Effect of Remittances on Household Consumption Patterns

Effect of Remittances on Household Consumption Patterns 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. 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 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) 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 ho Effect of Remittances on Household Consumption Patterns Effect of Remittances on Household Consumption Patterns 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. 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 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) 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 ho

Gender Bias in Education Essay

â€Å"Sitting in the same classroom, reading the same textbook, listening to the same teacher, boys and girls receive very different educations.† (Sadker, 1994) In fact, upon entering school, girls perform equal to or better than boys on nearly every measure of achievement, but by the time they graduate high school or college, they have fallen behind. (Sadker, 1994) However, discrepancies between the performance of girls and the performance of boys in elementary education leads some critics to argue that boys are being neglected within the education system: Across the country, boys have never been in more trouble: They earn 70 percent of the D’s and F’s that teachers dole out. They make up two thirds of students labeled â€Å"learning disabled.† They are the culprits in a whopping 9 of 10 alcohol and drug violations and the suspected perpetrators in 4 out of 5 crimes that end up in juvenile court. They account for 80 percent of high school dropouts and attention deficit disorder diagnoses. (Mulrine, 2001) This performance discrepancy is notable throughout Canada. In Ontario, Education Minister Janet Ecker said that the results of the standardized grade 3 and grade 6 testing in math and reading showed, â€Å"†¦persistent and glaring discrepancies in achievements and attitudes between boys and girls.† (O’Neill, 2000) In British Columbia, standardized testing indicates that girls outperform boys at all levels of reading and writing and in Alberta testing shows that girls, â€Å"†¦significantly outperform boys on reading and writing tests, while almost matching them in math and science.† (O’Neill, 2000) However, the American Association of University Women published a report in 1992 indicating that females receive less attention from teachers and the attention that female students do receive is often more negative than attention received by boys. (Bailey, 1992) In fact, examination of the socialization of gender within schools and evidence of a gender biased hidden curriculum demonstrates that girls are shortchanged in the classroom. Furthermore, there is significant research indicating steps that can be taken to minimize or eliminate the gender bias currently present in our education system. The socialization of gender within our schools assures that girls are made aware that they are unequal to boys. Every time students are seated or lined up by gender, teachers are affirming that girls and boys should be treated differently. When an administrator ignores an act of sexual harassment, he or she is allowing the degradation of girls. When different behaviors are tolerated for boys than for girls because ‘boys will be boys’, schools are perpetuating the oppression of females. There is some evidence that girls are becoming more academically successful than boys, however examination of the classroom shows that girls and boys continue to be socialized in ways that work against gender equity. Teachers socialize girls towards a feminine ideal. Girls are praised for being neat, quiet, and calm, whereas boys are encouraged to think independently, be active and speak up. Girls are socialized in schools to recognize popularity as being important, and learn that educational performance and ability are not as important. â€Å"Girls in grades six and seven rate being popular and well-liked as more important than being perceived as competent or independent. Boys, on the other hand, are more likely to rank independence and competence as more important.† (Bailey, 1992) This socialization of femininity begins much earlier than the middle grades. At very early ages, girls begin defining their femininities in relation to boys. One study of a third grade classroom examined four self-sorted groups of girls within the classroom: the nice girls, the girlies, the spice girls and the tomboys. Through interviews researcher Diane Reay found that ‘nice girls’ was considered a derogatory term indicating, â€Å"†¦an absence of toughness and attitude.† (Reay, 2001) Furthermore, the girlies were a group of girls who focused their time on flirting with and writing love letters to boys, the tomboys were girls who played sports with the boys, and the spice girls espoused girl-power and played ‘rate-the-boy’ on the playground. Reay’s research shows that each of the groups of girls defined their own femininities in relation to boys. (2001) The Reay study further demonstrates how socialization of girls occurs at the school level by tolerating different behaviors from boys than from girls. Assertive behavior from girls is often seen as disruptive and may be viewed more negatively by adults. In Reay’s study, the fact that the spice girls asserted themselves in ways contrary to traditional femininity caused them to be labeled by teachers as â€Å"real bitches†. (2001) This reinforces the notion that â€Å"†¦girls’ misbehavior to be looked upon as a character defect, whilst boys’ misbehavior is viewed as a desire to assert themselves.† (Reay, 2001) A permissive attitude towards sexual harassment is another way in  which schools reinforce the socialization of girls as inferior. â€Å"When schools ignore sexist, racist, homophobic, and violent interactions between students, they are giving tacit approval to such behaviors.† (Bailey, 1992) Yet boys are taunted for throwing like a girl, or crying like a girl, which implies that being a girl is worse than being a boy. According to the American Association of University Women Report, â€Å"The clear message to both boys and girls is that girls are not worthy of respect and that appropriate behavior for boys includes exerting power over girls — or over other, weaker boys.† (Bailey, 1992) Clearly the socialization of gender is reinforced at school, â€Å"Because classrooms are microcosms of society, mirroring its strengths and ills alike, it follows that the normal socialization patterns of young children that often lead to distorted perceptions of gender roles are reflected in the classrooms.† (Marshall, 1997) Yet gender bias in education reaches beyond socialization patterns, bias is embedded in textbooks, lessons, and teacher interactions with students. This type of gender bias is part of the hidden curriculum of lessons taught implicitly to students through the every day functioning of their classroom. In Myra and David Sadker’s research, they noted four types of teacher responses to students: teacher praises, providing positive feedback for a response; teacher remediates, encouraging a student to correct or expand their answer; teacher criticizes, explicitly stating that the answer is incorrect; teacher accepts, acknowledging that a student has responded. The Sadkers found that boys were far more likely to receive praise or remediation from a teacher than were girls. The girls were most likely to receive an acknowledgement response from their teacher. (Sadker, 1994) These findings are confirmed by a 1990 study by Good and Brophy that â€Å"†¦noted that teachers give boys greater opportunity to expand ideas and be animated than they do girls and that they reinforce boys more for general responses than they do for girls.