Social Cognitive Theory free essay sample

Baptist, A. P. , Ross, J. A. , Yang, U. , Song, P. X. , Clark, N. M. (2013). A randomized controlled trial of a self-regulation intervention for older adults with asthma. Journal of the American Geriatrics Society, 61(5), 747-753. Doi:10. 1111/jgs. 12218 To evaluate an asthma self-regulation intervention for older adults, specifically observing the effects on asthma quality of life and asthma control. Participants were outpatients aged 65 and older with asthma who were currently taking daily medications to control their asthma. There were 70 patients enrolled. The average age was 73. 3 years old. Women made up 27% of the enrolled population. The mean number of years diagnoses with asthma was 27. 3 years. Caucasians made up 28% of the enrolled population. Randomized, double-blind, controlled trial. Patients assigned randomly to control or intervention group. Control group (n=35) participants received standard asthma education administered by a health educator. Topics included proper inhaler technique, asthma triggers, asthma control, and signs of exacerbation. Intervention group (n=35) participants received standard care and participated in a six-session program conducted over the telephone and group sessions. We will write a custom essay sample on Social Cognitive Theory or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Participants selected an asthma-specific goal, identified problems, and addressed potential barriers. Outcomes were assessed at 1, 6, and 12 months and included the mini-Asthma Quality of Life Questionnaire (mAQLQ), Asthma Control Questionnaire (ACQ), healthcare utilization, fraction of exhaled nitric oxide level (FENO), and percentage of predicted forced expiratory volume in 1 second (FEV1%). The mean mAQLQ score was significantly higher in the intervention group than in the control group at 1, 6, and 12 months. Higher scores indicated greater quality of life. Mean ACQ score was significantly lower at 1 month for the intervention group than the control group and was lower at the 6 and 12-month time points as well. Lower scores indicated greater asthma control. Lung function was evaluated at 6 and 12 months. No difference was seen in FENO, but predicted FEV1% favored the intervention group. There was no significant difference found for hospitalizations or ED visits between the intervention and control groups, although both favored the intervention group. For a composite healthcare utilization index (hospitalization, ED, or unscheduled visit) the intervention group had significantly fewer visits than the control. A self-regulation intervention is effective for improving asthma quality of life, asthma control, and healthcare utilization in older adults. Byrne, J. , Khunti, K. , Stone, M. , Farooqi, A. , Carr, S. (2011). Feasibility of a structured group education session to improve self-management of blood pressure in people with chronic kidney disease: An open randomized pilot trial. BMJ Open, doi:10. 1136/bmjopen-2011-000381 To test a group educational intervention called Controlling Hypertension (HTN): Education and Empowerment Renal Study (CHEERS) to improve self-management of blood pressure in people with chronic kidney disease (CKD). Exploring the acceptability of the intervention. Patients with early CKD and HTN. There were 81 people enrolled in the study 30% of the 267 patients approached. Random controlled trial. Patients recruited from nephrology outpatient clinic. Control group received standard clinical management of HTN. Intervention group received routine standard care plus structured CHEERS patient education intervention. Measured: recruitment, retention, patient demographics, self-efficacy, and patient satisfaction. Lack of time 48% and lack of interest 44% were the main reasons for non-participation. 37. 5% of the intervention group failed to attend. These patients were older and had lower self-efficacy. The intervention was rated enjoyable and useful by 100% of participants. None of those who attended the education sessions accessed the additional support offered. Carr, L. J. , Dunsiger, S. I. , Lewis, B. , Ciccolo, J. T. , Hartman, S. , Bock, B. , Marcus, B. H. (2013). Randomized controlled trial testing an internet physical activity intervention for sedentary adults. Health Psychology, 32(3), 328-336. doi:10. 1037/a0028962 To test the efficacy of a newly enhanced internet (EI) intervention in relation to six standard internet (SI) intervention websites that are publicly available to promote physical activity (PA), for improving PA behavior in previously sedentary adults. Healthy sedentary (achieving less than 60 minutes of moderate-to-vigorous physical activity per week) men and women between ages 18 and 65 years of age. 66 participants were enrolled. 25 were randomly assigned to the EI group. 28 were randomly assigned to the SI group. More than half were college educated. More than 80% reported being non-Hispanic white. Both groups used an internet website to monitor and improve physical activity. Website use, physical activity, and patient satisfaction were measured. The EI included five SCT-influenced internet features including, physical activity tracking goal-setting calendar, regular peer activity updates, ask the expert QA forum, exercise videos, and geographic mapping function. At 3 months EI participants averaged 180. 