1 00:00:00,040 --> 00:00:02,390 The following content is provided under a Creative 2 00:00:02,390 --> 00:00:03,680 Commons license. 3 00:00:03,680 --> 00:00:06,630 Your support will help MIT OpenCourseWare continue to 4 00:00:06,630 --> 00:00:09,980 offer high quality educational resources for free. 5 00:00:09,980 --> 00:00:12,820 To make a donation or view additional materials from 6 00:00:12,820 --> 00:00:16,560 hundreds of MIT courses, visit MIT OpenCourseWare at 7 00:00:16,560 --> 00:00:17,810 ocw.mit.edu. 8 00:00:20,635 --> 00:00:25,330 PROFESSOR: So we now have, hopefully, I don't know. 9 00:00:25,330 --> 00:00:28,580 I've been going to some of the groups and we seem to be there 10 00:00:28,580 --> 00:00:30,500 for at least one of the groups I surveyed 11 00:00:30,500 --> 00:00:32,420 by the end of yesterday. 12 00:00:32,420 --> 00:00:34,420 We have a question. 13 00:00:34,420 --> 00:00:37,250 We know what we'd like to evaluate, and we have some 14 00:00:37,250 --> 00:00:39,470 kind of a design. 15 00:00:39,470 --> 00:00:46,160 So we know how we are going to get a sample. 16 00:00:46,160 --> 00:00:49,540 We know how we are going to randomize. 17 00:00:49,540 --> 00:00:54,890 And that's a good start. 18 00:00:54,890 --> 00:00:59,700 So what we are going to do today is getting to know that 19 00:00:59,700 --> 00:01:01,740 we have a way of randomizing. 20 00:01:01,740 --> 00:01:04,920 We're going to get into the details of how we are actually 21 00:01:04,920 --> 00:01:08,120 going to go about collecting the data for the evaluation. 22 00:01:08,120 --> 00:01:10,600 So it's not going to be so much about the program, it's 23 00:01:10,600 --> 00:01:14,390 going to be about thinking how we really 24 00:01:14,390 --> 00:01:15,880 want to do the survey. 25 00:01:15,880 --> 00:01:21,540 Thinking about what is the sample size we need, how many 26 00:01:21,540 --> 00:01:26,980 data points we need, and what data should we go about 27 00:01:26,980 --> 00:01:30,630 collecting, and how much is it going to cost, and what are 28 00:01:30,630 --> 00:01:33,170 the trade-offs we are facing when we are trying to address 29 00:01:33,170 --> 00:01:34,460 those questions. 30 00:01:34,460 --> 00:01:40,400 So we could be spending from one week to one 31 00:01:40,400 --> 00:01:41,790 year on these questions. 32 00:01:41,790 --> 00:01:44,880 In particular, on the survey design aspect. 33 00:01:44,880 --> 00:01:47,290 These are complicated questions. 34 00:01:47,290 --> 00:01:50,530 Not many people who are much more qualified, certainly, 35 00:01:50,530 --> 00:01:53,740 than me on this issue of how to ask the 36 00:01:53,740 --> 00:01:55,260 right question correctly. 37 00:01:55,260 --> 00:01:58,820 So we are not going to get into any of that. 38 00:01:58,820 --> 00:02:03,330 It's going to remain at a somewhat abstract level of 39 00:02:03,330 --> 00:02:04,040 business one. 40 00:02:04,040 --> 00:02:07,360 It's going to be both abstract and quite particular in a way. 41 00:02:07,360 --> 00:02:10,289 At the abstract level is how do we even think about the 42 00:02:10,289 --> 00:02:12,000 type of data we want to collect? 43 00:02:12,000 --> 00:02:15,180 And at the concrete level it's we're going to do it for one 44 00:02:15,180 --> 00:02:19,280 particular example, which is this Panchayat example which 45 00:02:19,280 --> 00:02:22,630 happens to be one that I know pretty well. 46 00:02:22,630 --> 00:02:24,260 AUDIENCE: [UNINTELLIGIBLE] 47 00:02:24,260 --> 00:02:26,245 PROFESSOR: Not making much progress, huh? 48 00:02:28,960 --> 00:02:36,440 So what are the questions we need to know the answer to 49 00:02:36,440 --> 00:02:39,640 before getting ready to start our evaluation? 50 00:02:39,640 --> 00:02:43,140 One is what data should we collect? 51 00:02:43,140 --> 00:02:44,690 It's kind of a big question. 52 00:02:44,690 --> 00:02:46,930 What should go in the questionnaire? 53 00:02:46,930 --> 00:02:49,530 Anybody among you who's been involved in doing 54 00:02:49,530 --> 00:02:50,810 questionnaires knows that you cannot 55 00:02:50,810 --> 00:02:53,240 ask a million questions. 56 00:02:53,240 --> 00:02:55,880 And so how do we choose what to put in? 57 00:02:55,880 --> 00:03:00,580 And then, what systems we should put in place to ensure 58 00:03:00,580 --> 00:03:02,380 that the quality of the data is good? 59 00:03:05,690 --> 00:03:07,650 And what sample size we need to plan. 60 00:03:07,650 --> 00:03:09,720 So not going to do sample size this morning. 61 00:03:09,720 --> 00:03:16,640 It has its own little lecture and a firm set of exercises. 62 00:03:16,640 --> 00:03:18,560 So that's going to go this afternoon. 63 00:03:18,560 --> 00:03:21,280 We are going to be doing a lot of that and 64 00:03:21,280 --> 00:03:24,400 a bit of that today. 65 00:03:24,400 --> 00:03:29,710 So let's start with the Panchayat example. 66 00:03:29,710 --> 00:03:33,590 So let's talk a bit about the setting. 67 00:03:33,590 --> 00:03:36,220 So the setting is a reservation for women in the 68 00:03:36,220 --> 00:03:39,550 Panchayat region in India. 69 00:03:39,550 --> 00:03:48,110 So what's the Panchayat and what's the reservation policy? 70 00:03:52,700 --> 00:03:54,300 So the rule of the game, I don't 71 00:03:54,300 --> 00:03:56,540 ask rhetorical questions. 72 00:03:56,540 --> 00:03:59,910 So when I ask a question, someone needs to answer 73 00:03:59,910 --> 00:04:00,550 otherwise we're stuck. 74 00:04:00,550 --> 00:04:03,793 AUDIENCE: It was a program designed to decentralize the 75 00:04:03,793 --> 00:04:06,538 allocation of public resources in villages 76 00:04:06,538 --> 00:04:08,534 to the lower level. 77 00:04:08,534 --> 00:04:11,650 And at the same time, you wanted to ensure that meant 78 00:04:11,650 --> 00:04:16,000 minorities and scheduled tribes in India got 79 00:04:16,000 --> 00:04:19,975 represented somehow, and the preferences and opinions at 80 00:04:19,975 --> 00:04:22,420 this Panchayat level. 81 00:04:22,420 --> 00:04:23,380 PROFESSOR: That's right. 82 00:04:23,380 --> 00:04:28,390 So the policy is Panchayat stands to 83 00:04:28,390 --> 00:04:30,460 consider five people. 84 00:04:30,460 --> 00:04:32,920 And maybe in some historical India there was this council 85 00:04:32,920 --> 00:04:35,970 of five people who make decisions for the villages. 86 00:04:35,970 --> 00:04:38,560 Or maybe not, but anyway, that's where 87 00:04:38,560 --> 00:04:40,520 the name comes from. 88 00:04:40,520 --> 00:04:44,840 And the idea, and that's not only India, it's something 89 00:04:44,840 --> 00:04:47,470 that you see in a lot of developed and developing 90 00:04:47,470 --> 00:04:52,890 countries today, is that decisions regarding local 91 00:04:52,890 --> 00:04:58,010 public goods such as drinking water infrastructure, the 92 00:04:58,010 --> 00:05:00,340 local wards, the buildings-- 93 00:05:00,340 --> 00:05:03,470 at least the buildings so, for example, schools, health 94 00:05:03,470 --> 00:05:05,720 facilities and such things-- 95 00:05:05,720 --> 00:05:12,310 would better represent people's need if people had a 96 00:05:12,310 --> 00:05:14,690 say in what they wanted. 97 00:05:14,690 --> 00:05:16,770 So it's very difficult to know if our bureaucrats sitting in 98 00:05:16,770 --> 00:05:20,280 Delhi that such and such village in the middle of Bihar 99 00:05:20,280 --> 00:05:23,110 needs a road as opposed to a water well. 100 00:05:23,110 --> 00:05:26,480 And so you can get this vast misallocation of 101 00:05:26,480 --> 00:05:28,150 money in this way. 102 00:05:28,150 --> 00:05:33,410 So the idea with the Panchayat is that now even though the 103 00:05:33,410 --> 00:05:36,960 revenue collection is still pretty much not coming from 104 00:05:36,960 --> 00:05:40,120 the villages where there is not much taxation ability, 105 00:05:40,120 --> 00:05:43,415 this one defending decision should be taken increasingly 106 00:05:43,415 --> 00:05:48,790 at this local level to ensure that better adequation, a 107 00:05:48,790 --> 00:05:52,830 better fit between what you build and what people want. 108 00:05:52,830 --> 00:06:02,770 The problem with that, of course, is that when you have 109 00:06:02,770 --> 00:06:05,660 local decision making it's who is going to be 110 00:06:05,660 --> 00:06:07,950 controlling the power. 111 00:06:07,950 --> 00:06:10,680 So India, like many other countries, perhaps even more 112 00:06:10,680 --> 00:06:14,200 than other countries, has this history of these conditions 113 00:06:14,200 --> 00:06:16,230 again some particular group. 114 00:06:16,230 --> 00:06:18,820 So, for example, the scheduled caste who are the former 115 00:06:18,820 --> 00:06:19,080 untouchable? 116 00:06:19,080 --> 00:06:22,410 The scheduled tribe who don't even have a caste? 117 00:06:22,410 --> 00:06:29,450 And so the worries that there are enough people, strong 118 00:06:29,450 --> 00:06:32,370 people, in these groups, that at the national level they can 119 00:06:32,370 --> 00:06:37,210 organize to make sure that there are policies that help 120 00:06:37,210 --> 00:06:38,980 these particular groups. 121 00:06:38,980 --> 00:06:39,930 Same thing for women. 122 00:06:39,930 --> 00:06:42,590 There definitely are very strong women in India. 123 00:06:42,590 --> 00:06:48,890 But in any local villages, you might not have a minority of 124 00:06:48,890 --> 00:06:54,780 scheduled casual tribal women who are able to take 125 00:06:54,780 --> 00:06:57,240 responsibility to ensure that their group is represented. 126 00:06:57,240 --> 00:06:59,910 So they might never be represented, you might have 127 00:06:59,910 --> 00:07:02,370 some tyranny of the majority. 128 00:07:02,370 --> 00:07:06,660 So that's why the system also introduced this reservation 129 00:07:06,660 --> 00:07:10,960 concept, which is to ensure some representation. 130 00:07:10,960 --> 00:07:15,270 So how does a reservation work? 131 00:07:15,270 --> 00:07:19,260 How did they put it in place? 132 00:07:24,170 --> 00:07:27,020 AUDIENCE: They reserved a certain number of seats for 133 00:07:27,020 --> 00:07:30,281 women in each council. 134 00:07:30,281 --> 00:07:31,120 PROFESSOR: Right. 135 00:07:31,120 --> 00:07:35,710 So there is one reservation at the level of the council. 136 00:07:35,710 --> 00:07:38,100 So we can say these are various councils. 137 00:07:41,050 --> 00:07:44,570 Each council has about, say, 10-12 representatives which 138 00:07:44,570 --> 00:07:48,250 represent a population of 10,000 to 12,000. 139 00:07:48,250 --> 00:07:50,960 And so the first reservation is within each council. 140 00:07:50,960 --> 00:07:52,896 How many seats for women? 141 00:07:52,896 --> 00:07:53,730 AUDIENCE: One third. 142 00:07:53,730 --> 00:07:55,050 PROFESSOR: One third. 143 00:07:55,050 --> 00:07:59,130 So each council gets one third for women. 144 00:07:59,130 --> 00:08:02,900 And what about SC and ST What do they get? 145 00:08:02,900 --> 00:08:04,508 STUDENT: In proportion to their population? 146 00:08:04,508 --> 00:08:06,480 PROFESSOR: In proportion to their population. 147 00:08:06,480 --> 00:08:08,080 Not in that particular village, but 148 00:08:08,080 --> 00:08:09,380 in the whole district. 149 00:08:09,380 --> 00:08:12,290 So, for example, in Birbhum, there is about, I think, 30% 150 00:08:12,290 --> 00:08:13,810 of scheduled caste. 151 00:08:13,810 --> 00:08:19,910 So every village needs to have 30% of scheduled caste, even 152 00:08:19,910 --> 00:08:21,380 if they have much less. 153 00:08:21,380 --> 00:08:23,680 Except that if they have less than five, they're exempted. 154 00:08:26,390 --> 00:08:29,020 If you have no scheduled tribe in the village, you can not 155 00:08:29,020 --> 00:08:31,320 have a scheduled tribe representative. 156 00:08:31,320 --> 00:08:33,870 So that's the first thing. 157 00:08:33,870 --> 00:08:39,570 So are we going to be able to evaluate the impact of having 158 00:08:39,570 --> 00:08:46,050 this policy which has, within each council, has one third of 159 00:08:46,050 --> 00:08:49,000 the seats to a woman. 160 00:08:49,000 --> 00:08:50,940 AUDIENCE: No, because there's no control. 161 00:08:50,940 --> 00:08:51,690 PROFESSOR: Right. 162 00:08:51,690 --> 00:08:52,750 That's going to be very difficult 163 00:08:52,750 --> 00:08:54,420 because there is no control. 164 00:08:54,420 --> 00:08:57,400 So certainly we won't be able to look at the impact of this 165 00:08:57,400 --> 00:09:02,610 policy on what the council does as a whole. 166 00:09:02,610 --> 00:09:06,640 You might ask whether the particular village where she 167 00:09:06,640 --> 00:09:09,220 comes from, or segment of the village where she comes from, 168 00:09:09,220 --> 00:09:11,270 gets different good, possibly. 169 00:09:11,270 --> 00:09:16,620 But certainly you're not going to be able to compare the GPs 170 00:09:16,620 --> 00:09:18,170 since they are different. 171 00:09:18,170 --> 00:09:21,110 So what is the second layer of the policy that's going to be 172 00:09:21,110 --> 00:09:22,690 more helpful? 173 00:09:22,690 --> 00:09:23,514 AUDIENCE: The Pradhan? 174 00:09:23,514 --> 00:09:25,490 PROFESSOR: At the level of the Pradhan. 175 00:09:25,490 --> 00:09:28,015 And how does that work? 176 00:09:28,015 --> 00:09:29,795 AUDIENCE: It's like a rotation? 177 00:09:29,795 --> 00:09:32,330 PROFESSOR: Right. 178 00:09:32,330 --> 00:09:36,768 So how does it work for any particular election? 179 00:09:36,768 --> 00:09:38,895 AUDIENCE: It's like it gets sorted or something? 180 00:09:38,895 --> 00:09:40,140 PROFESSOR: Right. 181 00:09:40,140 --> 00:09:46,340 So what the deal exactly, is that they rank them. 182 00:09:46,340 --> 00:09:50,170 So these GPs have a serial number, which is some number 183 00:09:50,170 --> 00:09:53,890 that they had forever and ever. 184 00:09:53,890 --> 00:09:57,080 So they rank them by their serial number and by 185 00:09:57,080 --> 00:09:58,840 geographic unit. 186 00:09:58,840 --> 00:10:03,310 So they first rank them by block, and within each block 187 00:10:03,310 --> 00:10:05,980 they rank them by their serial number. 188 00:10:05,980 --> 00:10:08,560 And then the constituent three lists. 189 00:10:08,560 --> 00:10:14,540 One list of what they call general, one list of SC, and 190 00:10:14,540 --> 00:10:18,120 one list of ST. So the general list means it's not reserved 191 00:10:18,120 --> 00:10:20,360 for an SC Pradhan. 192 00:10:20,360 --> 00:10:23,900 Those SC lists means it's there for a SC Pradhan, and ST 193 00:10:23,900 --> 00:10:25,610 means it's there for a ST Pradhan. 194 00:10:25,610 --> 00:10:29,950 And the way I did this selection is that they have to 195 00:10:29,950 --> 00:10:33,530 stay with a random number which they use to say, so if 196 00:10:33,530 --> 00:10:35,750 you need to reserve it's like a very long table. 197 00:10:35,750 --> 00:10:40,560 Because it tells you if you need to reserve five GPs, you 198 00:10:40,560 --> 00:10:45,880 pick number 1, 15, 17, and 21 for SC. 199 00:10:45,880 --> 00:10:48,840 So they do this list this way. 200 00:10:48,840 --> 00:10:53,570 And then this gives them three lists, which are ordered by 201 00:10:53,570 --> 00:10:55,400 serial number. 202 00:10:55,400 --> 00:10:58,460 And in each list, they count 1-2-3, 1-2-3, 1-2-3, in the 203 00:10:58,460 --> 00:11:00,830 first election. 204 00:11:00,830 --> 00:11:03,715 All the GP number one in all the lists got 205 00:11:03,715 --> 00:11:05,130 reserved for women. 206 00:11:05,130 --> 00:11:07,330 In the second election, again this goes 207 00:11:07,330 --> 00:11:08,640 through the same process. 208 00:11:08,640 --> 00:11:11,400 And all the GP number two are reserved, and the third 209 00:11:11,400 --> 00:11:14,020 election, all the GP number three were reserved. 