Capacity Building for Sustainable Human Development: A Panel PMG/ARDL Analysis of East and South Asia.
There has been an improvement in human development over the past couple of decades. The basic indicators of human development have been showing improvements such as life expectancy, per capita income, school enrolment of both boys and girls, nutrition of child and mother, and other basic social services. Both Millennium Development Goals (MDGs) by UNO in 2000 and the commitment of its member countries have contributed to the growth of human development. The improvement in some indicators of human development is shown in the following table.
However, human development has not been consistent across the globe and there exit a lot of deprivations. For instance, 1 out of 9 persons lives below the poverty line, one out of three persons is a victim of malnutrition. In every minute, approximately 11 children (under age 5) die and approximately 24 people are included in migrants or homeless. In every hour, 35 pregnant women die due to lack of delivery treatment, in everyday environmental problems cause the death of 18,000 people across the globe. And in every year, approximately 15 million girls marry before 18 and 2 million people are infected by HIV.
There are three types of challenges which are hurdles in improving human development across all the regions of the world. One is economic issues (such as the vicious circle of poverty) which are prolonged, other is social problems (like inequality, gender discrimination in economic activities) which are deepened, and finally concerns about the environment (e.g. CO2 emission) which are emerging.
These challenges are circulated around three sectors which are economy, society, and environment and when we include simultaneously all three sectors in development agenda it is called sustainability of people, planet and prosperity. There are two types of sustainability i.e. weak and strong sustainability.
Weak sustainability is equal to the constant sum of human-made capital (machinery, infrastructure etc.) and natural capital (air, water, soil, living organisms etc.), both human-made capital and natural capital are fully substitutes of each other. In weak sustainability, the constant sum of human-made capital and natural capital will be handed over to the future generation. Strong sustainability is equal to the constant sum of each capital since the productivity of one capital depends on the other and the substitution of each capital is possible only to some extent. Hence, according to the strong sustainability constant level of natural resources must be hand over to the next generation.
Under this concept of sustainability, in September 2015 UNO has approved 17 sustainable development goals and 169 targets to be achieved by 2030. The objects of Sustainable Development Goals (SDGs) people, planet and prosperity. Due to both the 2030 Agenda and Paris Agreement on Climate change, the concept of sustainability has been on main priority in policy making of all member countries of UNO. This increased attention is due to changes in climate and natural resources that we can all observe. Worse environmental changes have made it compulsory to modify our ways of production and consumption in order to save abilities & capabilities of natural and human resources prosperity of not only this generation but also of the future generation. Sustainable development is a ring of three sectors which are economy, society and environment (Hardi & Zdan, 1997; West Midlands Round Table, 2000)
The human development and 2030 Agenda are linked with similar features. Like, both agendas primarily stress on the well-being of everyone across the globe and hence these agendas are universal. The human development includes the concept of economic prosperity via a rise in per capita income, concepts of social prosperity via a rise in years of education per person, an increase in life expectancy. And 2030 also includes both concepts i.e. economic and social prosperity with additional concepts of environmental prosperity (UNDP, 2016). Hence, human development indicators can be used with additional indicators to assess the progress towards 2030 Agenda.
The term capacity building (CB) means an increase in one's own capabilities in order to achieve certain goals and targets. In this particular study, goals, and targets are circulated around the sustainability of human development in developing countries. Hence there is a need to identify such major determinants from the economy, society, and environment, which will enhance the capabilities of developing countries in order to achieve the goal of sustainable human development.
The role of women in the production process is crucial to enhance economic and social capabilities. According to the UN Food and Agriculture Organization, in Asia, the female labor force is 80 percent of the total labor force in the Agriculture sector and they are producing 50 percent of total agri-output. This share in agri-production can be increased by 20 to 30 percent by providing access to more resources used in agriculture and it will decrease extreme poverty by 12 to 17 percent in Asia. Hence, female participation in the production process of the economy can build the capacity of developing countries.
