NBER WORKING PAPER SERIESON WHAT STATES DO PRICES DEPEND? ANSWERS FROM ECUADOR
Craig Benedict
Mario J. Crucini
Anthony Landry
Working Paper 22506
http://www.nber.org/papers/w22506

NATIONAL BUREAU OF ECONOMIC RESEARCH
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Cambridge, MA 02138
August 2016

This paper was written while Mario Crucini was a visiting senior fellow at the Globalization andMonetary Policy Institute at the Federal Research Bank of Dallas. Mario Crucini gratefully
acknowledges the financial support of the National Science Foundation. The views in this paper
are our responsibility and should not be interpreted as reflecting the views of the Bank of Canada,
the Federal Reserve Bank of Dallas, the Federal Reserve System, or the National Bureau of
Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been
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official NBER publications.
© 2016 by Craig Benedict, Mario J. Crucini, and Anthony Landry. All rights reserved. Shortsections of text, not to exceed two paragraphs, may be quoted without explicit permission
provided that full credit, including © notice, is given to the source.

On What States Do Prices Depend? Answers From Ecuador
Craig Benedict, Mario J. Crucini, and Anthony Landry
NBER Working Paper No. 22506
August 2016
JEL No. E3,E58,F3,F41
ABSTRACTIn this paper, we argue that differences in the cost structure across sectors play an important rolein the decision of firms to adjust their prices. We develop a menu-cost model of pricing in which
retail firms intermediate trade between producers and consumers. An important facet of ouranalysis is that the labor-cost share of retail production differs across goods and services in the
consumption basket. For example, the price of gasoline at the retail pump is predicted to adjust
more frequently and by more than the price of a haircut due to the high volatility in wholesale
gasoline prices relative to the wages of unskilled labor, even when both retailers face a common
menu cost. This modeling approach allows us to account for some of the cross-sectional
differences observed in the frequency of price adjustments across goods. We apply this model to
Ecuador to take advantage of inflation variations and the rich panel of monthly retail prices.

Craig Benedict
SUNY Oswego
450 Mahar Hall
Oswego, NY 13126-3599
craig.benedict@oswego.edu
Mario J. Crucini
Department of Economics
Vanderbilt University
Box 1819 Station B Nashville,
TN 37235-1819 and NBER
mario.j.crucini@vanderbilt.edu

Anthony Landry
Bank of Canada
234 Laurier Ave. West
Ottawa, ON K1A 0G9
and The Wharton School of
the University of Pennsylvania
anthonylandry@bankofcanada.ca

1Introduction

A growing literature documents large cross-sectional variation in the frequency and sizeof price adjustment. To date, this literature has mostly focused on idiosyncratic shocks
specific to individual firms to explain these patterns. For example, Dotsey et al. (1999)
emphasize heterogeneous menu costs of price adjustment among firms, while Golosov andLucas (2007) and Midrigan (2011) emphasize idiosyncratic productivity shocks. Bothof these mechanisms generate cross-sectional variation in the frequency and size of pricechanges, but fail to address Boivin et al. (2009) finding that sector-specific shocks areimportant in explaining the frequencies and sizes of price changes. In particular, theyfind that disaggregated prices appear sticky in response to macroeconomic and monetary
disturbances but flexible in response to sector-specific shocks.
In this paper, we present a simple theoretical model to explain these facts and provide
microeconomic evidence to corroborate the underlying mechanism of our model. Specifically, we argue that differences in the cost structure across sectors play a central role in
the price adjustment process.1 For instance, a local franchise selling haircuts will have a
cost function that is relatively sensitive to local wage conditions whereas a local franchisethat sells gasoline will have a cost structure that is relatively sensitive to the wholesale
price of gasoline, which in turn is sensitive to the world price of oil (in domestic currency
units). Local in the context of our application is a city within Ecuador.
To study how different sectors react to a given cost shock, we develop a two-factor
menu cost model of a retail firm operating in a particular sector and selling goods orservices in a particular city. Retail firms purchase intermediate inputs (potentially locallysourced or imported from elsewhere in their own country or abroad) and hire local laborto make goods and services available for sale in a local retail outlet. In most cities, somegoods are locally sourced and others are imported. An example of the former is brewed
coffee and of the latter, a Porsche. For services, an example of local sourcing is any
service requiring the producer and consumer to be at arms-length (haircut, emergency
room treatment, etc.); an example of a traded service is the broadcast of World Cup
soccer.
To capture real frictions associated with intermediating trade between manufacturers
1