210 00:11:14,020 --> 00:11:16,960 So in principle there is a rotation, though it is not 211 00:11:16,960 --> 00:11:21,330 necessarily one for one in the sense that if a GP happens to 212 00:11:21,330 --> 00:11:26,210 be number one on this list in the election in 2003, and 213 00:11:26,210 --> 00:11:28,450 number two in this list in 2008, they get 214 00:11:28,450 --> 00:11:30,190 reserved twice in a row. 215 00:11:30,190 --> 00:11:34,110 Possibly if they become number three in this list, they would 216 00:11:34,110 --> 00:11:35,290 do that three times in a row. 217 00:11:35,290 --> 00:11:37,240 That could happen, too. 218 00:11:37,240 --> 00:11:40,400 But twice in a row is pretty frequent, actually. 219 00:11:40,400 --> 00:11:47,540 So this is more hopeful because now we have some GPs 220 00:11:47,540 --> 00:11:48,890 that are reserved for women. 221 00:11:51,490 --> 00:11:55,000 That says those three are reserved for women. 222 00:11:55,000 --> 00:11:57,620 And some who are not in the same year. 223 00:11:57,620 --> 00:12:00,820 So now we can compare them. 224 00:12:00,820 --> 00:12:04,110 Compare the decisions that are made in those places, compare 225 00:12:04,110 --> 00:12:07,090 anything we would like to compare, and we have a chance 226 00:12:07,090 --> 00:12:09,060 to actually identify the effects. 227 00:12:09,060 --> 00:12:09,470 Yeah. 228 00:12:09,470 --> 00:12:13,157 AUDIENCE: Can you compare if in the non-reserve you elect a 229 00:12:13,157 --> 00:12:16,370 woman, does that become part of the control group, or? 230 00:12:16,370 --> 00:12:20,830 PROFESSOR: So what's your thinking on that? 231 00:12:20,830 --> 00:12:25,790 So two things could happen, right? 232 00:12:25,790 --> 00:12:28,520 It could happen that in the places that are reserved for 233 00:12:28,520 --> 00:12:31,510 women, they elect a man anyway in defiance? 234 00:12:31,510 --> 00:12:33,040 So you don't have that problem, actually. 235 00:12:33,040 --> 00:12:34,590 They elected a woman. 236 00:12:34,590 --> 00:12:36,400 And the other thing that could happen is what you're talking 237 00:12:36,400 --> 00:12:41,710 about, is that some of those places might decide to elect a 238 00:12:41,710 --> 00:12:42,870 woman anyway. 239 00:12:42,870 --> 00:12:45,960 In fact, about 7% of them do. 240 00:12:45,960 --> 00:12:49,012 So what do you think we do with those guys? 241 00:12:49,012 --> 00:12:50,305 AUDIENCE: They're part of the control. 242 00:12:50,305 --> 00:12:52,650 PROFESSOR: We keep them in the control, right? 243 00:12:52,650 --> 00:12:55,900 Because we're going to spend more time on that tomorrow, 244 00:12:55,900 --> 00:12:59,940 but if we did put them in the treatment, then we don't have 245 00:12:59,940 --> 00:13:02,260 a random selection anymore because now in the treatment 246 00:13:02,260 --> 00:13:04,050 we have the people who are forced to have a 247 00:13:04,050 --> 00:13:05,410 woman that's on them. 248 00:13:05,410 --> 00:13:09,970 Plus the places that choose that's not random. 249 00:13:09,970 --> 00:13:17,540 So we'll be keeping them here, and this is going to mean that 250 00:13:17,540 --> 00:13:19,770 it's going to look a little bit like an encouragement 251 00:13:19,770 --> 00:13:21,990 design like you saw yesterday. 252 00:13:21,990 --> 00:13:25,310 Which is, in the places that are reserved for women, 100% 253 00:13:25,310 --> 00:13:27,720 of them have women. 254 00:13:27,720 --> 00:13:29,890 In the places that were not reserved, 255 00:13:29,890 --> 00:13:32,200 7% have women anyway. 256 00:13:32,200 --> 00:13:35,540 So the difference is not quite 100%. 257 00:13:35,540 --> 00:13:39,260 So tomorrow we'll deal with, see, so here we are going to 258 00:13:39,260 --> 00:13:42,890 focus on the impact of being reserved, and we're not going 259 00:13:42,890 --> 00:13:47,020 to be concerned so much about the impact of having a woman. 260 00:13:47,020 --> 00:13:49,460 So we are still going to always compare treatment 261 00:13:49,460 --> 00:13:50,460 versus control. 262 00:13:50,460 --> 00:13:53,680 Tomorrow you can see how, in fact, we can do something 263 00:13:53,680 --> 00:13:57,080 about thinking about how we go from the impact of being 264 00:13:57,080 --> 00:13:59,480 reserved, to the impact of being a woman if we are 265 00:13:59,480 --> 00:14:03,970 willing to make a couple more assumptions. 266 00:14:03,970 --> 00:14:05,920 So that's a better setting. 267 00:14:05,920 --> 00:14:12,440 We can now compare those places and even though the 268 00:14:12,440 --> 00:14:15,860 Indian government didn't do this beautiful experiment with 269 00:14:15,860 --> 00:14:18,460 the view of an evaluation in mind. 270 00:14:18,460 --> 00:14:19,650 And they might have. 271 00:14:19,650 --> 00:14:21,740 And then they might contact you and say, hey, we 272 00:14:21,740 --> 00:14:23,240 have this set up. 273 00:14:23,240 --> 00:14:28,060 How should we go about evaluating it? 274 00:14:28,060 --> 00:14:29,430 And so that's the name of the game. 275 00:14:32,570 --> 00:14:35,070 So I think we went through this maybe? 276 00:14:35,070 --> 00:14:40,620 We can spend a little more time on the goals. 277 00:14:40,620 --> 00:14:47,410 So what do we expect from the Panchayati raj? 278 00:14:50,080 --> 00:14:56,210 What will give us a first sense of what data we should 279 00:14:56,210 --> 00:14:59,990 think about collecting to know what's the goal of the 280 00:14:59,990 --> 00:15:01,240 institution. 281 00:15:05,960 --> 00:15:09,350 This big untraditional amendment took place in 1993. 282 00:15:09,350 --> 00:15:14,624 What was in the mind of the policy makers at that time? 283 00:15:14,624 --> 00:15:19,096 AUDIENCE: That there would be more accountability to the 284 00:15:19,096 --> 00:15:20,984 interest in priorities of interested countries? 285 00:15:20,984 --> 00:15:23,988 PROFESSOR: Right. 286 00:15:23,988 --> 00:15:26,398 AUDIENCE: Greater women's empowerment? 287 00:15:26,398 --> 00:15:31,550 PROFESSOR: So I don't know if it's a goal of the Panchayati 288 00:15:31,550 --> 00:15:35,575 raj amendment per se, but we could think it was something 289 00:15:35,575 --> 00:15:38,354 people had in mind in the reservation policies. 290 00:15:38,354 --> 00:15:40,018 AUDIENCE: That program selection would be more 291 00:15:40,018 --> 00:15:41,080 aligned with the local preferences? 292 00:15:41,080 --> 00:15:42,140 PROFESSOR: Right. 293 00:15:42,140 --> 00:15:44,860 Program selection might reflect local preferences. 294 00:15:44,860 --> 00:15:45,745 We should keep that in mind. 295 00:15:45,745 --> 00:15:48,189 AUDIENCE: To increase the quality and 296 00:15:48,189 --> 00:15:48,890 quantity of public goods? 297 00:15:48,890 --> 00:15:49,285 PROFESSOR: Right. 298 00:15:49,285 --> 00:15:51,910 We can see that there is more as a result of their 299 00:15:51,910 --> 00:15:55,620 accountability, there might be better and more public goods. 300 00:15:58,562 --> 00:16:00,745 AUDIENCE: More participation in public life? 301 00:16:00,745 --> 00:16:01,340 PROFESSOR: Right. 302 00:16:01,340 --> 00:16:05,550 We might also think that we might value the participation 303 00:16:05,550 --> 00:16:07,530 for its own good. 304 00:16:07,530 --> 00:16:09,850 I would think it's a good thing if people are involved 305 00:16:09,850 --> 00:16:10,865 in local democracy. 306 00:16:10,865 --> 00:16:11,956 AUDIENCE: They vote more? 307 00:16:11,956 --> 00:16:14,280 PROFESSOR: They vote more, participate in meetings, 308 00:16:14,280 --> 00:16:15,680 things like that. 309 00:16:15,680 --> 00:16:19,440 So that's kind of the sum of the objective of the Panchayat 310 00:16:19,440 --> 00:16:21,010 raj, generally. 311 00:16:21,010 --> 00:16:24,680 And within that there was this reservation. 312 00:16:24,680 --> 00:16:31,610 And the reservation policy is, was, still is in a sense, 313 00:16:31,610 --> 00:16:32,860 quite controversial. 314 00:16:35,210 --> 00:16:37,990 It is still controversial in the sense that the next step 315 00:16:37,990 --> 00:16:41,300 they are asking themselves now, and in fact I think has 316 00:16:41,300 --> 00:16:43,510 always been somewhat lurking in the background, is should 317 00:16:43,510 --> 00:16:46,130 there be reservations at other levels. 318 00:16:46,130 --> 00:16:48,850 For example, for foreign peace. 319 00:16:48,850 --> 00:16:51,280 Like, should there be reservations in the parliament 320 00:16:51,280 --> 00:16:55,760 modeled on the one [UNINTELLIGIBLE] model, but 321 00:16:55,760 --> 00:16:57,760 akin to what we have on the Panchayat. 322 00:17:00,410 --> 00:17:03,100 And that debate is lively in India, 323 00:17:03,100 --> 00:17:09,170 but it's also elsewhere. 324 00:17:09,170 --> 00:17:11,950 Many countries have reservation policies. 325 00:17:11,950 --> 00:17:15,349 Last I counted, hundreds of some kind of some form of 326 00:17:15,349 --> 00:17:17,780 mandated representation for women. 327 00:17:17,780 --> 00:17:24,099 So it is not a very prominent policy, and some countries are 328 00:17:24,099 --> 00:17:25,859 very opposed to that. 329 00:17:25,859 --> 00:17:27,770 For example, the US. 330 00:17:27,770 --> 00:17:36,810 So what are the potential pros and cons of 331 00:17:36,810 --> 00:17:40,040 a reservation policy? 332 00:17:40,040 --> 00:17:42,970 Since our evaluation is meant in a sense to inform this 333 00:17:42,970 --> 00:17:47,030 debate, we should think about what are the questions that 334 00:17:47,030 --> 00:17:50,285 are in people's minds? 335 00:17:59,100 --> 00:18:00,760 AUDIENCE: It's not democratic. 336 00:18:00,760 --> 00:18:02,370 It's not majority rule. 337 00:18:02,370 --> 00:18:02,900 PROFESSOR: Right. 338 00:18:02,900 --> 00:18:04,510 So that's a disadvantage. 339 00:18:15,820 --> 00:18:18,830 So it's not democratic, I'll just put it that way. 340 00:18:18,830 --> 00:18:22,020 We are constraining people's choices. 341 00:18:22,020 --> 00:18:25,000 They might not like it. 342 00:18:25,000 --> 00:18:27,833 So that reduces their utility. 343 00:18:27,833 --> 00:18:31,596 AUDIENCE: But there is this structural inequality, so you 344 00:18:31,596 --> 00:18:34,530 need to empower your women [INAUDIBLE] 345 00:18:34,530 --> 00:18:36,490 do it, [INAUDIBLE] 346 00:18:36,490 --> 00:18:38,420 this reservation [UNINTELLIGIBLE]. 347 00:18:38,420 --> 00:18:41,622 But at some point in history, you need to do it to push 348 00:18:41,622 --> 00:18:43,005 women farther. 349 00:18:43,005 --> 00:18:46,820 PROFESSOR: So I see more than one thing in that comment. 350 00:18:46,820 --> 00:18:51,270 One is that in the short run we need to ensure woman's 351 00:18:51,270 --> 00:18:52,520 representation. 352 00:18:57,690 --> 00:18:59,460 That's the short run. 353 00:18:59,460 --> 00:19:03,504 And I think what I also hear in your comment is that-- 354 00:19:03,504 --> 00:19:05,722 AUDIENCE: So many noises in the room. 355 00:19:05,722 --> 00:19:07,330 It's not working as it should. 356 00:19:07,330 --> 00:19:08,070 PROFESSOR: Right. 357 00:19:08,070 --> 00:19:10,650 So one of the reasons why you might want to ensure woman's 358 00:19:10,650 --> 00:19:12,700 participation is just because-- 359 00:19:12,700 --> 00:19:13,470 just like that. 360 00:19:13,470 --> 00:19:17,030 Not even because you think it will affect, again, like 361 00:19:17,030 --> 00:19:20,170 democracy here is an end in itself. 362 00:19:20,170 --> 00:19:22,280 You might think, well, democracy is not real because 363 00:19:22,280 --> 00:19:24,040 women are not participating. 364 00:19:24,040 --> 00:19:27,050 I'm interested in having women participating, period. 365 00:19:27,050 --> 00:19:29,360 Even if it doesn't change anything to the outcome, 366 00:19:29,360 --> 00:19:30,740 that's what makes democracy real. 367 00:19:30,740 --> 00:19:32,430 So that could be one outcome. 368 00:19:32,430 --> 00:19:36,600 So we are interested in that as an end in itself. 369 00:19:36,600 --> 00:19:40,460 And I think one thing that I overhear in your comment that 370 00:19:40,460 --> 00:19:44,400 might not be there in reality, is also that maybe that will 371 00:19:44,400 --> 00:19:47,440 help jump-start the process where that would not be needed 372 00:19:47,440 --> 00:19:48,960 in the long run. 373 00:19:48,960 --> 00:19:51,830 So it would ensure a woman's representation in the long 374 00:19:51,830 --> 00:19:56,195 run, and maybe change voter's view. 375 00:20:00,090 --> 00:20:07,290 View is such that in the long run, the effect would persist. 376 00:20:14,420 --> 00:20:19,990 In which case you might go to a bigger and better democracy 377 00:20:19,990 --> 00:20:21,850 where everybody's actually involved. 378 00:20:21,850 --> 00:20:25,620 In fact, it might be good for men as well to avail 379 00:20:25,620 --> 00:20:28,030 themselves of the possibility of half the population's 380 00:20:28,030 --> 00:20:33,640 talents which now, for some reason, becomes shut. 381 00:20:33,640 --> 00:20:37,360 So that's a first set of things which concerns the 382 00:20:37,360 --> 00:20:39,000 participation itself. 383 00:20:39,000 --> 00:20:41,485 AUDIENCE: So one of the advantages of ensuring women's 384 00:20:41,485 --> 00:20:47,950 representation is that they might then allocate more money 385 00:20:47,950 --> 00:20:52,270 to goods or services that benefit women's [INAUDIBLE]. 386 00:20:52,270 --> 00:20:53,790 PROFESSOR: So that is certainly 387 00:20:53,790 --> 00:20:56,930 something you hear a lot. 388 00:20:56,930 --> 00:21:05,190 So we allocate more money to goods and services which, 389 00:21:05,190 --> 00:21:07,740 sorry, can you repeat the end? 390 00:21:07,740 --> 00:21:09,996 AUDIENCE: Which benefit women in [UNINTELLIGIBLE]. 391 00:21:09,996 --> 00:21:11,710 PROFESSOR: Which benefit women. 392 00:21:15,020 --> 00:21:17,350 Which might be good if-- 393 00:21:17,350 --> 00:21:19,820 you were saying that woman were not represented 394 00:21:19,820 --> 00:21:24,436 adequately before, and all-- 395 00:21:24,436 --> 00:21:24,912 yeah. 396 00:21:24,912 --> 00:21:26,578 AUDIENCE: And then there might also, I mean, there's been 397 00:21:26,578 --> 00:21:29,160 some literature saying that there's been a spillover 398 00:21:29,160 --> 00:21:31,605 effect for children and families, but if [INAUDIBLE]. 399 00:21:40,896 --> 00:21:43,200 PROFESSOR: That's also something you hear a lot which 400 00:21:43,200 --> 00:21:47,050 is, so this is an argument in favor of redistribution. 401 00:21:47,050 --> 00:21:49,280 To half the population there is a group that doesn't get 402 00:21:49,280 --> 00:21:52,150 very much in the normal system. 403 00:21:52,150 --> 00:21:54,660 They need to get some part of the rent as well. 404 00:21:54,660 --> 00:21:56,830 And then that's sort of one step further which is an 405 00:21:56,830 --> 00:21:58,050 efficiency argument. 406 00:21:58,050 --> 00:22:01,540 You say, as you do that, you will also get a 407 00:22:01,540 --> 00:22:03,780 different type of goods. 408 00:22:03,780 --> 00:22:07,740 STUDENT: Increases a woman's empowerment. 409 00:22:07,740 --> 00:22:08,540 PROFESSOR: Right. 410 00:22:08,540 --> 00:22:16,450 So as you do that you might also increase a woman's 411 00:22:16,450 --> 00:22:17,920 empowerment. 412 00:22:17,920 --> 00:22:19,980 I think it's a double positive, but I'll 413 00:22:19,980 --> 00:22:21,340 leave it like that. 414 00:22:21,340 --> 00:22:22,590 Empowerment. 415 00:22:25,380 --> 00:22:31,230 Which may be good again as a redistribution or because we 416 00:22:31,230 --> 00:22:35,426 think that women in the house will do things differently. 417 00:22:35,426 --> 00:22:38,300 AUDIENCE: Maybe the same thing as empowerment, but maybe more 418 00:22:38,300 --> 00:22:41,950 measurable is that it claims to reverse the trend of 419 00:22:41,950 --> 00:22:44,645 increasing gender gap in the population? 420 00:22:44,645 --> 00:22:47,100 PROFESSOR: So one of the possible outcomes would be 421 00:22:47,100 --> 00:22:48,380 gender gap. 422 00:22:48,380 --> 00:22:50,400 You mean among children or among adults? 