4 weekly minutes of PA compared to 46. 3 minutes by SI participants. At 6 months EI participants averaged 171. 4 weekly minutes of PA compared to 121. 8 minutes by SI participants. The EI intervention facilitated quicker increases in PA behavior than the SI comparison group. An improvement in PA behavior was associated with improvements in various SCT constructs including social support, self-efficacy, outcome expectations, and self-regulation. Dorough, A. E. , Winett, R. A. , Anderson, E. S. , Davy, B. M. , Martin, E. C. , Hedrick, V. (2012). DASH to Wellness: Emphasizing Self-Regulation Through E-Health in Adults With Prehypertension. Health Psychology, doi: 10. 1037/a0030483 To assess the initial efficacy of an electronically delivered, lifestyle intervention for treating prehypertension (PHT) by increasing fruit and vegetable consumption, reducing dietary sodium through the adaption of the DASH eating plan, increasing physical activity, promoting use of home blood pressure monitoring, reducing weight, and decreasing BP thereby reducing risk of movement to stage I hypertension in middle-aged adults with PHT. 27 participants with a mean age of 54. 3, mean weight of 87. 8 kg, mean BMI of 31. 5, were 69. 5% female, 95% Caucasian, 5% Mexican American, were well educated with 52% reporting 4-year or postgraduate degrees, and 60. 8% reported a household annual income above $60,000. Participants were randomly assigned a group. The standard-of-care condition, DASH 2 wellness (D2W) only, or the intervention-treatment group, DASH 2 wellness plus (D2WP). At baseline both groups completed a 4-day food intake record and a 7-day step log. Baseline height, weight, and BMI were also recorded. This data was gathered again after the 10-week intervention. D2W group was provided the DASH eating plan guide, walking and weight program, a scale, and a pedometer. D2WP group was provided everything listed above along with an automatic blood pressure monitor, weekly electronic feedback in planning, goal setting, and tracking of intake, weight, exercise, and self-monitored blood pressure readings. D2WP had a large increase in average daily steps 2,900 vs. 636. D2WP had a larger decrease in systolic BP 15. 14 mmHg vs. 4. 61 mmHg. D2WP had a larger decrease in weight 10. 54 lbs. vs. 3. 23 lbs. Outcomes suggest the primarily electronically delivered approach was more effective than the standard of care in changing some health behaviors related to nutrition and physical activity, reducing body weight, and systolic blood pressure. All D2WP participants moved from the PHT category to a normal, even optimal BP except for one. Fjeldsoe, B. S. , Miller, Y. D. , Marshall, A. L. (2013). Social cognitive mediators of the effect of the MobileMums intervention on physical activity. Health Psychology, 32(7), 729-738. Doi: 10. 1037/a0027548 To explore whether improvements in physical activity (PA) following the MobileMums intervention were facilitated by changes in Social Cognitive Theory (SCT) constructs targeted in the intervention. Women less than 12-months postpartum were recruited from a database of women that agreed to be contacted for health-related research projects. Eligibility criteria included English comprehension, ownership of a mobile phone, engaged in less than five days per week of 30-minutes of moderate-to-vigorous physical activity, and intention to increase PA. 88 participants completed the baseline assessment and were randomized to either the intervention group, n=45, or control group, n=43. Seventy-seven percent of participants were reassessed at 6-weeks and 69% at 13-weeks. There were no meaningful or statistically significant differences between group demographic characteristics. Participants were randomly assigned to the control or intervention group. Control group participants received one face-to-face consultation with a behavioral counselor and given a PA information pack. MobileMum intervention group participants had two PA consultations with a behavioral counselor and one telephone consultation at 6-weeks. Counselor helped to set goals and plan exercise activities. Participants were also given weekly activity planning magnets to help self-monitor. Participants nominated a social support person. They also received 42 individualized phone messages providing strategies for behavior change and 11 goal check messages. Support person also received messages. Frequency of walking for exercise and the amount of moderate-to-vigorous PA was measured. Barrier self-efficacy, goal setting skills, outcome expectancy, social support, and perceived environmental opportunity were also measured. The MobileMum program increased the amount of walking for exercise and moderate-to-vigorous physical activity among postnatal women. An increase in physical activity was mediated in the short-term (6-weeks), by improvements in barrier self-efficacy and goal setting skills. Social support did not significantly mediate the intervention effects on physical activity. The intervention did not have a significant impact on outcome expectancy or perceived environmental opportunities for PA. Pilutti, L. , Dlugonski, D. , Sandroff, B. , Klaren, R. , Motl, R. (2013). Randomized controlled trial of a behavioral intervention targeting symptoms and physical activity in multiple sclerosis. Multiple Sclerosis Journal, doi: 10. 