Introduction: economies were born where education and

Introduction:

One
of the basic objectives of economics is to provide welfare to people most
efficiently. Human capital can be defined as the skills, knowledge and other
intangible assets which can be used to produce some economic value. A high
number of researchers have revealed that human capital plays a big role in economic
growth. Mincer (1958) first theorized human capital as an important proponent of
economic growth, as it raises output. This theory was used as a basis for
future studies on economic growth. Mankiew et al. (1992) utilize secondary
education enrollments. Barro and Lee (1993) and Bosworth et al. (1995) have used
average years of schooling. Education was viewed as the core factor of human
capital by macroeconomists, while health held equal stature according to
microeconomists as labour needs to be healthy to work efficiently. (Romer,
1986, 1990; Lucas, 1988; Rebelo, 1991) emphasized that human capital formation
was a major factor which explains difference in growth performance of developed
and under-developed countries.

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Classical
theory suggests that labor productivity is an exogenous variable which depends
upon workforce in terms of capital and technological advances. It fails to
capture the potential growth of productivity due to education, health or
training. Studies on theory of market value have shown that there is an
influence of intangible assets, such as R&D, intellectual properties of
companies and patents which lead ultimately to economic growth. Companies that
are deemed as the spine of the biggest economies were born where education and
health are of top quantity and quality. This has been observed throughout the
past two decades, as companies raised huge amount of assets through developing
technologies, which came through an incubating environment. Companies such as
Apple, Google, Microsoft, have created industries and made leaps in
technological advances, enjoy assets worth more than most countries. This
influx of money then leads to higher economic growth of their countries. Tesla
for a more recent example, have completely changed the automotive industry by
developing fully electric production cars. This technology for mass production
was deemed impossible for a decade into the future. The same can be said for
SpaceX.

The
incredible economic growth of countries with very few natural resources, the
likes of which relied on importing their input materials. Japan, Taiwan and
other countries of the sort relied heavily on their human capital formation and
bore the fruit, as they attained high growth rate and therefore greater
welfare. USA, Germany, Japan have had persistent high economic growth over a
100 years, and if growth of land and physical capital is taken into account
there should’ve been a rapid decrease in returns and eventually no growth. The
persistence can therefore be credited to technological advancement, which came
through formation of human capital. This scientific advancement gives high
value to education, training and other factors leading to it.

The
economic effects of population health can be seen both at the individual and
macroeconomic levels. There is consensus of health’s effect on economic growth,
however the magnitude of the effect is the area where there exists a difference
of opinions.  

Developing
countries have more human capital as compared to physical capital, so more
emphasis should be given to developing labor quality. Pakistan has a very large
labor population but had very low spending on health and education throughout
its history. In the past decade health expenditure revolved around .9% while
education averaged 2.4%. Comparing two neighboring countries Iran and India,
Pakistan ranks below both on life expectancy and average years of schooling.
Although it ranked closely on life expectancy at birth with India but trails
both countries on education and health by some margin according to the human
development index. It is currently ranked at 147 while India and Iran rank at
131 and 69 respectively.

Health
and Education indicators alone fail to capture development of capabilities and
skills of labor, so a broader measure is needed to study the effect of human
capital on economic growth. Variables such as unemployment, protection of
property rights, social security affect development of human capital. Unemployment
generates a negative effect on human capital, as there is lack of area to apply/develop
skill and knowledge acquired by the individual. Social security has the same
effect as people are demotivated to work/study in institutions due to
terrorism/crime. This study tries to capture the effect of these variables on
economic growth of Pakistan.

Literature
review:

The
term human capital was first coined by (Mincer, 1958), where he viewed labor
force as factor which can be invested in to increase output. He defined human
capital as “the stock of knowledge, habits, social and personality attributes,
including creativity, embodied in the ability to perform labor so as to produce
economic value”.

The
effect of human capital on economic growth is inconsistent throughout
literature, as some papers show a strong significant impact while other papers
report a negative relation. In this section, a review of previous literature
will be cited and cause of inconsistency will be adressed.

In
the 1960s, neoclassical model was used for the growth model as developed by
(Sollow, 1956). One feature of this model is the convergence property, which
means that lower the real per capita GDP, higher the predicted growth rate. If
all economies were the same and which is not the case, then convergence would
apply absolutely, because all economies differ in various ways, then
convergence would have a conditional effect. Meaning that growth rate tends to
be high if an economy begins below its own target position. Convergence
property is conditional because steady state levels and output per worker
depend on population growth, saving rates, government policies, protection of
property rights, so on and so forth. This property is derived from the
diminishing returns to capital in the neoclassical model. Low capital per
worker would lead to higher rate of returns and thus higher growth rate.