423 00:22:50,400 --> 00:22:53,960 AUDIENCE: Eventually it will be among the adults, but it 424 00:22:53,960 --> 00:22:56,250 can be noticed [INAUDIBLE] 425 00:22:56,250 --> 00:22:59,700 indicators would be like reduction in girl infanticide. 426 00:23:06,765 --> 00:23:10,050 PROFESSOR: So do we think there is a direct rule between 427 00:23:10,050 --> 00:23:15,130 what women policy makers can do, in terms of what type of 428 00:23:15,130 --> 00:23:16,510 public goods they can provide? 429 00:23:16,510 --> 00:23:20,960 Or do you think it's going to be all indirect to doing stuff 430 00:23:20,960 --> 00:23:23,540 that benefits women, which increases the power of women, 431 00:23:23,540 --> 00:23:27,495 which means that they make decisions that are better for 432 00:23:27,495 --> 00:23:29,462 girls in the household? 433 00:23:29,462 --> 00:23:31,366 AUDIENCE: And perception, it will quickly 434 00:23:31,366 --> 00:23:32,990 change in our society. 435 00:23:39,062 --> 00:23:41,600 PROFESSOR: So there is an issue of perception as well. 436 00:23:46,590 --> 00:23:49,584 AUDIENCE: [INAUDIBLE] 437 00:23:49,584 --> 00:23:55,516 that when you see [INAUDIBLE] 438 00:23:58,492 --> 00:24:02,590 it would be more [UNINTELLIGIBLE] to follow. 439 00:24:02,590 --> 00:24:03,120 PROFESSOR: Right. 440 00:24:03,120 --> 00:24:05,790 So you might think that might be something that people are 441 00:24:05,790 --> 00:24:08,950 also talked about is just, again, not just the aspect of 442 00:24:08,950 --> 00:24:10,420 what she might be doing. 443 00:24:10,420 --> 00:24:14,150 Just having the woman as the figurehead changes the image 444 00:24:14,150 --> 00:24:15,860 in a somewhat more permanent way. 445 00:24:15,860 --> 00:24:19,580 You don't just think well, she's there because of the 446 00:24:19,580 --> 00:24:20,130 reservation. 447 00:24:20,130 --> 00:24:23,560 You see a woman in a position of power and that might change 448 00:24:23,560 --> 00:24:26,105 your view of what's possible. 449 00:24:26,105 --> 00:24:29,240 AUDIENCE: As a disadvantage, you also have the potential of 450 00:24:29,240 --> 00:24:32,540 the women who have poor job and so the perception actually 451 00:24:32,540 --> 00:24:36,747 becomes negative, if it's perceived that the woman might 452 00:24:36,747 --> 00:24:38,480 be unqualified. 453 00:24:38,480 --> 00:24:43,140 PROFESSOR: So the first woman might be unqualified, and that 454 00:24:43,140 --> 00:24:45,440 would worsen perception. 455 00:24:45,440 --> 00:24:49,520 And then the other thing that your comment could be read as 456 00:24:49,520 --> 00:24:51,580 is maybe they're not particularly unqualified, but 457 00:24:51,580 --> 00:24:53,850 they are perceived as being unqualified. 458 00:24:53,850 --> 00:24:56,950 Because they are perceived as being there because of the 459 00:24:56,950 --> 00:24:58,190 reservation policy. 460 00:24:58,190 --> 00:25:01,500 And, in fact, it's makes it even more difficult for 461 00:25:01,500 --> 00:25:03,040 competent women to assert themselves. 462 00:25:03,040 --> 00:25:05,350 That's something that has been said a lot about affirmative 463 00:25:05,350 --> 00:25:07,920 action in the US. 464 00:25:07,920 --> 00:25:13,920 For blacks, for example, now every time you see a black 465 00:25:13,920 --> 00:25:15,520 person succeeding you're thinking, why? 466 00:25:15,520 --> 00:25:19,090 It's because they got some unfair advantage somewhere, 467 00:25:19,090 --> 00:25:21,590 and so that makes things worse. 468 00:25:21,590 --> 00:25:22,410 AUDIENCE: Backlash. 469 00:25:22,410 --> 00:25:25,730 PROFESSOR: So we can write it as backlash. 470 00:25:25,730 --> 00:25:27,050 That's a very good point. 471 00:25:29,610 --> 00:25:32,010 So that's a question that we can also ask. 472 00:25:32,010 --> 00:25:34,330 What's the perception of women, and women in power 473 00:25:34,330 --> 00:25:35,706 before and after? 474 00:25:35,706 --> 00:25:38,090 AUDIENCE: We also create this backlash. 475 00:25:38,090 --> 00:25:41,454 It's my perception of it, too, but it's a different issue. 476 00:25:41,454 --> 00:25:44,328 It's not necessarily clear that women will, in fact, 477 00:25:44,328 --> 00:25:45,786 represent women better. 478 00:25:45,786 --> 00:25:48,702 It is possible that women will feel the need to compensate 479 00:25:48,702 --> 00:25:53,070 and therefore go out of their way to identify with men's 480 00:25:53,070 --> 00:25:56,184 issues, and go out of their way to not be perceived as a 481 00:25:56,184 --> 00:25:57,112 feminine candidate. 482 00:25:57,112 --> 00:25:57,970 PROFESSOR: Right. 483 00:25:57,970 --> 00:26:05,925 So I'm looking for a term for that but I don't find one. 484 00:26:09,410 --> 00:26:15,790 Women may overcompensate and not 485 00:26:15,790 --> 00:26:17,040 represent women's interests. 486 00:26:26,138 --> 00:26:31,217 AUDIENCE: As an advantage having women on the councils 487 00:26:31,217 --> 00:26:35,720 that you bring more information that may benefit 488 00:26:35,720 --> 00:26:37,960 women but also may just benefit the entire society. 489 00:26:37,960 --> 00:26:40,340 Because you weren't getting this channel of information 490 00:26:40,340 --> 00:26:42,932 either directly from their communities, or from their 491 00:26:42,932 --> 00:26:45,528 constituents who don't feel comfortable with women 492 00:26:45,528 --> 00:26:46,330 [INAUDIBLE]. 493 00:26:46,330 --> 00:26:46,880 PROFESSOR: Right. 494 00:26:46,880 --> 00:26:48,140 You may be more information. 495 00:26:48,140 --> 00:26:52,020 For example, because women speak up more, and so they 496 00:26:52,020 --> 00:26:55,140 give their opinion on stuff and there is stuff they might 497 00:26:55,140 --> 00:26:57,400 just know better about, even if they don't particularly 498 00:26:57,400 --> 00:26:59,500 care more about. 499 00:26:59,500 --> 00:27:02,310 Everybody may have the same preferences so that might not 500 00:27:02,310 --> 00:27:03,150 be a conflict. 501 00:27:03,150 --> 00:27:06,010 If you never hear from the women that the water well is 502 00:27:06,010 --> 00:27:09,920 blocked, then you may never think of fixing it even though 503 00:27:09,920 --> 00:27:12,035 that's something that would benefit everybody ultimately. 504 00:27:16,685 --> 00:27:19,748 AUDIENCE: An advantage would be building capacity in women 505 00:27:19,748 --> 00:27:22,088 for leadership for the higher levels. 506 00:27:22,088 --> 00:27:22,910 PROFESSOR: Right. 507 00:27:22,910 --> 00:27:29,380 So we are now talking about spillover over time. 508 00:27:29,380 --> 00:27:33,120 That you have one woman one time, and people understand 509 00:27:33,120 --> 00:27:36,640 that they are good and then they can continue on. 510 00:27:36,640 --> 00:27:41,280 But it can also be, of course, different levels while you're 511 00:27:41,280 --> 00:27:48,550 building up a cadre of powerful women. 512 00:27:53,350 --> 00:27:55,938 AUDIENCE: I would think that you could maybe get an income 513 00:27:55,938 --> 00:27:59,120 of that, right away, on women. 514 00:27:59,120 --> 00:28:02,220 We vote next year to put in a water well. 515 00:28:02,220 --> 00:28:05,060 That means that there's a lot more time to do my trading 516 00:28:05,060 --> 00:28:08,175 business, [UNINTELLIGIBLE] business, so that right away I 517 00:28:08,175 --> 00:28:09,430 might make more money. 518 00:28:09,430 --> 00:28:10,070 PROFESSOR: Right. 519 00:28:10,070 --> 00:28:13,770 So one of the ways we can put it into women empowerment, I'm 520 00:28:13,770 --> 00:28:17,900 going to add "to," so it could be to income. 521 00:28:17,900 --> 00:28:20,810 It could be to time that you generate, because now you 522 00:28:20,810 --> 00:28:22,340 spend less time collecting water. 523 00:28:22,340 --> 00:28:25,730 Or it could be to the role models as already discussed. 524 00:28:25,730 --> 00:28:27,950 But just the fact that I have the public good that is 525 00:28:27,950 --> 00:28:30,440 convenient for me, that I need. 526 00:28:30,440 --> 00:28:33,780 And presumably if women's power increased, they're going 527 00:28:33,780 --> 00:28:37,710 to do some kind of public good that's good for them which 528 00:28:37,710 --> 00:28:40,680 might be these types of things that will free times for them. 529 00:28:43,680 --> 00:28:46,180 AUDIENCE: Kind of a counterpoint to the last 530 00:28:46,180 --> 00:28:48,426 disadvantage. 531 00:28:48,426 --> 00:28:51,528 It's also possible that because a woman doesn't have 532 00:28:51,528 --> 00:28:55,578 to compete against a man, they don't have to pander to men, 533 00:28:55,578 --> 00:29:00,542 and in a sense, overcompensate to men's preferences as much. 534 00:29:00,542 --> 00:29:01,310 PROFESSOR: Right. 535 00:29:01,310 --> 00:29:03,976 So in a reservation-- 536 00:29:03,976 --> 00:29:07,584 we have too many advantages. 537 00:29:07,584 --> 00:29:11,000 AUDIENCE: You add to disadvantage also which is 538 00:29:11,000 --> 00:29:15,990 that, I think, this may be more indirect in the sense 539 00:29:15,990 --> 00:29:18,770 that when you're electing someone on the premise that 540 00:29:18,770 --> 00:29:22,420 they'll able to deliver public goods to a specific 541 00:29:22,420 --> 00:29:30,470 constituency, you risk perhaps instituting a culture of 542 00:29:30,470 --> 00:29:34,990 paternalistic government that is elected just to deliver the 543 00:29:34,990 --> 00:29:36,710 pork to a specific constituency. 544 00:29:36,710 --> 00:29:37,470 PROFESSOR: Right. 545 00:29:37,470 --> 00:29:39,610 And that is something that has been discussed a lot. 546 00:29:39,610 --> 00:29:42,850 For example, with the scheduled caste is that you 547 00:29:42,850 --> 00:29:45,250 could have this shifting. 548 00:29:45,250 --> 00:29:48,870 So you go it's your turn, it's our turn, it's your turn, it's 549 00:29:48,870 --> 00:29:54,600 our turn, and then people are not really watching the 550 00:29:54,600 --> 00:29:58,170 scheduled caste guy because they understand this is the 551 00:29:58,170 --> 00:30:01,790 scheduled caste guy's time to take their 552 00:30:01,790 --> 00:30:03,640 turn, and vice versa. 553 00:30:03,640 --> 00:30:05,960 It might also go the other way in the sense that the 554 00:30:05,960 --> 00:30:09,610 scheduled caste people who were previously 555 00:30:09,610 --> 00:30:12,390 disenfranchised might now feel, well, we might as well 556 00:30:12,390 --> 00:30:15,500 get this guy to deliver really well while he is 557 00:30:15,500 --> 00:30:17,150 in power for us. 558 00:30:17,150 --> 00:30:18,360 Same thing for women. 559 00:30:18,360 --> 00:30:19,900 But the effect on accountability 560 00:30:19,900 --> 00:30:21,060 is it's a good point. 561 00:30:21,060 --> 00:30:23,260 Because since the whole Panchayat is about 562 00:30:23,260 --> 00:30:25,840 accountability to the people, the effect of reservation 563 00:30:25,840 --> 00:30:29,660 system on accountability, at best, ambiguous. 564 00:30:29,660 --> 00:30:32,715 So we can say this is related to this first point which is 565 00:30:32,715 --> 00:30:34,600 it's not democratic anymore. 566 00:30:34,600 --> 00:30:39,230 So any tampering with the democracy you might have story 567 00:30:39,230 --> 00:30:41,190 going either way. 568 00:30:41,190 --> 00:30:47,620 But that's a disadvantage showing, effect on 569 00:30:47,620 --> 00:30:48,870 accountability. 570 00:30:55,220 --> 00:30:57,210 You're now accountable to nobody. 571 00:30:57,210 --> 00:30:59,180 First, you are a lame duck. 572 00:30:59,180 --> 00:31:02,680 There is a very poor chance that you get reelected again, 573 00:31:02,680 --> 00:31:04,170 so that's your point here. 574 00:31:04,170 --> 00:31:06,440 Which in a sense makes you free to pander to your guys 575 00:31:06,440 --> 00:31:09,240 which is maybe what we wanted in this case, but on the other 576 00:31:09,240 --> 00:31:13,720 hand, makes you maybe less likely to deliver. 577 00:31:13,720 --> 00:31:14,838 Sorry, there was a-- 578 00:31:14,838 --> 00:31:17,766 AUDIENCE: Another potential disadvantage which is kind of 579 00:31:17,766 --> 00:31:20,478 still under whether or not women are in fact 580 00:31:20,478 --> 00:31:23,510 representing, is that if there's a culture of women not 581 00:31:23,510 --> 00:31:26,720 speaking up or not advocating, then you might end up with no 582 00:31:26,720 --> 00:31:28,070 representation at all. 583 00:31:28,070 --> 00:31:28,870 PROFESSOR: Right. 584 00:31:28,870 --> 00:31:31,900 So there is a question of who is in charge. 585 00:31:31,900 --> 00:31:36,420 So it might be that when it's an election you elect someone, 586 00:31:36,420 --> 00:31:37,600 they're in charge. 587 00:31:37,600 --> 00:31:41,970 Here it's like maybe no woman really wants to run, so you 588 00:31:41,970 --> 00:31:45,400 pick up any figurehead. 589 00:31:45,400 --> 00:31:48,750 It might go back to elite control. 590 00:31:48,750 --> 00:31:54,720 If nobody who's democratically elected is in a position to 591 00:31:54,720 --> 00:31:57,350 exercise the power, whoever is a natural leader 592 00:31:57,350 --> 00:31:58,180 will take it back. 593 00:31:58,180 --> 00:32:00,100 It might be her husband, it might be anybody else. 594 00:32:04,380 --> 00:32:07,940 It's a very pretty reasonably recent effort that local 595 00:32:07,940 --> 00:32:09,560 democracy at that level. 596 00:32:09,560 --> 00:32:15,070 And it might be crazy to go back to elite control. 597 00:32:15,070 --> 00:32:17,870 So who is in charge? 598 00:32:17,870 --> 00:32:20,250 And is there a risk of elite control? 599 00:32:20,250 --> 00:32:25,479 Which is of course not why we did this in the first place. 600 00:32:25,479 --> 00:32:28,972 AUDIENCE: I guess it builds upon the last point maybe 601 00:32:28,972 --> 00:32:31,966 overall less efficiency in elite administration functions 602 00:32:31,966 --> 00:32:34,128 because there may be resentment against the women, 603 00:32:34,128 --> 00:32:35,958 so less cooperation against them. 604 00:32:35,958 --> 00:32:37,954 So maybe there's more stalemate 605 00:32:37,954 --> 00:32:39,451 policies being passed. 606 00:32:39,451 --> 00:32:40,470 PROFESSOR: Yeah. 607 00:32:40,470 --> 00:32:42,300 So that's a risk of stalemate. 608 00:32:47,927 --> 00:32:51,270 So unless you have a burning point, then we stop here 609 00:32:51,270 --> 00:32:53,140 because I have no space anymore on the board. 610 00:32:53,140 --> 00:32:56,720 So that's it, that's budget constraint for thoughts. 611 00:32:59,400 --> 00:33:07,280 So there is kind of a lot of ideas and so somehow we are 612 00:33:07,280 --> 00:33:12,020 going to want them organized in order to go 613 00:33:12,020 --> 00:33:14,130 and collect our data. 614 00:33:14,130 --> 00:33:17,020 So one thing we could think of doing is that, oh let's 615 00:33:17,020 --> 00:33:17,835 postpone the big 616 00:33:17,835 --> 00:33:18,790 deorganization of the thoughts. 617 00:33:18,790 --> 00:33:23,620 We can see that it can go all over the place, so let's go 618 00:33:23,620 --> 00:33:40,670 and collect a bunch of outcomes and see how it goes. 619 00:33:40,670 --> 00:33:47,800 So given all of what we have discussed here, what are the 620 00:33:47,800 --> 00:33:53,096 possible things that might be affected by this policy? 621 00:33:53,096 --> 00:33:55,520 I'd like to have a board. 622 00:33:55,520 --> 00:33:58,200 I can just write. 623 00:33:58,200 --> 00:34:01,810 Given all the discussion we had, what are the things that 624 00:34:01,810 --> 00:34:03,750 we think might be affected by having 625 00:34:03,750 --> 00:34:07,380 this reservation policy? 626 00:34:07,380 --> 00:34:09,659 A lot of these things already came in, but we can make a 627 00:34:09,659 --> 00:34:10,699 little list for ourselves. 