# The Ruble Collapse in an Online Marketplace:
Some Lessons for Market Designers

John J. Horton111Thanks to Bo Cowgill, Josh Angrist, David Autor, Alan Benson,
Richard Zeckhauser, Alex Tabarrok, Jonathan Meer, Apostolos Filippas, Simon
Jäger, Paul Oyer, Bob Hall, Christina Patterson, and Judith Chevalier for
helpful comments. Latest draft available at http://www.john-joseph-
horton.com/papers/russia_shock.pdf This research was declared exempt by the
MIT COUHES, Project E-2154. .
MIT Sloan & NBER

###### Abstract

The sharp devaluation of the ruble in 2014 increased the real returns to
Russians from working in a global online labor marketplace, as contracts in
this market are dollar-denominated. Russians clearly noticed the opportunity,
with Russian hours-worked increasing substantially, primarily on the extensive
margin—incumbent Russians already active were fairly inelastic. Contrary to
the predictions of bargaining models, there was little to no pass-through of
the ruble price changes in to wages. There was also no evidence of a demand-
side response, with buyers not posting more “Russian friendly” jobs,
suggesting limited cross-side externalities. The key findings—a high extensive
margin elasticity but low intensive margin elasticity; little pass-through
into wages; and little evidence of a cross-side externality—have implications
for market designers with respect to pricing and supply acquisition.

##  1 Introduction

After a marketplace business has overcome the “cold start” or “chicken-and-
egg” problem (Rochet and Tirole, 2006; Caillaud and Jullien, 2003), it has to
decide much to invest in acquiring more buyers or sellers. This investment can
mean subsidizing participation for one side or the other, lowering the
platform’s take rate, building features that are attractive to market
participants, or spending on marketing. How should a platform think about
these decisions? Will these expenditures be effective at attracting more
participants? What will the additional participants do to prices and
quantities in the marketplace—and hence platform revenue under common price
structures? How much will acquiring one side stimulate usage from the other
side?

In this paper, I report the results of a natural experiment that approximated
a massive subsidy to a select group of sellers in an online labor market
(Horton, 2010). This “subsidy” was not a platform decision, but rather was
caused by the collapse of the Russian ruble, which nearly doubled the real
returns to Russians from working in the marketplace, as contracts in the
marketplace are dollar-denominated. This ruble collapse was not due to a
general appreciation of the US dollar, but rather had Russia-specific causes,
namely the imposition of sanctions following Russia’s annexation of Crimea and
military intervention in Ukraine; a fall in oil prices is also thought to have
played a role. As there are few employers from Russia in the marketplace, the
ruble collapse was not a demand shock.222 I use the terms “worker” and
“employer” to be consistent with the labor literature, and not as a commentary
on the relationships created on the platform.

To estimate effects of the ruble collapse, I primarily use a difference-in-
differences design, where Russians are the treated group and non-Russians are
the control group. Of course, as the market is international, Russians and
non-Russians compete, creating the possibility of spill-overs. To address this
concern, I exploit the fact that individuals from a particular country tend to
specialize in the same areas of work, and so countries and workers varied in
how “exposed” they were to the Russian influx. As it is, the degree of
competition seems to matter little, which is perhaps unsurprising given that
Russians were relatively small players in the market.

I analyze the Russian response at two levels: the “macro” country level and
the “micro” individual level, using a sample of workers active pre-collapse.
At the macro level, a 10% rise in the value of the US dollar measured in
rubles led to a 14% increase in the number of applications sent per week by
Russians. The number of Russians hired increase by a similar amount. These
macro estimates are about 5 times larger than the equivalent micro elasticity
estimates. The reason for the gap is that the macro estimates capture a
substantial extensive margin response.333 See Chetty et al. (2011) for the
macro/micro reconciliation in conventional settings.

On the intensive margin, the individual elasticity of hours-worked is 0.16,
though there is substantial individual heterogeneity in labor supply. I find
that the individual hours-worked and application elasticities are similar in
magnitude. This similarity implies Russian workers scale up their search
intensity and hours-worked in the same proportion, with no apparent loss of
job search efficiency.

Russian wages did not fall by an economically significant amount. In the micro
panel of incumbent Russians, the elasticity of the average wage to the US
dollar priced in rubles is just -0.03, implying little to no pass-through.
Although there is a slight decline in wages in the macro panel, this can be
explained by changes in composition.

The paper provides a case study of how workers respond to changes in real
returns on a marketplace, and offers insight how the market “works.” But it
also has several practical implications relevant to market designers.

All marketplace designers worry about attracting buyers and sellers to their
platform. The ruble episode shows that the supply side of the market is
collectively quite elastic with respect to the financial pay-off obtained on
the platform. Although it is perhaps no surprise that greater earning
possibilities would influence sellers, if we thought horizontal preferences
were very important in sellers choosing a marketplace (Hossain et al., 2011),
then effects could have been small. They were not. Furthermore, with labor, it
also not always widely accepted that labor supply curves are even upward
sloping, due to target earning (Camerer et al., 1997) or income effects.
Neither concern seems to have been borne out in practice. In short, money
matters.

Also relevant to would-be market designers is the source of additional supply.
Most of the supply increase was on the extensive margin, rather than the
intensive margin—Russians already active on the platform only expanded their
hours-worked slightly. This has implications for where more supply can
(cheaply) come from—namely from new suppliers rather than hoping existing
suppliers can increase output. For some marketplaces, we might expect a
different result—particularly when sellers are firms—but when sellers in a
marketplace are individual workers providing their own labor, they are
inherently supply-constrained (Horton, 2019). In this case, increases from
already active sellers are likely to be expensive.

For platforms that tax transactions, a concern with adding supply is that is
might lower prices and thus potentially revenue, depending on changes in
marketplace quantities. This concern was not borne out—added supply did not
discernibly lower wages, either at the market level or for the subsidized
sellers. The likely reason wages did not fall is that Russians are apparently
good substitute for other workers on the platform. As such, the increase in
Russians was not a large change in supply, properly defined. Although the
fraction of Russians increased dramatically, they are not a large component of
the market as a whole, and so it was not a market-moving shock: 1.4% of hired
workers at the start of the period I examine were Russian, compared to 2.4% at
the end.

One might have thought Russians would have lowered their wage asks in order to
obtain more work, consistent with bargaining models of price formation, but
this was not the case. The market was better characterized by the competetive
market model in which workers and employers are price-takers.

The classic way to think of price-setting in a two-sided marketplace, is that
the two sides—buyers and sellers—exert different cross-side externalities,
which in turn should affect the price the platform wants to charge, as well as
the cost of servicing that side and that side’s elasticity of demand
(Armstrong, 2006). In the case of the ruble collapse, there is no evidence of
a demand response—there was no discernible demand increase in “Russian
compatible” job posts. This might suggest cross-side externalities might be
weak in an established market, but recall the lack of a price effect. As there
was no pass-through of lower wages, there was no obvious additional benefit
accruing to buyers from more Russians. With a more massive shock that moved
on-platform prices, this would perhaps not be the case.444 Not all cross-side
externalities show up in prices—Hall et al. (2017) shows that when the number
of drivers in a city increases, wait-times falls.