1177/1352458513503391 To examine the efficacy of an internet delivered behavioral intervention for improving outcomes of fatigue, depression, anxiety, pain, sleep quality, and health related quality of life (HRQOL) in ambulatory persons with MS. This intervention has previously been tested to increase lifestyle physical activity among persons with MS. The second aim of this study is to replicate those results regarding change in physical activity. Sample consisted of 82 participants who were randomized into a control group, n=41, and an intervention group, n=41. Participant inclusion criteria: 18-64 years, diagnosis of MS, relapse-free for the past 30-days, internet access, ability to walk with or without an assistive device, physician approval, not maintaining 30 minutes of moderate-to-vigorous physical activity a day for more than 2 days. Sample was primarily female (76%). The level of disability of the sample was moderate and most participants (74%) did not use an assistive device. There were no statistically significant differences between groups in demographic or clinical characteristics, physical activity, or symptomatic and HRQOL outcomes pre-trial. Random controlled trial. The intervention group was provided a study website with information about becoming more physically active based on principles of SCT, self-monitoring, and goal-setting using a pedometer and activity logs, and one-on-one video coaching sessions for 6 months. Data measures included physical activity, fatigue, depression, anxiety, pain, quality of sleep, HRQOL, and disability. These measures were recorded at baseline and 6 months post intervention. Participants in the intervention group participated in significantly more self-reported physical activity compared to control group. They also spent more time in moderate-to-vigorous physical activity compared to the control group although not a significant difference. Symptoms of depression and anxiety were significantly lower in the intervention group. There was a favorable effects of the intervention on symptoms of pain and sleep quality post-intervention. Participants in the intervention group reported greater quality of life compared to control participants, although this difference did not reach statistical significance. Overall, it can be confirmed that a lifestyle intervention delivered through the internet can be effective for increasing everyday physical activity in persons with MS, and this had a positive effect on symptomatic outcomes. Amaya, M. , Petosa, R. (2012). An evaluation of a worksite exercise intervention using the social cognitive theory: A pilot study. Health Education Journal, 71(2), 133-143. Doi: 10. 1177/0017896911409731 To increase exercise adherence among insufficiently active adult employees. Employees (n=127) who did not meet current American College of Sports Medicine recommendations for exercise. Majority of subjects were female (83%). A majority of subjects had obtained a bachelor’s or post bachelor’s degree (61%). Nearly all subjects were married (77%). A majority of the study sample was Caucasian (84%). A quasi-experimental separate samples pre-test-post-test group design was used to compare treatment and comparison group. Intervention: An eight-week educational program targeting the social cognitive theory constructs. There were 6 one-hour classroom-based sessions and multiple meetings with a trainer. The intervention taught subjects self-regulation skills, including focusing on self-monitoring, goal setting, and time management. Taught self-efficacy skills, overcoming barriers and exercise preferences. Taught social support for exercise, outcome expectations and expectancies, reasons to exercise and its value. Measures: free-living exercise, self-regulation, self-efficacy, social support, and outcome expectations and expectancies. Measurements were taken at pre-test, post-test, one month and three months post-intervention. There was a significant difference between groups for moderate intensity exercise and vigorous intensity exercise at post-test and follow-up. There was a significant difference between groups for self-regulation at post-test and follow-up. There was not a significant difference between groups for self-efficacy or outcome expectancies. Family and friend social support group differences were non-significant at post-test and at one-month follow-up, but were significant at three-month follow-up. The educational intervention was effective in increasing the exercise rates of employees at the worksite. Social Cognitive Theory Framework Paper Framework Description, Components, and Synthesis Social Cognitive Theory (SCT) emerged primarily from the work of Albert Bandura. Social cognitive theory is a learning theory based on the idea that people learn by observing others. The theory is an expansion of the