† (Marshall, 1997) Beyond teacher responses, special services in education appear to be applied more liberally to boys than to girls. Research shows that boys are referred for testing for gifted programs twice as often as girls, which may be because, â€Å"†¦giftedness is seen as aberrant, and girls strive to conform.† (Orenstein, 1994) Boys represent more than two-thirds of all students in special education programs and there is a higher the proportion of male  students receiving diagnoses that are considered to be subjective. While medical reports indicate that learning disabilities occur in nearly equal numbers of in boys and girls, it may be the case that, â€Å"Rather than identifying learning problems, school personnel may be mislabeling behavioral problems. Girls who sit quietly are ignored; boys who act out are placed in special programs that may not meet their needs.† (Bailey, 1992) Gender bias is also taught implicitly through the resources chosen for classroom use. Using texts that omit contributions of women, that tokenize the experiences of women, or that stereotype gender roles, further compounds gender bias in schools’ curriculum. While research shows that the use of gender-equitable materials allows students to have more gender-balanced knowledge, to develop more flexible attitudes towards gender roles, and to imitate role behaviors contained in the materials (Klein, 1985) schools continue to use gender-biased texts: Researchers at a 1990 conference reported that even texts designed to fit within the current California guidelines on gender and race equity for textbook adoption showed subtle language bias, neglect of scholarship on women, omission of women as developers of history and initiators of events, and absence of women from accounts of technological developments. (Bailey, 1992) Clearly the socialization of gender roles and the use of a gender-biased hidden curriculum lead to an inequitable education for boys and girls. What changes can be made to create a more equitable learning environment for all children? First, teachers need to be made aware of their gender-biased tendencies. Next, they need to be provided with strategies for altering the behavior. Finally, efforts need to be made to combat gender bias in educational materials. A study by Kelly Jones, Cay Evans, Ronald Byrd, and Kathleen Campbell (2000) used analysis of videotaped lessons in order to introduce teachers to their own gender-biased behavior. Requiring in-service programs to address gender bias in the classroom will make teachers more aware of their own behaviors: â€Å"As a teacher, I was struck by the Sadkers’ research on classroom exchanges and was forced to acknowledge the disproportionate amount of time and energy, as well as the different sorts of attention, I give to male students.† (McCormick, 1995) Once teachers have recognized their gender-biased behaviors, they need to be provided with resources to help them change. In their study focusing on how the effects of  a gender resource model would affect gender-biased teaching behaviors, Jones, Evans, Burns, and Campbell (2000) provided teachers with a self-directed module aimed at reducing gender bias in the classroom. The module contained research on gender equity in the classroom, specific activities to reduce stereotypical thinking in students, and self-evaluation worksheets for teachers. The findings from this study support the hypothesis that â€Å"†¦female students would move from a position of relative deficiency toward more equity in total interactions†¦.† (Jones, 2000) This demonstrates that teachers who are made aware of their gender-biased teaching behaviors and then provided with strategies and resources to combat bias are better able to promote gender equity in their classrooms. However, beyond changing their own teaching behaviors, teachers need to be aware of the gender bias imbedded in many educational materials and texts and need to take steps to combat this bias. Curriculum researchers have established six attributes that need to be considered when trying to establish a gender-equitable curriculum. Gender-fair materials need to acknowledge and affirm variation. They need to be inclusive, accurate, affirmative, representative, and integrated, weaving together the experiences, needs, and interests of both males and females. (Bailey, 1992) â€Å"We need to look at the stories we are telling our students and children. Far too many of our classroom examples, storybooks, and texts describe a world in which boys and men are bright, curious, brave, inventive, and powerful, but girls and women are silent, passive, and invisible.† (McCormick, 1995) Furthermore, teachers can help students identify gender-bias in texts and facilitate critical discussions as to why that bias exists. Gender bias in education is an insidious problem that causes very few people to stand up and take notice. The victims of this bias have been trained through years of schooling to be silent and passive, and are therefore unwilling to stand up and make noise about the unfair treatment they are receiving. â€Å"Over the course of years the uneven distribution of teacher time, energy, attention, and talent, with boys getting the lion’s share, takes its toll on girls.† (Sadker, 1994) Teachers are generally unaware of their own biased teaching behaviors because they are simply teaching how they were taught and the subtle gender inequities found in teaching materials are often overlooked. Girls and boys today are receiving separate and unequal educations due to the gender  socialization that takes place in our schools and due to the sexist hidden curriculum students are faced with every day. Unless teachers are made aware of the gender-role socialization and the biased messages they are unintentionally imparting to students everyday, and until teachers are provided with the methods and resources necessary to eliminate gender-bias in their classrooms, girls will continue to receive an inequitable education. Departments of education should be providing mandatory gender-equity resource modules to in-service teachers, and gender bias needs to be addressed with all pre-service teachers. Educators need to be made aware of the bias they are reinforcing in their students through socialization messages, inequitable division of special education services, sexist texts and materials, and unbalanced time and types of attention spent on boys and girls in the classroom. â€Å"Until educational sexism is eradicated, more than half our children will be shortchanged and their gifts lost to society.† (Sadker, 1994) References  Bailey, S. (1992) How Schools Shortchange Girls: The AAUW Report. New York, NY: Marlowe & Company. Jones, K., Evans, C., Byrd, R., Campbell, K. (2000) Gender equity training and teaching behavior. Journal of Instructional Psychology, 27 (3), 173-178. Klein, S. (1985) Handbook for Achieving Sex Equity Through Education. Baltimore, MD: The Johns Hopkins University Press. Marshall, C.S. & Reihartz, J. (1997) Gender issues in the classroom. Clearinghouse, 70 (6), 333-338. McCormick, P. (1995) Are girls taught to fail? U.S. Catholic, 60, (2), 38-42. Mulrine, A. (2001) Are Boys the Weaker Sex? U.S. News & World Report, 131 (4), 40-48. O’Neill, T. (2000) Boys’ problems don’t matter. Report/ Newsmagazine (National Edition), 27 (15), 54-56. Orenstein, P. (1994) Schoolgirls: Young Women, Self-Esteem and the Confidence Gap. New York, NY: Doubleday. Reay, D. (2001) ‘Spice girls’, ‘Nice Girls’, ‘Girlies’, and ‘Tomboys†; gender discourses. Girls’ cultures and femininities in the primary classroom. Gender and Education, 13 (2), 153-167. Sadker, D., Sadker, M. (1994) Failing at Fairness: How Our Schools Cheat Girls. Toronto, ON: Simon & Schuster Inc.