Simon Kuznets presented the relationship between the emission of [CO.sub.2] and economic development in form of a curve, and this curve is called the Environmental Kuznets Curve (EKC). According to this curve, the growth of the economy and degradation of environmental change in the same direction but only to some point. After that point, the relationship starts to change in opposite direction i.e. more growth of economy with low degradation of the environment. And economies also experience structural changes during the achievement of human development, for instance, developing countries move from the agriculture sector to the production sector and from the production sector to sophisticated service base structure. During this transformation of economic structure, developing countries experience hypothesis of EKC. Hence, the effect of emission of [CO.sub.2] is important in analyzing the capabilities of the economy and environment. The vicious circle of poverty can be break by injecting capital flows in the economy, however, there is a requirement of domestic capabilities for sustainable human development. In order to build domestic capabilities, domestic saving and educated labor force are important. It will increase the speed of economic activities in the form of investment and production.
The Objectives of the Study
There are two main objectives of the study:
i. The objective is to find out Significant determinants to enhance the capacity building (CB) of countries for sustainable human development. The selected countries are from two regions i.e. East Asia and South Asia.
ii. Increase awareness about strong sustainability.
The Significance of the Study
i. There has been an improvement in human development since the last couple of decades, however, this improvement is not yet in each region of the world. The major portion of the world's population live in East Asia and South Asia as we can see in the following table, so these regions are selected for analysis.
ii. The concept of the Capacity building is widely acknowledged by UNO and policymakers ((Fukuyama, 2013; Hyden & Mease, 2004; Rothstein, 2011) and human development cannot be sustained without a domestic capacity building. Hence this study will be helpful to illustrate the determinants which will build domestic capabilities of selected countries. )
iii. The idea of this study is developed from 2030 Agenda and selected counties are members of UNO. Hence, this study will promote further research on 2030 Agenda and contribute to relevant policy-making in selected countries.
Section II presents a literature review and hypothesis of the study. Model specification, methodology, & data are described in section III, and result in interpretation in section IV. Finally, conclusion and policy recommendations are given in the last section.
In the last decade, the concept of sustainability has gained the main attention of researchers, policy makers, and government. It is the reason for growing literature on the sustainability of the economy, society, and environment. This study develops an empirically link between economy, society, environment, and sustainability of human development on the bases of relevant literature, some of this literature is given below.
Kim et al. (2017) examined the effects of institutions on individual preferences. They made analysis by using data on NK refugees and SK students and found NK refugees showed more references for marked based economy and democracy than those of SK students. They also found a difference in distributional preferences between NK and SK subjects is not only significant but also large.
Joshi et al. (2015) constructed three indices of governance for 183 countries and found positive prospects of governance in all selected countries. These found that governance contributed to progress in human development i.ee health, education, and income and concluded that governance is a necessary source for sustainable development.
Carbonnier et al. (2012) made a comparison of governance outcome in developed and developing countries. They found that some countries (Australia etc.) fully utilized their natural resources and included in the high-income group, some countries (South Africa etc.) utilized their natural resources and included in upper-middle income group, and finally some countries (Niger etc.) under-utilized natural resources and included in a poor income group. The basic difference in these groups of countries was the quality of governance. Rizk (2012) gave empirical evidence of a decrease in poverty by increasing the quality of governance. He used data of 71 countries from 1996 to 2008 and showed in estimated results that a one percent increase in quality of governance will decrease 1.75 percent in poverty.
Baliamoune (2011) empirically analyzed the contribution of institutions in the growth of the economy. They collected data from 30 countries and used methods of fixed effects and Arellano-Bond GMM to estimate results. They found strong evidence that social capital increases the contribution of political institutions and weak evidence in case of civil liberties. Furthermore, institutions and social capital have a positive effect on economic growth and human capital.
Ozcan and Bjonskoy (2011) have tested the relationship between social trust and development. They collected primary data on social trust from 86 countries and used HDI as a proxy for development. The estimated results showed significant social trust and the speed of development in selected countries.
