Gaurav Chiplunkar on the Frictions in India’s Labor Market | Ideas of India

Hi Gora, welcome to the show. It is such a pleasure to have you here and do this in person. >> Thank you so much for having me Shorty. >> Yeah, we do this all the time but we just don't record it on the podcast. So I feel like it's high time we recorded on the podcast and a bunch of your co-authors have been here like Ritum most recently Ashinidesh Pani I'm sure there are more. Uh so I'm excited. I want to start with uh your papers. I mean you are my go-to person for all things labor market in India because you you're somehow studying it from all different angles. demand side, supply side, field experiments, uh you know, RCTs, general equilibrium models. Somehow you you've managed to cover the gamut. >> But I want to start with female labor force participation. >> Okay. uh so I mean the the headline news is in India female labor force participation is both low and declining and also that there's a fair amount of variation between the states and it's just difficult to get a sense of what the problem is or what the solution is because there could be so many different factors demand side factors and supply side factors and uh I want to start with the one particular paper of yours um your latest in econometrica congratulations uh with Penny Goldberg where you build a quantitative general equilibrium model where you look at India's entire labor market and you look at both men and women and whether they choose to work whether they choose to run firms whether they are formalizing or if they're hiring workers and so on and uh what you model in particular is gender specific barriers at each step and uh you also treat every state as its own closed economy which is a lovely way to model this because India has so much uh regional variation and the the the big sort of takeaway from the paper is that womenowned firms end up hiring more women. But before we get to the results, can you walk us through how you actually model something as complicated as the Indian labor market in particular femaleled firms and female employment and then what is the exact mechanism through which this multiplier effect is happening? So I mean first of all thank you so much for having me. I think I've been a great fan of the show and your podcast and it's so exciting that I'm finally here and we're doing this. So thank you for having me. Um to your question I think there were let me preface the thought behind that paper with two things. One thing which I learned very early on is that you can never generalize anything in India because what works in one part just doesn't work in the other part. And so making some general claim about this is the problem or that is the problem in some cases is true but in many cases is not because one can always find exceptions and so that's coming to your sort of insight about why we look at different states and not like you know just the aggregate is because things might be very different now particularly with this this I think so two things one is that the aggregate in some sense matters because of course there's a rich you know randomized control trial literature trying to look different barriers to female labor force participation whether it's work whether it's the type of work whether it's where you work how you work get renumerated for it so on and so forth the aggregate kind of becomes important from a policy perspective because once you start implementing this at scale >> then you have changes in different channels through you know just market responses right which could be uh women entering the workforce starting these firms or not starting them if they're facing barriers um and then you might might you know uh crowd out or crowd in depending on what uh uh the the data tells you uh either same gender or or men or different male entrepreneurs so on and so forth wages prices like there are lots of other factors that once you start scaling up become important to understand now the model is obviously a simplification of how this world works so we don't want to overclaim in terms of like you know this is exactly how the world works but I think it provides an interesting insights in terms of understanding this problem >> and you do build the model with real world data on the Indian labor market and Indian firms just to be clear. So it it's not an exact one-on-one map of the real world but it's a pretty close approximation. >> Absolutely. So I mean uh it's inspired by this like almost I would say now 25 year old paper by she and 2009 or 2008 uh paper it's been 25 years >> it's been almost I has it I don't know but 15 years maybe with the idea being that look like if you observe certain types of distributions in the data can you in those might be because of two reasons one is that the fundamentals themselves might be different so you know for example women you know might face different prices or different markets or skill levels might differ so on and so forth. Or it could be these uh non-economic barriers in some sense which could be culture, it could be norms, it could be other things that are very hard to move uh uh from from sort of a policy standpoint. Um and so some of these frameworks I find also helpful because they help you know very transparently to the extent that you of course believe the framework. Um uh help you decompose and understand where things are coming from as opposed to an abstract uh uh way of just thinking about uh this is how I want to make the world work. And so like somehow fitting the model as opposed to letting the data actually tell you >> uh which of these forces are actually uh more dominant in in terms of what we are seeing in the data. And so that's kind of the motivation behind that. I think the the the thing that also caught our attention which has been very underemphasized in the literature and it's not an India specific story though the paper is about India is that womenowned uh firms hire more women. >> Yeah. >> And and for some reason uh we didn't find a lot of literature trying to look at that across the spectrum. So these are not only small informal firms, these are also large once they get larger. These are in the formal sector. informal sector. These are actually then we use the World Bank data to then look at across countries and the pattern is robust across countries including advanced economies and so on and so forth and that we were really struck by. Um now again you might think about is this uh like you know as you said like women do they end up hiring other women because of a lack of options or is that a preference uh because of other things that might make the say the workplace more uh uh you know whether it's safety whether it's whether it is about amenities all these other things. >> Yeah. And it could also within entrepreneurship be the demand versus the supply side, right? Like either men have more opportunities so they become more competitive so the wages are higher so women are cheaper to hire. That's one possibility. The other is the within firm transactions cost which is men are harder to manage for women entrepreneurs. Exactly. Depending on the social norms and so on or men may simply not want to work for a female boss and so on and so forth. Right. So even there it's nice to study this as the broader general equilibrium pattern because you can see that the pattern is sticky. Now whether you give it whether the mechanism is culture or whether the mechanism is skills or the mechanism is wages and competition that's you know the >> I think that's one of the limitations or I would say I mean I view all of these as tools in a toolbox and so some things are great for some things and not for other things and one of the things that one should be cautious about in these models is at least the ones that that this paper talks about is that it's not a policy prescription. So it's not going to be able to tell you like should you have policy A versus policy B. I don't think this model or this framework is geared towards that. What it is geared towards is trying to understand how important are these things as opposed to other things that might uh uh crowd in or crowd out participation of women along different dimensions and it's very rich in terms of a framework in terms of looking at this not only at the state level but also state sector level. So we actually have agriculture manufacturing services we have barriers to entry as entrepreneurs. We have barriers to formalization of firms. So is it really the fact that you have a lot of informal employment where women work but it the real barrier is the fact that they can't formalize their firms and grow their firms or is it really for example on the demand side like hiring women versus uh versus men. So uh you know we explore a lot of these channels in this in this paper which is why I mean I think it's it's pretty insightful in terms of some of the patterns that >> so what are the broad patterns that you can share with us without you know discussing every single result in the paper but what are the broad patterns especially when it comes to different states and different kinds of firms. I think that might be the most interesting part. >> Yeah. So I so I think two patterns. one is that the the the women hiring women uh I think is interesting is and particularly from an Indian standpoint because after actually this paper I have gotten a lot more interested in looking at labor demand side policies for female labor force participation as opposed to supply side policies because in some sense like norms and culture as we have seen is extremely hard to move and uh you know obviously there's little policy can do about it >> I'm also not a huge fan of that literature I mean there's variation within the literature we've had some very good people some on this podcast talk about it. But also like just the idea that women especially Indian women in certain states and certain casts are like this exotic thing completely like bound and trapped by culture and norms. I mean it's too much of a deviation for me from homoeconomically everywhere else markets and prices seem to work. >> Women are rational too. We just got to figure out what that cost barrier is. Right. So I don't like the supply side literature sometimes because I think it makes Indian women a little too exotic in a way that I'm very uncomfortable. >> Totally. And I think it's in some sense I mean of course much of it is true as well. So there's there's some water to that story but I think in many ways it's not the entirety of the problem that you know oh like people are just chained to their houses and they can't move and so on and so forth. So I think it's a combination of the two but I think the latter is extremely underemphasized in terms of how the narrative around female labor force participation in India is built at least to my knowledge of course uh uh there might be other opinions on this. So again in this paper that's that's that's I think one of the things that that I think would be nice to emphasize which is the labor demand side of things and and the reason that female entrepreneurs hire more women and so therefore this connects back to this policy of like in the data there are very very few female entrepreneurs to begin with and so then the obvious question is if you have policies that promote female entrepreneurship apart from just a social equity standpoint this can actually be efficient for the economy in terms of generating growth and act as a multiplier effect on female labor force participation because By creating one entrepreneur, if that entrepreneur hires five women, then your labor force participation goes up not by one but by six people or by six women actually. So there's these labor force participation multipliers that can come in and then of course you know that can lead to other things in the economy once you start aggregating aggregating things up. But that was one result that was that was very neat and robust across states and across contexts that we find. In fact the the other paper we started working on was uh uh the India policy forum uh which is organized every year. They invited us to then look at this with more recent data uh in the Indian context and again we find very similar things in terms of the barriers to entrepreneurship versus uh versus work in in some sense. The the second thing which I want to which which which was interesting is trying to break the break entrep when we think of entrepreneurship I think it's it's important to understand whether this is self-employment whether this is really owner operated enterprises or whether this is really firms in the true entrepreneurial sense and and the big sort of margin is is the fact that you know well there's a barrier to just women not working though I think there's some recent literature trying to look at this from a measurement standpoint right and and there's obviously work that needs to be done around this but of course even if we were to measure all of these things correctly I don't it's still too low it is still low and I think you know and now whether it's declining or whether it's on the up in the past few years or not I mean all of that is a local trend in some some sense rather than a global than a global uh uh optimum but but what we do is then you know try and look at is