On graduation

A few days ago I took my last final online and, without ado, graduated. I thought I would celebrate that evening with something more than sleep and Netflix, and also very naively thought I would be able to get back on track on all my personal projects the very next day, but my Netflix binge has lasted two whole days (I watched the entire season of Ryan Murphy’s Hollywood, a series which was very entertaining, even if not at all critically acclaimed).

I am now (maybe, sort of) back in the right headspace, well-rested and ready to work again, and desperately trying to finish everything I planned to complete in the limbo between graduation and the start of work. I had hoped to use this time to travel (to Australia, to visit my brother), but that, of course, is not possible now. I do not mean to sound bitter: I know that I am extraordinarily fortunate just to have found employment prior to graduation, so I’m at the very least not adrift in the ensuing economic storm. But back on topic: graduation.

Earlier this year, upon learning that I’m graduating this semester, my friend said, “Finally.” I retorted with “What do you mean by finally? I took just the right amount of time.” But right now, especially having spent the last weeks of my undergraduate education in isolation, it does feel like finally. It also feels like I matriculated last year, because all my memories of that semester are so vivid, and most of the intermediate semesters are a blur. I guess rites of passage draw up emotional clichés.

I remember being set on studying Economics when I entered NUS. Back then it was mostly because I had done relatively well in H2 Economics, and wanted to work in a bank (I swiftly realized some time after matriculating that I had very little passion for finance). It was secondly because I had not done well enough to be admitted to law school, and most of everything else did not interest me. It was thirdly because at that time I expected that I would be able to muddle through most other majors I had a vague interest in – Political Science, Global Studies – because I had a natural affinity for these subjects, and wanted to challenge myself doing something more mathematical, having never been too good at math. I suppose this reason should be labelled youthful hubris.

Anyway, I began studying Economics, and it was serendipitous: if I had done better in the A Levels, I would have chosen Law; if I had not taken a gap year to work and chosen to enter university a little earlier, I might have been keener to tread the path of less resistance, and chosen to study something like Political Science instead. But I ended up studying Economics, and found that I love empirical work, and I love applied micro, and I love experimental work, and I love all the cool theories in papers I have and haven’t read. I remain not quite literate and certainly inarticulate in Economics, and struggle through papers sometimes (most of the time), and when I attend theory seminars sometimes (most of the time) I have no idea what’s going on. But I do love Economics, and want to get better at reading Economics, and I guess this may be read as a commitment to regular updates on this blog, as work permits.

Competitively debating and coaching debate (alongside completing other RA duties) while trying to take as many courses as I can to maximize learning in 4 years, approximately SGD 32,000, has not been easy. I think if I could go back I would have chosen to do many things differently. Coaching at two schools was definitely a bad idea, and I should have limited myself to one from the beginning. Maybe I should not have coached at all, but that would have left me financially stranded, so I don’t know. I wish I had obtained better grades in my time at NUS, but they are what they are, and the past is past. I hope there will be subsequent opportunities to study Economics again, and I hope that in these opportunities I will be able to devote myself full-time to my education.

A simple model of gun ownership and crime rates

Does gun ownership increase or reduce violent crime? I’m not sure whether a model for this already exists, but in this post, I attempt to model the causal effect of gun ownership on violent crimes in this post (I’ll share any relevant literature I find, if I find any, in subsequent posts). I’ll jump straight into the model setup, with explanations injected as I go along.

Model setup

  • We assume that a person makes the decision about whether or not to commit a violent crime based on two variables: their innate desire to commit the crime (\beta_{i} \sim U(0,1)) and their probability of success (\chi_{i}). We represent this with \kappa_{i} = 0.5(\beta_{i} + \chi_{i}).
  • Their probability of success depends on two factors: some innate ability to use force (a_{i} \sim U(0,1)) and the ability of the other party to use force (a_{-i} \sim U(0,1)). Their innate ability is known to them. The ability of the other party is not. We represent this with \chi_{i} = a_{i} - a_{-i} + 0.5, i.e. if you are better able to use force than the other party (a_{i} > a_{-i}), it is likely that you successfully commit the crime and subdue them with force, with your probability of success increasing linearly as the gap between your ability and the other party’s ability grows. If you are less able to use force than the other party (a_{i} < a_{-i}), the converse applies. If your ability is equal to that of the other party (a_{i} = a_{-i}), your probability of success is \chi_{i} = 0.5, i.e. you have a 50% chance of success and 50% chance of failure.
  • If \kappa_{i} crosses some threshold, \bar{\kappa}, 1 > \bar{\kappa} > 0.5, then the person will commit a crime. If it doesn’t, then they will not. We set the upper bound on \bar{\kappa} such that there will always be people who commit violent crimes (\bar{\kappa} < 1 = maximum \kappa). We set the lower bound on \bar{\kappa} such that there will always be people who deeply want to commit violent crimes (\beta_{i} = 1) but who are completely unable to (\chi_{i} = 0) and so \kappa_{i} = 0.5 < \bar{\kappa}, and these people do not commit violent crimes. Similarly, people who are very able to commit violent crimes (\chi_{i} = 0) but really don’t want to (\beta_{i} = 0) have \kappa_{i} = 0.5 < \bar{\kappa} and don’t commit violent crimes.