628 00:34:10,699 --> 00:34:12,924 AUDIENCE: Choice of which public goods-- 629 00:34:12,924 --> 00:34:15,810 PROFESSOR: So one is definitely the public goods. 630 00:34:15,810 --> 00:34:20,460 So the disadvantage I'm going to-- 631 00:34:20,460 --> 00:34:22,449 so outcomes-- 632 00:34:22,449 --> 00:34:25,670 oh, wow. 633 00:34:25,670 --> 00:34:28,670 I don't usually use boards, so I'm going to-- 634 00:34:28,670 --> 00:34:31,400 ta da. 635 00:34:31,400 --> 00:34:32,650 That has to be MIT. 636 00:34:34,460 --> 00:34:36,530 So we have a whole [UNINTELLIGIBLE], so one is 637 00:34:36,530 --> 00:34:41,270 clearly public good, and potentially we 638 00:34:41,270 --> 00:34:42,159 have lots of them. 639 00:34:42,159 --> 00:34:45,147 What are the public goods that we can see in villages? 640 00:34:45,147 --> 00:34:45,901 AUDIENCE: Water. 641 00:34:45,901 --> 00:34:46,530 PROFESSOR: Water. 642 00:34:46,530 --> 00:34:47,205 AUDIENCE: Roads. 643 00:34:47,205 --> 00:34:48,019 PROFESSOR: Roads. 644 00:34:48,019 --> 00:34:48,770 AUDIENCE: Schools. 645 00:34:48,770 --> 00:34:49,690 PROFESSOR: Schools. 646 00:34:49,690 --> 00:34:50,520 AUDIENCE: Hospitals. 647 00:34:50,520 --> 00:34:51,350 AUDIENCE: Health centers. 648 00:34:51,350 --> 00:34:52,989 PROFESSOR: Small health centers, yeah. 649 00:34:52,989 --> 00:34:53,980 AUDIENCE: Young centers. 650 00:34:53,980 --> 00:34:55,946 PROFESSOR: Young centers. 651 00:34:55,946 --> 00:34:57,254 AUDIENCE: Large buildings. 652 00:34:57,254 --> 00:35:03,985 PROFESSOR: Large buildings, irrigation, biogas. 653 00:35:03,985 --> 00:35:04,895 AUDIENCE: Electricity. 654 00:35:04,895 --> 00:35:05,490 PROFESSOR: Sorry? 655 00:35:05,490 --> 00:35:06,480 AUDIENCE: Electricity. 656 00:35:06,480 --> 00:35:08,000 PROFESSOR: Electricity, potentially. 657 00:35:08,000 --> 00:35:09,470 Sanitation. 658 00:35:09,470 --> 00:35:14,670 So like a long list of public goods could go either a way, 659 00:35:14,670 --> 00:35:15,920 so that's a long list. 660 00:35:19,230 --> 00:35:20,480 Where else? 661 00:35:25,825 --> 00:35:26,890 AUDIENCE: Perceptions about women. 662 00:35:26,890 --> 00:35:28,427 PROFESSOR: Perception of women. 663 00:35:33,910 --> 00:35:38,180 So if you want we'll talk a bit more about how we measure 664 00:35:38,180 --> 00:35:39,490 perception about women. 665 00:35:39,490 --> 00:35:40,480 AUDIENCE: Participation. 666 00:35:40,480 --> 00:35:42,000 PROFESSOR: Political participation. 667 00:35:46,240 --> 00:35:50,550 So attendance at meetings, voting. 668 00:35:50,550 --> 00:35:52,280 And of course we have men and women. 669 00:35:52,280 --> 00:35:54,530 That might be different. 670 00:35:57,185 --> 00:35:59,130 AUDIENCE: Better governance? 671 00:35:59,130 --> 00:36:01,650 PROFESSOR: So that's kind of the same thing. 672 00:36:01,650 --> 00:36:03,110 So better governance. 673 00:36:03,110 --> 00:36:06,760 In practice that's going to be whether there is graft, maybe 674 00:36:06,760 --> 00:36:08,100 budget utilization. 675 00:36:08,100 --> 00:36:10,860 So some measure of corruption. 676 00:36:10,860 --> 00:36:13,510 Bribes. 677 00:36:13,510 --> 00:36:15,480 You didn't say it actually in the advantages or 678 00:36:15,480 --> 00:36:17,160 disadvantages. 679 00:36:17,160 --> 00:36:19,250 The women are less corrupt, more corrupt. 680 00:36:19,250 --> 00:36:22,420 I guess it came up in the accountability. 681 00:36:22,420 --> 00:36:22,845 What else? 682 00:36:22,845 --> 00:36:24,716 AUDIENCE: Household income. 683 00:36:24,716 --> 00:36:28,060 PROFESSOR: Household income. 684 00:36:28,060 --> 00:36:29,820 And while we are in the household? 685 00:36:29,820 --> 00:36:31,452 AUDIENCE: Health education. 686 00:36:31,452 --> 00:36:32,755 PROFESSOR: Health education. 687 00:36:36,700 --> 00:36:39,940 Any gender differences in these things, both for the 688 00:36:39,940 --> 00:36:42,660 household and for the kids. 689 00:36:42,660 --> 00:36:44,340 We were talking about gender 690 00:36:44,340 --> 00:36:46,110 discrimination within the household. 691 00:36:46,110 --> 00:36:49,370 So again it's like a long list of household stuff 692 00:36:49,370 --> 00:36:51,270 potentially. 693 00:36:51,270 --> 00:36:53,670 Was a woman participating in savings, 694 00:36:53,670 --> 00:36:54,920 groups, blah, blah, blah? 695 00:37:05,250 --> 00:37:08,600 There is both perception of women politicians, and we also 696 00:37:08,600 --> 00:37:13,150 discussed about perception of women in general. 697 00:37:13,150 --> 00:37:15,020 What else could we need? 698 00:37:15,020 --> 00:37:17,450 AUDIENCE: Maybe greater employability of women? 699 00:37:17,450 --> 00:37:18,130 PROFESSOR: Right. 700 00:37:18,130 --> 00:37:21,730 So that's going to be maybe an income and then employment. 701 00:37:21,730 --> 00:37:25,590 That's part of the long list of stuff we might 702 00:37:25,590 --> 00:37:26,840 collect in a household. 703 00:37:29,810 --> 00:37:32,006 AUDIENCE: Social cohesion. 704 00:37:32,006 --> 00:37:32,710 PROFESSOR: Right. 705 00:37:32,710 --> 00:37:35,100 So that's maybe political 706 00:37:35,100 --> 00:37:38,660 participation and social cohesion. 707 00:37:38,660 --> 00:37:41,720 Don't know how you measure that, but we can 708 00:37:41,720 --> 00:37:42,970 think about it later. 709 00:37:46,722 --> 00:37:48,189 AUDIENCE: Sustainability. 710 00:37:48,189 --> 00:37:50,030 Or sort of decentralization of power. 711 00:37:54,340 --> 00:37:57,153 The majority that's been in the government, it's like a 712 00:37:57,153 --> 00:37:57,485 vicious cycle. 713 00:37:57,485 --> 00:38:00,467 They'll do everything to keep the minorities out of the 714 00:38:00,467 --> 00:38:04,443 governance, so if you had jump started this process by 715 00:38:04,443 --> 00:38:06,440 introducing 30% of women-- 716 00:38:06,440 --> 00:38:07,360 PROFESSOR: Right, right, right. 717 00:38:07,360 --> 00:38:13,890 So it's perception of women politicians is in a woman's 718 00:38:13,890 --> 00:38:17,560 future electoral success. 719 00:38:17,560 --> 00:38:21,090 Is it the case that once a place is reserved for a woman, 720 00:38:21,090 --> 00:38:22,550 obviously you are the woman. 721 00:38:22,550 --> 00:38:25,650 How about the next time when it's not reserved anymore, do 722 00:38:25,650 --> 00:38:27,130 you have more woman candidates? 723 00:38:27,130 --> 00:38:30,170 Do you have a woman elected? 724 00:38:30,170 --> 00:38:32,040 Women's future electoral successes. 725 00:38:34,660 --> 00:38:36,670 That's one of the first things we had discussed. 726 00:38:39,480 --> 00:38:41,645 More? 727 00:38:41,645 --> 00:38:45,170 I think it's a pretty long list already. 728 00:38:45,170 --> 00:38:48,380 And so now that we have this long list, the issue 729 00:38:48,380 --> 00:38:51,200 is what do we do? 730 00:38:51,200 --> 00:38:52,870 Suppose you had no money problem. 731 00:38:56,090 --> 00:38:59,250 Suppose budget was not a problem. 732 00:38:59,250 --> 00:39:01,800 Would you just take this long list? 733 00:39:01,800 --> 00:39:06,040 So one possible thing is to say, well, we'll think about 734 00:39:06,040 --> 00:39:07,320 this thing later. 735 00:39:07,320 --> 00:39:11,490 That makes a long list of outcomes, and 736 00:39:11,490 --> 00:39:13,550 go and collect data. 737 00:39:13,550 --> 00:39:15,320 So we're going to do a household survey and a 738 00:39:15,320 --> 00:39:20,450 community survey, and then an audit of what's there in the 739 00:39:20,450 --> 00:39:24,490 villages, and we are going to collect a bunch of data. 740 00:39:24,490 --> 00:39:26,470 And then this data will come back, and we'll 741 00:39:26,470 --> 00:39:28,250 start to look at it. 742 00:39:28,250 --> 00:39:31,930 So what are the pluses and minuses of that approach? 743 00:39:31,930 --> 00:39:34,350 AUDIENCE: [INAUDIBLE] 744 00:39:34,350 --> 00:39:35,802 statistical significance. 745 00:39:35,802 --> 00:39:38,948 If you collect so much data, eventually you'll randomly 746 00:39:38,948 --> 00:39:41,448 stumble upon a result, and maybe that's the result that 747 00:39:41,448 --> 00:39:42,094 you end up reporting. 748 00:39:42,094 --> 00:39:43,090 PROFESSOR: Right. 749 00:39:43,090 --> 00:39:48,180 So in particular, since we have seen at least some of the 750 00:39:48,180 --> 00:39:51,740 groups starting to discuss power, so you've seen 751 00:39:51,740 --> 00:39:52,990 hypothesis testing. 752 00:39:52,990 --> 00:39:56,070 So if I ran a hundred regression and looked for 753 00:39:56,070 --> 00:39:59,570 significant results and then independently looked for 754 00:39:59,570 --> 00:40:01,830 results in each of them, how many of them would count 755 00:40:01,830 --> 00:40:03,720 significant at the 5% level? 756 00:40:03,720 --> 00:40:04,626 AUDIENCE: Five. 757 00:40:04,626 --> 00:40:06,320 PROFESSOR: Five. 758 00:40:06,320 --> 00:40:09,730 So if I run 20, I would get two. 759 00:40:09,730 --> 00:40:12,505 So by example, suppose I collect 20 public goods, which 760 00:40:12,505 --> 00:40:15,720 is not such a large number. 761 00:40:15,720 --> 00:40:21,970 And we find that water wells go up in places which have 762 00:40:21,970 --> 00:40:28,320 more women, and irrigation goes down, and anything else 763 00:40:28,320 --> 00:40:29,570 doesn't change. 764 00:40:33,770 --> 00:40:38,090 What can I conclude if I have just gone on to this big 765 00:40:38,090 --> 00:40:40,832 fishing expedition? 766 00:40:40,832 --> 00:40:43,302 AUDIENCE: [INAUDIBLE] 767 00:40:43,302 --> 00:40:45,460 PROFESSOR: Well, you don't know 768 00:40:45,460 --> 00:40:46,730 whether it was just random. 769 00:40:46,730 --> 00:40:48,680 You can definitely make up a story. 770 00:40:48,680 --> 00:40:51,300 What is a story you could easily make up on the basis of 771 00:40:51,300 --> 00:40:52,550 those results? 772 00:40:52,550 --> 00:41:00,197 AUDIENCE: That women invest in wells because collecting water 773 00:41:00,197 --> 00:41:04,508 used to be a woman's job, and men invest in irrigation 774 00:41:04,508 --> 00:41:07,861 because farming is a man's occupation. 775 00:41:07,861 --> 00:41:08,850 PROFESSOR: Exactly. 776 00:41:08,850 --> 00:41:11,960 So you could say, well, women are not going to benefit from 777 00:41:11,960 --> 00:41:14,230 irrigation anyway, they're going to benefit from drinking 778 00:41:14,230 --> 00:41:15,400 water a lot. 779 00:41:15,400 --> 00:41:18,450 So this is quite consistent with what we were saying about 780 00:41:18,450 --> 00:41:22,300 women leaders investing in the goods women want, and so 781 00:41:22,300 --> 00:41:24,130 that's great. 782 00:41:24,130 --> 00:41:28,260 The only problem is, if we've done that ex-post, someone 783 00:41:28,260 --> 00:41:31,450 else could say, well, or an alternative interpretation is 784 00:41:31,450 --> 00:41:34,020 you've run 20 regression, you've found two significant, 785 00:41:34,020 --> 00:41:37,680 and you're making up a story ex-post 786 00:41:37,680 --> 00:41:39,490 to explain the results. 787 00:41:44,170 --> 00:41:47,730 It's not that it's morally wrong to do that, but if 788 00:41:47,730 --> 00:41:52,920 you're not sure it is always going to be some suspicion. 789 00:41:52,920 --> 00:41:55,090 And then it's a little bit sad to have spent so much money 790 00:41:55,090 --> 00:42:00,760 collecting so much data and not to be very sure how to 791 00:42:00,760 --> 00:42:02,290 interpret the results. 792 00:42:02,290 --> 00:42:06,130 So maybe that's not the right approach. 793 00:42:06,130 --> 00:42:09,615 Maybe we need to do something slightly different, and that 794 00:42:09,615 --> 00:42:13,680 is we need to try and put a little bit more thought into 795 00:42:13,680 --> 00:42:16,820 why we are collecting each piece of data and where it's 796 00:42:16,820 --> 00:42:23,060 going to fit in our global explanation of the results. 797 00:42:23,060 --> 00:42:30,360 So if you take this specific example, instead of doing what 798 00:42:30,360 --> 00:42:34,000 we just suppose we did, which is making this long list of 799 00:42:34,000 --> 00:42:36,880 results and hope for the best. 800 00:42:36,880 --> 00:42:39,810 Instead, if we had said we are going to go after this one 801 00:42:39,810 --> 00:42:43,260 question, potentially we can have we can have more than one 802 00:42:43,260 --> 00:42:44,380 big question. 803 00:42:44,380 --> 00:42:46,850 But we are at least going to go after this one question 804 00:42:46,850 --> 00:42:50,580 which is, what is it the case that the women leader do what 805 00:42:50,580 --> 00:42:52,640 women want? 806 00:42:52,640 --> 00:42:58,130 And we had given ourselves the means to first find out what 807 00:42:58,130 --> 00:43:01,840 women want for real, and not making it up from the results. 808 00:43:01,840 --> 00:43:05,690 The key is to try to not be in a position where you're going 809 00:43:05,690 --> 00:43:08,760 to have to reverse engineer your explanation 810 00:43:08,760 --> 00:43:11,920 of the result ex-post. 811 00:43:11,920 --> 00:43:15,850 If you want me to be completely honest, it's very 812 00:43:15,850 --> 00:43:17,110 difficult not to do that. 813 00:43:17,110 --> 00:43:20,140 Because once you see the results you always want to 814 00:43:20,140 --> 00:43:22,960 explain them to yourself, and explain to others. 815 00:43:22,960 --> 00:43:27,980 So I'm not about to tell you that from a position of where 816 00:43:27,980 --> 00:43:31,400 I sit and say well, don't you do that or you will be damned 817 00:43:31,400 --> 00:43:32,360 and go to hell. 818 00:43:32,360 --> 00:43:35,320 But the truth is that we do it, but the truth is the less 819 00:43:35,320 --> 00:43:37,410 we have to do it the better. 820 00:43:37,410 --> 00:43:42,330 And you have to do it less if you've been thinking ex-ante 821 00:43:42,330 --> 00:43:47,330 about what it is that you want to test, and what are the 822 00:43:47,330 --> 00:43:52,760 steps that align themselves in order to get to 823 00:43:52,760 --> 00:43:54,210 where you want to be. 824 00:43:54,210 --> 00:43:59,680 And this result might have been totally fine if we had a 825 00:43:59,680 --> 00:44:02,210 good way to say this is what woman are really interested 826 00:44:02,210 --> 00:44:06,050 in, and we had collected data on that. 827 00:44:06,050 --> 00:44:12,940 And then where our test will not be good by good, is it the 828 00:44:12,940 --> 00:44:15,150 case that women do different things. 829 00:44:15,150 --> 00:44:17,750 But we ask the questions that generally do 830 00:44:17,750 --> 00:44:20,330 women go in that direction. 831 00:44:20,330 --> 00:44:21,490 And what is the case? 832 00:44:21,490 --> 00:44:27,480 Yes, we're missing a moral explaining to us why it is. 833 00:44:27,480 --> 00:44:30,740 How do we go from this hypothesis we have that women 834 00:44:30,740 --> 00:44:34,220 are going to do what women want, to how is it going to 835 00:44:34,220 --> 00:44:36,630 translate into the public goods. 836 00:44:36,630 --> 00:44:46,230 So the hypothesis to test must be defined before the 837 00:44:46,230 --> 00:44:49,070 beginning of the experiment or we don't know how to assist 838 00:44:49,070 --> 00:44:50,330 their validity. 839 00:44:50,330 --> 00:44:53,010 And what we missed when we did this big list of outcomes, or 840 00:44:53,010 --> 00:44:56,120 what we would have missed if we just did this big list of 841 00:44:56,120 --> 00:44:58,770 outcomes, and then go out and collect the data and then 842 00:44:58,770 --> 00:45:02,410 think about how to interpret them, is these steps. 843 00:45:02,410 --> 00:45:07,060 Which is go from a discussion which is very likely at an 844 00:45:07,060 --> 00:45:09,700 implicit level at the back of our minds. 845 00:45:09,700 --> 00:45:13,130 In fact, now it's very much in the front of our minds since 846 00:45:13,130 --> 00:45:14,510 we just had it. 847 00:45:14,510 --> 00:45:17,120 But usually when you do an evaluation, it's everybody 848 00:45:17,120 --> 00:45:22,420 sort of had that in mind, but it might remain implicit and 849 00:45:22,420 --> 00:45:24,630 it's much better to make it explicit. 