Other papers have considered the effect of sector-specific shocks on aggregate and disaggregate prices,
but none that we know of rely on the cost structure to explain the cross-sectional variations in thefrequencies and sizes of price adjustments. Carvalho (2006) generalizes the Calvo model to allow forheterogeneity in price stickiness across sectors, while in the models of Gertler and Leahy (2008) andMackowiak and Wiederholt (2009), firms pay more attention to firm-specific conditions.

1and final consumers, we incorporate heterogeneous distribution margins to create distinct
pricing decision responses to an identical shock. As in most menu cost models of priceadjustment, firms hold their prices constant until the difference between their optimal
price and their current price is sufficient to justify paying the menu cost to adjust theprice. However, in our model, the inducement to adjust prices depends both on the sizeof the shock to the manufacturing good price and its share in the total cost of making
the good available to final consumers.
We use Ecuadorian data to test and calibrate the model because it has two attractive
properties. First, Ecuador’s macroeconomic history provides three regimes where theinflation rate, import price, and wages have distinct stochastic properties. Comparing
across these three regimes allows us to relate changes in macroeconomic states to changes
in the average frequency of price changes. Secondly, using a developing country allows
larger shocks and movements in the input prices and inflation rate which can be exploited
to more easily observe changes in the optimal pricing behavior of firms in the menu costframework.
We first look at trends in the frequency of price adjustment to show that all firms
reprice more frequently in a higher inflation environment. While this is common in
the theoretical state-dependent pricing literature, a number of empirical studies, suchas Klenow and Kryvtsov (2008), have shown that inflation and price adjustment frequencies are not highly correlated. Our empirical results are closer to those of Gagnon (2009),
which used Mexican data to show that when annual inflation is greater than 10%-15%
annually, the correlation between inflation and price adjustment frequency intensifies.
Put differently, when inflation changes by a substantial amount, as is certainly true in
Ecuador and Mexico, it is easier to detect the positive relationship between aggregate
inflation and average price adjustment frequencies. In mild inflationary environments, idiosyncratic factors specific to particular goods or markets obscure the impact of aggregate
inflation.
Our second finding relates to differences in the frequency of price adjustment acrosssectors within a given inflationary regime. In our data we find that traded goods always
reprice more frequently than services. Our explanation is simple: the variance of realimport prices is over three times larger than that of real wages. Therefore, firms that sellgoods that utilize a larger share of traded goods in their production need to update prices
more frequently due to the more volatile nature of their cost function.
This paper elucidates the states upon which a firm’s price depends. Our results showthat current state-dependent pricing models fail to account for key features present in the2data. By incorporating firms cost structure into current state-dependent pricing models,
policymakers can better predict price movements for individual firms and improve theeffectiveness of monetary policy. While the relevance of our model is demonstrated in
the case of Ecuador, our findings are likely to carry over to more stable, low inflation
environments. For example, our model provides a natural explanation for the relatively
frequent and volatile movements in food and energy, sectors which epitomize our definitionof retail goods which are high in traded input content on a cost basis.
This paper is organized as follows. Section 2 provides the context for using Ecuador
as a natural experiment and presents key stylized facts from the data. Section 3 lays out
our theoretical framework which we use to generate a set of predictions for how prices
should respond given the state of macroeconomic conditions in Ecuador. In Section
4, we calibrate and simulate the model to assess its ability to capture salient featuresof the observed frequencies of price changes across goods for three distinct inflationary
environments experienced in Ecuador. Section 5 concludes.

2

A Brief Monetary History of Ecuador

In this section, we review Ecuadorian monetary history from 1995 to 2003 to give
context to the model and present key stylized facts which help motivate our analysis.
We show that the distribution of the frequency of price changes exhibits a stable crosssectional pattern across goods as Ecuador moves from one inflation regime to another.
This pattern will serve as a key motivation for the model presented in Section 3.

