How to Conduct an Opportunity Assessment and Identification

Executive Summary 

Every day, nations face challenges that tend to slow down their rate of development. Most of the challenges, especially those faced by developing countries, revolve around the structural constraints to development. Some of these problems include poverty, inadequate infrastructure, and a high mortality rate, among others. The odd of developing economies to overcome these challenges is relatively low as compared to the developed nations. The developing nations have the required resources that make it possible for them to manage the problems at a very early stage. However, for developing nations, their resources are minimal, and sometimes investing entirely in solving one problem leaves another sector ailing. Therefore, development becomes slower as these nations try to meet their economic needs. However, this paper will investigate the drivers of infant mortality in Malaysia using the autoregressive model framework. However, the paper will discuss the finding and provide a recommendation on what the Malaysian government should do to reduce the mortality rate. 

Introduction 

High infant mortality rates have been an ongoing challenge that seems to threaten the economic development in Malaysia. Malaysia is one of the rapidly emerging economies and has been attracting world attention over the years. However, the infant mortality rate seems to be a big issue in Malaysia since its infant mortality rate has increased by one percent for the last five years. Right now, the Malaysian infant mortality rate stands at 7.3%. However, such decline reveals a higher degree of heterogeneity when weighed against the probability of various social groups experiencing different infant mortality levels. However, to reduce child deaths, the states need to address the factors influencing infant mortality. To do so, one can use frameworks as used by traditional demographers to explain these factors. One can use time-series data to model the contingency table, evaluating the inter-relationship between the child death rate and factors that influence this mortality rate. 

Selection of Frameworks/ Theories 

Review 

Due to the rapid economic and social change, the three stages proposed by the theory of demographic Transitions do not accurately describe Malaysia's position, particularly on infant mortality rate. Therefore, it is essential to examine infant deaths alone based on Malaysian economic growth and developing stages by evaluating probable factors that influence infant deaths. Analyzing the economic conditions, one will realize that the citizen's income level is inversely related to infant mortality. It is believed that individuals that have higher incomes have access to quality healthcare for their children. Understanding that quality health care is expensive means that those that lack the money will not enjoy quality healthcare (Siah & Lee, 2015) . Therefore, the children born to low-income earners suffer the risk of mortality if they fall asleep. 

Moreover, the child's parent's education level plays a vital role in curbing the mortality rate. Children with well-educated parents, particularly mothers, have a higher chance of survival. Such is because the mother can decide the health needs of a child before it is too rate. Through education, parents are provided with an opportunity to study various aspects of life, and healthcare becomes a more prevalent sector in the Malaysian education system. Therefore, the parents who have gone through school have more comprehensive knowledge about the need of a child than someone who never attended school. 

Access to healthcare is also a factor that contributes significantly to infant mortality. Sometimes the country may have made it possible to provide quality and affordable health care, but environmental conditions may prevent one from getting to the nearby health care facility. This comes to be due to especially regions that experience heavy rains and flooding, which hinders mobility by distracting many transport modes. For such cases getting to the hospital becomes a challenge, and sometimes the child may succumb before getting them to the hospital. More so, some areas have no hilly and scarce places, and sometimes one may find that there is only one healthcare facility serving a larger region. Accessing the facility becomes problematic, especially for people living far from the facility, which increases the risk of infant death. However, apart from environmental conditions, female fertility increases infant mortality risk (Renton et al., 2012). The rate of being children is inversely related to infant deaths. Such is because every child born suffers the risk of death, and therefore, the more children someone gets, the higher the risk of death. As the fertility rate increases, the infant mortality rate also increases. 

Therefore, from these explanations, one can tell the significant indicators of infant deaths are the income level, education level, access to health care facilities, and female fertility. However, from these indicators, most people affected by infant mortality are the under-privilege individuals in society. Although poverty in Malaysia has reduced by 16.7 percent, 56% of the Malaysian population earns median income and below. And this is the primary group experiencing a higher infant mortality rate since there is no luxury of enjoying quality infant healthcare due to their limited disposable income. 

