15. Suppose you are hired by the MOH to study the determinants of child health. In additionto several factors which can be measured quantitatively each child falls in one incomequintile from first quintile (Q1)—the poorest 20% of the household population to thefifth quintile (Q5)—the wealthiest 20%. How many dummy variables should be includedin the regression equation for income?A. 7B. 5C. 6D. 416. In the following equation, GDP refers to gross domestic product, and FDI refers toforeign direct investment. In(GDP) = 2.65 +0.222ln(BANKCREDIT) + 0.527FDIWhich of the following statements is then true?A. If GDP increases by 1%, bank credit increases by 0.222%, the level of FDI remainingconstant.B. If bank credit increases by 1%, GDP increases by 0.222%, the level of FDI remainingconstant.C. If GDP increases by 1%, bank credit increases by log(0.222)%, the level of FDIremaining constant.D. If bank credit increases by 1%, GDP increases by log(0.222)%, the level of FDIremaining constant.17. The following model predicts annual savings of individuals on the basis of annual incomeand bank account ownership.SAVINGS = Bo+ B₁BACCOUNT + B₂INCOME+u.The variable 'BACCOUNT' takes a value of 1 if the person owns a bank account and 0 ifthe person has no bank account. INCOME is income of the individual. The inclusion ofanother binary variable in this model that takes a value of 1 if a person has no bankaccount and 0 if the person has a bank account, will give rise to the problem ofA. omitted variable biasB. autocorrelationC. perfect collinearityD. heteroskedastcity18. In a regression based on 30 annual observations, farm income was related to fourindependent variables-grain exports, government subsidies, population, and a dummyvariable for bad weather years. The model was fitted by least squares, resultingin a Durbin-Watson statistic(d) = 1.12. which decision and conclusion is correct?A. Reject Ho at 5% significance level and conclude that the error terms has positiveautocorrelationB. Do not reject Ho at 5% significance level and conclude that the error terms haspositive autocorrelationC. Reject Ho at 5% significance level and conclude that the error terms has negativeautocorrelationD. Do not reject Ho at 5% significance level and conclude that the error terms hasnegative autocorrelation

A dummy variable is a binary (0/1) variable used in statistical models to represent a categorical…

15. Suppose you are hired by the MOH to study the determinants of child health. In addition to several factors which can be measured quantitatively each child falls in one income quintile from first quintile (Q1)—the poorest 20% of the household population to the fifth quintile (Q5)-the wealthiest 20%. How many dummy variables should be included in the regression equation for income? A. 7 B. 5 C. 6 D. 4

15. Suppose you are hired by the MOH to study the determinants of child health. In addition to several factors which can be measured quantitatively each child falls in one income quintile from first quintile (Q1)—the poorest 20% of the household population to the fifth quintile (Q5)-the wealthiest 20%. How many dummy variables should be included in the regression equation for income? A. 7 B. 5 C. 6 D. 4

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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Paddy has lots of cousins. With a family reunion in the near future, Paddy decides to collect income information for himself and all his cousins. He obtains the following data points: Incomes: $52, 000 $22, 000 $92, 000 $8, 000 $118, 000 $62, 000 $38, 000 $14, 000 $132, 000 $46, 000 $26, 000 $96, 000 $54, 000 $110, 000 $80, 000 What is the percentage of income received by the third quintile of the income distribution of Paddy's cousins? Round your answer to the nearest tenth.
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Cristina is excited with these averages and tells her public relations team to prepare a glossy brochure to showcase this $250 increase in household income. which of the following must be true to confidently conclude that this $250 difference in average income was caused by access to a UThere The herders who bought UThere phones would have had higher income than the herders who did not even if they had not purchased the UThere phone. The herders who bought UThere phones would have had the same income as the herders who did not even if they had not purchased the UThere phone. We don’t need to worry about Selection Bias since Nepal and Tibet are small countries with more or less similar people. The equation for Selection Bias does not provide enough information since we do not know the counterfactuals.
23. An education economist estimated the wage equation to determine the rate of return to an additional year of education and obtained ln(WAGE) = -0.5220+0.1075×EDUC. it is anticipated that ability ( attributes, such as intelligence, creativity, perseverance and industriousness) is positively correlated with UDUC. If a measure of a bility is omitted from the model the estimated return to education A. Is biased downwards B. Is biased upwards C. Cannot be estimated D. All the above
Consider the population regression of log earnings [Y;, where Y,= In(Earnings,;)] against two binary variables: whether a worker is married (D₁, where D₁;= 1 if the th person is married) and the worker's gender (D2;, where D₂;= 1 if the th person is female), and the product of the two binary variables Y₁ = Po+B₁D₁+P₂D2i + P3 (D₁¡ × D₂i) + Hi- The interaction term: O A. indicates the effect of being married on log earnings. B. does not make sense since it could be zero for married males. C. allows the population effect on log earnings of being married to depend on gender. D. cannot be estimated without the presence of a continuous variable.
Suppose we are interested in evaluating the impact on wages of being a college graduate and of residing in California. To this end we define the following dummy variables if college graduate if not college graduate coll = ncoll = if not college graduate if college graduate 1 cali = if residing in California S 1 if not residing in California ncali = if not residing in California if residing in California and for wages denoting yearly wages, we estimate the following model of returns to education wages = B1 coll+ B2ncoll + B3 cali + B4(coll x cali) + e where E[e|coll, cali] = 0. Which parameter or combination of parameters measures the increase in expected wages from obtaining a college degree (comparing to not having a college degree) when residing California? O a. None of the other options is correct O b. Bi – B2 O c. B1 – B2 + ß4 O d. B4 O e. Bi + B3 – ßi – B4 O f. Bi + B4 – ß2 – B4 Clear my choice
According to Grossman model, what would happen to the demand for health stock if wages go down? A) Would MEI shift to the right/left/stay the same? B) would MC curve shift up/down/stay the samer C) would optimal level of heath stock go up/down/stay the same?
Which of the following hypotheses argues that a third variable, "patient", determines both health and wealth: Fuchs hypothesis efficient producer hypothesis thrifty phenotype hypothesis allostatic load hypothesis access to care hypothesis
What are the main models for analyzing quasi-experiment data? Please briefly describe the main ideas and differences between these models.