Undersatnding People Essay -- essays research papers

Different people see the world from different perspectives. In our society, there will always be misunderstandings between people and those people’s reactions will differ. Some choose to mope, groan, and even get angry about the way that themselves or others are treated while some decide to try and do something about it. Still, there are others who think to themselves that maybe the best way to deal with the issues among people is to try and comprehend what they mean and just live by them. As Benedict Spinoza put it, â€Å"I have striven not to laugh at human actions, not to weep at them, nor to hate them, but to understand them.† Authors now try to understand actions that people make instead of ridiculing them. The following explains these authors and how they have been able to identify with others.   Ã‚  Ã‚  Ã‚  Ã‚  Ã¢â‚¬Å"Under the Influence† is an essay written by Scott Russell Sanders. In this writing he tells how he grew up with an alcoholic as a father. His life was not at all easy. He had to deal with issues that young children should not have to deal with. In this essay he makes the reader understand what an alcoholic is and how the actions of one person who has this disease can affect himself and so many other people. Sanders does this by explaining the horror that he and his family had to go through during the years of his father’s problem. The family was never sure whether they would be happy at the sight of the father or horrified by his presence. As a child, S...

Honesty Versus Justice and Due Process Versus Crime Control

Honesty versus Justice and Due Process versus Crime Control Lisa Moore University of Phoenix Ethics in Justice and Security CJA 530 March 07, 2010 Roger Long JD Honesty versus Justice and Due Process versus Crime Control The criminal justice system is built on a foundation of honesty and justice. For justice to work, the justice system, and criminal justice professionals must be honest. The word honesty, describes an individual that doesn't lie, cheat, steal, or abuse to get ahead for personal or professional gain. The word justice describes the concepts of moral rightness based on ethics, rationality and fairness. How can there be justice if the guilty go free or if the innocent pay for crimes he or she never committed. â€Å"In order for this to occur, our legal system must be one that demands absolutely honesty, such as when someone is called to testify he or she is asked, Do you swear to tell the truth, the whole truth, and nothing but the truth? If false accusations and false evidence are presented against the innocent, they will be punished as if they are guilty† (RealPolice, 2000). The place of honesty in a system that promotes justice has sparked a controversial issue among the Criminal Justice world, that honesty could be put on the back-burner when pursuing justice. For example when officers present false evidence to prosecutor in order to have a case against the accused when they believe he or she will not be found guilty. A good example of deception by law enforcement occurred in 1993, when â€Å"Craig D. Harvey a New York State Police trooper was charged with fabricating evidence. Harvey admitted he and another trooper lifted fingerprints from items the suspect, John Spencer, touched while in Troop C headquarters during booking. He attached the fingerprints to evidence cards and later claimed that he had pulled the fingerprints from the scene of the murder. The forged evidence was used during trial and John Spencer was sentenced to 50 years to life in prison† (New York Times, 1993. ) It is â€Å"important that the officials within the justice system be held to higher standards in moral and ethical behavior. As the law enforcement arm of our legal system, Law enforcement officers take a front-line role in activating the laws our legislators create. If they arrest those they know are innocent, create fake evidence against the innocent, or otherwise undo the requirement of honest and honorable behavior, they undo the processes of the justice system from the very beginning, and therefore corrupt the whole process† (RealPolice, 2000). Honesty and justice are the entities that we depend on to distinguish between right and wrong, good and evil, legal and illegal. We depend on justice to keep us safe from evil and allow us to live our lives in harmony and peace, without chaos. According to John Locke (1690), we, as citizens, give up sovereignty to a government or other authority in order to receive or maintain social order through the rule of law under the Social Contract Theory. Crime control and due process models are â€Å"two competing systems of values operating within criminal justice, the tension between the two accounts for the conflict and disharmony that now is observable in the criminal justice system† (Hoffman, p. 12, 2000). Although they are both different systems both impact the way our judicial system is ran. â€Å"When comparing the due process and crime control models, it should be kept in mind that proponents of both models embrace constitutional values† (Hoffman, p. 11, 2000). The concept of Crime