If the market were different and Russians offered some kind of benefit to
would-be employers that was distinctive and differentiated, then perhaps we
would have observed some demand response. However, it is not clear that even
for large “shocks” we would see large changes if the supply curve is _de
facto_ horizontal. In a fairly commodified market with low barriers to entry
and lots of atomistic sellers, marketplace supply could be inherently highly
elastic. Although this paper only adds some evidence for this point of highly
elastic supply curves, results from several other online marketplaces all
point towards highly elastic supply.

In terms of related literature, there are several papers that have a plausibly
exogenous increase in sellers due to policy changes or market mergers. For
example, Reshef (2020) examines the role of additional sellers on a
marketplace, finding that the market expansion effects were generally positive
for incumbents, but not for lower quality incumbents who experience a business
stealing effect. Farronato et al. (2020) examines the effects of a platform
acquisition, finding that although some incumbent users benefited. There was
enough platform differentiation that users of the acquired platform
experienced worse outcomes. There are also papers looking at entirely new kind
of competitor, as in Seamans and Zhu (2014), or a new kind of product offering
in an ecosystem, as in Li and Agarwal (2017). There are no papers I am aware
of that have a distinct group of sellers entering who are not necessarily
strongly differentiated form incumbents.

The rest of the paper is organized as follow. I first discuss the empirical
context in Section 2. Results at the “macro” country-level are presented in
Section 3. Results at the “micro” individual level are presented in Section 4.
In Section 5, I show how little effect the Russian influx had on the demand
side of the market. Section 6 discusses the results and Section 7 concludes.

##  2 Empirical context

The empirical context is a large online labor market (Horton, 2010; Horton et
al., 2017; Agrawal et al., 2015). In these markets, firms hire workers to
perform tasks that can be done remotely, such as computer programming, graphic
design, data entry, research and writing. Markets differ in their scope and
focus, but common services provided by the platforms include maintaining job
listings, hosting user profile pages, arbitrating disputes, certifying worker
skills and maintaining feedback systems.

Although the context is online, many of the same market features found in
conventional labor markets also exist in this market. Employers post job
descriptions which workers can search for and apply to. Employers can assess
candidates through interviews and negotiate over wages. In online labor
markets more generally, there are still substantial search frictions (Horton,
2019, 2017a), barriers to entry (Stanton and Thomas, 2016; Pallais, 2014),
information asymmetries (Benson et al., 2019), and even compelling evidence of
employer monopsony power (Dube et al., 2020).

Several marketplace features are worth noting. The platform can observe nearly
all job search behavior: it observes which job openings job-seekers apply to,
and at what terms; it also observes whether the employer interviewed the
applicant and ultimately hired them, forming a contract.555 Observing job
search effort and intensity is fairly rare, though there are exceptions.
Mukoyama et al. (2018) examine job search intensity over the business cycle
using the ATUS. Typically, job search intensity is only seen indirectly, such
as through reduced unemployment duration, as in Woodbury and Spiegelman (1987)
in response to a time-limited bonus for finding a new job. Adams et al. (2018)
does find some increases in search intensity among those already looking for
work in response to minimum wage increases.  If a contract is formed, the
platform observes how many hours are worked, and at what wage. Although some
workers and firms do haggle, most employers seem to take worker wage bids as
take-it-or-leave-it offers (Barach and Horton, Forthcoming). There is no
collective bargaining or even channels for workers to confer with each
other—factors that might otherwise lead to sticky wages, as in Saez et al.
(2019).

To work on hourly contracts, workers use a kind of digital punch clock, and so
hours are measured essentially without error. Hourly contracts are the focus
on this market, though fixed price contracts are allowed. For this paper, I
exclusively focus on hourly contracts. For hourly contracts, the hired worker
is free to bill hours as they see fit, though employers can cap hours-per-week
_ex ante_ and can file a dispute if they object to the quality of the work.
Disputes are fairly rare, as employers can observe worker effort in real-time
through platform-mandated screen capture software and offer guidance or
criticism.

In addition to market wages and wage bids, the platform can also observe a
worker’s “profile wage” or the wage they list on their worker profiles, which
is a kind of online resume. Employers view these profiles when deciding whom
to recruit, and so it tends to be close to what a worker is actually willing
to accept.

In terms of market composition, most employers are from the US and the
remainder of the demand side are typically from other high-income countries.
The supply side is far more diverse. The platform knows which country each
worker is from, and all market participants can observe the country of every
other market participant (who also know this). Regardless of which countries
the employer and worker are from, all contracts are denominated in US dollars.

Earnings from contracts are kept in account for the worker. Workers are free
to keep their earnings with the platform, but the account is not interest
bearing. This gives workers an incentive to frequently transfer from the
platform to their home country bank—at least for workers from countries where
transfer fees are _ad valorem_ and with a minimum fixed component. The
platform transfers earned money to the worker’s bank when the worker requests
it. The worker’s bank then does the currency conversion. My understanding is
that at the time of the ruble collapse, Russian workers faced small
transactions costs in transferring their earnings from the platform to Russian
banks.

Prior to the Russian financial crisis, the exchange rate between the ruble and
the dollar had been fairly stable since the great recession. The ruble began
to fall in earnest in the middle of 2014. Although I will plot time price over
time, to give a sense of the fall, in the 2 week of 2014, one US dollar cost
about 33 rubles; by the 2 week of 2015, one US dollar cost about 63 rubles at
official Bank of Russia exchange rates.

Although much of the platform literature has focused on platform competition,
the Russian outside option was not necessarily of being on some other two-
sided platform, but simply working in the larger labor market or enjoying
leisure. This is likely commonplace in many online marketplaces where there is
still a substantial offline counterpoint; people still find spouses, drive
taxis, walk dogs, assemble furniture, and so on outside of a computer-mediated
platform context.

###  2.1 Conceptual issues

Before getting into the details of how Russians on the platform behaved, it is
useful to consider what can be learned from this context. The Russian
financial crisis changed many things that could potentially have had an
independent effect on Russians. These other factors could cause an under- or
over-statement of the effects of changes in the ruble price. Furthermore, we
need a framework for interpreting what we observe in terms of economic theory.

The claim that the ruble decline increased the real returns to on-platform
work deserves greater scrutiny. For earning dollars to become more attractive
to Russians, we need to assume that Russians were mostly consuming in rubles.
This seems likely. But we also need to know about the price level in Russia,
both for consumption and in the labor market. There was substantial inflation
in goods prices in Russia (Liefert et al., 2019). I have no good evidence on
Russian home country wages for my population—which is mainly software
developers—but overall, in 2014 and 2015, inflation was overcoming any nominal
wage growth and Russian real wages were likely declining, at least on
average.666 “How falling wages are squeezing Russian households,” World
Economic Forum, https://www.weforum.org/agenda/2015/08/how-falling-wages-are-
squeezing-russian-households/ Accessed Online: 3/29/2020.