850 00:45:24,630 --> 00:45:27,230 First because you're more likely to collect the data 851 00:45:27,230 --> 00:45:29,040 that you actually will need. 852 00:45:29,040 --> 00:45:31,290 For example, here we might have missed collecting women's 853 00:45:31,290 --> 00:45:31,730 preferences. 854 00:45:31,730 --> 00:45:34,660 It's not in the list. 855 00:45:34,660 --> 00:45:37,280 It's not in the list of data that's here. 856 00:45:37,280 --> 00:45:37,650 Why? 857 00:45:37,650 --> 00:45:39,240 Because it's not an outcome. 858 00:45:39,240 --> 00:45:42,570 So if were just thinking about, oh, what are the 859 00:45:42,570 --> 00:45:45,280 effects, and we forget to collect women's preferences. 860 00:45:45,280 --> 00:45:48,130 But then we have no good way to interpret the preferences 861 00:45:48,130 --> 00:45:51,690 in the context of that model if it was the model. 862 00:45:51,690 --> 00:45:54,650 So if we don't do this thinking implicitly, we might 863 00:45:54,650 --> 00:45:56,220 be missing a key step. 864 00:45:56,220 --> 00:45:59,560 In particular, in what we call the intermediate variable that 865 00:45:59,560 --> 00:46:03,130 might be needed not as a measure of the impact of the 866 00:46:03,130 --> 00:46:07,390 program, but as what is going to help us interpret the 867 00:46:07,390 --> 00:46:09,860 impact of this program. 868 00:46:09,860 --> 00:46:13,620 So you need to try and define the hypotheses before the 869 00:46:13,620 --> 00:46:15,300 beginning of the experiment. 870 00:46:15,300 --> 00:46:19,050 And this is actually an exercise that's very useful in 871 00:46:19,050 --> 00:46:24,150 my experience working with implementation partners. 872 00:46:24,150 --> 00:46:26,820 It's very useful for both sides. 873 00:46:26,820 --> 00:46:32,710 Because from the side of the evaluation team, strive to 874 00:46:32,710 --> 00:46:35,200 understand what is the program you're evaluating. 875 00:46:35,200 --> 00:46:38,410 How it connects to what you know about, say, developments, 876 00:46:38,410 --> 00:46:41,540 poverty, et cetera. 877 00:46:41,540 --> 00:46:47,310 From the side of the partner, many of you have more 878 00:46:47,310 --> 00:46:48,600 experience than me on that. 879 00:46:48,600 --> 00:46:52,350 At least the half that are actually into implementation. 880 00:46:52,350 --> 00:46:55,250 It's like why are we doing this? 881 00:46:55,250 --> 00:47:02,282 Sometimes the answer is not as forthcoming as you might hope. 882 00:47:02,282 --> 00:47:05,590 AUDIENCE: The other thing that was noticeable to me when we 883 00:47:05,590 --> 00:47:09,232 were doing this is that there are different levels maybe of 884 00:47:09,232 --> 00:47:11,802 importance, or that a lot of them are 885 00:47:11,802 --> 00:47:12,825 subsets of other things. 886 00:47:12,825 --> 00:47:14,260 PROFESSOR: Exactly. 887 00:47:14,260 --> 00:47:19,810 So one of the things that you might do in this process is to 888 00:47:19,810 --> 00:47:23,890 prioritize what is sort of the big news, sort of the million 889 00:47:23,890 --> 00:47:25,390 dollar question? 890 00:47:25,390 --> 00:47:27,690 What are subsidiary? 891 00:47:27,690 --> 00:47:30,530 What are things that are going to enlighten 892 00:47:30,530 --> 00:47:32,620 whatever impacts you find? 893 00:47:32,620 --> 00:47:35,920 So you might also, we are going to do that in a minute, 894 00:47:35,920 --> 00:47:39,476 is to think depending on how much money you have-- 895 00:47:39,476 --> 00:47:41,312 is it that good? 896 00:47:41,312 --> 00:47:42,548 So sorry. 897 00:47:42,548 --> 00:47:46,070 We are trying to do something. 898 00:47:46,070 --> 00:47:49,370 Given how much money you have, you might be thinking of some 899 00:47:49,370 --> 00:47:52,130 small things, or some things that might-- 900 00:47:52,130 --> 00:47:55,190 for example, some of the things, like say, one of the 901 00:47:55,190 --> 00:47:58,940 outcomes we are thinking is relative mortality of girls. 902 00:47:58,940 --> 00:48:01,640 You might think realistically two years after the women's 903 00:48:01,640 --> 00:48:04,635 empowerment is less likely to happen. 904 00:48:04,635 --> 00:48:06,895 So if you had an infinite amount of money, you might 905 00:48:06,895 --> 00:48:09,275 still collect it as a subsidiary outcome, but you 906 00:48:09,275 --> 00:48:12,190 wouldn't want it in a long list up there with whether or 907 00:48:12,190 --> 00:48:15,850 not they invest in water wells. 908 00:48:15,850 --> 00:48:20,730 There's no way to do this prioritization unless you are 909 00:48:20,730 --> 00:48:25,490 in good cause the exercise of thinking to your causal model, 910 00:48:25,490 --> 00:48:30,150 linking whatever intervention you have to the results. 911 00:48:30,150 --> 00:48:34,280 AUDIENCE: In this discussion I believe you also raised the 912 00:48:34,280 --> 00:48:38,340 possibly of doing like a factor analysis to create an 913 00:48:38,340 --> 00:48:42,547 index of different factors so that we could reduce the 914 00:48:42,547 --> 00:48:45,281 number of variables. 915 00:48:45,281 --> 00:48:46,090 PROFESSOR: Right. 916 00:48:46,090 --> 00:48:49,590 So that's a very good idea. 917 00:48:49,590 --> 00:48:53,760 But on the other hand, you have to think about how to-- 918 00:48:53,760 --> 00:48:56,400 your instinct is the right one, which is to say how are 919 00:48:56,400 --> 00:48:59,800 we going to combine this stuff? 920 00:48:59,800 --> 00:49:02,390 So if you take, for example, the 20 public goods. 921 00:49:02,390 --> 00:49:05,350 If you're like, but these women are more efficient, 922 00:49:05,350 --> 00:49:07,440 they're going to build more goods. 923 00:49:07,440 --> 00:49:12,150 Then you might want to do that, or you might want to sum 924 00:49:12,150 --> 00:49:14,630 them or to average them somehow, which is what making 925 00:49:14,630 --> 00:49:16,900 an index is. 926 00:49:16,900 --> 00:49:20,340 To see whether in general all go positive. 927 00:49:20,340 --> 00:49:25,270 But if your model is along this line which is women are 928 00:49:25,270 --> 00:49:28,880 doing what women want, then you might not get more goods. 929 00:49:28,880 --> 00:49:32,800 You may get some of some, and less of others, so you might 930 00:49:32,800 --> 00:49:38,190 need something else than an index to deal with this mess. 931 00:49:38,190 --> 00:49:40,970 But you're exactly right with the idea that what we want is 932 00:49:40,970 --> 00:49:47,410 a way to not have many, many, many hypothesis, but fewer. 933 00:49:47,410 --> 00:49:49,250 So, for example, it can be one, which is a woman does 934 00:49:49,250 --> 00:49:50,910 what women want. 935 00:49:50,910 --> 00:49:52,580 That would be the first one, and then what 936 00:49:52,580 --> 00:49:56,090 comes out of that. 937 00:49:56,090 --> 00:49:59,420 If that's your hypothesis, the index you create would be 938 00:49:59,420 --> 00:50:02,050 something different than what you would get 939 00:50:02,050 --> 00:50:03,720 from a factor analysis. 940 00:50:03,720 --> 00:50:07,060 If, on the other hand, it's an education intervention, and 941 00:50:07,060 --> 00:50:12,500 you have a math result, and an English result, and science 942 00:50:12,500 --> 00:50:14,490 result, and [UNINTELLIGIBLE] results, and geographic 943 00:50:14,490 --> 00:50:20,010 results, then you know that they should all up. 944 00:50:20,010 --> 00:50:21,260 Or at least that's your hypothesis they should all go 945 00:50:21,260 --> 00:50:25,410 up, then you can do something like that to average the 946 00:50:25,410 --> 00:50:28,311 effect across the outcomes. 947 00:50:31,137 --> 00:50:34,630 AUDIENCE: I understand why you don't want to throw in 20 948 00:50:34,630 --> 00:50:39,830 different outcomes. 949 00:50:39,830 --> 00:50:45,650 But in terms of how you prioritize the outcomes along 950 00:50:45,650 --> 00:50:48,540 with their causal model, there are some fields where it's 951 00:50:48,540 --> 00:50:49,446 going to be fairly clear. 952 00:50:49,446 --> 00:50:51,545 You know, in the health field, they would say, OK, well, our 953 00:50:51,545 --> 00:50:54,286 objective is to reduce malaria, so we 954 00:50:54,286 --> 00:50:55,953 didn't reduce malaria. 955 00:50:55,953 --> 00:50:59,390 But if something that has more intermediate steps along the 956 00:50:59,390 --> 00:51:00,640 way, such as a-- 957 00:51:04,330 --> 00:51:05,580 PROFESSOR: What was that about? 958 00:51:10,289 --> 00:51:12,650 AUDIENCE: --such as like a conflict mitigation program, 959 00:51:12,650 --> 00:51:24,193 where you're saying, OK, we are going to get children into 960 00:51:24,193 --> 00:51:29,980 clubs because we believe that this is going to create more 961 00:51:29,980 --> 00:51:33,724 solidarity among ethnic groups, and that this is going 962 00:51:33,724 --> 00:51:36,610 to lead to conflict reduction. 963 00:51:36,610 --> 00:51:39,440 When you have, like, six or seven different steps along 964 00:51:39,440 --> 00:51:43,180 this causal model, how and at what point do you prioritize, 965 00:51:43,180 --> 00:51:46,810 this is the impact that we're looking for, versus these are 966 00:51:46,810 --> 00:51:50,170 the steps that we have take to get to this impact? 967 00:51:50,170 --> 00:51:52,830 PROFESSOR: So that's a great question, and you can answer 968 00:51:52,830 --> 00:51:54,620 it at two levels in a way. 969 00:51:54,620 --> 00:52:01,860 One is, where and when you're going to see an impact first. 970 00:52:01,860 --> 00:52:04,250 For example, in this case, you might think that ultimately 971 00:52:04,250 --> 00:52:06,750 what we care is not the number of water wells. 972 00:52:06,750 --> 00:52:11,400 Ultimately what we care is the growth of the Indian economy 973 00:52:11,400 --> 00:52:13,150 through these complicated channels. 974 00:52:13,150 --> 00:52:17,320 But this is not what we are trying to do, because we think 975 00:52:17,320 --> 00:52:18,250 it's going to take a bit more time to 976 00:52:18,250 --> 00:52:19,490 arrive to this question. 977 00:52:19,490 --> 00:52:23,010 Or ultimately what we care about is girl's mortality. 978 00:52:23,010 --> 00:52:25,380 And we're thinking that through this complicated path 979 00:52:25,380 --> 00:52:28,210 we're going to get there eventually, but this is not 980 00:52:28,210 --> 00:52:30,430 the yardstick by which you measure the success of the 981 00:52:30,430 --> 00:52:34,030 program because it's in too long time, and after too many 982 00:52:34,030 --> 00:52:36,230 other things have diluted this. 983 00:52:36,230 --> 00:52:38,010 But at least you want to know that you're going in the right 984 00:52:38,010 --> 00:52:42,550 direction, so you might decide to stop by the water wells for 985 00:52:42,550 --> 00:52:44,470 the beginning. 986 00:52:44,470 --> 00:52:47,540 Likewise in your program, you might say you're going to 987 00:52:47,540 --> 00:52:50,950 produce into groups to work together. 988 00:52:50,950 --> 00:52:55,400 You might say, well our first measure of success with this 989 00:52:55,400 --> 00:53:00,230 program is whether or not the use opinion of the other guys 990 00:53:00,230 --> 00:53:04,040 has changed in a way that we can reliably measure. 991 00:53:04,040 --> 00:53:05,830 And then you're going to ask yourself the question of the 992 00:53:05,830 --> 00:53:17,260 measurement, and then you can take it at various steps. 993 00:53:17,260 --> 00:53:21,020 And one is, am I still going to find an effect after all 994 00:53:21,020 --> 00:53:23,270 the things that have happened? 995 00:53:23,270 --> 00:53:26,080 So is it fair to my program? 996 00:53:26,080 --> 00:53:28,800 And the other is what can I measure? 997 00:53:28,800 --> 00:53:31,460 Some things are easier and harder to measure, and they 998 00:53:31,460 --> 00:53:33,530 might be at different levels in the chain. 999 00:53:33,530 --> 00:53:36,060 It might be that what is happening first is easier to 1000 00:53:36,060 --> 00:53:37,710 measure, sometimes it's what's happening a little later 1001 00:53:37,710 --> 00:53:38,730 that's easier to measure. 1002 00:53:38,730 --> 00:53:41,870 For example, perceptions might be very hard to measure. 1003 00:53:41,870 --> 00:53:45,530 But whether or not people are willing to work together to 1004 00:53:45,530 --> 00:53:50,370 build something might be very easy to measure, so you might 1005 00:53:50,370 --> 00:53:50,900 go for that. 1006 00:53:50,900 --> 00:53:52,690 AUDIENCE: And at the same time, you do want to make sure 1007 00:53:52,690 --> 00:53:55,962 that you're doing something further enough along the chain 1008 00:53:55,962 --> 00:53:58,243 that there isn't actually impact, and not just an output 1009 00:53:58,243 --> 00:53:58,558 measure, or-- 1010 00:53:58,558 --> 00:53:59,660 PROFESSOR: Exactly. 1011 00:53:59,660 --> 00:54:04,520 So this is where this 1012 00:54:04,520 --> 00:54:06,580 conversation's going to be helpful. 1013 00:54:06,580 --> 00:54:12,100 What is our program hoping to do, and how. 1014 00:54:12,100 --> 00:54:14,330 And sometimes you realize in that conversation is that what 1015 00:54:14,330 --> 00:54:18,260 is your program hoping to do so squishy that maybe we 1016 00:54:18,260 --> 00:54:20,560 should do something else. 1017 00:54:20,560 --> 00:54:22,770 But not the case of your program that you just 1018 00:54:22,770 --> 00:54:26,360 described which sounded actually quite specific in 1019 00:54:26,360 --> 00:54:27,800 what it was hoping to achieve. 1020 00:54:36,350 --> 00:54:37,710 So here's our example. 1021 00:54:37,710 --> 00:54:41,000 So we have to define the hypothesis. 1022 00:54:41,000 --> 00:54:43,750 So, for example, we can take here, we could take several. 1023 00:54:43,750 --> 00:54:46,850 One possibility is to take this one which is public goods 1024 00:54:46,850 --> 00:54:51,540 favored by women are more likely to be chosen by women. 1025 00:54:51,540 --> 00:54:57,080 So to test that, we need to know what women want, and then 1026 00:54:57,080 --> 00:54:58,810 we need to collect the data on public goods. 1027 00:54:58,810 --> 00:55:01,640 And then the one thing we are going to do is, we are 1028 00:55:01,640 --> 00:55:05,100 creating this index of wanted by women more than by men, 1029 00:55:05,100 --> 00:55:07,390 which is the way we would aggregate. 1030 00:55:07,390 --> 00:55:10,310 And then the key test would be are the public goods moving 1031 00:55:10,310 --> 00:55:12,460 towards what women want? 1032 00:55:12,460 --> 00:55:15,900 And so the one little snag is how are we going to measure 1033 00:55:15,900 --> 00:55:17,790 women's preferences? 1034 00:55:17,790 --> 00:55:20,080 What is it that women want? 1035 00:55:20,080 --> 00:55:22,350 So what do you guys think? 1036 00:55:22,350 --> 00:55:26,770 What would be possibilities to measure a woman's preferences? 1037 00:55:26,770 --> 00:55:28,710 AUDIENCE: Household surveys? 1038 00:55:28,710 --> 00:55:30,970 PROFESSOR: So we could ask them, in household surveys. 1039 00:55:30,970 --> 00:55:33,860 That would be one way. 1040 00:55:33,860 --> 00:55:35,110 What they care about. 1041 00:55:37,936 --> 00:55:39,186 Any other ideas? 1042 00:55:42,162 --> 00:55:46,210 AUDIENCE: If there were communities where women were 1043 00:55:46,210 --> 00:55:49,200 well represented, what were the choices of those 1044 00:55:49,200 --> 00:55:50,660 communities, historically? 1045 00:55:50,660 --> 00:55:52,980 PROFESSOR: Well, it's slightly circular because-- 1046 00:55:55,560 --> 00:55:56,020 AUDIENCE: [INAUDIBLE] 1047 00:55:56,020 --> 00:56:02,440 that have a majority of women on the local council have 1048 00:56:02,440 --> 00:56:03,990 higher percentage of wells. 