Review of Economics and Development Studies Vol.2, No 2, December 2016

129

Volume and Issues Obtainable at Center for Sustainability Research and Consultancy

Review of Economics and Development Studies
ISSN:2519-9692 ISSN (E): 2519-9706

Volume 2: Issue 2 December 2016

Journal homepage: www.publishing.globalcsrc.org/reads

The Role of Human Capital Formation in Poverty Mitigation: A Co-integration

Analysis from Pakistan

1
Romaisa Arif,

2
Muhammad Zahir Faridi,

3
Fatima Farooq

1
M.Phil Scholar, School of Economics, Bahauddin Zakariya University, Multan Pakistan,

romaisa_pasha@yahoo.com
2
Professor, Department of Economics, Bhauddin Zakariya University Multan, Pakistan,

zahirfaridi@bzu.edu.pk
3
Assistant Professor, School of Economics, Bahauddin Zakariya University Multan, Pakistan,

fatimafarooq@bzu.edu.pk

ARTICLE DETAILS ABSTRACT
History

Revised format: Nov 2016

Available online: Dec 2016

The present study tries to explore the dynamic relationship between

human capital formation and poverty mitigation by adopting the course

of investment in education and health substances. For this sake, study

takes heath expenditure and infant mortality rate as health indicators

while status of education is captured with literacy rate and enrollment in

higher education. Time series data is employed ranges from 1973-2013.

The properties of time series data are inspected with the ADF test whilst

PP test is employed for the robustness of unit root results. Mixed order of

integration of data compels us to make use of ARDL technique for the

estimation. Similarly, one unit change in health expenditures lead to

reduce 0.251 units of poverty and one unit change in infant mortality

cause to reduce poverty by 0.04 units. In last, one unit increase in

literacy rate changes 1.03 units in poverty and one unit change in higher

education results in 0.003 unit’s change in poverty. The results of the

study leave us with a clear finale for an optimal policy formulation that,

Pakistan is in sturdy need of investment in health and education

substances for a noteworthy accumulation of human capital for a right

way poverty mitigation policy.

NonCommercial 4.0

Keywords

Health and Education,

Human Capital,

Poverty,

ARDL,

Co-integration,

Pakistan

JEL Classification

C23, H75, J24, P36

Corresponding author’s email address: romaisa_pasha@yahoo.com

Recommended citation: Arif, R., Faridi, M. Z. and Farooq, F. (2016). The Role of Human Capital Formation in

Poverty Mitigation: A Co-integration Analysis from Pakistan. Review of Economics and Development Studies, 2

(2) 129-138

DOI: https://doi.org/10.26710/reads.v2i2.130

http://www.publishing.globalcsrc.org/reads
mailto:romaisa_pasha@yahoo.com
mailto:zahirfaridi@bzu.edu.pk
mailto:fatimafarooq@bzu.edu.pk
mailto:romaisa_pasha@yahoo.com
https://doi.org/10.26710/reads.v2i2.130

Review of Economics and Development Studies Vol.2, No 2, December 2016

130

1. Introduction

The notion of human capital formation has attained a lot of entice in the recent years as it involves

efficiency in the manufacturing mechanism that leads to high economic growth and turns into the

outgrowth in term of boosting up the living standard of people. Among others, studies like Romer

(1986) and Lucus (1988) provided the significance of investment in human capital as it is a key

contributor to economic development and poverty mitigation, Schultz (1999); Sen(1999). On the same

argument the New Millennium Development Goals (MDGs) focus on human capital accumulation as the

majority of MDGs merely rely on the health and education substances. Different quantitative studies on

economic growth in the west show that the growth of human capital plays a significant role in economic

growth, Todaro (10
th

edition). Sturdy linkages are observed between economic growth and poverty

mitigation when growth is channeled from investment in education and health as it goes for trickledown

effect that remove inequality, Scheikman (2002).