The Framework 

To articulate the above factors are genuinely the indicators of higher mortality rates, one will need to use the logarithmic analysis framework to study the factors. Malaysian time data series on the infant mortality rate (IMR0), income levels (GD), female fertility (FR), level of education (EDC), and access to healthcare (PHY) will be used. The data will be extracted from World Development Indicators (World Bank., 2014a) . The model must follow a sequential process for an empirical strategic estimate of the logarithmic equation. Such will enable testing the unit root followed by the co-integration analysis, which will help understand the long and short terms impacts of the determinant of infant mortality. The resulting equation will be: 

?????? = ? 0 + ? 1 ????? + ? 2 ?????? + ? 3 ?????? + ? 4 ???????? + ? t 

The µ represents the stochastic error while β stands for the intercept term. However, the other variables are previously defined. 

Application of the Framework/ Theories 

First, the test roots need to be conducted for all the variables to identify how they are related without mixing them. To strengthen the findings, Zivot and Andrew root test is recommended to capturing the points of breaking for every data category utilized in the test. The ZA unit testing can identify the trends in the time series, which helps identify the indicators that are playing at hand during that period. However, understanding that the data could have various integration orders, then autoregressive distribution lag is better used in testing the co-integration. This method is more robust when evaluating the long-term relations between the infant deaths rate and the factors contributing to these deaths. Such leads to the testing of the null hypothesis for no co-integration among the variables. Such can be done by applying the joint significance test on the lagged variables. Therefore, a null hypothesis is then formulated, which states that all the lagged variables' coefficients equal to zero. 

However, for better results, one needs to use the F-test to articulate how these indicators are related to the infant mortality rate on long term basis. For those that shows existence long-term relationship, the analysis results will indicate the variable that will be normalized. However, when determining whether long term relationships exists between the variable, the f-test values are always greater than those of an upper critical boundary. However, if the calculated f-test results are lower than the lower critical boundary, there is no long-term relationship. Nevertheless, if the formulated f-test value lies between the upper and lower critical boundary, then one concludes that there the long-term relationships text was inconclusive. 

Results 

Malaysia descriptive data from the framework analysis 

IMR FR PHY GNI EDUC 

Mean 1.153 0.495 -0.324 3.419 1.733 

Median 1.113 0.538 -0.385 3.454 1.760 

Maximum 1.638 0.687 0.204 4.018 1.873 

Minimum 0.838 0.293 -0.634 2.602 1.440 

Std. dev. 0.267 0.121 0.264 0.374 0.116 

Jarque-Bera 3.804 3.711 3.514 1.5465 7.587 

(0.149) (0.156) (0.172) (0.461) (0.022) 

The unit test results 

ADF ZA 

Variables I(0) I(1) I(0) I(1) 

IMR -2.293 -2.995** -3.167 -5.198*** 

FR -1.149 -4.464* -5.152** -6.553* 

PHY -2.377 -5.591* -2.702 -8.423* 

GNI -1.987 -3.229** -4.226 -5.190** 

EDUC -3.892* -4.832* -2.804 -6.303* 

The model outcome will show that the data extracted about the infant mortality rate is normally distributed. The unit test root results indicate that the Malaysian data on infant mortality follows a stationary process. However, from the ZA test, the results say that one needs to reject the null hypothesis. However, the autoregressive vector framework determines Malaysia's optimal lag, which was used to tell the short and long-term relationship between the variables and infant mortality rate. Moreover, the f-test will indicate that there are among the indicators and children's death since testing it against a 1 percent significance level, the f-test results tend to be bigger than the critical boundary on upper side. The model's main result is a long-term relationship between the level of education, income level, access to health care facilities, fertility rate, and infant mortality rate. 