2.1

The Data

Our primary source of data is a monthly database of retail prices from the National Institute of Statistics and Census (INEC), the official national statistical agency of Ecuador
and a subdivision of its Central Bank. These data comprise monthly retail prices in 12 different Ecuadorian cities spanning both the Western Coastal region and the Central Sierra
region, including both the country’s capital, Quito, and largest city, Guayaquil. These
prices cover a wide variety of goods and services of varying distribution shares, ranging
from an automobile with a non-traded input share of 0.167 to postage for a letter, which
has a non-traded input share of 1. The data are described in detail in Peñaloza (2005). 2
2

The Ecuadorian micro-price panel was obtained from INEC (the national statistical agency of
Ecuador) by Peñaloza (2005) who studied Ecuadorian real exchange rates with respect to the United

3

2.2

Three Inflation Regimes

Prices were recorded monthly from January 1997 to April 2003. These years represent
a tumultuous period in Ecuadorian history spanning three distinct inflation regimes. The
first regime is referred to as the Moderate regime and represents a period of relative
stability. At this time, Ecuador was on a crawling peg to the US dollar, and although
Ecuador’s monthly inflation rate of 2.8 percent seems high compared to a developed
country like the US, it was typical for a Latin American country over this time period.
In mid-1998, Ecuador was hit with a series of exogenous, negative shocks. El Nino
had negative effects on agriculture and the warming trend reduced the global price of oil,
an important Ecuadorian export. Only a year prior, the Asian financial crisis appeared
to leave developing and emerging markets susceptible to capital flight. Ecuadorian GDP
per capita fell by more than 7 percent from 1998 to 1999. During our second regime,
which we call the Crisis regime, Ecuador experienced a large bout of hyperinflation of
approximately 5.1 percent per month which contributed to the paralysis of its economy.
Unable to reign in inflation using standard monetary policy actions, in January 2001,
the Ecuadorian government announced that it would replace the Sucre by the US dollar
for all retail transactions. The results of dollarization were impressive, with inflation
falling from 5 percent to a mere 0.7 percent per month between 2001 and 2003, the end
point of our sample.
Figure 1 plots our monthly inflation measure together with INEC’s official Consumer
Price Index. Our measure is an equally weighted average of inflation across all goods andcities. Comparing the two lines, it is obvious that our monthly database looks almost
exactly like that of the official CPI. The inflation rate is shown in tri-color, displaying ourthree regimes: blue for the Moderate regime, red for the period of financial and exchange
rate crisis known as the Crisis regime, and green for the Dollarization regime.
Table 1 presents our summary statistics across the three inflation regimes. The firstrow conveys the narrative history of inflation in Ecuador. In the first regime, inflationis very high compared to industrial countries, averaging 2.2 percent per month. In thesecond regime, during the financial and exchange rate crisis, inflation reaches hyperinflationary levels. The average is a bit deceptive in the sense that some inflationary spikes
extended to more than 10 percent per month. The inflationary situation moderated in
the third regime, with inflation stabilizing to 0.7 percent per month, presumably as a consequence of the dollarization together with a commitment to open trade and integration
States in his Ph.D. dissertation.

4

with international capital markets.