Portney (2013) tested empirically the Environmental Kuznets Curve (EKC), The author used data of 55 largest cities of USA, time period from 1990 to 2009 and estimated results showed that citizen of these cities expects from public policymakers to follow sustainability.
Dempsey et al (2011) presented a detailed survey of social sustainability within the urban context. He classified two dimensions of social sustainability, one is equitable access and other is the sustainability of the community. Equitable access means target to meet the needs of not only the present generation but also the future generation. The sustainability of community means the ability of society to survive as well as to reproduce itself
Custancet and Hillier (1998) examined the contribution of statisticians in describing indicators of sustainable development. Statisticians presented criteria to select an indicator. According to these criteria, the indicator must be sound, easy to understand, capable of being updated, able to indicate the whole scenario. Rennungs and Wiggering (1997) found that instead of a strong positive correlation between sustainable environment and economy, the slogan of sustainability is looked a concern only for ecological economics and neoclassical economics focused on only materialistic development. They presented criteria to merge both environment and economy, this criterion is based on social cost and benefit analysis of environmental degradation.
Nayar (1994) highlighted sustainable development is only a fashionable buzz, and since it is against the developed countries. The north region is responsible for environmental pollution by industrialization, they not only degraded their own environment but also of the south region. He also linked theoretically environmental degradation with poverty.
Bravo (2015) updated his previous version of human sustainable development index and used emission of [CO.sub.2] as a proxy for the sustainability of the environment. The advantage of an updated version of the index is the emission of [CO.sub.2] is a major concern and has effects on many economic and social indicators.
Bravo (2014) developed the new form of human development index by including the role of the environment and named it Human Sustainable Development Index (HSDI). He used data taken from the UN, however, this index remained insufficient in its representation of sustainability of the environment.
The Hypothesis of the Study
Above literature emphasized the role of institutions, environment, and society in sustainability. Hence, the following variables are chosen to build the capacity of selected countries for sustainable human development.
H1: Domestic Saving, total natural resources, female participation in the production process of the economy, renewable energy, food production, and institutions have a significant relationship with sustainable human development.
METHODOLOGY AND MODEL SPECIFICATION
The following function describes the model of study Sustainable Human Development = f (Capacity Building) Where capacity building depends on economically sustainable development, social Sustainable development, and environmental development.
The specification of the model is illustrated in the following chart
In order to find an elasticity of independent variables the lin-log model is used in this study and given below
[HDI.sub.it] = [[beta].sub.0i] + [[beta].sub.1] log([ANS.sub.it]) + [[beta].sub.2]log([LEFF.sub.it]) + [[beta].sub.3]log([CO.sub.2]) + [[beta].sub.4]log([TNTR.sub.it]) + [[beta].sub.5]log([RL.sub.it]) + [[beta].sub.6]log([FPI.sub.it]) + [[beta].sub.7]log([REO.sub.it]) + [[mu].sub.it] .... 1.1
t = 1.2.3...........10
t = 1996,1997,1998................2016
HDI = Human Development Index
ANS = National Savings
LFF = Female Labor Force Participation in Economic Activities
[CO.sub.2] = Emission of [CO.sub.2] (kt)
TNR = Total Natural Resources include Rent on Oil, Gas, Coal, Mineral, and Forest
RL = Rule of Law
FPI = Food Production Index
REO = Renewable Electricity Production as a percentage of Total
Production of Electricity
The panel data is used for 10 countries from East Asia and South Asia. In order to represent approximately the whole population of East Asia and South Asia, countries are selected on population size. Countries include Pakistan, India, Bangladesh, Sri Lanka, Indonesia, Malaysia, Republic of Korea, China, Thailand, and the Philippines. The data of Institution is collected from WGI by WB, data of HDI from UNDP, and data of remaining variables from WDI by WB. The time period is from 1996 to 2016.
Unit Root Test. Since data of 20+ years for each country is used, so the unit root of each series of data is tested. And there are also differences among selected countries in their population size so following panel unit root test is applied in this study.