However, the model evaluated the impact these variables have on children's mortality rate. There exists an elasticity between these variables when it comes to death rates. To illustrate that, when there is a 5 percent increase in access to health care, then the infant death rate will lower by 1 percent. The framework articulated the main drivers of child deaths are access to health care facilities which, if carter for, will reduce infant mortality by 5 percent, and the level of education, which proves to reduce child death by 10%. Increasing access to healthcare facilities and increasing citizen's education, especially females, will significantly reduce infant mortality. The health services tend to alter the risks of mortality, and therefore providing maternal clinics and physicians to the remote will reduce infant mortality. Contradicting the research done by (Gruber, Hendren, & Townsend., 2014) , the results show the impact of access to health care facilities. (Kaplan, Hooper, J, & Gurven, 2015) provides a plausible explanation since the study identified that re mote regions with limited reach to health services indicate shows durations between births which raises the risks of death by fourfold. 

In Malaysia, income is also a significant determinant of the child mortality rate. Agreeing with the study by (Erdogan, Ener, & Arica, 2013) , an increase of income by 1% will eventually lower infant deaths by 0.7%. Such is because the access to quality infant health care increases as the disposable income increases. The level of education is also a dominant factor that affects children mortality. Such suggest that women exposed to higher education have more excellent knowledge about infant health care, which indirectly tends to lower infant mortality. Such relates directly to a higher fertility rate. With higher education, women can manage and plan their children-bearing patterns. Therefore, despite having to bear children just because they are capable, educated women will bring forth children they can manage, consequently reducing the infant mortality rate. 

Conclusion 

The infant mortality rate has become a massive problem in Malaysia which hinders the rate of development. Therefore, investigating this challenge, the paper tackled the drivers of the infant mortality rate in Malaysia. To understand this, an autoregressive model framework was used to investigate the co-integration while identifying the long-term relationship between child deaths and access to education, level of education, disposable income levels, and the fertility rate among citizens. The results found these factors are inversely related to the infant mortality rate. However, the people who suffer from infant mortality rate are underprivileged community members, particularly medium-income earners and below. Nevertheless, the Malaysian government can but some initiatives to reduce the infant mortality rate but raising access to facilities such as hospitals and education. 

Recommendation 

Having seen that level of education, income level, fertility rate, and access to health care are inversely related to infant mortality rate; the policymakers should prioritize raising these factors, which will significantly reduce the children's death. However, below are few recommendations that can help the Malaysia policymakers during the formulation of initiatives that are meant to reduce infant mortality: 

The country needs to increase more maternal clinics in remote areas and deploy more doctors to those areas. Such will tend to increase access to health care services which will reduce the mortality rate. 

The country should restructure the education system where they offer affordable and quality education to its citizens. Such will increase women's chance to access education, which translates to a better understanding of infant health needs. Moreover, for the potential mothers that lack the opportunity to study, the state should provide programs that educate them about infant care through community-based outreach. 

The country needs to develop affordable health care plans such as insurance policies that enable individuals, especially the average income earners, to access quality health care. Such can be done by having state agreements with employers and business people to pay monthly health insurance cover. 

The government needs to engage health care practitioners and social workers in educating households on family planning for the higher fertility rate. Such will allow people to sire children to raise and afford quality health services for their children, which will reduce the infant death rates. 

References 

Erdogan, E. .., Ener, M., & Arica, F. (2013). The strategic role of infant mortality in economic growth is an application for high-income OECD countries. Procedia Social and Behavioral Sciences, 99 , 19-25. 

Gruber, J., Hendren, N., & Townsend., R. M. (2014). The Great Equalizer: Health Care Access and Infant Mortality in Thailand. American Economic Journal: Applied Economics, 6 (1), 91-107. 

Kaplan, H., Hooper, P., J, & Gurven, M. (2015). The causal relationship between fertility and infant mortality: prospective analyses of a population in transition. In Population in the Human Sciences: Concepts, Models, Evidence , 326-376. 

Renton, A., Wall, M., & Lintott., J. (2012). Economic growth and decline in mortality in developing countries: an analysis of the World Bank development datasets. Public Health, 126 (7), 551-560. 

Siah, A. K., & Lee, G. H. (2015, May 28). Female labor force participation, infant mortality, and fertility in Malaysia. Journal of the Asia Pacific Economy, 20 (4), 613-629. Retrieved from https://doi.org/10.1080/13547860.2015.1045326 

World Bank. (2014a). World Development Indicators. Washington DC.