHT
-
-
-
-
GDPCAP
3.40E-05
(2.27E-05)
2.85E-05
(2.23E-05)
MODEL C
8.15E-01
(3.00E-02)
2.42E-05
(2.24E-05)
3.63E-05
(2.29E-05)
.026
(.013)
2.99E-05
(2.24E-05)
CONSTANT
238.216
(45.676)
241.160
(46.064)
220.154
(45.158)
232.194
(45.173)
233.625
(45.378)
N / GROUPS
294/19
294/19
294/19
294/19
294/19
R SQ
.770
.769
.766
.770
.769
Dependent variable is public expenditure on education as a % of total government expenditure. Crosssectional time-series regressions with random effects. Cases are limited to the 19 members of the OECD
covered by the Huber Stephens dataset and range from 1960 to 1994. Coefficients with p<0.05 in bold,
coefficients with p<0.10 in italics.
5. The Competitiveness Model – Is there Diffusion in Human Capital Investment?
So far we have analyzed states’ decisions over their level of human capital investment in
an independent manner – each state’s decision is affected by other of its policy variables
(e.g. openness, other government expenditure) or through partisan bargaining. We have
not yet examined how any one state’s human capital investment decisions might affect
those of another state. Yet, given the emphasis made earlier on the importance of the
global economy in explaining why human capital investment rates have increased over
the past few decades it would seem negligent not to examine the interaction between
states’ decisions. The openness argument above is comparable to market structure
arguments about perfect competition – in our case the returns to human capital move
from being set within the state to being set by the global market of competing states. Any
change in a state’s relative endowment of skills cannot affect the global rate of return.
However, the international economy is clearly far from being a perfectly competitive
market. Instead, it may be that states are influenced by the investment decisions of their
trading partners. Thus human capital investment becomes interdependent rather than
independently derived.
The voluminous literature on diffusion posits several reasons why states might be
influenced by the policy decisions of other countries. Dobbin, Garrett and Simmons
suggest a number of key dynamics: economic competition, historical similarities, best
practice, learning and imitation. Almost certainly, all of these different mechanisms have
had an impact on the responses of states to other countries’ level of human capital
investment. Some of this may be through imitation of the educational systems of
historically sympathetic states, as in the similarities between the German and Austrian
educational systems or the higher education institutions of the English speaking world.
The economic success of high growth / high skill economies like the Asian and Celtic
Tigers certainly provides a glowing example of best practice. However, in order to keep
the analysis in this section comparable to that undertaken in the previous section, I limit
my focus to political economy reasons for diffusion. In particular, I focus on the
competitive forces that lead to convergence around an international equilibrium rate of
human capital investment. While this does not precisely conform to Simmons’ definition
of diffusion as a process whereby the decisions of other states affect the probability of a
given state deciding to implement a policy (it is harder to see how this might operate with
an interval level policy like investment as compared to a binary variable like law
accession), it does permit a detailed analysis of how other states’ decisions over human
capital investment impact the equilibrium choices of any particular country.
To examine why states might converge to an international equilibrium we can develop a
very simple two-state model of human capital policy interaction. Let us assume there are
two states, home and foreign, and each can create human capital Hi by taxing income yi at
a rate ti. Thus the level of investment in human capital perfectly determines the rate of
taxation, and vice versa, for any given level of national income. However, each state is
constrained in the level of human capital it can provide by competition over both human
capital and over tax rates with the other state. Let us assume that this competition
emerges because firms in the two states use human capital in their production process and
are able to move between the two states without friction.16 Human capital is a cheaper
input in the state in which it is more abundant incentivizing firms to move to the state in
which there is higher human capital provision. However, producing human capital costs
incomes, which has to be paid for through higher taxes. Thus, if tax rates in one country
are higher than in the other, companies will move to the latter state. States are thus
constrained in both directions in their level of human capital investment and will
converge to an equilibrium inter-state rate. For each state, their utility can be written as:

