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ECOS2002 Assignment 1 Due: Week 4 1. Class survey Go to Canvas and answer the questions in the Economic Growth and Expectations of Inflation Quiz. 2. Data Exercise Go to https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project- database-2020 and download the Maddison Dataset. (a) Find the value of GDP per capita for Australia and China in 1820 and 2018. Calculate the average annual growth rate for the two countries (b) Find the value of GDP per capita for Australia and China in 1990. Calculate the average annual growth rate between 1990 and 2018 (c) Using the rule of 70, approximately how many years will it take for GDP per capita to double in Australia if GDP per capita growth continues at the 1990-2018 pace? How many years will it take for China’s GDP per capita to double? 3. Immigration in the Simple Model of Production: Using graphs, show the effect of decreased immigration (L ⇓) due to a pandemic, for example, on output, real wages, and real rental rates. Explain your answer. 4. Immigration in the Solow-Swan Model: (a) What is the effect of decreased immigration on the steady state level of capi- tal? Explain your answer. (b) What is the effect of decreased immigration on steady state GDP per capita? Explain your answer. 5. Real Wages and Capital Accumulation: The pandemic has caused many com- panies and individuals to increase investment in video conferencing technologies such as Zoom. This investment has increased the capital stock. 1 (a) Using the simple model of production, graph the effect of an increase in the capital stock on the real wage. (b) Now using the Solow-Swan model, consider an increase in investment/saving (s ⇑). What does such an increase imply for real wages in steady state? Explain your answer. (Hint: Think about how the Solow-Swan model and the simple model of production are related.) (c) Using the Solow-Swan model, draw a picture of what the transition path of real wages would look like between the low investment rate steady state and the high investment rate steady state when s ⇑. 6. Consider the following equations Kt+1 = sYt + (1− δ)Kt (1) Lt+1 Lt = (1 + n) (2) (a) Define in words what equation (2) means. (b) Divide equation (1) by Lt and use equation (2) to express the capital accumu- lation equation in per worker variables (kt = Kt/Lt and yt = Yt/Lt). Show your work. (This question has a bit of trick to it. Note that Lt+1/Lt+1 = 1 and pay careful attention to the time subscripts.) (c) Now substitute in the production function into your answer for (b) and cal- culate the steady state for capital per worker. How does capital depend on (1 + n) on the balanced growth path? Show your work. Microsoft Word - Assignment1_2021v5.docx 1 ATHK1001 ANALYTIC THINKING: ASSIGNMENT 1, 2021 Due date: 11:59pm Friday, April 23rd (Week 7). Late penalty of 5% per calendar day applies. Online submission: All submissions are to be made online on the ATHK1001 Canvas website. Submissions will be checked for plagiarism. Incorrect submissions: If you discover before the closing date that the file you submitted on Turnitin was incorrect, and let us know, you may be given the option to resubmit a corrected version which will incur a 50% penalty or the relevant lateness penalty, whichever is greater. Word length: 750 words across all questions (excluding references in Question 12). A penalty of 10% will apply to papers that exceed this limit by more than 10%, a 20% penalty if you exceed 20% of the limit, and 30% if you exceed the limit by 30%. Total marks: 60 (17.5% of total grade for class) Background and Aims Sometimes people do not have access to the data they need, so they have to make informed estimates. However, there can be biases in people’s estimates. Tversky and Kahneman (1974) identified one such bias they called “anchoring” (amongst other biases in decision making). They showed that when people tried to estimate the numerical answers to a question they do not know, they can be influenced by a number they have just seen. For example, if people estimate the proportion of African countries in the United Nations, then they give higher estimate if they first had to say if the answer was higher or lower than 65 rather than 10. This was true even though they were told that the number was randomly generated. The first number appeared to anchor their estimate and drag the estimate towards the anchor. In the experiment you did during Week 2 tutorials explored the anchoring bias to estimation. Although participants in Tversky and Kahneman’s (1974) study was told that the number was randomly generated, perhaps they did not believe the experimenter and instead thought the number they were given was actually related to the true answer, so rather than the number they were given being an anchor it may have been regarded as useful information. To test this possibility, in our experiment for one of our tasks we had participants generate a number based on their own phone number, so they knew it was unrelated to the question. Participants then said whether or not the