The Im, Pesaran, and Shin (IPS) Test. IPS unit root test allows heterogeneity on the coefficient of the [Z.sub.it_1] variable. The test procedure is based on the average of the individual statistic of each cross section. The Equation of the IPS test is given below
[mathematical expression not reproducible] 1.2
[H.sub.0] : [p.sub.i] = 0
[H.sub.1] : p < 0
Alternative hypothesis describes that coefficient of at least one cross section is less than zero. After checking the unit root test of data, the appropriate methodology is chosen for estimation. Since the value of HDI is effected by the previous value of HDI, hence dynamic model is used in the study. To estimate the dynamic model of this study, the pooled mean group estimator is chosen.
The Pooled Mean Group (PMG) Estimator
In PMG estimation, only the long run coefficients are constrained to be the same across countries, while short-run coefficients are allowed to vary. By using the equation of PMG estimator developed by Pesaran and Smith (1995), the unrestricted specification for the ARDL equation of the model of this study is
[mathematical expression not reproducible] 1.3
Where [X.sub.it] is vector [7*1 in this study] of explanatory variables. Here error term of the model is assumed independently distributed with zero mean and variance >0, and distributed independently of the regressors. [micro]i represents the fixed effects and p and q may vary across the countries. The error correction model for this study is given below
[mathematical expression not reproducible] 1.4
Where the [beta]i is the long run parameters are the error- correction parameters. The error correction parameter indicates the speed of adjustment. If = 0, then there is no evidence that variables have long run the association. It is expected, that is negative and statistically significant under the prior supposition that variables indicate a convergence to long-run equilibrium in case of any disturbance. Since, the restriction in PMG estimator is that [beta] is common across the countries, which is shown in the following model
[mathematical expression not reproducible] 1.5
ESTIMATED RESULT AND INTERPRETATION Results of Unit Root Test
IPS unit root test is applied to check the stationarity of data used in this study, and results are shown in the following table
Variable of female labor force participation (LFF) is stationary on the level while remaining all variables are stationary at first difference. Since variables are stationary at mixed order i.e. either at I (0) or I (1), hence PMG estimator is appropriate to estimate a model of study. PMG estimator requires selection of appropriate lag length for equations of an individual country. In this study, the Schwarz Bayesian
Criterion is used for appropriate lag length and results are given in the following table
When we applied Schwarz Bayesian Criterion on different lags length, the minimum value is calculated at lag length 1, hence we chose lag length 1 to estimate our model, and short-run results are given in the following table
In the above table, for all selected countries the value of ECM is significant and also negative which means convergence towards equilibrium in the next period. However, the speed of adjustment from the current period to the next period is different in each country. The high speed of adjustment in Bangladesh and Pakistan, low speed of adjustment in China, India, and Sri Lanka, and average speed of adjustment in remaining selected countries. Here ECM = -0.64 in case of Pakistan which describes that speed of adjustment towards equilibrium is 64% from current period to next period in Pakistan, and value of ECM for each selected country has the same explanation
All variables in the case of each selected country (except variable LFF in the case of China) are significant. However, the magnitude and sign of each variable for a specific country of study are different. But we will rely on long-run behavior of our variables for interpretation.
Importantly, ECM also represents the existence of a long-run relationship among variables if it has a significant and negative value (Baek, 2016). As ECM value in this model is also significant and negative, hence there exist long-run relationship among variables of the model. So, the long run results are estimated and given in the following table
Following all interpretations belong to table 3. Since probabilities given in table 3 are less than 5%, so we reject our null hypothesis of study which means all variables are significant in long run. Since the lin-log model is used this study, hence long-run results are showing the percentage change in explanatory variables but an absolute average unit change independent variable.
The coefficient of variable [CO.sub.2] is confirming the hypothesis of Environment Kuznets Curve (EKC) in the case of East Asia and South Asia. That is, as the economy and human development improve the emission of [CO.sub.2] will also increase. But when the goal of sustainable human development achieves, emission of [CO.sub.2] will start to decrease in these regions. Positive coefficients of both ANS and TNR are confirming the importance of domestic savings and natural resources for sustainable economic development. The role of the female in sustainable human development is also proved important as the coefficient of LFF is significant and positive.