Among the pioneers, Schultz (1993) was the first in defining the human capital who narrated that human

capital formation includes; primary and high education; training and learning activities for the

development of skills; and, investment in children education. The activities like education, training,

health, wisdom, intelligence, attitude and tendencies may also be an indicator or criteria of human

resources.

A strong causality is observed between the household income and their children’s schooling and

educational attainment. In particular, in the developing countries, parents with high income are more

like to take children education as a normal commodity and increase its demand with every level of their

income, Schultz (1993). While it is most common among poor that risk aversion or the credit constraint

is the factor that compels parents to the under invest for children education, even when returns on

investment are fairly good, Parish and Willis (1993). The impact human capital in eradicating poverty is

also observed from the good health and nutrition conditions by narrowing the income divide—sourced

from the improved education attainments, Eastwood and Lipton (1999); Girma and Kedir (2005).

With reference to Pakistan, present study is aimed to answer some fundamental question that are; does

education attainment a starting place from where poverty is initiated to trim down? Does heath status a

factual pathway through which poverty diminishes? The study uses ARDL technique to quantify the

brief and deep impact of health and education substances in eliminating poverty through the course of

human capital concentration.

One of the crux objectives of the study is to strive for providing an extensive review of the studies aimed

at exploring the linkages between human capital formation and poverty elimination through the course

of development process. The literature on the concerned issue is in surfeit. Yet Study makes a healthy

endeavor to integrate worthy empirical findings that will leads to point out some important dimensions

on that issue, Gundlach, (1996); Abbas (2000); khan (2005); Qadri et al. (2011); Oluwatoyin (2011);

Hanushek (2013).The above narratives identify the importance of the human capital formation and

poverty mitigation nexus for P akistan. The termination of segment draws a conclusion that for long

term economic performance and social well being is strongly tied with the education and health status.

For that reason, this study attempts to estimate deep and brief impact of human capital formation for

Pakistan through dynamic analysis.

After the brief introduction, section two is consisted on the trends and patterns about how the human

capital evolves over the period of time against poverty. Description of data and Methodology is given in

Review of Economics and Development Studies Vol.2, No 2, December 2016

131

section three, while results and discussions are presented in section four. Six section is based on

concludes and policy recommendations.

2. Human Capital and Poverty in Pakistan: Trends And Size

Trends are the most important part of all studies. The present segment has presented the human capital

and poverty scenario in Pakistan. Therefore, literacy rate is used as a proxy of education and total health

expenditures (million rupees) as a proxy of health. The head count ratio is used as a measurement of

poverty (in percentage).

Figure 1: Human Capital and Poverty Trends in Pakistan

Source: Pakistan Economic survey (various issues)

Figure 1 shows the ten years trends of human capital and poverty in Pakistan. According to economic

survey of Pakistan and Jamal (2006), in 2000, Pakistan has found 27.61 percent poverty rate and in 2005

it has declined to 23.9 percent. Pakistan has reached 20.7 percent poverty rate in 2010 and 12.45 percent

in 2013. Now, the scenario of human capital proxies by literacy rate and total health expenditures in

Pakistan is presented. According to economic survey of Pakistan, in 2000, Pakistan has found 49 percent

literacy rate and it has increased to 54 percent in 2005. Subsequently, in 2010, the literacy rate reached

at 57.68 percent in Pakistan and 60 percent in 2013. In Pakistan the total health expenditures are

21475.47 million rupees in 2000 and it has increased to 38000 million rupees in 2005. The total health

expenditures have increased day by day— in 2010, these were 79000 million rupees and, in 2013,

reached to 32000 million rupees in Pakistan according to the information of economic survey of

Pakistan.

3. Data and Methodology

This section is based on data source and methodology. At first, we briefly explained the description of

variables and present statistical analysis. Further, we have used ADF and PP for unit root results, and

ARDL approach for the estimation of the results and the Wald test (F-stat) is used for bound of co-

integration.