the so there's a clear barrier to labor force participation but conditional work we actually find that women are over represented in these self-employed owner operated enterprises relative to men. Right? So it's not the case that entrepreneurship is hard. >> Yeah. >> Right. It's not the case that I just can't start say a sewing shop in my house. >> And that is the classic one. Most female entrepreneurs are actually doing sewing work. >> Exactly. And so so it's not that. So there is a barrier of participation but conditional participation. >> It's not really entrepreneurship that is the problem. It is really growing your firms to be larger. Uh which seems to be kind of the barrier. Yeah. Um and again as I said like these are not policy prescriptions in terms of like but it it points you towards where we should be thinking about from a policy from a policy standpoint. So that's kind of the second result that we we we find interesting. Now the third result which is coming back to this women hiring more women I find it to be fascinating because I think there are two sides to this in terms of a preference as opposed to a constraint story right and I would love to you know in the future kind of dig into this a little bit more in terms of trying to understand like what are these constraints or are these actually just preferences like I mean there's a homophy literature on this right like men hiring more men promoting more men like there's the entire literature of homopholy on this and as opposed to constraints uh which to the ones that you were talking about Do men not want women bosses? Do women not want to hire junior men? Like it can go in many ways. >> It could be something as simple as if most of these women who are entrepreneurs are like one or two people shops in a simple room in the home or joining the home. They may not want a strange man to come to the house. It's just much easier to have a stranger who's a woman come to the house. So the constraints could sometimes be that basic and we just don't know what they are yet. >> Absolutely. And I think this is this is like ripe ground for randomized control trials to take place to be able to actually figure out at a very micro level uh trying to understand some of these barriers and learn from you know why what are the barriers for hiring uh or growing growing their firms. So that's where the econometrica paper is kind of situated and the follow-up like IPF paper on this which is using more recent data. So you know one other question on the econometrica paper itself uh what are the gender specific barriers to entrepreneurship which are different for men versus women like one you said obviously is scale women are more likely I mean India is a country of microenterprises so that's I think true across the board but a microenterprise is under 10 people >> uh I imagine women cluster more around the you know 0 to 5 or 0 to four kind of level so There's obviously a scale barrier. What are the other kinds of barriers? Is it like is it regulatory at all? Is it just the same barriers you have towards work, you know, workforce participation, which is you can't leave the house or there isn't good enough public transport or is it access to credit? >> Yeah. So the IPF paper actually digs into that a little bit more because where we what we do there is we go to this other data set called the global entrepreneurship monitor and and there they actually ask people questions about you know aspirations about constraints about how how your uh uh like whether it's society, media, prestige like all of these other things. The World Bank enterprise surveys uh on the uh also and they now have this nice informality like informal firms model as well. They actually you can look at the owner of the owner of the firm or in many cases you know whether a majority of the owners in the firm are women or not and then and then they have these modules on different types of inputs like electricity, water, licensing and then you can look at you know is that a barrier or not and so on and so forth. There we actually find that many of this also come through just accessing services, accessing credits um you know where women report uh uh facing larger barriers to accessing these services. And again we need to dig into more about whether these areformational constraints whether these are you know other constraints in terms of just going to a bureaucratic office and sitting there and getting things done like uh that's where I feel like you know there there is a nice match to be made between uh these aggregate sort of patterns and models with like really microfunded well done work um uh using either RCTs or even descriptive data would be very helpful to be honest to just understand what are the you know functional barriers that people face on the ground and it's a combination of many. I think that's the that's the hesitation in terms of hanging your hat on one. >> Yeah. >> Uh because it's it's going to be a combination of all of these things. But I think just to be able to understand which context where how I think just doing some deep research on some of these I think I think would be you know really unlocking this last mile barrier in terms of like what where the constraints are and the IPF paper does talk a lot about this right so like uh it's not really skills it's not really confidence it's not really all of these other things you might be worried about like are women under confident they're not like are women underkilled they're not like you know and of course this could be self-reported but if anything self-reported data should bias it even more >> even more yeah that's the main issue if that's the main issue. And so in some sense like the fact that we don't find this in and of course this part is not causal but you know it's still informative and descriptive. Um uh but it's access to services, it's access to credit, it's access to you know taxes uh like you know being able to pay them being able to you'll have to bribe you'll have to deal with these inspectors as you know a lot on the reg labor regulation standpoints right like so there's a lot of this um uh uh that happens and you see that in the in some of these data. >> Yeah. But you know I find the bribe thing is quite interesting because you know if a traffic cop stops me in Delhi which is you know where I'm from >> uh he's more likely to have a conversation with the driver even though I'm the one who's actually going to pay the bribe but they don't want to have this conversation with what they call ladies log you know they just don't want to have they will make the demand I can't even like kind of elbow my way and have the conversation absolutely right. So it's it's really on both sides. It's like there is a uh there's a friction but it could also be like this is where we have so much rich descriptive work on the supply side constraints but we don't have it on the demand side constraints and it could be something as simple as maybe the local bank officer doesn't see the potential >> in like work that is sewing and embroidery. He just doesn't understand the market and he understands like a cycle or a mechanic shop a little bit better. Right? So maybe the constraint might be we need better educated bank officers or we need we need more women in [laughter] you know loan officers. So it could be something that basic and we just don't have a good sense of it. Which are the states that are doing well relative to the ones that are not? >> Actually Northeast is doing excellent in terms of female labor force powers. The south is doing much better. So it's the usual suspects that you might think about where you know it's a it's a concern. And the places that do well on labor force participation also do well on entrepreneurship. And you've just explained to us why because of the multiplier effect, right? >> 100%. And and coming back to your early point which I which I which I want to double click on uh a little bit is is this idea about like you know uh if you have uh like you know the men won't talk to you or you know loan officers won't give you loan. I think there are randomized control trials that show these biases exist. >> Yeah. I feel where the conversation kind of needs to move um is not so much towards documenting there is a bias but like what is the source of this bias which is obviously hard to measure um but but I don't think we have a good handle on that right like in a very simple way is it just stays based or statistical is if it is stays based then you know certain things that we can think about statistical certain things that we can think about but also like you know loan officers giving or not giving loans um uh to women versus men for what I mean the problem with working with labor demand as I have routinely faced is that just getting firms is hard >> right like no entrepreneur especially for these big firms wants to risk uh uh and it's very blatant once you talk to them >> they're like oh like gender is a big issue right like if if you come out and tell us that that you know we're not doing things right uh we're going to get into trouble >> right and so really partnering uh with these firms is extremely hard especially for RCTs but I feel even them releasing their own data Yeah. >> Or because many firms actually do have a lot of great data. Like I've worked with a bunch of job portals and you know they have rich data on applications on postings on this and that they just don't want to share it. Many of them just don't want to share it which is a shame. >> But that's also because we've pushed this completely on the firms and nothing not enough on the structural broader issues. Right. Because if the answer is you need it's public safety. >> Yeah. uh you know the the the immediate media and policy lens will be on the firm or why aren't you providing safe transportation for them right so it's a it's just the way we think about the problem is a little bit twisted because we've kind of given up on the state solving a lot of structural barriers >> no exactly in fact there [laughter] are papers if there are papers that that that that have non-disclosure agreements that need to not even let alone the firm not even mention India so they have things like there's a large developing economy or something like that and obviously one knows what you're talking about. But that's the extent to which you know legal teams require you to go to hide identities uh uh in in many cases. >> Yeah. And I don't blame them. >> Exactly. And so, but that limits our understanding of unpacking and I think that's a more I've gotten excited really a lot about the labor demand side of things in in in the last like many years because I think that's the margin we can actually tangibly move and will have long-term spillover effects once we start like I mean a great paper was the Gura paper by Rob Jensen back in the day that shows when call centers opened up >> exactly >> neighboring villages women started skiing themselves because there were these jobs that were accessible that you know were quote unquote like female friendly in these >> MC >> at MNC's. Exactly. Exactly. And so u and so you know playing around with labor demand or having policy that is directed towards that can actually have these again spillover effects on labor supply. Maybe not in the short run but definitely like in the in the in the medium run and I think that's that's a policy uh uh push that we need to go does not mean uh I we study a lot on regulation does not mean overburdening you know the onus on like there needs to be a needle that needs to be thread very carefully when it comes to addressing these issues um from the labor demand standpoint >> and you know the other reason I think labor demand is so critical is I think we are a little too obsessed with labor supply not just in the case of women but overall >> and this brings me to your paper with Rytham and uh Vidya um >> if you obsess so much over labor supply constraints then you're like oh they don't get paid enough or they don't have you know period leave or maternity leave or this that and the other and without looking at the labor demand side you actually implement policy what you find is that everything becomes upside down so I mean for those who haven't heard the episode with rhythm we briefly discussed the paper it's a fantastic Veler, you look at something so specific which is contract workers versus regular workers. And once you start tagging on all these benefits to contract workers, what firms do is not just hire fewer contract workers, they prefer to be informal rather than formal. So actually the the supply side motivated regulation can sometimes be so burdensome that we actually flip it like you you really tilt it every firm and you move them back into informality which is actually worse for both you know when it comes to labor for men or women. So is this like >> this I think is one thing I'm learning from your work on why we should pay more attention to labor demand but there are other things you find like that. So specifically this paper I think another margin which I find fascinating is I think I mean apart from the fact that I've written it so I should like it but >> it's a great paper. This is the second time I'm discussing it in two months. So I must [laughter] really like the paper. Apart from