The
concept of capital in the neoclassical model can be broadened to include human
capital, as education, experience and health play a role in it (Lucas, 1988),
(Mulligan and Sala-i-Martin, 1993), (Barro and Sala-i-Martin, 1995). A country
that tends to have a high labor to capital ratio tends to grow more rapidly,
because physical capital is much easier to manage and can be allocated
efficiently in a short time. (Ben Habib and Spiegel, 1994) suggest that if the
GDP depends more on a countries initial level of per capita output then the
starting amount of human capital is high.

However
this rate must diminish as it reaches its steady state. But the long run data
of countries show that a steady positive growth sustains over a century or more.
Neo classical theory then fails to predict long run per capita growth. One exogenous
variable in the model which successfully predicts the long run growth is rate
of technological progress.

Endogenous
growth theory thus tries to fill the gap by including technological progress.
These models include private incentives to discover new products or production
methods. These incentives can be encouraged by patent protection or government
subsidies or direct government involvement. This incorporated theory was
initialized by (Romer, 1987, 1990) and includes contributions by (Grossman and
Helpman, 1991) and (Agion and Howitt, 1992).

(Becker,
1962) also popularized investment in human capital. He studied the change in
income due to change in investment cost and rate of returns. He emphasized to
invest in education, healthcare and training. (Schultz, 1971) also worked along
these lines and found causal relationship in education and healthcare and found
a positive effect of these variables on economic growth.

Early
cross-country studies find a significant impact of human capital on economic
growth. (Rosenzweig, 1990) reported out that major determinant of high growth rate
of developed countries and poor growth rate of developing countries is
difference in the human capital growth. (Sachs and Werner, 1997) also reported
a positive relation between healthcare and growth but found that increase in
health expenditure increases economic growth but a decreasing rate. (Steward et
al, 1998) studied cross country data from 1970-1992 between human development
and economic growth and found a strong two-way causation. However, strength of
the relationship from economic growth to human development depends on female
education and social services expenditure whereas income distribution and
investment rate determine the strength of relationship from human development
to economic growth. (Lucas, 1993):

The main engine of growth is the accumulation of human capital –
or knowledge – and the main source of differences in living standards among
nations is a difference in human capital. Physical capital plays an essential
but decidedly subsidiary role.

A
rapid decrease in mortality rates lead to the population explosion in the 19th
and 20th century. Increased survival rate and decrease in mortality
led to a population boom, the most significant increase was found in infant
mortality rate so there was a large increase in young people. In the long run,
reductions in infant mortality lead to a fall in desired fertility, creating a
one?time
baby?boom
cohort. As this large cohort ages, the resultant changes in population age
structure can have significant economic implications. Population growth is the
difference between birth and death rates and the global population explosion in
the twentieth century is attributable to improvements in health and falling
death rates. Health advancement in developing countries lead to an initial
increase in the number of children. Reduced infant mortality, increased numbers
of surviving children, and rising wages for women can lower desired fertility (Schultz,
1997) which leads to smaller cohorts of children in future generations. This
process creates a “baby boom” generation that is larger than both preceding and
succeeding cohorts. Subsequent health improvements tend primarily to affect the
elderly, reducing old?age
mortality and lengthening the lifespan. In many theoretical models a population
explosion reduces income per capita by putting pressure on scarce resources and
by diluting the capital–labor ratio. In these models population declines spur
economic growth in per capita terms. For example, the very high death rates and
decline in population due to the Black Death in fourteenth century Europe
appear to have caused a shortage of labor, leading to a rise in wages and the
breakdown of the feudal labor system (Herlihy, 1997). However, in modern population
there appears to be little connection between overall population growth and
economic growth; indeed the twentieth century saw both a population explosion
and substantial rises in income levels.

 

Proxy
of the monetary value of life (the willingness to pay to avoid a small risk of
death) are often very large (Viscusi and Aldy, 2003). We can use these
estimates of the value of life to compare the welfare improvements that have
come about due to improvements in population health and the improvements due to
economic growth and higher incomes. We can measure the money value of health
improvements by the amount of money people, would be willing to pay to forgo
these improvements. For example we survey people living with today’s income,
health, and life expectancy as to what level of income would be required for
them to accept living with average life expectancy and health a century ago.
The income gain they would require is a measure of value of health and
longevity in monetary terms, which can be very large. Such comparisons suggest
that in many countries the value of health gains has been comparable to, or has
even surpassed, the value of income gains (Nordhaus, 2003). In addition,
although income gaps between countries have been very persistent over the last
50 years, there has been large?scale
convergence in life expectancy, suggesting that overall world welfare levels
have been converging (Bourguignon and Morrisson, 2002; Becker, Philipson and
Soares, 2005). The large monetary value of health gains gives a rationale for
investing in health quite apart from its instrumental value as an input into
productivity.