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013Poverty

Education

Health

Review of Economics and Development Studies Vol.2, No 2, December 2016

132

a) Data

In this article, we have used annual time series data from 1973 to 2013. The data is collected from

different sources, economic survey of Pakistan (various issues), World Development Indicator,

handbook of statistics, Jamal (2006) and some data has not available that’s why we interpolating by

econometrics software, Eviews 9.

In the analysis, poverty has estimated in percentage with head count ratio. Independent variables

included per capita GDP is measured in terms of million rupees, inflation rate is taken in percentage.

While health expenditures are measured in million rupees and infant mortality rate is measured in term

of per 1000 population. However, trade openness is calculated as the ratio of imports and exports to

GDP in terms of percentage; literacy rate is measuring in 10 year and above population (percentage), in

last, the enrollment in higher education is measured in numbers.

Table: Description of Variables

Description of variables
Measuring

units
Sources of Data

POV Poverty, head count ratio Percentage

Jamal, H., (2006), The

PDR 45(3), pp. 439–459

and Economic Survey of

Pakistan 2014

GDP
Gross domestic product

per capita

Million

rupees

Economic survey of

Pakistan (various issues)

INF Inflation rate Percentage
Economic survey of

Pakistan (various issues)

HEX Total health expenditures
Million

rupees
Handbook of statistics

MOR Infant mortality rate
Per 1000

population

Economics survey of

Pakistan

TOP Trade openness

Imports +

exports /

GDP

Economic survey of

Pakistan

LITR Literacy rate Percentage
Economic survey of

Pakistan

HED
Enrollment in higher

education
Numbers

Economic survey of

Pakistan

b) Properties of Time Series Data
We examines the properties of time series data by using ADF (1979) as well as PP (1988) unit root test

to analyze the stationarity of data. Now the following specification of ADF test where the optimal lag

length is based on Schwarz information criteria (SIC);
1

t-1 t

= c + x + + +
k

t t j

D D L   






 

Where, Dt is representing all time series variables i.e., POV, GDP, INF, HEX, MOR, TOP, LITR, HED

and L represents the time trend. In short, ∆ shows the first difference operator and µ is the disturbance

term. The null hypothesis (ɸ = 0) means that the unit root exist in equation and alternative hypothesis (ɸ

≠ 0) means that the stationary exist in our model. The following equation is ADF test statistics for non

parametric adjustment.

Review of Economics and Development Studies Vol.2, No 2, December 2016

133

t-1 t = c + L + D +
2

t
c

D t  
 
 

 
Where, Dt is representing all the time series variables i.e., POV, GDP, INF, HEX, MOR, TOP, LITR,

HED and c is the sample size while {t-c/2} is the time trend and µ is the disturbance term.

c) Econometric Methodology

The ARDL co-integration approach is diverged from the other Engle and Granger (1987) and Johansen

(1990) co integration approaches. The ARDL co-integration model is newly developed but it’s popular

in recent years, Jayaraman and Choong (2009). There are many positive edge of ARDL co integration

approach. Firstly, ARDL approach has used in small sample size of time series variables, Ghatak and

Siddiki (2001). However, Johansen co integration mechanism has used in large sample size. Secondly,

ARDL approach has examined the diverge order of integration i.e., I(0) and I(1) but Johansen co

integration examined the same order of integration i.e., I(1), it means that all variables have integrated in

first difference stationarity, Pesaran et al. (2001). Thirdly, the ARDL approach has different optimal lags

of all time series variables, Ozturk and Acaravci (2011).