Control Model is to get the criminal off the street and to protect the innocent. â€Å"The Crime Control Model could perhaps be seen in a negative mannerism due to the fact that it assumes the alleged criminal is guilty even before they step foot into the court, this model supports those actions of the police and prosecutors to the fullest extent† (Zalman, p. 3, 2002). This â€Å"model moves the alleged criminal through the system with the forethought that everyone is guilty until proven otherwise, and also limits the amount of plea-bargaining and appeals. The main objective of the criminal justice process should be to discover the truth or to establish the guilt of the accused† (Hof fman, p. 11, 2000). The â€Å"Due Process Model resembles an obstacle course† (Zalman, p. 13, 2002). â€Å"This system is far more realistic in the fact that it leaves room for error. It does not automatically assume that the alleged criminal is guilty before the case is proven. This system does not want to risk prosecuting an innocent person† (Zalman, p. 13, 2002) it â€Å"demands the prevention and elimination of mistakes to the extent possible. The Due Process Model is said to be â€Å"suspicious of those who are power hungry and merely looking to convict. The difference between the two models in this sense is that the Crime Control Model is based upon factual guilt and the Due Process Model is based upon legal guilt† (Zalman, p. 14, 2002). â€Å"Due Process is also based upon equal treatment of the defendant. The reason that this is believed is because it is felt that errors are the cause for an invalid conviction. While the Crime Control Model strongly contradicts this view it can sometimes hinder a person’s rights within the system† (Zalamn, p. 14, 2002). In the case of Charles Manson, the crime control model, was swift and took the criminal off the streets. â€Å"Manson was found guilty of conspiracy to commit the Tate and LaBianca murders, carried out by members of the group at his instruction. He was convicted of the murders themselves through the joint-responsibility rule, which makes each member of a conspiracy guilty of crimes his fellow conspirators commit in furtherance of the conspiracy's object† (Linder, 2002). The case of â€Å"Roe v. Wade is a good example of the due process model, making it a crime in under Texas law to assist a woman to get an abortion violated her due process rights. The Court held that a woman's right to an abortion fell within the right to privacy protected by the Fourteenth Amendment. The decision gave a woman total autonomy over the pregnancy during the first trimester and defined different levels of state interest for the second and third trimesters† (Oyez Project, 2010). Both models have been opposing each other for years, the crime control model used by law enforcement is based on the assumption that the evidence in a case is reliable and factual not fabricated. Under the due process model the individual charged with a crime will have his or her rights protected To determine that one model is better than the other one would have â€Å"to make a value judgment. Crime control reflects conservative values, whereas due process model reflects liberal values. In my opinion the due process model is unbiased, and follows the principles of the Declaration of Independence† (Hoffman, p. 1, 2000). Too bad the two models cannot come together to form a model that would work for everyone. References (1993) â€Å"Police Investigation Supervisor Admits Faking Fingerprints† The New York Times Retrieved March 7, 2010 from http://www. nytimes. com (2000) â€Å"Honesty in the Justice System† RealPolice Retrieved March 7, 2010 from http://forums. rea lpolice. net Hoffman, D. (2000) â€Å"Great Debate in Criminal Justice: Should the Crime Control Model or the Due Process Model Prevail† Criminal Justice Cliff-Notes pgs. 1-12 Retrieved March 7, 2010 Linder, Doug (2002) â€Å"The Charles Manson (Tate-LaBianca Murder) Trial† UMKC Law Retrieved March 7, 2010 from http://www. wikipedia. com Locke John (1690) â€Å"Two Treatises Government† Project Gutenberg (10th edition)Retrieved March 7, 2010 from http://www. gutenberg. org/dirs/etext05/trgov10h. htm The Oyez Project, â€Å"Roe v. Wade, 410 U. S. 113 (1973)† Retrieved March 7, 2010 from http://oyez. org/cases/1970-1979/1971/1971_70_18 Zalman, M. (2002) â€Å"Analysis of the Crime Control and Due Process Models† Criminal Procedure: Constitution and Society Retrieved March 7, 2010 from http://www. associatedcontent. com

Introduction Statement of the Problem - Free Essay Example