that I think I think one of the learnings for me was this is exactly the test case for why uh aggregate GE kind of things matter because if you were to purely do a policy evaluation so the policy just to you know uh tell your listeners is you know Andhra Pradesh back in the day basically said oh like you know firms are evading uh formal like regular workers because once with regular workers if you're in the formal sector you have to give all of these benefits yada yada yada we know all of that. Um so you know firms how do they f how do firms get around this is you know hire a bunch of contract people on contract so they don't have to comply with all of these >> and this is still all formal employment this is not informal contract employment which is a whole other beast >> absolutely absolutely and so Andhra Pradesh one day decides that you know we're going to ban firms from hiring contract workers they're formal sector firms ban them from hiring contract workers push them to hire more regular workers again a very well-intentioned policy because it solves a real problem uh of you know workers getting their b their due benefits within firms and so from uh quote unquote like reduced form perspective one would actually evaluate this and find a lot of success because that's what we find like formal sector firms actually do move towards more regular workers and they do shed uh their contract workers now I think where the aggregation and equilibrium effects come in is once you start building up and saying now let's endize a firm's decision >> to be formal or not right and there you actually find the margin that you know the informal sector is just growing because obviously no crazy entrepreneur is going to want to you know formalize and get in the ambit bit of all of these inspectors. Um, and so what do you do to a some extent like you do find uh the informal sector basically growing which >> this sort of marries kind of nicely these margins along like you know here's a well-intentioned policy that wanted to solve a real labor problem >> and you just made contract workers more precarious. >> Exactly. >> Right. As opposed to less precarious. >> Exactly. >> So you know one thing that comes from that particular paper in Andhra Pradesh is that the the it's very very sensitive to price and wage. Right. I mean these are workers. I mean the products are clearly in a highly elastic market. A little bit higher wage is fundamentally difficult for them to pay. So the firms prefer to be informal or the regulation is so ownorous that even in a not a very highly elastic market it's not feasible. Right. So it could go either way. >> Correct. >> But there's another issue about skill. >> Yes. >> That the firms are not willing to pay a certain price because the skill level is simply not there and they have to spend a lot of money skilling. Now is this a problem across genders? Because there's a lot of work that's done on female labor force participation and skilling but to me it seems like men and women in India when it comes to the labor market there is both less skill and there is mismatch of skill that is like complete misallocation in the labor market. So what what can you tell us about something like that from this kind of an experiment? >> Yeah. So skilling is I think uh I mean there there have been some work that has shown uh for example like Achadwaru Namatakala Anand Nishadam they have this paper with the good business lab that shows that firms really underinvest in skilling their own workers and the returns to skilling are huge. Yeah. >> Right. Yet if you go I've spent a lot of time talking to entrepreneurs across the spectrum and they just like you know if we skill them they're going to leave this. It's as simple as that. They're going to leave especially in sectors where there is a lot of churn. >> Yeah. >> Especially in urban areas for example. People are just afraid that the returns to skilling. >> Yeah. >> Are not going to be boned by the firm themselves. And that gets even more precarious once you have contract workers where you don't have any any way of holding workers back um for a few months or a you know a considerable amount of time for the firm to actually reap the benefits of the skilling itself um >> or women if they go on maternity leave things like that. >> Exactly. And so now coming to the gender aspect of this, this quantifies uh sorry this multiplies in in in many ways because there are other constraints that interact with the skill acquisition itself and then constraints that might impact the way you supply labor to that firm and so you know it's a complicated question in terms of like you know what should be done about it but I think I think that the the basic reason is everybody is appreciative of the fact that more skill in fact we ran a small survey um this is back in the day in Uttar Pradesh and you you ask you know a worker uh You ask uh youth like 21 year olds who are just entering the labor market. U you know they're skilled. So they're coming out of these undergraduate colleges degrees vocational training they're clearly skilled. Uh uh where do you want to find work? And they name all these like skilled firms and you know where they want to work. [snorts] Now you go to the flip side and ask these firms what's your biggest problem. The number one thing they'll tell you is skilled workers. >> Yeah. >> Right. So, so again like coming back to this like I think it's not so I mean there's of course a skill acquisition problem but I think there is a major problem in terms of matching and more than that like retention. >> Yeah. >> Right. Um and and it's not obvious always that retention uh or the lack of retention is a bad thing if people are climbing up the job ladder but that does not seem to be the case. It's not like people who are working for delivery kind of jobs are suddenly quitting their jobs to be able to go and become you know some other uh you know a highly qualified job somewhere 6 months down the line. No, it's basically because they're doing something else which is kind of parallel in terms of the skill requirements, in terms of pay, in terms of uh uh and then I think in some sense there's also a problem about just documenting panel level data in India, right? Like I haven't seen a good you know data set that has a high frequency uh collection of data on just how people are moving around in jobs >> for an extended amount of time. People are piecing together >> uh stitching data >> Yeah. and making inferences based on you know what we find but there is no real good panel data especially for >> because of the level of informality and most of our firms are tiny. >> Yeah. Exactly. Exactly. And so >> it's also the micro firm nature is one of the reasons retention is so bad. Right. There isn't that much place to move up. The best people usually want to move up. They don't want to work in a four people shop or a 10 people shop. Right? They want to if they get skilled they want to move around. If they get married they move. The number one reason for migration especially for women is marriage. So there are so many things going on and micro firms are micro because they don't want to be formal which means we can't get the data. So we have this problem. I mean your data problem is your regulatory problem is your job problem. It's all >> intertwined. But I think you need to start somewhere. [laughter] >> No no no absolutely you need to start somewhere. So one of the other things that you've looked at when it comes to both skilling and women and this is I mean this is like an allst star list of people working on female labor force participation. So this is your paper with Ashwini Desh Pande, Kaneka Mahajan, Nandini, Niharika Singh and it's forthcoming in the JD. Um, so what I looked at is the design stage because this is a pre-registered RCT and you're looking at a small sliver of the labor market which is highly educated women in formal employment in IT services. Right? So just uh disclaimer out there that this is not the entire labor market. This is not your gender equilibrium paper kind of thing, right? And you actually randomize them into three groups. And there's of course the control group that you know this is women who are coming back uh from maternity leave uh 3 years or more. So there's obviously a control group which basically just gets integrated into the workforce. Uh and then there are two treatment groups groups people who get some training and professional support and then people who get you know professional support plus like technical skill training right so these are the three groups now I don't know if you have results yet but I just wanted to touch upon this. what are your priors on what you expect to find? >> Yeah. >> And if you have any results and you're willing to share them, that would be great. >> So, I think uh the reason we got excited about this was in some of my earlier work with vocational trainees u these were you know 21 18 to 21 year olds um where we worked was entirely in Uttar Pradesh. Um we the number one thing you when you ask you know the girls is is you know what about jobs? They're clearly extremely excited about going and working in the labor market, but the number one thing they'll tell us is that as you said earlier, like marriage, right? Like I'm going to get married and then, you know, it's going to be a whole different beast in terms of being able to bargain my way into the workforce later. So maybe I'm going to try this later once I have been married and once I've settled in, then you know, we can think about long-term work work strategies. So that kind of got us excited. That was an anecdote. But then you know once you look into the data actually women who want to come back after a career break uh that population is non-trivial in India it's actually a large population of women >> and that solves many of these constraints of trying to move the needle on 21 year olds but then what if you're a 25 26 year old right like you've been married you probably have a kid or two and you know they're going um you know they're probably starting school soon and so now you're at home and you have a bunch of free time and so so that's the set of population that we're trying to study. uh in this and as you rightly said this is not about informal work at all. This is and that's largely because of of the partner that we're working with who has these training programs for for you know these highly qualified IT skilled women. Nevertheless like I think it solves many of these problems or I mean we can examine many of these. So what are we doing? So it's not I mean it's still in the field so I don't have results unfortunately to share but you know stay tuned. I mean it's I think I'm >> what are your priors on something like this? >> Having talked to a few people I think so we we're doing basically two things. One is to give them hard skills. There's a the one thing you can think about is skill depletion. I'm out of the labor for 5 years. This is it. So it's coding especially now with AI I guess even more. >> Uh so one is just hard skills. Second is actually the software side of things right interview skills, CV building skills, how to just like you know answer a bunch of questions. Um >> now having talked to a few few participants uh the one thing we are finding is that they're searching more intensely because this is as I said this is in the field. So we don't actually have outcomes and employment and this this was just basically after they finished the training program. You know we just chatted with a few of them just to understand what they were thinking about and how they were going to talk about applying in the labor market. Looks I mean it looked like they were all going to search uh uh more quote unquote like efficiently in the sense that they know what they want. They know exactly where their skills land because now they've done the training program. They know exactly what skills uh they need to work on versus what they can actually remember. So on and so forth. I think the soft skill is a huge part of this because many of them going in before the training program they were like I don't I haven't been in an interview for many years now right what questions will they ask right like who's going to interview me many of them are actually concerned that you know they might have to take a a a drop down on their career and they were like how am I going to go and work with an 18-year-old you know analyst uh I've been ex like manager in my d and now I have to go and work as an analyst for a few years before I get promoted again so these are very real questions And so to the extent that we can measure them and capture them either through qualitative and quantitative uh data those are kind of the things. So my prior basically if you ask me is it's not going to be as slam dunk of a result as we would hope but I think on the margin trying to understand like who is this working for. >> Yeah >> right I think will be the more interesting part for me. >> For me actually I love this study for a slightly different reason. I mean like maternity leave as good as countries which seems a little bit ridiculous for a country at its sort of GDP per capita but that aside the biggest problem for women is not enough maternity leave whereas the biggest barrier might be