Baseline case: No guns

The individual decides on whether or not to commit a violent crime, i.e., he considers his desire and his probability of success. Not knowing the ability of the other party, he takes the expected ability of the other party (E[a_{-i}] = 0.5, since we are considering a uniform distribution over the interval [0,1]), i.e.

\kappa_{i} = 0.5E[\beta_{i} + a_{i} - a_{-i} + 0.5] = 0.5(\beta_{i} + a_{i})

If 0.5(\beta_{i} + a_{i}) \geq \bar{\kappa}, the individual attempts a crime (this doesn’t say anything about whether or not he succeeds, only that he attempts it).

Everyone has a gun

Now we consider the case in which guns are provided to everyone. What happens when a gun adds \alpha to each person’s ability to be violent? We note that this does not change the number of violent crimes committed, since

\chi_{i} = 0.5(a_{i} + \alpha - (a_{-i} + \alpha) + 1) = 0.5(a_{i} - a_{-i} + 0.5)

Let’s say that the value a gun adds to someone’s ability to be violent is increasing in \kappa_{i}, i.e. people who have decided to or have very high likelihood of deciding to commit a violent crime are more able to use a gun to their advantage than people who don’t want to commit a violent crime. Intuitively, this seems reasonable, because violent criminals have the advantage of surprise, and their victims, even when carrying guns, are unable to reach for their guns in time or don’t want to because they don’t want to risk angering their attackers and being shot. For simplicity, let’s say \alpha_{i} is a function that takes the form \gamma\kappa_{i} + c, where \gamma < 1, c > 0.

What we then have is

\kappa_{i} = 0.5E[\beta_{i} + (a_{i} + \gamma\kappa_{i} + c - (a_{-i} + \gamma\kappa_{-i} + c) + 0.5] = 0.5(\beta_{i} + a_{i} + \gamma\kappa_{i} - 0.5\gamma)

\kappa_{i} - \gamma \kappa_{i} = 0.5(\beta_{i} + a_{i} - 0.5\gamma)

\kappa_{i} = \frac{0.5(\beta_{i} + a_{i}) - 0.25\gamma}{1-\gamma}

What we see is that for sufficiently large \gamma (\gamma > 0.5), \frac{0.5(\beta_{i} + a_{i}) - 0.5}{1-\gamma} > 0.5(\beta_{i} + a_{i}), no matter what \beta_{i} and a_{i} are.

What is the implication of this?

Let’s take a \bar{\kappa} of 0.7 and \gamma of 0.5. I previously had

\kappa_{i} = 0.5(0.6 + 0.6) = 0.6 < \bar{\kappa}

Now, I have

\kappa_{i} = \frac{0.5(0.6 + 0.6) - 0.5(0.5)}{1-0.5} = 0.7 = \bar{\kappa}

So, given \gamma > 0.5, we will see an increase in the number of people committing violent crimes.

Ban on guns, but individuals can obtain them illegally

Next, we introduce a situation with a ban on guns but the ability to obtain weapons illegally. We assume the probability of an individual obtaining a gun is dependent on \beta_{i} (the intuition behind this is that the more dogged they are in their pursuit of a gun to act out their violent tendencies, the more likely it is that they get the gun), with some cutoff: \beta_{i} > \bar{\beta} > 0.5 means that the individual has a gun. What this means is that the average person does not have a gun, and the expectation that someone is carrying a gun is 1 - \bar{\beta}.

For an individual with \beta_{i} > \bar{\beta}, and with the same expectations as laid out before, it’s easy to observe that

\kappa_{i} = 0.5(\beta_{i} + a_{i} + \gamma\kappa_{i} - (1-\bar{\beta})(0.5\gamma+ \bar{\beta}c)

\kappa_{i} = \frac{0.5(\beta_{i} + a_{i} + \bar{\beta}c - 0.5\gamma(1-\bar{\beta}))}{1-\gamma}

Since 1 - \gamma < 1 and c > 0, \kappa_{i} here is obviously higher than in the case with no guns at all. It is also higher than \frac{0.5(\beta_{i} + a_{i}) - 0.25\gamma}{1-\gamma}, derived from the case where everyone carries a gun.