Guidance and Counselling Thesis

Comprehensive Guidance Programs That Work II Norman Gysbers and Patricia Henderson A Model Comprehensive Guidance Program Chapter 1 Norman C. Gysbers The Comprehensive Guidance Program Model described in this chapter had its genesis in the early 1970s. In 1972, the staff of a federally funded project at the University of Missouri-Columbia conducted a national conference on guidance and developed a manual to be used by state guidance leaders as a guide to developing their own manuals for state and local school district use. The manual was published in early 1974 and provided the original description of the Comprehensive Guidance Program Model. From the 1940s to the 1970s, the position orientation to guidance dominated professional training and practice in our schools. The focus was on a position (counselor) and a process (counseling), not on a program (guidance). Administratively, guidance, with its position orientation, was included in pupil personnel services along with other such services as attendance, social work, psychological, psychiatric, speech and hearing, nursing, and medical (Eckerson & Smith, 1966). The position orientation had its beginnings when guidance was first introduced in the schools as vocational guidance. As early as 1910, vocational counselors had been appointed in the elementary and secondary schools of Boston, and by 1915 a central office Department of Vocational Guidance had been established with a director, Susan J. Ginn. The vocational counselors in Boston were teachers who took on the work with no financial return and often no relief from other duties (Ginn, 1924). What were the duties of vocational counselors? The Duties of a Vocational Counselor: 1. To be the representative of the Department of Vocational Guidance in the district; 2. To attend all meetings of counselors called by the director of Vocational Guidance; 3. To be responsible for all material sent out to the school by the Vocational Guidance Department; 4. To gather and keep on file occupational information; 5. To arrange with the local branch librarians about shelves of books bearing upon educational and vocational guidance; 6. To arrange for some lessons in occupations in connection with classes in Oral English and Vocational Civics, or wherever principal and counselor deem it wise; 7. To recommend that teachers show the relationship of their work to occupational problems; 8. To interview pupils in grades 6 and above who are failing, attempt to find the reason, and suggest remedy. 9. To make use of the cumulative record card when advising children; 10. To consult records of intelligence tests when advising children; 11. To make a careful study with grade 7 and grade 8 of the bulletin â€Å"A Guide to the Choice of Secondary School†; 12. To urge children to remain in school; 13. To recommend conferences with parents of children who are failing or leaving school; 14. To interview and check cards of all children leaving school, making clear to them the requirements for obtaining working certificates; 15. To be responsible for the filling in of Blank 249 and communicate with recommendations to the Department of Vocational Guidance when children are in need of employment. (Ginn, 1924, pp. 5-7) As more and more positions titled vocational counselor were filled in schools across the country, concern was expressed about the lack of centralization, the lack of a unified program. In a review of the Boston system, Brewer (1922) stated that work was â€Å"commendable and promising† (p. 36). At the same time, however, he expressed concern about the lack of effective centralization: In most schools two or more teachers are allowed part-time for counseling individuals, but there seems to be no committee of cooperation between the several schools, and no attempt to supervise the work. It is well done or indifferently done, apparently according to the interest and enthusiasm of the individual principal or counselor. p. 35) Myers (1923) made the same point when he stated that â€Å"a centralized, unified program of vocational guidance for the entire school of a city is essential to the most effective work† (p. 139). The lack of a centralized and unified program of guidance in the schools to define and focus the work of vocational counselors presented a serious problem. If there was no agreed-upon, centralized structure to organize and direct the work of building-level vocational counselors, then â€Å"other duties as assigned† could become a problem. As early as 1923 this problem was recognized by Myers (1923). Another tendency dangerous to the cause of vocational guidance is the tendency to load the vocational counselor with so many duties foreign to the office that little real counseling can be done. The principal, and often the counselor himself, has a very indefinite idea of the proper duties of this new office. The counselor’s time is more free from definite assignments with groups or classes of pupils than is that of the ordinary teacher. If well chosen he has administrative ability. It is perfectly natural, therefore, for the principal to assign one administrative duty after another to the counselor until he becomes practically assistant principal, with little time for the real work of a counselor. (p. 141) During the 1920s and 1930s, as formal education was being shaped and reshaped as to its role in society, a broader mission for education emerged. Added to the educational mission was a vocational mission. How did education respond to these additional tasks and challenges? One response was to add pupil personnel work to the education system. What was pupil personnel work? According to Myers (1935), â€Å"pupil personnel work is a sort of handmaiden of organized education. It is concerned primarily with bringing the pupils of the community into the educational environment of the schools in such condition and under circumstances as will enable them to obtain the maximum of the desired development† (p. 804). In his article, Myers (1935) contrasted pupil personnel work and personnel work in industry. He then listed eight activities he would include in pupil personnel work and the personnel who would be involved, including attendance officers, visiting teachers, school nurses, school physicians, as well as vocational counselors. In his discussion of all the activities involved in pupil personnel work and the personnel involved, he stated that â€Å"Probably no activity in the entire list suffers so much from lack of a coordinated programs as does guidance, and especially the counseling part of it† (p. 807). In the late 1920s, in response to the lack of an organized approach to guidance, the services model of guidance was initiated to guide the work of individuals designated as counselors. Various services were identified as necessary to provide to students, including the individual inventory service, information service, counseling service, placement service, and follow-up service (Smith, 1951). By this time too, the traditional way of describing guidance as having three aspects – vocational, educational, and personal-social – was well established. Vocational guidance, instead of being guidance, had become only one part of guidance. By the 1940s and 1950s, guidance was firmly established as a part of pupil personnel services with its emphasis on the position of counselor. Beginning in the 1960s, but particularly in the 1970s, the concept of guidance for development emerged. During this period, the call came to re-orient guidance from what had become an ancillary set of services delivered by a person in a position (the counselor) to a comprehensive, developmental program. The call for reorientation came from diverse sources, including a renewed interest in vocational-career guidance (and its theoretical base, career development), a renewed interest in developmental guidance, concern about the efficacy of the prevailing approach to guidance in the school, and concern about accountability and evaluation. The work of putting comprehensive guidance programs into place in the schools continued in the 1980s. Increasingly, sophisticated models began to be translated into practical, workable programs to be implemented in the schools. As we near the close of the 1990s, comprehensive guidance programs are rapidly encompassing the position orientation to guidance. Comprehensive guidance programs are becoming the major way of organizing and managing guidance in the schools across the country. This chapter begins with a brief review of traditional organizational patterns for guidance. Next, the development of a Comprehensive Guidance Program Model that had its genesis in the early 1970s is presented. The content of the model is described, ollowed by a presentation of the structure of the program, the processes used in the program, and the time allocations of staff required to carry out the program. Finally, there