Although
population health measures are highly predictive of future economic growth,
there is a debate about how to interpret the link. The health effect could be
interpreted as the macroeconomic counterpart of the worker productivity effect
found in individuals. However (Acemoglu, Johnson and Robinson, 2003) argue that
health differences are not large enough to account for much of the cross?country difference in incomes, and
that the variations in political, economic, and social institutions are more
central factors. They argue that health does not have a direct effect on
growth, but serves in growth
regressions as a proxy for the pattern of European settlement, which was more successful
in countries with a low burden of infectious disease.

One way to
address the issue is to see how the effect of health caries with the inclusion
of other variables in the growth regression that may account for potential
omitted variables. (Sala?i?Martin, Doppelhofer, and Miller, 2004) test 67 potential variables that might affect economic growth. They
start by putting an equal probability of
affecting growth on each variable. They run possible models of a particular size (for example, 5, 7, 9, and 11
explanatory variables) and perform Bayesian
updating on the results to find the find the posterior probability of each variable being included. If the model has
only five explanatory variables, they select the
East Asia dummy, primary schooling, price of
investment goods, initial income, and fractional tropical area as the most
likely explanations of economic growth. However
extending the model to include nine explanatory
variables adds life expectancy, malaria prevalence, the fraction of the population Confucian, and the population
density in coastal areas, indicates that the predictive power of health for
economic growth is robust to the specification
of the growth regression.

Acemoglu
and Johnson (2007) raise a second objection to the argument that health affects economic growth. They instrument health using
the initial disease burden and worldwide
technological progress in disease?specific
interventions. They find that instrumented health does
not predict the level of income. This result is
subject to the criticism of lag times; it may take time for health technologies to be implemented and time for the health
improvements in children to work their way into
productivity improvements. However, the major innovation
in the paper is the argument that health improvements increase longevity and spur population growth and this population
growth puts a strain on other factors, causing
income per capita to fall.

There
are many issues which could lead to inconsistent estimation of health’s effect
on economic growth. Firstly and the most major problem, is that health is
measured in different ways, different proxies are used to measure health.
Secondly there is a problem of inconsistent data, this is because there is evidence
of adult cognition and production efficiency affected by their childhood
health. Thirdly there is a case for causality, so it is possible that income
affects health and health affects income so it becomes difficult to measure.

So
a solution to this measure difference is to address the root cause for
difference in adult health indicators. There is consistence in the health data
collected about children which very strongly influences adult health. This was seen
by (Case, Fertig, and Paxson, 2005) who used education and parental influences to
show childhood health has a strong impact on adult health. (Schultz, 2002) regresses
adult height with childhood health and nutrition to argue that each centimeter
gain in height due to improved inputs as a child in Ghana and Brazil leads to a
wage increase of between 8 and 10 percent.

Another
solution could be to derive data ourselves using quasi-experiments. (Thomas and
Frankenberg, 2002) advocated this approach. (Bleakley, 2003) studied the
effects of the eradication of hookworm and malaria in the United States in the
1910s and 1920s. Controlling for normal wage gains in areas that were not
infected, showed that persons who were born after eradication had higher wages
as compared to persons born before eradication. However the rate of returns
which can be achieved due to investment in health is not addressed.

(Barro,
2001) studied education’s effect and found a strong impact. He reported that
high ratio of human capital tends to generate higher growth through two
channels, firstly through more absorption of physical capital due to lower
labor to capital ratio and secondly due to efficient adjustment of physical
capital.

Direct
effects of education such as increased individual wages follow from the assumption
that education results in learning, which increases a worker’s productivity. If
workers are paid the value of their marginal product, it means that
better-educated workers should earn higher wages.

In
addition to the direct effects of education, a number of indirect effects have emerged
in the literature. (Michaelowa, 2000) found a positive effect of a
mother’s schooling on her children’s health in developing countries. Healthier
children may be more productive than unhealthy children and the result may be
higher performance in school. Similarly, better-educated parents tend to make
more informed decisions with regard to family planning – the result being
smaller family sizes. Smaller family size enables more parental involvement in
each child’s education (as parents’ time is scarce). Increased parental involvement
in a child’s education may enable the child to perform better in school and
encourage him or her to pursue additional years of education. An individual’s
choice to pursue further education may improve the earnings of his or her
neighbors. (Michaelowa
and Katharina, 2000) offers the example of an educated farmer who implements
new agricultural techniques. Neighbors may observe the new methods used by the
educated farmer and imitate them. Learning through observation is a mechanism by
which such educational benefits may be spread within a community.

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