1049 00:56:03,990 --> 00:56:05,010 PROFESSOR: Well, yeah. 1050 00:56:05,010 --> 00:56:12,200 That's a little bit circular, because that means that what 1051 00:56:12,200 --> 00:56:14,930 we can do that only, A, if we believe that, in fact, it is 1052 00:56:14,930 --> 00:56:18,470 true that women better do what women want, which is what we 1053 00:56:18,470 --> 00:56:19,440 are trying to test. 1054 00:56:19,440 --> 00:56:22,570 And B, if we think there was no selection in which village 1055 00:56:22,570 --> 00:56:27,890 elected a woman, and we don't believe that which is the 1056 00:56:27,890 --> 00:56:29,190 whole reason why we are going through 1057 00:56:29,190 --> 00:56:30,510 this tortuous exercise. 1058 00:56:30,510 --> 00:56:31,648 So that might-- 1059 00:56:31,648 --> 00:56:36,379 AUDIENCE: I know in the exercise it talked about the 1060 00:56:36,379 --> 00:56:39,328 transcripts from some of the meetings, so you could look to 1061 00:56:39,328 --> 00:56:42,674 see if there were patterns for what issues were raised. 1062 00:56:42,674 --> 00:56:45,542 Whether or not the council voted on them, it was what 1063 00:56:45,542 --> 00:56:46,976 they brought to the table. 1064 00:56:46,976 --> 00:56:47,740 PROFESSOR: Right. 1065 00:56:47,740 --> 00:56:49,070 So you could do that. 1066 00:56:49,070 --> 00:56:51,560 In fact, this is what we did in this case. 1067 00:56:51,560 --> 00:56:54,960 Cheaper than household survey, is to say, in general, what do 1068 00:56:54,960 --> 00:56:58,220 women ask about in those meetings? 1069 00:56:58,220 --> 00:57:00,930 Preferably, not in places where the woman is the head, 1070 00:57:00,930 --> 00:57:03,100 because that might have changed the dynamic, but in 1071 00:57:03,100 --> 00:57:05,480 places which are not reserved. 1072 00:57:05,480 --> 00:57:07,690 What are women talking about? 1073 00:57:07,690 --> 00:57:09,220 What are men talking about? 1074 00:57:09,220 --> 00:57:13,120 And the advantage of a household survey is that it's 1075 00:57:13,120 --> 00:57:15,760 a little bit costly to go and talk about something. 1076 00:57:15,760 --> 00:57:18,980 In a meeting, you speak up and other people look at you. 1077 00:57:18,980 --> 00:57:22,850 If you have to go to the leader and have a written 1078 00:57:22,850 --> 00:57:25,670 complaint it takes some time, and people wouldn't do that 1079 00:57:25,670 --> 00:57:27,160 unless they cared about it. 1080 00:57:27,160 --> 00:57:28,870 So maybe that's one way to proceed. 1081 00:57:28,870 --> 00:57:31,550 But household surveys is also a good way to proceed. 1082 00:57:31,550 --> 00:57:33,490 AUDIENCE: But if you're measuring whether women 1083 00:57:33,490 --> 00:57:38,630 represent women, like you've assumed at that point, but if 1084 00:57:38,630 --> 00:57:40,630 you're looking at the discussion within the council, 1085 00:57:40,630 --> 00:57:42,093 you've assumed at that point that the 1086 00:57:42,093 --> 00:57:43,605 woman that was selected-- 1087 00:57:43,605 --> 00:57:44,855 PROFESSOR: Alright. 1088 00:57:44,855 --> 00:57:45,645 AUDIENCE: --for women. 1089 00:57:45,645 --> 00:57:46,350 PROFESSOR: Alright. 1090 00:57:46,350 --> 00:57:49,645 So I was thinking of not the small council, but the big 1091 00:57:49,645 --> 00:57:53,030 council when they have this big meeting. 1092 00:57:53,030 --> 00:57:55,500 What she was referring to is the transcript of the 1093 00:57:55,500 --> 00:57:58,140 [UNINTELLIGIBLE] meetings. 1094 00:57:58,140 --> 00:58:02,320 Or even when women have gone to the office of the Pradhan 1095 00:58:02,320 --> 00:58:03,570 and asked them. 1096 00:58:05,600 --> 00:58:09,510 So that's different ways, but the key is, we 1097 00:58:09,510 --> 00:58:10,600 need to have that. 1098 00:58:10,600 --> 00:58:12,430 Because then we can see it doesn't 1099 00:58:12,430 --> 00:58:13,990 go into that direction. 1100 00:58:13,990 --> 00:58:16,950 And now we don't have 20 public goods, we have one 1101 00:58:16,950 --> 00:58:17,600 hypothesis. 1102 00:58:17,600 --> 00:58:20,609 AUDIENCE: Is it necessarily critical that what women are 1103 00:58:20,609 --> 00:58:23,062 speaking about when they go visit to large meetings that's 1104 00:58:23,062 --> 00:58:25,278 what they want, and what the men are speaking about, that's 1105 00:58:25,278 --> 00:58:25,980 what they want? 1106 00:58:25,980 --> 00:58:27,384 What if a man is speaking on behalf 1107 00:58:27,384 --> 00:58:28,830 of his wife's interests? 1108 00:58:28,830 --> 00:58:29,320 PROFESSOR: Right. 1109 00:58:29,320 --> 00:58:32,290 So it is not entirely clear. 1110 00:58:32,290 --> 00:58:37,920 And so this measure of women's and men's preference might not 1111 00:58:37,920 --> 00:58:39,920 be the best you can think of. 1112 00:58:39,920 --> 00:58:42,100 The key is to have a good one. 1113 00:58:42,100 --> 00:58:45,290 I think you're right, which is it could be that women never 1114 00:58:45,290 --> 00:58:48,110 speak for example, or never complain about anything, and 1115 00:58:48,110 --> 00:58:50,390 they would still have preferences so what would have 1116 00:58:50,390 --> 00:58:53,990 to figure something out to get them. 1117 00:58:53,990 --> 00:58:57,940 To the extent that both genders speak, then you might 1118 00:58:57,940 --> 00:59:00,840 think that they speaking about what they care about as long 1119 00:59:00,840 --> 00:59:09,890 as the house is not a fully harmonious unit. 1120 00:59:09,890 --> 00:59:11,720 Or you might think that it's not, and then for example, 1121 00:59:11,720 --> 00:59:14,450 women tend to talk about water just because they know better 1122 00:59:14,450 --> 00:59:16,990 about water as someone suggested earlier. 1123 00:59:16,990 --> 00:59:21,430 And it doesn't represent needs, it just represents 1124 00:59:21,430 --> 00:59:24,120 relative advantages. 1125 00:59:24,120 --> 00:59:27,790 So that is something that this particular wealth measuring 1126 00:59:27,790 --> 00:59:31,250 preferences might not be the best one, but the 1127 00:59:31,250 --> 00:59:34,590 key is to have one. 1128 00:59:34,590 --> 00:59:37,042 More than one would be even better. 1129 00:59:37,042 --> 00:59:40,300 So in this case, that's the only when we had so it was a 1130 00:59:40,300 --> 00:59:42,300 bit of a gamble. 1131 00:59:42,300 --> 00:59:46,600 But the key thing is that we want to do that. 1132 00:59:46,600 --> 00:59:49,010 Then there are other things we might be interested in. 1133 00:59:49,010 --> 00:59:53,860 So once we have that, do women invest more in public goods, 1134 00:59:53,860 --> 01:00:00,430 then we can ask the next question which is do investing 1135 01:00:00,430 --> 01:00:04,530 more once we find this that kind of can be our study 1136 01:00:04,530 --> 01:00:05,610 number one. 1137 01:00:05,610 --> 01:00:09,165 Which is yes, we are showing that women invest more in the 1138 01:00:09,165 --> 01:00:10,660 public goods that women prefer. 1139 01:00:10,660 --> 01:00:12,970 Then there was this other question that also came about, 1140 01:00:12,970 --> 01:00:16,160 a completely different question, which is, does 1141 01:00:16,160 --> 01:00:19,590 having a woman as a policy maker change the perception of 1142 01:00:19,590 --> 01:00:21,810 women as policy makers. 1143 01:00:21,810 --> 01:00:25,470 And that we can think of a separate hypothesis altogether 1144 01:00:25,470 --> 01:00:27,180 that can be tested separately. 1145 01:00:27,180 --> 01:00:32,210 And it could be that women as leaders do different things 1146 01:00:32,210 --> 01:00:36,030 and do what women want, but that there is no lingering 1147 01:00:36,030 --> 01:00:39,330 effect of having a woman because that doesn't affect 1148 01:00:39,330 --> 01:00:41,345 people's preferences or because there is a backlash. 1149 01:00:45,410 --> 01:00:48,390 So we can think of these two as different bins. 1150 01:00:48,390 --> 01:00:50,160 We can even do one study and not the 1151 01:00:50,160 --> 01:00:53,090 other, of both of them. 1152 01:00:53,090 --> 01:00:57,050 But they are kind of separate tracks to which to go. 1153 01:00:57,050 --> 01:01:02,830 So if we take a minute on people's perception, do women 1154 01:01:02,830 --> 01:01:08,380 affect the perception of voters of women leaders? 1155 01:01:08,380 --> 01:01:10,480 How would you go about thinking about this? 1156 01:01:10,480 --> 01:01:15,090 How would you go about trying to measure people's political 1157 01:01:15,090 --> 01:01:16,861 preferences? 1158 01:01:16,861 --> 01:01:19,506 AUDIENCE: Asking questions about the role of women in the 1159 01:01:19,506 --> 01:01:21,190 household, or women in the community. 1160 01:01:21,190 --> 01:01:23,490 PROFESSOR: So you can ask questions. 1161 01:01:23,490 --> 01:01:25,340 So what is an issue is this asking 1162 01:01:25,340 --> 01:01:27,185 questions in this context? 1163 01:01:27,185 --> 01:01:29,420 Do you like women leaders? 1164 01:01:29,420 --> 01:01:30,994 What do you think of-- 1165 01:01:30,994 --> 01:01:32,965 AUDIENCE: People might feel like there's a right answer. 1166 01:01:32,965 --> 01:01:35,580 PROFESSOR: People may feel there is a right answer. 1167 01:01:35,580 --> 01:01:38,820 And what's the right answer, do you think, in this context? 1168 01:01:38,820 --> 01:01:41,280 AUDIENCE: I see that you can tell the story right away that 1169 01:01:41,280 --> 01:01:46,230 it's important to believe in equality between genders. 1170 01:01:46,230 --> 01:01:52,400 Or that if you're a male, in front of your other male 1171 01:01:52,400 --> 01:01:54,916 friends you want to appear like a tough guy. 1172 01:01:54,916 --> 01:01:56,302 AUDIENCE: Or who's asking the questions. 1173 01:01:56,302 --> 01:01:58,180 PROFESSOR: Or who is asking the questions. 1174 01:01:58,180 --> 01:02:00,240 Or you might think that it's the right time to send a 1175 01:02:00,240 --> 01:02:04,800 message to say those people from the capital who are 1176 01:02:04,800 --> 01:02:06,920 imposing this woman on us, better tell them that really 1177 01:02:06,920 --> 01:02:09,170 we don't like it. 1178 01:02:09,170 --> 01:02:11,680 So it could really go either way, we don't know. 1179 01:02:11,680 --> 01:02:14,300 And in a sense that is something that is interesting 1180 01:02:14,300 --> 01:02:21,500 as well, is to ask which way is what people are willing to 1181 01:02:21,500 --> 01:02:22,480 reveal to you? 1182 01:02:22,480 --> 01:02:25,170 In which direction is it best? 1183 01:02:25,170 --> 01:02:29,636 So then we would need some measure of "to" preferences. 1184 01:02:29,636 --> 01:02:35,460 To willingness to consider women as policymakers. 1185 01:02:35,460 --> 01:02:38,148 AUDIENCE: Their voting preferences in the next cycle? 1186 01:02:38,148 --> 01:02:38,920 PROFESSOR: Yeah. 1187 01:02:38,920 --> 01:02:42,100 So it seems that the litmus test here would be voting. 1188 01:02:42,100 --> 01:02:46,370 Which is after one cycle of reservation, or two cycles of 1189 01:02:46,370 --> 01:02:50,930 reservation, are women more likely to be elected. 1190 01:02:50,930 --> 01:02:54,880 And that seems to be that it would be like the place easier 1191 01:02:54,880 --> 01:02:57,060 to start or at least to end, which is does it make a 1192 01:02:57,060 --> 01:02:57,730 difference? 1193 01:02:57,730 --> 01:03:03,590 And along the way, if you find that you can think, well, 1194 01:03:03,590 --> 01:03:05,550 maybe this is because of various things. 1195 01:03:05,550 --> 01:03:09,000 Maybe this is women started to develop their networks. 1196 01:03:09,000 --> 01:03:14,970 Maybe women figured out that they could do this. 1197 01:03:14,970 --> 01:03:18,310 So if we wanted to know that it's really true, the change 1198 01:03:18,310 --> 01:03:21,630 in the perception of women as policy makers, you would try 1199 01:03:21,630 --> 01:03:23,310 and get a measure of perception. 1200 01:03:23,310 --> 01:03:27,720 Again, more to eliminate the end line result, and to try to 1201 01:03:27,720 --> 01:03:31,908 get the measure of pure, just one sort of-- 1202 01:03:31,908 --> 01:03:34,255 AUDIENCE: Could you also-- 1203 01:03:34,255 --> 01:03:36,790 so there are women elected to these councils. 1204 01:03:36,790 --> 01:03:37,570 PROFESSOR: Yeah. 1205 01:03:37,570 --> 01:03:42,420 AUDIENCE: Could you also though measure the women's 1206 01:03:42,420 --> 01:03:46,785 ascendancy to other kinds of more management posts, like at 1207 01:03:46,785 --> 01:03:53,125 school level, or at community groups or something like that, 1208 01:03:53,125 --> 01:03:56,645 as stepping stones on the way to that? 1209 01:03:56,645 --> 01:03:57,900 PROFESSOR: Right. 1210 01:03:57,900 --> 01:04:00,350 That goes into this margin on women's participation. 1211 01:04:02,910 --> 01:04:04,690 So you're thinking [STUDENT NAME] 1212 01:04:04,690 --> 01:04:07,070 earlier was thinking up, like do we see more women-- 1213 01:04:07,070 --> 01:04:07,865 AUDIENCE: At higher levels. 1214 01:04:07,865 --> 01:04:09,390 PROFESSOR: --going at higher level. 1215 01:04:09,390 --> 01:04:12,300 Or you could say do we see more women in position of 1216 01:04:12,300 --> 01:04:13,350 power within the village. 1217 01:04:13,350 --> 01:04:14,150 AUDIENCE: Sink or swim. 1218 01:04:14,150 --> 01:04:17,190 PROFESSOR: School headmasters, things like that. 1219 01:04:17,190 --> 01:04:20,020 School council, other things like that. 1220 01:04:20,020 --> 01:04:25,280 Going back to the perception question, do you have an idea 1221 01:04:25,280 --> 01:04:29,210 of how we could go about measuring people's perception 1222 01:04:29,210 --> 01:04:30,460 of women as policymakers? 1223 01:04:33,195 --> 01:04:36,153 AUDIENCE: We could have an index of the satisfaction they 1224 01:04:36,153 --> 01:04:39,357 have with investments that women have made, and try to 1225 01:04:39,357 --> 01:04:44,041 relate it to how much they like or dislike the woman who 1226 01:04:44,041 --> 01:04:45,027 decided about this investment? 1227 01:04:45,027 --> 01:04:47,700 PROFESSOR: So we could try satisfaction of the goods. 1228 01:04:47,700 --> 01:04:51,860 Thinking that in particular if we had an objective quality of 1229 01:04:51,860 --> 01:04:54,480 the goods, we could say that if their satisfaction of the 1230 01:04:54,480 --> 01:04:59,100 goods is lower even though the goods are the same, it 1231 01:04:59,100 --> 01:05:01,550 indicates that they like women less. 1232 01:05:01,550 --> 01:05:04,360 But that requires having a very good measure of the 1233 01:05:04,360 --> 01:05:06,120 quality of the goods. 1234 01:05:06,120 --> 01:05:09,760 Otherwise, someone could always say they are worse in 1235 01:05:09,760 --> 01:05:12,330 some dimension that you didn't observe. 1236 01:05:12,330 --> 01:05:16,030 AUDIENCE: We could do some kind of hypothetical question 1237 01:05:16,030 --> 01:05:20,402 that involves psychology, or you're trying to-- 1238 01:05:20,402 --> 01:05:22,280 PROFESSOR: So yeah, great. 1239 01:05:22,280 --> 01:05:25,474 Continue in this direction. 1240 01:05:25,474 --> 01:05:30,310 AUDIENCE: Where you're interviewing people or doing 1241 01:05:30,310 --> 01:05:36,850 surveys, and you asked them to imagine a scenario where you 1242 01:05:36,850 --> 01:05:41,450 have two different candidates, and you give them the 1243 01:05:41,450 --> 01:05:44,720 backgrounds and one's a man and one is a woman, and ask 1244 01:05:44,720 --> 01:05:46,620 them who they would pick. 1245 01:05:46,620 --> 01:05:49,740 I mean, that would be a poorly disguised question-- 1246 01:05:49,740 --> 01:05:53,420 PROFESSOR: If you have the two candidates and you ask them to 1247 01:05:53,420 --> 01:05:56,150 compare, you might get into the same issue. 1248 01:05:56,150 --> 01:05:58,690 But just go one step further with this same idea. 1249 01:06:03,080 --> 01:06:03,810 You're almost there. 