For an individual with \beta_{i} < \bar{\beta}, and with the same expectations as laid out before, we observe that

\kappa_{i} = 0.5(\beta_{i} + a_{i} - (1-\bar{\beta})(0.5\gamma + c))

The individual has a lower \kappa_{i} than in the case with no guns, as well as a lower \kappa_{i} than in the case with everyone carrying a gun.

What this tells us is that allowing everyone to carry a gun (relative to a situation in which we ban guns but there is still illegal gun ownership) decreases the probability that someone who has high pre-existing violent desire (\beta_{i} < \bar{\beta}) commits a crime, but increases the probability that someone who has lower pre-existing violent desire (\beta_{i} > \bar{\beta}) commits a crime. What this means for the overall violent crime rate depends on what \bar{\beta} is: the implication being that if there’s a very low proportion of people who manage to obtain guns in the presence of a gun ban, then we should ban guns to increase general safety.

I’ll stop here for now. In a later post, I will further extend this model to look at the case of screening gunowners so that a “trustworthy” proportion of the population owns a gun.

There are, of course, assumptions made in this model. One is that the ability is exogenously determined, and independent of desire to be violent. Another important one is that the decision to commit a violent crime is made based on an expectation of the average ability of all victims to be violent and retaliate, where in reality, violent criminals are choosing their victims because these victims are perceived to be of lower ability to retaliate.

A duopolistic setting: Marketplace or reseller?

I attempt to model the choice between marketplace and reseller in a duopolistic setting, with each intermediary deciding whether to be a marketplace or a reseller, while revising for my Industrial Organization final. Please read to the end to see my very trivial results and let me know if there are any mistakes!

It is based on the model I’ve posted about here (I employ the same notation as in the paper in this post, so if you haven’t read the paper or the post, or taken EC4322, you probably should take a look). I try this extension for my own learning in this post.

Assume we have two intermediaries (Intermediary 1, Intermediary 2) deciding on whether to operate in reseller-mode or in marketplace-mode. The timing of events is the same as in the paper. We first examine the case of simultaneous entry, then look at sequential entry.

Simultaneous entry

We first assume that supplier i will only sell their products to or through one intermediary (otherwise this will be no different from the monopolist case). All products have identical buyer demand 2[m - (a_{i} - a_{i}^{*})^{2}], where each intermediary gets m - (a_{i} - a_{i}^{*})^{2} buyer demand. As with the previous paper, a_{i}^{*} = \theta + \gamma_{i} + \delta_{i}. Suppliers only care about the profit they make, and have no preference for either intermediary.

Reseller

We first look at the situation in which both intermediaries choose to sell in reseller mode.

It is easy to see here that when a reseller r \in \{1,2\} raises the fee \tau_{r} at which it purchases goods from the suppliers to sell, more suppliers will be willing to sell to it, and when a reseller lowers the fee at which it purchases goods from the suppliers to sell, fewer suppliers will be willing to sell to it. We have, in essence, Bertrand competition here, where the two intermediaries 1 and 2 bid up the fee to the point at which they make zero profit. This is an equilibrium: if Reseller 1 lowers its fee below that which Reseller 2 is paying it will also gain zero profit (since no suppliers are selling to it, it does not have any products to sell). If Reseller 1 increases its fee above that which Reseller 2 is paying at this point it will make a loss on every good sold (its marginal cost is larger than the revenue it makes on each good sold here).

At this symmetric equilibrium, the two resellers split the market. Each gets \frac{n}{2} product types to sell from latex \frac{n}{2}$ suppliers. In this equilibrium, we have

\pi_{R}^{1}(n_{1}) = (v-f)\sum_{i=1}^{n_{r}}E_{R}[m-(a_{i}-a_{i})^{2}] - n_{r}(F + \tau_{r})

We know (from the original model) that resellers will set

a_{i} = \theta + \gamma_{i}

So we have that

\pi_{R}^{1}(n_{r}) = n_{r}(v - f)(m - V_{\delta}) - n_{r}(F + \tau_{r})

From the previous analysis, we have that

\tau_{r} = (v - f)(m - V_{\delta}) - F

So the supplier makes (v - f)(m - V_{\delta}) - F profit and each reseller makes zero profit.

Marketplace

We now look at the case in which both intermediaries choose marketplace-mode.