something like this. >> Yes. >> Right. So if we do have to focus our energies whether as a firm giving an incentive or as the governmenting policy the policy solution may be somewhere else to bring women back into the work force post maternity leave and it may not be longer maternity leave. In fact it may be something completely different and something much cheaper. >> I agree and there have been papers that have shown that once you like think about how firms are going to respond they reduce hiring of women in these uh in these fertility ages in these fertility ages. So, so I mean again like I think holistically looking at this is I think the right answer to trying to at least figure out like what is the right way of designing policy support and I think reintegration is is important. I think there's a lot I mean again in India you multiply anything by infinity and it becomes a really large number because just over the scale at which we operate but I think the scale also gives us a nice petra dish to be able to observe different kinds of constraints at really minute detail which I don't think any other country on earth has the luxury of doing >> right and I think really drawing in lessons from the the almost like the variation within the data >> is I think extremely informative to see how things might work under different constraints and how what might work in one context and not so in this paper for example example, we have women who are across different cities. So, it's not concentrated either in one city or not. It's across different cities. It's across a couple of uh different occupations as well. And so, I'm really excited to see like uh uh what comes. So, we are in the endline phase right now. So, we're actually uh 6 months uh out after the training program. So, we we we're trying to gather data on their employment outcomes. So, we'll see. Fingers crossed. >> Yeah. No, I'm excited for this paper. But more generally like everything we've been talking about in some sense has been like let's look at you know broad patterns in India let's look at you know not just one supply side constraint or demand side constraint but the biggest thing about labor force participation other than the multiplier effect is what that leads to which is obviously economic growth >> right so that's the big ticket item that we're all after women working >> has been either a symptom or a consequence or a cause and it's complicated to disentangle these three of every single country that has had high growth for lots of years. Right? So you have this great like broad-based study that you've done with Tatiana Kleinberg. This is female labor force participation but looking at it from the point of view of structural transformation across like a 100 countries. >> So what is the lesson for us more broadly? India should be studied you know in its own right for various reasons including you know female labor force participation is a really big problem but what is it that we can learn from patterns across the world >> yeah so I think and that's precisely why we started working on this because if you look at so on this paper what we're doing is basically looking at a 100 countries over the last five decades or so right putting together rich data um uh on you know the type of work that you're doing uh you know what occupations are you working in on the job ladder what sectors you're working on and how does that correlate with economic development. Yeah. >> So the first part is just basically documenting a whole bunch of patterns which I find extremely interesting and sort of the big pattern is if you go back to like econ 101 development econ rather 101 the first pattern you see is that oh like as countries develop people move out of agriculture into manufacturing and then services. Now of course in the last 15 years we've seen this jump from agriculture to services but it's really like you know all of this turns out that if you actually look at you know this from a gender perspective all of what I just said is a male-dominated story. >> Yes. for women what the actual patterns we find is you get out of agriculture and this is again Claudia Golden's you know famous famous work so it's nothing none of this part is really new >> curve as we have seen it before >> exactly so none of this is actually new what is I think kind of exciting and new is therefore once we open up the box of also occupations right because from a you know again if you think of it from an economic development point of view it's probably helpful for economic growth to get excess people off the farms and into the firms but then the question is if all the women are going to become receptionist and all the men are going to become the managers, right? It has very different implications for economic growth than if men and women have equal chances of becoming a manager at a firm. And so that part I think is is what we dig into much more carefully in this paper. And it's exactly what you find. So in the low-income countries uh there is a gap uh in terms of the probability that you know uh uh you are a manager versus if you are you know I don't know a worker let's say. Uh but there's no gender gap in some sense right and specifically between manufacturing and services. >> Yeah. Now what happens is as countries develop right uh service sector gap basically closes in terms of managerial occupations. So rich countries are actually able to close this gender gap in managerial occupations. But that's a service sector story. >> In manufacturing the line is basically flat. Whatever you had in low-inccome countries in terms of male to female manager ratios is almost the same as in in rich countries as poor countries. So therefore a sector plays kind of a role. And again this is not to say that we know what is going on. I think that's that we need more microeconomic studies to be able to you know unpack some of these u micro foundations of why is what is it about services as opposed to manufacturing. However, the other other kind of interesting thing is that if you look at clerical occupations in rich countries women are three times more likely to be working in these clerical occupations as opposed to men. Right? Whereas in the poorer countries uh again there is a gender gap that favors men in terms of these occupations. So again with development you know the how it percolates into gender is extremely uneven right and that brings the so you know a couple of times that we talked to a bunch of uh uh people about this I think where people were pushing us to think about more is that some of this could just be economics right if you think about classic stories of comparative advantage if you think about operating machines versus computers kind of uh stories it could be skill acquisitions like you know in low-income countries like the first person to get out of school is a woman because the man the boy still needs to go to school where whereas the girl child can drop out. So this could be skill differences this could be comparative advantage differences and so on and so forth that could explain why these patterns are the way they are. And so the next part is again uh going back to a more quantitative story. >> Yeah. But we try to again similar to the econometric paper like build in all of this machinery uh and then say okay like giving it the best frontier models that are out there right now right can we explain the story completely by just economic gaps in terms of skills comparative advantage so on and so forth and the answer is no but then the quantification kind of is interesting and of course this is all contingent on the model and there are lots of like asterisks attached conditions apply like all of that uh all of that there but what we find is that gender barriers which are these non-economic barriers to either work or earnings explain about 25 to 30% >> of the economic of the economic growth that that we have experienced in these countries over the last 50 years. Right? So it's not trivial 30% at all 25 to 30% of your growth >> is is explained by the fact that you know the workplace or the the labor market has become more gender equal >> and then the other 70% is because you know skill gaps have closed because you know technology has developed and so there's a lot to see also about just good old-fashioned development like get people in school right like you know like these kinds of things do work but then there's a non-trivial layer on top which is you know 25 to 30% uh that is just about changing some of these underlying constraints that women face which are not really economic constraints. So you know there are two sets of outliers in the global story right in the rich world the Scandinavian countries are the outlier when it comes to gender participation in the sort of developing country and now very much in the middle income or developed country world it's the East Asian countries that have this incredible story where it was partially manufacturing driven there for women so is there something we can learn maybe not from individual countries but largely these two clusters which have some commonality and the timing of it also matches in these countries. What is it that we can learn in India from these two ends of the spectrum? >> I think that's a great question. Um and I think and now I'm completely going to you know make claims that are unfounded in any of you know the data or anything but I think for example >> that's what I do for a living. >> So like Bangladesh and Vietnam being the two uh kind of uh posters of you know really high growth in female labor force participation. Sorry, as a random aside, Bangladesh is the number one reason I hate a lot of the supply side constraint literature. It's like anytime someone throws this norms and women in India are exotic and whatever and cultural, I'm like Bangladesh, Bangladesh, Bangladesh. >> Oh, 100%. And I think that so in fact one one of my advisers have put it very nicely and it's like if you if if your family is starving, people don't really care whether it's a man or a woman who's working, right? So, so there is a huge constraint in terms of the jobs >> um that are being there. In fact, like Fzana I remember has this amazing paper that I like on the fact that you know they they connected a whole bunch of women to job portals and if I remember correctly from that paper what they find is that women actually don't take up these jobs but their husbands are more likely to get better jobs [laughter] right and so that's telling >> in terms of how technologies for example can can percolate. Now coming back to your your original question, I think it's just about good old I mean many of these the larger scheme to my understanding seems to be from the government sector again right which is again a sector in which we have seen a lot of women labor for even in India like even in India like that's where we and we've seen a lot of big firms uh enter these enter these sectors which have been able to draw women into into them. So maybe one lesson in in all of this is probably like if it is manufacturing and if you want to view manufacturing from a gender equity standpoint then looking at sectors where there is possibility for for women to come and contribute um uh effectively [snorts] >> Scandinavia is a completely different uh story and and I think um it's probably not the right counterfactual to look at from the Indian perspective because again we have these laws and I think you know like as you were saying earlier like maternity leave in India is amazing on paper right uh but then once actually look at enforcement once you actually look at you know how firms are able to get around it for example like these issues become way more uh uh uh sort of binding and so maybe like instead of trying to emulate Scandinavia in terms of forming laws maybe it's also time to like just as you have argued in many of your pieces like just reduce regulatory burden on on many of these things that really allow firms to to you know grow and some of this growth in the short term might have consequences in the longer term. conversations and you know there's a distributional consequence there's a long-term short-term trade-off that you have to make and in some sense like whether you do it or not I don't think people even discuss it uh in in in greater >> so you know my bias for starting this even though there are distributional consequences and yes uh you know I mean this is the kind of awful thing economists say to each other and to econ students We say horrible things like the number of you know fires in a factory the optimal number will live with it because we understand that there is a trade-off right between extreme safety regulation and you know extremely high and good care of maternity leave versus actually what happens on the ground and whether the firm can absorb the cost. But the reason I have a bias against the regulatory barriers other than just the fact that I mean it just makes they're stupid and they make people unfree is growth compounds. >> Yeah. And if you can get firms to start growing early, if you can get economic growth like you know little green shoots in a particular labor market that really compounds over 10, 15, 20 years in a way that if you wait for things to happen >> and just say you know we'll one day reach the level of development