According to these properties of ARDL model, we prepared the specified equations of ARDL bound

testing approach as follow;

0 1 2 3

1 0 0

4 5 6 7

0 0 0 0

( ) = + ( ) + ( ) + ( ) +

( ) + ( ) + ( ) + ( ) +

(

a b c

t i t i i t i i t i

i i i

f gd e

i t i i t i i t i i t i

i i i i

POV POV GDP INF

HEX MOR TOP LITR

HED

   



  

  

   

   

   



  

   

9 t-1 10 t-1 11 t-1 12 t-1 13 t-1

14 t-1 15 t-1 16 t-1 t

) + (POV) + (GDP) + (INF) + (HEX) + (MOR) +

(TOP) + (LITR) + (HED) +

t i

    

   







Where, ∆ is showed the short time period and α9 to α16 showed the long time period while µt is the

disturbance term in ARDL equation. We have selected the different lags length based on Schwarz

Bayesian criterion (SBC). The next step is to check the co integration exist in our variables through

Wald test for bound statistics, if co integration exist in our analysis so we are distinguished the short run

and long run equations of ARDL modeling as follow;

1 2 3

4 5 6 7

0 1 2 3

1 0 0

4 5 6 7

0 0 0 0

( ) = + ( ) + ( ) + ( ) +

( ) + ( ) + ( ) + ( ) +

(

g g g

t i t i i t i i t i

i i i

g g g g

i t i i t i i t i i t i

i i i i

POV POV GDP INF

HEX MOR TOP LITR

   



  

  

   

   

  



  

   
8

9 t-1 t

) + (POV) +
g

t i

ED  



And

Review of Economics and Development Studies Vol.2, No 2, December 2016

134

1 2 3

4 5 6 7

0 1 2 3

1 0 0

4 5 6 7

0 0 0 0

( ) = + ( ) + ( ) + ( ) +

( ) + ( ) + ( ) + ( ) +

m m m

t i t i i t i i t i

i i i

m m m m

i t i i t i i t i i t i

i i i i

POV POV GDP INF

HEX MOR TOP LITR

   



  

  

   

   

   

  

   
8

8 t-1 t

( ) + (ECM) +
m

i t i

HED  




The term λ shows the error correction model. ECM represents the speed of convergence from

disequilibrium to equilibrium, and it is always negative, and highly significant, Pesaran et al. (2001);

Narayan (2005); Haliciogul and Anno (2009); Arif et al. (2013).

4. Results and Discussions
5.

The results of the study are based on ARDL co-integration. First, we have discussed the descriptive

analysis, and then we have discussed the ADF (1979) as well as PP (1988). Furthermore we have

estimated the deep and brief nexus between human capital and poverty.

Table: Descriptive Statistics

Variables Mean St. Dev. Observations Probability

POV 25.54 7.61 41 0.14

GDP 512.26 314.49 41 0.01

INF 9.57 5.35 41 0.00

HEX 17977.6 20004.2 41 0.00

MOR 92.11 16.69 41 0.32

TOP 33.59 2.90 41 0.64

LITR 38.92 13.60 41 0.14

HED 1331.37 759.10 41 0.16

Source: Authors Calculation (Eviews 9)

In this analysis, there are 41 observations and have based on average value of mean and standard

deviation, to find out the direct effect of human capital on poverty in Pakistan. We have calculated the

human capital through education and health. However, literacy rate and enlistment in higher education

are used as a proxy of education and health expenditures and infant mortality rate are used as a proxy of

health, both health and education are representing the human capital.

Table: Tests for Stationarity

Level

At 1st

Difference

Order

Integration

Variables ADF PP ADF PP

Inter.

trend

Inter.

trend

Inter.
Inter. &

trend
Inter.

Inter.

trend

POV -2.24 -2.11 -2.16 -2.22 -4.80 - -4.87 - I(1)

GDP 2.75 0.84 2.75 0.73 -4.55 - -4.61 - I(1)

INF -3.20 - -3.37 - - - - - I(0)

HEX 2.89 0.55 -1.43 -2.65 -5.01 - -5.98 - I(1)

MOR -0.73 -3.26 -0.66 -2.36 -6.27 - -6.28 - I(1)

Review of Economics and Development Studies Vol.2, No 2, December 2016

135

TOP -3.31 - -3.31 - - - - - I(0)

LITR 0.38 -1.99 0.33 -2.08 -5.96 - -5.97 - I(1)

HED 1.69 -1.54 1.69 -1.54 -4.67 - -4.67 - I(1)

Source: Authors Calculations (Eviews 9)

According to this table, some variables are integrated at level and some time series variables are

integrated at first difference. However, inflation (INF) and trade openness (TOP) have integrated at level

and all other variables such as poverty (POV), GDP per capita (GDP), total health expenditures (HEX),

infant mortality rate (MOR), literacy rate (LITR) and enrollment of higher education (HED) have

integrated at first difference. No one variable has integrated at second difference that’s why we can

apply the Autoregressive distributed lag techniques (ARDL) of co integration.