It seems likely working online was becoming more desirable for Russians. How
much more desirable is hard to say, but the dramatic increase in Russian
participation makes the sign of the effect clear. Furthermore, the close
tracking of the ruble price and the Russian response (which I will show)
suggests it was primarily what Russians were responding to—or they were
responding to something that was moving more or less in lockstep with the
ruble. All these issues aside, my interest is less in precisely estimating
various labor supply elasticities but more in seeing what steps Russians took
to act on their changed preferences, and how these decisions affected
outcomes, without caring too much why those preferences changed, or by how
much.

It is important to note that even if the ruble collapse likely changed the
real returns to working, it only had the _potential_ to change market wages.
The effects on realized wages depends on how Russian workers changed their
wage bidding, if at all, and how employers reacted to those wage bids. This
point is not typically applicable in the labor supply literature, which starts
from the assumption that workers are price-takers with respect to the wage and
are costlessly adjusting hours-worked in response.777 Obviously, many workers
on conventional markets face hours restrictions (Ham and Reilly, 2002), but
the competitive perspective has workers choosing employers offering different
hours-worked bundles or even flexibility in light of their preferences, even
if it takes some time to act on those preferences (Kuhn, 2004). There is
substantial evidence that at least some workers value flexibility in hours-
worked (Chen et al., 2019), though how widespread and strong these preferences
is less clear (Mas and Pallais, 2017).

Assuming we can treat the ruble collapse as a _de facto_ wage shock, there are
obstacles if the goal were to estimate a life-cycle model of labor supply. We
do not know what Russians believed about the time courses of the ruble, nor do
we know their wealth, consumption, non-labor sources of income, or what
borrowing constraints they faced. That being said, given that the proximate
cause was geopolitical events that could in principle be resolved quickly, it
seems likely that Russians viewed the higher real wage opportunity as
unexpected and temporary. Furthermore, if Russians believed in long-run
purchase price parity, they would expect this change to be temporary, though
given how persistent gaps exist between nominal and real exchange rate
fluctuations, how “temporary” is not clear. Even if the course of the ruble
was unclear, Russians surely know about the changes, unlike, for example,
workers needing to learn about UI benefit generosity before being affected by
them (Lemieux and MacLeod, 2000). As a transient change observed more or less
immediately by Russians, the elasticity estimated in a panel is arguably
“Frisch-like” even though we lack some of the right measurements for this
claim (MaCurdy, 1981).

##  3 The “macro” view of the ruble collapse

To begin, I compare country-level outcomes over time in a panel: numbers of
active workers, the number of applications sent, the number of hires made, the
average wage bid, and so on. I compare Russia to non-Russian countries that
experienced relatively small currency fluctuations during the period covered
by the data.

###  3.1 Panel definition

I use 9 different countries as the comparison units, with Russia included.
These are the largest participating countries by worker count, excluding
Ukraine which was removed because it was also affected directly by the Russian
financial crisis.888 Some other countries were removed because of data quality
issues.  I construct a balanced weekly panel, with 61 weeks per country, for a
total of 549 observations. The panel starts on 2014-01-08 and ends on
2015-03-19, which is where my data end. This panel is constructed from all
applications sent during the period covered by the panel.

Table 1: Country-level measures in the 2 week of 2014 and 2015, during which ruble lost approximately half its value against the US dollar |  |  | Means |  |
---|---|---|---|---|---
Year | Active | Apps/Active | Wage (log) | Win Rate | Hires | Rus-Index
Philippines |  |  |  |  |  |
2014 | $10,204$ | $5.77$ | $1.49$ | $0.029$ | $1,703$ | $0.007$
2015 | $9,615$ | $5.71$ | $1.67$ | $0.026$ | $1,413$ | -
Kenya |  |  |  |  |  |
2014 | $693$ | $5.34$ | $1.82$ | $0.033$ | $121$ | $0.008$
2015 | $776$ | $5.10$ | $2.01$ | $0.027$ | $106$ | -
Bangladesh |  |  |  |  |  |
2014 | $9,748$ | $10.43$ | $1.17$ | $0.013$ | $1,354$ | $0.008$
2015 | $9,392$ | $9.45$ | $1.54$ | $0.013$ | $1,180$ | -
United States |  |  |  |  |  |
2014 | $5,241$ | $4.36$ | $2.75$ | $0.040$ | $919$ | $0.014$
2015 | $5,149$ | $4.45$ | $2.82$ | $0.039$ | $886$ | -
United Kingdom |  |  |  |  |  |
2014 | $877$ | $4.69$ | $2.64$ | $0.030$ | $124$ | $0.016$
2015 | $981$ | $4.80$ | $2.79$ | $0.041$ | $195$ | -
Pakistan |  |  |  |  |  |
2014 | $5,196$ | $7.56$ | $1.78$ | $0.020$ | $795$ | $0.018$
2015 | $5,117$ | $7.69$ | $1.98$ | $0.018$ | $706$ | -
Canada |  |  |  |  |  |
2014 | $709$ | $5.20$ | $2.65$ | $0.039$ | $144$ | $0.020$
2015 | $755$ | $5.51$ | $2.84$ | $0.038$ | $160$ | -
India |  |  |  |  |  |
2014 | $19,078$ | $8.54$ | $2.09$ | $0.011$ | $1,873$ | $0.022$
2015 | $21,454$ | $9.15$ | $2.17$ | $0.009$ | $1,771$ | -
Russia |  |  |  |  |  |
2014 | $623$ | $5.51$ | $3.00$ | $0.027$ | $92$ | -
2015 | $1,480$ | $6.01$ | $2.96$ | $0.026$ | $234$ | -

_Notes:_ Country-level comparisons of activity in an online labor market
across the second week of January, in 2014 and 2015. The column labeled “Rus-
Index” is measure of how closely workers from a country compete with Russians,
on average prior to the ruble collapse.

Table 2 presents data from the panel from two points in time: the 2 week of
2014 and the 2 week of 2015. I use the 2 week to avoid the New Year’s holiday
week. Comparing the Russia rows, we can see a more than 3x increase in the
number of active Russians, where “active” is defined as sending at least one
job application. Most other countries show no large changes. There is also an
increase in applications per active Russian worker, though other countries
also show changes of similar or greater magnitude. The Russian win rate—the
probability they are hired for a given application—does not change.

The Russian average wage bid is slightly lower at the end of the period
covered by the panel. Some countries experience large wage increases over the
time period, particularly countries that started with relatively low wages at
the start of the panel This was due to a minimum wage change on the platform,
which I will discuss at length.

The number of Russians hired per week increased substantially, going from 92
to 234, while other countries show no changes nearly as large. Some of the
lower wage countries show fairly large declines; some of the higher wage
countries show increases. This is also likely the result of the minimum wage,
as it caused a substantial degree of labor-labor substitution towards
relatively high-wage workers.