that will fit these bad regulations. Uh I don't think you get the same result and I think we underestimate how much compounding matters in poorer areas. So I completely agree with that. I think just to add on to that uh I think the other part so you know talking to bureaucrats about it's not that people don't know this or appreciate this. They might be underappreciated but they know that you know this is the need of the hour and I think that's where I think in economics we need more of political economy as mainstream models that can account not only like for example much of my work in fact you'll be able to tell me oh these are the efficiency costs on economy right you go to a bureaucrat and you tell this person like look like this is they're like yes we know this right of course women should be working of course we should have larger firms of course we should >> so it's not like they don't you can tag a number on it and make make them realize the severity of the issue. where the rubber hits the road to my minister. This is not going to be politically feasible at all. >> Yeah. And you know the other part of it is when we have tried reforms we haven't tried wholesale reforms like the last time we had wholesale reforms was you know like sort of Rakkesh moan and group removing license permit wholesale. So you remove the entire command and control structure for a wide part of the economy. The trouble is if you do this peace meal, you don't know which the binding constraint is right. So okay, we can adjust maternity laws a little bit. It still doesn't improve female labor force participation because you don't know if the binding constraint is demand side or supply side or for women of reproductive age or what. So unless we reform it in like a more broad-based manner, I don't think we are going to get the kind of political benefit that previous reforms saw. So I think it's also the way we advise our politicians. we make them do tiny narrow things uh because we do RCTs on them or something and that's just not how the rubber hits the road when when it's on the ground >> and in fact like the econometric paper kind of does this very nicely because these frameworks allow you to say of course we don't observe counterfactuals but to the extent that you believe the estimated model you can run counterfactuals and you can say in a simulated world if I were to only remove one barrier at a time would that really generate growth and basically the econometric paper is very clear no right like it's not the compounding is huge. So you really need you really need multiple barriers being alleviated. Now how much of that can policy push with versus not is a separate conversation but to the extent that one needs to think about these big bang reforms in some sense again in in Indian labor force and and just the labor market I think is is a no-brainer. Now the question is why aren't politicians willing to bet their money on this and and say why don't why don't we push push this through. Now the current government has put women as an agenda like a priority agenda and so now there is some excitement that's being generated around this. >> I'm a little alarmed about that and your research has made me more alarmed and I'll explain how it is. You know the we have two big policy goals. One is formalization of firms. Yeah. >> And the other is increase in female labor force participation. Your papers show us that sometimes these goals may not be quite compatible especially the Andhra Pradesh work which is you know uh when you burden uh uh a particular firm with more goals more policy objectives which are highly targeted or more regulation and they actually flip back into informality. So that's what alarms me about this dual goal problem where like governments say we need more formality and that's going to be good for women and so now let's push the female labor force participation agenda to force more formality. Does that make sense? I think it does and I think there's a large part again I think that's where some of these economic frameworks can be helpful because you can exactly model these tensions right and you can at least do a first order try and understand how these tensions might interact uh you know from a macroeconomic standpoint and I totally agree with you like is formal sector really the goal especially from a gender perspective not really if flexibility if informality is is what is valued because that's the society we live in right obviously everybody understands people are too poor to pay for the goods that get produced in the formal sector. >> Yes. But some of them can be you know made available cheaply for example like data is a good example where you know clothes are another good example like where economies of scale might really reduce the costs but to the larger point that absolutely like if you if you have a ton of laws and come down with a hammer on them and force people to do it like firms are not going to do it like they're going to find ways to not do it and if anything jugar in India is great. So, so you know >> juggar in India is great and bad because jugar can only get you up to 10 work. >> Exactly. No, exactly. No, I meant more like formal sector firms are going to figure out around this. >> Oh, that also. Yes. >> Right. Like they're going to figure out a jagard around this. So before we get to the jugard and the political connections uh you know and what firms do in the formal part of the economy I still want to stick with labor because there's another set of papers that you have which is not so much about like women and demand and supply side necessarily or even like the regulatory barriers and the regulatory cholesterol that that we face. It is frictions that exist in any job market. Right? So the job market we tend to treat it as this aggregate thing but it's actually hugely fractionalized when you start studying the micro data there is a lot of matching >> problems and there's a lot of allocation questions right so not every software engineer is the same as another software engineer though that is one particular job where there is a vast amount of similarity and overlap uh but they're still different so now I want to discuss like some of these papers and I want to get to perhaps my most favorite paper of yours. Right. So this is titled who gets the job and this is with Eron Kelly and Greg Lane and this is coming out in Restat shortly. Okay. So the the broad thing that you are trying to understand is information and how social networks are kind of a conduit for information flows in particular when it comes to job opportunities. Right. >> Uh you have a lovely field experiment. This is an experiment uh in Bombay University where you look at a particular cohort across different uh you know sub fields and colleges >> and you randomly vary whether the job opportunity for that particular cohort is going to be rival or non-rival and by rival it means they have to compete to get that job with the rest of the cohort. Non-rival is if you get the information you'll get the job right >> and what happens to information flows within this group. Yes. >> And what you find is not comforting. So first, okay, before we get to the results, maybe you can set up how you got to this experiment and you know what was interesting about it because field experiments are fascinating and then I have lots of questions about the result. >> [laughter] >> So I this came back to my college days and you know on the job market as a PhD PhD student as well which is in many cases like the way people know about jobs uh is if you ask look at literally any survey in literally any developing countries friends and family right and so there's a lot of control over who gives their information and Abij Matt Jackson all of these guys have amazing RCTs in back in like the Karnataka area that show like you know gossip matters like nodes matter and so on and old work on rumors and so on, right? Like that's prec. Yeah. >> Exactly. And so and so and to couple that I think there's this entire literature on on information flow in agriculture that basically shows that oh if my farmer my neighboring farmer does you know something with with a new technology you know I'm more likely to do it. And with jobs I think the or with labor market information the the big wedge in this information flow is the fact that you know information is rival. If I tell you about or can be rival and so if I tell you about this great job opportunity and you decide to apply for it then I'm competing with you for the same job and so that gives me incentives to withhold uh this information from you. Uh and so that's basically and we started thinking about where we wanted to go do this. >> No and that also has macro effects like this wasn't just a cute experiment right because it changes the applicant pool fundamentally at the aggregate level in the labor market. Sorry keep going. No, absolutely. And that that was I think the the reason we landed with college students was twofold. One was where are labor market uh you know uh information frictions probably the highest where the consequences are going to be you know uh quite severe. One pool is college students because they're entering the labor market. It's going to be their first job. We know from a lot of papers that this is an important phase of their careers. And so a that and and b was really to be able to work with a group of students where we could observe exactly how information was shared right because these are collegegoing students they're in the same classroom they know each other in networks are very well defined in some sense right and so for a lot of these reasons we landed here >> but more broadly the information you know I want to clarify is not like a job posting correct >> because most people think of information as like a job posting or a classified ad or a LinkedIn post whereas actually the information is runs many layers deeper than that. One is about is this within the feasibility set? Yes. >> Right. The other is is it respectable? Is this actually a high uh you know is this a kind of job that's going to be good for me? Then the third layer of it is matching. Is this actually good for my skills? The fourth is rejection. Do I have a shot at it? And then so there is layers upon layers and layers of information. And I love that you did this in Bombay University because that's exactly the kind of stacked >> layers of information you can both study and parse out. Right. Absolutely. Absolutely. And so this job was not exactly a job from a firm standpoint. We were the firm basically. And so the World Bank hired uh they needed a bunch of interns. And so and so this was basically an internship opportunity for for these students which they valued a lot. Uh and so the key thing that we wanted to vary was really try and see like how much is this competition playing a role number one. Number two are there forces that can actually overcome competition. So for example, like you and I are great friends and so you know even though I know this we are going to be competing for the for the same job the closeness of our friendship might might mitigate some of these competitive concerns right and so >> or you're going to find out eventually anyway if you're such close friends I might as well tell you before >> exactly no exactly no exactly exactly >> sorry for such a cynical view but reading your paper did not leave me optimistic about human race. >> No I mean yeah women yes I'm more optimistic about because you find that these when we come to the results we can we can talk more about the gender angle of this but that's where we started out and and so now if you look at so the other wrinkle we wanted to add on this was the following like if if this is going to affect the pool of applicants a firm is going to get because the smartest guy in the room never knows about this job. So it really at the fundamental level depends on the correlation between your ability and ability to gain information right and so if these are positively correlated which means that the smarter people also are more connected in the in the in in in their social networks then this is not a problem because these are smarter people they're also but if you know a lot of Bollywood and just reality the nerds are probably not the most connected in their in their social networks >> but there's a second element to it right like what you find is something much better which is the nerds are deliberately kept out Exactly. No, but that was the surprising and not perhaps expost and unsurprising thing which is what we randomize is basically who so after mapping out the social network and you know getting to know who your friends are in the classroom so on and so forth. What really what we randomize every week is who in your class gets information about this internship opportunity right and then we really very carefully track how that information flows across students within the classroom. >> That's amazing. And so there is what what you were saying like the one thing we find is that the nerds don't get to know about the are less likely let's say >> the nerds are people who are the higher ability candidates >> and so it's not just a porative we're using for like the studioious kids right so if someone gets information about a rival uh job >> yes >> uh where