Social Learning Theory (SLT) originally proposed by Neal Miller and John Dollard and later expanded by Bandura himself. Social Learning Theory theorizes that people learn new behaviors by observing others, imitating their behavior, and then being reinforced by the observed outcomes. Bandura’s SCT differs from SLT by its emphasis on the role of self-efficacy and the concept of reciprocal triadic causation. Self-efficacy is a person’s confidence and desire to perform a behavior. It reflects not only a person’s actual know-how to perform a behavior, but also their skepticism or willingness in performing the behavior. Behavior is theorized in SCT to occur in a social context and influenced by the active shared interaction between the person, environment, and behavior, the reciprocal triadic causation. Never is a behavior not influence by all three. There are five key concepts that make up the Social Cognitive Theory, those concepts are knowledge, perceived self-efficacy, outcome expectation, goal formation, and sociostructural factors. Knowledge is often the starting point for most health promotion programs. Many people will obtain knowledge through observation, verbal or written description, video or audio recordings, and other forms of knowledge delivery (DiClemente, 2013). The DARE program is a good example of a program that delivers knowledge to kids about the risks of alcohol and drugs to hopefully prevent their use and abuse. However, behavior change after obtaining knowledge is often dependent on four inner-related processes involving attention, retention, production, and motivation. Regardless of having the knowledge there must be underlining drives to commit to a behavior change. Building off the last example if a kid were to be a valued member of a sports team they may be move motivated to abstain from alcohol and drugs to avoid a decline in performance. Since knowledge alone may not initiate a behavior change all of the study articles listed above provided education for all participants at baseline. The Baptist study provided asthma education on how to administer medication, asthma triggers, control, and signs of exacerbation. The Pilutti study provided online material on becoming more physically active and healthy. Perceived self-efficacy can be explained as a person’s inner confidence in performing a task. This inner confidence has been influenced by past performances, the observation and verbal persuasion of others, and their physiological state (Bandura, 2004). Low self-efficacy can be caused by fear and can defeat any effort to perform a given task. Having a high level of self-efficacy will increase a person’s initiation and drive for a particular task. There are four methods to improve self-efficacy; learn to reduce the fear and other negative emotions that may be associated with the task, verbal persuasion, watch others perform the task successfully, and be physically guided or coached through the task (DiClemente, 2013). Many of these methods were used in the study articles listed above. By providing education, participant knowledge is increased, which helps to improve self-efficacy. The Carr study provided online exercise videos. In the Fjeldsoe study participants were assigned a counselor and a support person to provide encouragement. Participants in the Amaya study met for one-hour classroom sessions and had group discussions discussing fears, barriers, and exercise technique. Outcome expectations are individual beliefs about what consequences are most likely to occur if a particular behavior is performed. People anticipate the consequences of their actions before engaging in a behavior, and these anticipated consequences could influence the successful completion of the behavior (Bandura, 2004). The benefit of the behavior is the driving force for action. Educators, trainers, and counselors work well to help people recognize the possible positive outcomes. The Baptist, Amaya, and Fjeldsoe studies all used a person as part of their intervention to help participants visualize the possible outcomes of their efforts. Outcome expectations can also include negative perceptions or costs. For example a person thinking about going back to school may see the benefit in an advanced degree and pay raise or the negative outcome of tuition costs and time spent. Goal setting is an important aspect of SCT. Setting small achievable goals that progress to the final goal is an effective way to keep focused and maintain spirit throughout the process. With the attainment of small sub goals self-efficacy perceptions are likely to increase and the experience of positive outcomes will increase and improve change efforts (DiClemente, 2013). Further, goals are an important prerequisite for self-regulation because they provide objectives to strive towards and are levels against which to judge progress (Bandura, 2004). Participants of the Fjeldsoe study carried mobile devices that monitored progress and sent individualized messages with strategies to meet goals. Those participants also receive goal check messages and depending on participant responses they either received more advice or applause and encouragement to keep up the