Sticky Prices and the Value of Public Information:

What Can Financial Markets Tell Us?∗

Jin Xie† Juanyi (Jenny) Xu‡

This version: January 2018

Abstract

We construct a simple measure of output-price transparency using staggereddisclosures of product indices by the Bureau of Labor of Statistics (BLS). Themeasure tells the extent to which investors or firms of a granular sector observeprice changes from closely neighbored sectors. Transparency mitigates informationasymmetry in the debt market and thereby reduces sticky-price firms’ financingcosts. Transparency also leads sticky-price firms to respond more to cost shocks,which reduces their exposure to systematic risks. Surprisingly, transparency delaysflexible firms’ responses to shocks and thereby increases the risk exposure of theirstocks. We perform textual analysis on transcripts of earnings conference calls andfind that managers of flexible firms have more private signals about the trend ofinput costs. To address endogeneity, we exploit the cross-sector heterogeneity ofJanuary 2004 coverage expansion by the Office of Publications at BLS.

JEL classification: E12, E44, G32, G12, D83

Keywords: Sticky Prices, Public Information, Debt Financing, Stock Returns,Government Statistics Agency, BLS

∗We are indebted to Michael Weber for numerous helpful discussions and making the data of frequencyof price adjustment at the sector level publicly available. We thank Doron Avramov, David Cook, KangShi, and Yizhou Xiao for helpful comments. We thank Scott Sager at the Bureau of Labor of Statisticsfor answering numerous questions about the producer price index (PPI) program. Xiang Shi, Yihui Zengand Yue Zhou provided excellent research assistance. Xie and Xu acknowledge research support from theChinese University of Hong Kong and Hong Kong University of Science and Technology, respectively. Allerrors are our own.

†The Chinese University of Hong Kong, Cheng Yu Tung Building, No.12, Chak Cheung Street, Shatin,N.T., Hong Kong. Email: XieJin@cuhk.edu.hk.

‡Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. Email:jennyxu@ust.hk.

1 Introduction

Government agencies periodically disclose information to guide decisions undertaken by

private sectors that are often uninformed about aggregate fundamentals. The officially

released data are often noisy, and range from macroeconomic statistics, most notably,

the gross domestic product (GPD), to microdata such as corporate profits and personal

income. One important question is whether a public release creates social value. More

public information is generally beneficial when a single agent’s decision is made in

isolation. However, this conclusion might not hold if public information has a coordinating

role and agents are influenced by the action of others. Morris and Shin (2002) and

Amador and Weill (2010), among others, show the release of economic statistics might

be detrimental to social welfare if agents with coordination motives “overreact” to public

information or rely less on their own private signals.1 Yet, despite extensive theoretical

research, with a particular focus on government disclosure in the conduct of monetary

policy, empirical evidence on this issue has been sparse.

In this paper, we provide novel evidence on the impact of public information on

financial market outcomes via firms’ pricing decisions under imperfect information. For

three reasons, we propose U.S. public corporations with heterogeneous price stickiness as

our laboratory. First, a poor information environment within which firms operate can

cause prices to be sticky (Blinder et al. (1997); Zbaracki et al. (2004); Fabiani et al.

(2005). Second, price stickiness can also contribute to information frictions between firms

and outside investors (Gorodnichenko and Weber (2016); D’Acunto et al. (2017)). Third,

it is well observed that firms as price setters are often interested parties in the pricing

actions of other firms (Ball and Romer (1991)). For these reasons, public information

has attributes that make it a double-edged instrument in influencing both the cause and

consequence of price stickiness.

Using data of frequency of price adjustment, we document two distinct channels

through which the supply of public information by the Bureau of Labor of Statistics

(BLS) is valuable for sticky-price firms. In the first channel, BLS disclosure mitigates

information asymmetry between investors and sticky-price firms and thereby reduces their

1Angeletos et al. (2016) allow an informational friction to inhibit the coordination of a firm’s productionand pricing decisions, and show the social value of public information depends on both the real andnominal rigidities, on the source of business cycle, and on the conduct of monetary policy.

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financing costs. In the second channel, more public information causes sticky-price firms

to be more responsive to cost shocks, which in turn reduces inflation inertia and their

exposure to systematic risks. Surprisingly, we find perverse effects of public information on

flexible-price firms. In a spirit similar to Morris and Shin (2002) and Morris et al. (2006),

we show that managers of sticky and flexible firms have access to private signals about

the trend of input costs at different levels of precisions, which explains the differential

impact of BLS publication coverage on risk exposures of the two types of stocks.

To empirically quantify the public information environment, we construct a simple

measure of output-price transparency using the staggered disclosure of product indices

underling the Producer Price Index (PPI) program compiled by BLS. The measure

contains both new information and noise that are relevant to a firm’s pricing decisions.

BLS adds indices to, or deletes them from, the PPI program via a resampling process

conducted at the 6-digit North American Industry Classification System (NAICS) level.2

Within each 3-digit NAICS sector, we calculate the number of granular product indices

published by BLS in month s as a fraction of the maximum possible number of underlying

products in that sector. We call it “BLS publication coverage.” The measure describes

the extent to which investors and firms in granular sector j observe price adjustments for

outputs made by sector j’s closely neighbored sectors, j′. Because cost structures of j

and j′ are imperfectly correlated, prices from sector j′ contains both new information and

noises for sector j.3

One important concern is that other industrial conditions might drive the variation

of BLS publication both across sectors and over time. To address this concern, we first

show our measure is not confounded by industry concentration and firm-level measures

of market power and concentration. Second, we propose an instrumental variable (IV)

strategy that utilizes a quasi-natural experiment in which, by a single exogenous event,

the Office of Publications at BLS differentially changed its publication coverage across

2We will discuss the institutional details in section 2.3Without BLS publications, firms indeed do observe prices of certain products sold by firms from

neighboring sectors. However, for any firm to have perfect information about its peers’ prices is almostimpossible. Firms keep their product pricing strategies confidential for a variety of strategic motives.Public firms request the US Securities and Exchange Commission (SEC) to redact product prices insales contracts that have to be disclosed in financial statements (Verrecchia and Weber (2006); Boone,Floros, and Johnson (2016)). When contacting firm participants for surveys, BLS makes a pledge ofconfidentiality to respondents.

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3-digit NAICS sectors.4

We form two hypotheses arguing that BLS’s disclosure of product indices creates

value for sticky-price firms. In our first hypothesis, price stickiness suppresses the

revelation of a firm’s private information about its marginal cost of production, which

exacerbates information asymmetry between firms and outside investors. BLS disclosure

allows investors to infer a sticky-price firm’s private information about marginal costs

from observing price adjustments made by closely neighbored peer firms. We test

this hypothesis by comparing interest rates between bond and loan markets paid by

sticky-price firms. Compared to banks, investors of diffusely owned public bonds are not

well informed about a borrower’s marginal costs, because they suffer from a coordination

problem when monitoring private information (Diamond, 1991a,b). As a result, bond

investors charge a higher spread. We expect the gap between loan and bond interest rates

will be negative correlated with BLS disclosure.

To measure price stickiness, we use the frequency of price adjustments for each

granular NAICS sector provided by Pasten, Schoenle, and Weber (2017).5 We classify

a Compustat firm as a sticky-price firm if the adjustment frequency of this firm’s NAICS

sector is ranked below the sample median. A flexible firm is defined analogously. Although

both sticky- and flexible- price firms pay a higher spread in the bond market than in the

loan market, BLS disclosure attenuates the spread difference only among sticky-price

firms. Industry characteristics such as competition and market power do not confound

our results. A one-standard-deviation increase in our transparency measure reduces the

“bond premium” by 32.5 basis points (see column 1 of Table 3). Similar to D’Acunto

et al. (2017), we estimate these magnitudes after controlling for stock-return volatility,

firm size (measured by sales), tangibility, profitability, the book-to-market ratio, firm-level

measures of market power, concentration, and firm and year fixed effects. Our IV

estimation yields similar results (see Table 5).

In our second hypothesis, firms have imperfect information about a permanent shock

to the marginal cost of production. The presence of imperfect information and higher-

order expectations leads to a delay in the price adjustment to shocks. However, public

4We will discuss the details in section 2.5The authors use the confidential microdata underlying the producer price indices from the BLS. On

the sample period of from 2002 to 2012, the authors aggregated frequencies of price adjustment at thegoods level into NAICS sectors of different granularities. The frequency measure is time invariant.

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information makes firms’ private information more accurate. Given the same adjustment

frequency (i.e., Calvo rate), a firm with more precise information becomes more responsive

to cost shocks. In the online appendix, we link BLS publication coverage, price stickiness,

and risk premium, relying on the theoretical framework by Nimark (2008). Similar to

Li and Palomino (2014) and Weber (2015), the central mechanism generating a cross-

sectional return premium for sticky-price firms in the model is a higher cyclicality of cash

flows for sticky price firms after shocks to marginal utility. We also hypothesize that

flexible firms can often reset prices after information is improved and therefore is less

exposed to systemic risks.

For the second hypothesis, we find evidence consistent for sticky-price firms but not

for flexible-price firms. First, we find BLS publication increases the speed at which sticky-

price, granular sectors pass input-cost shocks into output prices. Second, we explore the

asset-pricing implication of BLS disclosure for sticky-price firms. In each year, we double-

sort eligible stocks based on price stickiness and BLS publication.6 Returns monotonically

decrease in the degree of transparency in sticky-price firms. On the sample period from

July 1997 to June 2013, we show that portfolios of sticky-price firms sorted on BLS

publication generate a return differential of 7.7 percentage points per year between stocks

with the highest and lowest levels of BLS publication coverage. Such a return differential

becomes 4.4 percentage points when we extend the sample period from July 1983 to June

2013.7 Third, we show market power, industry concentration, industry fixed effects, and

other standard cross-sectional return predictors cannot explain the premium in firm-level

panel regressions. Last, we test whether differential exposure to systematic risk can

explain the difference in returns between the two extreme portfolios for sticky-price firms.

Although the capital asset-pricing model (CAPM) cannot explain the level of double-

sorted portfolio returns, it does explain a significant portion of cross-sectional dispersion:

less transparent, sticky-price firms are, on average, more sensitive to the market excess

return (with a β close to 1.3). βs in sticky-price firms decrease monotonically with BLS

publication, resulting in a difference in exposure to market risk of about 0.13 between

sticky-price stocks in the most and least transparent portfolios.

Our most surprising results come from flexible firms. We find that, compared to those

with low BLS publication coverages, flexible firms with high coverage adjust less to cost

6We focus on domestic common stocks that are publically listed on NYSE, AMEX, and NASDAQ.7The sample of Compustat firms covered by the NAICS system ranges from 1983 to now.

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shocks and have higher risk exposures to systematic risks. We interpret these findings

as evidence consistent with Morris and Shin (2002) and Amador and Weill (2010). In

Morris and Shin (2002), the detrimental effect comes from agents’ overreaction to noises

contained in public information release. In Amador and Weill (2010), firms overweight

public information release and underweight their own private information, making sector-

wide prices less informative. The initial overweighting on public information prompts firms

to put even less weight on their private information, making prices even less informative,

and so on.

We argue that less efficient managers, or managers with higher attention costs,

might acquire less information about the trend of costs and thereby adjust output prices

less frequently. To empirically validate our conjecture, we perform textual analysis on

transcripts of earnings conference calls. An earnings conference call is a teleconference,

or webcast, in which managers discuss the financial results of a reporting period. The

SEC’s Regulation Fair Disclosure (Reg FD) requires managers to disclose material private

information to all investors at the same time via conference calls or press releases.

Managers can have more private signals about cost trends if they use more exact numbers

(e.g., “we anticipate a 5% increase” or “costs will decrease by $1.2 million”) to predict

input costs of their own company.

Compared to managers of sticky firms, we find managers of flexible firms disseminate

more quantitate, nonpublic information about the trend of certain cost items on a

voluntary basis through the avenue of earnings conference calls. On a sample of more

than 79,000 earnings call transcripts for the period of 2002 to 2012, we count the number

of sentences in which managers forecast the company’s future costs in a quantitative

manner. Based on the frequency of price adjustment, we sort our sample firms into

four portfolios. We find that both the incidence and intensity of managerial forecasts

monotonically decreases with the level of price stickiness. As for incidence, there are

44% and 55% of transcripts in the most sticky and most flexible portfolios, respectively,

contain such discussions. As for intensity, managers of the stickiest firms have used 2.1

sentences per conference call to make quantitative forecasts about input costs, whereas

managers of most flexible firms have used about 3.

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1.1 Related Literature

Our paper adds to a recent literature on the macroeconomic determinants of default risk

and equity premium in the framework of New Keynesian economics. Li and Palomino

(2014) conclude the traditional New Keynesian framework to capture asset-pricing

dynamics has a significant shortcoming. Weber (2015) examines the asset-pricing

implications of nominal rigidities and finds firms that adjust product prices inflexibly earn

an equity premium by a magnitude of 4% per year. The central mechanism explaining

his results is a higher cyclicality of cash flows for sticky-price firms after shocks to

marginal utility. Gorodnichenko and Weber (2016) show that after monetary policy

announcements, the conditional volatility of stock market returns, as well as company

operating income, increases more for firms that cannot freely adjust prices. D’Acunto

et al. (2017) find flexible-price firms have a higher long-term financial-leverage ratio than

inflexible-price firms. The authors argue the asymmetric-information problem is more

severe for firms with rigid prices when bank monitoring is costly. We study the impact

of public information on the financial-market consequence introduced by nominal price

rigidities. In particular, we put our analysis in a context in which firms as price setters

use public signals not only to predict fundamentals but also to coordinate their pricing

decisions.

The paper also speaks to both economics and finance literatures studying the welfare

effect of information disclosure. The economic literature find public information can

play a role akin to that of sunspots, possibly contributing to higher volatility and lower

welfare. In their seminal work, Morris and Shin (2002) conclude that public signals have

a disproportionate effect on equilibrium outcomes relative to what is warranted on the

basis of their informational content regarding fundamentals alone. Amador and Weill

(2010) find that although a public release has the direct beneficial effect of providing new

information, it may increase uncertainty about the monetary shock and reduce welfare.

Angeletos, Iovino, and La’O (2016) highlight how information dispersion can be the source

of both real and nominal rigidities.

The role of public information has also been studied in the finance literature.

When financial- market agents (e.g., lenders and equity investors) have an incentive to

coordinate, their actions are more sensitive to public than to private information, because

public information better forecasts the actions of others. This notion has been applied to

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the understanding of a variety of financial-market phenomena, including creditor and bank

runs (Morris and Shin (2004); Goldstein and Pauzner (2005)), borrower runs (Bond and

Rai (2009)), financial crises (Goldstein (2005)), currency attacks (Morris and Shin (1998);

Hellwig, Mukherji, and Tsyvinski (2006); Goldstein, Ozdenoren, and Yuan (2011)), and

asset-price volatility (Ozdenoren and Yuan (2008)). In a novel empirical study, Hertzberg,

Liberti, and Paravisini (2011) use a credit-registry expansion as a natural experiment to

isolate the coordination channel from lenders’ joint reaction to new information. The

authors find that lower financing by one lender reduces firm creditworthiness and causes

other lenders to reduce financing.