Uh   Hh  H
*
f

2
H *f 
Hi
c H
  h 
 where  i 
2  y h
y f 
yi
Note that because taxes perfectly determine human capital rates, we can substitute for
them in the utility function. The optimal rate of taxation can be calculated:
U h
0
H h

y
 
H h*    y h2  H *f  h
y
c
 f

  A  y h *f


where A 

c
y h2
Note that the optimal rate of human capital investment for the home state depends
critically on the equilibrium rate of investment in the foreign state adjusted for the
relative size of the economies and the parameters α and c. This is a perfectly symmetrical
Nash equilibrium: thus each country’s best response is to set human capital investment
rates according to the utility maximization above. To see the best response function we
16
A very extreme assumption but one which simplifies this stylized model of convergence. Adding
transaction costs would, of course, permit states more leeway to diverge at the margin.
analyze how the equilibrium home human capital policy is affected by the foreign level
of human capital investment. As can be readily seen, the response rate will equal unity if
countries are the same size – that is, any given change in the foreign level of human
capital will lead to an equal, same-directional response in home levels.
H h*  y h 

y 
H *f
 f 
This simple model of interactive effects on human capital investment suggests that we
should see convergence among states in terms of the level of public human capital
provision. In order to test this hypothesis I analyze whether there has been convergence
in human capital investment using a sample of OECD states from 1960 to 2002. In
particular, I want to know whether countries that diverge from the international mean in a
given time period or pushed back toward that mean in later time periods. In order to
conduct this analysis I use a simplified version of the methodology of Franzese Jr. and
Hays.17 Franzese Jr. and Hayes develop a model to examine spatial relationships in
policymaking, by creating a spatial diffusion matrix which represents the effect of each
other country in the dataset on a particular state’s policy. They then calculate the
residuals from a regression using this spatial correlation matrix – these residuals indicate
the degree to which a country’s policies are in disequilibrium. While the authors use
capital taxation policy as their example, a policy with very apparent forces demanding
convergence, I use the level of public investment in human capital, which appears
unlikely prima facie to be as competitive as tax policy – hence, my analysis provides a
17
Franzese Jr., Robert and Jude Hays, 2003.
strong test of the logic of convergence. My analysis also differs from Franzese Jr. and
Hays’ in that it does not develop a full spatial correlation matrix – rather the mean level
of human capital investment is used as the equilibrium policy. While this means
neglecting the nuances of which countries affect a particular state’s policy more than
others, it still permits fruitful analysis of the key effect of disequilibrium on a state’s
investment policies.
The analysis proceeds in two stages. In the first stage I take the one-year lag (t-1) of the
dependent variable (percentage of GDP spent on education or PUBED) and the mean of
all other states’ values on the dependent variable in that same one-year lagged period. I
regress the lag for each state on the mean of the other states and from this regression I
extract the residuals. These residuals demonstrate the extent to which a particular state’s
level of investment in human capital exceeds, or fails to reach, the equilibrium policy in
time t-1, thus the residuals act as a measure of the level of disequilibrium for a given
policy. I then use these residuals as independent variables in a second regression, for
which the dependent variable is the change in the percentage spent on education since the
lagged period (in this case the change in PUBED from t-1 to t). In this second equation I
also control for changes in overall government expenditure (GOVEX), and in GDP and
population, as well as for a time trend. The regression uses fixed effects for each country
and robust standard errors. I repeat the same procedure for lagged periods of two, three,
four, five, ten, fifteen, and twenty years. In each case I regress the t-n value of the
dependent variable on the mean international level for that time period. I then perform the
second regression by regressing the change in PUBED from t-n to t on the residuals from
the first regression. I expect that the coefficient on this second equation should increase
with n, that is, the effects of convergence to the equilibrium policy should be stronger
over the long term than over shorter periods.
Table Seven, below, demonstrates the results of this data analysis. The coefficients on the