they have to compete they are more likely first they're less likely to share it overall but within conditional upon sharing they're more likely to share compare it with people who are lower on the ability scale than them than those on higher on the ability scale than them which basically means the nerds are left out. That's where we're going with this. >> Yes, absolutely. And in I was a nerd. So you know in that sense like I feel like there is there's much to be said there's much to be said about [laughter] some of these things but but but jokes apart I think that's exactly right and I think because we know exactly the social network we can actually do an analysis at the at the pair level like the pair of friends and really look at you know who on a onetoone basis who gets the information and not exactly this. Now the surprising thing is that making a job non-rival. Now, how do we do this is basically to say like look if I tell Shuty um you know non-rival just means if you get this information you've already got the job. >> Yeah. >> Right. >> So everyone who has the information has the job they don't have to compete. >> Exactly. And so that removes your incentive of just purely competing for this job. But we do encourage you to still share it >> as we do in the rival case. Uh encourage this uh sharing between your classmates. We find this nerd gap let's call it uh disappears completely. >> Right? So it's nothing to do with, you know, networks. It's purely competition. It's just purely about the fact that I know this guy is smart. If I apply and this person applies, like he or she's going to get it. Um, and so >> which also brings me back to if I had to scale up your experiment, demand side really matters. >> The number of jobs available overall in an economy really, really, really matters, right? >> Absolutely. I mean, yes. >> Yeah. Nothing to add to that. >> Yeah. It's it's like the same story over and over again. And you've studied this using different methods. You've studied this in different sectors. You've studied. So it's it's an incredible story. Now one of the more interesting things that you found found is this nerd effect. I mean first men are way more competitive than women. >> Men share less information. There is a bigger effect when it comes to the nerd effect. Men like to leave men of higher ability out. But these things disappear with women. What's going on? >> I don't know. You tell me. You're the woman in in our conversation. like >> I have grown up entirely in male networks in economics. So I don't know anymore uh if I have the same uh I I mean women share information. We understand this. Uh but I think one thing that might be going on and this may have some supply side factors related to it is if two of my close friends are also get the same job my parents are more likely to allow me to go do it and things like that, right? Or it'll be more fun or we can travel together. And you see this in a lot of field experiments which other people have done. Uh so I think that might be one part of it where sharing information and actually doing applying to the job together, interviewing together or even doing the job together is safer. Yes. >> You know, quote unquote, whether it's in terms of social norms or everything else. >> Yes. No. In fact, like Smith Gard has this like really nice paper um that I was thinking about that looks at exactly this issue in terms of like traveling together for a job. Uh fortunately for us like ours was online and so there was no like traveling or interviewing or anything anything like that that that that was the issue. But I agree like I mean men having close male friends is a disaster if you're looking for labor market information because they're competitive >> uh male friends is a disaster if they're in the same field as yours and competing for the same pool of jobs. >> That's a well caveed statement. Let's put it that way there. Yes. But that's exactly right. if you're looking for jobs and that's I mean we've talked to a few we've talked to a few there's no clear answer like the more we've talked I mean it's some of what you say like it's some of the fact that oh look like I'm actually close friends with I mean like she's a close friend of mine and so like why would I not tell her this information right which is pure altruism as opposed to you know like oh like but this is only a oneweek opportunity like you know I really you know it's fine like at the end of the day there's some reciprocity to it like you know she has helped me out in the past and so like I'm going to do so there are multitude of reasons that at least from these qualitative conversations we learn about now why men don't value it I have no idea I mean >> so I my hunch is just the Indian job market in terms of really good jobs >> but these are internships >> I know but there is a mindset problem right the mindset comes from what is going on >> more broadly >> we have too few seats whether it's at IIT whether it's at Bombay University or whether it is Infosys hiring or anywhere else so everything is a race >> and uh you can't blame them in thinking at a very local level that this is a zero sum game. >> Yeah. No, absolutely. No, absolutely. >> And there's no more pressure on men. No question. >> Yeah. Absolutely. I mean, it's a combin as I said there's a combination of all of these things. Um and again like but the surprising thing is again when you make jobs on rival none of this matters. >> None of this matters. Exactly. >> None of this matters. >> Right. So that's the thing. If there are enough jobs, men would also behave in a sane way. >> Exactly. >> Is this why all your co-authors are women because you've done this study and you know exactly what you're doing? >> No. It's a happy coincidence. There are lots I mean you you've [laughter] worked with a lot of different people >> uh from a lot of different institutions and countries and whether it's working on female labor force participation or not you have an extraordinary number above average for sure of female co-authors. Thank you. >> So I'm wondering if this study has >> they want to work with me. >> No but I'm wondering if this study has something to do with that. You're like the men are just going to cut me down [laughter] >> because of that nerd effect. True. You're like the women who are >> I really internalized my own research to a degree that I don't know about it. [laughter] >> At least my female co-authors will let me know the opportunity. >> No, I'm just I I'm just very happy. It's Yeah, I'm grateful that they want to work with me. [laughter] >> But someone else did point to me a few weeks back that oh like you you've got a lot of amazing female co-authors. >> Did they land on the same reason or am I >> No, this is the first time I'm hearing about this reason about like it's probably not them. It's me who has internalized my own research foundation. Yeah, exactly. Exactly. No, but sorry, but just to continue that conversation, I think that the other thing that we find with this which I find fascinating is that [snorts] this is not cute just from a college information because we we actually give them the jobs. you can actually monitor their entire performance on the job and it really does affect outcomes like you know the the pool shrinks in terms of the quality the performance of the job and so the last wrinkle that we add to this which I find interesting is we say okay like if I'm a firm what am I going to do to attract talent well I'm going to probably hire offer higher wages >> higher wages so you double the wages >> so we actually offer double the wages and so we randomize who gets these >> but you know what if you make jobs more attractive competition goes up >> exactly >> right and so in some sense >> they're even less likely to share it exacerbates some of these effects. It overcomes some of these constraints uh in terms of the just information flow because people are now more interested more generally in these jobs but it exacerbates these kind of competitive uh effects. And so the traditional ways in which one would say like you know oh like high quality jobs you know just offer higher wages and people will come. Yes, people will come but there is selection in terms of like who is coming and I think this this kind of is nice to be able to study that very cleanly from an experimental standpoint. >> Yeah. also matters who your node is within an information network, right? That's the biggest thing. Your nodes better be really high quality and more likely women. >> No, exactly. Absolutely. And so there's there was a lot to learn from it from like uh how information from a policy standpoint should you know so for example like online job portals or you know these new technologies are they really overcoming some of these information barriers because now people are finding I think that's an interesting question to study >> right >> so you know okay so I want to stick with the information thing so this online portal thing is super interesting because this gets me to your next paper which is actually a much older paper this is your paper with Abjid Banerjee also studying uh you know uh labor market frictions This is very concrete like old school matching exercise, right? And here you also have a middleman. So you have people looking for job, you have firms hiring and then you have a middleman which is a sort of like a placement coordinator or a hiring manager or something like that. And now there's a question not just of information flow between the appropriate opportunity from the point of view of the applicant. It's also for the firm not just in aggregate terms but for this particular applicant and now the placement coordinator and what kind of precise information they have about the particular candidate and if they can actually match them because matching is really the problem we need to solve not just hiring random people right >> and uh when you ask like what you guys do is when you ask hiring managers or like placement coordinators or HR to figure out what are preferences of individual candidates, they actually do a terrible job of figuring that out even though that's their primary role. Absolutely. So, first of all, what is the information friction going on? Because on paper, a lot of these jobs, I mean, you're looking at a standard degree kind of thing. >> You know that a skill from this person in this college is going to be about at this level. So, this is really about do they wish to work this job? Do they wish to work these hours? Do they wish to travel? So what is it about all this information which is not as explicit as maybe the skill and the degree signal >> and how do we overcome it? >> Yeah. So I think uh the placement officers was kind of an interesting exercise because they play a huge role as you were saying in terms of like >> I didn't know I thought they were doing nothing. No, I mean one would argue effectively [laughter] but but to the extent that they're incentivized to do a lot. Um and so I think um the the key part was trying to figure out like preferences are multi-dimensional in some sense. And so the thing that they get right unsurprisingly is that higher paid jobs are preferred more right duh. I mean uh that's not very hard to figure out. So where do they get it wrong is basically all the non-monetary dimensions, right? How much do you prefer uh you know characteristic X of a particular job and especially whether that's with respect to distance whether that's respect to the work hours how active the job is do you really have to go on a delivery scooter halfway across Lucknau now versus a cushy office job where you just type on a computer all day in an air conditioned office right like so these kinds of amenities I think people and there are two things one is that preferences are varied even amongst individuals >> and we assume as economists that they are given to us correct from the people. We're not distorting their preferences. >> Correct. Exactly. And so students themselves have varied preferences right across these jobs. And and so what we learned from that experiment was basically yes like you know one part of it was this information friction especially on the non-monetary dimension. And so that begs the obvious question suppose we know students preferences because we measured them very carefully. What would happen if you just give preferences to to to these placement officers? Fortunately, we do find they do pay attention to what we were telling them, which >> they won't go looking for the information, but once you give them the information, they act on it. >> Exactly. So, they they did act on it. People were placed in jobs that, you know, if you take a locus of how far from your most preferred job, because I mean, it's multi-dimensional, so we have an index blah blah, >> but they're closer to their preferred index, but they don't actually stick to their jobs. So, that's the So, there were two things that were surprising. One, one part was the fact that, you know, why don't placement officers wait this uh to begin with? And I think when we talked to a lot of these placement officers, one part was they were under so they had no clue. They accepted the fact that you know sure like you might have these one part of it coming back to your earlier point is that you might have these