2 Producer Price Index (PPI) Program at BLS

2.1 General Background

Established in 1884, The Bureau of Labor Statistics of the US Department of Labor is the

principal Federal agency responsible for measuring price changes, labor-market activity,

and working conditions in the economy. Its mission is to collect, analyze, and disseminate

essential economic information to support public and private decision-making.

The Producer Price Index (PPI) is a family of indexes that measures the average

change over time in the selling prices received by domestic producers of goods and services.

The PPI program at BLS measures and publishes price movements for the net output of

producers. The program tracks prices of all goods-producing industries, such as mining,

manufacturing, gas, and electricity, as well as the service sector. The PPI covers about

three-quarters of the service-sector output.

Each PPI index is an aggregation of prices for individual goods at different granular

levels. The BLS follows the following procedures to determine the individual goods

included in the PPI. The BLS selects establishments using a systematic sampling from

a listing of all firms that file with the Unemployment Insurance System. After a firm is

selected and agrees to participate in the survey, a probability-sampling technique called

disaggregation is used to determine which specific products or services will be included.

Disaggregation is a process in which iterative steps are taken to select items based on their

proportionate value to the manufacturer’s overall revenue. A respondent breaks down

the type of items shipped into categories, which are then divided by price-determining

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characteristics such as options, color, and size. Differentiating between types of buyers

or discounts then could be necessary. Disaggregation continues until a specific product

sold to a specific buyer is selected. Establishments selected must continue to report until

a new sample is selected for the industry after seven to eight years.

2.2 BLS Conversion of SIC into NAICS

In January 2004, the PPI program switched its basis for industry classification from the

1987 Standard Industrial Classification (SIC) system to the NAICS system. This profound

reform was made in response to increasing criticism about the inability of SIC system

to handle rapid changes in the US economy.8 Two major dimensions in which NAICS

improves upon SIC make our instrument especially appealing. First, NAICS provides

more complete coverages of new and emerging sectors at different granularities. These new

sectors are reflective of the changes in how people produce and consume.9 Second, NAICS

groups establishments into industries on the basis of their production function, whereas

SIC uses a mixture of ways to categorize economic activities. The unified approach creates

more homogeneous categories that are better suited for economic analysis.1011

For price indices prior to 2004, BLS reorganizes them under the NAICS according to

the rule as follows. The PPI treats the SIC-to-NAICS comparison as continuous if 80%

or more of the weight of the SIC-based index comprises at least 80% of the weight of the

NAICS-based index. All index series that have passed this test are published under the

NAICS structure using the index base date and price-index history established by the

SIC-based index.

8Developments in information services, new forms of health care, an expansion in the service sector,and the advent of high-tech manufacturing are examples of industrial changes that could not be studiedunder SIC.

9These new and emerging industries include semiconductor and related device manufacturing, cellularand other wireless telecommunications, and Internet punishing and broadcasting.

10For example, establishments using similar raw-material inputs, similar capital equipment, and similarlabor are classified under the same industry.

11The third advantage of NAICS relative to SIC is that NAICS is not only used by the United Statesbut also by Canada and Mexico. The conversion makes NAICS a consistent tool for measuring andcomparing the economies within the North American Free Trade Agreement. See Walker and Murphy(2001) for more institutional details.

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3 Hypothesis Development

In this section, we illustrate mechanisms for the two distinct channels to affect the

interaction between BLS publication and sticky-price firms in the financial market.

Output-price transparency captures the extent to which the interested parties in 6-digit

NAICS sector j (investors and firms) observe price movements for goods produced by

sector j′ , sector j’s close neighbors in the 3-digit sector.

3.1 First Channel

In this channel, public information partially resolves information frictions between sticky-

price firms and investors. Here, investors, namely, large bond investors, make decisions in

isolation and public information is unambiguously beneficial according to Morris and Shin

(2002). Firms receive shocks to the marginal cost of production but differ in the frequency

of price adjustment. Flexible firms can adjust prices to shocks more frequently and keep

their profits at the optimal level. Sticky-price firms adjust prices less often so their profits

are more volatile. As a result, price stickiness exacerbates information asymmetry because

investors cannot observe marginal costs and relative prices and thus are uncertain about

firms’ profits (Gorodnichenko and Weber (2016); D’Acunto et al. (2017)).

Output-price transparency helps investors of a sticky-price firm i form better

expectations about the firm’s profit. A higher level of transparency indicates that within

each 3-digit NAICS sector, more peer firms’ product prices are publicly available. That

is, investors can infer i’s marginal cost more accurately via observing price adjustments

made by other firms in i’s closely neighbored sectors.

However, differentials of financial costs in empirical data also reflect differentials of

other firm characteristics that are correlated with price inflexibility. To clearly identify

information cost, we compare the effects of BLS publication through this channel

between two financial environments. First, the firm borrows through the public bond

market. Second, the firm borrows from banks. Investors of diffusely owned public

bonds are not well informed about borrowers’ marginal costs, because they suffer from a

coordination problem when monitoring private information (Diamond (1991a), Diamond

(1991b)). Bond investors respond to their information disadvantage by charging a

higher spread. Compared to bond investors, banks are much better informed due to

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either repeated lending relationships or monitoring. As a result, loan spreads are less

sensitive to information asymmetries arising from price stickiness. Everything else

equal, the difference between bond and loan spreads speaks to the degree of information

asymmetries. We therefore aim to test the following hypothesis in the data.12

Hypothesis 1: When BLS discloses more product indices for a 3-digit NAICS sector,

sticky-price firms in this sector pay a lower bond spread but not loan spread. Such a

relation between BLS publication coverage and spread does not exist among flexible-price

firms.

3.2 Second Channel

In this subsection, we focus on the second channel by which product-price transparency

causes firms to become more responsive to a cost shock, which in turn reduces price

stickiness and makes sticky-price firms less risky. Here, firms as price setters are

imperfectly informed about a marginal cost shock and are interested in knowing pricing

actions taken by other firms. We first discuss the information structure underlying a shock

to a firm’s marginal cost. In Appendix A, we present Nimark (2008) partial-equilibrium

model to better illustrate the mechanism at work.

In each granular sector j, a continuum of monopolistic firms is distributed on a unit

interval [0, 1] indexed by i.13 Each firm produces differentiated goods. The customer

combines these differentiated goods to produce a consumption good for a specific sector

j. At time t, firm i in sector j is subject to a real marginal cost shock mcjt(i). The shock

is composed of two parts, a sector-wide component mcjt and a firm-specific component

µjt(i). Firm i only observes the sum of the two components as follows:

mcjt(i) = mcjt + µjt(i), (1)

where µjt(i) is an idiosyncratic component and µjt(i) ∼ N(0, σ2µ) for i ∈ (0, 1). The

12Duffie and Lando (2001) recognize that managers are either not able or not willing to communicate alltheir private information in the financial statements. They find that compensation for investors includesnot only the “structural” credit risk premium, but also the “imperfect information” risk premium. Fordeterminants of corporate bond spreads, see Huang and Huang (2012).

13In our empirical setting, j corresponds to a granular sector (e.g., 6-digit NAICS) belonging to acoarsely defined sector (e.g., 3-digit NAICS).

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sector-wide average marginal cost shock comes from an aggregate cost shock. We assume

it follows an exogenous AR(1) process:

mcjt = ρ×mcjt−1 + vt (2)

where vt ∼ N(0, σ2v) is an aggregate cost-push shock.

This idiosyncratic component µjt(i) may come from two sources. First, the impact of

a sector shock differs across firms, and firm i is privately informed about this impact.14

Second, each firm acts on the basis of its own perception regarding the state of the

sector-wide shock. µjt(i) comes from a subjective error that is specific to each individual

observer (Woodford, 2003). As in Sims (1998) and Sims (2003) , these errors might come

from the limited capacity of firms in collecting and processing information. The cross-firm

difference in such a capacity leads to a cross-firm difference in µjt(i).

Due to Calvo-type price stickiness, individual firms set price infrequently in response

to marginal cost shocks. However, imperfect information implies firms cannot perfectly

infer the sectoral marginal cost shock (mcjt) by observing their own marginal cost (µjt(i)).

They have to form expectations about the sectoral marginal cost and price level. As

Nimark (2008) shows, the presence of imperfect information and higher-order expectation

delays the adjustment of prices in response to marginal cost shocks and thereby creates

inflation inertia.

Given the information structure in equation (1), a decrease ofσµ2

σv2— the relative

variance of the idiosyncratic component to that of the average marginal cost innovation

– implies an increase in the accuracy of an individual firm’s private information. We

argue that BLS publication reducesσµ2

σv2for the following reason. The cost structures of

firms in sector j and j′ are highly correlated. Hence, knowing prices in sector j′ will help

sector j firms to predict average marginal cost and price more accurately based on their

own information on µjt(i). An increase in price transparency will reduce firms’ cost of

collecting and processing information and thereby increase the information content of

their observation of µjt(i) , which in turn makes firms more responsive to cost shocks. We

state our second hypothesis as follows:

14For example, how much an increase in oil price will affect production costs differs across firms. Inanother example, a firm can negotiate with its suppliers to share the profit loss under wage inflation. Thenegotiation outcome between the two contracting parties is not publicly disclosed. Different firms havebargaining powers, which implies cross-firm difference in the impact of wage inflation on costs.

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Hypothesis 2: When product-price transparency is high, granular product-price

indices are more responsive to aggregated cost shocks.

In Appendix A2, we develop a one-sector general equilibrium model to rationalize

the negative relation between return premium for sticky-price firms and BLS publication

coverage. Similar to Weber (2015), the central mechanism generating a cross-sectional

return premium for sticky-price firms in the model is a higher cyclicality of cash flows

for sticky-price firms after shocks to marginal utility. However, our model predicts that

output-price transparency may reduce risk exposures only for sticky-price firms, because

flexible firms can reset prices shortly after the information environment is improved.

In the model, the economy consists of households who supply labor and consume

goods, firms that produce differentiated goods and set prices and a monetary policy

authority that sets the nominal interest rate. Households are subject to economy-wide

shock to their (dis)utility of supplying labor. The labor supply shock is not directly

observable by firms but influences the marginal cost of production. In addition, firms’

marginal costs are also affected by specific wage bargaining shock and firms cannot by

direct observation distinguish between the economy-wide labor supply shock and the

idiosyncratic bargaining shock. Firms have to form higher order expectation of average

marginal cost in order to set prices optimally. In Appendix B, we provide the details of

the model.

Hypothesis 3: An increase in product- price transparency reduces sticky-price firms’

risk premium.

Figure 2 provides some graphical evidence on the decrease in the excess equity

premium after increase in information precision. We plot the relationship between

aggregate dividend Dt, and marginal utility C−γt , as a function of aggregate output, Yt.

We simulate the one-sector general equilibrium model for 200 periods 500 times, sort the

difference in dividend and marginal utility based on the realization of aggregate output,

and take the average across simulations. We simulate three scenarios, corresponding to the

case with low precision (σ2u

σ2v

= 0.2), medium precision (σ2u

σ2v

= 1) and high precision(σ2u

σ2v

= 5).

From Figure 2, we can see that, in times of low aggregate output and high marginal

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utility, the dividend corresponding to the low information precision case (black line) is

lower than in the higher-information-precision case (blue and red line). The covariance

between marginal utility and the aggregate dividend when σ2u

σ2v

= 0.2, 1, and 5 are −0.0865,

−0.0954, and −0.1080, respectively, suggesting lower information precision implies lower

relative dividends. Negative relative payoffs in times of high marginal utility are key

for positive return premia. Therefore, when information precision increases following an

incrase in BLS publication coverage, the return premium will decrease, as suggested by

our empirical evidence.

What leads to the decrease in the excess return premium? From the simulation, we

can show that when the information precision increases, the term related to marginal

cost and price dispersion will decrease, and inflation inertia will decrease as well. So,

more accurate private information helps the sticky-price firms form better predictions of

the sectoral marginal-cost shock and makes prices less sticky, which in turn leads to a

decrease of equity premium. This finding is consistent with Weber (2015)’s finding on

the impact of nominal rigidity on the excess return premium. He also emphasizes the

important of price dispersion in explaining the return premium.

4 Data

In subsection 4.1, we discuss the choice of sample period. In sections 4.2 and 4.3,

we describe the constructions of product-price transparency and the frequency of price

adjustment. To unify terminology, we refer to 3-digit NAICS codes as “coarsely defined

sectors”, and to 5- and 6-digit NAICS codes as “granular sectors”. In section 4.4, we

describe financial data.

4.1 Sample Period

For several reasons, we restrict our main analysis to the sample period of 1997-2012.

First, the NAICS system was first established in 1997 by the Bureau of Economic

Analysis (BEA). To convert SIC into NAICS for years before 1997, BEA relied heavily

on concordances developed in 1997. Such a single-year static concordance becomes

increasingly unreliable in early years before 1997 as the true relationship between NAICS

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and SIC changes over time. Because both our BLS publication and price-stickiness

measures are prepared in accordance with the NAICS system, we choose 1997 as the

beginning of sample. By doing so, we avoid inconsistence conversion from SIC into NAICS

in early years.15

Second, industry concentration is an important factor that is correlated with price

stickiness (D’Acunto et al., 2017). We mainly use the firm-level Hoberg-Phillips HHI

index (HP HHI) to measure product-market concentration. HP HHI was constructed

by Hoberg and Phillips (2010) and Hoberg and Phillips (2016) based on the distance

between firms in the product space, using textual analysis to assess the similarity

of firms’ product descriptions from the annual 10-K filings. The authors show that

relative to existing industry classifications, the text-based classifications offer economically

large improvements in their ability to explain differences in key characteristics such as

profitability, sales growth, and market risk across industries. We use concentration in

year t-1 to control for confounding factors affecting independent variables. Becuase HP

HHI is only available starting from 1996, 1997 becomes a natural start of the sample

period.

Third, we rely on the Dealscan-Compustat link file complied by Chava and Roberts

(2008) to merge syndicated loans with Compustat firms. The link applies to facilities

started from August 5, 1987, until August 31, 2012, which justifies the end period of the

sample.

4.2 Output-Price Transparency

In this subsection, we formally discuss how we measure BLS publication coverage. The

measure describes the extent to which investors and firms of a granular sector j observe

price adjustments made by closely-neighbored, granular sectors within the same coarsely

defined sector. In each month s (1 ≤ s ≤ N), Pub% is defined as

Pub%k,s =nk,s

max(nk,1...nk,N)(3)

where k denotes a 3-digit NAICS sector. nk,s is the number of product indices within

sector k disclosed by BLS in month s. max(nk,1...nk,N) is the maximum number of

15See Yuskavage (2007) for an introduction on how BEA converted historical industry time-series datafrom SIC to NAICS.

15

product indices for sector k on our observation period of January 1997 through May

2016. We set our observation period longer than the sample period to exhaust all the

product indices within 3-digit sector published by BLS. We exclude miscellaneous items.