Food production has been increased during the last few decades due to the adoption of green revolution by developing countries. It helped to improve human development, the positive coefficient of FPI in this study confirming this positive trend between food production and human development.
There are traditional societies in most of the selected countries, especially in South Asia. In traditional society, the staff of institutions, as well as people of societies, are reluctant to follow the rules and regulation. So, institutions are negatively contributing to capacity building. However, after some period when traditional societies convert into modern society, the institution will start to contribute positively to capacity building. And developing countries are lacked technical and financial resources to build Dams for the production of cheap and renewable energy. These countries have to obtain these types of assistance from developed countries. And in return, developed countries make influence to domestic policies of these countries in their own interests. Hence contribution of renewable energy in the improvement of human development.
The main purpose of this research work is to identify indicators of the economy, society, and environment which are helpful to build the capacity of selected countries for sustainable human development. After empirical analysis in this study, the following indicators are found for this purpose. These are, domestic savings, utilization of natural resources, renewable energy, control on emissions of [CO.sub.2], female participation in all economic activities are min indicators for capacity building. However, in order to make an effective & positive role of the institution, there is a need to convert traditional societies of these regions especially of South Asia into modern societies. In order to enhance the capacities of countries for sustainable human development, the following policy recommendations are given on the bases of long-run estimates of this study
* First of all, to increase the participation rate of public and institutes in achieving both sustainable human development and SDGs, there should be more conferences, research seminars and lectures in educational institutes and policymaking institutes.
* Instead of focusing only on economic development, policymakers should include social as well as environmental preference, by allocating more fiscal resources on human capital and environment.
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Rana Khalid Mehmood (1), Hafeez Ur Rehman (2)
(1) Department of Economics, University of the Punjab, Lahore, Pakistan, Email: email@example.com
(2) Department of Economics, University of Management & Technology, Lahore
Table 1: Improvement in Human Development Variables Across the Globe 1990 2015 Number of Extreme 1,926 million 836 million Poor Malnourished 23.3 % of the 12.9 % of the People total population total population Literacy Rate 83% 91% Among Youth (15-24) Number of Child 12.7 million 6 million Mortality annually annually Maternal Mortality 380 210 per 100,000 The ratio of Birth 55% of total 64% of total Control Among women care women care Women about birth about birth control control Access to Improved 76% of the total 91% of the total Drinking Water population population Source Variables Across the Globe Improvement from 1990 to 2015 Number of Extreme 1,090 million people have come out of the circle Poor of extreme poverty Malnourished 10.4 percentage point improvement People Literacy Rate 8 percentage point improvement Among Youth (15-24) Number of Child 6.7 million annual decreases in child mortality Mortality Maternal Mortality Decrease in