Sample details Pages: 15 Words: 4500 Downloads: 4 Date added: 2017/06/26 Category Statistics Essay Did you like this example? Do remittances affect the consumption pattern of the Filipino households? Objectives The objective of this paper is to formulate structural models to illustrate the change in consumption pattern of the Filipino households. In this study, our aim is to use an advanced econometric approach to find out if there is indeed such change in the consumption pattern of the household receiving remittances as compared to those who only get their income from domestic sources. Don’t waste time! Our writers will create an original "Introduction Statement of the Problem" essay for you Create order Review of Related Literature There are several studies regarding the consumption patterns of household. One of which is the study made by Taylor and Mora (2006), they studied about the effect of migration in reshaping the expenditure of rural households in Mexico. The conclusion that they made is that remittances has positive effects on total expenditures and investment. They also found out that as the remittances of rural household increases, the proportion of the income on consumption decreases (Taylor Mora, 2006). Another one is the study of Rasyad A. Parinduri Shandre M. Thangavelu (2008), wherein they used the Indonesia Family Life Survey data to observe the effect of remittances to the consumption patterns of the Indonesian households. In their study, they used the matching and difference-in-difference matching estimators to observe the relationship. They found out that remittances do not improve the living standard of the households, nor do remittances have an effect on economic development. They used t he education and medical expenditure as indicators of economic development. The major findings that they have are that most of the Indonesian households used the remittances in terms of investing them into luxury goods such as house and jewelries (Parinduri Thangavelu, 2008). Using the same study, we intend to observe the consumption pattern of the households, based not only on the remittances but also to other sources of income. In addition to that, instead of looking at economic development, we intend to look at the consumption goods that households normally consume, and see if there are indeed changes in the consumption patterns of the selected households. Theoretical Framework Engelà ¢Ã¢â€š ¬Ã¢â€ž ¢s Law Methodology and Data In the methodology and data part, our main concern is to find ways to observe the consumption patterns of the Filipino households here in this country. In order to do that, we tried to find a dataset that will explain such relationship. Based from the available datasets here in the country, we would say that the Family Income and Expenditure Survey or the FIES best suits our study. The dataset enlists all the possible consumption goods that were being consumed by the households during a specific year. In addition to that, we can also determine the source of income of the different households that was made available in the dataset. By examining the relationship of consumption and income, we will be able to observe the behavioral aspect of the Filipino householdsà ¢Ã¢â€š ¬Ã¢â€ž ¢ consumption based from the income that they received. Due to the inaccessibility of the latest data, we settled for the 2003 edition. Based on this data, we will be able to observe the impact of the different sources of income to the kind of goods that the Filipino families consume, using an advanced econometric approach called the simultaneous equation model (SEM). After acquiring the right dataset for this study, we must next formulate the different structural equations to illustrate the consumption patterns. In this paper, we have formulated four equations, one of which is based from the Engelà ¢Ã¢â€š ¬Ã¢â€ž ¢s Law, which again, states that when an individualà ¢Ã¢â€š ¬Ã¢â€ž ¢s income increases, his/her percentage of consumption decreases (Engelà ¢Ã¢â€š ¬Ã¢â€ž ¢s Law, n.d.). As for the other three other equations which are mainly composed of different sources of income, mainly wages, domestic source, and foreign source, we have used other studies conducted by (SOURCE) ,to see what are the factors that affects or determine the different sources of income. After formulating the equations, we decided to use the log-log model for the estimation, simply because our study aims to observe the income elasticity of the different goods. With the use of the log-log model, we will be able to determine the elasticity of the different consumption goods, by just looking at their respective estimated coefficients. Another reason why we chose the log-log model is because of the limited information about the domestic and foreign source of income in the FIES data. There are several households in the data who either do not receive domestic or foreign source of income, or the data gatherers failed to obtain these data from the respective respondents. By using the log-log model, we will be able to exclude those unrecorded observations, so that the results will be not inconsistent and will not be affected by the people who do not receive income from either domestic or foreign source. After citing the reasons for the construction of the model, next, we will be observing three consumption goods, particularly the total food expenditures, the total non food expenditures, and the tobacco-alcohol consumption. Model 1: Food Consumption Equation 1: Equation 2: Equation 3: Equation 4: Where: food = total food expenditures Condo = domestic source of income Conab = foreign source of income Wage = wages or salaries of the household Wsag = wages or salaries from agricultural activities Wsnag = wages or salaries from non-agricultural