2.3

Price Changes in Ecuador

We now turn to individual prices and present new stylized facts observed in our novel
dataset. To help answer our question about the states upon which price adjustment
depends, we begin with an analysis of the frequency of price change in Ecuador. Looking
at Table 1, we see higher inflation periods are also periods with more frequent pricechanges consistent with a state-dependent or menu-cost theory of price adjustment. We
see this pattern consistently across the three regimes of our sample with the frequency of
price changes increasing from 50% (Regime 3) to 54.1% (Regime 1) and then to 67.8%(Regime 2) as we move from the lowest to highest inflation regime. These frequencies areabout twice as high as those reported in Nakamura and Steinsson (2008) for the UnitedStates. In addition, this strong correlation between frequency of price change and inflation
runs counter to much of the empirical literature (e.g. Klenow and Kryvtsov (2008)).
Both of these differences are accounted for by the fact that inflation is much higher in
Ecuador than in the US, even during the most stable period of dollarization. A more
appropriate comparison of inflation rates is Gagnon (2009), who studies the frequency of
price changes in Mexico from 1994 to 2002 and shows inflation is strongly correlated withprice change frequency when inflation is over 10%. Even during the stable Dollarization
regime, Ecuadorian annual inflation is about 9% annually.
As other authors have pointed out, the frequency of price adjustment differs substantially across items in the consumption basket. This heterogeneity of frequencies
across goods is a universal feature of micro-price data. An important question to ask
is whether the cross-sectional variance in frequencies reflects economic structure thatmacroeconomists should be building into their models or uninteresting noise. We suspectthat structure underlies these patterns.
As an intuitive metric to elucidate such structure we ask if the frequency of priceadjustments maintains its cross-sectional pattern as we move from one regime to another.
Figure 2 accomplishes this by plotting the frequency of price adjustment by good in our
micro-sample as individual data points. The x-coordinates of this figure are the frequencies
of price adjustment in Regime 1, the sample with the inflation rate closer to the historicalmean. The y-coordinates for the red and green dots are frequencies in the Crisis regime(Regime 2) and the Dollarization regime (Regime 3), respectively. The higher (lower)
average adjustment frequencies in Regime 2 (Regime 3) are evident with the red (green)5scatter lying above (below) the 45- degree line. Here we see an underlying pattern tothe data across goods: The cross-sectional distribution of price-adjustment frequencies ispreserved across eras with a slightly elevated (reduced) mean price-adjustment frequency
in high (low) inflation regime compared to normal times. This pattern is inconsistent
with menu-cost models in which the heterogeneity in price adjustments comes from firms
drawing randomly from a common distribution of menu costs (e.g., Dotsey et al. (1999)
and the open-economy versions of Landry (2009, 2010)) or from a common distribution
or productivity shocks (e.g., Golosov and Lucas (2007)). In these cases, we would expect
a cloud of points tightly clustered around the mean frequency in each regime with little
or no pattern in relation to the 45 degree line.
Evidence of a structural relationship comes from the fact that the cross-sectional
distribution of price-adjustment frequencies is preserved across regimes. That is, thefrequencies of price changes across goods is strongly positively correlated across regimes(i.e., the green and red scatter diagrams show strong positive correlation with each other).
What this suggests is that there is some factor specific to an individual good that induces
more or less frequent price adjustments and this has little to do with the inflation regime.
Next, we turn to our explanation for this stable cross-sectional distribution of priceadjustment frequencies.

3

The Model

Like traditional menu-cost models, firms must pay a fixed menu cost in order toadjust their price. However, unlike these models, each firm’s cost function may have a
different weight on its factors of production and therefore, different exposure to different
cost components. Naturally, then, firms with a higher cost share of the more volatile
input price will adjust their prices more frequently. In most macroeconomic settings, themore volatile input price in retail goods is the traded input component. Food and energy
provide good examples relative to labor-intensive items such as services. We turn, now,
to the structural details of the model.

3.1

Menu Cost Model of Price Adjustment

We develop a partial equilibrium model in which a continuum of firms belongs to asector that combines labor (i.e., retail services) and a wholesale good purchased in global
markets to produce a differentiated final good. The production function for firm i is6iyit = litαi m1−αitwhere yit is the final good the consumer purchases, lit denotes retail services involved in
making the good available to the final consumer and mit is the intermediate imported
good.
Consumers have CES preferences over goods and thus the demand for the good i is:
yit = y

pit
Pt

−θ

where pit is the nominal price of good i relative to the CPI price index. Real aggregate
demand (and income) is y, and θ is the elasticity of demand. We normalize output tounity in what follows.
Firm i maximizes the expected discounted value of its profits:
Et∞X