good work. Lastly, sociostructural factors are important as they can strongly impact self-efficacy and consequently behavior. Sociostructural factors are any actual or perceived external factor that influences for better or for worse the likelihood of a behavior (DiClemente, 2013). For example, a positive sociostructural factor for an individual trying to loose weight and eat healthy would be a number of local gym options. A negative factor may be the lack of grocery stores that offer organic food options. The Carr study, which investigated the effectiveness of an Internet site to improve physical activity, had a geographic mapping function to help participants locate resources and plan outdoor activity. Major Research and Practice Areas Based on the journal articles above much of the research is on the effectiveness of interventions that target the patient SCT constructs to improve illness management and physical activity. These interventions mostly focus on all five of the SCT concepts. The programs typically provided education, group, online, technical, and personal support, help with goal formation and strategic planning, and also coaching and counseling. The research is looking for interventions to improve self-management, quality of life, and the increase of health promoting behaviors such as blood pressure monitoring, eating healthy, and being physical activity. Social Cognitive Theory interventions can be used in practice to improve the management of chronic illnesses such as asthma, chronic kidney disease, and hypertension. It can be used to improve health-promoting behaviors such as increasing exercise, dieting and weight loss, and monitoring blood pressure or blood sugars. Framework Population The populations of the studies found above using SCT involved older adults over the age of 65, patients with multiple diagnoses, adults with borderline diagnoses, sedentary adults, postpartum women, and adults managing disability related to an illness. All of the listed populations are at risk for low levels of self-efficacy, which can inhibit change. Therefore, these populations may require extra support and motivation, which can be provided by interventions using SCT to support change. Framework and Risk Factor In the Dorough study the population of focus was adults with prehypertension and their risk factor was physical inactivity and poor diet and nutrition. The study aimed to use an intervention influenced by SCT to promote physical activity and reduce hypertension. Features of the intervention that were SCT based were the nutrition and exercise education, the nutrition guide an outlined exercise program, and lastly the electronic resource provided for logging progress, goal setting, and goal attainment advice. The results of the intervention included a significant increase in daily steps, decrease in blood pressure, and weight loss. All but one participant moved from prehypertension to normal even optimal blood pressure. Application of Framework Stroke survivors can have a low perceived level of self-efficacy and this puts them at an increased risk for not meeting their rehabilitation goals for recovery. Stroke survivors want to return to the varied roles they had before their stroke. However, commonly these survivors become victim to themselves as they begin to lose interest, become unmotivated, and become difficult to get going. Depression and apathy are common consequences of stroke with the sudden loss of independence and self-reliance. An intervention that aims to change patient SCT constructs to improve physical activity in rehab may work to improve the patient’s self-efficacy and their progression in rehabilitation. The desired outcome is increased participation in physical rehabilitation. The factor that is limiting that outcome is the patient’s low level of perceived self-efficacy. One study revealed that the functional level at 6 months post stroke could predict long-term survival and disability (Jones, 2010). Therefore, an intervention that promotes a positive spirit, provides support, and creates an environment that facilitates goal attainment is absolutely necessary for these patients to increase their functional ability and decrease their level of dependency, which is of utmost importance. An ideal SCT intervention for this population would begin with an educational session on stroke covering topics such as common side effects, medications, needs and goals of rehabilitation. The recovery process is long and therefore multiple small goals should be set weekly to help maintain motivation and recognize progression however small. Therapy should be conducted in groups to allow patients to motivate each other, witness each other’s progression, and support one another through the recovery process. Nurses, and physical, speech, and occupational therapist should help the patients monitor and log their progress, coach exercises, and motivate. To measure the effectiveness of the program therapy participations may be measure based on time, patient exertion and attitude. The level of self-efficacy before and after would be a good measure along with patient’s outlook on current progression and further progress.