Table: Bound Testing for Co integration

Equation F-Statistic Upper Bound

Critical Value

Conclusion

POV/GDP, INF, HEX,

MOR, TOP, LITR, HED

13.73

[0.00]

5.96

(1%)
Integration exists

Source: Authors Calculations. Note: f-statistic: 13.73 (Significant at 1% marginal values).

Critical Values at k =8-1=7 is cited from Narayan (2005), Case v: unrestricted intercept and unrestricted

trend. The numbers in parenthesis shows the probabilities of F-statistic. We checked the existence of co

integration in this analysis through Wald bound testing. The value of F-statistic is greater than the upper

bound critical value; it’s mean to show that the co integration exists. We follows the Narayan (2005) to

compare the critical value of co integration exists. Finally the long run relationships exist in this

analysis.

Table: The Effect of Human Capital on Poverty Samples 1973-2013

Variables Coefficient t-statistics

ARDL estimate intercept 45.438 4.768

GDP -.017 -2.795

INF .563 4.062

HEX -.251 -.592

MOR -.040 -.619

TOP -.932 -3.839

LITR 1.030 2.972

HED -.003 -.665

Error correction coefficient

ECMt-1 -0.596 -6.115

Diagnostic test (p-values)

Χ2sc .567 NA

Χ2ff .487 NA

X2nor .985 NA

X2het .554 NA

Source: Authors calculations using Microfit 4.1.

Table shows the effect of human capital on poverty in Pakistan, the time period is 1973 to 2013. We

have estimated the human capital through education and health while literacy rate and enlistment in

higher education is used as a proxy of education, health expenditures and infant mortality rate is used as

Review of Economics and Development Studies Vol.2, No 2, December 2016

136

a proxy of health. However, GDP per capita, inflation rate and trade openness are controlled time series

variables.

The statistically significant variable GDP per capita is negative shows that economic growth increasing

edge to decline poverty in Pakistan, Amjad & Kemal (1997); Forgha (2006); Ahmad & Riaz (2010). In

addition, the inflation coefficient (0.563) shows the positive relationship between inflation and poverty,

it is highly statistically significant at 1%. This implies that increase in inflation leads to increases

poverty in Pakistan because the value of money declines that’s why poverty increases, Amjad & Kemal

(1997); Forgha (2006). Now we have interpreted the results of education and health— proxies for

human capital. One unit change in health expenditures lead to reduces 0.251 units of poverty but it is

statistically insignificant. It’s mean that people have a good health and high will power to compete the

economic problems that’s why total health expenditures reduce poverty. Similarly, the one unit change

in infant mortality cause to reduce poverty by to 0.04 and it is insignificant.

Furthermore we have discussed about trade openness and poverty relationship. The one unit change in

trade openness leads to 0.93 units reduce in poverty and it is statistically significant at 1% level. Trade

openness may stimulate poverty in the presence of high imbalance in income distribution. It is evident

that mostly rich people are the major investor in Pakistan; big businesses are in the hands of few people

who are involved in export business. Any increase in the level of business activity at open economy

level benefits high investing business community leaving major portion of poor people i.e. workers at

their marginalized condition. Now we have explained the relationship between literacy rate and poverty;

one unit increase in literacy rate changes 1.03 units in poverty. Literacy rate means that people are just

read or write, if people have low educated and have a low level of working knowledge that’s why

poverty rises. In last, the one unit change in higher education results in 0.003 unit’s change in poverty.