dt,s πiss=twhere dt,s is the discount factor between period t and future period s and πis is the flowlevel of real profits (nominal profits divided by the CPI price level) for the firm in periods. Flow profits in real terms equal,
πit = pit yit − wt lit − pm
it mit − χwt Iit
where Iit = 1 if the firm adjusts its price pit and 0 otherwise.
We normalize the real wage and the import price such that optimal real price equals
unity on average in the steady-state. The indexation of the nominal wage means that asa firm leaves its nominal price constant, demand for its product increases and real profits
also rise provided θ > 1 (when the demand elasticity is unitary, the increase in demand
generated by the falling price of the good exactly offsets the effect of the price decline onreal revenue, so real revenues remain unchanged. The firm then decides on the extensive(timing) and the intensive (size) margins of price adjustment to maximize the expecteddiscounted value of profits.
The pricing decision rule of a firm in our model has two dimensions: First, the priceadjustment decision is a function of the import price and the aggregate price level. Second,
the size of the inaction region and the magnitude of the price jumps are different for each7

component of marginal cost and depend on the cost share of labor and traded inputs into
production.
To see this more clearly, consider a log-linearized version of the nominal cost function
implied by the model:
cit = αi wt + (1 − αi )pm
it
The two state variables are the wage rate in Ecuador, wt , and the import price index,pm
it .
In our setting, the variance of the marginal cost function depends on the variance ofwages, the variance of import prices, and their covariance. It also depends on the costshare of the two inputs, αi . In Golosov and Lucas (2007), the frequency of price changes
is increasing in the variance of the microeconomic productivity shocks experienced byfirms because this translates into higher variance in marginal cost. In our setting, thefrequency of price adjustments is rising in the cost share of the imported input because
these microeconomic prices tend to be more volatile than the nominal wage. Moreover,import prices tend to be more volatile than domestic prices, on average, and more so
the more complete is exchange rate pass-through. A similar point was made in Crucini,
Shintani and Tsuruga (2013). Namely, flexible prices amplify the transmission of realshocks across locations while sticky prices amplify the transmission of nominal shocks.
Figure 3 shows this connection in the Ecuadorian data. In the figure, each dot represents a different good in the dataset. The x-coordinate is the distribution share of the good
and the y-coordinate is the frequency of price adjustment. In all three regimes and in thefull sample we see an inverse relationship between the two variables. As the distribution
share increases, firms are less likely to adjust their prices, presumably because movementin the import price outweighs movement in wages. The slope becomes steeper in both thecrisis regime and the full sample. With this theoretical discussion as background we turnto the calibration of our benchmark parameterization.

3.2

Calibration of Shock Parameters

Due to the lack of monthly wage and import price data available for Ecuador, we maketwo simplifying assumptions. The first is that the price of haircuts can be used as a proxy
for the wage. After setting the nominal wage, Wt , equal to the price of haircuts, we relyon the model, our observed retail price, Pit , and estimates of the distribution cost shareto back out the import price, Pitm . That is, the empirical counterpart to the cost functionis:8

pit = αi wt + (1 − αi )pm
itwhere lowercase denotes the log of the variable. Using simple alegbra will give us theimport price:pm
it =pit − αi wt(1 − αi )Using pm
it , we then estimate the parameters in our model from the following system of
equations.
pt = µr + pt−1 + tmpm
it − pt = µim + pit−1 − pt−1 + νit

where pm
it represents the nominal import price. The error terms are assumed to be normally2. Table 2 gives the results of this
distributed with mean 0 and variances of σ2 and σiνestimation excercise. Figure 4 is a histogram showing the estimated variances of the2import price shocks, σiνfor each of the regimes and the full sample. The y-axis shows
the number of goods in each bucket corresponding to the values along the x-axis. As
expected, the import price shocks are much more widely distributed in regime 2 and thefull sample. This dispersion of the import price shocks is what drives our results andleads firms with a greater reliance on traded goods to adjust their price more frequently.
Based on our estimation exercise, a number of facts are readily apparent. The mostsignificant is that the median import price shock of 0.005 is over four times larger than the
inflation rate shocks of 0.0013 in the full sample. The dominance of import price shocks
relative to inflation shocks generates a prediction that goods with larger distribution
shares (i.e., retail labor) will exhibit less frequent price changes due to a lower overallvariability in the firm’s cost function.
Our calibration parameters are displayed in Table 2. We allow the mean inflation
rate, the variance of inflation and the variance of the relative price of imports to varyacross regimes to match the historical data of Ecuador. The distribution cost shares andthe menu cost do not change over time. The distribution cost shares are taken from US
NIPA estimates by sector and matched to the Ecuadorian retail price data by assigningeach good or service to a US NIPA sector. The distribution shares range from 0.19
for gasoline to 0.85 for haircuts. The median across the 223 goods and services in the
Ecuadorian micro-panel is 0.52. Following the literature, menu costs are calibrated to9