The Art Of Writing News

The Art Of Writing News The Art Of Writing News The Art Of Writing News By Sharon News writing is a key skill for journalists, but it helps with other types of writing as well. Thats because news writing is about telling a story quickly and concisely. Anyone can learn to do this, with a bit of help. Heres how you can write the news and get your story across. The technique also works well for writing press releases. News Writing Structure News writing has its own structure. Its called the inverted pyramid. This upside down triangle serves as a guide for how you include information in the story. Using the inverted pyramid means starting with the most important information, then putting the next most important info and so on. It can also serve as a guide for writing each paragraph in the story. Start with the most important point, then the next most important and so on. The inverted pyramid has an interesting history. Before digital printing and desktop publishing, news was laid out manually. If a late breaking story came in and the editor needed to make room, then the editor would order another story to be cut. Having the most important information at the top meant that readers always got the essential parts of the story. Writing The Facts Another way to think of the inverted pyramid is that you start with the facts and then add the background. So, how do you know what background to add? Its easy. You can use the 6Ws. Strictly speaking, there arent six Ws, there are actually 5Ws and 1H, but the formula seems to work. That mnemonic reminds us to include the who, where, what, why, when and how of a story. Why is this? Think about how you tell a story to your friends. You might say: Youll never believe WHO I just saw! Then you might go on to tell the story of where the person was, what they were doing, and why its scandalous. We all want to hear about people – and thats what news is about? Look at any news story and you will see that all of this information is in the first two paragraphs. Anything after that is background to the story. Let me give another example. If I were writing about a car crash, I would say who was involved, when and where it happened, why it happened and how it happened. Those would be the main points and my story might look something like this: Two people sustained serious injuries in a car crash at Hill Road at 6am today. The collision happened when Mr. Smith swerved into the opposite lane to avoid a dog in the road. Ms Jones, who was in that lane, was unable to stop in time. Both Mr. Smith and Ms Jones have been taken to the local hospital. This is not a perfect example, but you get the idea – and now you can write the news too. Want to improve your English in five minutes a day? Get a subscription and start receiving our writing tips and exercises daily! Keep learning! Browse the Freelance Writing category, check our popular posts, or choose a related post below:Program vs. ProgrammeBroadcast vs Broadcasted as Past FormWhat Is the Meaning of "Hack?"

How has the EU-US Open Skies Agreement Affected EU Citizenship of Essay

How has the EU-US Open Skies Agreement Affected EU Citizenship of Germany - Essay Example How has the EU-US Open Skies Agreement Affected EU Citizenship of Germany? The EU-US Open Skies Agreement generally reflects the formation of the ‘US-EU Open Aviation Area Agreement’. In the year 2007, both the regions i.e. the US and the EU had signed a momentous pact in order to liberalise open global transportation and air travel on their respective business markets over the Atlantic Ocean. This pact or treaty is popularly acknowledged as The EU-US Open Skies Agreement. This significant agreement intends to deregulate the concern of air traffic over the Atlantic Ocean through various ways that have been discussed in the following section. It is worth mentioning that this particular agreement has facilitated any airline belonging to the US and the EU to fly to any particular point between these two regions. Apart from lessening the increased level of regulation of air transportation between the aforesaid two regions, the agreement also tends to undertake certain significant steps specifically for normalisation of the global aviation industry (Peterson & Graham, 2008). With this concern, the essay intends to discuss the EU-US Open Skies