is discussion of the program resources required for the model if it is to function effectively. Traditional Organizational Patterns By the 1960s, the evolution of guidance in the schools had reached a peak. The guidance provisions of the National Defense Education Act of 1958 (Public Law 85-864) caused the nu mber of secondary counselors in schools to increase substantially. Later, due to an expansion of the guidance provisions of the act, elementary guidance was supported and as a result, the number of elementary counselors in schools increased rapidly. Counselors put their expertise to work in schools where three traditional organizational patterns for guidance were prevalent, often under the administrative structure called pupil personnel services or student services; the services model, the process model, or the duties model. In many schools, combinations of these three approaches were used. Services The ervices model had its origins in the 1920s and consists of organizing the activities of counselors around major services including assessment, information, counseling, placement and follow-up. Although the activities that are usually listed under each of these services are important and useful, it is a limited model for three reasons. First, it is primarily oriented to secondary schools. Second, it does not lend itself easily to the identification of student outcomes. And third, it does not specify how the time of counselors should be allocated. Processes The process model had its origins in the 1940s. It emphasizes the clinical and therapeutic aspects of counseling, particularly the processes of counseling, consulting, and coordinating. This model is appealing because it is equally applicable to elementary and secondary counselors. However, the process model has some of the same limitations as the services model: It does not lend itself easily to the identification of student outcomes and it does not specify allocations of counselor time. Duties Often, instead of describing some organizational pattern such as the services model or the process model, counselor duties are simply listed (duties model). Sometimes these lists contain as many as 20-30 duties and the last duty is often â€Å"and perform other duties as assigned from time to time. † Although equally applicable to elementary school and secondary school counselors, student outcomes are difficult to identify and counselor time is almost impossible to allocate effectively. Position Oriented Rather Than Program Focused One result of these traditional organizational patterns has been to emphasize the position of the counselor, not the program of guidance. Over the years, as guidance evolved in the schools, it became position oriented rather than program focused. As a result, guidance was an ancillary support service in the eyes of many people. This pattern placed counselors mainly in a remedial-reactive role – a role that is not seen as mainstream in education. What was worse, this pattern reinforced the practice of counselors performing many administrative-clerical duties because these duties could be defended as being â€Å"of service to somebody. † Because of the lack of an adequate organization framework, guidance had become an undefined program. Guidance had become the add-on profession, while counselors were seen as the â€Å"you-might-as-well† group (â€Å"While you are oing this task, you might as well do this one too†). Because of the absence of a clear organizational framework for guidance, it was easy to assign counselors new duties. Counselors had flexible schedules. And, since time was not a consideration, why worry about removing current duties when new ones were added? Origin of the Comprehensive Gu idance Program Model In October of 1969, the University of Missouri-Columbia conducted a national conference on career guidance, counseling and placement that led to regional conferences held across the country during the spring of 1970. Then in 1971, the University of Missouri-Columbia was awarded a U. S. Office of Education grant under the direction of Norman C. Gysbers to assist each state, the District of Columbia, and Puerto Rico in developing models or guides for implementing career guidance, counseling and placement programs in their local schools. Project staff in Missouri conducted a national conference in St. Louis in January of 1972 and developed a manual (Gysbers & Moore, 1974) to be used by the states as they developed their own guides. The manual that was published in February of 1974 provided the first description of an organizational framework for the Comprehensive Guidance Program Model that was to be refined in later work (Gysbers, 1978; Gysbers & Henderson, 1994; Gysbers & Moore, 1981; Hargens & Gysbers, 1984). The original organizational framework for the Comprehensive Guidance Program Model contained three interrelated categories of functions, and on-call functions. The curriculum-based category brought together those guidance activities which took place primarily in the context of regularly scheduled courses of study in an educational setting. These activities were a part of regular school subjects or were organized around special topics in the form of units, mini courses, or modules. They were based on need statements and translated into goals and objectives and activities necessary for the development of all students. Typical topics focused on self-understanding, interpersonal relationships, decision making, and information about the education, work, and leisure worlds. School counselors were involved directly with students through class instruction, group processes, or individual discussions. In other instances, school counselors worked directly and cooperatively with teachers, providing resources and consultation. Individual facilitation functions included those systematic activities of the comprehensive guidance program designed to assist students in monitoring and understanding their development in regard to their personal, educational, and occupational goals, values, abilities, aptitudes, and interests. School counselors served in the capacity of â€Å"advisers,† â€Å"learner managers,† or â€Å"development specialists. Personalized contact and involvement were stressed instead of superficial contact with each student once a year to fill out a schedule. The functions in this category provided for the accountability needed in an educational setting to ensure that students’ uniqueness remained intact and that educational resources were used to facilitate their life career development. On-call functions focused on direct, immediate responses to stu dents needs such as information seeking, crisis counseling, and teacher/parent/specialist consultation. In addition, on-call functions were supportive of the curriculum-based and individual facilitation functions. Adjunct guidance staff (peers, paraprofessionals, and volunteers/support staff) aided school counselors in carrying out on-call functions. Peers were involved in tutorial programs, orientation activities, ombudsman centers, and (with special training) cross-age counseling and leadership in informal dialogue centers. Paraprofessionals and volunteers provided meaningful services in placement and followup activities, community liaison, career information centers, and club leadership activities. The 1974 version of the model focused on the importance of counselor time usage by featuring â€Å"time distribution wheels† to show how counselors’ time could be distributed to carry out a developmental guidance program. A chart was provided to show how counselors’ time could be distributed across a typical school week using the three categories as organizers. REFINEMENTS TO THE COMPREHENSIVE GUIDANCE PROGRAM MODEL In 1978, Gysbers described refinements that had been made to the model since 1974. By 1978, the focus was on a total comprehensive, developmental guidance program. It included the following elements: definition, rationale, assumptions, content model, and process model. The content model described the knowledge and skills that students would acquire with the help of activities in the guidance program. The process model grouped the guidance activities and processes used in the program into four interrelated categories: curriculum-based processes, individual-development processes, on-call responsive processes, and systems support processes. It is interesting to note the changes that had been made between 1974 and 1978 in the model. The concepts of definition, rationale, and assumptions had been added. The model itself was now organized into two parts. The first part listed the content to be learned by students, while the second part organized into four categories the guidance activities and processes needed in a program. The category of individual facilitation was changed to individual development, the word responsive was added to on-call, and a new category – systems support – was added. Also in 1978, Gysbers described seven steps required to â€Å"remodel a guidance program while living in it†: 1. Decide you want to change. 