1.5% of labor income to match the frequency of price adjustment for the median goodin the full sample. Given the median frequency of price changes is 0.6, roughly 0.9% of
aggregate labor income is spent on price adjustment by retail firms.

4

Results

Having estimated the stochastic processes for the two components of the firm’s costfunction, we are in a position to compare our model’s predictions for the frequencies of
price change across sectors and regimes to the micro-data.
To gain some intuition for the relationship between menu costs and price adjustment,
Figure 5 plots the policy function for a representative firm facing a constant import price.
This is a common figure within the menu-cost literature, which shows the price-adjustment.
cutoffs as the firm’s real price fluctuates around the optimal price, given by pPt−1t
Our model, however, differs from conventional menu cost models because firms havedifferent price-adjustment cutoffs based on their distribution cost share. To show this,Figure 6 plots the policy functions of three firms, each with a different distribution costshare. In this graph the aggregate price level Pt is held constant. We see that when1−α.
firms choose to reset to the optimal price, they choose pt such that it is equal to (pmit )Furthermore, we observe that as the distribution share αi increases, firms have a wider
band of inaction and therefore become less sensitive to a traded input shock. Therefore,a firm with a higher distribution cost share will experience a smaller marginal revenue
deviation, thus reducing the likelihood that the firm reprices.
In our first exercise, we test the ability of our model to match the median frequency of
prices changes across all three regimes. Here we restrict the model to have only aggregate
price level shocks and abstract from heterogeneity in the distribution share across goods.
That is, the distribution share of retail production is set at 0.52, the median across goods.
Figure 7 presents the median frequencies of price adjustments by regime for each city inthe Ecuadorian micro-panel as well as the predictions of the baseline calibration of themodel. The black stars are the predictions from the model while the red, blue and green
dots are measured using the micro-data. For example, the cluster of green dots represents
the median frequencies of price adjustments across goods, city-by-city for the dollarization
regime (Regime 3). With inflation of about 3 percent per quarter, the frequency of priceadjustments is about 50 percent per month. Inflation during the financial crisis is above
14 percent per quarter and the median frequencies are between 60 to 70 percent. The

10menu cost is parameterized to match the overall average frequency, which is the third
black star from the bottom left. We see that the model also fits the Crisis regime, but
underestimates the frequency of price adjustments in Regime 3, the Dollarization regime.
Our next exercise is to see how the model fares in accounting for the heterogeneityof price-adjustment frequencies. Figure 8 displays the simulated frequency of price adjustments averaged over each distribution share. Each dot represents a distribution share
across the regimes–in the same fashion as the data were presented in Figure 2. Regime
1’s simulations are on the x-axis and Regime 2’s and 3’s are the y-coordinates. It should
be kept in mind that we have fewer distribution shares than goods, which limits to someextent the cross-sectional variance that results. That being said, the variation is muchless than we saw in Figure 3 earlier. We suspect that this is partly due to our adherence
to a common variance of import price shocks across goods. While a full model could
potentially include heterogeneity in productivity shocks and possibly wages as well, we
abstract from these issues and our results therefore are likely to have less variability as aresult.
Figure 9 displays the frequency of price adjustment for each good in the model compared to its distribution share on the x-axis. All regimes show a negative correlation
between a good’s distribution share and its frequency of price adjustment. Our model not
only accounts for the fact that goods maintain a certain frequency of price adjustment
pattern across regimes, but also it distinguishes which goods are more likely to repricebased on their cost structure. Besides the general downward trend of the slope, we canagain observe the under-prediction of frequency in the Dollarization regime and the overprediction in the Crisis regime. The lines in the figure are fitted values from a linear
regression of the frequencies of the price changes in the Ecuadorian data on sector-level
distribution shares from the US NIPA. The appendix provides more details about these
constructs.
Together these results indicate that our menu cost model can account for many of thestylized facts we found in Section 2 regarding the price adjustment process. In addition
to matching the positive correlation between frequencies of price changes and aggregate
inflation, our structural model provides a novel explanation for the different frequencies
of price changes observed across the distribution of goods in the CPI.