Reason is that the poverty is reduced with higher education increase and higher education is negatively

related to poverty, it is insignificant.

The coefficient of ECM is 0.59 and has a statistically significant with negative sign. It is implies that,

the 59% of the short run disequilibrium in poverty to the long run equilibrium in present year.

The diagnostic test results are presented in the lower part of table. The evidence shows that there is no

serial correlation, functional form specification, normality or heteroscedasticity. In last step of ARDL

model is to analyze the stability of data and this graph is here in appendix. The graph of CUSUM is

within the boundaries but the graph of CUSUMSQ is little bit without the boundaries. Many studies

have unstable data means CUSUM and CUSUMSQ are without the boundaries, Dritsakis (2010);

Ahmad and Riaz (2010); Ozturk and Acaravci (2011).

5. Conclusions
The study has presented strong connections of human capital accumulation with poverty mitigation

policies in Pakistan. We measured the human capital through education and health; while, education is

measured through literacy rate and enrolment in higher education and, health is measured with health

expenditures and infant mortality rate. The connections between human capital and poverty have

checked through ARDL co integration approach by employing data ranges from 1973 to 2013. Study

also uses many other supporting variables such as per capita GDP, inflation rate, and trade openness.

The results show that per capita GDP is negatively and significantly related to poverty while inflation

rate is positively and highly significantly associated to poverty. When growth increases in Pakistan, the

poverty starts to decline. Likewise, health expenditures, infant mortality rate, trade openness and

enlistment in higher education are negatively associated to poverty while the literacy rate is positively

Review of Economics and Development Studies Vol.2, No 2, December 2016

137

associated to poverty. The results of the study leave us with a clear finale for an optimal policy

formulation that is— Pakistan is in sturdy need of investment in health and education substances for a

noteworthy accumulation of human capital as a right way poverty mitigation policy.

References

Bils, M., &amp; Peter, J. K. (2000). Does schooling cause growth? American Economic Review, 90(5),

1160-1183.

Levine, R., &amp; David, R. (1992). A sensitivity analysis of cross-country growth regressions.

American Economic Review, 82(4), 942-963.

Abbas, Q. (2000). The role of human capitan in economic growth: a comparative study of Pakistan and

India. The Pakistan development review, 39(4), 451-473.

Ahmad, K., &amp; Riaz, A. (2010). An econometrics model of poverty in Pakistan: ARDL approach to

co integration. Asian journal of business and management sciences, 1(3), 75-84.

Amjad, R., &amp; Kemal, A. R. (1997). Macroeconomic policies and their impact on poverty

alleviation in Pakistan. The Pakistan development review, 36(1),39-68.

Barro, R. J. (1991). Economic growth in a cross section of countries. Quarterly Journal of Economics,

106(2), 407-443.

Chaudhry, M. O., Faridi, M. Z., Farooq, F., &amp; Arif, R. (2013). Contribution of health outcomes to

economic growth in Pakistan. Pakistan journal of social sciences, 33(2), 281-295.

Dickey, D., &amp; Fuller, W. A. (1979). Distribution of the estimate for autoregressive time series with

a unit root. Journal of American Statistical Association, 74, 427–31.

Dritsakkis, N. (2010). Demand for money in Hungary: an ARDL approach. Economic and social

science, 1-28.

Eastwood, R., & Lipton, M. (1999). The impact of changes in human fertility on poverty. The Journal of

Development Studies, 36, 1–30.

Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: representation, estimation,

and testing. Econometrica, 55, 251–76.

Forgha, N. G. (2006). Econometric model of poverty: in Cameroon: a system estimation approach.

International review of business research papers, 2(2), 30-46.

Ghatak, S., & Siddiki, J. (2001). The use of ARDL approach in estimating virtual exchange rates in

India. Journal of Applied Statistics, 11, 573–583.

Girma, S., & Kedir, A. (2005). Heterogeneity in returns to schooling: econometric evidence from

Ethiopia. Journal of Development Studies, 41, 1405–1416.