From the analysis above, it also follows that if either marketplace z \in \{1,2\} charges suppliers any P_{z} > P_{-z}, suppliers will not join the marketplace. So in Bertrand pricing, we have p = 0. Here, the supplier makes (v - f)(m - V_{\gamma}) - F and each marketplace makes zero profit.

Marketplace and reseller

What if one intermediary chooses marketplace-mode and the other chooses reseller-mode?

Suppliers will choose to join the intermediary that offers them higher profit. The reseller can offer any \tau up to (v - f)(m - V_{\delta}) - F and the marketplace can offer the supplier any profit up to (v - f)(m - V_{\gamma}) - F. If (v - f)(m - V_{\delta}) - F > (v - f)(m - V_{\gamma}) - F, i.e. V_{\delta} < V_{\gamma}, basically we have something like Bertrand competition with asymmetric costs.

The reseller will offer \tau_{r} = (v-f)(m - V_{\gamma}).

The marketplace will randomise P over [(v-f)(m - V_{\gamma}), (v-f)(m - V_{\gamma}) + \epsilon] (offering anything within this interval will give the marketplace zero profit).

At this point, the reseller makes positive profit either \frac{N}{2}(v-f)(V_{\gamma} - V_{\delta}) - \frac{N}{2}F or N(v-f)(V_{\gamma} - V_{\delta}) - NF.

  • If (v - f)(m - V_{\gamma}) - F > (v - f)(m - V_{\delta}) - F, i.e. V_{\gamma} < V_{\delta}, the marketplace will offer \tau_{r} = (v-f)(m - V_{\delta}).
  • The reseller will randomise \tau_{r} over [(v-f)(m - V_{\delta}), (v-f)(m - V_{\delta}) + \epsilon (offering anything over this interval will give the reseller zero profit).
  • We assume that in both cases, the intermediary that is offering lower profit always charges the minimum price.
  • At this point, the marketplace makes positive profit either \frac{N}{2}(v-f)(V_{\delta} - V_{\gamma}) - \frac{N}{2}F or N(v-f)(V_{\delta} - V_{\gamma}) - NF.

If V_{\delta} < V_{\gamma}, the Nash equilibrium is (Reseller-mode,Reseller-mode).

Marketplace-modeReseller-mode
Marketplace-mode0,0 0,>0 (see above)
Reseller-mode>0 (see above),00,0

If V_{\gamma} < V_{\delta}, the Nash equilibrium is (Marketplace-mode, Marketplace-mode).

Marketplace-modeReseller-mode
Marketplace-mode0,0>0 (see above),0
Reseller-mode0, >0 (see above)0,0

These are the results derived in the original model as well.

Sequential entry

Say Intermediary 1 enters the market first. We assume that suppliers can costlessly switch between Intermediary 1 and Intermediary 2. We also assume that Intermediary 1 has to commit to the same price throughout period 1 and period 2. There are m - (a_{i} - a_{i})^{2} buyers for each product in both period 1 and period 2. We represent the discount factor Intermediary 1 applies to profits in period 2 with \eta (I know the usual notation is \delta but it’s already representative of the private information known to the supplier).

Intermediary 1 enters in reseller mode

The condition on \tau_{1} is that

N[(v-f)(m - V_{\delta}) - F - \tau_{1}] + \eta n_{1}[(v - f)(m-V_{\delta}) - F - \tau_{1}] \geq N[(v-f)(m - V_{\delta}) - F]

i.e. the lower bound on \tau_{1} is given by

\tau_{1} \geq \frac{\eta n_{1}(v-f)(m-V_{\delta}) - F}{N - \eta n_{1}u}

Intuitively, this is because if Intermediary 1 charges any \tau_{1} < \frac{\eta n_{1}(v-f)(m-V_{\delta}) - F}{N - \eta n}, it gets lower profit than it would have if it had charged \tau_{1} = 0 and gotten N[(v-f)(m - V_{\delta}) - F] in period 1, and nothing in period 2 (from Intermediary 2 undercutting and offering suppliers a higher surplus than 0 in period 2).

Intermediary 2 enters in period 2. Note that if Intermediary 1 has entered in reseller-mode, it indicates that entering in reseller-mode is the dominant strategy (V_{\delta} < V_{\gamma}),, and Intermediary 2 will never enter in marketplace-mode.