11

5

Conclusion

In making decisions about changes in the Federal Funds rate, it is essential that monetary policymakers distinguish generalized inflationary impulses from changes in relative
prices that may affect some, but not all market prices. The structure of our model helps
to elucidate these differences. Changes in the prices of imported goods are often large andinduce frequent changes in the retail prices of these goods. Essentially, this is why foodand energy are typically excluded in measures of core inflation. The typical explanation
for the volatility of these prices is that the markets for them are subject to particularly
large shocks. Our approach generalizes this conventional wisdom by recognizing that final
goods have distinct production functions in the sense of requiring different intensities ofretail labor in making them available to final consumers. This allows us to parse theinflationary impulse of, say, an indexed wage (typically the cost-push dimension of monetary policy) from shocks that are idiosyncratic to the good or sector (such as imported
goods). Ecuador provides an ideal setting to explore this mechanism by virtue of highfrequency micro-price data by good and city spanning a varied inflationary experience.
Our hope is that our work will motivate similar studies in other countries to validate themenu cost model developed here in a broader cross-section of nations and in inflationary
environments closer to that of the United States.

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[20] Midrigan, Virgiliu. 2011. “Menu Costs, Multiproduct Firms, and Aggregate Fluctuations ”Econometrica, 79(4), 1139-1180.
[21] Nakamura, Emi and Jon Steinsson. 2008. “Five Facts About Prices: A Reevaluationof Menu Cost Models.” Quarterly Journal of Economics, 123(4), 1415-1464.
[22] Nakamura, Emi and Jon Steinsson. 2010. “Monetary Non-neutrality in a Multisector
Menu Cost Model.” Quarterly Journal of Economics, 125(3), 961-1013.
[23] Peñaloza-Pesantes, Roberto. 2005. ”Dollarization and Price Dynamics.” Ph.D. Dissertation, Vanderbilt University.

14

Table 1. Summary of Monthly Price Facts
Full sample

Regime 1

Regime 2

Regime3

1997:01-2003:04

1997:01-1998:07

1998:08-2000:12

2001:01-2003:04

2.8%
57.7%
43.2%
14.4%
7.1%

2.2%
54.1%
42.8%
11.3%
5.9%

5.1%
67.8%
54.7%
13.1%
9.8%

0.7%
50.0%
31.8%
18.2%
3.9%

Inflation
Price adjustment frequency
Price increases
Price declines
Size of price changes

Size of price adjustments are average absolute values across goods and time
periods.

Table 2: Stochastic Properties of Shocks
Full sample

Regime 1

Regime 2

Regime 3

1997:01-2003:04

1997:01-1998:07

1998:08-2000:12

2001:01-2003:04

Inflation, µ

2.8%

2.2%

5.1%

0.7%

ση
σν

0.00130.00020.00210.00020.00500.00190.00890.020

αiHaircut

0.85

Menu cost

1.5%

Gasoline

0.19

λ

0.60

Median0.52

E(Menu)0.9%

15

Figure 1: Monthly CPI inflation in Ecuador by Regime

Moderate
Inflation

15%

Currency and Banking
Crisis10%

Dollarization

Offical CPI Inflation(National Average)

5%

0%
1997

1998

1999

2000

2001

2002

2003

-5%

Monthly inflation rate over the sample period of January 1998 to April 2003, calculated as an equally weighted averageof inflation across all goods and cities from our dataset. The black line represents the official CPI from INEC, Ecuador’snational statistical agency.