Figure 1 reports the time series of mean and 95% confidence intervals for Pub% on

the sample period 1997-2012. The Pub% was 0.6 in January 1997 and steadily increased

to 0.7 before experiencing a discontinuous jump of 15 percentage points in January 2004,

when the PPI program replaced the SIC codes with the NAICS codes. The size of such

jump is 21% of the average ratio of Put% as of November, 2003.16

4.3 Frequency of Price Adjustment

To measure price stickiness, we use the frequency of price adjustment at the granular

sector level in accordance with the NAICS. The data are proprietary and are provided by

Pasten, Schoenle, and Weber (2017) using the confidential microdata underlying the PPI

from the BLS.

On the sample period of 2002-2012, Pasten et al. (2017) calculated the frequency of

price adjustment at the goods level as the ratio of the number of price changes to the total

number of sample months. For example, if an observed price path is $5 for three months

and then $10 for another two months, one price change occurs during five months, and

the frequency is 1/5. The authors then aggregate goods-based frequencies into NAICS

industries at different granularities.

Table 1 presents descriptive statistics of BLS publication (Pub%) and the frequency

of price adjustment (FPA) across the 3-digit NAICS sectors. Two patterns emerge. First,

the frequency of price adjustment across coarsely defined sectors contains substantial

heterogeneity, ranging from as low as 9% for professional, scientific, and technical services

to 93.75% for oil and gas extraction. The numbers imply the two sectors will keep prices

constant for 1.3 and 23.1 months, respectively.17

Second, output-price transparency also substantially varies across sectors. In the

sample period of 1997-2012, only less than 50% of product-price indices were published

16The maximum number of product indices does not always reach the end of our sample period.Product indices go out of publication if they meet either of the two following conditions. First, the indexmust have cooperation from a minimum number of reporting units (establishments) Second, in any givenmonth, the index must have actual prices from a minimum number of reporting units.

17We use −1/ log(1-adjustment frequency) to calculate implied durations.

16

for sectors such as merchant wholesalers, whereas almost 100% of indices were published

for sectors such as education services and furniture.

4.4 Financial Data

We collect stock returns from the monthly stock- return file from the Center for Research in

Security Prices (CRSP). We collect financial and balance-sheet variables from Compustat.

We construct two samples for debt financing during the period 1997-2012. The

first sample covers all firms with public debt (bonds) issued. We obtain the data on

the issuances of public bonds from the Securities Data Company (SDC) database. The

second sample is based on the deals from the private debt market. This sample is a set

of syndicated loan issuances from the Dealscan database provided by the Loan Pricing

Corporation (LPC). We collapse a package with multiple facilities contracted on the

same date into one observation. Deal amount, maturity, and spread in each package are

calculated as the sum of the amount across facilities, the average maturity, and the average

all-in-drawn spread over the London Interbank Offered Rate (LIBOR). The average values

are weighted by the amount of each facility within the package. We then match these

loans to Compustat via the August 2012 version of the Dealscan-Compustat linking table

introduced by Chava and Roberts (2008).

4.5 Earnings Conference Call

We obtain earnings conference call transcript data from Thomson Reuters, specifically

from the StreetEvents data feed. We collect the complete transcripts of all US conference

calls for the period of 2002 to 2012. We match the call transcripts with Compustat and

CRSP data based on the start date and company ticker of the firm conducting the call.

We focus on the presentation by the CEO and CFO during earnings conference calls and

count the frequency of cost-related sentences with future tense.

17

5 Price Stickiness, BLS Publication, and Borrowing

Costs

In this section, we test hypothesis 1, namely, that BLS disclosure creates value for sticky-

price firms, resolving information frictions in the debt market. We exploit the different

responses of the bond and loan spread to the change of output-price transparency to pin

down the impact of public information on financing costs paid by sticky-price firms.

5.1 Ordinary Least-Squares Analysis

Our first empirical specification is ordinary least- squares (OLS) equation. We combine

issuances of syndicated loans and bonds together and form two subsamples based on

whether a borrower is a sticky-price firm or not. Sticky-price firms are defined as firms

whose price-adjustment frequency is below the sample median (FPA=18%). Flexible

firms are defined analogously. Within each subsample, our most general specification is

the following OLS equation:

Spreadq,j,s = α+β×Bondi,s+γ×Bondi,s×Pub%k,s+λ×Pub%k,s+X′i,t−1×θ+ηt+ηi+εq,j,s

(4)

Spreadq,j,s is spread for firm i in deal q as of month s. Pub%k,s is measured as in equation

(3) and it varies across 3-digit NAICS sector k and over month s. Bondi,s is an indicator

variable equal to 1 if firm i borrows a public bond in month s, and zero if the firm borrows

a loan. X is a set of control variables, including stock-return volatility, profitability, sales,

book-to-market ratio, intangibility, long-term debt, price-to-cost margin, and industry

concentration. All the control variables are measured as of year t-1. ηt is a set of year

fixed effects absorbing time-varying shocks all firms face when borrowing occurs, and ηi is

a set of firm fixed effects absorbing time-invariant characteristics that differ across firms.

In alternative specifications, we replace ηi with η′k, a set of industry fixed effects. k′ refers

to coarsely defined sectors other than NAICS, namely, Hoberg-Philips 50 or Fama-French

48 industries. Standard errors are clustered at the 3-digit NAICS level. Our results are

virtually unchanged if we cluster standard errors at the 6-digit level.

We select control variables following D’Acunto et al. (2017). Total Vol is the

annualized return volatility in the previous calendar year using daily data. We set the

18

volatility to missing if we have less than 60 daily return observations. Profitability is

operating income over total assets (Profitability). Ln (Sales) is the log of sales. BM is

the book-to-market ratio. Intangibility is intangible assets defined as total assets minus

the sum of net property, plant, and equipment; cash and short-term investments; total

receivables; and total inventories to total assets. PCM is the ratio of net sales minus the

cost of goods sold to net sales. HP HHI is the concentration within the Hoberg-Phillips

industries.18 We exclude the period from December 2007 to June 2009 to circumvent the

concerns associated with the effects of the Great Recession. We also exclude firms from

financial and utilities sectors. Including these two sectors, however, does not change our

results.19

Table 2 provides descriptive statistics of the bond and syndicated loan samples in

Panel A, and correlations across variables in Panel B. On average, bond borrowers pay

49-basis-points higher spreads than loan borrowers. Compared to loan borrowers, bond

borrowers are more profitable, are of larger firm size, have lower book-to-market ratios,

and have higher long-term debt ratios and profit margins. In addition, bond borrowers

are from sectors in which the frequency of price adjustment is higher and for which the

BLS has published more product indices. Panel B shows the contemporeous correlations

among variables in Panel A.

Table 3 reports the baseline results. In columns (1)-(4) of Table 3, Pub% is the

continuous measure of BLS publication. In columns (5)-(8), it is a dummy variable that

equals 1 for firms in the top 50% of the distribution based on Pub%, and 0 for firms in

the bottom 50% of the distribution. In columns (1)-(2) and (5)-(6), we report results

for sticky-price firms; in columns (3)-(4) and (7)-(8), we report results for flexible firms.

We only exploit variation within firms and years. Our results are the same if we exploit

variation within HP 50 or Fama-French 48 industries.

In column (1) of Table 3, we regress Spread on Bond, Bond×Pub%, Pub%, other

standard determinants of interest rate, and year and firm fixed effects, as well as

measures of market power and market concentration, both at the firm level. Compared

to loan borrowers, borrowers of public bonds on average pay an extra 242 basis

18As a robust check, we also use HHI of annual sales at the Fama-French 48-industry level. Our resultsare not materially altered.

19For the periods during the Great Recession, we follow the US business-cycle expansion andcontractions define by the National Bureau of Economic Research (NBER).

19

points. Product-price transparency, however, largely reduces the premium for public

bonds. A one-standard-deviation increase in BLS publication (0.18) is associated with a

32.5-basis-point decrease in bond spread, which is 13% of the size of the bond premium.

In column (2), we add stock-return volatility (Total Vol) to control for time-varying

risk aversion, fades, or noise trader risk. Although total volatility is strongly positively

associated with spread, coefficients for variables of our main interest are not changed.

In columns (3)-(4), we repeat the same analysis on flexile firms. As column (3)

shows, flexible-price borrowers of public bonds on average pay an extra 175 basis points,

which is 67 basis points lower than the bond premium paid by sticky-price borrowers.

Pub% still reduces the bond premium, but its magnitude becomes economically and

statistically insignificant. A one-standard-deviation increase in Pub% (0.18) is associated

with a 13-basis-point decrease in bond spread, which is only 7.5% of the size of the bond

premium.

In columns (5)-(8), we replace the continuous measure of Pub% with the dummy

variable HPub% as defined above. Both sticky-price and flexible bond issuers again

pay a premium ranging from 127 to 132 basis points, and sticky-price firms with higher

BLS publication coverage pay about 50 basis points less, which is 38% of the total bond

premium. By contrast, BLS publication does not reduce the spread paid by flexible-price

firms.

In Table A4 of online appendix, we introduce triple interactions among the frequency

of price adjustment, BLS publication and a dummy variable indicating bond issuance. We

find that (1) sticky-price firms pay a higher bond spread than flexible firms, and (2) BLS

publication reduces the bond premium paid by sticky-price firms. The pattern is especially

significant when we use the continuous version of BLS publication (see columns (1)-(2) of

Table A4).

5.2 Instrumental Variable (IV) Approach

To identify the impact of transparency on bond spread, our strategy is similar to D’Acunto

et al. (2017). We regard price stickiness as a highly persistent firm characteristic.20 We

test whether an exogenous shock to the supply of BLS disclosure reduces the bond spread

20For example, in their sample of S&P 500 firms, D’Acunto et al. (2017) find a firm-level regression ofpost-1996 price flexibility on pre-1996 price flexibility yields a slope coefficient of 93%, and they fail toreject the null that the coefficient equals 1.

20

of sticky-price firms more than that of flexible-price firms.

With the release of data for January 2004, the PPI program of BLS changed its

basis for industry classification from the 1987 SIC system to the NAICS. We use the

cross-sector variation in the new coverage expanded by BLS under the NAICS system

to instrument for Pub% after January 2004. For each granular sector j, the publication

of product indices for j’s close neighbors should be orthogonal to any economic factors

within j after the expansion. To do so, we calculate ∆Pub%2004k , the difference in Pub%

between November 2003 and January 2004 for each 3-digit NAICS sector k.

Table A3 presents descriptive statistics of ∆Pub2004k across sectors. The change of

Pub%k across coarsely defined sectors contains substantial heterogeneity, ranging from as

low as 0% for forestry and logging to 73% for general merchandise stores. The first-stage

regression of the IV estimation is then given by:

Pub%i,k,s = α + β ×∆Pub%2004k × Post+X ′i,t−1 × θ + ηt + ηk + εi,s (5)

Post is an indicator equals to 1 if month s is within the period from January 2004 through

December 2012, and zero otherwise. ηk and ηt are a set of sector- and year-firm fixed

effects, respectively. Table 4 reports the estimation results for the first-stage regression.

Columns (1)-(3) report the regression results when the continuous version of Pub% is

used as the dependent variable. Columns (4)-(6) report results when BLS publication is

used as a dummy variable. The coefficient on the interaction term, ∆Pub%2004k ×Post, is

both economically and statistically significant, and the results hold for both sticky- and

flexible-price-firm subsamples. F -statistics show ∆Pub%2004k ×Post is a strong instrument.

Table 5 presents the IV-estimation results. In columns (1)-(4), we perform IV

estimations in which both Pub% and Bond × Pub% are instrumented. The results are

similar to those reported in columns (1)-(4) of Table 3. For flexible firms, however,

although the magnitude of the interaction term becomes larger, it loses statistical

significance (see columns (3)-(4) of Table 5). In columns (5)-(8), we also show that

when BLS publication is measured as an indicator variable, it again only reduces the

bond premium in sticky-price firms.

21

5.3 Additional Tests

Table A5 of the online appendix presents the estimated effects of the interaction between

price stickiness and BLS publication on a variety of loan-contract outcomes. Columns

(1)-(6) report OLS results, and columns (7)-(12) report IV-estimation results. One

concern with the interpretation of results in Tables 3 and 5 is that the loan spread

is not the only vehicle through which banks express their concern about information

asymmetry. Banks could monitor or screen borrowers using covenants or collaterals

(Rajan and Winton (1995); Sufi (2007); Ball and Bushman (2008)). If our conjecture

is right, we should not observe that banks change contract terms for sticky-price firms

when BLS publication coverage increases. The dependent variables we select include

lead lender’s ownership, restrictions on dividend payouts and capital expenditures, and

financial and capital covenants. We do not find any systematic pattern suggesting banks

use monitoring to substitute interest rates for sticky-price borrowers.

In the previous exercises, we regressed a persistent variable (bond or loan spread)

on other persistence variables (BLS publication). In Table A6 in the online appendix,

we collapse our data at the firm level and run a single cross-sectional regression. Three

variables of interests – BLS publication coverage, price stickiness, and interest rates –

mostly vary at the firm or sector level. In Table A6, we show our results are economically

and statistically similar.

In Table A7 in the Online Appendix, we examine whether price stickiness

predicts future stock-return volatility, an alternative measure of information asymmetry,

conditioning on current return volatility. For each June of year t, we calculate our

dependent variables – total volatility and idiosyncratic volatility – using data of daily

stock returns from July t to June t+1. We use CAPM model to estimate residual stock

returns and then calculate idiosyncratic volatility. Columns (1)-(6) report results for

OLS estimations and columns (7)-(12) report results for IV estimations. Our results

are consistent with price stickiness capturing cash flows volatilities and BLS disclosure

reducing such volatility in sticky-price firms.

22

6 Price Stickiness, BLS Publication, and Risk Pre-

mium

In this section, we test hypothesis 2, which predicts the disclosure of price information

by BLS improves sticky-price firms’ precision of private signals concerning idiosyncratic

shocks to the marginal cost and leads to more frequent price adjustments. In particular, we

test the value of public information for firms by recognizing the fact that price stickiness

constitutes a source of macroeconomic risk that is priced in the cross section of stock

returns.21 We hypothesize that BLS disclosure has less of an effect on flexible-price firms.

6.1 BLS Publication and Cost Pass-Through

In this subsection, we empirically validate the theoretical prediction that disclosures of

neighborhood prices encourage firms with imperfect common knowledge and private mea-

surement error to adjust more to cost shocks. We specify the following cost-pass-through

equation:

πj,s = α+18∑h=1

βs−h×vj,s+18∑h=1

γs−h×vj,s×HPub%k,s+18∑h=1

δs−h×HPub%k,s+ηj+εj,s (6)

The dependent variable is πj,s, the inflation rate for product j at the 6-digit sector as of

month s. vs is a cost shock at the aggregate level as of month s. ηj is a set of product

fixed effects at 6-digit level. The MA representation in equation (6) echoes the prediction

of Nimark (2008). The lagged vs will have a positive impact on current inflation only

if the average marginal cost follows a persistent process. If ρ in equation (2) becomes

zero, lagged inflation does not hold any information relevant to the firm’s price-setting

problem, and inflation becomes a white-noise process.22 We measure vs as follows

vj,s = ∆PPIs × Input%j,s (7)

21Using S&P 500 firms in the sample period of 1982-2007, Weber (2015) shows sticky-price firms arerisky and command an additional 4% return premium compared to firms with flexible prices.