activities S1021_age = household head age S1041_hgc = household head highest grade completed S1101_employed = total number of family employed with pay Lc10_conwr = contractual worker indicator In order to observe the consumption patterns of the Filipino household based from the different sources of income, we will be modifying the first equation of the model, by replacing one good to the other good, while maintaining the same structural forms. For example, in the initial first model, we have chosen food expenditure as our first consumption good. Later on, we will be observing other consumption goods such as non food expenditure, and alcoholic tobacco-alcohol consumption, and we will replace the food consumption with these other goods. This is because consumption goods are all affected by the income, and we have chosen the different income sources based from the availability of the FIES data, which was released on 2003. A-priori expectation insert a-priori here Given the interrelationship of the equations, it seems like we have to solve the equations simultaneously to estimate for the unknown variables. Before we can use the simultaneous equation model (SEM) approach, there are several identification problems that we must solve in order to know whether SEM is an appropriate method or not. According to Gujarati and Porter (2009), the identification problem process consists of the following tests: a. order and rank condition, b. Hausman specification test, which is also known as the simultaneity test, and c. exogeneity test. Identification Problem Order and rank condition Before we proceed with the order and rank condition, we must first define the different variables that we will be using in order to test whether the equations are under-identified, exactly identified or over-identified. Legend: M à ¯Ã†â€™Ã‚  number of endogenous variables in the model m à ¯Ã†â€™Ã‚  number of endogenous variables in the equation K à ¯Ã†â€™Ã‚  number of exogenous/predetermined variables in the model k à ¯Ã†â€™Ã‚  number of exogenous/predetermined variables in the equation Order Condition The order condition is a necessary but not sufficient condition for identification (Gujarati and Porter, 2009). This test is used to see whether an equation is identified by comparing the number of excluded exogenous/predetermined variables in a given equation with the number of endogenous variables in the equation less one. There will be three instances where we can determine if the equation is identified or not. First, if K-k (number of excluded predetermined variables in the equation) m-1 (number of endogenous variables less one), then the equation is deemed to be under-identified. According to Gujarati and Porter, K-k must be greater than or equal to m-1, for the order condition to be satisfied. In the first model, there are four endogenous variables namely lnfood, lnwages, lncondo, and lnconab (M=4). And there are also six exogenous variables in the equation which are the variables that were not named (K=6). With that, the order condition of the food consumption is illustrated below: Equation K-k m-1 Conclusion Lnfood 6 3 Over Lnwages 4 0 Over Lncondo 2 0 Over Lnconab 2 0 Over In the first case, all the equations are considered to be over-identified, simply because K-k m-1. In the order condition, we have concluded that the model is identified. However, the order condition is not sufficiently enough to justify whether an equation is identified or not, that is why there is another condition that must be satisfied before we can proceed to the estimation process, which is the rank condition. Rank Condition The rank condition is a necessary and sufficient condition for identification. In order to satisfy the rank condition, à ¢Ã¢â€š ¬Ã…“there must be at least one nonzero determinant of order (M-1) (M-1) can be constructed from the coefficients of the variables excluded from that particular equation but included in the other equations of the modelà ¢Ã¢â€š ¬?(Gujarati and Porter, 2009). Ys Xs Eq. Food Wages condo conab 1 wssag wsnag hh_age hh_hgc employed conwr lnfood 1 0 0 0 0 0 0 lnwages 0 1 0 0 0 0 0 0 Lncondo 0 0 1 0 0 0 Lnconab 0 0 0 1 0 0 We simplify the variableà ¢Ã¢â€š ¬Ã¢â€ž ¢s notation, but ità ¢Ã¢â€š ¬Ã¢â€ž ¢s basically the same as the variables in the model, it only lacks the à ¢Ã¢â€š ¬Ã…“lnà ¢Ã¢â€š ¬? in some variables, and some variablesà ¢Ã¢â€š ¬Ã¢â€ž ¢ descriptions are shortened. We can observed that the (M-1) x (M-1), which in this case is 3 x 3 matrices, have at least one nonzero determinant, therefore the rank condition is satisfied. We can now proceed to the other identification test. Hausman specification test The Hausman specification test is to test whether the equations exhibits simultaneity problem or not. According to Gujarati and Porter (2009), if there is not simultaneity problem, then OLS is BLUE (best linear unbiased estimator). But if there is simultaneity problem, then OLS is not blue, because the estimated results will be bias and inconsistent. With that, we have to use the different estimation techniques of the SEM in order to regress the given equations. The Hausman specification test involves the following