If Intermediary 2 offers \tau_{2} higher than \tau_{1}, it gets the full market in period 2, and its expected profit is

N[(v-f)(m - V_{\delta}) - F - \tau_{2}]

Intermediary 1’s profit is

N[(v-f)(m - V_{\delta}) - F - \tau_{1}]

If Intermediary 2 offers suppliers the same profit as Intermediary 1 does, it gets half the market in period 2

Intermediary 2’s profit is

\frac{N}{2}[(v-f)(m - V_{\delta}) - F - \tau_{2}]

And Intermediary 2’s profit is

N[(v-f)(m - V_{\delta}) - F - \tau_{1}] + \eta \frac{N}{2}[(v-f)(m - V_{\delta}) - F - \tau_{1}]

If Intermediary 2 offers suppliers a higher profit than Intermediary 1 does, it gets no market share in period 2. Therefore,

Intermediary 2’s profit is 0 and Intermediary 1’s profit is

(1+\eta)N[(v-f)(m - V_{\delta}) - F - \tau_{1}]

So we know that Intermediary 1, in seeking to maximize profits, must charge \tau_{2} \leq \tau_{1}. Within the interval \tau_{1} \geq \frac{\eta n_{1}(v-f)(m-V_{\delta}) - F}{N - \eta n_{1}}, Intermediary 1 makes more profit from lowering its \tau_{1} to match \tau_{2}. Therefore, it will lower its price all the way down to \frac{\eta n_{1}(v-f)(m-V_{\delta}) - F}{N - \eta n_{1}}. We know that n_{1} can only take the values 0, \frac{1}{2} or 1. However, since Intermediary 2 will always set \tau_{2} \leq \tau_{1} down to \tau_{2} = 0, where it makes zero profit as well, we have that n_{1} can only take the values of 0 or \frac{1}{2}. So in equilibrium, we have that

\tau_{2} = \frac{\eta \frac{N}{2}(v-f)(m-V_{\delta}) - F}{N - \eta \frac{N}{2}}

and

Intermediary 1 randomizes over setting

\tau_{1} = \frac{\eta \frac{N}{2}(v-f)(m-V_{\delta}) - F}{N - \eta \frac{N}{2}}

or

\tau_{1} = 0

At this point, suppliers make either (1+\eta)\frac{\eta \frac{N}{2}(v-f)(m-V_{\delta}) - F}{N - \eta \frac{N}{2}} or \eta\frac{\eta \frac{N}{2}(v-f)(m-V_{\delta}) - F}{N - \eta \frac{N}{2}} in profit.

Intermediary 1 makes profit

\pi_{R}^{2} = \frac{N}{2}[(v-f)(m - V_{\delta}) - F - \frac{\eta \frac{N}{2}(v-f)(m-V_{\delta}) - F}{N - \eta \frac{N}{2}}]

or

\pi_{M}^{2} = N[(v-f)(m - V_{\gamma}) - F - \frac{\eta \frac{N}{2}(v-f)(m-V_{\gamma}) - F}{N - \eta \frac{N}{2}}]

Intermediary 2 makes profit

\pi_{R}^{1} = N[(v-f)(m - V_{\delta}) - F]

If, instead, Intermediary 1 enters in marketplace-mode (because V_{\delta} > V_{\gamma} then it is quite easy to observe that

Suppliers make either (1+\eta)\frac{\eta \frac{N}{2}(v-f)(m-V_{\gamma}) - F}{N - \eta \frac{N}{2}} or \eta\frac{\eta \frac{N}{2}(v-f)(m-V_{\gamma}) - F}{N - \eta \frac{N}{2}} in profit.

Intermediary 2 makes profit

\pi_{M}^{2} = \frac{N}{2}[(v-f)(m - V_{\gamma}) - F - \frac{\eta \frac{N}{2}(v-f)(m-V_{\gamma}) - F}{N - \eta \frac{N}{2}}]

or

\pi_{M}^{2} = N[(v-f)(m - V_{\gamma}) - F - \frac{\eta \frac{N}{2}(v-f)(m-V_{\gamma}) - F}{N - \eta \frac{N}{2}}]

Intermediary 1 makes profit

\pi_{M}^{1} = N[(v-f)(m - V_{\gamma}) - F]

Basically, we’re just substituting V_{\gamma} for V_{\delta}. With sequential entry in a two-period model, it is easy to observe that both Intermediary 1 and Intermediary 2 make higher profits.

Multihoming

Assume that suppliers will multihome whenever the increase in their profits is non-negative. Instead of a case in which all products have identical buyer demand 2[m - (a_{i} - a_{i}^{*})^{2}], where each intermediary has its own captive potential buyers m - |a_{i} - a_{i}^{*}| ,we have [m - (a_{i} - a_{i}^{*})^{2}] (for simplicity, let m = \gamma_{i}^{2} + \delta_{i}^{2}) split between the two suppliers if a_{i} is the same, but otherwise with buyers choosing which intermediary to purchase from.