maternal mortality by 170 per 100,000 per 100,000 The ratio of Birth 9 percentage point increase in women population Control Among who care about family planning Women Access to Improved 15 percentage point increase in the population who Drinking Water has access to an improved drinking water source Source Source. Report of UNO on MDGs 2015 Table 2: Population Region Population (2017, in Share in Total million) Population World 7536 Asia 4494 59.64% of World Population South Asia 1845 77.21% of Asia & East Asia 1625 46.04% of World Population Source: www.worldpopdata.org Table 3: Results of Unit Root Test Variable At Level With With Intercept At First Difference With Intercept and Trend Intercept HDI -0.59125 2.99539 -4.91523 (**) L ANS -3.66857 (**) -1.08662 -10.3709 (**) L [CO.sub.2] 1.86410 0.66950 -10.8370 (**) L LFF -1.29376 (*) -2.20879 (**) L TNR 0.01552 2.74601 -9.95475 (**) L RL -2.11956 (**) -0.48968 -8.63761 (**) L FPI -1.39092 (*) 2.05225 -10.3907 (**) L REO -1.17300 -4.17596 (**) -10.6653 (**) Variable At First Difference With Intercept and trend HDI -4.56296 (**) L ANS -7.93104 (**) L [CO.sub.2] -9.66887 (**) L LFF L TNR -9.77634 (**) L RL -6.93321 (**) L FPI -7.85843 (**) L REO -8.83812 (*) (*), and (**) represent significant at 10% and 5% respectively, Source: Authors own calculation. Table 4: Schwarz Criterion for Selection of Lag Length Lag Length SIC 0 -6.125931 1 -33.84735 (*) 2 -32.73141 3 -31.32002 4 -29.37451 5 -27.64441 6 -26.10883 7 -24.59703 (*) represent minimum value of SIC. Source: Authors own calculation Table 5: Individual Short Run Estimates Variable PAK IND BGD LKA IDN ECM - - - - - 0.64 0.03 0.78 (**) 0.09 0.23 (**) (**) (**) (**) D(L AN - - - - 0.01 S) 0.02 0.01 0.02 (**) 0.02 (**) (**) (**) (**) D(L C - 0.01 0.0068 - - O2) 0.03 (**) 59 0.01 0.02 (**) (**) (**) D(L FP - 0.07 0.03 (**) - - I) 0.02 (**) 0.03 0.14 * (**) (**) D(L LF 0.01 - 0.92 (**) - 0.48 F) (**) 0.04 0.04 (**) (**) (**) D(L RE 0.01 0.02 0.02 (**) 0.01 - O) (**) (**) (**) 0.02 (**) D(L RL - 0.08 0.01 (**) - 0.01 ) 0.01 (**) 0.01 (**) (**) (**) D(LTN - - - - - R) 0.02 0.01 0.02 (**) 0.01 0.01 C (**) (**) (**) (**) - - - - - 0.22 0.01 0.35 (**) 0.01 0.08 (**) (**) (**) (**) Variable MYS KOR CHN THA PHL ECM - - - - - 0.12 0.12 0.03 0.06 0.28 (**) (**) (**) (**) (**) D(L AN 0.02 0.02 0.04 - 0.03 S) (**) (**) (**) 0.01 (**) (**) D(L C 0.08 - 0.04 0.02 0.02 O2) (**) 0.04 (**) (**) (**) (*) 8 D(L FP - - 0.13 0.09 0.06 I) 0.05 0.08 (**) (**) (**) (**) (**) D(L LF 0.03 0.69 0.34 0.25 - F) (**) (**) (**) 0.08 (**) D(L RE 0.02 0.02 0.02 - - O) (*) 8 (**) (**) 0.02 0.01 (**) (**) D(L RL - 0.02 - 0.01 0.02 ) 0.04 (**) 0.01 (**) (**) (*) 8 (**) D(LTN - - 0.01 - - R) 0.03 0.06 (**) 0.01 0.05 C (**) (**) (**) (**) - - - - - 0.03 0.02 0.01 0.02 0.08 (**) (**) (**) (**) (**) (*) and (**) represent significant at 10% and 5% respectively. Source: Authors own calculation Table 6: Long Run Estimates Variable Coefficient Standard Probability Error t-Statistic L_ANS 0.044927 (**) 0.008170 5.498815 0.0000 L_CO2 KT 0.084297 (**) 0.019657 4.288367 0.0000 L_FPI 0.092502 (**) 0.033808 2.736094 0.0073 L_LFF 0.218021 (**) 0.018460 11.81044 0.0000 L_REO -0.07210 (**) 0.009035 -7.98000 0.0000 L_RL -0.02658 (**) 0.004432 -5.99839 0.0000 L_TNR 0.027588 (**) 0.003334 8.274026 0.0000 ECM -0.23989 (**) 0.083544 -2.87067 0.0050 C -0.08280 (**) 0.037304 -2.21972 0.0286 (**) represent significant at 5%. Source: Authors own calculation.
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|Title Annotation:||Pooled Mean Group/Autoregressive Distributed Lag|
|Author:||Mehmood, Rana Khalid; Rehman, Hafeez Ur|
|Date:||Jul 1, 2018|
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