2. Form work groups. . Assess current programs. 4. Select program model. 5. Compare current program with program model. 6. Establish transition timetable. 7. Evaluate. Between 1978 and 1981, further refinements were made in the model. These refinements appeared in Improving Guidance Programs by Gysbers and Moore (1981). By then, the basic structure of the model was est ablished. The terms â€Å"content model† and â€Å"process model† had been dropped. Also, the steps for remodeling a guidance program, first delineated in 1978, formed the basis for the organization the chapters in Improving Guidance Programs and were described in detail. Between 1981 and 1988, the model was being used by state departments of education and local school districts with increasing frequency. During these years, two school districts in particular became involved: St. Joseph School District, St. Joseph, Missouri and Northside Independent School District, San Antonio, Texas. Hargens and Gysbers (1984), writing in The School Counselor, presented a case study of how the model was implemented in the St. Joseph School District. The work in the Northside Independent School District became the basis for much of the most recent description of the model (Gysbers & Henderson, 1994). As the 1980s progressed, a number of states and a number of additional school districts across the country began to adapt the model to fit their needs. In 1988, the first edition of Gysbers and Henderson’s book Developing and Managing Your School Guidance Program was published by the American Association for Counseling and Development, AACD (now the American Counseling Association, ACA). Using the framework of the model presented in 1981, Gysbers and Henderson expanded and extended the model substantially. Building upon the experiences of a number of local school districts and states and with particular emphasis on the experiences of the Northside Independent School District, the planning, design, implementation, and evaluation phases of the model were elaborated upon in much more detail. Sample forms, procedures, and methods, particularly those from Northside, were used extensively to illustrate the model and its implementation. The second edition of the book Developing and Managing Your School Guidance Program by Gysbers and Henderson was published in 1994. DESCRIPTION OF THE COMPREHENSIVE GUIDANCE PROGRAM MODEL Conceptual Foundation The perspective of human development that serves as the foundation for the model and as a basis for identifying the guidance knowledge, skills, and attitudes (competencies) that students need to master is called life career development. Life career development is defined as self-development over a person’s life span through the integration of the roles, setting, and events in a person’s life. The word life in the definition indicates that the focus of this conception of human development is on the total person – the human career. The word career identifies and relates the many often varied roles that individuals assume (student, worker, consumer, citizen, parent); the settings in which individuals find themselves (home, school, community); and the events that occur over their lifetimes (entry job, marriage, divorce, retirement). The word development is used to indicate that individuals are always in the process of becoming. When used in sequence, the words life career development bring these separate meaning words together, but at the same time a greater meaning evolves. Life career development describes total individuals – unique individuals, with their own lifestyles (Gysbers & Moore, 1974, 1975, 1981). The meaning of the word career in the phrase life career development differs substantially from the usual definition of the term. Career focuses on all aspects of life as interrelated parts of the whole person. The term career, when viewed from this broad perspective, is not a synonym for occupation. People have careers; the marketplace has occupations. Unfortunately, too many people use the word career when they hould use the word occupation. All people have careers – their lives are their careers. Finally, the words, life career development do not delineate and describe only one part of human growth and development. Although it is useful to focus at times on different areas (e. g. , physical, emotional, and intellectual), it is also necessary to integrate these areas. Life career development is an organizing and integrating concept f or understanding and facilitating human development. Wolfe and Kolb (1980) summed up the life view of career development as follows: Career development involves one’s whole life, not just occupation. As such, it concerns the whole person, needs and wants, capacities and potentials, excitements and anxieties, insights and blind spots, warts and all. More than that, it concerns his/her life. The environment pressures and constraints, the bonds that tie him/her to significant others, responsibilities to children and aging parents, the total structure of one’s circumstances are also factors that must be understood and reckoned with, in these terms, career development and personal development converge. Self and circumstances – evolving, changing, unfolding in mutual interaction – constitute the focus and the drama of career development. (pp. 1-2) COMPREHENSIVE GUIDANCE PROGRAM MODEL ELEMENTS The model program (see Figure 1. 1) consists of three elements: content, organizational framework, and resources. CONTENT There are many examples today of content (student knowledge and skills) for guidance. The content is generally organized around areas or domains such as career, educational, and personal-social. Most often, the content is stated in a student competency format. For purposes of this chapter, the three domains of human development that are featured in the life career development concept are presented here: self-knowledge and interpersonal skills; life roles, setting and events; and life career planning (Gysbers & Henderson, 1994; Gysbers & Moore, 1974, 1981). Student competencies are generated from these domains to provide example program content for the model. Self-knowledge and Interpersonal Skills In the self-knowledge and interpersonal skills domain of life career development, the focus is on helping students understand themselves and others. The main concepts of this domain focus on students’ awareness and acceptance of themselves, their awareness and acceptance of others, and their development of interpersonal skills. Within this domain, students begin to develop an awareness of their interpersonal characteristics – interests, aspirations, and abilities. Students learn techniques for self-appraisal and the analysis of their personal characteristics in terms of a real-ideal self-continuum. They begin to formulate plans for self-improvement in such areas as physical and mental health. Individuals become knowledgeable about the interactive relationship of self and environment in such a way that they develop personal standards and a sense of purpose in life. Students learn how to create and maintain relationships and develop skills that allow for beneficial interaction within those relationships. They can use self-knowledge in life career planning. They have positive interpersonal relations and are self-directed in that they accept responsibility for their own behavior. See Figure 1. 1 Below The model program consists of three elements: content, organizational framework, and resources. Comprehensive Guidance Program Elements Content Organizational Framework, Activities, Time Resources COMPETENCIES †¢ †¢ †¢ Student Competencies Grouped by domains STRUCTURAL COMPONENTS †¢ Definition †¢ Assumptions †¢ Rational PROGRAM COMPONENTS SAMPLE PROCESSES Guidance Curriculum Structured Groups Classroom presentations Individual Planning Advisement Assessment Placement & Follow-up †¢ Responsive Services Individual counseling Small group counseling Consultation Referral System Support Management activities Consultation Community outreach Public relations †¢ †¢ RESOURCES †¢ Human †¢ Financial †¢ Political SUGGESTED DISTRIBUTION OF TOTAL COUNSELOR TIME Elementary School 35-45% 5-10% 30-40% 10-15% Middle/Junior School 25-35% 15-25% 30-40% 10-15% High School 15-25% 25-35% 25-35% 15-20% Guidance Curriculum Individual Planning Responsive Services System Support Life Roles, Settings, and Events The emphasis in this domain of lif e career development is on the interrelatedness of various life roles (learner, citizen, consumer), settings (home, school, work, and community), and events (job entry, marriage, retirement) in which students participate over the life span. Emphasis is given to the knowledge and understanding of the