In Table 2, countries are ordered from least to most similar to Russian in
terms of application overlap i.e., workers from the Philippines tend to work
in very different categories from Russians, while workers from India compete
more directly, on average. However, a comparison of summary statistics for
India and Russia show that they are not that similar on many
dimensions—Russians have far higher wages, send fewer applications and have
nearly double the per-application win rate. In terms of application behavior
and outcomes, Russians are more similar to workers from countries like Canada
and the United States.

###  3.2 Market quantities over time

The by-week log price of one US dollar in rubles, is plotted in the top facet
of Figure 1. Each facet below the ruble facet shows the time series for
country-level outcomes. All time series are de-meaned to 0 at the start of the
panel. Russia is a heavy black line in shape, while all other countries are
light gray lines. The average for all non-Russian countries is a heavy dashed
line.

Figure 1: The ruble collapse and weekly country-level market outcomes, de-
meaned

_Notes:_ This figure shows the time series of a collection of country-level
outcomes. All series are de-meaned to the first period. The non-Russian
countries are the same as those listed in Table 2.

In the second facet from the top, labeled “Log number of active workers (at
least one application),” we can see the dramatic increase in Russians engaged
in job search on the platform. For Russia, the log number active line tracks
the log US dollar in ruble line, but with a larger magnitude. The number
active line tops out at nearly 1, despite the US dollar-to-ruble line topping
out at about 0.75, giving an eyeballed elasticity greater than 1.

The third facet from the top, labeled “Log number of applications sent” shows
a large increase in applications from Russians. The curve closely tracks the
number of active workers, not just in shape but also in magnitude. An
implication of the “active” and “applications” curves having the same
magnitude is that there is no evidence of an intensive margin effect (i.e.,
number of applications send conditional upon sending any). However, it is
important to remember that given the large extensive margin effects, an
aggregate intensive margin calculation is likely masking important composition
changes. The kinds of Russians joining the platform in response to the ruble
collapse might have relatively little capacity to take on more work, and thus
send relatively few applications compared to “incumbent” Russians already
active. To explore these issues, I will turn to an individual worker panel
analysis in Section 4.

The facet labeled “Mean log wage bid proposed by job-seekers” we can see that
the Russian line eventually dips below 0, but eventually returns to 0 at the
end of the panel. However, other countries go up: in the light gray lines, we
can see that he mean wage for some other countries shows a substantial and
sudden increase indicated at the vertical dashed line. As mentioned earlier,
the cause is the imposition of a platform-wide minimum wage of $3/hour in mid-
November 2014, which strongly increased average wages in several low income
countries (Horton, 2017b). This platform wide minimum wage will require us to
take care when doing a difference-in-differences analysis.

Prior to the minimum wage imposition, note that the Russian average is right
with the other countries, despite there already being enormous Russian
increases in market participation by then. This is suggestive evidence of
little pass-through of the ruble decline into wage bids. A gap emerges later
between Russia and other countries, but to the extent there is a real relative
Russian decline, it could be a composition effect—new entrants could be less
productive—or a market effect, with increased competition in Russian-heavy
categories.

The outcome in the bottom facet of Figure 1 is the “Log number of hires.” For
Russians, we can see hires curve closely tracks the applications curve and has
a similar magnitude. The similarity in magnitude implies no change in per
application win rates—the added Russian applications seemingly convert into
hires at the same rate as before.

The count of hires is not the same as the count of hours-worked. However, the
size of contracts won (in terms of hours-worked) did not change on average,
and so this hires elasticity is likely equivalent to the hours-worked
elasticity, though there are some conceptual issues with this claim, and labor
supply is better investigated at the micro level.999 I could plot hours-worked
here, but there is an issue of whether to allocate those hours to the date the
contract was formed, or the date the hour was actually worked. As much
contracts are short in duration, this not likely to create much of a
difference empirically.

###  3.3 Russian work specialization

The increase in the number of applications from Russians was not evenly spread
across all kinds of work. Figure 2, shows how the fraction of applicants from
Russia changed over time. The right panel, labeled “collapse year”, shows the
fraction of applications during two periods: The pre-period is April 1, 2014
to July 1st, 2014, which is indicated by a circle, and a post period from
January 1st, 2015 to March 1st, 2015, which is indicated by triangle. The left
panel, labeled “1 year before collapse year” is the same as the right panel,
but with the observations shifted back one year. In collapse year, in both the
pre- and post-periods (in the right panel) and the same comparisons pushed one
calendar year in the past (in the left panel). A circle corresponds to the
“pre” period and the triangle to the “post” period.

We can see that in certain categories and sub-categories, the collapse of the
ruble lead to large increases in the fraction of applications coming from
Russians, whereas in other areas, there was minimal impact. The plot makes it
clear that the influx of Russians was concentrated in categories of work that
Russians already focused on before the ruble collapse.

Figure 2: Changes in the fraction of applicants from Russia

_Notes:_ This figure shows fraction of applicants from Russia, per the
platform sub-category. The right panel, labeled “collapse year”, shows the
fraction of applications during two periods: The pre-period is April 1, 2014
to July 1st, 2014, which is indicated by a circle, and a post period from
January 1st, 2015 to March 1st, 2015, which is indicated by triangle. The left
panel, labeled “1 year before collapse year” is the same as the right panel,
but with the observations shifted back one year. Point sizes are scaled by the
number of total job openings in that sub-category. The sample is restricted to
categories with at least 300 total openings in both pre- and post periods for
the collapse year and the year before the collapse.

###  3.4 Accounting for competition among countries and the minimum wage issue

One limitation of the graphical approach of Figure 1 is that it does not
account for the fact that some countries are closer competitors with Russians
than others.101010 Coincidentally, this “ethnic specialization” design mirrors
the design of Borjas and Doran (2012), which relied on a previous Russian
economic crisis to estimate the effects on American mathematicians that were
in Russian-heavy sub-fields of mathematics.  A sudden influx of Russians
affected certain kinds of work—namely work that Russians tend to specialize
in—which in turn could affected certain workers—namely non-Russians that tend
to focus on the same kinds of work as Russians. This could make some countries
bad controls.

To create a measure of Russian competition exposure, I start with each
application sent by any worker from any country and compute the fraction of
their fellow applicants to that job opening that were Russian. I then average
this measure over all workers in a particular country. This is done with
applications prior to the start of the panel. In a regression, I can then
interact this measure with the ruble price, allowing close competitors to
Russians to be more affected.

I can do something similar for the minimum wage, by computing the lowest pre-
minimum wage average wage per country and interacting this with a post minimum
wage imposition indicator. This allows countries that were affected by the
minimum wage to have a different response after the minimum wage imposition.
As it turns out, the competition/overlap issue seems to be more of a problem
in theory than in practice; the minimum wage issue matters more.