preferences we are never going to be able to find the jobs that fit these preferences right because the way many of these placements actually work is bulk placements. >> Yeah. >> There is one employer who wants to hire 40 people and so I don't give two woots about your preferences. You're going to >> the margins on which you could still make it work is what I got from the paper. A few a few right but but uh the primary thing on what placement officers told us was the reason they underinvest in this kind of exploration is because in many cases their jobs are just bulk hiring and so they or in Lucknau for this kind of I mean this was in UP and so we had luck now Delhi Kpur like these were the main placement areas um they basically were constrained with the terms the kinds of options that they were that they were getting to place their students in which is why they underinvested in this. The second part is that we actually followed these guys over time the students themselves once they were. So technically if you were placed in these jobs you should love it and you should be happy about this right literally like 6 or 9 months later I would say very very very very few people are actually sticking to these jobs >> and which goes back to your earlier point of they don't want to invest in skilling because this churn is real. >> Absolutely. And so >> and the churn comes from mismatch. >> Correct. And so but this was like after reducing mismatch. >> Yeah. >> Right. You still don't have retention. Right. And so so that was fascinating. And so there were two or three reasons that came up. And so I'll go from the most like logical story to the to the so the one thing people told us that they were trying for public exams. Yes. >> Right. Uh government school uh government u mangal and all his research horribly that distorts the labor market. >> Exactly. And so again these were 21 year olds and so this was up again and so again like context might differ and this might have worked very differently in other contexts but at least in this context the one thing they wanted to do was to appear for government exams and they were not interested in some random delivery job in which begs the question why did they want to take the vocational training course to begin with and I have some theories on that that that I'm happy to you know talk about later but but that that was reason number one reason number two especially and this is where gender again comes in very interestingly women are actually more likely uh to stick to these jobs than than men and the reason was very different. Men it was the first time that they were living out of their household. So the Rajabu syndrome was shattered >> handle anything >> was shattered right. So they would rather come back to their village and just hang out in their in their in their homes than actually take these employment opportunities in these cities that that that were nearby. >> And the cities are cruel in fairness. and and we asked a lot about who they were living with. So they were usually sharing apartments with four or five people. They had to cook their own food. Work hours were grueling. Work hours were grueling. And so it was very difficult for them to adjust to this life in a in a very short. >> So the aspiration they have of what kind of job they want and the kind of job they get in this particular job market. There's a huge gap. So suddenly you feel a little bit better about applying to the government job lottery. >> Exactly. And so for for many of the men this was what was going on in terms of trying to you know government jobs a combination of just the labor the job itself actually wasn't so we asked them this distinction between the quality of life versus the the work itself and people expected the work to be what it is reassuringly because they were in a vocational training program. So they had seen some of this at least in theory. So that wasn't where the update was. Most of the update in terms of their uh uh priors came from just living in a city and just trying to understand that you know in a 20,000 rupee job uh I'm probably not going to get the quality of life that I envision myself doing >> which is heartbreaking >> which is heartbreaking and then for women on the other hand the story so we were surprised and cautiously optimistic that you know women are actually sticking these jobs way more but for them it was the first time they were allowed to step out of their household and they were relishing it right it was >> and it probably means that they've already overcome the previous barrier. They've figured out their PG situation or they're living with a family member or an extended family member or something. >> Exactly. And and you know, of course, there were you know, constraints in terms of like for example, women were more likely to go to Lucknau than Delhi, for example. So, there were these obvious constraints, but to the extent that they were able to do that, uh uh it was the first time that they actually relished their own freedom. And in fact, we find that women were more likely to send a larger fraction of their salary back home >> uh as opposed to the to the men. And one of it was again an income story that you know they were earning 20 25,000 rupees in in these jobs and they used to send a regular income back home and so and it was almost like a uh in a very unfortunately the price that that they put on you know the fact that they could live in these cities and work in these cities. >> The big drop of course that we still see after that is just marriage. >> Marriage. Yeah. >> Right. I mean the minute that they have these talks and these are qualitative conversations we've had with a few. So we didn't follow them large because no one stuck to their jobs. So, so we didn't have a much longer follow-up on this, but to a few people who we were able to call and and talk to them, marriage was still the biggest barrier. So, the minute the family started talking about marriage, they quit their jobs, they were back home. >> Yeah. So, you know, one thing I mean, now I'm thinking about this given what you just said when I was reading the paper, I thought it was exactly the way you had set it up, which is, you know, it is the placement officer who doesn't have the information about all this. Now I'm wondering if one big problem is the individual applicants are either not happy to reveal their preference >> right >> or they don't even know their preference >> right because they are not that familiar with this kind of a job market especially in a big city >> given what's happening with the structural transformation >> so how much of that is playing a role in this women are just clearer about what their preferences are because in some sense their life has been mapped out for them right like families just tell them you're allowed to study you're allowed to work for a few years then you're going to get started by this age and this is it. >> Yeah. No, and I think uh so to your first part of whether can people like how much do we believe these preferences right? So we did actually within the experiment we had a subexperiment where we actually incentivized uh individuals in the following way. We told them that look like you can take this here and the way we actually got these preferences were real world jobs that were offered to previous cohorts. We mapped them out more carefully to look at like what dimensions were varying in these jobs and we presented students with a list of about 8 to 10 jobs and we were like you know why don't you go just go and rank them in terms of order of preferences. So we didn't want to make explicit you know certain dimensions that we wanted to prime them on etc. And these were real world job offers. So these were like hypothetical scenarios of ideal offers and so on and so forth. And the way we incentivized them were to say look like here are 10 jobs and it was factually true which have been offered to previous cohorts. So your preference is going to matter because we're going to ask the placement officer to search in these dimensions depending on what you have. So we increase the stakes of just you know randomly just ranking these job orders. We find no difference in terms of like whether the incentivized and the the non-incentivized individuals in terms of the rankings that we get on on these 10 jobs actually don't differ. >> Do they worry that they won't get the job at all if there is too much of a strong preference for one kind of thing versus another? potentially that could be going on but again these were jobs that were offered to previous cohorts. So there were jobs that had come by uh in in in previous in in previous batches and so it could be that you know if everybody just says job one then you know we're all going to compete for job one and so you know >> so not so much that it's like oh these people are going to think I have like I'm picky and I have these preferences like this is me speaking as a woman thinking if if we express too much about what our strong preferences are like no one's going to hire us >> potentially but I mean it's again like this wasn't a a preference elicitation in terms of like we going to tell employers this is what >> exactly this this is more about like tell us because we want to match you to jobs that you actually want to work in. Um and so it was set up in that way but we find no real difference in any of this. So it's not really the fact that people weren't willing to and again if you talk to these students they actually want jobs that they want they're really excited about this coming from a vocational training program they want to go and experience the city and you know they want to really live in these uh uh you know labor markets and so much of it I think was the update um afterwards right and so in fact I remember like the two key things Abijit was excited about in terms of you know trying to unpack further was you know should therefore we have internship programs in the college, right, where people actually go to these labor markets that they're going to work in and actually have firms uh uh you know uh uh hire on a temporary bas >> which is how we all did it. >> Exactly. >> We all figured out what our preferences are and what our skill matched set is based on internships. >> Exactly. And so and so that was one and the second thing is then then trying to look at it more from what how should the placement process itself work for these vocational training programs to be able to uh because clearly this is not a case where retention meant you were going to get better jobs and look now this was your first g and by the way to be clear there were some people who did very well right they really went into these jobs they loved it they found better opportunities for themselves and there are success stories I don't want to underemphasize the success story part of this as well But on average that wasn't the story that was coming out of it. So I'm just focusing on the average as opposed to you know the the there were a few students who did really well on this um as as as well in terms of just breaking through the luck now market and you know finding themselves better opportunities and so and the second part is what should we do about the public sector in terms of you know how it is either distorting I mean in this case clearly distorting uh labor market opportunities and Niharika and Konal kind of digging into this digging into this more deeply but these were kind of the two things on how and then you couple this with the social experiment the social network experiment right in terms of if there are these few well sought-after jobs then it really matters like it gives you you know power over who holds that information and how that is conveyed to other people. Interestingly let me add one quick thing which is in some follow-up work that uh is paused right now but we hope to pick it up at some point was I was really excited to see whether job market job portals specifically right can break this uh uh this barrier. So if this is all about the fact that look like the placement officer just doesn't know who in Lucknau uh and you know I just have to go and contact Domino's who wants to hire 50 delivery delivery people the job portals can solve this problem because you know exactly uh how the Lucknau labor market can look like uh through jobs that are posted on these job portals. We did a small pilot in Jarken uh uh and and and basically what we find is the following. We find that actually play placement officers do put in more effort and so to be clear what was the what what did we try doing? We basically train students on how to search on these job portals. Okay. So the experiment was we just show up we give them a two-day training on on a job portal. We help them create their profiles. We'll tell them how to filter how jobs what to look for in jobs and so on and so forth. Then like 3 months later we follow them up and we ask them okay how did you get a job? placements through job market through online job portals is very very low right so it's not that people are and they applied to jobs they searched for jobs they applied to jobs we asked them how did you find your job and they were like oh our placement officer like our placement manager basically put so >> so networks >> so networks matter and