process: First, we regress an endogenous variable with respect to all of the exogenous/predetermined variables in the system, after which we obtain the value of the residual, in which it is the predictedThe second step is to regress the endogenous variable with respect to the other endogenous variables plus the predicted . If the is statistically significant, this means that we have all the evidence to reject the null hypothesis, which states that there is no simultaneity bias in the model. But if it is insignificant, we have no evidence to reject the null hypothesis, and if that happens, there is no simultaneity problem. The variable that exhibits no simultaneity bias should not be treated as an endogenous variable. (Gujarati and Porter, 2009) Dependent variable: lnwages P-values Independent variables: lncondo 0.370 lnconab 0.014 uhat 0.000 For the simultaneity test in the first model, we follow the steps in the Hausman specification test. After that, we observed the predicted uhat in this regression and we can see that the predicted uhat here is 0.000. This means that the null hypothesis is rejected, and there exist simultaneity bias in the first model, therefore we should use other estimation techniques other than OLS, to produce unbiased and consistent estimates. Exogeneity test After the simultaneity test, we must also test for the other exogenous/predetermined variables, to check whether these variables are truly exogenous or not. The process is similar to the Hausman specification test, but instead of regressing the endogenous variables, we regress each exogenous/predetermined variable with respect to the . If the is statistically significant, then we have to reject the null hypothesis that it is truly an exogenous variable. But if the p-value of the is 1.000, this means that we have no evidence to reject the null hypothesis, and we conclude that the corresponding variables are truly exogenous or truly predetermined variables. Exogenous variables à ¢Ã¢â€š ¬Ã¢â‚¬Å" 2nd equation Resulting p-values for uhat Lnwsag 1.000 lnwsnag 1.000 Exogenous variables à ¢Ã¢â€š ¬Ã¢â‚¬Å" 3nd equation Resulting p-values for uhat s1021_age 1.000 s1041_hgc 1.000 s1101_employed 1.000 lc10_conwr 1.000 Exogenous variables à ¢Ã¢â€š ¬Ã¢â‚¬Å" 4nd equation Resulting p-values for uhat s1021_age 1.000 s1041_hgc 1.000 s1101_employed 1.000 lc10_conwr 1.000 Based from the table given above, each exogenous variable is regressed against the predict uhat and looking at the respective p-values, which are all 1.000. This means that we have no evidence to reject that these variables are indeed truly exogenous variables in each of the equations. Model 2: Non Food Consumption Equation 1: Equation 2: Equation 3: Equation 4: Where: nonfood = total non food expenditure In model 2, we basically changed the total food expenditure with the total non food expenditure. Before we can regress the model, this model should also undergo series of identification problem process to see if whether the model is identified or not. We will also test if the nonfood expenditure model exhibits simultaneity bias and if all of its exogenous variables are truly exogenous. Order and Rank Condition Order Condition Equation K-k m-1 Conclusion Lnnonfood 6 3 Over Lnwages 4 0 Over Lncondo 2 0 Over Lnconab 2 0 Over Similar to the food consumption order condition, the non food consumption is also identified based on the order condition. All equations are concluded to be over-identified; therefore we can say that the model is identified. But again, we must use the rank condition to further validate if the equations are truly identified or not. Rank Condition Ys Xs Eq. nonfood wages condo conab 1 wssag wsnag hh_age hh_hgc employed conwr lnnonfood 1 0 0 0 0 0 0 lnwages 0 1 0 0 0 0 0 0 lncondo 0 0 1 0 0 0 lnconab 0 0 0 1 0 0 Based from the sub 33 matrices, we can say that there exists at least one nonzero determinant in the equation, therefore rank condition is satisfied. This means that the equations are identified. Hausman specification test Dependent variable: lnwages P-values Independent variables: lncondo 0.533 lnconab 0.011 uhat2 0.001 For the simultaneity test in model 2, we can see that uhat2 is statistically significant, meaning there exists a simultaneity bias in the model. Therefore we must use the SEM estimation techniques similar to model 1, to estimate the impact of income and consumption goods. Exogeneity test Exogenous variables à ¢Ã¢â€š ¬Ã¢â‚¬Å" 2nd equation Resulting p-values for uhat2 Lnwsag 1.000 lnwsnag 1.000 Exogenous variables à ¢Ã¢â€š ¬Ã¢â‚¬Å" 3nd equation Resulting p-values for uhat2 s1021_age 1.000 s1041_hgc 1.000 s1101_employed 1.000 lc10_conwr 1.000 Exogenous variables à ¢Ã¢â€š ¬Ã¢â‚¬Å" 4nd equation Resulting p-values for uhat2 s1021_age 1.000 s1041_hgc 1.000 s1101_employed 1.000 lc10_conwr 1.000 Similar to the food consumption model, the exogenous variables in the nonfood model are truly exogenous, since all the resulting p-values for uhat2, are all 1.000. Model 3: Tobacco-Alcohol Consumption Equation 1: Equation 2: Equation 3: Equation 4: Where: at = tobacco-alcohol consumption The same process in model 2 was made here in model 3, we