sociological, psychological, and economic dimensions and structure of their worlds. As students explore the different aspects of their roles, they learn how stereotypes affect their own lives and others’ lives. The implications of futuristic concerns is examined and related to their current lives. Students learn the potential impact of change in modern society and the necessity of being able to project themselves into the future. In this way, they begin to predict the future, foresee alternatives they may choose, and plan to meet the requirements of the life career alternatives they may choose. As a result of learning about the multiple options and dimensions of their worlds, students understand the reciprocal influences of life roles, settings, and events, and they can consider various lifestyle patterns. Life Career Planning The life career planning domain in life career development is designed to help students understand that decision making and planning are important tasks in everyday life and to recognize the need for life career planning. Students learn about the many occupations and industries in the work world and of their groupings according to occupational requirements and characteristics, as well as learning about their own personal skills, interests, values, and aspirations. Emphasis is placed on students’ learning of various rights and responsibilities associated with their involvement in a life career. The central focus of this domain is on the mastery of decision-making skills as a part of life career planning. Students develop skills in this area by learning the elements of the decision-making process. They develop skills in gathering information from relevant sources, both external and internal, and learn to use the collected information in making informed and reasoned decisions. A major aspect of this process involves the appraisal of personal values as they may relate to prospective plans and decisions. Students engage in planning activities and begin to understand that they can influence their future by applying such skill. They accept responsibility for making their own choices, for managing their own resources, and for directing the future course of their own lives. ORGANIZATIONAL FRAMEWORK The model program (see Figure 1. 1) contains seven components organized around two major categories: structural components and program components (Gysbers & Henderson, 1994; Gysbers & Moore, 1981). The three structural components describe the student focus of the program and how the program connects to other educational programs (definition), offer reasons why the program is important and needed (rational), and provide the premises upon which the program rests (assumptions). The four program components delineate the major activities and the roles and responsibilities of personnel involved in carrying out the guidance program. These four program elements are as follows: guidance curriculum, individual planning, responsive services, and system support. Structural Components Definition The program definition includes the mission statement of the guidance program and its centrality within the school district’s total educational program. It delineates the competencies that individuals will possess as a result of their involvement in the program, summarizes the components, and identifies the program’s clientele. Rational The rationale discusses the importance of guidance as an equal partner in the educational system and provides reasons why students need to acquire the competencies that will accrue as a result of their involvement in a comprehensive guidance program. Included are conclusions drawn from student and community needs assessments and statements of the goals of the local school district. Assumptions Assumptions are the principles that shape and guide the program. They include statements regarding the contributions that school counselors and guidance programs make to students’ development, the premises that undergird the comprehensiveness and the balanced nature of the program, and the relationships between the guidance program and the other educational programs. Program Components An examination of the needs of students, the variety of guidance methods, techniques, and resources available, and the increases expectations of policy-makers and consumers indicates that a new structure for guidance programs in the schools is needed. The position orientation organized around the traditional services (information, assessment, counseling, placement, and follow-up) and three aspects (educational, personal-social, and vocational) of guidance is no longer adequate to carry the needed guidance activities in today’s schools. When cast as a position and organized around services, guidance is often seen as ancillary and only supportive to instruction, rather than equal and complementary. The â€Å"three aspects† view of guidance frequently has resulted in fragmented and eventoriented activities and, in some instances, the creation of separate kinds of counselors. For example, educational guidance is stressed by academic-college counselors, personalsocial guidance becomes the territory of mental health counselors, and vocational guidance is the focus of vocational counselors. If the traditional structures for guidance in the schools are no longer adequate, what structure is needed? One way to answer this question is to ask and answer the following questions: Are all students in need of specific knowledge, skills, and attitudes that are the instructional province of guidance programs? Do all students need assistance with their personal, educational, and occupational plans? Do some students require special assistance in dealing with developmental problems and immediate crises? Do educational programs in the school and the staff involved require support that can be best supplied by school counselors? An affirmative answer to these four questions implies a structure that is different from the traditional position model. A review of the variety of guidance methods, techniques, and resources available today and an understanding of the expectations of national and state policy-makers and consumers of guidance also suggests the needs for a different model. The structure suggested by an affirmative answer to the four questions and by a review of the literature is a program model of guidance techniques, methods, and resources organized around four interactive program components: guidance curriculum, individual planning, responsive services, and system support (Gysbers & Henderson, 1994; Gysbers & Moore, 1981). The curriculum component was chosen because a curriculum provides a vehicle to impart guidance content to all students in a systematic way. Individual planning was included as a part of the model because of the increasing need for all students to systematically plan, monitor, and manager their development and to consider and take action on their next steps personally, educationally, and occupationally. The responsive services component was included because of the need to respond to the direct, immediate concerns of students, whether these concerns involve crisis counseling, referral, or consultation with parents, teachers, or other specialists. Finally, the system support component was included because, if the other guidance processes are to be effective, a variety of support activities such as staff development, research, and curriculum development are required. Also, system support encompasses the need for the guidance program to provide appropriate support to other programs in including assuming â€Å"fair share† responsibilities in operating the school. These components, then, serve as organizers for the many guidance methods, techniques, and resources required in a comprehensive guidance program. In addition, they also serve as a check on the comprehensiveness of the program. A program is not comprehensive unless counselors are providing activities to students, parents, and staff in all four program components. Guidance Curriculum This model of guidance is based on the assumption that guidance programs include content that all students should learn in a systematic, sequential way. In order for this to happen, counselors must be involved in teaching, team teaching, or serving as a resource for those who teach a guidance curriculum. This is not a new idea; the notion of guidance curriculum has deep, historical roots. What is new however, is the array of guidance and counseling techniques, methods, and resources currently available that work best as part of a curriculum. Also new is the concept that a comprehensive guidance program has an organized and sequential curriculum. The guidance curriculum typically consists of student competencies (organized by domain) and structured activities presented systematically through such strategies as the following: †¢ Classroom Activities Counselors teach, team teach, or support