22In Appendix A, we introduce partial-equilibrium model originlly developed by Nimark (2008) toillustrate the mechanism of the second channel.

23

where ∆PPIs is the growth rate of producer price index (PPI) at the aggregate level

from month s-1 to s. For each 6-digit NAICS sector j, we calculate Input% as the total

intermediate goods as a percentage of total output. Therefore, we allow for differential

impacts of a one-unit PPI shock on different products.23

Table 6 presents estimation results. Columns (1)-(2) report regression results when

HPub%k,s is defined based on the distribution of Pub%k,s. The relations between BLS

publication and price response to cost shocks differ between sticky and flexible firms.

Columns (3)-(4) report regression results for the IV estimation, where HPub% is equal

to 1 if the predicted value of Pub% is above the sample median, and zero otherwise.

Figure 3 plots the pass-through coefficients. The statistic of eventual interest is the

sum of the coefficients on the lagged vs: β(n)L =∑h=1

h=n βs−h if HPub%=0 and β(n)H =∑h=1h=n βs−h if HPub%=1. These coefficients reflect the impact the change in the current

PPI index has on future micro inflation over time. Our objective is to compare these

estimates across firms as the number of lags included in the specification is increased from

1 to 18. In Panel A of Figure 2, sticky-price firms with higher BLS publication coverage

in their corresponding 3-digit sector adjust more than do those with lower coverage. A

difference is visible around the seventh month. Our most surprising findings are from

Panel B. The plot shows that flexible firms with more BLS coverage adjust more slowly

in the first seven months than those firms with less coverage.

Figure 4 plots coefficients when HPub% is set to be 1 if the predicted value of Pub%

(P ub%) is above the sample median, and zero otherwise. P ub% is the predicted value

according to the following equation:

Pub%j,k,s = α + β ×∆Pub%2004k × Post+ ηt + ηk + εj,s (8)

The difference in the role of BLS disclosure on cost pass-through between sticky- and

flexible-price firms is even more evident. This time, flexible firms with high BLS

publication coverage adjust even much less than those with low coverage from the

beginning of the shock all the way to the 18th month.

Our findings on flexible firms are not consistent with intuition but are more consistent

23An alternative test for the mechanism in Hypothesis 2 is to examine whether an increase in pricerelease at the level of coarsely defined sectors (3-digit NAICS) reduces price stickiness or inflation inertia.However, this test is not feasible given the data availability. The frequency of price adjustment providedby Pasten, Schoenle, and Weber (2017) is time invariant.

24

with public information imposing negative effects on firms, especially when they have

independent sources of information. In section 6, we discuss a possible mechanism through

which public information hurts flexible-price firms.

6.2 Portfolio

In this subsection, we double sort stocks unconditionally by the frequency of price

adjustment and BLS publication coverage. We aim to test if differences in price stickiness

and output-price transparency are associated with dierences in returns.

Our main sample period is from July 1997 to June 2013. In each year t, we first

assign stocks into two baskets based on the frequency of price adjustment. Because the

frequency of price adjustment does not vary over years, we follow Weber (2015) to only

sort once at the beginning of the sample period to minimize concerns about measurement

error. We then assign stocks independently into four baskets based on the measure of

product-price transparency (Pub%).

Table 7 presents descriptive statistics. Panel A summarizes time-series averages of

annual means and standard deviations of the return predictors. We have on average

more than 3,400 distinct firms per year. The sample consists of domestic stocks listed on

NYSE, AMEX, and NASDAQ from 1997 to 2013. Domestic stocks are defined as firms

whose headquarters are based in the United States. They have a mean size of more than

$240 million and a beta of 0.81. Panel B presents contemporaneous correlations. We see

that firms with more flexible prices have higher book-to-market ratios and long-term-debt

ratios, but also lower betas and price-to-cost margins.

In Panel A of Table 8, we report average equally weighted annual returns on the

sample period of July 1997 to June 2013. P1 to P4 are portfolios sorted on Pub% in

ascending order. Since Pub% increases over time, we rebalance P1-P4 portfolios annually

to remove the time effects. Stocks in P1 and P4 have the lowest and highest levels of BLS

publication coverage, respectively. Sticky and flexible portfolios are labeled “S” and “F”,

respectively. The double sorting generates several patterns for the return spread. First,

within sticky-price firms, mean returns decrease monotonically in the level of output-price

transparency. The spread is 15.75 percentage points per year and is statistically and

economically large. Second, the spread in returns between sticky- and flexible-price firms

appears only when publication coverage by BLS is low. The spread is about 9.8 percentage

25

points per year in the lowest decile of BLS publication and decreases as transparency

increases. Third, within flexible-price firms, mean returns increase monotonically with

product-price transparency, and the return spread is about 12 percentage points per year.

In Panel B of Table 8, we report returns adjusted for firm characteristics to

disentangle a premium of our interest from well-known cross-sectional return predictors.

Following Daniel et al. (1997), we report portfolio mean returns after adjusting for firm

characteristics. We calculate benchmark-adjusted returns by subtracting the assigned

portfolio returns from the individual stock returns. For sticky-price firms, the spread

between the transparency portfolios of decile 1 and 4 becomes 9.5 percentage points per

year; for flexible-price firms, the spread between the two extreme portfolios becomes 9.8

percentage points.

In Panels C and D of Table 8, we report average equally weighted annual returns on a

longer sample period, from 1983 to 2013. The patterns in these two panels resemble those

in Panels A and B. For example, for sticky-price firms, the characteristics-adjusted return

spread between the transparency portfolios of deciles 1 and 4 becomes 10.8 percentage

points per year; for flexible-price firms, the spread between the two extreme portfolios is

24 percentage points per year. In addition, sticky-price premia are found in decile 1 and

2. In decile 3 and 4, flexible-price stocks commend a higher premium than sticky-price

stocks.

6.3 Panel Regressions

To control for more confounding factors, we use the following panel-regression equation:

Ri,t = α+β×Stickyj+γ×Stickyj×Pub%k,t+δ×Pub%k,t+X′i,t−1×θ+ηt+ηk+εi,t (9)

Ri,t is annual return (in percent) calculated from July of year t to June of year t+1 for

firm i. Pub%k,t is measured for each 3-digit NAICS sector k as of June of year t. X is a

set of control variables, chosen following Weber (2015), including market capitalization,

β, turnover, bid-ask spread, book-to-market ratio, leverage, cash flow, price-cost margin,

and firm-level HP HHI. Standard errors are adjusted at the 3-digit NAICS sector level.

Size of year t is the natural logarithm of the total market capitalization (in thousands)

in June of year t. β is the regression coefficient in rolling time-series regressions of monthly

26

excess returns on a constant and the excess returns of the CRSP value-weighted index over

the previous 252 trading days. We set β to missing if we have less than 60 daily returns

in the estimation period. Turnover is the ratio of volume to shares outstanding. Spread

is the monthly average of the daily bid-ask spreads from the CRSP Daily Stock file. The

book-to-market (BM ) ratio of year t is then the book equity for the fiscal year ending in

calendar year t-1 over the market equity as of December t-1. Leverage (Lev) is the ratio

of total long-term debt and debt in current liabilities over the sum of the numerator and

shareholders’ equity. Cash flow (CF ) is the sum of income before extraordinary items

and depreciation and amortization over total assets.24

Table 9 reports OLS regression results for annual, non-overlapping percentage

returns. The coefficient on Sticky in column (1) is zero. In column (2), we add

Fama-French 48 industry-fixed effects but again fail to find risk premium for sticky-price

firms. In column (3), we interact Sticky with Pub%. Sticky turns out to be significantly

positive and Sticky × Pub%, significantly negative. Holding Pub% at the mean (0.74),

sticky-price stocks only commend a 2-percentage-point (42-54 × 0.74) higher premium

per year than flexible-price stocks. A one-standard-deviation increase in Pub% (0.21)

reduces the return premium on sticky-price firms by only 1.26 percentage points. For

flexible-price firms, a one-standard deviation increase in Pub% is associated with a

10.1-percentage-point premium.

In column (4), we report results by exploiting variation within Fama-French 48

industries. The economic and statistical magnitudes of Pub% are largely reduced. For

example, a one-standard-deviation increase in Pub% (0.21) reduces 4.7 percentage points

of return premium on sticky-price firms. A one-standard deviation increase in Pub%

increases 5.1 percent points premium for flexible-price firms, but the statistical significance

is weak. In column (5), we exploit variation within 6-digit NAICS sectors, but the

marginal impact of Pub% on sticky firm returns is largely reduced. In column (6)-(8), we

add all the firm characteristics and return predictors and our results are similar. Pub%

now becomes a strong return predictor despite the addition of industry fixed effects. In

column (9)-(14), we replace Pub% with HPub%, a dummy variable equal to 1 if Pub% is

above the sample median, and zero otherwise. The interaction term Sticky ×HPub% is

significantly negative.

24We follow Weber (2015) to select control variables.

27

One concern about the interpretation of results in Table 9 is that BLS publication

coverage simultaneously captures many other economic factors at the 3-digit NAICS

sector level. For example, the number of products captures the substitution across goods

within a sector. Intuitively, firms in sectors experiencing such an increase face greater

competition and therefore become riskier. In fact, this positive effect can dominate the

effect of transparency. Following the same IV strategy in section 4.2, we instrument BLS

publication coverage.

Table 10 reports IV-estimation regression results. Sticky is significantly positive

in most columns, and Sticky × Pub%, or Sticky × HPub%, is significantly negative.

However, Pub% or its dummy version becomes a weak predictor of stock returns, and its

economic magnitude is smaller than that of the interaction term. This finding implies that

(1) annualized returns for sticky-price stocks will unambiguously, monotonically decrease

with the level of BLS publication, and (2) returns for flexible-price firms will weakly but

monotonically increase with the level of BLS publication. In other words, the results in

Table 10 resemble the pattern of mean portfolio returns in Table 8. Table A8 reports IV

estimation results on the sample period from July 1983 to June 2012.

In Table 8, we find that stocks of sticky-price firms command a higher return premium

only in sectors where BLS publication coverage is low. However, we only form two

portfolios based on the frequency of price adjustment. A limitation of the portfolio

analysis is that returns may differ across portfolios for other reasons. In Table 11, we

run panel regressions of stock returns at the firm level on a continuous measure of price

stickiness and a dummy variable indicating whether BLS publication is above or below

sample median. We multiply the frequence of price adjustment by minus 1 to obtain the

continuous measure price stickiness. Columns (1)-(8) report results for OLS regressions

and columns (9)-(13) report results for IV estimations.

The results are in line with the statistic pattern in Table 8. When BLS publication

coverage is low, price stickiness positively predicts stock returns. The results survive if we

only exploit variation of price stickiness within industries. We use column (4) to illustrate

the economic magnitude. For firms located in sectors with low output-price transparency,

a one-standard-deviation increase in stickiness increases annual return premium by 9.2

percentage points (0.17 × 54.5). However, the relation between price stickiness and return

premium reverses when BLS publication coverage ratio becomes high.

28

6.4 Conditional CAPM

We perform time-series tests of the CAPM to explore whether differential exposure to

market risk can explain the impact of BLS publication on the premium for sticky-price

stocks. Following Lewellen and Nagel (2006) and Weber (2015), we estimate the

conditional CAPM monthly on a rolling basis over the previous 12 months as follows:

Rp,s = αp + βp ×Rm,s + εp,s, (10)

where Rp,s is the portfolio excess return, αp is a constant, and Rm,s is the excess return of

the CRSP value-weighted index. The CAPM predicts that exposure to market risk fully

explains the expected excess return, which implies α is zero.

Table 12 reports βs for the conditional CAPM and αs (in percent) per month. We

report Newey and West (1987) corrected t-statistics in parentheses. Panels A and B

report results on the sample period from July 1997 to June 2013. In sticky-price firms,

βs monotonically decreases from 1.27 for portfolio P1 to 1.14 for portfolio P4. When the

frequency of price adjustment is low, the difference in annual returns between stocks in

the two extreme Pub% portfolios is definitely pertaining to their differential exposures

to market risk. However, the conditional CAPM cannot explain the portfolio returns.

Monthly αs are negative, economically large, and statistically significant. They partially

explain return spreads both between sticky-and flexible-price portfolios, conditioning on

BLS publication, and between P1 and P4, conditioning on price stickiness. For flexible-

price firms, β increases from 1.04 for portfolio P1 to 1.20 for portfolio P2, and slightly

declined to 1.16 in P4. This pattern suggests BLS publication coverage increases the risk

exposure of flexible firms, but not monotonically. In Panels C and D, we report results

on the sample period from July 1983 to June 2013. We find similar results.

7 Firms’ Own Private Signals

In this section, we explore the reason why public information becomes a double-edged

sword in the second channel. On a global analysis, when the private signal has precision

no lower than the precision of the public signal, welfare is lower with the public signal

than without (Morris and Shin, 2002; Morris et al., 2006). The intuition is that, although

29

public information is effective in influencing actions, firms also overreact to the noise in

the public signal. Amador and Weill (2010) propose a different mechanism in which the

informativeness of a fundamental is the weight that agents collectively assign to their

forecasts using private information. However, the release of public information leads

agents to put less weight on their private forecasts, making prices less informative. This

prompts agents to put even less weight on their private forecasts, making prices even

less informative, and so on. In this mechanism, public information increases agents’

uncertainty.

We hypothesize that the reason why BLS disclosure has different value implications

for firms is that sticky- and flexible-firms have private signals at different levels of

precisions. In particular, managers of flexible-price firms have much better private signals

that do those of sticky-price firms. While menu costs could be an alternative explanation

for price stickiness, less efficient managers, or managers with higher attention costs, are

directly responsible for firms’ lack of private information, which leads to sticky-output

price.

Unfortunately, it is impossible to infer from metrics from CEO/CFO profiles to know

whether they are more or less informed about firm marginal costs. Thus, we utilize

earnings conference call transcripts to shed some lights on the issue. A conference call is

a teleconference, or webcast, through which security analysts have access management’s

private information. The prescriptions of Regulation FD require public firms to use

conference calls to prompt documentation and dissemination of material information to

all analysts. Regulation FD thereby eliminates the informational advantage an analyst

would otherwise have from private discussions with management. Therefore, managers’

discussions during earnings conference calls provide us a unique setting to gauge the extent

to which managers are privately informed about future marginal costs of their companies,

which is central to a firm’s pricing strategy.

Following several criteria, we count the number of sentences from each transcript that

are related to a manager’s outlook for production cost of his own company. First, the

cost-related-word list includes: cost(s), expense(s), expenditure(s), spend, and spending.25

Second, cost-related sentences are in future tense. Third, more important, we require

exact numbers to be paired with cost-related words. Fourth, we exclude scripts with less

25We only focus on managerial discussions on production costs. We therefore exclude wordingsindicating expenses related to capital expenditure, compensation, mergers and acquisitions and pensions.