From this, we know that if Intermediary 1 enters as a reseller and sets a_{i} = \theta + \gamma_{i}, it gets m - \delta_{i}^{2} buyers. There are \delta_{i}^{2} buyers left over for each product i.

The intermediary, entering in Stage 2, can choose to enter as a reseller or as a marketplace.

If it enters as a reseller, both intermediaries will pay the supplier i the minimum they can = its marginal cost = 0 (recall that if the supplier is multihoming whenever the increase in its profits is non-negative, the two suppliers are no longer in competition). At this point, each reseller will sell product i to half the buyers who buy at a_{i} = \theta_{i} + \gamma_{i}. We have the following profit expressions.

\pi_{R}^{1} = N[(v-f)(m - V_{\delta}) - F + \eta N[(v-f)\frac{1}{2}(m - V_{\delta}) - F]

\pi_{R}^{2} = N[(v-f)\frac{1}{2}(m - V_{\delta}) - F)]

If Intermediary 2 chooses to enter as a marketplace,

Supplier i‘s optimal choice is to set a_{i} = \theta_{i} + \delta_{i}, and the buyer demand for its product here is m - \gamma_{i}^{2}.

What this tells us is that if the supplier i multihomes when Intermediary 1 is a reseller and Intermediary 2 is a marketplace, it gets buyer demand m - \delta^{2} + m - \gamma^{2}. Since we know m = \delta_{i}^{2} + \gamma_{i}^{2}, this simplifies to buyer demand = m, i.e. supplier i is able to sell to the entire market. At this point, Intermediary 1 (the reseller) pays \tau_{1} = 0 and its profit is

\pi_{R}^{1} = (1+\eta)N[(v-f)(m - V_{\delta}) - F]

Intermediary 2 (the marketplace) is able to charge (1+\eta)N[(v-f)(m - \gamma_{i}^{2}) - F, i.e. its profit is

\pi_{M}^{2} = N[(v-f)(m - V_{\gamma}) - F]

Therefore, even if V_{\delta} < V_{\gamma}, under the conditions laid out above, if \pi_{M}^{1} = V_{\delta} > \frac{1}{2}\gamma, Intermediary 2 has an incentive to enter in marketplace-mode when Intermediary 1 is in reseller mode. The converse applies for the case in which Intermediary 1 has an incentive to set up in marketplace-mode (V_{\gamma} < V_{\delta}).

You may ask whether these results may be inferred from other papers. The answer is yes. But the question you should be asking is whether I have too much time on my hands, and, well:

COVID-19: Migrant worker-related searches in Singapore



Popular Youtuber Preetipls’ campaign for donations to migrant workers in Singapore

After I posted about US search trends in sinophobic terms after the COVID-19 outbreak, a friend told me it would be interesting to look at trends in Singapore. As the COVID-19 pandemic disproportionately affects migrant workers in Singapore, many have taken to social media platforms to encourage donations to migrant worker NGOs, raising awareness of migrant workers’ living conditions and quarantine environment. Those keeping up to date on COVID-19 statistics will also know that infection rates among migrant workers are significantly higher than those among locals, and perhaps be curious about migrant workers.

I therefore visualized migrant worker-related search trends in Singapore.

Data from Google trends

Weekly data was collected on the past year in searches, from the week in 21 April 2019 to the week in 12 April 2020 (1st to 52nd week on the x-axis in the graph). I visualized some data about search results on migrant worker issues and migrant worker NGOs in Singapore, with search terms binned into broad topics (categorization described below).

TopicSearch terms
Migrant“construction worker”; “migrant workers”, “migrant”
Dormitory“dormitories”; “dormitory”
Migrant Workers’ Centre (MWC)“mwc”; “mwc singapore”; “migrant workers centre”
Transient Workers Count Too (TWC2)“twc2”; “transient”; “transient workers”

As shown from the data above, there has been increased interest in migrant workers in Singapore, their living conditions, and NGOs that promote their welfare. Has this interest translated into action to help migrant workers? There are currently various COVID-19 specific campaigns set up on Giving.sg to raise funds to provide food and other essentials to migrant workers in isolation facilities. I tried to compare differences in amount donated in COVID-19 related migrant worker welfare campaigns and other migrant worker welfare campaigns, but it’s difficult to say what the difference is at this time, considering that many of the COVID-19 campaigns have not ended (also, a quick note: Giving.sg does not show campaigns that have ended, so it’s difficult to collect data – perhaps a data request is in order).

Hope this was interesting to you! If you’re Singaporean and would like to show some Solidarity through donating your S$600 cash payout to the ongoing campaigns raising funds for migrant workers on Giving.sg, here’s a link to the campaigns.