Gundlach, E. (1996). Human capital and economic development: a macroeconomic assessment. Kieler

arbeitspapiere, 778.

Halicioglu, F., & Anno, R. (2009). An ARDL model of unrecorded and recorded economics in Turkey.

MPRA, 25763.

Hanushek, E. (2013). Economic growth in developing countries: the role of human capital. Stanford

University.

Jamal, H., (2006). Does Inequality Matter for Poverty Reduction? Evidence from Pakistan’s Poverty

Trends. The Pakistan Development Review, 45(3), 439–459.

Jayaraman, T., & Choong, C. K. (2009). Growth and oil price: a study of causal relationships in small

Pacific island countries. Energy Policy, 37(6), 2182–2189.

Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on co integration

with applications to the demand for money. Oxford Bull Econ Stat, 52, 169–210.

Khan, M. S. (2005). Human capital and economic growth in Pakistan. The Pakistan development

review, 44(4), 455-478.

Review of Economics and Development Studies Vol.2, No 2, December 2016

138

Lucas, R. E. J. (1988). On the mechanic of economic development. Journal of Monetary Economics,

22(1), 3–42.

Ministry of Finance, Government of Pakistan, Economic survey of Pakistan, various years.

Narayan, P.K. (2005). The saving and investment nexus for China: evidence from co-integration tests.

Applied economics, 37(17), 1979-1990.

Narayan, S., Narayan, P.K., & Mishra, S. (2010). Investigating the relationship between health and

economic growth: Empirical evidence from a panel of 5 Asian countries. Journal of Asian

Economics, 21, 404-411.

Oluwatoyin, M. A. (2011). Human capital investment and economic growth in Nigeria: the role of

education and health. knowledge management, information management, learning management,

14, 266-277.

Ozturk, I., & Acaravci, A. (2011). Electricity consumption and real GDP causality nexus: evidence from

ARDL bounds testing approach for 11 MENA countries. Applied Energy, 88, 2885–2892.

Parish, W. L., & Willis, R. J. (1993). Daughters, education, and family budgets: Taiwan experiences.

Journal of Human Resources, 28 (4), 863–898.

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level

relationships. J Appl Econom, 16, 289–326.

Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika,

75(2), 336-346.

Qadri, F. S., & Waheed, A. (2011). Human capital and economic growth: time series evidence from

Pakistan. Pakistan business review, 815-833.

Romer, P. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1002-

1037.

Scheikman, J. A. (2002). A Agenda Perdida–diagno´ sticos e propostas para a retomada do crescimento

com maiorjustic¸a social. Rio de Janeiro, Instituto de Estudos do Trabalho e Sociedade (IETS).

Schultz, T. P. (1993). Mortality decline in the low income world: Causes and consequence. Economic

Growth Center Discussion Paper No. 681. New Haven, CT: Yale University.

Schultz, T. P. (1999). Health and schooling investments in Africa. Journal of Economic Perspectives,

13(3), 67–88.

Sen, A. (1999). Development as freedom. New York: Alfred A. Knopf Inc.

The World Development Indicators (2013), The World Bank, Washington D.C.

Todaro, M. P., & Smith, S. C. (10th edt.). Economic development.

World Bank (1995). Priorities and strategies for education: A World Bank review. Washington, DC:

World Bank.

World Bank (2003). World development indicators. Washington, DC: World Bank.

World Bank (2007). World Economy Gravity Models. In World Development

Appendix

Review of Economics and Development Studies Vol.2, No 2, December 2016

139

Plot of Cumulative Sum of Squares
of Recursive Residuals

The straight lines represent critical bounds at 5% significance level

-0.5

0.0

0.5

1.0

1.5

1975 1980 1985 1990 1995 2000 2005 2010 2013

Plot of Cumulative Sum of Recursive
Residuals

The straight lines represent critical bounds at 5% significance level

-5

-10

-15

1975 1980 1985 1990 1995 2000 2005 2010 2013