30

than 200 words in the presentation section. For example, the sentence “we obviously will

have an input cost inflation of about 3% to 4% throughout the Group” satisfies the three

of our criteria. We seperately search for sentences that are included in the presentation

and Q&A sections. This is because existing studies find that managers discriminate

among analysts during the Q&A section based on how favorably the analyst views the

firm (Mayew, 2008; Cohen et al., 2017).

Figure 4 documents the novel stylized fact. We sort US domestic firms listed on

NYSE/ NASDAQ/AMEX into four equally sized groups with increasing output-price

flexibility. Panel A plots the probability of management quantitatively predicting future

input expenses during the presentation section of a conference call. Moving from firms

with the most rigid output prices to firms with the most flexible output prices increases

the probability from around 43% to over 56%. Panel B plots the mean for the number

of related sentences discussed by CEO/CFO in each conference call. Moving from firms

with the most rigid output prices to firms with the most flexible output prices increases

the number from 2.1 to about 3.

To assess the magnitude of the correlation between price stickiness and managers’

private signals about the trend of input costs, we specify the following linear probability

regression equation:

Discussioni,t = α + β × Sticky%j +X ′i,t−1 × θ + ηk′ + ηt + εi,t. (11)

Discussioni,t is a dummy variable that equals to 1 if CEOs or CFOs provide exact numbers

when discussing the trend of firm i’s input costs in the earnings conference call held in

quarter t, and zero otherwise. Sticky% is a continuous measure of price stickiness, which

is calculated as −1 × FPA at the level of 6-digidit of NAICS sectors. In an alternative

specification, we also create a dummy variable (Sticky10pctl) that equals 1 for the firms

in the bottom 10% of the distribution based on the frequency of price adjustment, and

zero otherwise.

Xi,t−1 is a set of control variables measured as of the beginning of year-quarter t. We

include the dispersion of analysts’ one-year-ahead forecast of earnings per share (scaled

by lagged stock prices), the percentages of equity holdings by institutional investors,

the number of security analysts covering a firm, the number of managers attending the

event, the number of analyst participating the conference calls, market capitalization,

31

book-to-market ratio, price-to-cost margin, and industry concentration. ηt is a set of

year-quarter fixed and ηk is a set of Fama-French-48 industries fixed effects. Standard

errors are clustered at the 6-digit NAICS level.

Table 13 reports the estimates for the coefficients in equation (13). In columns

(1)-(4), managers’ discussion of our interest is searched based on the entire transcript

of conference call. In columns (5)-(8) and (9)-(12), related sentences define the measure

of Discussion are searched based on the presentation and Q&A sections, respectively.

Across all specifications, the sign of the estimated coefficients, β, are in line with the

pattern in Figure 5. Managers of firms with higher degree of price stickiness provide less

detailed estimations about the company’s future input costs. In column (1), the estimated

coefficient for Sticky% is -0.21 (t=5.5). Thus, a one-standard-deviation increase in price

stickiness implies a reduction in the likelihood of managerial discussion of input costs by

3.5% (0.21 × 0.18)– about 8.8% of sample mean. Our results hold in both presentation

and Q&A sessions and are not materially affected if we use a dummy variable to measure

price stickiness. In addition, the results also survive if we only exploit variation within

Fama-French 48 industries, and hence we absorb any time-invariant determinant of the

dependent variable at the industry level.

8 Conclusion

Firms differ in the frequency with which they adjust output prices. A poor information

environment in which firms operate can either cause or be caused by price stickiness.

This simple observation raises an interesting yet unanswered research question: Does a

transparent information environment for product prices create value for sticky-price firms?

In this paper, we measure the transparency of output prices using the staggered

disclosure of product indices compiled by the BLS. An increase in transparency implies

interested parties of a firm can observe more price adjustments made by closely

neighbored, granular sectors. We find two distinct channels by which transparency

creates value for sticky-price firms. First, transparency resolves information frictions

between sticky-price firms and investors. We test this channel by exploiting the

differential of information asymmetries between private and public debt markets. Second,

output-price transparency improves the precision of individual firms’ private signals

32

and encourages them to become more responsive to persistent marginal cost shocks.

This mechanism effectively reduces sticky-price firms’ exposure to systematic risks. To

overcome endogeneity, we exploit an exogenous event in which the Office of Publications

at BLS differentially expands its publication coverage across 3-digit NAICS sectors.

Surprisingly, we also show that more BLS disclosures might negatively affect flexible-

price firms. These firms might have both high quality private information (concerning

cost shocks) as well as strategic motives to coordinate. Our results on cost-pass through

in flexible price sectors are broadly consistent with theoretical predictions proposed by

Morris and Shin (2002) and Amador and Weill (2010), among others, that the detrimental

effects of public information can dominate in equilibrium when firms that have access to

independent sources of information.

Academics and policy makers have heatedly debated the questions why firms

have rigid prices and whether sticky prices burden firms. A growing finance and

macroeconomics literature document novel facts showing that firms with rigid output

prices are more exposed to macroeconomic shocks, experience high volatilities around

monetary policy shocks, and borrow less long-term debt (Weber (2015); Gorodnichenko

and Weber (2016); D’Acunto et al. (2017)). Utilizing data from financial markets, we

contribute to the understanding of the nature of the real effects of nominal price rigidities.

In particular, our empirical results speak to the role of government statistical agencies to

guide firms to make decisions.

33

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36

Figure 1: BLS Publication over Time: January 1997 - December 2012

This figure shows the time series of the mean and standard deviation of the measure of product price

transparency (y-axsis) at the monthly frequency. In each month s (1≤s≤N), Pub% is defined as

Pub%k,s =nk,s

max(nk,1, ..nk,N ),

where k denotes a 3-digit NAICS sector. nk,s is the number of product indices within sector k disclosed by

BLS in month s. max(nk,1, ..nk,N ) is the maximum number of product indices for sector k in our observation

period of January 1997 through May 2016.

.5.6

.7.8

.9

199701 200301200001 200601 200901 201201

37

Figure 2: Difference in Sector Dividends and Marginal Utility

The figure plots the relationship between aggregate dividend Dt, and marginal utility C−γt , as a function of

aggregate output, Yt. We simulate the one-sector general equilibrium model for 200 periods and 500 times,

sort the difference in dividend and marginal utility based on the realization of aggregate output, and take

the average across simulations. We simulate three scenarios, corresponding to the case with low precision

(black line), medium precision (blue line) and high precision (red line). The dividend is measured on the

right y-axis, whereas marginal utility is measured on the right axis.

38

Figure 3: BLS Publication and Pass-Through of Cost Shocks

This figure plots the accumulative pass-through coefficients (y-axsis) estimated from

πj,s = α+

18∑h=1

βs−h × vj,s +

18∑h=1

γs−h × vj,s ×HPub%k,s +

18∑h=1

δs−h ×HPub%k,s + ηj + εj,s,

j, k, and s denote 6- and 3-digit NAICS sectors and month, respectively. vj,s is measured as ∆PPIs ×Input%j. ∆PPIs is the growth rate of the producer price index (PPI) at the aggregate level from month

s-1 to s. For each 6-digit NAICS sector, Input%j is measured as the total intermediate goods as a

fraction of total output for sector j. 1997, 2002, and 2007 input-output accounts data are obtained from

the Bureau of Economic Analysis (BEA). HPub% is a dummy that equals 1 if Pub% is above the sample

median, and zero otherwise. Pub% is measured as the number of product indices published by BLS in a

month as a fraction of the maximum number of products observed in that sector (see equation (3) for a

detailed description). ηj is a set of product fixed effects. Sticky- and flexible-price firm subsamples are

formed based on whether the frequency of price adjustment (FPA) is below or above the sample median.

FPA is measured at the granular sector level (see Table 1 for a detailed description). The sample period

is 1997-2012.

Panel A: Sticky Firms

0.2

.4.6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Low Pub% High Pub%

Panel B: Flexible Firms

.2.4

.6.8

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Low Pub% High Pub%39

Figure 4: Predicted BLS Publication and Pass-Through of Cost Shocks

This figure plots the accumulative pass-through coefficients (y-axsis) estimated from

πj,s = α+

18∑h=1

βs−h × vj,s +

18∑h=1

γs−h × vj,s ×H Pub%k,s +

18∑h=1

δs−h ×H Pub%k,s + ηj

+ εj,s,

j, k, and s denote 6- and 3-digit NAICS sectors and month, respectively. vj,s is measured as ∆PPIs ×Input%j. ∆PPIs is the growth rate of the producer price index (PPI) at the aggregate level from month

s-1 to s. For each 6-digit NAICS sector, Input%j is measured as the total intermediate goods as a fraction

of total output for sector j. 1997, 2002, and 2007 input-output accounts data are obtained from the Bureau

of Economic Analysis (BEA). HPub% is a dummy that equals 1 if P ub% is above the sample median,

and zero otherwise. P ub% is the predicted value of Pub% (see equation (5) for detail description). Pub%

is measured as the number of product indices published by BLS in a month as a fraction of the maximum

number of products observed in that sector (see equation (3) for a detailed description). ηj is a set of

product fixed effects. Sticky- and flexible-price firm subsamples are formed based on whether the frequency

of price adjustment (FPA) is below or above the sample median. FPA is measured at the granular sector

level (see Table 1 for a detailed description). The sample period is 1997-2012.

Panel A: Sticky Firms

0.1

.2.3

.4.5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Low Pub% High Pub%

Panel B: Flexible Firms

.2.4

.6.8

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Low Pub% High Pub%40

Figure 5: Price Stickiness and Managers’ Dissemination of Private Information

This figure plots the average incidence and intensity of managers’ dissemination of private information

about the trend of input costs during earnings conference calls for groups of firms with increasing frequency

of price adjustment. The sample is restricted on common stocks (listed on NYSE, AMEX, and NASAQ)

of firms whose headquarters are based in the United States. Utilities and Financial sectors are excluded.

Sample firms are assigned to four baskets based on the frequency of price adjustment (FPA). FPA is

measured at the level of 6-digit North American Industry Classification System (NAICS). Panel A plots

the average probability of managers providing quantitative estimation about the trend of input costs during

the earnings conference call. Panel B plots the average number sentences related to quantitative estimation

about the trend of input costs. The sample period is 2002-2012.

Panel A: Probability

.4.4

5.5

.55

.6P

roba

bilit

y

1 23 4

Panel B: Number of Sentences

22.

22.

42.

62.

83

Num

ber o

f Sen

tenc

es

1 23 4

41

Table 1: BLS Publication and the Frequency of Price Adjustment across Sectors

This table reports descriptive statistics for BLS publication Pub% and the frequency of price adjustment

(FPA) across 3-digit sectors according to the North American Industry Classification System (NAICS).

For each 3-digit sector k, Pub% is measured as the number of product indices published by BLS in a

month as a fraction of the maximum number of products observed in that sector (see equation (3) for a

detailed description). The end of the observation period is May 2016. In the sample period of 2002-2012,

the frequency of price adjustment is calculated by Pasten, Schoenle, and Weber (2017), by aggregating

frequencies of price adjustment at the goods level into 5- and 6-digit NAICS sectors. Equally weighted

probabilities of price adjustments at the goods level are calculated using the micro-data underlying the

Producer Price Index (PPI) constructed by the Bureau of Labor Statistics (BLS). The granularity for

FPA is at the 6-digit level. If 6-digit FPA is not available, the 5-digit FPA is used.

Industry Name NAICS Pub% PAFMean Std Mean Std

Forestry and Logging 113 0.70 0.10 0.19 0.00Oil and Gas Extraction 211 0.89 0.12 0.82 0.08Mining (except Oil and Gas) 212 0.87 0.09 0.30 0.23Support Activities for Mining 213 0.84 0.09 0.14 0.05Utilities 221 0.87 0.12 0.47 0.13Construction of Buildings 236 0.69 0.23 0.29 0.09Specialty Trade Contractors 238 0.97 0.05Food Manufacturing 311 0.82 0.08 0.29 0.14Beverage and Tobacco Product Manufacturing 312 0.67 0.11 0.22 0.07Textile Mills 313 0.75 0.12 0.18 0.04Textile Product Mills 314 0.80 0.10 0.17 0.10Apparel Manufacturing 315 0.67 0.19 0.12 0.02Leather and Allied Product Manufacturing 316 0.63 0.16 0.13 0.07Wood Product Manufacturing 321 0.76 0.23 0.27 0.12Paper Manufacturing 322 0.72 0.14 0.28 0.11Printing and Related Support Activities 323 0.76 0.18 0.14 0.04Petroleum and Coal Products Manufacturing 324 0.83 0.10 0.35 0.22Chemical Manufacturing 325 0.83 0.11 0.26 0.11Plastics and Rubber Products Manufacturing 326 0.80 0.15 0.25 0.06Nonmetallic Mineral Product Manufacturing 327 0.85 0.11 0.21 0.09Primary Metal Manufacturing 331 0.72 0.14 0.31 0.07Fabricated Metal Product Manufacturing 332 0.83 0.10 0.16 0.03Machinery Manufacturing 333 0.80 0.10 0.13 0.03Computer and Electronic Product Manufacturing 334 0.85 0.13 0.14 0.05Electrical Equipment, Appliance, and Component Manufacturing 335 0.84 0.13 0.20 0.07Transportation Equipment Manufacturing 336 0.73 0.18 0.20 0.13Furniture and Related Product Manufacturing 337 0.79 0.17 0.13 0.03Miscellaneous Manufacturing 339 0.88 0.04 0.15 0.03Merchant Wholesalers, Durable Goods 423 0.48 0.27 0.26 0.00Merchant Wholesalers, Nondurable Goods 424 0.43 0.21 0.34 0.00Wholesale Electronic Markets and Agents and Brokers 425 0.81 0.14 0.15 0.00Recyclable Materials 429 0.94 0.05Motor Vehicle and Parts Dealers 441 0.82 0.26 0.31 0.24Furniture and Home Furnishings Stores 442 0.98 0.09 0.22 0.04Electronics and Appliance Stores 443 0.51 0.27Building Material and Garden Equipment and Supplies Dealers 444 0.93 0.23 0.27 0.07Food and Beverage Stores 445 0.71 0.12 0.25 0.05Health and Personal Care Stores 446 0.81 0.16 0.17 0.03Gasoline Stations 447 0.60 0.19 0.30 0.06Clothing and Clothing Accessories Stores 448 0.55 0.18 0.17 0.05Sporting Goods, Hobby, Musical Instrument, and Book Stores 451 0.63 0.12 0.17 0.02General Merchandise Stores 452 0.81 0.29 0.27 0.02Miscellaneous Store Retailers 453 0.71 0.18 0.16 0.04Nonstore Retailers 454 0.59 0.24 0.36 0.20Air Transportation 481 0.69 0.17 0.20 0.14Rail Transportation 482 0.81 0.09 0.40 0.00Water Transportation 483 0.71 0.31 0.25 0.05Truck Transportation 484 0.56 0.29 0.23 0.10Pipeline Transportation 486 0.86 0.17 0.23 0.02Support Activities for Transportation 488 0.65 0.17 0.14 0.03Postal Service 491 0.48 0.04 0.21 0.00Couriers and Messengers 492 0.71 0.31 0.19 0.09Warehousing and Storage 493 0.80 0.16 0.12 0.01Publishing Industries (except Internet) 511 0.63 0.16 0.12 0.03Broadcasting (except Internet) 515 0.73 0.25 0.27 0.07Telecommunications 517 0.80 0.11 0.33 0.22Data Processing, Hosting, and Related Services 518 0.88 0.15 0.11 0.00Other Information Services 519 1.00 0.00Credit Intermediation and Related Activities 522 0.85 0.09 0.50 0.01Securities, Commodity Contracts, and Other Financial Investments and Related Activities 523 0.84 0.26 0.19 0.13Insurance Carriers and Related Activities 524 0.79 0.25 0.21 0.07Real Estate 531 0.69 0.28 0.19 0.10Rental and Leasing Services 532 0.78 0.21 0.18 0.09Professional, Scientific, and Technical Services 541 0.56 0.19 0.09 0.02Administrative and Support Services 561 0.68 0.19 0.13 0.02Waste Management and Remediation Services 562 0.68 0.31 0.11 0.00Educational Services 611 1.00 0.00Ambulatory Health Care Services 621 0.34 0.11 0.13 0.01Hospitals 622 0.60 0.07 0.11 0.02Nursing and Residential Care Facilities 623 0.47 0.24 0.17 0.00Amusement, Gambling, and Recreation Industries 713 0.87 0.24 0.14 0.04Accommodation 721 0.60 0.32 0.21 0.05Repair and Maintenance 811 0.56 0.00Administration of Environmental Quality Programs 924 0.97 0.04