###  3.5 Country-level regression evidence

To address the competition/overlap and the platform minimum wage issues, I
switch to a regression framework. My preferred specification is:

$\displaystyle y_{ct}$ $\displaystyle=\beta_{1}\left(\log
p_{t}\times\textsc{Russian}_{c}\right)+$ (1) $\displaystyle\beta_{2}\left(\log
p_{t}\times
RI_{c}\right)+\beta_{3}\left(\textsc{Post}_{t}\times\underline{w}_{c}\right)+\delta_{t}+\gamma_{c}+\epsilon_{c},$

where $y_{ct}$ is some outcome of interest, such as log applications sent per
week by workers from country $c$ during week $t$, $p_{t}$ is the price of one
US dollar in rubles at the start of week $t$, $\textsc{Russian}_{c}$ is an
indicator for whether the observed country $c$ is Russia, $RI_{c}$ is the
Russian competition index (set to 0 for Russia), $\underline{w}_{c}$ is the
lowest observed log wage for a country $c$, $\textsc{Post}_{t}$ is an
indicator for periods after the imposition of the minimum wage, and finally,
$\delta_{t}$ and $\gamma_{c}$ are week- and country-specific fixed effects. I
report four different specifications:

  * •

Russian Index Interaction and Min Wage Interaction (full Equation 1)

  * •

No Interaction ($\beta_{2}:=0$ and $\beta_{3}:=0$).

  * •

Min Wage Interaction only ($\beta_{2}:=0$).

  * •

Russian Index Interaction only ($\beta_{3}:=0$).

Figure 3 reports $\beta_{1}$ from Equation 1, with each facet reporting the
effect on a different outcome. All of the specifications are shown; my
preferred specification is in black, while the others are in gray.111111 The
full regressions in table form are in Appendix A.1.  Standard errors are
clustered at the level of the country.121212 To check for Bertrand et al.
(2004) problems, I also did block bootstrap on the country (leaving Russia in
every sample). The bootstrap standard errors were nearly identical, and so I
report the conventional standard errors with the appropriate clustering.  In
the top facet, the outcome is the log number of active workers. The point
estimates are similar regardless of the specification: the number of active
Russians increased dramatically, with a 10% associated with about a 13%
increase in active Russians.

Figure 3: Effect of ruble price changes using a country-week panel

_Notes:_ This panel reports estimates of Equation 1. These results in table
form are in Appendix A.1.

In the next facet, the outcome is the log number of applications. Again, the
precise specification does not seem to matter much, in that the effects are
similar to what we saw with the number of active Russians: 10% increase in the
value of a US dollar in rubles is associated with 14% more applications from
Russians.

Given that the effects of the ruble decline has similar percentage effects on
the number of active workers and the total number of applications, we expect
little effect on the intensive margin with respect to applications. In the
third facet from the top, the outcome is the log of the number of applications
sent divided by the number of workers active. Given the large extensive margin
effect relative to the overall increase, there is not much room for an
intensive margin effect among Russians. Indeed, the point estimates are all
close to zero. As mentioned earlier, this country-level regression is not
capturing an intensive margin effect—I will turn to this issue in the micro
analysis.

The third facet from the bottom shows the effect on win rates. All the
specifications are close to zero, implying no change. As such, we should
expect the total number of hires to be proportional to the number of
applications. In the second panel from the bottom, we see that prediction is
borne out: the number of Russian hires increases dramatically, with elasticity
point estimates similar to what we observed for applications and active
workers.

In the bottom facet, the outcome is the mean log wage bid. For this outcome,
the specification matters. Although all point estimates are negative, the two
specifications that have the minimum wage interaction term have estimates
closer to zero. As the minimum wage imposition occurred when the ruble was
near its minimum, the inclusion of minimum-wage affected countries (without
$\beta_{3}:=0$) makes Russian wages look worse when this effect is not
captured with the appropriate specification.

##  4 The “micro” view of the ruble collapse

I now take an individual worker perspective, using only workers that were
active pre-collapse, thus fixing the composition of the panel. To avoid the
complexities associated with the minimum wage policy change (and the unnatural
“exit” this created for at least some low-wage workers), I restrict the
countries to: Canada, Russia, United Kingdom and the United States. These are
higher wage countries that are also in somewhat less competition with Russian
workers.

To define the sample, I take all workers that were active—defined as sending
at least one job application—from 2013-01-01 to 2014-01-01. There are a total
of 106,879 unique workers, of whom 5,789 are Russian. Mirroring the country-
level panel, the individual level panel covers 2014-01-07 to 2015-03-17. There
are 63 one week periods, giving a panel of 6,733,377 worker-week
observations.131313 It is slightly larger that the country panel because of
slight differences in panel construction.

Given the lack of importance of the “overlap” measures and the exclusion of
minimum-wage affected countries, I use a simpler specification:

$\displaystyle y_{it}=\beta_{1}\left(\log
p_{t}\times\textsc{Russian}_{i}\right)+\delta_{t}+\gamma_{i}+\epsilon_{i},$
(2)

where $y_{it}$ is some weekly outcome for worker $i$ in week $t$,
$\textsc{Russian}_{i}$ is an indicator for the worker being from Russia,
$p_{t}$ is the average price of one US dollar in rubles during the week $t$,
$\delta_{t}$ is a week-specific fixed effect and $\gamma_{i}$ is a worker-
specific fixed effect. Standard errors are clustered at the level of the
individual worker.

The first outcome of interest is the extensive margin, or whether the worker
was active on the platform in the sense of earning some amount of money.
Unlike the other individual outcomes, this cannot be estimated using a log
specification, and the change has to be compared relative to some baseline to
construct an elasticity. Using the Russian baseline probability of working
some number of hours in the first 4 weeks of the start of the panel, the
implied extensive margin elasticity with respect to the log price of the US
dollar in rubles is 0.24.

For the rest of the outcomes, the sample is conditioned on the worker being
either working some number of hours and/or sending an application. These
estimates of $\beta_{1}$ from Equation 2 are presented in Figure 4.

Figure 4: Individual-level estimates of effects of ruble prices changes on
individual outcomes

_Notes:_ This figure reports estimates of $\hat{\beta}_{1}$ from Equation 2.
These results in table form are in Appendix A.1.

In the top facet of Figure 4, the outcome is the log number of applications
sent by a worker per week, conditional upon sending any. The outcome, in
levels, has summary statistics $\hat{\mu}=4.63$;$\hat{\sigma}=6.13$. The
implied elasticity with respect to the US dollar price in rubles is 0.21. This
is considerably smaller than the application macro estimate—which included
extensive and intensive margin effects—but it is also non-zero, whereas in the
macro panel, there was no evidence of an intensive margin increase in
applications.

In the second facet from the top, the outcome is the log number of hours-
worked by a worker per week, conditional upon any.141414 Manual hours are
possible, giving implausibly high values. I cap hours-per-week at 100,
replacing those entries with 100 exactly. This affects less than 1% of
observations in the panel.  This outcome, in levels, has summary statistics
$\hat{\mu}=18.24$;$\hat{\sigma}=20.20$. The hours-worked elasticity is 0.16,
which is similar to the application intensity elasticity.