so it could be the fact so the now I think the reason it's paused is because we're trying to unpack kind of like what could be driving this so there are two kind of channels one channel is I've just increased your outside option as a student so you're no longer at my mercy as the placement officer and so now I have to work hard to earn my incentive because now you can just go and find a job. I like that story a lot. >> Or the second option is basically purely giving you information doesn't matter and my real monopoly is not on the information. It's about the connection to the manager at Domino's who's going to hire you, >> right? And so therefore, you might do everything you want on the job portal, >> but you're not going to get that job. But in me knowing a manager at Domino's, so but these are two very very, you know, the opposite end of the spectrum in terms of what >> and showing knowing seeing what your other research has shown is probably the latter. >> No, exactly. And so so now we are trying to dig into that a little bit more carefully and trying to understand whether it's a monopoly on information or connections um as opposed to an outset option that's going up for you which is why I'm working hard to earn this incentive. >> I'm less I mean you've done other work on information right I mean this is literally your paper again with Penny Goldberg on the digital age and places leaprogging in terms of technology by getting like 2G 3G networks and uh using these phones to have more information. the information story is not quite as compelling for the structural transformation and also matching the skills with the jobs. So am I reading too much into your paper with Penny Goldberg on leaprogging and how that result is really mixed like you find a few more opportunities through information but they're not great opportunities and they're certainly not the kinds that you're talking about which is you know through a placement officer I can get matched to something higher. >> Yeah. No, absolutely. And I think uh that's a better summary of the paper than >> Okay. [laughter] So why don't you tell me about the paper then? >> So no it's absolutely right. So I think I think >> sorry I've read all your work all together much more recently than you have. So this tends to happen >> and and as I've told you before we started this podcast I think the way you're threading through all of this work I would have never been able to do that. I'm really grateful that you are able to see and connect all of the which I mean you're obviously fantastic at doing that and you're better at you know mirroring my own research to me which I'm now discovering is great in terms of learning a lot of things about the labor market. So you know uh you read marginal revolution and you know uh you know Tyler and Alex uh one of the things I learned from them very early on maybe this is like 15 20 years ago is don't look at papers but look at literatures >> correct >> and I took this very seriously very early on so I actually like reading papers in clusters and I find that reading them in clusters actually tells you a lot more about what's going on and and what we do as economists is pattern recognition and you know kind of putting the pieces together also most people are doing partial equilibrium stuff when it comes to these kinds of big questions. So it's just easier for me now I think having done this for many many years to just read papers as literature and yours has to me a very very clear uh through line but going back to your paper you know with Penny this is really so relevant for India because you're looking at you know this leaprogging technology and putting a phone in the hands of people uh so one does it matter for structural transformation and what are the differences between men and women which is back to the big >> exactly so I think does it matter infrastructural transformation. No, it's what we find, right? But there is a nuance. >> It matters for jobs. >> Yes. So, so to be clear, what is this paper? This is basically putting together data on 3G coverage. So, these are smartphones. Uh improvement of that over a bunch of countries um and over time. And so, think about this more as like I live in a particular region and this region now has access to 3G. What do I do with that new technology that comes in? And the good thing is that we have data also on 2G which is kind of the not smartphones. >> Uh and so this is not about cell phone dumb phones as someone told me in a talk. >> So they're not smartphones they're dumb phones. And so so it's not so much about cell phone access as much as it is about internet on the smartphone. >> So it's actually information and not networks which is the important distinction >> or communication. It could be WhatsApp. It could be you know all these online job >> program. Whereas with the older version of the phones we were just talking to people being new effectively. >> Absolutely. And so what do we find? We basically find basic two things. One is that uh employment goes up. People are more likely to be employed. People are more likely to work in in wage uh jobs. Uh and of course because this is cross country we're very limited in terms of the depth that we can go in. But the patterns seem to be very very broadly consistent across across countries. And now there is a literature also country specific and you know people are finding and re echoing some of these these findings again and again which is basically the fact that people are more likely to be employed they're more likely to transition from unpaid employment into into into you know paid paid wage uh paid wage jobs >> and here you know just to caveat unpaid employment is basically jobs around the house if you're on a farm like you know you'd be doing work on the farm which is technically unpaid but it is employment. >> Absolutely. And like Exactly. So it's like >> that matters for Exactly. And that matters a lot for poorer countries and poorer families which are at one end not yet into the structural transformation pipeline which is why it matters so much. >> Yes. In fact and it matters for women because that's that's where we find the biggest gender divergence. Right. So what we find in terms of the gender implications of this is that why does it not affect structural transformation because men are actually moving out of these unpaid household type of jobs into either wage paid jobs or or many of them actually also into owner operated enterprises. So like self-employment in in some sense. Women on the other hand uh female labor force participation is going up. >> Yeah. >> Right. So more women entering the workforce but basically they're working on the job ladder that the men have just vacated. So basically they're the ones filling up on these farm opportunities. Some of them do go into wage jobs in the service sector. So there's a again a spectrum of uh doing that but the large picture seems to be so everyone in that sense is climbing up the job ladder. is just not genderneutral in terms of how that that progress is is being made. And so on on net if you look at average this is great because you know on average everyone's better off. >> Exactly. So the result you find is that a 10 percentage point increase in 3G coverage raises female labor force participation by about 4.9% which is not as much as the men but it's nothing to sneeze at. No, absolutely, absolutely. But again, it matters where these women are going and working in the workforce, right? And so and so now we actually we have a follow-up work that we're just trying to wrap up uh soon in Mexico. So we've taken one example where we have great data. We have a good way of identifying these causal effects. Then we can actually dig more into formal sector versus informal sector earnings versus not and so on and so forth. And so one thing that I'm fairly sure will survive all the subsequent analysis that's going to happen on the paper before we can release it publicly is that men are actually moving into better paying jobs. >> Yes. >> Right. And because there is an entry of women on the extensive margin also an entry into unpaid jobs on average earnings of women are not actually going up. >> Yeah. >> Right. And so we're trying to unpack some of those results. But it's fascinating again in terms of like how technology can empower uh certain things versus like the inequalities that might still persist despite this technology. Again with Mexico it's really nice because there is time use data on pe on people just being asked how do you use your smartphones right and so again we find some gender disparity in terms of you know information communication. Fortunately for us, everybody uses it for entertainment, which is great, but also like for example, like information communication channels matter more um for for women than they matter than they matter for men. >> No. And this again like I mean I don't mean to keep harping on this, but again this study shows so much that if you don't figure out or crack your labor demand puzzle, having relaxing the supply side constraint for women is only going to reduce their wages. >> Yes. So absolutely >> right. So this is like a really I mean this is again something I see over and over again in what you're doing. You're looking at labor supply, labor demand, all kinds of different factors. But if you don't solve labor demand, I'm not saying you're doing women a disservice. I think having a job even if it's slightly less paid is better than having no job as far as female labor force participation is concerned especially in the long run for economic growth. But you are going to suppress their wages. >> No, absolutely. And I think the econometric paper makes that point very cleanly, right? Like if you only reduce in a counterfactual simulation where you only suppose in a in the best case scenario we eliminate all labor supply barriers right you have I mean you go from 29 over 24 percentage point female labor force participation all the way up to like 70%. >> But if if there are no jobs because all these demand side constraints exist >> and then they're going to be treated horribly and paid very little. >> Yeah. I mean that's a natural extension of this uh uh to make. And so again like there are different goalposts and I think in much of the conversations that I have read slash interacted with like I think people have their own goalpost on what welfare means and it's always such a hard thing. uh I mean economics takes one stand on it but you know people more generally what do you mean is it about as you were saying like there's a there's a really nice paper actually with a co my my co-author Edin Kelly and others have in Bangladesh on on the psychological value of work right u which basically shows that just and these are refugees and so it's it's a different context and a different uh uh uh place of the world but basically showing that just the fact that people have a job >> really matters to them from from a mental perspective and so seeing it in the developed world with all the AI conversation people are like no we need to work just abundance and productivity is not going to hack >> it absolutely and so so again like I think the goalpost is I think what is kind of uh in and and there's no reason to pick one over the other but I think sticking to one and defining that and then looking at and evaluating a policy based on that I think is is much more constructive at least in terms of my thinking because then you can understand exactly the trade-offs yeah >> with with impacting whatever measure of welfare or job or earnings um and it speaks to what you were saying like you know sure you'll get more pe women to work and maybe that's what you're gearing for >> but if jobs don't go up then wages are going to go down there's only so much that you can say about complicating things and you have to be fine with that right um so these are that and coming back to like why we need more frameworks and why we need to take them more seriously I mean usually it's like throwing the baby out with the bath water in some sense right like because people are like oh like this is a model like why do we learn from this and why should we care about this this is not the world and true but also I think it gives you these insights that are very hard to Unless you have really rich um long-term data from a RCD perspective. >> Google Maps is a model and I learn a lot from it without having to walk every street. Exactly. >> So there is something to be said for well well put together models from actual real empirical work. This was such a pleasure actually. You know I feel bad or maybe good. We've covered only about half your papers. So you have to come back and talk to us about a lot of the political economy work which I'm even more excited about. I mean this is still George Mason University and we love to talk about rent seeking and political corruption. Uh so hopefully you will come back but this was such a pleasure and I mean really like kudos to you for just looking at the labor force participation problem in different countries different sectors different income levels different methods you know and you're really looking at it from many many different lenses and I got a lot out of it. Thank you for doing this. >> Pleasure has been all mine. Thank you for having me.