residual, or disequilibrium, variable are statistically significant at a p<0.0001 for all
regressions, despite the inclusion of control variables and a time trend (coefficients for
these controls are not shown). Moreover, the expected increase in the size of the
coefficient on the residual variable as the lag periods increase in length is also met. In the
first year, the effect of being four percent points away from the equilibrium policy in
period n-1, is a move toward the equilibrium policy of only one percent point in period n.
However, a state that is four percent points away from equilibrium in period n-20, moves
five percent points toward the equilibrium policy by period n and so may actually
overshoot the international mean. It should be noted be noted that because the sample
only ranges from 1960 to 2002, the number of cases analyzed drops dramatically as the
length of the lag periods increases. Nonetheless, the number of states under analysis
remains almost constant and the coefficients remain statistically extremely significant
even for the longest lag periods. Thus, it appears that there is indeed cross-national
convergence in human capital policy, and given that a time trend has been incorporated
into the regression, we can remain fairly comfortable that this convergence effect is not
merely a function of the secular increase in the level of human capital investment crossnationally, but is also apparent when the mean follows cyclical dips and peaks.
Table Seven – Convergence Patterns in Public Expenditure on Education
COEFFICIENTS ON
CHANGE IN PUBED
N / GROUPS
1 YEAR RESIDUAL
-.262
(.036)
517 / 31
2 YEAR RESIDUAL
-.455
(.049)
383 / 31
3 YEAR RESIDUAL
-.646
(.053)
361 / 31
4 YEAR RESIDUAL
-.712
(.056)
350 / 31
5 YEAR RESIDUAL
-.588
(.054)
424 / 30
10 YEAR RESIDUAL
-.926
(.081)
298 / 28
15 YEAR RESIDUAL
-1.127
(.126)
182 / 26
20 YEAR RESIDUAL
-1.270
(.206)
97 / 26
Cross-sectional time series regressions with fixed effects (by state). Each regression is specific to a
particular time period change in the dependent variable (public education as a % of GDP): e.g. the 3 year
residual is regressed on the 3 year change in the dependent variable. All coefficients are significant at a p <
0.0001. Data is for all OECD countries 1960 to 2002. Coefficients with p<0.05 in bold.
6. In Conclusion
Since the 1960s the unlikely pairing of globalization and increased public investment
have walked hand in hand. Most analyses of international political economy either
portray a convergence to residual neoliberal economic policy or show how divergence
can continue to exist despite globalization. This paper makes a somewhat different
argument – that in fact globalization can lead to convergence upwards in some forms of
social spending – in particular, investment in human capital. The paper argues that
globalization, by permitting individuals to engage in the global labor market through
trade, allows higher levels of skilling than would be possible in a closed economy
because there is no downward push on skilled wages when skill supply is expanded
domestically. Countries can indeed choose to specialize in skilled production without
reducing the rate of return to skills. The paper noted also that strong partisan effects
interact with the openness story. While recent literature has asserted that left-wing parties
invest more heavily in the absolute amount of human capital investment – an assertion
tested and supported here – if skilled individuals no longer have their wages reduced by
increased skill supply, they will prefer a higher relative amount of human capital
investment (vis-à-vis other government spending). Thus right-wing parties are shown to
favor higher relative expenditure on human capital than left-wing parties.
Finally, this paper examined whether a given state’s human capital policy might be
interdependent with the decisions made by other states. A convergence model was
developed and tested with a variety of lags. The paper noted that in the long run states
will move back toward the international equilibrium rate of human capital investment.
This outcome should give caution to the thought that human capital investment is a
monotonic good for an individual state – it may not in fact be optimal to have too high a
rate of investment vis-à-vis other states, presumably because the cost of providing the
investment may become suboptimally high. Nonetheless, there appears to be a chaingang effect as nearly every state is drawn to an ever-increasing average level of human
capital investment. Presumably some upper bound to this investment exists. It is not
clear, however, that we are yet near that point. The global economy is likely to push,
cajole and force states to educate their workforces just to remain competitive and this
portends a much greater role for the state than many commentators have imagined.