42

Tab

le2:

Sum

mary

Sta

tist

ics

and

Corr

ela

tions:

SD

C/D

ealS

can-C

om

pust

at

Merg

ed

Sam

ple

16.5

cmT

his

tabl

ere

port

sdes

crip

tive

stati

stic

sfo

rB

LS

pu

blic

ati

onPub%

an

dth

efr

equ

ency

of

pri

ceadju

stm

ent

(FP

A)

acr

oss

3-d

igit

sect

ors

acc

ord

ing

toth

eN

ort

hA

mer

ican

Indu

stry

Cla

ssifi

cati

on

Sys

tem

(NA

ICS

).F

or

each

3-d

igit

sect

or

k,Pub%

ism

easu

red

as

the

nu

mbe

rof

pro

du

ctin

dic

espu

blis

hed

byB

LS

ina

mon

thas

afr

act

ion

of

the

maxi

mu

mn

um

ber

of

pro

du

cts

obs

erve

din

that

sect

or

(see

equ

ati

on

(3)

for

adet

ail

eddes

crip

tion

).T

he

end

of

the

obs

erva

tion

peri

odis

May

2016.

Inth

esa

mple

peri

odof

2002-2

012,

the

freq

uen

cyof

pri

ceadju

stm

ent

isca

lcu

late

dby

Past

enet

al.

(2017),

by

agg

rega

tin

gfr

equ

enci

esof

pri

ceadju

stm

ent

at

the

good

sle

vel

into

5-

an

d6-d

igit

NA

ICS

sect

ors

.E

quall

yw

eigh

ted

pro

babi

liti

esof

pri

ceadju

stm

ents

at

the

good

sle

vel

are

calc

ula

ted

usi

ng

the

mic

ro-d

ata

un

der

lyin

gth

eP

rodu

cer

Pri

ceIn

dex

(PP

I)co

nst

ruct

edby

the

Bu

reau

of

Labo

rS

tati

stic

s(B

LS

).

The

gran

ula

rity

for

FP

Ais

at

the

6-d

igit

leve

l.If

6-d

igit

FP

Ais

not

ava

ilabl

e,th

e5-d

igit

FP

Ais

use

d.

Pan

elA

:M

ean

s,M

edia

ns

an

dS

tan

dard

Dev

iati

on

s

Poole

dB

on

dL

oan

NM

ean

p50

std

NM

ean

p50

std

NM

ean

p50

std

Deb

tS

pre

ad

12,1

50

175.4

4150.0

0133.7

52471

214.3

4158.0

0158.2

59679

165.5

0150.0

0124.8

2F

PA

12,1

50

0.2

90.2

00.2

22471

0.3

50.2

60.2

49679

0.2

70.1

90.2

1P

ub

%12,1

50

0.7

50.7

40.1

82471

0.7

90.7

60.1

59679

0.7

40.7

40.1

9P

rofi

tab

ilit

y12150

0.0

80.0

80.0

92471

0.1

00.0

90.0

89679

0.0

80.0

80.1

0L

n(S

ale

s)12,1

50

7.2

87.3

61.9

12471

8.5

08.6

61.4

39679

6.9

77.0

21.8

9B

M12,1

50

0.6

90.5

41.0

92471

0.5

80.4

90.5

79679

0.7

10.5

61.1

8In

tangib

ilit

y12,1

50

0.3

00.2

70.2

02471

0.3

00.2

90.1

79679

0.3

00.2

70.2

0L

ev12,1

50

0.2

50.2

30.1

92471

0.2

80.2

70.1

59679

0.2

40.2

20.1

9P

CM

12,1

50

0.1

80.1

50.1

92471

0.2

10.1

90.1

69679

0.1

70.1

40.1

9H

PH

HI

12,1

50

0.2

10.1

50.2

02471

0.1

90.1

30.1

79679

0.2

20.1

50.2

0

Pan

elB

:C

onte

mp

ora

neo

us

Corr

elati

on

sD

ebt

Sp

read

FP

AP

ub

%P

rofi

tab

ilit

yL

n(S

ale

s)B

MIn

tan

gib

ilit

yL

evP

CM

HP

HH

ID

ebt

Sp

read

1.0

0F

PA

0.0

31.0

0P

ub

%0.0

10.2

01.0

0P

rofi

tab

ilit

y-0

.36

-0.1

00.0

11.0

0L

n(S

ale

s)-0

.35

0.0

50.1

30.2

91.0

0B

M0.0

70.0

90.0

0-0

.10

-0.0

51.0

0In

tan

gib

ilit

y-0

.05

-0.3

00.0

20.0

50.1

8-0

.04

1.0

0L

ev0.2

40.2

40.0

0-0

.09

0.0

5-0

.05

0.0

71.0

0P

CM

-0.1

90.3

10.1

50.5

10.1

50.0

0-0

.03

0.1

31.0

0H

PH

HI

0.0

6-0

.27

0.0

40.0

3-0

.12

-0.0

20.1

1-0

.07

-0.1

21.0

0

43

Tab

le3:

BL

SP

ublica

tion,P

rice

Sti

ckin

ess

,and

Borr

ow

ing

Cost

s:January

1997-

Dece

mb

er

2012,O

LS

Regre

ssio

n

This

tabl

ere

port

sth

ere

sult

sfo

res

tim

ati

ng

the

foll

ow

ing

lin

ear

equ

ati

on

:

Spreadq,j,s

=α

+β×Bondi,s

+γ×Bondi,s×Pub%

j,s

+X′ i,t−

1×θ

+η t

+η i

+ε q,j,s,

The

Gre

at

Rec

essi

on

peri

od(D

ecem

ber

2007

toJ

un

e2009)

isex

clu

ded

.U

tili

ties

an

dF

inan

cial

sect

ors

are

excl

uded

.q,

i,j,

k,s,

an

dt

den

ote

dea

l,fi

rm,

6-

an

d3-d

igit

NA

ICS

sect

ors

an

dm

on

th,

an

dye

ar,

resp

ecti

vely

.S

tick

y-an

dfl

exib

le-p

rice

-firm

subs

am

ple

sare

form

edba

sed

on

whet

her

the

freq

uen

cy

of

pri

ceadju

stm

ent

(FP

A)

isbe

low

or

abo

veth

esa

mple

med

ian

.F

PA

ism

easu

red

at

the

gran

ula

rse

ctor

leve

l(s

eeT

abl

e1

for

adet

ail

eddes

crip

tion

).

Spre

ad

isth

ediff

eren

cebe

twee

nth

ebo

nd,

or

syn

dic

ate

dlo

an

,in

tere

stra

tean

dth

eri

sk-f

ree

rate

.B

on

dis

an

indic

ato

rva

riabl

eeq

ual

to1

ifa

firm

borr

ow

sa

pu

blic

bon

d,

an

dze

roif

the

firm

borr

ow

sa

loan

.Pub%

ism

easu

red

as

the

nu

mbe

rof

pro

du

ctin

dic

espu

blis

hed

byB

LS

ina

mon

thas

a

fract

ion

of

the

maxi

mu

mn

um

ber

of

pro

du

cts

obs

erve

din

that

sect

or

(see

equ

ati

on

(3)

for

adet

ail

eddes

crip

tion

).HPub%

isa

du

mm

yth

at

equ

als

1

ifPub%

isabo

veth

esa

mple

med

ian

,an

dze

rooth

erw

ise.

X′ i,t−

1is

ave

ctor

of

addit

ion

al

con

trols

(see

Tabl

e2

for

adet

ail

eddes

crip

tion

).S

tan

dard

erro

rsare

clu

ster

edat

the

3-d

igit

NA

ICS

leve

l.

Sti

cky

Fle

xib

leS

tick

yF

lexib

le(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)B

on

d242.3

6***

240.4

2***

175.3

6***

180.1

2***

131.1

1***

131.5

5***

126.9

4***

129.2

8***

(6.6

5)

(6.6

5)

(4.6

9)

(4.7

9)

(10.3

8)

(10.2

3)

(9.5

1)

(9.3

6)

Bon

d×

Pu

b%

-180.7

4***

-176.4

6***

-72.9

6-7

6.8

0(-

4.1

9)

(-4.1

5)

(-1.4

3)

(-1.5

1)

Pu

b%

-8.3

812.6

7-2

.06

13.9

2(-

0.3

2)

(0.6

2)

(-0.0

7)

(0.5

4)

Bon

d×

HP

ub

%-4

9.1

7***

-47.7

0***

-12.7

1-1

3.7

1(-

4.3

7)

(-4.0

3)

(-0.7

1)

(-0.7

5)

HP

ub

%-1

.12

1.4

2-8

.26

-4.8

0(-

0.1

8)

(0.2

7)

(-0.7

4)

(-0.4

2)

Tota

lV

ol

2304.5

8***

2337.6

6***

2294.1

6***

2311.7

5***

(9.3

1)

(7.9

6)

(9.4

3)

(7.6

2)

Pro

fita

bilit

y-2

56.1

8***

-171.6

9***

-109.1

9-8

6.6

1-2

52.8

8***

-169.1

9***

-107.9

7-8

3.6

1(-

4.7

2)

(-4.0

3)

(-1.4

6)

(-1.2

9)

(-4.6

8)

(-3.9

7)

(-1.4

7)

(-1.2

6)

Ln

(Sale

s)-2

8.2

4***

-24.6

8***

-26.0

8***

-20.0

5***

-28.4

7***

-24.9

7***

-27.3

4***

-21.2

8***

(-5.6

2)

(-5.0

0)

(-3.6

5)

(-2.9

6)

(-5.5

9)

(-5.0

4)

(-4.0

5)

(-3.3

7)

BM

2.7

12.9

0-1

.45

-0.6

22.7

82.9

5-1

.30

-0.6

0(0

.94)

(1.1

2)

(-0.4

7)

(-0.2

0)

(0.9

8)

(1.1

4)

(-0.4

1)

(-0.1

9)

Inta

ngib

ilit

y-2

6.6

8-1

2.3

729.6

639.0

9-2

9.4

3-1

5.4

729.6

239.6

8(-

1.1

4)

(-0.5

9)

(0.5

9)

(0.8

2)

(-1.2

5)

(-0.7

3)

(0.5

9)

(0.8

3)

Lev

93.0

8***

75.6

4***

112.9

2***

94.6

3***

93.1

6***

75.8

5***

115.8

6***

97.6

1***

(5.6

8)

(4.2

9)

(3.5

9)

(3.2

3)

(5.6

6)

(4.2

9)

(3.5

7)

(3.2

3)

PC

M-6

7.3

5***

-56.6

8**

-34.5

4***

-24.2

3-6

7.8

3***

-57.7

6**

-34.9

1***

-24.9

2(-

3.6

5)

(-2.2

1)

(-3.2

7)

(-1.5

9)

(-3.6

5)

(-2.2

4)

(-3.2

8)

(-1.6

6)

HP

HH

I15.9

213.3

432.7

7**

25.8

2*

16.9

014.4

133.4

5**

26.2

6*

(1.2

1)

(1.1

7)

(2.1

6)

(1.8

7)

(1.2

9)

(1.2

7)

(2.2

1)

(1.8

8)

Con

stant

463.2

7***

337.9

2***

393.6

9***

265.0

9***

450.7

2***

343.7

1***

403.6

8***

286.5

1***

(9.8

4)

(7.5

9)

(5.9

8)

(4.0

6)

(10.5

6)

(8.4

1)

(6.6

1)

(4.7

8)

Yea

rF

EY

esY

esY

esY

esY

esY

esY

esY

esF

irm

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

N6526

6525

4161

4160

6526

6525

4161

4160

ad

j.R

20.6

50.6

70.6

40.6

50.6

50.6

70.6

30.6

5

t-st

ati

stic

sin

pare

nth

eses

∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

44

Table 4: First-Stage Regression: January 1997-December 2012

This table reports the results for estimating the following linear equation:

Pub%i,k,s

= α+ β ×∆Pub%2004k × Post+X ′i,t−1 × θ + ηk + ηt + εi,s,

i, k, and s denote firm, 3-digit NAICS, and month, respectively. The Great Recession period (December

2007 to June 2009) is excluded. Utilities and Financial sectors are excluded. Pub%2004k is the difference

in Pub% between November 2003 and January 2004 for each 3-digit NAICS sector k. For each k, Pub%

is measured as the number of product indices published by BLS in a month as a fraction of the maximum

number of products observed in that sector (see equation (3) for a detailed description). Post is an

indicator equal to 1 if month s is from January 2004 through December 2012, and zero otherwise. ηkis a set of 3-digit sector fixed effects. ηt is a set of year fixed effects. X ′i,t−1 is a vector of additional

controls (see Table 2 for a detailed description). Standard errors are clustered at the 3-digit NAICS level.

In columns (1) - (3) the dependent variable is Pub%. In columns (4) - (6), the dependent variable is

HPub%. HPub% is a dummy that equals 1 if Pub% is above the sample median, and zero otherwise.

Sticky- and flexible- price subsamples are formed based on whether the frequency of price adjustment

(FPA) is below or above the sample median. FPA is price adjustment frequency at the granular sector

level (see Table 1 for a detailed description).