Attila the Hun was defeated at the Battle of Chalons on a date before or after the year equal to their phone number (plus 100). They then estimated the true answer. If a knowingly random number can anchor an estimate, then the higher a participant’s phone number the higher the estimate should be. The anchoring bias seems to imply that any random number we are exposed to could influence us whenever we have to try to estimate a numerical answer. So an important question is how wide is the scope of the anchoring effect? We tested this using a task based on that used by Strack and Mussweiler (1997). We gave participants a set of pairs of questions. The first question in the pair asked them whether the answer to the question was higher or lower than a given answer (i.e., the anchor), the second question in the pair asked them to either give a numerical answer to the same question or to a different question. The anchor was either substantially higher than the true answer or substantially lower than it. If to be influential an anchor must be directly related to the number being estimated, then when the anchor is high estimates should be higher when the second question in the pair is the same than when it is different, and when the anchor is low estimates should be lower when the second question in the pair is the same than when it is different. In the class experiment we examined hypotheses about both of these tasks. Method Participants A total of 294 students from analytic thinking course (ATHK1001) participated as part of a class experiment. Additional students participated but either did not complete the experiment or did not consent 2 to having their data analysed. Of these 177 were female, 117 were male and they had a mean age 19.5 years). Materials For the Phone task participants answered three questions: “Think of the last 3 digits of your telephone number, now add 100 to its value, then write the answer in the box below” (adding 100 was a way to make participants focus on the number). “Looking at the number you created above did the following event occur before or after this date AD: Attila the Hun was defeated at the Battle of Chalons” “Provide your estimate of what year AD the event occurred” (true answer is 451) An example of the questions asked in the Paired task was the following: “Please indicate whether you think the true answer for the quantity is higher or lower than the random number in blue. The population of New York City in 2019 was [anchor number in blue] ”. They then answered on the following two questions: “What is the population of New York City in 2019?” [Same question condition] “What was the number of babies born in the USA in 2018?” [Different question condition] The eight other questions in the Same question condition were: What was the total livestock population of ducks in France in 2008? In 2014, what was the Gross National Debt of the Republic of Congo (US$)? What is the length of time an American person spends eating dinners per year in minutes? What was the weight of King Henry 8th of England (in pounds)? What is the total area (square kilometres) of Chile? What is the height of the mountain K2 (in feet)? What is the annual consumption of dairy products per person (in pounds)? What was the total worldwide gross of the film 'How to Train Your Dragon II' (US$)? The eight other questions in the Different question condition were: In 2012, what was the annual consumption of electricity (megawatt hours) in Kazakhstan? What was the lowest daily value of shares traded on New York Stock Exchange in Year 2003 (US$)? What is the average time spent by a person per year on social networking online (minutes)? How much does a typical refrigerator weight (in pounds)? What is the population of Suriname? What is the average distance a car travels per year (in km)? What is the average annual water consumption per household (in Liters)? What were the global iPhone sales during 2018 (units)? The high anchors were randomly generated but always had a magnitude one greater than the true answer. The low anchors were randomly generated always with a magnitude one less than the true answer. Design and Procedure The experiment had two independent variables: Question condition, either same or different; Anchor condition, either high or low. These independent variables were varied between participants, each participant received all their paired questions in either the same or different condition, and they received either all the high anchor high or all the low anchors. During tutorials for the class Analytic Thinking at the University of Sydney participants completed the experiment individually on computers in class or online. They then completed the experiment in a set of steps. First participants answered some demographic questions, then they did the Paired task, and then the Phone task. After completing the experiment participants indicated whether or not they consented to having their data included in the data set. 3 Hypotheses We proposed four hypotheses related to anchoring effects. First, we will test whether there