Hate in the time of corona

There has been an increase in the number of hate crimes against those of East Asian descent since the pandemic hit Western countries (you can read about it here, here, here, here, and here). Living as part of the Singapore Chinese majority, I obviously have no personal encounters with COVID-19 related racism to add to the conversation (for clarity, since a friend pointed it out, what I mean is that people have not been racist towards ME). I’ll just say that I’m now looking at postponing graduate study because I’m afraid to live overseas in the near future, and I’m worried about the safety of my friends who are still studying abroad. I was in California just last summer, and Italy, France and Greece last December. It’s fortunate that I traveled so much last year, because I don’t know when I’ll be able to visit all these places again.

What I can contribute is what I’ll post today: a visualization of the frequency at which sinophobic search terms have been keyed into Google recently (very rough, hastily thrown together, but I just wanted to quickly share it). Graph data is taken from weekly Google Trends search data and limited to searches in the US. It goes back to one year ago (i.e. it spans the week of 21 April 2019 to the week starting on 12 April 2020); the x-axis represents Week 1, Week 2, …, Week 52. We see an explosion in the number of searches in the topics I looked at in the 40th week (the week of 19 January 2020).

Data from Google trends, visualized in R using the amazing and beautiful Wed Anderson palette library (Darjeeling1)

For each topic, I collect data on a number of related popular search terms. It should be noted that the data is incomplete, because there may be a variety of search terms I have not tried since they didn’t pop up on the list of relevant keywords Google Trends recommended. Actual numbers may be significantly higher. I have a table of the search terms on which I collected Google frequency data on below, and the topic they fall under.

TopicSearch terms
Chinese virus“chinese virus 2020”; “chinese virus us”; “chinese virus”; “china virus”
Dirty“dirty chinese”; “china dirty”
Eating bats“chinese people eat bats”; “why do chinese eat bats”; “do chinese people eat bats”; “why chinese eat bats”; “why do chinese eat bats” “do the chinese eat bats”; “did chinese eat bats”; “why do chinese people eat bats”; “why do the chinese eat bats”; “does chinese eat bats”; “do chinese eat bats and snakes”
Eating dogs“chinese eat dogs”; “why do chinese eat dogs”; “in china they eat dogs”; “do they eat dogs in china”; “do people in china eat dogs”
Pejoratives“chink virus”; “chink”; “yellow virus”

As you can observe from the graph above, why Chinese people eat bats is something people in the US are very curious about. I tried the search term “why do chinese eat bats” as well and the first five search results (outside of a Wikipedia page about bats as food) are below.

Article

Article

Article

Article

Article

Out of the five articles, three perpetuate that the consumption of wildlife is prevalent among Chinese people (including diasporas). I couldn’t find any sales data for bats as food in China, or in Chinatowns across the world, so I won’t try to claim that this prevalence is a myth, but it certainly is an allegation that requires more empirical validation.

Another observation I made is that while Statesman News Network disputed the attribution of blame to Chinese culture, this showed up in the search results snippet text, which I think is rather misleading about the content of the actual article.

Google search results

Other observations from the data: While COVID-19 did not originate in dogs, it appears that people living in the US are once again interested in the Chinese tradition of eating dogs! Donald Trump’s term for the virus has also caught on, with “Chinese virus” becoming a popular search term. People also increasingly think that the Chinese are dirty, and are retreating to the territory of racial slurs.

Of course, I know not everyone in the US is searching these terms. In fact, it is impossible to determine what people’s intentions are when they enter these search terms into Google, so I won’t even say that all of those who are making these searches are sinophobes. But what people search for tells us a lot about the current climate, while official COVID-19 related hate crime statistics have not been consolidated (and are likely to be underreported anyway). I’ll end on this gloomy note.

Online seminars

Caltech Econ Theory on Twitch

I’ve been noting dates and planning to attend numerous online Economics seminars. They’re hosted by different organizations, so I’ve compiled a list (available below, with links). This is maybe one of the better things to have come out of this virus.

On a side note, I’ve penciled in some time to study for my Industrial Organization and Behavioral Public Policy finals, and probably will be posting notes on the papers covered in the syllabus or related papers so I can revise and prepare. Excited for my exams to end so I can focus entirely on reading more new papers!

(Update: I’ve been told that the formatting for this table is a bit wonky on mobile platforms. It’s best viewed on desktop!)