The outcome in the third facet from the top is the log earnings by a worker
per week, conditional upon any. The estimated elasticity is also similar in
magnitude to the hours-worked and application-intensity elasticities. That the
effects on hours and earnings are similar foreshadows the effect on
wages—namely that there is little effect.

For wages, I report the effect on three outcomes: the log average wage bid,
the log average wage worked at, and the log average profile rate. Summary
statistics for the actually worked-at wages are, in levels, are
$\hat{\mu}=21.62$;$\hat{\sigma}=15.75$. There is some slight evidence of
decline in the average wage and a decline in the wage bid; the profile wage is
unchanged. The effect on the individual wage estimate is negative and
statistically significant, but fairly close to zero: the implied elasticity is
just -0.03. Note that this estimate is about 1/10th the size of the macro
panel estimate, with the difference likely explained by composition effects.

There is no evidence that workers lowered their wage bids by an economically
significant amount to obtain more hours of work. If we take the bargained wage
view, this results implies workers have nearly all the bargaining power.
However, a more sensible interpretation is that job-seekers and employers act
as price-takers and a bargaining lens is the wrong lens.

###  4.1 Intensive margin labor supply elasticity by current hours-per-week

Given the large extensive margin effects revealed in the macro panel, it is
useful to consider whether Russians lightly attached to the market pre-
collapse were particularly elastic. A stylized fact from the labor supply
literature is that workers who are less “attached” to the market tend to be
more elastic (Blundell and MaCurdy, 1999). Adapting this to our setting, it
seems likely that incumbent Russian workers would respond differently
depending on how many hours they were already working pre-collapse.

To assess whether intensive margin elasticities vary by pre-collapse hours-
worked, I compute the fraction of all weeks that workers were working at least
some number of hours in the 6 months prior to the start of the panel. Note
that this sample is defined by actually working, not just sending an
application. For weeks where they were active, I compute the average hours per
week, and I compute the fraction of weeks active. I then divide workers into
quartiles based on the their average hours per week, conditional upon working.

Figure 5, in the upper left facet, shows the average hours-worked by quartile
pre-collapse. The highest quartile is working close to full time, on average,
while the bottom quartile is only working a few hours per week. In the top
right facet, the per-week probability of being active pre-collapse is
reported. Even in the top quartile it is only 60%, but recall that in the pre-
period, workers are “joining” the platform and so some of these non-working
weeks were before they had joined.

Figure 5: Hours-worked and estimated intensive margin elasticities for workers
active before the ruble collapse

_Notes:_ This figure reports characteristics of Russian workers by decline in
terms of pre-collapse activity. The bottom panels report estimates of
$\beta_{k}$ from Equation 3.

To estimate effect of the ruble collapse, I use the specification

$\displaystyle y_{it}=\sum_{g}\beta_{g}\left(\log
p_{t}\times\textsc{Russian}_{i}\times
H_{g}\right)+\sum_{g}\delta_{gt}+\gamma_{i}+\epsilon_{i},$ (3)

which has the Russian indicator and log US dollar price in rubles interacted
with the quartile indicators. There is now a separate time effect,
$\delta_{gt}$, for each quartile, which is indexed by $g$, as well as an
individual fixed effect, $\gamma_{i}$. Standard errors are clustered at the
level of the individual worker.

For the probability of being active, in the bottom right facet of Figure 5, we
can see there is an increase across quartiles, with effects largest in the
lowest quartile. However, effects are generally imprecise.

In the bottom left facet of Figure 5, the outcome is the log hours-worked,
conditional upon any. For the intensive margin, we see fairly high point
estimates for workers that where weakly attached, but the estimates are highly
imprecise. The elasticity declines with greater pre-collapse usage, in the top
quartile, the point estimate is about 0.12.

Despite the limitations the setting imposes on the interpretability of the
elasticities, if we take them at face value, the results are broadly inline
with other Frisch labor supply estimates in Blundell and MaCurdy (1999),
though they are considerably smaller than the estimates in settings where the
changes are much more obviously temporary (Angrist et al., 2017; Fehr and
Goette, 2007). The finding of heterogeneity in elasticity by labor market
attachment is also found in conventional settings.

##  5 Evidence of a demand response?

Employers posting jobs that would appeal to Russians presumably received more
applications and—our panel evidence notwithstanding—perhaps lower wage bids.
This could cause them to post more Russian-friendly jobs or be more likely to
fill them. To see how this affected the employers, we need to switch to the
job post as our unit of analysis. A key complication is that we now need some
measure of how appealing a job opening would be to Russian applicants.

To construct a measure of Russian exposure, I first construct a historical
dataset of pre-collapse job openings, recording the fraction of those
applicants that were Russian. Then, I use the full document-term-matrix for
the skills required for that opening, as well as other characteristics set by
the employer as predictors. I then use gradient boosting (using the `xgboost`
R package) to train a linear model (Friedman et al., 2000; Chen et al., 2015).
I use the fitted model out of sample to make predictions for all job openings
posted during our panel year. The predictive model can explain about 25% of
the variance in the realized fraction of Russian applicants in the panel
sample.

Figure 6, in the right panel, I plot the mean predicted score (the fraction of
Russian applicants), by quantile (with the cut points determined by pooling
over the entire panel) and by week. We can see that the predicted score shows
no time trend in any level. This is consistent with there being no demand
shift or compositional shift in the kinds of jobs being posted. If instead
more Russian-friendly jobs were posted, these time series would rise as the
model would observe more jobs with Russian-friendly characteristics and
predict a greater fraction of Russians. This is not the case. In short, there
is no evidence employers posted more Russian-compatible job openings.

In the left panel, I plot the mean actual, realized fraction of Russian
applicants, but by predicted quantile. At first, the predicted and actual
match during the period before the ruble began to strongly depreciate.
However, as the ruble depreciates, we can see somewhat of an increase in every
quantile—even the lowest—there is an increase in the fraction of actual
applicants, though the increase is concentrated among those job openings
expected to receive the largest number of Russian applicants. Among the top
25%, the fraction of Russian applicants nearly doubles.

Figure 6: Comparison of the predicted fraction of Russian applicants (based on
job opening characteristics) versus the realized fraction

_Notes:_ This figure shows the predicted and actual fraction of Russian
applicants for job openings, with the predictions derived from the text of the
job description, skill required and job title. The different lines correspond
to the different quantiles of the predicted Russian applicant fraction
distribution.

###  5.1 Job opening level outcomes

To see how this Russian supply shock affected the openings, I discretize the
Russian compatibility score in a collection of $k$ percentile intervals, from
lowest score to highest score. I then estimate regression of the form

$\displaystyle y_{j}$
$\displaystyle=\sum_{k}\alpha_{k}\textsc{RcsInt}_{k(j)}+\sum_{k}\beta_{k}\left(\textsc{RcsInt}_{k(j)}\times\log
p_{t(j)}\right)+\delta_{t(j)}+\epsilon$ (4)

where $y_{j}$ is some outcome for job opening $j$, $\textsc{RcsInt}_{k(j)}$ is
an indicator for whether the Russian-compatibility score for job opening $j$
is in the $k$th interval, and $p_{t(j)}$ is the price of one US dollar in
rubles when opening at time $t$, which is when opening $j$ was posted, and
finally, $\delta_{t}$ is day fixed effect.