now check for the identification problems for the tobacco-alcohol consumption Order and Rank Condition Order Condition Equation K-k m-1 Conclusion Lnat 6 3 Over Lnwages 4 0 Over Lncondo 2 0 Over Lnconab 2 0 Over Order condition is satisfied here in model 3, since all of the equations are concluded to be over-identification. We now proceed to the rank condition to check if the equations are ultimately identified. Rank Condition Ys Xs Eq. at wages condo conab 1 wssag wsnag hh_age hh_hgc employed conwr lnat 1 0 0 0 0 0 0 lnwages 0 1 0 0 0 0 0 0 lncondo 0 0 1 0 0 0 lnconab 0 0 0 1 0 0 Rank condition is satisfied because there is at least one nonzero determinant here in the sub 33 matrices. Hausman specification test Dependent variable: lnwages P-values Independent variables: lncondo 0.911 lnconab 0.063 uhat3 0.003 In model 3, there is no simultaneity problem because uhat3 is statistically significant. Therefore, we have all the evidence to reject the null hypothesis that there is no simultaneity bias in the equation. The same procedure as for food and nonfood model, we will be using the different estimation techniques to estimate these unknown variables. Estimation Techniques and Results Estimation Techniques After the identification problems of the simultaneous equation problem, we proceed to the estimation techniques. As discussed by Gujarati and Porter (2009), they provided three estimation techniques in order to solve for SEM, namely the ordinary least squares (OLS), indirect least squares (ILS), and the two-stage least squares (2SLS). The OLS is used for the recursive, triangular, or causal models (Gujarati and Porter, 2009). Meanwhile, the ILS focuses more on the reduced form of the simultaneous equations, wherein there exists only one endogenous variable in the reduced form equation and it is expressed in terms of all existing exogenous/predetermined variables in the model. It is estimated through the OLS approach, and this method best suits if the model is exactly identified (Gujarati and Porter, 2009). Lastly, the 2SLS approach, wherein the equations are estimated simultaneously. Unlike ILS, 2SLS can used to estimate exact and over-identified equations. (Gujarati and Porter, 2009 ) The three approaches discussed by Gujarati and Porter (2009) are all based from the single equation approach. If there are CLRM violations such as autocorrelation and heteroscedasticity in the models, we must use the system approach, particularly the three-stage least squares (3SLS), to correct these violations. The only drawback of the 3SLS method is that if any errors in one equation will affect the other equations. Ordinary Least Squares (OLS) Since all three models suffer from simultaneity bias, we will not use the OLS in this paper. This is because if we used the OLS in estimating the equation which there exist simultaneity bias, the results will be biased and inconsistent. Therefore, OLS is not a good estimator for the three models. Indirect Least Squares (ILS) Food consumption model reduced form: Where: | Nonfood model reduced form: Where: | Tobacco-Alcohol model reduced form: Where: | We will not estimate anymore the coefficient for the ILS, because our main goal is to observe the relationship of consumption goods with the different sources of income and not the other determinants of the different sources of income. The ILS results will not yield standard error for the structural coefficients; therefore it will be hard to obtain the values of the structural coefficients. In addition to that, all of our equations are over-identified, therefore ILS is an inappropriate method to estimate the coefficients. Two-stage least squares (2SLS) Consumption Goods Food (948 obs) Non Food (1078 obs) Tobacco-Alcohol (634 obs) 1st Equation Coefficients (P-value) Coefficients (P-value) Coefficients (P-value) constant 6.428484 (0.000) 1.401963 (0.070) 12.94298 (0.001) lnwages 0.2235283 (0.000) 0.2880426 (0.000) 0.7781965 (0.000) lncondo 0.0223739 (0.622) 0.2036453 (0.013) -1.47202 (0.000) lnconab 0.205797 (0.001) 0.5110999 (0.000) 0.6098058 (0.121) 2nd Eq. lnwages Coefficients (P-value) Coefficients (P-value) Coefficients (P-value) constant 2.122649 (0.000) 2.122649 (0.000) 1.884011 (0.000) lnwsag 0.3611279 (0.000) 0.3611279 (0.000) 0.42199 (0.000) lnwsnag 0.5175117 (0.000) 0.5175117 (0.000) 0.483135 (0.000) 3rd Eq. lncondo Coefficients (P-value) Coefficients (P-value) Coefficients (P-value) constant 7.75861 (0.000) 7.75861 (0.000) 7.887869 (0.000) s1021_age -0.0003422 (0.903) -0.0003422 (0.903) 0.0014345 (0.720) s1041_hgc 0.0346237 (0.000) 0.0346237 (0.000) 0.1302147 (0.000) s1101_employed -0.023387 (0.450) -0.023387 (0.450) -0.0601213 (0.111) lc10conwr 0.1583353 (0.345) 0.1583353 (0.345) 0.0871853 (0.710) 4th Eq. lnconab Coefficients (P-value) Coefficients (P-value) Coefficients (P-value) constant 10.39914 (0.000) 10.39914 (0.000) 9.947326 (0.000) s1021_age 0.004519 (0.169) 0.004519 (0.169) 0.0145833 (0.002) s1041_hgc 0.0210221 (0.000) 0.0210221 (0.000) 0.150857 (0.000) s1101_employed 0.0420871 (0.245) 0.0420871 (0.245) 0.0273189 (0.541) lc10conwr -0.6848394 (0.000) -0.6848394 (0.000) -0.7780885 (0.005) Since FIES is a cross