the teaching of guidance curriculum learning activities or unites in classrooms. Teachers also may teach such units. The guidance curriculum is not limited to being part of only one or two subjects but should be included in as many subjects as possible throughout the total school curriculum. These activities may be conducted in the classroom, guidance center, or other school facilities. †¢ Group Activities Counselors organize large-group sessions such as career days and educational/college/vocational days. Other members of the guidance team, including teachers and administrators, may be involved in organizing and conducting such sessions. Although counselors’ responsibilities include organizing and implementing the guidance curriculum, the cooperation and support of the entire faculty are necessary for its successful implementation. Individual Planning Concern for individual student development in a complex society has been a cornerstone of the guidance movement since the days of Frank Parsons. In recent years the concern for individual student development has intensified as society has become more complex. This concern is manifested in many ways, but perhaps is expressed most succinctly in a frequently stated guidance goal: â€Å"Helping all students become the persons they are capable of becoming. † To accomplish the purposes of this component of the Model, activities and procedures are provided to assist students in understanding and periodically monitoring their development. Students come to terms with their goals, values, abilities, aptitudes, and interests (competencies) so they can continue to progress educationally and occupationally. Counselors become â€Å"person-development-and-placement specialists. † Individual planning consists of activities that help students to plan, monitor, and manage their own learning and their personal and career development. The focus is on assisting students, in close collaboration with parents, to develop, analyze, and evaluate their educational, occupational, and personal goals and plans. Individual planning is implemented through such strategies as: †¢ Individual Appraisal Counselors assist students to assess and interpret their abilities, interests, skills, and achievement. The use of test information and other data about students is an important part of helping them develop immediate and long-range goals and plans. †¢ Individual Advisement Counselors assist students to use self-appraisal information along with personal-social, educational, career, and labor market information to help them plan and realize their personal, educational, and occupational goals. †¢ Placement Counselors and other educational personnel assist students to make the transition from school to work or to additional education and training. Responsive Services Problems relating to academic learning, personal identity issues, drugs, and peer and family relationships are increasingly a part of the educational scene. Crisis counseling, diagnostic and remediation activities, and consultation and referral must continue to be included as an ongoing part of a comprehensive guidance program. In addition, a continuing need exists for the guidance program to respond to the immediate information-seeking needs of students, parents, and teachers. The responsive services component organizes guidance techniques and methods to respond to these concerns and needs as they occur; it is supportive of the guidance curriculum and individual planning components as well. Responsive services consist of activities to meet the immediate needs and concerns of students, teachers, and parents, whether these needs or concerns require counseling, consultation, referral, or information. Although counselors have special training and possess skills to respond to immediate needs and concerns, the cooperation and support of the entire faculty are necessary for this component’s successful implementation. Responsive services are implemented through such strategies as: †¢ Consultation Counselors consult with parents, teachers, other educators, and community agencies regarding strategies to help students deal with and resolve personal, educational, and career concerns. †¢ Personal Counseling Counseling is provided on a small-group and individual basis for students who have problems or difficulties dealing with relationships, personal concerns, or normal developmental tasks. The focus is on assisting students to identify problems and causes, alternatives, possible consequences, and to take action when appropriate. †¢ Crisis Counseling Counseling and support are provided to students or their families facing emergency situations. Such counseling is normally short term and temporary in nature. When necessary, appropriate referral sources are used. †¢ Referral Counselors use other professional resources of the school and community to refer students when appropriate. These referral sources may include: mental health agencies employment and training programs vocational rehabilitation juvenile services social services special school programs (special or compensatory education) The responsive services component also provides for small-group counseling. Small groups of students with similar concerns can be helped by intensive small-group counseling. All students may not need such assistance, but it is available in a comprehensive program. Adjunct guidance staff—peers, paraprofessionals, volunteers—can aid counselors in carrying out their responsive activities. Peers can be involved in tutorial programs, orientation activities, ombudsman functions and, with special training, cross-age counseling and leadership in informal dialog. Paraprofessionals and volunteers can provide assistance in such areas as placement, follow-up, and community-school-home liaison activities. System Support The administration and management of a comprehensive guidance program require an ongoing support system. That is why system support is a major program component. Unfortunately, it is often overlooked or only minimally appreciated. And yet, the system support component is as important as the other three components. Without continuing support, the other three components of the guidance program are ineffective. This component is implemented and carried out through such activities as the following: †¢ Research and Development Guidance program evaluation, follow-up studies, and the continued development and updating of guidance learning activities are some examples of the research and development work of counselors. †¢ Staff/Community Public Relations The orientation of staff and the community to the comprehensive guidance program through the use of newsletters, local media, and school and community presentations are examples of public relations work. †¢ Professional Development Counselors must regularly update their professional knowledge and skills. This may include participation in school inservice training, attendance at professional meetings, completion of postgraduate course work, and contributions to the professional literature. †¢ Committee/Advisory Boards Serving on departmental curriculum committees and community committees or advisory boards are examples of activities in this area. †¢ Community Outreach Included in this area are activities designed to help counselors become knowledgeable about community resources, employment opportunities, and the local labor market. This may involve counselors visiting local businesses and industries and social services agencies. Program Management and Operations This area includes the planning and management tasks needed to support the activities of a comprehensive guidance program. Also included in the system support component are activities that support programs other than guidance. These activities may include counselors being involved in helping interpret student test re sults to teachers, parents, and administrators, serving on departmental curriculum committees (helping interpret student needs data for curriculum revision), and working with school administrators (helping interpret student needs and behaviors). Care must be taken, however, to watch the time given to these duties because the primary focus for counselors is their work in the first three components of the comprehensive guidance program. It is important to realize that if the guidance program is well run, focusing heavily on the first three components, it will provide substantial support for other programs and personnel in the school and the community. Program Time Counselors’ professional time is a critical element in the Model. How should professional certified counselors spend their time? How should this time be spread across the total program? In this Model, the four program components provide the structure for making judgments about appropriate allocations of counselors’ time. One criterion to be used in making such judgments is the concept