1250 01:06:03,810 --> 01:06:06,080 Or at least that's one way of doing it. 1251 01:06:09,778 --> 01:06:13,970 AUDIENCE: You could present two hypothetical CVs to people 1252 01:06:13,970 --> 01:06:16,110 and say, would like to vote for this person. 1253 01:06:16,110 --> 01:06:19,030 But on one of the hypothetical CVs, make it up as a woman, 1254 01:06:19,030 --> 01:06:22,915 and the other make it up as a man, but they have all the 1255 01:06:22,915 --> 01:06:23,365 same qualifications. 1256 01:06:23,365 --> 01:06:26,200 And just see if there's a sense that people would vote 1257 01:06:26,200 --> 01:06:27,830 more for one versus the other. 1258 01:06:27,830 --> 01:06:28,255 PROFESSOR: Exactly. 1259 01:06:28,255 --> 01:06:28,960 You are [? interpreting ?] 1260 01:06:28,960 --> 01:06:31,290 values to different people. 1261 01:06:31,290 --> 01:06:35,050 Nothing forces you to present the same questionnaire to 1262 01:06:35,050 --> 01:06:36,540 every person. 1263 01:06:36,540 --> 01:06:39,060 You could randomize within your questionnaire what you're 1264 01:06:39,060 --> 01:06:40,980 going to show them. 1265 01:06:40,980 --> 01:06:43,120 So you're going to show them, for example, exactly the same 1266 01:06:43,120 --> 01:06:45,940 CV and ask them, are you going to vote for this person? 1267 01:06:45,940 --> 01:06:49,990 Or you could present a scenario saying, so this and 1268 01:06:49,990 --> 01:06:53,290 this happened, and there was a choice to be made, and they 1269 01:06:53,290 --> 01:06:55,610 decided to do this and what do you think? 1270 01:06:55,610 --> 01:06:57,350 Was it a good decision? 1271 01:06:57,350 --> 01:07:01,040 And in one case it's like Mr. So and So decided to do this, 1272 01:07:01,040 --> 01:07:04,790 and in one case Mrs. So and So decided to do this. 1273 01:07:04,790 --> 01:07:06,860 If you ask both, you're exactly right, 1274 01:07:06,860 --> 01:07:07,860 it was poorly disguised. 1275 01:07:07,860 --> 01:07:10,010 But if you ask only one, people 1276 01:07:10,010 --> 01:07:13,950 will answer the question. 1277 01:07:13,950 --> 01:07:16,820 And they don't have enough information to really judge 1278 01:07:16,820 --> 01:07:18,260 the person fully. 1279 01:07:18,260 --> 01:07:20,520 Especially if you give them a small scenario and then ask 1280 01:07:20,520 --> 01:07:22,260 them, will you vote for them? 1281 01:07:22,260 --> 01:07:25,280 So then we would bring in, presumably, whatever else 1282 01:07:25,280 --> 01:07:28,020 their other views on the person. 1283 01:07:28,020 --> 01:07:32,280 So then you will have compare across surveys whether people 1284 01:07:32,280 --> 01:07:40,160 tend to rank more highly the survey that has Mr. So and So, 1285 01:07:40,160 --> 01:07:43,850 versus the survey that has Mrs. So and So. 1286 01:07:43,850 --> 01:07:46,390 The way we did it in the follow up study to the one 1287 01:07:46,390 --> 01:07:50,090 that is in the case is we actually taped speeches. 1288 01:07:50,090 --> 01:07:53,410 So we had speeches that someone had given, and we had 1289 01:07:53,410 --> 01:07:56,930 a bunch of women record the same speech. 1290 01:07:56,930 --> 01:08:00,840 And we then played a tape and said, so what do you think? 1291 01:08:00,840 --> 01:08:03,080 So the advantage is, we don't even have to insist on that 1292 01:08:03,080 --> 01:08:04,880 it's Mr. So and So, it's just would you 1293 01:08:04,880 --> 01:08:05,820 listen to this speech. 1294 01:08:05,820 --> 01:08:09,590 And the voice immediately tells you and then you can 1295 01:08:09,590 --> 01:08:11,800 compare people's answers. 1296 01:08:11,800 --> 01:08:14,765 And so now it becomes you have to compare people's answers to 1297 01:08:14,765 --> 01:08:18,460 this question in the villages that were reserved and the 1298 01:08:18,460 --> 01:08:21,060 villages that were not reserved, and see whether any 1299 01:08:21,060 --> 01:08:22,790 gap changes. 1300 01:08:22,790 --> 01:08:25,779 An interesting fact is that then you can correlate that to 1301 01:08:25,779 --> 01:08:30,160 what they actual tell you and see whether, for example, it 1302 01:08:30,160 --> 01:08:33,359 might be that the real gap doesn't change, but what they 1303 01:08:33,359 --> 01:08:35,650 tell you narrows, or it could be the opposite. 1304 01:08:35,650 --> 01:08:40,590 If they're trying to signal their dislike of the policy or 1305 01:08:40,590 --> 01:08:41,790 something like that. 1306 01:08:41,790 --> 01:08:44,290 And at the end of the day, all of this leads to sort of I'll 1307 01:08:44,290 --> 01:08:47,819 find a hypothesis of did this work, in a sense. 1308 01:08:47,819 --> 01:08:50,029 Which would be what are the vote shares 1309 01:08:50,029 --> 01:08:52,370 for women in politics? 1310 01:08:52,370 --> 01:08:55,819 Or if you were less ambitious, something like what are the 1311 01:08:55,819 --> 01:08:58,220 share of women who are represented in positions of 1312 01:08:58,220 --> 01:09:02,770 power within the village as you suggested earlier. 1313 01:09:02,770 --> 01:09:04,960 So that's something for day two because we were on 1314 01:09:04,960 --> 01:09:06,100 measurement of outcomes. 1315 01:09:06,100 --> 01:09:11,130 I just wanted to give you one example so we don't lack of 1316 01:09:11,130 --> 01:09:14,189 various ways we can collect outcomes. 1317 01:09:14,189 --> 01:09:15,430 That's one way. 1318 01:09:15,430 --> 01:09:22,620 Going back to the example of do women leaders better 1319 01:09:22,620 --> 01:09:26,270 represent the preferences of women. 1320 01:09:28,930 --> 01:09:33,470 So what we try and do with an evaluation is to try to find 1321 01:09:33,470 --> 01:09:37,200 out whether the program's just, whether the program is 1322 01:09:37,200 --> 01:09:42,950 effective, but also why it's really more helpful? 1323 01:09:42,950 --> 01:09:46,710 Both because well, we have a richer understanding, and 1324 01:09:46,710 --> 01:09:53,670 because it will enrich our understanding of the program 1325 01:09:53,670 --> 01:09:57,190 and also make it easier to draw more general lessons. 1326 01:09:57,190 --> 01:10:00,800 Because for example, this program you evaluate in West 1327 01:10:00,800 --> 01:10:02,580 Bengal and someone can say, well we 1328 01:10:02,580 --> 01:10:03,890 need to work in Rajasthan. 1329 01:10:03,890 --> 01:10:07,360 So one way is to go and do it in Rajasthan. 1330 01:10:07,360 --> 01:10:09,070 But then you can say, well, but it worked 1331 01:10:09,070 --> 01:10:10,200 in Rajasthan, too. 1332 01:10:10,200 --> 01:10:13,200 We need to work in South India and it cannot replicate 1333 01:10:13,200 --> 01:10:14,970 everywhere, everywhere. 1334 01:10:14,970 --> 01:10:19,910 So eventually you want to have the ability to say something. 1335 01:10:19,910 --> 01:10:24,870 It's not necessarily we'll go with some hypothesis, but to 1336 01:10:24,870 --> 01:10:27,160 say something that what is your take at the end of the 1337 01:10:27,160 --> 01:10:31,815 day of the reasons why this program will have such and 1338 01:10:31,815 --> 01:10:32,830 such effect. 1339 01:10:32,830 --> 01:10:37,220 So if you say, well what I think is what's happening is 1340 01:10:37,220 --> 01:10:41,510 when women are elected, when there is a reservation for 1341 01:10:41,510 --> 01:10:44,540 women they are doing stuff that women prefer. 1342 01:10:44,540 --> 01:10:47,470 Then you can say, well, in West Bengal what they want is 1343 01:10:47,470 --> 01:10:49,470 water so that's what they'll do. 1344 01:10:49,470 --> 01:10:52,580 In, say, Tamil Nadu our water's not so important-- 1345 01:10:52,580 --> 01:10:55,310 well, it is very important to me-- so in any place where 1346 01:10:55,310 --> 01:10:58,210 it's not so much of an issue, I guess that wouldn't be 1347 01:10:58,210 --> 01:11:02,120 India, then they'll do that. 1348 01:11:02,120 --> 01:11:05,000 So as long as you tell me what women care about, I can tell 1349 01:11:05,000 --> 01:11:08,360 you that it's going to go in this direction. 1350 01:11:08,360 --> 01:11:10,730 It has the advantage that it makes the replication more 1351 01:11:10,730 --> 01:11:15,480 interesting because if you're doing North Bengal and then 1352 01:11:15,480 --> 01:11:21,690 you say, well now some goods go up, some goods go down. 1353 01:11:21,690 --> 01:11:24,930 And then say you replicate in Rajasthan a new level of tests 1354 01:11:24,930 --> 01:11:27,100 that you are willing to subject yourself to. 1355 01:11:27,100 --> 01:11:31,190 If some goods go up, some goods go down, it's like well, 1356 01:11:31,190 --> 01:11:33,090 maybe you don't even need to do the evaluation because 1357 01:11:33,090 --> 01:11:34,600 probably you'll find it. 1358 01:11:34,600 --> 01:11:40,860 Whereas after the West Bengal study you say, whatever I find 1359 01:11:40,860 --> 01:11:44,870 from this however imperfect way to collect preferences is 1360 01:11:44,870 --> 01:11:48,500 what women prefer, this is the same direction it's going to 1361 01:11:48,500 --> 01:11:50,100 move in Rajasthan. 1362 01:11:50,100 --> 01:11:51,770 So, in fact, in here in this case it's 1363 01:11:51,770 --> 01:11:52,710 exactly what happened. 1364 01:11:52,710 --> 01:11:56,990 Because we first did West Bengal, and we find that women 1365 01:11:56,990 --> 01:12:01,500 preferred water, according to the measure of preferences, 1366 01:12:01,500 --> 01:12:03,290 and goods went to water. 1367 01:12:03,290 --> 01:12:06,360 And men prefer schools, which was surprising to us and 1368 01:12:06,360 --> 01:12:07,840 others, but that's the way it is, and the 1369 01:12:07,840 --> 01:12:09,740 goods went to schools. 1370 01:12:09,740 --> 01:12:12,050 So now for Rajasthan, we say, well we are going to do the 1371 01:12:12,050 --> 01:12:12,500 same thing. 1372 01:12:12,500 --> 01:12:15,370 Find out what women want, what men want in the same way. 1373 01:12:15,370 --> 01:12:17,200 It's going to go in this direction. 1374 01:12:17,200 --> 01:12:19,530 And we find that there in Rajasthan, also women prefer 1375 01:12:19,530 --> 01:12:23,100 water, but men love roads. 1376 01:12:23,100 --> 01:12:25,030 So we should have less roads. 1377 01:12:25,030 --> 01:12:26,470 More water, less roads. 1378 01:12:26,470 --> 01:12:29,330 While in West Bengal, women also likes the road, for the 1379 01:12:29,330 --> 01:12:32,290 reason that they work on them. 1380 01:12:32,290 --> 01:12:35,620 They are the people who do the roads, so it's employment 1381 01:12:35,620 --> 01:12:37,070 activities for them. 1382 01:12:37,070 --> 01:12:41,460 So it's interesting because we have different predictions. 1383 01:12:41,460 --> 01:12:44,000 Our prediction is that the road will go up in West Bengal 1384 01:12:44,000 --> 01:12:46,490 where you have women reservation, and they will go 1385 01:12:46,490 --> 01:12:48,380 down in Rajasthan. 1386 01:12:48,380 --> 01:12:50,580 We know why, because it's related to the thing. 1387 01:12:50,580 --> 01:12:54,500 And in fact we can make this leap of faith beforehand. 1388 01:12:54,500 --> 01:12:58,380 And so that makes it much more powerful once you replicate, 1389 01:12:58,380 --> 01:13:02,840 rather than I'll replicate as I can. 1390 01:13:02,840 --> 01:13:08,240 To say I will replicate with a good sense of what I'm 1391 01:13:08,240 --> 01:13:10,960 expecting to find. 1392 01:13:10,960 --> 01:13:13,130 Which brings me to the same thing which is our saying it's 1393 01:13:13,130 --> 01:13:18,170 a very difficult ex-post not to use the data to learn more 1394 01:13:18,170 --> 01:13:22,210 than what the hypothesis was at the beginning because, you 1395 01:13:22,210 --> 01:13:23,660 know, it's sad. 1396 01:13:23,660 --> 01:13:25,840 So you could think about it in a more constructive way, which 1397 01:13:25,840 --> 01:13:28,760 is you could think well, this is what was my hypothesis was 1398 01:13:28,760 --> 01:13:30,740 in the beginning, this is what I find. 1399 01:13:30,740 --> 01:13:34,490 In addition, I have also these interesting, tantalizing 1400 01:13:34,490 --> 01:13:36,690 tidbits of results. 1401 01:13:36,690 --> 01:13:39,460 I'm putting it in front if you admitting that it was not my 1402 01:13:39,460 --> 01:13:43,130 hypothesis to start with, but I am contesting that it should 1403 01:13:43,130 --> 01:13:44,540 be the hypothesis of the next study. 1404 01:13:47,310 --> 01:13:50,340 I'll give you one very good example of that. 1405 01:13:50,340 --> 01:13:56,330 There was one project by David McKenzie from the World Bank, 1406 01:13:56,330 --> 01:14:00,570 and Suresh de Mel, who works in Sri Lanka, and Chris 1407 01:14:00,570 --> 01:14:04,860 Woodruff in UCSD, and they were interested in the return 1408 01:14:04,860 --> 01:14:07,720 to capitol for very small entrepreneurs. 1409 01:14:07,720 --> 01:14:11,730 So what they did is they gave people in Sri Lanka a grant. 1410 01:14:11,730 --> 01:14:15,060 Small entrepreneurs, people who had about $200 of working 1411 01:14:15,060 --> 01:14:17,460 capital, that's just what we call a 1412 01:14:17,460 --> 01:14:19,650 helicopter drop of money. 1413 01:14:19,650 --> 01:14:23,420 So you get $100 grant or $200 grant. 1414 01:14:23,420 --> 01:14:26,260 And they did that and they found that at first you get 1415 01:14:26,260 --> 01:14:28,780 the average and they found great returns to capital. 1416 01:14:28,780 --> 01:14:30,590 Very high returns to capital. 1417 01:14:30,590 --> 01:14:33,340 So very beneficial to give people of the other 1418 01:14:33,340 --> 01:14:35,100 5% percent a month. 1419 01:14:35,100 --> 01:14:37,850 So very high return to capital. 1420 01:14:37,850 --> 01:14:39,320 Great. 1421 01:14:39,320 --> 01:14:42,800 And then they decided to do it separately at women and men. 1422 01:14:42,800 --> 01:14:46,530 And oh, surprise, they found no return whatsoever for the 1423 01:14:46,530 --> 01:14:50,760 women, and huge return for the men. 1424 01:14:50,760 --> 01:14:55,350 So you can say, sorry, it was not in your original design. 1425 01:14:55,350 --> 01:14:58,430 It was not stratified by gender, so we have really no 1426 01:14:58,430 --> 01:15:00,010 intents that you are-- so we have to 1427 01:15:00,010 --> 01:15:02,090 throw this result away. 1428 01:15:02,090 --> 01:15:03,910 Of course, we don't want to throw this result away because 1429 01:15:03,910 --> 01:15:06,690 that's so surprising and striking that we kind of want 1430 01:15:06,690 --> 01:15:08,360 to think about it. 1431 01:15:08,360 --> 01:15:09,910 So what's the idea? 1432 01:15:09,910 --> 01:15:13,140 You write this up being very explicit that we found this 1433 01:15:13,140 --> 01:15:16,570 ex-post, but it seems like really robust. 1434 01:15:16,570 --> 01:15:19,700 We are going to go and do a new experiment, so we could 1435 01:15:19,700 --> 01:15:22,630 redo it in Sri Lanka or do it in somewhere else. 1436 01:15:22,630 --> 01:15:27,730 And in this case, our hypothesis is the age zero is 1437 01:15:27,730 --> 01:15:29,950 the return to capital are the same for men and women, that's 1438 01:15:29,950 --> 01:15:31,750 what you are trying to reject. 1439 01:15:31,750 --> 01:15:34,230 So these scores are good to think about. 1440 01:15:34,230 --> 01:15:38,090 These evaluations are part of a process, we are not alone. 1441 01:15:38,090 --> 01:15:39,790 A lot of people are working on this, there will be 1442 01:15:39,790 --> 01:15:42,570 replication either by you or by others. 1443 01:15:42,570 --> 01:15:47,870 And being explicit up front about what was your first 1444 01:15:47,870 --> 01:15:49,990 hypothesis and your current model. 1445 01:15:49,990 --> 01:15:53,050 And was is it that you found out as well? 1446 01:15:53,050 --> 01:15:56,070 Our goal is to not get mixed up, and at the same time not 1447 01:15:56,070 --> 01:15:57,930 to lose the information that is going to be 1448 01:15:57,930 --> 01:15:59,180 useful in the future. 