sectional data, the model maybe exposed to the violations of multicollinearity and heteroscedasticity. As shown in the appendix1, under the CLRM violations, there exists no multicollinearity in the equations, but there exists heteroscedasticity three out of four equations in the model. The only way to correct for the heteroscedasticity problem is by estimating the simultaneous equations using the three-stage least squares method, which is considered to be full information approach. Three-stage least squares (3SLS) Consumption Goods Food (948 obs) Non Food (1078 obs) Tobacco-Alcohol (634 obs) 1st Equation Coefficients (P-value) Coefficients (P-value) Coefficients (P-value) constant 6.383871 (0.000) 0.7926094 (0.289) 18.63624 (0.000) lnwages 0.2224267 (0.000) 0.2831109 (0.000) 0.7374008 (0.000) lncondo 0.0245077 (0.582) 0.3151916 (0.000) -2.405262 (0.000) lnconab 0.2101956 (0.001) 0.4810778 (0.000) 0.9024638 (0.020) 2nd Eq. lnwages Coefficients (P-value) Coefficients (P-value) Coefficients (P-value) constant 2.142826 (0.000) 2.126479 (0.000) 1.895235 (0.000) lnwsag 0.3560053 (0.000) 0.3594587 (0.000) 0.419183 (0.000) lnwsnag 0.5203181 (0.000) 0.5187091 (0.000) 0.4846674 (0.000) 3rd Eq. lncondo Coefficients (P-value) Coefficients (P-value) Coefficients (P-value) constant 7.66644 (0.000) 7.420188 (0.000) 8.252266 (0.000) s1021_age 0.0000462 (0.987) -0.0005333 (0.840) 0.0042572 (0.224) s1041_hgc 0.0344578 (0.000) 0.0327889 (0.000) 0.0972984 (0.002) s1101_employed -0.0109756 (0.720) 0.030168 (0.302) -0.0811008 (0.009) lc10conwr 0.173369 (0.296) 0.234941 (0.151) -0.0362562 (0.860) 4th Eq. lnconab Coefficients (P-value) Coefficients (P-value) Coefficients (P-value) constant 9.635422 (0.000) 9.760654 (0.000) 9.899007 (0.000) s1021_age 0.0025551 (0.394) 0.0034051 (0.195) 0.0140427 (0.003) s1041_hgc 0.0212975 (0.000) 0.0171248 (0.000) 0.1589354 (0.000) s1101_employed 0.1534522 (0.000) 0.1464836 (0.000) 0.0291422 (0.510) lc10conwr -0.484862 (0.011) -0.5302148 (0.004) -0.761339 (0.006) By using the 3SLS, the models are now corrected and it is free from any CLRM violations. Therefore, the table shown above is already the final model of estimation, and we can now interpret the results equation per equation basis. Check for equality and unit elasticity As indicated in the appendices (last part), we also check if there lnwages and lnconab in the food consumption equation are indeed equal. We used the test command in STATA, to see if the two variables are equal, by looking at its p-value. The resulting p-value of the test is 0.8614, meaning we have no evidence to reject the null hypothesis that the two variablesà ¢Ã¢â€š ¬Ã¢â€ž ¢ coefficients are equal. We made the same process for the lnwages and lncondo in the nonfood consumption equation, and the resulting p-value of the test is 0.6846, which means that lnwages and lncondo are also equal in the estimation. Aside from the check for equality, we also check if the lnconabà ¢Ã¢â€š ¬Ã¢â€ž ¢s income elasticity to tobacco-alcohol consumption is equal to 1. The resulting p-value for the test is 0.8007, which means that the income elasticity of lnconab to tobacco-alcohol consumption is 1, meaning it is unit elastic. Results Model 1 à ¢Ã¢â€š ¬Ã¢â‚¬Å" Food Consumption In the first model, which is the total food expenditure model, the variable domestic source of income in the 1st equation is considered to be statistically insignificant. This means that it will be meaningless to interpret the results of that particular variable. As for wages and foreign source of income, we can see that the two coefficients are very similar, which means that for every one percent increase in wages and foreign source of income, food consumption increases by 0.22 and 0.21 percent respectively. The results are clearly consistent with Engelà ¢Ã¢â€š ¬Ã¢â€ž ¢s Law of food consumption that the proportion of food expenditure decrease as an individualà ¢Ã¢â€š ¬Ã¢â€ž ¢s income increases. For the 2nd equation, which is the wage equation, the result shows that the impact of non-agricultural activities is greater compared to agricultural activities. This is consistent with our a-priori expectation of one having a larger impact than the other. In reality, we can see that non-agricultural activities result to higher income due to its high value added products that it produces. The higher the value added the work is, the higher the changes are that wages or salaries received will be also higher. For the 3rd and 4th equation, which is considered to be similar except for the source of income where it comes from, the results show that only highest grade completed is considered to be statistically significant in the 3rd equation, while in the 4th equation, the household headà ¢Ã¢â€š ¬Ã¢â€ž ¢s age is the only one which is statistically insignificant. For the domestic source of income, we can observed that people who has a larger share of the wages or salaries in the company, have typically higher educational attainment compared to those who have lower educational attainment. The result of the 3rd equation maybe attributed to that factor. For the 4th equation, it is the same explanation for the highest grade completed