of program balance. The assumption is that counselor time should be spread across all program components, but particularly the first three. Another criterion is that different grade levels require different allocations of counselor time across the program components. For example, at the elementary level, more counselor time is spent working in the curriculum with less time spent in individual planning. In the high school, these time allocations are reversed. How counselors in a school district or school building plan and allocate their time depends on the needs of their students and their community. Once chosen, time allocations are not fixed forever. The purpose for making them is to provide direction to the program and to the administrators and counselors involved. Since the Model is a â€Å"100 percent program,† 100 % of counselors’ time must be spread across the four program components. Time allocations are changed as new needs arise, but nothing new can be added unless something else is removed. The assumption is that professional counselors spend 100 % of their time on task, implementing the guidance program. What are some suggested percentages? As an example, the state of Missouri (Starr & Gysbers, 1997) has adopted suggested percentages of counselor time to be spent on each program component. These suggested percentages were recommended by Missouri counselors and administrators who had participated in the field-testing of the Missouri adaptation of the Comprehensive Guidance Program Model: Percent ES M/JH HS Guidance Curriculum 35-45 25-35 15-25 Individual Planning 05-10 15-25 25-35 Responsive Services 30-40 30-40 25-35 System Support 10-15 10-15 15-20 Resources Human Human resources for the guidance program include such individuals as counselors, teachers, administrators, parents, students, community members, and business and labor personnel. All have roles to play in the guidance program. While counselors are the main providers of guidance and counseling services and coordinators of the program, the involvement, cooperation, and support of teachers and administrators is necessary for the program to be successful. The involvement, cooperation, and support of parents, community members, and business and labor personnel also is critical. A SchoolCommunity Advisory Committee is recommended to bring together the talent and energy of school and community personnel. The School-Community Advisory Committee acts as a liaison between the school and community and provides recommendations concerning the needs of students and the community. A primary duty of this committee is to advise those involved in the guidance program. The committee is not a policy- or decision-making body; rather, it is a source of advice, counsel, and support and is a communication link between those involved in the guidance program and the school and community. The committee is a permanent part of the guidance program. A community person should be the chairperson. The use and involvement of an advisory committee will vary according to the program and the community. It is important, however, that membership be more than in name only. Members will be particularly helpful in developing and implementing the public relations plan for the community. Financial The financial resources of a comprehensive guidance program are crucial to its success. Examples of financial resources include budget, material, equipment, and facilities. The Model highlights the need for these resources through its focus on the physical space and equipment required to conduct a comprehensive program in a school district. To make the guidance curriculum, individual planning, responsive services, and system support components function effectively, adequate guidance facilities are required. Traditionally, guidance facilities have consisted of an office or suite of offices designed primarily to provide one-to-one counseling or consultation assistance. Such arrangements have frequently included reception or waiting areas that serve as browsing rooms where students have access to displays or files of educational and occupational information. Also, this space has typically been placed in the administrative wing of the school so that the counseling staff can be near the records and the administration. The need for individual offices is obvious because of the continuing need to carry on individual counseling sessions. A need also exists, however, to open up guidance facilities and make them more accessible to all students, teachers, parents, and community members. One way to make guidance facilities more usable and accessible is to reorganize traditional space into a guidance center. A guidance center brings together available guidance information and resources and makes them easily accessible to students. The center is used for such activities as group sessions, student self-exploration, and personalized research and planning. At the high school level, students receive assistance in areas such as occupational planning, job entry and placement, financial aid information and postsecondary educational opportunities. At the elementary school level, students and their parents receive information about the school, the community, and parenting skills; they also read books about personal growth and development. An area for play therapy can be provided in the guidance center. Although the center is available for use to school staff and community members, it is student centered, and many of the center activities are student planned as well as student directed. At the same time, the center is a valuable resource for teachers in their program planning and implementation. Employers, too, will find the center useful when seeking part-time or full-time workers. Clearly, the impact of the center on school and community can be substantial. If community members and parents are involved in the planning and implementation of the center and its activities, their interest could provide an impetus for the involvement of other community members. When parents and community members become involved in programs housed in the center, they experience the guidance program firsthand. Through these experiences, new support for the program may develop. The guidance center is furnished as comfortably as possible for all users. Provision is made for group as well as individual activities. Coordinating the operation of the guidance center is the responsibility of the guidance staff, but all school staff can be involved. It is recommended that at least one paraprofessional be a part of the staff to ensure that clerical tasks are carried out in a consistent and ongoing manner. Political Education is not simply influenced by politics, it is politics. The mobilization of political resources is key to a successful guidance program. Full endorsement of the guidance program by the Board of Education as a â€Å"program of studies of the district† is one example of mobilizing political resources. Another example is a clear and concise school district policy statement that highlights the integral and central nature of the school district’s comprehensive guidance program to other programs in the school district. Putting It All Together What does the Program Model look like when all of the Model’s elements are brought together? Figure 1 (see page 12) presents the Model on one page so that the three program elements can be seen in relationship to each other. Notice that the three program elements (program content, program structure, processes, and time, and program resources) represent the â€Å"means† of the program. Without these means in place, it is impossible to achieve the full results of the program and to fully evaluate the impact of the program on the students, the school, and the community. Some Final Thoughts The Program Model, by definition, leads to guidance activities and structured group experiences for all students. It de-emphasizes administrative and clerical tasks, one-toone counseling only, and limited accountability. It is proactive rather than reactive. Counselors are busy and unavailable for unrelated administrative and clerical duties because they have a guidance program to implement. Counselors are expected to do personal and crisis counseling as well as provide structured activities to all students. To fully implement the Program Model it is important that the program be as follows: 1. Understood as student-development oriented, not school maintenance-administrativeoriented. 2. Operated as a 100 % program; the four program components constitute the total program; there are no add-ons. 3. Started the first day of school and ended on the last day of school; not started in the middle of October with an ending time in April so that administrative, nonguidance tasks can be completed. . Understood as program focused, not position focused. 5. 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