Pub% HPub%Pooled Sticky Flexible Pooled Sticky Flexible(1) (2) (3) (4) (5) (6)

∆ Pub%2004 × Post 0.88*** 1.00*** 0.82*** 1.66*** 1.67*** 1.81***(12.77) (14.27) (10.51) (4.77) (2.76) (4.30)

Profitability 0.01 -0.00 0.02 -0.00 0.05 -0.03(1.33) (-0.13) (1.23) (-0.03) (0.61) (-0.28)

Ln(Sales) 0.00 -0.00 -0.00 0.00** 0.01*** 0.00(0.39) (-0.39) (-0.10) (2.35) (2.93) (0.95)

BM 0.00 -0.00 -0.00 -0.00 -0.00 -0.00(0.02) (-1.17) (-0.38) (-0.16) (-0.03) (-0.15)

Intangibility -0.00 0.00 0.00 0.03 0.04 -0.00(-0.79) (0.44) (0.08) (0.98) (0.93) (-0.10)

Lev -0.01 -0.00 -0.01 -0.01 -0.02 0.03(-1.63) (-0.90) (-1.40) (-0.41) (-0.59) (1.15)

PCM 0.01 0.01 0.01 0.01 -0.02 0.02(1.31) (0.71) (1.63) (0.29) (-0.46) (0.61)

HP HHI -0.00 0.00 -0.03* -0.02 0.00 -0.05(-0.44) (0.98) (-1.76) (-0.84) (0.03) (-0.94)

Constant 0.77*** 0.76*** 0.81*** 0.63*** 0.59*** 0.69***(47.54) (35.63) (52.62) (7.17) (4.22) (5.14)

Year FE Yes Yes Yes Yes Yes YesNAICS3 FE Yes Yes Yes Yes Yes YesF-Stat 163.18 203.66 110.53 22.71 7.63 18.53N 13836 7328 4715 13836 7328 4715adj. R2 0.94 0.95 0.93 0.72 0.71 0.74

t-statistics in parentheses∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01

45

Tab

le5:

BL

SP

ublica

tion,

Pri

ceSti

ckin

ess

,and

Borr

ow

ing

Cost

s:January

1997-D

ece

mb

er

2012,

IVE

stim

ati

on

This

tabl

ere

port

sth

ere

sult

sfo

res

tim

ati

ng

the

foll

ow

ing

lin

ear

equ

ati

on

:

Spreadq,j,s

=α

+β×Bondi,s

+γ×Bondi,s×Pub%

j,s

+δ×Pub%

j,s

+X′ i,t−

1×θ

+η i

+η t

+ε q,j,s,

See

equ

ati

on

(5)

for

the

des

crip

tion

of

the

IVappro

ach

.T

he

Gre

at

Rec

essi

on

peri

od(D

ecem

ber

2007

toJ

un

e2009)

isex

clu

ded

.U

tili

ties

an

dF

inan

cial

sect

ors

are

excl

uded

.q,

i,j,

k,s,

an

dt

den

ote

dea

l,fi

rm,

6-

an

d3-d

igit

NA

ICS

sect

ors

,m

on

th,

an

dye

ar,

resp

ecti

vely

.S

pre

ad

isth

ediff

eren

cebe

twee

n

the

bon

d,

or

syn

dic

ate

dlo

an

,in

tere

stra

tean

dth

eri

sk-f

ree

rate

.B

on

dis

an

indic

ato

rva

riabl

eeq

ual

to1

iffi

rmi

borr

ow

sa

pu

blic

bon

d,

an

dze

roif

the

firm

borr

ow

sa

loan

.F

or

each

k,Pub%

ism

easu

red

as

the

nu

mbe

rof

pro

du

ctin

dic

espu

blis

hed

byB

LS

ina

mon

thas

afr

act

ion

of

the

maxi

mu

m

nu

mbe

rof

pro

du

cts

obs

erve

din

that

sect

or

(see

equ

ati

on

(3)

for

adet

ail

eddes

crip

tion

).X′ i,t−

1is

ave

ctor

of

addit

ion

al

con

trols

(see

Tabl

e2

for

a

det

ail

eddes

crip

tion

).S

tan

dard

erro

rsare

clu

ster

edat

the

3-d

igit

NA

ICS

leve

l.S

tick

y-an

dfl

exib

le-

pri

cesu

bsam

ple

sare

form

edba

sed

on

whet

her

FP

A

isbe

low

or

abo

veth

esa

mple

med

ian

.F

PA

isth

efr

equ

ency

of

pri

ceadju

stm

ent

at

the

gran

ula

rse

ctor

leve

l(s

eeT

abl

e1

for

adet

ail

eddes

crip

tion

).

Sti

cky

Fle

xib

leS

tick

yF

lexib

le(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)B

on

d207.5

6***

205.3

7***

251.0

1**

254.0

9**

129.6

5***

131.0

7***

162.3

8***

164.5

1***

(3.8

4)

(3.9

9)

(2.5

2)

(2.5

8)

(9.1

5)

(9.6

3)

(4.0

1)

(4.0

7)

Bon

d×

Pu

b%

-136.5

9**

-131.9

9**

-169.5

8-1

71.2

9(-

2.0

6)

(-2.0

9)

(-1.4

3)

(-1.4

6)

Pu

b%

6.4

329.4

4-8

.83

4.9

8(0

.31)

(1.6

0)

(-0.2

4)

(0.1

5)

Bon

d×

HP

ub

%-4

6.7

9**

-46.6

5**

-64.7

7-6

5.4

8(-

2.3

8)

(-2.4

9)

(-1.3

7)

(-1.3

9)

HP

ub

%1.4

514.2

0-5

.45

1.4

7(0

.13)

(1.3

2)

(-0.3

5)

(0.1

0)

Tota

lV

ol

2317.4

1***

2336.6

7***

2311.3

8***

2317.4

6***

(10.8

8)

(9.1

3)

(11.2

5)

(8.9

5)

Pro

fita

bilit

y-2

57.9

8***

-172.9

8***

-108.8

7-8

6.0

3-2

53.6

3***

-169.9

3***

-108.4

5-8

4.4

7(-

5.5

4)

(-4.7

9)

(-1.6

1)

(-1.4

0)

(-5.3

7)

(-4.5

2)

(-1.6

3)

(-1.4

1)

Ln

(Sale

s)-2

8.1

7***

-24.5

9***

-26.2

6***

-20.2

7***

-28.5

1***

-25.1

9***

-28.2

6***

-21.7

4***

(-6.6

8)

(-5.8

9)

(-4.3

7)

(-3.5

6)

(-6.6

4)

(-5.9

8)

(-5.1

8)

(-4.0

5)

BM

2.7

22.9

2-1

.71

-0.8

92.7

62.9

1-1

.30

-0.5

9(1

.13)

(1.3

5)

(-0.6

3)

(-0.3

4)

(1.1

5)

(1.3

2)

(-0.4

5)

(-0.2

1)

Inta

ngib

ilit

y-2

7.3

6-1

2.8

627.7

037.1

4-2

9.5

4-1

5.1

722.2

132.2

6(-

1.3

5)

(-0.7

1)

(0.6

1)

(0.8

5)

(-1.4

7)

(-0.8

3)

(0.4

9)

(0.7

4)

Lev

92.4

6***

75.1

2***

112.4

4***

94.2

0***

92.8

2***

76.2

5***

117.5

7***

98.4

4***

(6.5

7)

(4.9

7)

(4.0

3)

(3.5

9)

(6.5

6)

(4.9

4)

(3.9

0)

(3.4

2)

PC

M-6

6.2

4***

-55.5

4**

-34.9

8***

-24.6

9*

-67.5

7***

-56.9

6***

-36.9

0***

-26.8

1*

(-4.2

7)

(-2.5

6)

(-3.5

7)

(-1.7

6)

(-4.4

0)

(-2.6

7)

(-3.4

1)

(-1.8

4)

HP

HH

I15.9

113.3

431.5

1**

24.5

7**

16.7

914.0

732.2

7**

25.0

5**

(1.3

9)

(1.3

5)

(2.3

0)

(1.9

9)

(1.4

9)

(1.4

2)

(2.3

9)

(2.0

3)

Yea

rF

EY

esY

esY

esY

esY

esY

esY

esY

esF

irm

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

N5923

5922

3935

3933

5923

5922

3935

3933

t-st

ati

stic

sin

pare

nth

eses

∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

46

Table 6: BLS Publication, Price Stickiness, and Cost Pass-Through

This table reports the results for estimating the following linear equation:

πjs = α+

18∑h=1

βs−h × vs,j +

18∑h=1

γs−h × vs ×HPub%k,s +

12∑h=1

δs−h ×HPub%k,s + ηj

+ εj,s,

j, k, and s denote 6- and 3-digit NAICS sectors and month, respectively. vs is measured as ∆PPIs ×Input%j. ∆PPIs is the growth rate of the producer price index (PPI) at the aggregate level from month

s-1 to s. For each 6-digit NAICS sector, Input%j is measured as the total intermediate goods as a fraction

of total outputfor sector j. 1997, 2002, and 2007 input-output accounts data are obtained from the Bureau

of Economic Analysis (BEA). For each k, Pub% is measured as the number of product indices published

by BLS in a month as a fraction of the maximum number of products observed in that sector (see equation

(3) for a detailed description). ηj

is a set of product fixed effects. Standard errors are clustered at the

product level. The sample period is 1997-2012.

OLS IVSticky Flexible Sticky Flexible(1) (2) (3) (4)

vs−1 0.05*** 0.28*** 0.05*** 0.44***(8.63) (12.44) (5.23) (10.35)

vs−2 0.02*** 0.17*** 0.03*** 0.11***(4.01) (13.87) (3.00) (8.38)

vs−3 0.04*** 0.12*** 0.00 0.07***(7.38) (11.61) (0.38) (5.67)

vs−4 0.03*** 0.04*** 0.02** 0.02*(6.63) (3.50) (2.53) (1.92)

vs−5 0.04*** 0.06*** 0.02** 0.05***(7.63) (7.44) (2.39) (5.68)

vs−6 0.02*** 0.01 0.01* 0.01(4.34) (0.77) (1.69) (0.48)

vs−7 0.04*** 0.03*** 0.02** 0.01(7.82) (3.16) (2.54) (0.49)

vs−8 0.03*** -0.00 0.03*** -0.01(6.71) (-0.43) (5.38) (-1.29)

vs−9 0.03*** 0.02** 0.04*** 0.00(7.33) (2.19) (5.89) (0.07)

vs−10 0.04*** 0.04*** 0.04*** 0.07***(10.71) (5.04) (5.13) (6.05)

vs−11 0.03*** 0.02*** 0.04*** 0.03***(6.78) (2.87) (4.81) (3.08)

vs−12 -0.00 0.01 -0.01 0.02(-0.00) (0.62) (-1.56) (1.45)

vs−13 0.04*** -0.01 0.01 -0.04***(6.68) (-0.81) (1.30) (-2.95)

vs−14 -0.01 0.01 -0.02** -0.01(-1.51) (1.57) (-2.54) (-1.18)

vs−15 0.02*** 0.03*** -0.01 0.02*(4.94) (3.52) (-1.00) (1.69)

vs−16 0.02*** 0.03*** 0.01 0.04***(5.71) (3.87) (1.40) (3.68)

vs−17 0.00 -0.00 0.00 0.01(0.50) (-0.09) (0.08) (0.55)

vs−18 0.01** -0.00 0.02*** 0.03***(2.35) (-0.00) (2.59) (2.90)

vs−1 × HPub% 0.01 0.01 -0.01 -0.29***(0.79) (0.53) (-0.55) (-6.44)

vs−2 × HPub% 0.01* -0.07*** -0.00 0.08***(1.87) (-3.91) (-0.08) (4.33)

vs−3 × HPub% -0.02*** -0.07*** 0.04*** 0.05***(-2.91) (-3.89) (4.29) (2.71)

vs−4 × HPub% 0.01* 0.07*** 0.02** 0.07***(1.80) (4.16) (2.34) (4.18)

vs−5 × HPub% 0.00 0.02* 0.02*** 0.01(0.54) (1.66) (2.77) (0.45)

vs−6 × HPub% 0.01 0.05*** 0.01* 0.02(1.35) (4.03) (1.78) (1.28)

vs−7 × HPub% 0.01 0.01 0.04*** 0.06***(1.39) (0.47) (4.50) (4.42)

vs−8 × HPub% -0.02** 0.02* -0.02*** 0.02(-2.20) (1.80) (-2.96) (1.42)

vs−9 × HPub% 0.02** 0.03** -0.00 0.06***(2.31) (2.38) (-0.46) (4.34)

vs−10 × HPub% -0.00 -0.00 0.01 -0.04***(-0.72) (-0.20) (0.81) (-3.12)

vs−11 × HPub% 0.03*** -0.01 0.01 -0.03**(3.73) (-0.44) (1.08) (-2.39)

vs−12 × HPub% -0.00 0.02 0.01* -0.00(-0.06) (1.61) (1.81) (-0.04)

vs−13 × HPub% -0.01 0.03* 0.02 0.06***(-1.41) (1.87) (1.64) (3.93)

vs−14 × HPub% 0.02* -0.03** 0.02* 0.02*(1.92) (-2.18) (1.88) (1.65)

vs−15 × HPub% -0.00 -0.03** 0.04*** -0.01(-0.59) (-2.37) (4.28) (-0.52)

vs−16 × HPub% 0.01* -0.04*** 0.02** -0.03**(1.82) (-2.77) (2.39) (-2.29)

vs−17 × HPub% 0.02*** -0.01 0.01 -0.03**(2.71) (-0.80) (1.00) (-2.15)

vs−18 × HPub% 0.01* 0.04*** -0.02* -0.04***(1.67) (3.43) (-1.96) (-2.87)

HPub% -0.00*** -0.00 0.00*** 0.00***(-5.70) (-1.09) (6.54) (11.17)

Constant 0.00*** 0.00*** 0.00*** 0.00***(29.17) (14.09) (16.38) (7.93)

N 213,925 197,501 212,245 196,417

adj. R2 0.03 0.04 0.03 0.05

t-statistics in parentheses∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01

47

Tab

le7:

Sum

mary

Sta

tist

ics

and

Corr

ela

tions:

CR

SP

-Com

pust

at

Merg

ed

Sam

ple

This

tabl

ere

port

sdes

crip

tive

stati

stic

sfo

rth

eva

riabl

esu

sed

inth

eem

pir

ical

an

aly

sis

for

ass

etpri

cin

gin

Pan

elA

an

dco

rrel

ati

on

sacr

oss

vari

abl

es

inP

an

elB

.T

he

sam

ple

peri

odis

from

Ju

ly1997

toJ

un

e2013.

The

sam

ple

isre

stri

cted

toco

mm

on

stoc

ks(l

iste

don

NY

SE

,A

ME

X,

an

dN

AS

AQ

)of

firm

sw

hose

hea

dqu

art

ers

are

base

din

the

Un

ited

Sta

tes.

Uti

liti

esan

dF

inan

cial

sect

ors

are

excl

uded

.F

PA

isth

efr

equ

ency

of

pri

ceadju

stm

ent

at

the

gran

ula

rse

ctor

leve

l(s

eeT

abl

e1

for

adet

ail

eddes

crip

tion

).F

or

each

k,Pub%

ism

easu

red

as

the

nu

mbe

rof

pro

du

ctin

dic

espu

blis

hed

byB

LS

ina

mon

thas

afr

act

ion

of

the

maxi

mu

mn

um

ber

of

pro

du

cts

obs

erve

din

that

sect

or

(see

equ

ati

on

(3)

for

adet

ail

eddes

crip

tion

).R

etu

rnin

year

tis

the

an

nu

ali

zed

stoc

kre

turn

calc

ula

ted

from

Ju

lyof

year

tto

Ju

ne

of

t+1.