Table 3 reports the $\hat{\beta}_{k}$ of the regressions where the outcomes
are various job opening level measures. In Column (1), the outcome is the log
number of Russian applications received, plus 1. For job openings with
relatively lower Russian-compatibility scores, the effect is positive but not
large in magnitude. However, the effect is increasing in the Russian
compatibility score. At the highest band—the most Russian compatible
openings—the increase is large and the elasticity is 0.71.

Table 3: Effects of the ruble collapse on per-vacancy measures of competition,
wage bidding, hires and total wage bill

| Dependent variable:
---|---
| Log # Russian apps | Log # apps | Log # non-Russians apps
| (1) | (2) | (3)
$\log p_{t(j)}\times\textsc{RcsInt}=(1.8e-06,0.1]$ |  $-$0.002 |  $-$0.021 |  $-$0.005
| (0.171) | (0.681) | (0.666)
$\log p_{t(j)}\times\textsc{RcsInt}=(0.1,0.25]$ | 0.001 |  $-$0.010 | 0.003
| (0.171) | (0.595) | (0.584)
$\log p_{t(j)}\times\textsc{RcsInt}=(0.25,0.5]$ | 0.004 | 0.015 | 0.028
| (0.168) | (0.563) | (0.552)
$\log p_{t(j)}\times\textsc{RcsInt}=(0.5,0.75]$ | 0.102 |  $-$0.113 |  $-$0.097
| (0.162) | (0.561) | (0.550)
$\log p_{t(j)}\times\textsc{RcsInt}=(0.75,1]$ |  0.709∗∗∗ | 0.376 | 0.307
| (0.173) | (0.502) | (0.482)
Day FE | Y | Y | Y
Mean outcome (in levels) | 0.3 | 31.81 | 31.51
Observations | 303,104 | 303,104 | 303,104
R2 | 0.216 | 0.007 | 0.008
Adjusted R2 | 0.216 | 0.007 | 0.008

_Notes_ : This table reports regressions on vacancy level outcomes on changes
in the price of the USD (measured in rubles) and its interaction with
indicators how Russian-compatible that opening is, based on a model fit with
historical data. Significance indicators: $p\leq 0.05:*$, $p\leq 0.01:**$ and
$p\leq.001:***$.

How this Russian influx translated into the size of the actual applicant pool
is considered in Column (2), which reports the results of a regression where
the outcome is the log number of applicants. The results are highly imprecise
and none of the effects are statistically significant. Similarly, in Column
(3), the outcome is the log number of non-Russian applicants, plus 1 and
again, the point estimates are imprecise. In short, there is little that can
we said about the effects of Russian entrants on applicant pool sizes. As it
will be more informative to view the collection of elasticities for other
outcomes graphically, in Figure 7, we re-report the coefficients from Table 3.

Figure 7: Elasticities of per-application measures of supply with respect to
the price of the US dollar, in rubles

_Notes:_ This figure plots the $\beta_{k}$ coefficients from an estimate of
Equation 4 for several outcomes.

Now we turn to wage bidding and hiring. In Figure 8, I turn to the effects of
the ruble collapse on measures of wage bids and hiring at the job opening
level. In the leftmost panel, the outcome is the average log wage of
interviewed candidates. There is no evidence of that the ruble collapse had
any effect on the wage bid of interviewed applicants, even among the most
Russian-compatible openings. In the next facet, we look at whether the
employer hired anyone at all. The point estimates are all centered around zero
and there is no evidence that more Russian-compatible openings had higher
hiring rates.

Figure 8: Elasticities of per-application measures of wage bids and hiring
with respect to the price of the US dollar, in rubles

_Notes:_ This figure plots the $\beta_{k}$ coefficients from an estimate of
Equation 4 for several outcomes.

In the rightmost panel, the outcome is whether the employer hired a Russian.
Here we see positive effects from about the 75 percentile onward, and large
effects at the highest percentile.

In short, at the level of the applicant, even for job openings most exposed to
the Russian influx, the effects on realized wage bids and hiring was non-
existent. The only finding that seems fairly clear is that more Russians were
ultimately hired among the most Russian compatible job openings.

##  6 Discussion

One way to characterize the effects of the ruble collapse is with a simple
supply and demand model. In the left panel of Figure 9, I draw overall supply
and demand curves in the market for labor to the platform. The ruble collapse
pushes out the supply curve from $S$ to $S^{\prime}$, but given that Russians
make up a small fraction of the market, it is a small shift. This shift causes
little change to overall quantities or wages.

Figure 9: Simple supply and demand treatment of a Russian supply shock to a
global online labor market

Now imagine there is also a market for Russian labor specifically, illustrated
in the right panel of Figure 9. There is a demand curve for Russian labor, but
because a would-be buyer of Russian labor has many good substitutes—namely
workers from other countries with similar skills—the demand curve is _de
facto_ horizontal. In the market for Russian labor, the supply shift from the
ruble collapse is obviously much larger. However, because of the horizontal
demand curve, the supply shock leads to a change in quantities ($Q_{RUS}$ to
$Q^{\prime}$) rather than prices. This explains the Figure 3 result of large
increases in hires (and thus hours-worked) for Russians, but no apparent
changes in wages. This simple supply and demand explanation succinctly
characterizes all of the main results of the paper.

###  6.1 What if the supply curve to a marketplace is _de facto_ horizontal?

The left panel of Figure 9 still draws the market supply curve as upward
sloping. The explanation for why there is no evidence of a Russian decline in
wages is that the implied supply shock was small. But what if this curve was
horizontal, in which case even large supply shocks would do little to prices?

Horizontal supply curves in the long-run are found in industries where there
are 1) limited capital requirements, 2) more or less free entry and 3) there
are constant returns to scale in production. This seems like a good
description of many online marketplaces. For (1), in most online marketplaces,
capital requirements (both physical and human) are minimal or at least common
and not specialized (e.g., ride-sharing requires a car). For (2) platforms try
to make it easy to join and remove entry barriers. And for (3) when the supply
side is a large number of smaller sellers, constant returns to scale is
reasonable—doubling the number of sellers likely doubles platform output.
Furthermore, even if the industry supply curve was not horizontal, platform
competition would tend to make it _de facto_ horizontal as market participants
multi-home or switch platforms.

If horizontal supply curves are a reasonable characterization in many online
marketplaces, we should expect prices in the market to be quite stable,
regardless of demand. There is some evidence for this from multiple online
marketplace contexts. Hall et al. (2017) shows Uber driver hourly earnings
rates being largely insensitive to the platform imposed price due to a supply
response by drivers. Horton and Tambe (2020) finds no evidence that workers
specializing in Flash saw their wages decrease as the demand for Flash
plummeted, as workers quickly switched to other skills. Cullen and Farronato
(2020) finds evidence from TaskRabbit—a marketplace for in-person
services—that despite huge changes in demand, prices stayed roughly constant
and that large changes in scale had little effect on matching efficiency.
Agrawal et al. (2015) presents roughly contemporaneous times series evidence
on average platform wages, showing remarkable stability despite the platform
growing rapidly during this period.