by the household head as in the 3rd equation. While for the total family members employed with pay, it has a positive relationship, simply because if there are larger number of family members who are working and receiving salaries, the cumulative source of income wi ll be larger, compared to those families who have fewer number of family members working with pay. The last variable in the 4th equation, which is the dummy variable contract worker, we can see in the result that if an individual is a contract worker, generally, that individual will receive lower wages compared to those regular employees. This is because contractual workers are given limited period of time to work for certain companies, and companies hire contractual workers for short term uses. With that, companies usually pay lower amount of wages to these short term workers. Model 2 à ¢Ã¢â€š ¬Ã¢â‚¬Å" Non food consumption For the 2nd model, the nonfood consumption model, all the variables in the 1st equation are all statistically significant. The coefficients of wages and domestic source of income are similar, but there is a disparity between these two variables and the foreign source of income, which resulted to a higher coefficient. The higher coefficient means that the foreign source of income is more sensitive to nonfood consumption compared to the initial two variables à ¢Ã¢â€š ¬Ã¢â‚¬Å" wages and domestic income. We can see in the result that a household who receives foreign source of income are more inclined to nonfood expenditures. In reality, we observed that children of the household who rely on foreign source of income or remittances, typically has more gadgets or technological advances stuff compared to the children of the household who rely only to domestic source of income. This may explains that households have larger incomes if they receive remittances compared to those who only rely o n domestic source of income. The results of the 2nd, 3rd, and 4th equations are the same as food consumption model. Therefore, there is no need to interpret the results of these equations, since it has been interpreted in the previous model. Model 3 à ¢Ã¢â€š ¬Ã¢â‚¬Å" Tobacco-Alcohol Consumption The last model, which is the tobacco-alcohol consumption model, the results were a bit unexpected. This is because the domestic source of income is negatively related to the tobacco-alcohol consumption, as compared to wages and foreign source of income, which are positively related to the tobacco-alcohol consumption. This means that households who rely only to domestic source of income have the tendency to reduce their vices as their income increases. While the implication of the positive relationship between foreign source of income and the tobacco-alcohol consumption is that, the long distance relationship between the household heads and the family who stayed in the country may have contributed some psychological factors which lead to the increase in the vices consumption of the households living domestically. Aside from the positive and negative relationship of the variables, we also test the income elasticity of the foreign source of income to the tobacco-alcohol consumption. Based from the test results, it shows that the foreign income elasticity of the good is 1, which indicates that tobacco-alcohol consumption is unit elastic in the case of remittances. Conclusion and Recommendation Conclusion Based from the results of the three consumption goods, food consumption pattern remains unchanged regardless of the source of income. As for the nonfood consumption pattern, the foreign source of income became more sensitive in terms of having higher income elasticity as compared to wages and domestic source of income. This means that people who are receiving remittances or other form of foreign source of income, are more inclined to purchase nonfood items, this can be in the form of gadgets, household equipment, and the like. As for the tobacco and alcohol consumption, we can see a significant change in the consumption pattern. When a household rely only on domestic source of income, they are discourage to consume these vices as their income increases. But when a household receives remittances, they tend to consume more tobacco and alcohol as indicated in the result. The reason behind this kind of relationship can be attributed to the psychological aspect of the Filipino households. Our overall conclusion in this paper is that remittances have contributing factors in changing the consumption patterns of the Filipino households. As indicated in the introduction, since the year 2003 up until the present, the OFW remittances has more than doubled, which indicates that the consumption pattern of these households who are receiving remittances might have some changes in their way of consuming different goods or services. Recommendation Our recommendation in this study is to use the latest version of the FIES to see whether the food and nonfood consumption patterns have changed. In addition to that, the latest FIES data might have more observations containing the domestic and foreign income information that might actually improve the results of the study.