16

Figure 2: Goods-by-goods frequencies of price changes in the data

Frequency of Price Adjustment

1

Regime 2
Regime 3

0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Regime 1
Comparison of price adjustment frequencies across regimes. Each dot represents a specific good. The x-coordinates represent
the price-adjustment frequencies in Regime 1, while the y-coordinates represent the price adjustment frequencies in the Crisis
regime (Regime 2) and the Dollarization regime (Regime 3).

17Figure 3: Frequencies of price changes by non-traded input share
Full Sample

1

Moderate Regime

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

0
0

0.2

0.4

0.6

0.8

1

0

0.2

Distribution Share
Crisis Regime

1

0.4

0.6

0.8

1

Distribution Share
Dollarization Regime

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

0
0

0.2

0.4

0.6

0.8

1

0

Distribution Share

0.2

0.4

0.6

0.8

1

Distribution Share

The frequency of price adjustment in each regime by distribution margin. Goods with a larger non-traded input share areless likely to reprice in each of the three regimes and the full sample.

18

Figure 4: Estimated distribution of import price shocks

Full Sample

Regime 1

60

60

40

40

20

20

0

0
0

0.01

0.02

0

Regime 2

0.01

0.02

Regime 3

60

60

40

40

20

20

0

0
0

0.01

0.02

0

0.01

0.02

The distribution of import price shocks. Eras 1 and 3 have a larger number of goods with import price shocks close to 0.

19

Figure 5: Policy function for a representative firm
Policy Function
1.05
1.04
1.03
1.02
1.01

pt
Pt

1

0.99
0.98
0.97
0.96
0.95
0.95

1

p t−1
Pt

Representative policy function for a firm with α = 0.167 holding the import price at unity.

20

1.05

Figure 6: Policy function for three representative firms
Policy Function
1.05
α = .167
α = .219
α = .366

1.04
1.03

( )

pt
(1−α)
m
pt

1.02
1.01
1
0.99
0.98
0.97
0.96
0.95

0.94

0.96

0.98

1

p t−1

1.02

1.04

1.06

1.08

(1−α)

(pmt )

Representative policy function for 3 firms holding the aggregate price level constant, each with a different non-traded input
share assuming Pt = 1. Figure 6 shows that firms’ price adjustment cutoffs is based on a firms’ non-traded input share The
figure shows that firms with lower distribution shares are less sensitive to a change in an import price shock.

21

Figure 7: Median price change adjustment by regime, data, and model

Frequency of Price Adjustment

1
0.9
0.8
0.7
0.6

Regime 1
Regime 2
Regime 3
Model

0.5
0.4
0.3
0.2
0.1
0
0

2

4

6

8

10

12

14

16

18

20

22

Quarterly Inflation Rate
Comparison of the frequency of price adjustment in both the data and the model. The black stars represent the model’s
predicted frequency for the median good in each regime. The colored dots represents the median frequency of priceadjustment in each of the 12 cities in Ecuador.

22

Figure 8: Monthly CPI inflation in Ecuador by Regime

Frequency of Price Adjustment

1

Regime 2
Regime 3

0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Regime 1
Comparison of the frequency of price adjustment across regimes in the model. Each dot represents a specific good. The
x-coordinate reflects the good’s frequency of price adjustment in Regime 1 while its y-coordinate represents the same good’s
frequency of price adjustment in Regime 2 or Regime 3.

23

Figure 9: Frequencies of price changes by non-traded input share
Full Sample

1

Moderate Regime

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

0
0

0.2

0.4

0.6

0.8

1

0

0.2

Distribution Share

0.4

0.6

0.8

1

Distribution Share

Crisis Regime

Dollarization Regime

1

1

0.8

0.8
0.6
0.6
0.4
0.4
0.2

0.2
0

0
0

0.2

0.4

0.6

0.8

1

0

Distribution Share

0.2

0.4

0.6

0.8

1

Distribution Share

The frequency of price adjustment in each regime by distribution margin as predicted by our model. As was in the data,
goods with a larger non-traded input share are less likely to reprice in each of the three regimes and the full sample. The
red lines represent the trend lines in the data for reference.

24