One could imagine the lack of prices effects from these difference cases is
the result of a sloped supply curve moving in at exactly the right rate to
keep prices constant despite enormous growth—and surely some demand growth has
to be met by supply growth—but this seems less plausible than an alternative,
which is that supply curve was _de facto_ horizontal and so prices never
changed despite rapid changes in demand.

If supply to a platform is highly elastic, it means that sellers will always
get about some market-determined rate of return. If the platform say, pays
bonuses of some kind, then under free entry, these would likely get dissipated
away by competition. This, in a nutshell, is the main finding of Hall et al.
(2017). From the profit-motivated market designer’s perspective then, the main
supply strategy in an established marketplace is to try to lower on-platform
costs for sellers. For example, they could lower costs by creating feature
that ease matching frictions or, in the spirit of the two-sided markets
literature, try to bring more buyers to the platform. These benefits would, of
course, still be dissipated by competition but can be paired with prices
increases or with greater revenue from an expanded market with greater
quantities. In short, the platform strategy in a world of horizontal supply
curves is to try to lower costs for buyers and sellers and then raising
prices, leaving buyers and sellers with unchanged pay-offs but with the
platform’s revenue increased.

###  6.2 Implications of the results for the take rate

Although the much of the two-sided platform pricing literature has focused on
membership fees, many marketplace businesses use ad valorem charges rather
than membership/entrance fees. Despite the theoretical advantages of taxing
membership and pricing transactions at cost (Oi, 1971)—and their use in some
settings—many online platforms use ad valorem charges, as they scale with user
value (e.g., a person doing 2 rides on Uber would find any fixed membership
fee too high if the fee was priced to extract value from the 15 rides per week
person). Furthermore, as the platform intermediates the transaction, it
observes and can tax that transaction, which might not be the case in other
offline settings (e.g., Jin and Rysman (2015)). The other advantage of only
taxing transactions is that users do not have to know their valuation ex ante.

The ruble episode strongly suggests that lowering the platform ad valorem fees
would attract more supply. However, the economics of this being profit-
maximizing are challenging. Consider that the elasticity of Russians appears
to be about 1—a 10% increase in returns increases hours-worked from Russians
by 10%. As wages did not change, platform revenue is proportional to hours-
worked, as the platform taxes the wage bill (hours-worked times the wage).

If the platform charges say 10%, even if the increase in hours-worked was
entirely incremental (not cannibalizing any other hours from non-Russians),
and the platform lowers its fee from 10% to 9%, this is a 10% reduction in
revenue but only leads to a 1% increase in platform earnings and a 1% increase
in hours-worked. The change in revenue is percentage change in revenue is
$0.90*1.01\approx 0.91$. This of course suggests higher fees would increase
revenue. However, the long-run elasticity is not known and is surely greater
than 1, as long-run elasticities are typically larger than short-run
elasticities. It is not obviously the case than the platform can profitably
raise prices, or lower prices for that matter.

Pricing is hard and many platforms choose something simple and tend to stick
with it. While we should not assume what is is necessarily optimal, that so
many platform marketplaces choose a simple price structure suggests most
platform competition is about features and marketing rather than finely tuned
price structures and levels. This perspective might be quite sensible,
particularly for emerging platforms where even small differences in rates of
platform growth are likely to be far more important to ultimate platform
value.

##  7 Conclusion

For a would-be market designer, the ruble experience offers several lessons.
First, it is clear that workers responded strongly to the financial
opportunities created on the platform. While platforms can and do make
“horizontal” changes to their platforms to attract buyers and sellers, the
ruble experience illustrates the paramount role played by financial returns in
explaining market participation—even if lowering the take rate is not a viable
way to increase supply, given the relevant elasticities.

Second, the price effects of adding more supply could be safely ignored.
Although the influx of Russians was small relative to the total collection of
workers—and so we would not expect market wages to fall—it was conceivable
that incoming Russians would have underbid incumbents to get more work, as in
bargaining models, potentially lowering platform revenue. This was not the
case. There was not apparent downside to adding more supply with respect to
price. However, neither was there was there evidence of any positive cross-
side externality either—there was no evidence of more Russian friendly job
openings being posted. Given the lack of price effects, this is perhaps
unsurprising—though the lack of price effects was less predictable.

In terms of generalization, it is important to note that the ruble-induced
influx was not a large supply shock at the market level. Larger shocks could
have different effects. Furthermore, this was a shock that occurred in an
established marketplace well past the chicken-and-egg stage. Marginal supply
for smaller markets could be worth a great deal more.

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##  Appendix A Online Appendix

###  A.1 Table versions

Table A1 has country panel results.

Table A1: Effects of the ruble collapse whether the worker worked any hours in
a week

| Dependent variable:
---|---
| log(num.active) | log(num.apps) | log(num.hires) | mean.wage
| (1) | (2) | (3) | (4)
$\log p_{t}\times\textsc{Russian}_{c}$ |  1.321∗∗∗ |  1.326∗∗∗ |  1.165∗∗∗ |  $-$0.208∗∗
| (0.157) | (0.157) | (0.146) | (0.080)
$\log p_{t}\times RC$ | 1.952 | 3.601 |  $-$6.444 |  $-$5.232
| (7.787) | (7.246) | (6.871) | (5.029)
$\textsc{Post}\times\underline{w}_{c}$ |  $-$0.005 | 0.051 |  0.100∗∗ |  $-$0.119∗∗∗
| (0.035) | (0.035) | (0.042) | (0.023)
Country FE | Y | Y | Y | Y
Week FE | Y | Y | Y | Y
Observations | 549 | 549 | 549 | 549
R2 | 0.998 | 0.997 | 0.994 | 0.997
Adjusted R2 | 0.998 | 0.997 | 0.993 | 0.996

_Notes_ : Significance indicators: $p\leq 0.05:*$, $p\leq 0.01:**$ and
$p\leq.001:***$.

Table A2 has individual panel results.

Table A2: Effects of the ruble collapse on individual outcomes

| Dependent variable:
---|---
| Apps | Hours | Earnings | Avg. Wage
| (1) | (2) | (3) | (4)
$\log p_{t}\times\textsc{Russian}_{i}$ |  0.213∗∗∗ |  0.174∗∗∗ |  0.138∗∗ |  $-$0.029
| (0.043) | (0.051) | (0.053) | (0.015)
Worker FE | Y | Y | Y | Y
Observations | 251,999 | 213,966 | 211,715 | 211,715
R2 | 0.435 | 0.611 | 0.681 | 0.940
Adjusted R2 | 0.363 | 0.592 | 0.666 | 0.937

_Notes_ : Significance indicators: $p\leq 0.05:*$, $p\leq 0.01:**$ and
$p\leq.001:***$.