Agreement and its implications on different EU nations along with the US. Moreover, the way in which this agreement has affected EU citizenship of Germany will also be taken into concern in the essay. The financial interrelation of the US and the EU has been witnessed to contribute in ascertaining greater commercial success on both the sides of the Atlantic. This can be justified with reference to the fact that the formation along with the maintenance of smooth financial interrelation between the aforesaid two nations have opened prospects for better investments, fostered trade in products or services and most vitally facilitated in enhancing the mobility of the individuals through undertaking various major initiatives. One of the initiatives in this regard can be apparently observed as the formation of the Visa Waiver Program. It is determined that the EU and the US are regarded as the t wo biggest air transportation markets throughout the globe. This is owing to the reason that both of these markets together account for in excess of half of all worldwide scheduled passenger travel and 71.7 percent of the globe’s freighter fleet. The formation of the EU-US Open Skies Agreement, which had been signed in the year 2007 and became effective in the year 2008, can be duly considered as a historic decision, as it not only broadly supports the aspect of trade liberalisation but also promotes the development of better international trade as well. In the context of analysing the EU-US Open Skies Agreement, it can be affirmed that aviation often plays a decisive role in driving the vital aspect of globalisation, contributing in expanding travel along with tourism and enabling the business entrepreneurs to make substantial investments. Furthermore, it also plays an imperative part in facilitating trade through bringing business people along with conducting their respecti ve operational functions jointly and developing the products or services in relation to the respective industry. By taking into concern these valuable roles played by aviation, it can be stated that both the regions i.e. the US and the EU had signed the agreement in order to transform and enhance the existing procedure of air travel and trade throughout the Atlantic (Alford &

Islam in America Essay Example | Topics and Well Written Essays - 750 words

Islam in America - Essay Example In early days of Islam in America people from Arab countries and Asian countries moved alone as well as with their families to America. They came purposely to have good earning from the America and planned to return to their home countries, but many of them had become the part of the land where they were earning. Muslim migrants were not able to make full conversation in English language in early days and preferred to work as labor and the peddling in their new living places of America. They worked as cheap labor for not having the proper skills and language barriers. In the early days of Islam in America, Muslims felt difficulties aroused from cultural differences, and low-level technical experience. American Muslims faced social challenges as their rituals differed from Native Americans. Muslims required religious institutions and prayer places. The Muslims were not entertained in educational institutes and business organizations to perform their daily prayers. The fasting in the month of Holy Ramadan has no accommodation for the American Muslims. The initial Muslim migrates tackled these problems with courage that maintained their identity as a Muslim in the community. The American Muslims were shifted from other countries, and they started their small businesses like coffee shops, restaurants, bakeries and small type of grocery stores. Traditional dishes of Muslims are well known among the communities living in America. With the passage of time, some of the famous Arabian dishes like Shawarma, and Tabular became famous among all communities o f The United States. The people other than the white Americans are commonly called ‘colored’ due to a little darker color. In the early 1930s, Muslims of Lebanese established a praying place called the Masjid or Mosque in the little Town Ross. It was the first Mosque built in America. The second Mosque was built in the Rapid City of Lowa. An Islamic center was constructed in 1914 in the Michigan City, Indiana.

