 I'm from the University of Notre Dame. Unfortunately, that's not in Paris. It's in Indiana, the USA. So I'm here today to present about my paper on labor migration in Indonesia, and in particular, looking at the effects of parental labor migration on the health of left-behind children. So the main motivation behind this piece of research is the question of whether temporary migration of parents away from home for work has an effect on the health of their children. And if so, in what direction is this effect? Is it negative or is it positive? There's no good theory behind it. So in my mind, it's more of an empirical question. So I looked at data that I will talk about in a moment to try to answer this question. So the takeaway from this is that my results seem to suggest that this net effect of parental migration on the health of children varies by sex of the child. Sorry, not by sex of the child, but by sex of the parent. So in particular, it seems that when mothers move away from work, the health of their children is negatively affected. On the other hand, I don't seem to find evidence to suggest that father's migration has this impact. So a little bit about the country that I'm studying. It's Indonesia. It's on the equator. It's a pretty large country, a fourth largest country in the world in terms of population. Yes, I'll get to that soon. Thanks. That's a great question. Yeah, it has the world's 16th largest economy in terms of absolute GDP. But in per capita terms, it's a growing economy, so the per capita GDP is about 4,000 US dollars. And this, I believe, is 2016 numbers. And someone in the audience asks if, by migration, I mean internal migration or international migration. And the answer is both. But I have here some information on where Indonesia stands in terms of remittances from abroad. That being said, this paper isn't focused on just outward migration. It isn't just focused on international migration. So Indonesia is the 14th ranked receiving country for remittances from abroad. And this was in 2015. And as you can see, in relative terms, I mean relative to GDP, it's not a very large percentage of its GDP. It's only about 1%. So it's not quite like, say, the Philippines or Mexico, where it's a huge proportion of the economy. So by migration, the definition that I use here is just whether or not the parent moved away from home for the sole purpose of work. So unfortunately, as it stands right now, my research doesn't quite distinguish between whether it's rural to urban migration or whether it's internal migration or whether it's international migration. So Indonesia has a very large number of women, especially who move to neighboring countries and the Middle East to work as domestic help. This paper doesn't try to discern whether or not it doesn't really consider the reasons for moving. So right now, it's preliminary. And all I look at is whether or not the parents move away from home for work. And implicitly, this is a temporary migration question because the data that I look at is a longitudinal survey. So the people who are in the survey were re-contacted. So by implication, you move away not for good. You move away for a few years for work and then you come back. And this isn't from my paper. This is by two researchers at the University of Indonesia. They broke down the numbers based on the same data set. So the duration of moves in, I guess, the most relevant years in this are the last two columns, year 2000 and 2007. So people who migrated internally moved for about four years on average. So that sort of gives you a sense of the duration of moves for people who we know migrated internally. OK, the health measure that I look at, in contrast to many of the papers that I've seen in this field, is not the subjective rank your health on a one to five scale or whatever. Rather, it's an anthropomorphic measure of health. So I look at height for age and weight for age. So and these were standardized by age and sex group. So the interpretation is if you have a height, for example, a height for age or age for short, if you have a height for age of negative one, then you are one standard deviation below the reference median for your age and sex category. And over there is the exact formula for how age was calculated. And I actually did not compute this myself. I just used this data model that was developed by someone else. It's called Zscore06. So it takes a World Health Organization tables, age, sex, reference values, and takes a host of variables from my data and spits out the Zscores for weight for age and height for age. So this is, I think, better than simply asking someone how healthy he or she feels. And in the survey that I employ, the Indonesian Family Life Survey, I haven't even mentioned it yet. In the most recent wave, they looked at biomarkers. So they took a blood sample and looked at biomarkers testing for red blood cell count and stuff like that. So that's another route that those of us working in health might want to consider. And I guess the main advantage of using Zscores is because it's linear, it makes interpretation a lot easier. And in particular, there's no difference. The difference between someone with, say, HAZ negative 1 to 0.5 is exactly the same as someone with HAZ 0.7 to 1.2. So it just makes interpretation a whole lot easier. And because these scores have been standardized by age and sex grouping, it's completely gender-neutral, gender-independent. And just to give you an idea of the prevalence of male nourishment in Indonesia, you can see a general downward trend in terms of weight for age. So this is a measure of underweight for children. But even though there's a downward trend, it's still pretty high. In 2015, you have 20% of children below the age of five who we would consider underweight. And for the most relevant years in my study, 2002, 2007, we do see a bit of an improvement. Maybe I should speed up. And this is a height for age data. And again, there's a general downward trend. But between 2000 and 2007, unlike weight for age, height for age actually became worse. All right. And a little bit about my sample, it's a panel data set. So children, I extracted children from the data set age 0 to 7 in the year 2000 and looked at the same children in 2007. And this allows me to control for time-invariant characteristics that are both observable and unobservable. So this removes a huge source of omitted variable bias, as you guys know. And I excluded outliers, so HAZ and WAZ, that are less than negative 6 or more than 6, I exclude. So the final sample is about 3,000 children. And from these children, I get information about their parents. And so I picked children aged between 0 and 7 in 2000 for the specific reason that I wanted to avoid confounding the effects of parents' choices from the sole effect of parental migration on children. So if children who are aged 0 to 7 are pretty reliant on their parents, right? So if the parents move, it's more plausible that whatever effect on health that is seen on children aged 0 to 7 was hugely affected, was hugely because of the parents' absence of the parent. Right. So this is again, sorry, this is comparing the descriptive characteristics of children with one parent who migrated versus the full sample and versus children for whom no parents migrated. So in my data, I actually did not have both parents migrated, right? Either the father migrated or the mother migrated. So parents with one, children with one parent who migrated actually were healthier, at least in terms of high 4-H and weight 4-H, Z scores in 2000. So in the initial year, children with one migrant parent were healthier. But strangely, we see this pattern reversed in 2007. And IFLS, as I said, is an ongoing longitudinal study. I won't talk about this because we are out of time. I use the waves from 2000 to 2007. This is some related research. And OK. So what was interesting about migration research is the focus mostly on international migration, right? Looking at remittances, there's like tons of papers that have been done about that. But when it comes to internal migration, it's sort of a newer thing. It's only recently that more and more focus has been on internal migration. And I think it's for the simple reason that micro data is not as widely available as macro data. And those studies that look at that internal migration tended to focus on more easily measurable things like school attendance and performance in school. And here's my regression model. It's very simple. It's just a linear model. And I just run a fix-effects linear regression. And crucial to this analysis, it might just render the whole paper meaningless if there wasn't enough variation in the explanatory variable of interest. So because I look at the same children, if I had no parents who moved in Wave 1 and Wave 2, or parents who moved in Wave 1 and the same parents moved in Wave 2, then there's no variation. And there's nothing to look at. But fortunately for me, of children whose parents migrate, OK, sorry, that went by faster than I thought. So here's my results. And again, this is preliminary. I find a negative significant effect for whether or not a mother migrated for work on health and health for age Z score. And no such effect for weight for age Z score, even though the sign is the same. And the effects for fathers migration is insignificant across the board. So this is suggestive of a gendered pattern. Thanks.