It's Microeconomics question.
1. The impact of gender quotas in electoral lists To address the scarcity of women in politics, in recent years many countries have adopted gender quotas in candidate lists requiring the presence of a minimum share of female candidates. By construction, these quotas increase the share of female candidates. However, it remains an empirical question whether they also lead to an increase in the share of women getting elected or reaching top political positions. Let us focus in the case of local elections in Spain. Within a proportional representation electoral system with closed lists, a quota requiring the presence of at least 40% of candidates of each gender on the ballot was implemented in 2007 in municipalities with more than 5,000 inhabitants. (a) One could use a difference-in-differences (DID) strategy to analyse the impact of these quotas on the probability that a woman is elected as mayor. Explain briefly how would you do it: write down the equation that you would estimate for a simple DID and define all its terms. Explain also briefly what are the key requirements that would make the DID strategy adequate in this context and potential threats to its validity. (3 marks) (b) Let us consider the possibility of using a regression discontinuity design (RDD) to estimate how quotas affect the probability that a woman becomes mayor. Explain briefly what are the key requirements that would make the RDD strategy adequate in this particular context. (3 marks) (c) Let us now compare the RDD and the DID estimates. Do they identify the impact of the treatment on the same type of municipalities? Which one is likely to provide more precise estimates? Which one is likely to provide more consistent estimates? (4 marks)The government of a country suffering from hyperinflation has sponsored an economist to monitor the price of a "basket" of items in the population's staple diet over a one-year period. As part of his study, the economist selected six days during the year and on each of these days visited a single nightclub, where he recorded the price of a pint of lager. His report showed the following prices: Day (i ) 8 29 57 92 141 148 Price ( P; ) 15 17 22 51 88 95 In P 2.7081 2.8332 3.0910 3.9318 4.4773 4.5539 [i= 475 [i =54,403 [InP, = 21.5953 _(InP,)2 = 81.1584 Liln P; = 1,947.020 The economist believes that the price of a pint of lager in a given bar on day i can be modelled by: In P, = a + bite; where a and b are constants and the e; 's are uncorrelated /(0,o) random variables. (i) Estimate a , b and oz . [5] (ii) Calculate the linear correlation coefficient r. [1] (iii) Obtain a 99% confidence interval for b . [2] (iv) Determine a 95% confidence interval for the average price of a pint of lager on day 365: (a) in the country as a whole (b) in a randomly selected bar. [7] [Total 151The effectiveness of a tablet containing :31 mg of drug 1 and :32 mg of drug 2 was being tested. In trials the following results were obtained: 92.5 23.3 94.9 39.3 25.2 94.1 49.2 93.9 95.2 2}: =439.2 25:1 = 235 252 = 232.3 211'\": =11,2o2.33 23% =12,333.42 2ny = 22,323.23 2153:: =19.32o.22 2.31.32 =3.935.93 [i] Using the multiple linear least square regression model: }' = '1 +51I1+ 3212 +3 (a) Show that the least squares estimates of or . ,51 and 132 satisfy: 2 I": =H+ 1512591 + 1322122 2 F2121 = I51'23'3'14' 512131 + 522122121 2 39122 = 33211.2 + 23121313. + 3221312 (b) Hence. using the above data, nd their numerical values. ['2'] [ii] Pmclict the percentage effectiveness for a tablet containing 51.3 mg of chug :31 and 13.3 mg of drug 1:2. [2] [Total 9]