Law Essay Example | Topics and Well Written Essays - 3750 words

Law - Essay Example These damages can be physical injuries, damage to property, and pure economic loss. The pure economic loss does not necessarily have to be with the presence of physical loss and damage to property. Sometimes there are losses that are purely economic and are caused by the breach of a duty of care. The duties of care that result in pure economic loss are mostly related to the business scenarios where the loss may occur in the form of economic for the business or for any specific employee (Edwards, 2008, 357). In one scenario, because of the power cut the business had to stop their production and was forced to shut down their factory temporarily. In this scenario, the loss was purely economic as the business claimed for the loss of profit that they could have gained in the time period when the factory was shut down. The courts held that this loss was neither calculable nor recoverable. The heads of the factory had a duty of care towards the business to make sure that power supply is pro vided to the factory so that no damage to property or economic loss can occur. In another scenario, if the business has provided the employees with the damaged or defected equipment and during the working hours, they get injured due to the fault of the equipment, the business will be held liable for breaching their duty of care towards the employees to provide them safety at work, unless the work is risky or the risk is reasonably foreseeable. Under the principles of negligence, the business will have to compensate the physical injury of the employee. And the employees can also claim for economic loss by claiming for the absence at work because of which they couldn’t earn. The courts will also order the business to compensate to those losses (Okrent, 2009, 58). The defense that the business can use in the case when there is a physical injury is contributory negligence. This is a common law defense which stated that anyone who was partly responsible for the harm done to them c ould not recover in tort. In the business scenario where the equipment provided was damaged and moreover, the employee worked with it in an improper way which caused the injury, the employee will be considered contributory negligent and no liability will be imposed on the business. To make sure that contributory negligence has taken place, it is important to first find out whether the defendant was negligent or not. Thus, it was seen that in many cases the claimant’s behavior was negligent which contributed to the accident and the damages (Statsky, 2011, 131). Volenti non fit injuria is also a defense which can be used in the business scenarios. This Latin phrase means ‘no injury can be done to a willing person’. It describes a defense which applies where the claimant has in some way consented to what was done by the defendant, on the basis that in giving consent the claimant was voluntarily taking the risk of harm. This applies in many business scenarios where t he jobs are mostly risky and may need technical expertise. For these jobs, the employees should voluntarily consent to the acceptance of the risk that will be throughout their job. Thus, this defense can be used by the businesses where consent can be proven (Barnes, Best, 2007, 151). Applying elements of vicarious liability to business scenarios: Vicarious liability occurs where

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

San Francisco and Chinatown Essay -- California Place History American

San Francisco and Chinatown Gilded age San Francisco stood as a beacon for travelers bound for the western coast of the United States. The most prominent city in the developing west during the latter parts of the nineteenth century and the opening of the twentieth, San Francisco encompassed a range of conflicting identities. This time period marked a transitory stage in the development of San Francisco, evolving from a booming â€Å"frontier town† to a â€Å"civilized metropolis,† the emerging San Franciscan identity retained qualities from both poles of this spectrum. Chinatown, existing as a city within the city, shared this relationship of extremes with San Francisco. To travelers visiting San Francisco, Chinatown was a necessary stop. The writings in travelogues published during this period describe Chinatown through a mix of revulsion and curiosity, its inhabitants virtuous and sub-human. In short, within the developing city of San Francisco, an expedition into Chinatown remaine d a visceral exploration of a foreign and exciting environment. Emily Faithful, an Englishwoman writing in 1884, traveled through America in order to explore the changing position of women during the nineteenth century.[1] Faithful remarked, â€Å"San Francisco is a city of strange contrasts. Perhaps there is not a faster place in the world, and yet there are few more conspicuous for works of true benevolence. There is more drinking, and more fanatical total abstinence than I ever encountered elsewhere†¦Ã¢â‚¬ [2] Faithful focused mainly upon the moral decay accompanying San Francisco’s prosperity, however she closed her description of San Francisco by contrasting the decadence of the â€Å"so-called society set,†[3] to the equally large â€Å"cultured... ...ities (Philadelphia: Hubbard brothers, 1883), 455 and Alfred Falk, Trans-Pacific sketches; a tour through the United States and Canada (Melbourne: G. Robertson, 1877), 23. [8] Glazier, Peculiarities of American Cities, 464. [9] Nicholas Everitt, Round the world in strange company; America, British Columbia and the west (London: T. W. Laurie Ltd., 1915), 270. [10] Green, Notes, 65. [11] Glazier, Peculiarities of American Cities, 468. [12] Glazier, 469. [13] Catherine Bates, A Year in the Great Republic (London, Ward & Downey, 1887), 140. [14] Ibid. [15] Bates, A Year in the Great Republic, 141. [16] Ibid. [17] ibid, 142. [18] Glazier, Peculiarities of American Cities, 469. [19] Green, Notes on New York, San Francisco, and Old Mexico, 71. [20] Ibid. [21] Green, Notes, 71. [22] Glazier, Peculiarities, 470. [23] Ibid, 471.