1449 01:16:05,610 --> 01:16:10,010 So we stop at 12:00 whenever we started, or what are the 1450 01:16:10,010 --> 01:16:11,565 social norms in this? 1451 01:16:11,565 --> 01:16:12,912 AUDIENCE: As long as you want. 1452 01:16:12,912 --> 01:16:16,050 PROFESSOR: Oh. 1453 01:16:16,050 --> 01:16:18,080 I have a phone call at 12:30, otherwise-- 1454 01:16:18,080 --> 01:16:19,280 AUDIENCE: It's 12:15. 1455 01:16:19,280 --> 01:16:23,510 PROFESSOR: I'd probably be finished before that. 1456 01:16:23,510 --> 01:16:26,840 I don't have time to finish what's on the slides anyway. 1457 01:16:26,840 --> 01:16:31,460 So I just wanted to give you a sense of what might be a 1458 01:16:31,460 --> 01:16:32,710 causal model in this case. 1459 01:16:35,670 --> 01:16:43,100 The whole perception and goal is not there, it's the public 1460 01:16:43,100 --> 01:16:45,056 goods thing. 1461 01:16:45,056 --> 01:16:49,350 And it's a way of disciplining all of the outcomes, as well 1462 01:16:49,350 --> 01:16:52,090 as the various things we spoke about. 1463 01:16:52,090 --> 01:16:54,800 So you start from reservation, so one thing that reservations 1464 01:16:54,800 --> 01:16:56,360 definitely do is that they will lead 1465 01:16:56,360 --> 01:16:59,520 to more women Pradhan. 1466 01:16:59,520 --> 01:17:02,790 And then the question is whether or not having more 1467 01:17:02,790 --> 01:17:08,420 women Pradhan will change the public good, and in what way? 1468 01:17:08,420 --> 01:17:13,120 And there are really two channels to change the 1469 01:17:13,120 --> 01:17:15,190 preferences, which we have discussed. 1470 01:17:15,190 --> 01:17:20,830 One is the women as the Pradhan do what women want. 1471 01:17:20,830 --> 01:17:23,590 And you've not really discussed that, but that comes 1472 01:17:23,590 --> 01:17:26,840 with its own set of assumptions which is on the 1473 01:17:26,840 --> 01:17:31,680 one hand the Pradhan are not representing the majority. 1474 01:17:31,680 --> 01:17:33,750 As always, the majority hasn't changed. 1475 01:17:33,750 --> 01:17:39,200 We shouldn't see a difference because even if you are 1476 01:17:39,200 --> 01:17:42,680 saying, well, you don't have to be accountable to the men. 1477 01:17:42,680 --> 01:17:45,660 That's not true, because they still do it for you. 1478 01:17:45,660 --> 01:17:45,830 Ex-ante. 1479 01:17:45,830 --> 01:17:53,500 Several woman compete, and the issue is what platform are you 1480 01:17:53,500 --> 01:17:55,930 going to run on? 1481 01:17:55,930 --> 01:17:59,020 Well you should be running on the platform that is going to 1482 01:17:59,020 --> 01:18:00,790 get you elected. 1483 01:18:00,790 --> 01:18:03,440 And whether or not you're a man or a woman, you're elected 1484 01:18:03,440 --> 01:18:05,540 by the same group of people. 1485 01:18:05,540 --> 01:18:09,700 So in a totally stand out model where democracy is 1486 01:18:09,700 --> 01:18:12,720 perfect, who is in charge doesn't matter. 1487 01:18:12,720 --> 01:18:17,320 Because who is in charge is representing the desire of the 1488 01:18:17,320 --> 01:18:20,580 majority, what we call the median voter. 1489 01:18:20,580 --> 01:18:23,510 So if you had perfect democracy, that channel would 1490 01:18:23,510 --> 01:18:27,680 be killed, and we wouldn't see an impact. 1491 01:18:27,680 --> 01:18:31,950 On the other hand, if all the decisions were made by a group 1492 01:18:31,950 --> 01:18:36,260 of elite villagers, that again wouldn't matter because who is 1493 01:18:36,260 --> 01:18:38,180 in charge doesn't matter. 1494 01:18:38,180 --> 01:18:44,290 So the identity of the Pradhan is going to make a difference 1495 01:18:44,290 --> 01:18:46,330 only in some 1496 01:18:46,330 --> 01:18:48,810 middle-of-the-road kind of [? ward ?] 1497 01:18:48,810 --> 01:18:53,280 where the politician has some control over what is going on. 1498 01:18:56,570 --> 01:18:59,170 It is not completely controlled by a bureaucracy or 1499 01:18:59,170 --> 01:19:01,600 by elite, and is not completely 1500 01:19:01,600 --> 01:19:03,550 accountable to the people. 1501 01:19:03,550 --> 01:19:05,190 But who is he? 1502 01:19:05,190 --> 01:19:09,100 He cannot fully commit, for example, to a platform. 1503 01:19:09,100 --> 01:19:10,660 That doesn't seem unrealistic. 1504 01:19:10,660 --> 01:19:12,660 People make electoral promises and sometimes 1505 01:19:12,660 --> 01:19:14,770 they go against them. 1506 01:19:14,770 --> 01:19:18,820 Almost never, but sometimes. 1507 01:19:18,820 --> 01:19:21,920 But that's something. 1508 01:19:21,920 --> 01:19:27,750 So if we do learn that the public good prefers to change, 1509 01:19:27,750 --> 01:19:30,770 we have learned something broader than just impact to 1510 01:19:30,770 --> 01:19:32,410 this program. 1511 01:19:32,410 --> 01:19:37,790 We have learned something about politics in India, which 1512 01:19:37,790 --> 01:19:39,760 is OK, there is some democracy. 1513 01:19:39,760 --> 01:19:40,960 Some people are contesting that. 1514 01:19:40,960 --> 01:19:46,390 Some people say the Panchayat is just a face. 1515 01:19:46,390 --> 01:19:49,510 There is no democracy really. 1516 01:19:49,510 --> 01:19:52,570 So if you find a difference by the identity of the Pradhan, 1517 01:19:52,570 --> 01:19:57,050 it shows you by the by that there is some reality in the 1518 01:19:57,050 --> 01:19:59,100 democratic system, but it's not perfect democracy. 1519 01:19:59,100 --> 01:20:01,100 So if we've learned something, it can be broader than a new 1520 01:20:01,100 --> 01:20:02,540 program as well. 1521 01:20:02,540 --> 01:20:06,810 And that is a lesson you can take elsewhere. 1522 01:20:06,810 --> 01:20:12,300 Another channel by which a woman having reservation can 1523 01:20:12,300 --> 01:20:16,500 influence the representation of women is to more 1524 01:20:16,500 --> 01:20:17,160 representation. 1525 01:20:17,160 --> 01:20:19,620 For example, we were talking about more political 1526 01:20:19,620 --> 01:20:22,290 participation of women if the woman is the head. 1527 01:20:22,290 --> 01:20:25,920 So one thing that could be true is that they're more 1528 01:20:25,920 --> 01:20:27,450 likely to show up in meetings, that they're more 1529 01:20:27,450 --> 01:20:28,900 likely to speak up. 1530 01:20:28,900 --> 01:20:31,530 For example, because the women have said that the Pradhan has 1531 01:20:31,530 --> 01:20:34,510 to be at the village meeting, so she better put it at the 1532 01:20:34,510 --> 01:20:36,040 time where she can go. 1533 01:20:36,040 --> 01:20:38,580 So not in the middle of the night in a field. 1534 01:20:38,580 --> 01:20:43,080 And so as long as she can go, a woman can also go. 1535 01:20:43,080 --> 01:20:53,060 So to either of these channels, you'd have the fact 1536 01:20:53,060 --> 01:20:55,710 that the public good will reflect better women's 1537 01:20:55,710 --> 01:20:57,210 preferences. 1538 01:20:57,210 --> 01:21:00,500 We have to add another assumption, is that women have 1539 01:21:00,500 --> 01:21:02,720 different preferences. 1540 01:21:02,720 --> 01:21:05,770 And if that's the case, then the public good will be 1541 01:21:05,770 --> 01:21:10,180 different in a specific way, which is towards those 1542 01:21:10,180 --> 01:21:12,150 different preferences. 1543 01:21:12,150 --> 01:21:17,280 And then you might have different outcomes. 1544 01:21:17,280 --> 01:21:22,080 More income for the women, better health and education 1545 01:21:22,080 --> 01:21:25,130 outcome, if it comes out that it's what women care about. 1546 01:21:25,130 --> 01:21:29,720 And you'll be able to follow exactly the trace that, you 1547 01:21:29,720 --> 01:21:31,960 know, if you find like in West Bengal more water. 1548 01:21:31,960 --> 01:21:34,410 Maybe you're going to be interested in diarrhea. 1549 01:21:37,490 --> 01:21:39,800 If you find less schools, you're going to be interested 1550 01:21:39,800 --> 01:21:42,430 in education to see whether education goes down, and 1551 01:21:42,430 --> 01:21:43,930 things like that. 1552 01:21:43,930 --> 01:21:46,120 So now we have the complete channel. 1553 01:21:46,120 --> 01:21:49,630 And we can now think about all of our variables that we had 1554 01:21:49,630 --> 01:21:52,780 collected, and we're going to slot them into, well, what are 1555 01:21:52,780 --> 01:21:55,040 they going to do for us? 1556 01:21:55,040 --> 01:21:57,400 So all the public goods go here. 1557 01:21:57,400 --> 01:22:00,020 We need to collect women's preferences 1558 01:22:00,020 --> 01:22:02,810 somewhere, it will go here. 1559 01:22:02,810 --> 01:22:08,180 We want to know as a woman are empowered, so we are going to 1560 01:22:08,180 --> 01:22:11,700 be collecting all of these. 1561 01:22:11,700 --> 01:22:13,730 Whether people come to the meeting, whether they speak 1562 01:22:13,730 --> 01:22:16,420 up, how they are answered to once they speak up. 1563 01:22:16,420 --> 01:22:19,270 This is all going to go here. 1564 01:22:19,270 --> 01:22:23,130 So now, if we have an infinite amount of money we're still 1565 01:22:23,130 --> 01:22:26,910 going to collect a large amount of data, but we know in 1566 01:22:26,910 --> 01:22:29,420 advance what it is we are going to do with them. 1567 01:22:29,420 --> 01:22:32,050 We can write it down, put it in an envelope, send it to 1568 01:22:32,050 --> 01:22:37,550 your grandmother, and this is the thing that really gives a 1569 01:22:37,550 --> 01:22:39,340 lot of credibility to what you're going to do. 1570 01:22:42,100 --> 01:22:44,340 Another version of sending it to your grandmother that we 1571 01:22:44,340 --> 01:22:48,340 are going to try and implement here at J-PAL is to allow you 1572 01:22:48,340 --> 01:22:53,590 to put it on a website, to upload it somewhere where 1573 01:22:53,590 --> 01:22:56,085 nobody can see it but you, but it's secure, 1574 01:22:56,085 --> 01:22:57,500 and the data is mapped. 1575 01:22:57,500 --> 01:23:02,690 So this is whatever it was your analysis plan at the time 1576 01:23:02,690 --> 01:23:04,580 of the beginning of your study. 1577 01:23:04,580 --> 01:23:08,650 So you are tying your hand behind your back. 1578 01:23:12,458 --> 01:23:17,761 AUDIENCE: Excuse me, but why would you do that? 1579 01:23:17,761 --> 01:23:19,340 PROFESSOR: Because we want to-- 1580 01:23:19,340 --> 01:23:22,500 or maybe someone can answer that question. 1581 01:23:22,500 --> 01:23:23,570 Why do we do that? 1582 01:23:23,570 --> 01:23:26,520 AUDIENCE: I guess it's just so people aren't relying on your 1583 01:23:26,520 --> 01:23:27,550 personal integrity. 1584 01:23:27,550 --> 01:23:30,000 You're saying this was our hypothesis. 1585 01:23:30,000 --> 01:23:33,086 We didn't ex-post change our hypothesis. 1586 01:23:33,086 --> 01:23:34,040 PROFESSOR: Right. 1587 01:23:34,040 --> 01:23:37,710 So the reason why you want to say what was your hypothesis 1588 01:23:37,710 --> 01:23:40,480 in advance, is because then you can attest it. 1589 01:23:40,480 --> 01:23:42,570 Whereas if you take something you can always reverse 1590 01:23:42,570 --> 01:23:43,480 engineer it. 1591 01:23:43,480 --> 01:23:48,040 And again, I have no big issue with that, with reverse 1592 01:23:48,040 --> 01:23:48,780 engineering. 1593 01:23:48,780 --> 01:23:51,000 Personally, I think it's useful, but it needs to be 1594 01:23:51,000 --> 01:23:54,010 very clear what was there before and what was reverse 1595 01:23:54,010 --> 01:23:56,810 engineered after, otherwise we have no notion 1596 01:23:56,810 --> 01:23:59,096 of statistical tests. 1597 01:23:59,096 --> 01:24:01,510 AUDIENCE: I'm just pushing out a little bit further, too. 1598 01:24:01,510 --> 01:24:04,070 What if you just had a question where you just don't 1599 01:24:04,070 --> 01:24:04,890 know, right? 1600 01:24:04,890 --> 01:24:07,490 You're like, wow, we really think there might be an effect 1601 01:24:07,490 --> 01:24:10,500 of this on that, or we're not sure whether the effect would 1602 01:24:10,500 --> 01:24:11,590 be upward or downward. 1603 01:24:11,590 --> 01:24:15,250 Do you still have to just pick one for the sake of having a 1604 01:24:15,250 --> 01:24:16,190 hypothesis that you're testing? 1605 01:24:16,190 --> 01:24:22,000 PROFESSOR: I think you wouldn't want to embark in an 1606 01:24:22,000 --> 01:24:26,390 evaluation without at least having a sense of why it would 1607 01:24:26,390 --> 01:24:28,380 go up and why it would go down. 1608 01:24:28,380 --> 01:24:30,990 So take this specific example. 1609 01:24:30,990 --> 01:24:33,900 To start with, you shouldn't really know whether the water 1610 01:24:33,900 --> 01:24:36,460 wells are going to go up and down, because it's going to 1611 01:24:36,460 --> 01:24:40,510 depend on what women want. 1612 01:24:40,510 --> 01:24:42,105 So I'm not making a stance. 1613 01:24:45,500 --> 01:24:50,200 It would be silly to write to my grandmother. 1614 01:24:50,200 --> 01:24:54,580 I'm betting that the women water wells will go up. 1615 01:24:54,580 --> 01:24:56,210 Because they could go up or down depending 1616 01:24:56,210 --> 01:24:56,990 on what women want. 1617 01:24:56,990 --> 01:24:58,880 Of course, you may have a very strong prior that it's what 1618 01:24:58,880 --> 01:25:05,500 women want, but the statement would be of the form, if it is 1619 01:25:05,500 --> 01:25:08,450 the case that women have a strong preference for water, 1620 01:25:08,450 --> 01:25:10,520 water should go up. 1621 01:25:10,520 --> 01:25:12,400 So if you have some uncertainty, it's probably 1622 01:25:12,400 --> 01:25:14,250 because there is an if somewhere that you're not 1623 01:25:14,250 --> 01:25:18,200 thinking about that you're expliciting now. 1624 01:25:18,200 --> 01:25:20,480 I think if thinking sufficiently hard about 1625 01:25:20,480 --> 01:25:23,340 something, you can know in what condition you 1626 01:25:23,340 --> 01:25:24,310 would go up and down. 1627 01:25:24,310 --> 01:25:26,310 There are a lot of programs which could go up and down, 1628 01:25:26,310 --> 01:25:28,830 that's why we evaluate them. 1629 01:25:28,830 --> 01:25:32,930 I mean, sometimes you think that they should really go up, 1630 01:25:32,930 --> 01:25:34,550 but it could also be zero. 1631 01:25:34,550 --> 01:25:40,210 And it's good to know, if this and this happened then this 1632 01:25:40,210 --> 01:25:41,520 effect would be expected. 1633 01:25:41,520 --> 01:25:44,240 If this and this doesn't happen, then I wouldn't see 1634 01:25:44,240 --> 01:25:45,610 this effect, so you could write that. 1635 01:25:48,620 --> 01:25:51,470 And in fact, I think these types of statements, in a 1636 01:25:51,470 --> 01:25:54,670 sense, are almost more informative 1637 01:25:54,670 --> 01:25:57,720 than this will happen. 1638 01:26:00,360 --> 01:26:01,810 So let me stop here. 1639 01:26:01,810 --> 01:26:07,217 What's in the rest of the slides is kind 1640 01:26:07,217 --> 01:26:10,010 of little bit random-- 1641 01:26:10,010 --> 01:26:13,080 not randomized, but not random in the sense of randomized, 1642 01:26:13,080 --> 01:26:18,500 but random in the sense of every which way I pass out the 1643 01:26:18,500 --> 01:26:23,460 advice on how to collect data and how to enter data and 1644 01:26:23,460 --> 01:26:24,510 things like that. 1645 01:26:24,510 --> 01:26:27,750 For those of you who are going to IPA training after this, 1646 01:26:27,750 --> 01:26:30,200 you're going to be sick of it by the end of the three days 1647 01:26:30,200 --> 01:26:31,360 so that's not needed. 1648 01:26:31,360 --> 01:26:38,330 For the other ones, it's in the slides and it's really-- 1649 01:26:38,330 --> 01:26:40,750 that's the problem of it being too short anyway. 1650 01:26:40,750 --> 01:26:45,330 So it is pretty self-explanatory and 1651 01:26:45,330 --> 01:26:49,910 relatively common sense and not sufficient anyway, but a 1652 01:26:49,910 --> 01:26:51,660 starting point. 1653 01:26:51,660 --> 01:26:52,910 Thank you very much.