DateTime (UTC+8)PaperHost
14 April23:30Elizabeth Tipton (Northwestern University). “Will this Intervention Work in this Population? Designing Randomized Trials for Generalization”.Online Causal Inference Seminar
15 April03:00Brad Shapiro (Chicago Booth). “Generalizable and Robust TV Advertising Effects” (with Gunter Hitsch and Anna Tuchman).(IO)2 Seminar
15 April22:00John Van Reenen (MIT). “Do tax incentives increase firm innovation?  An RD Design for R&D” (joint with Antoine Dechezleprêtre, Elias Einiö, Ralf Martin, and Kieu-Trang Nguyen).Virtual IO Seminar Series
16 April03:00Scott Kominers (Harvard University). “Redistribution through Markets” (with Piotr Dworczak and Mohammad Akbarpour).Caltech Econ Theory
16 April21:00Nageeb Ali (Penn State University) “Voluntary Disclosure and Personalized Pricing”.MaCCI EPoS Virtual IO Seminar
18 April02:00Benny Moldovanu (University of Bonn). “Extreme Points and Majorization: Economic Applications” (with Andreas Kleiner and Philipp Strack).Penntheon
22 April03:00Daniel Ershov (TSE), “Consumer Product Discovery Costs, Entry, Quality and Congestion in Online Markets”.(IO)2 Seminar
23 April21:00Carlo Reggiani (University of Manchester). “Exclusive Data, Price Manipulation and Market Leadership”.MaCCI EPoS Virtual IO Seminar
29 April03:00Adam Dearing (Ohio State), “Efficient and Convergent Sequential Pseudo-Likelihood Estimation of Dynamic Discrete Games” (with Jason Blevins).(IO)2 Seminar
29 April22:00Leslie Marx (Fuqua School of Business). “Countervailing Power” (with Simon Loertscher).Virtual IO Seminar Series
30 April03:00Cesar Martinelli (George Mason University). “Assignment Markets: Theory and Experiments” (with Arthur Dolgopolov, Daniel Houser and Thomas Stratmann).Caltech Econ Theory
2 May00:00John Asker (UCLA), “A Computational Framework for Analyzing Dynamic Auctions: The Market Impact of Information Sharing” (with Chaim Fershtman, Jihye Jeon, and Ariel Pakes).(IO)2 Seminar
6 May03:00Jason Abaluck (Yale). “A Method to Estimate Discrete Choice Models that is Robust to Consumer Search” (with Giovanni Compiani).(IO)2 Seminar
6 May22:00Lorenzo Magnolfi (Wisconsin). “The Competitive Conduct of Consumer Cooperatives” (with Marco Duarte and Camilla Roncoroni).Virtual IO Seminar Series
7 May03:00Mira Frick (Yale University). “Stability and Robustness in Misspecified Learning Models” (with Ryota Iijima and Yuhta Ishii).Caltech Econ Theory
7 May21:00Michael Kummer (University of East Anglia). “Competition and Privacy in Online Markets: Evidence from the Mobile App Industry”.MaCCI EPoS Virtual IO Seminar
12 May03:00Alex MacKay (Harvard University). “Competition in Pricing Algorithms” (with Zach Brown).(IO)2 Seminar
14 May21:00Tobias Salz (MIT). “The Economic Consequences of Data Privacy Regulation: Empirical Evidence from GDPR”.MaCCI EPoS Virtual IO Seminar
16 May00:00Elisabeth Honka (UCLA). “Consumer Search in the U.S. Auto Industry: The Value of Dealership Visits” (with Dan Yavorsky).(IO)2 Seminar
20 May03:00Steve Berry (Yale). “Jobs as Differentiated Products”.(IO)2 Seminar
21 May21:00Leonardo Madio (TSE). “Data Brokers Co-Opetition”.MaCCI EPoS Virtual IO Seminar
23 May00:00Matthijs Wildenbeest (Indiana University). “Agency Pricing and Bargaining: Evidence from the E-Book Market” (with Babur De los Santos and Daniel O’Brien).(IO)2 Seminar
29 May00:00Ali Yurukoglu (Stanford GSB). “Quantitative Analysis of Multi-Party Tariff Negotiations” (with Kyle Bagwell and Robert Staiger).(IO)2 Seminar

About this blog

Just a picture of my workspace

Hi! I’m a final year Honours student majoring in Economics.

I am taking some time off pursuing grad school to work, and want to keep myself engaged in the study of Economics. I have therefore decided to start this blog to keep track of the Economics papers that I’ve read (both to motivate myself to give them closer reading, as well as to have a record of them so I can come back to read my own summaries later).

Since you’re already reading this, it would be of great help if you could comment whenever you find my analysis to be incorrect or incomplete. All papers are filed under the categories that I’m interested in. I’ll also be posting some questions that I struggle with as I work through some standard Economics textbooks!

Thanks for reading! Hopefully I’ll have my first paper post up soon!