Siz

eis

the

natu

ral

loga

rith

mof

the

tota

lm

ark

etca

pit

ali

zati

on

(in

thou

san

ds)

inJ

un

eof

year

t.B

Mof

year

tis

the

book

equ

ity

for

the

fisc

al

year

endin

gin

cale

ndar

year

t-1

ove

rth

em

ark

eteq

uit

yas

of

Dec

embe

rt-

1.β

is

the

regr

essi

on

coeffi

cien

tin

roll

ing

tim

e-se

ries

regr

essi

on

sof

mon

thly

exce

ssre

turn

son

aco

nst

an

tan

dth

eex

cess

retu

rns

of

the

CR

SP

valu

e-w

eigh

ted

index

ove

rth

epre

viou

s252

tradin

gdays

.W

ese

tβ

tom

issi

ng

ifw

ehave

less

than

60

dail

yre

turn

sin

the

esti

mati

on

peri

od.

Lev

isth

elo

ng-

term

deb

t

ove

rto

tal

ass

ets.

CF

isca

shfl

ow

sove

rto

tal

ass

ets.

Tu

rnove

ris

the

rati

oof

volu

me

tosh

are

sou

tsta

ndin

g.S

pre

ad

isth

em

on

thly

ave

rage

of

the

dail

y

bid-a

sksp

reads

from

the

CR

SP

Dail

yS

tock

file

.P

CM

isth

epri

ce-t

o-c

ost

marg

in.

HP

HH

Iis

the

firm

-lev

elm

easu

reof

pro

du

ct-s

pace

con

cen

trati

on

base

don

the

Hobe

rg&

Phil

lips

300

indu

stri

es.

All

bala

nce

-shee

tva

riabl

esare

mea

sure

dat

the

end

of

the

fisc

al

year

endin

gat

the

late

stin

Dec

embe

r

of

cale

ndar

year

t-1.

All

the

vari

abl

esare

win

sori

zed

at

the

2.5%

leve

l.

Pan

elA

.M

ean

san

dS

tan

dard

Dev

iati

on

s

Ret

urn

PA

FP

ub

%Siz

eB

Mβ

Lev

CF

Tu

rnover

Sp

read

PC

MH

PH

HI

Mea

n15.1

90.2

10.7

312.3

90.7

50.8

60.5

90.0

10.1

50.0

50.3

10.2

5P

50

0.8

70.1

70.7

412.3

30.4

50.8

10.2

30.0

70.1

00.0

40.3

50.1

7S

td101.0

40.1

70.2

02.1

41.0

50.5

91.1

30.2

00.1

40.0

30.3

90.2

1N

55,8

25

41,6

66

48,7

64

55,7

48

55,7

71

55,8

02

55,5

73

55,6

14

55,7

95

55,7

95

54,8

88

49,8

09

Pan

elB

:C

onte

mp

ora

neo

us

Corr

elati

on

Ret

urn

PA

FP

ub

%S

ize

BM

βL

evC

FT

urn

over

Sp

read

PC

MH

PH

HI

Ret

urn

%1.0

0P

AF

0.0

21.0

0P

ub

%0.0

00.1

51.0

0S

ize

0.0

00.1

30.1

51.0

0B

M0.0

60.0

2-0

.09

-0.3

51.0

0β

-0.0

1-0

.01

0.2

20.4

0-0

.17

1.0

0L

ev-0

.01

0.1

3-0

.04

0.0

50.0

3-0

.03

1.0

0C

F0.0

40.0

9-0

.05

0.3

10.0

0-0

.02

0.0

41.0

0T

urn

over

-0.0

20.0

50.1

80.3

8-0

.14

0.5

00.0

00.0

01.0

0S

pre

ad

-0.0

2-0

.05

-0.1

3-0

.47

0.2

20.0

6-0

.03

-0.4

10.1

11.0

0P

CM

0.0

40.0

0-0

.03

0.1

4-0

.02

-0.0

1-0

.01

0.4

70.0

1-0

.18

1.0

0H

PH

HI

-0.0

4-0

.12

0.0

8-0

.20

0.0

4-0

.17

0.0

20.0

4-0

.16

-0.0

20.0

11.0

0

48

Table 8: Mean of Double-Sorted Portfolio Returns

This table reports time-series averages of annual equally weighted, double-sorting portfolio raw returns in

Panels A and C and characteristic-adjusted (DGTW) returns following Daniel et al. (1997) in Panels B

and D. The sample is restricted to common stocks (listed on NYSE, AMEX, and NASAQ) of firms whose

headquarters are based in the United States. Utilities and Financial sectors are excluded. In each June

of year t, stocks are first assigned to two baskets based on the frequency of price adjustment (FPA). FPA

is the frequency of price adjustment at the granular sector level (see Table 1 for a detailed description).

The two FPA baskets are only sorted once based on the median value of FPA at the granular sector level.

Stocks are then independently assigned to four baskets based on BLS publication (Pub%). For each 3-digit

NAICS sector k, Pub% is the number of product indices published by BLS as a fraction of the maximum

number of products observed in that sector (see equation (3) for a detailed description). The four Pub%

baskets are annually rebalanced. In Panels A and B, the sample period is from July 1997 to June 2013.

In Panels C and D, the sample period is from July 1983 to June 2013.

P1 P2 P3 P4 P4-P1Panel A: Annual Mean Returns: July 1997 – June 2013

S 23.63*** 14.93*** 13.00*** 7.87*** -15.75***(16.41) (12.14) (10.81) (4.75) (-7.11)

F 13.81*** 10.13*** 21.13*** 25.96*** 12.15***(8.08) (8.06) (14.88) (16.31) (4.85)

S-F 9.82*** 4.80** -8.13*** -18.09***(4.12) (2.72) (-4.39) (-7.77)

Panel B: DGTW Adjusted Returns: July 1997 – June 2013S 13.86*** 8.06*** 7.68*** 4.41*** -9.45***

(10.66) (7.46) (7.14) (3.03) (-4.79)

F 7.66*** 4.72*** 13.75*** 17.54*** 9.88***(5.18) (4.29) (10.70) (12.33) (4.47)

S-F 6.20*** 3.34** -6.06*** -13.13***(2.94) (2.16) (-3.64) (-6.37)

Panel C: Annual Mean Returns: July 1983 – June 2013S 44.65*** 32.14*** 31.62*** 33.85*** -10.80***

(25.37) (20.23) (20.24) (14.68) (-3.64)

F 30.91*** 29.87*** 46.20*** 54.84*** 23.93***(14.85) (17.69) (25.34) (26.12) (7.68)

S-F 13.73*** 2.27 -14.58*** -20.99***(4.72) (0.97) (-6.11) (-6.57)

Panel D: DGTW Adjusted Returns: July 1983 – June 2013S 20.00*** 14.63*** 12.65*** 13.70*** -6.30**

(12.71) (10.57) (9.08) (6.74) (-2.41)

F 9.38*** 12.87*** 24.27*** 29.30*** 19.91***(5.42) (8.74) (15.04) (15.55) (7.23)

S-F 10.62*** 1.76 -11.63*** -15.59***(4.15) (0.87) (-5.48) (-5.49)

t-statistics in parentheses∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01

49

Tab

le9:

Pan

el

Regre

ssio

ns

of

Annual

Sto

ckR

etu

rns

on

Pri

ceSti

ckin

ess

(dum

my)

and

BL

SP

ubli

cati

on:

July

1997-J

une

2013,

OL

SR

egre

ssio

n

This

tabl

ere

port

sth

ere

sult

sfo

res

tim

ati

ng

the

foll

ow

ing

lin

ear

equ

ati

on

:

Return

i,t

=α

+β×Sticky j

+γ×Sticky j×Pub%

k,t

+δ×Pub%

k,t

+X′ i,t−

1×θ

+η k′+η t

+ε i,t,

i,j,k

,an

dt

den

ote

firm

,6-a

nd

3-d

igit

NA

ICS

sect

ors

,an

dye

ar,

resp

ecti

vely

.T

he

sam

ple

isre

stri

cted

toco

mm

on

stoc

ks(l

iste

don

NY

SE

,A

ME

X,

an

dN

AS

AQ

)of

firm

sw

hose

hea

dqu

art

ers

are

base

din

the

Un

ited

Sta

tes.

Uti

liti

esan

dF

inan

cial

sect

ors

are

excl

uded

.R

etu

rn(i

npe

rcen

t)is

the

an

nu

ali

zed

stoc

kre

turn

calc

ula

ted

from

Ju

lyof

yeart

toJ

un

eoft

+1.Sticky j

isan

indic

ato

rva

riabl

eeq

ual

to1

ifth

efr

equ

ency

of

pri

ceadju

stm

ent

(FP

A)

ingr

an

ula

rse

ctorj

isbe

low

the

sam

ple

med

ian

,an

dze

rooth

erw

ise.

FP

Ais

the

freq

uen

cyof

pri

ceadju

stm

ent

at

the

gran

ula

rse

ctor

leve

l

(see

Tabl

e1

for

adet

ail

eddes

crip

tion

).F

or

each

3-d

igit

NA

ICS

sect

ork

,Pub%

ism

easu

red

as

the

nu

mbe

rof

pro

du

ctin

dic

espu

blis

hed

byB

LS

as

a

fract

ion

of

the

maxi

mu

mn

um

ber

of

pro

du

cts

obs

erve

din

that

sect

or

(see

equ

ati

on

(3)

for

adet

ail

eddes

crip

tion

).HPub%

isa

du

mm

yva

riabl

eth

at

equ

als

1ifPub%

isabo

veth

esa

mple

med

ian

,an

dze

rooth

erw

ise.X′ i,t−

1is

ase

tof

con

trol

vari

abl

es(s

eeT

abl

e7

for

adet

ail

eddes

crip

tion

).A

llth

e

vari

abl

esare

win

sori

zed

at

the

2.5%

leve

l.S

tan

dard

erro

rsare

clu

ster

edat

the

3-d

igit

NA

ICS

leve

l.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

Sti

cky

0.6

2-3

.15

41.8

5***

33.4

1***

42.2

6***

37.3

9***

12.2

17.0

114.2

3**

11.8

1(0

.12)

(-0.5

3)

(2.9

1)

(2.8

6)

(3.4

2)

(3.2

3)

(1.4

3)

(0.7

8)

.(2

.12)

(1.6

5)

.Sti

cky×

Pub%

-54.4

1***

-46.6

9***

-32.8

0**

-52.2

1***

-46.8

5***

-40.6

3**

(-3.2

8)

(-3.7

8)

(-2.1

5)

(-3.4

4)

(-3.5

6)

(-2.6

3)

Sti

cky×

HP

ub%

-17.1

6**

-13.7

3**

-11.0

8-1

7.4

4***

-15.2

9***

-14.2

0**

(-2.2

9)

(-2.3

3)

(-1.6

7)

(-2.7

8)

(-2.9

2)

(-2.4

2)

Pub%

48.4

3***

24.2

929.0

261.4

6***

36.1

9**

43.9

7**

(3.1

2)

(1.5

3)

(1.6

4)

(3.9

0)

(2.1

5)

(2.2

6)

HP

ub%

20.2

5***

14.5

3**

12.4

622.6

5***

15.8

1**

15.4

3*

(2.7

9)

(2.1

0)

(1.6

7)

(3.1

2)

(2.0

6)

(1.7

9)

Siz

e-1

.62**

-1.8

7**

-1.8

2*

-1.6

0**

-1.8

8**

-1.8

0*

(-2.1

4)

(-2.6

0)

(-1.8

2)

(-2.1

6)

(-2.6

4)

(-1.7

9)

BM

5.7

7***

6.9

4***

7.4

3***

5.7

7***

6.9

7***

7.4

5***

(2.9

0)

(3.8

7)

(4.8

9)

(2.8

7)

(3.9

0)

(4.9

0)

Beta

1.0

61.5

91.4

31.3

91.7

61.5

4(0

.48)

(0.7

2)

(0.6

6)

(0.6

3)

(0.7

9)

(0.7

0)

Lev

-0.7

60.0

50.9

6-0

.83

0.0

30.9

1(-

0.9

1)

(0.0

6)

(1.2

0)

(-1.0

2)

(0.0

4)

(1.1

3)

CF

12.2

622.0

1**

27.2

0***

11.7

522.1

4**

27.4

1***

(1.2

6)

(2.4

9)

(2.9

4)

(1.2

0)

(2.4

8)

(2.9

5)

Turn

over

-18.9

0**

-20.2

7**

-23.2

0***

-18.1

2**

-19.4

3**

-22.7

4***

(-2.4

3)

(-2.5

8)

(-3.2

5)

(-2.2

9)

(-2.4

2)

(-3.1

8)

Spre

ad

21.7

0-1

1.8

6-2

4.6

524.9

7-1

0.6

1-2

2.7

0(0

.32)

(-0.1

6)

(-0.3

6)

(0.3

7)

(-0.1

5)

(-0.3

3)

PC

M2.1

41.9

0-2

.33

2.1

02.0

3-2

.39

(0.3

2)

(0.3

2)

(-0.3

8)

(0.3

2)

(0.3

4)

(-0.3

9)

HP

HH

I-2

7.6

7***

-21.3

9**

-15.6

5*

-27.1

7***

-21.4

3**

-15.5

6*

(-3.9

9)

(-2.6

3)

(-1.9

9)

(-3.8

6)

(-2.6

1)

(-1.9

8)

Const

ant

24.5

7***

29.5

1***

-12.1

311.0

020.8

0-1

.88

24.3

137.6

511.0

017.4

5*

22.6

4***

28.7

6*

40.5

9**

47.0

9**

(5.3

6)

(6.1

0)

(-0.9

8)

(0.6

5)

(1.1

7)

(-0.1

0)

(1.0

8)

(1.5

4)

(1.4

3)

(1.8

0)

(3.7

0)

(1.8

0)

(2.3

5)

(2.5

0)

Year

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

FF

48

FE

No

Yes

No

Yes

No

No

Yes

No

No

Yes

No

No

Yes

No

NA

ICS6

FE

No

No

No

No

Yes

No

No

Yes

No

No

Yes

No

No

Yes

N41,6

66

41,6

66

40,6

34

40,6

34

40,6

34

35,8

62

35,8

62

35,8

62

40,6

34

40,6

34

40,6

34

35,8

62

35,8

62

35,8

62

R2

0.0

20.0

30.0

20.0

30.1

00.0

30.0

40.1

10.0

20.0

30.1

00.0

30.0

40.1

1

t-st

ati

stic

sin

pare

nth

ese

s∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

50

Tab

le10

:P

an

el

Regre

ssio

ns

of

Annual

Sto

ckR

etu

rns

on

Pri

ceSti

ckin

ess

(dum

my)

and

BL

SP

ubli

cati

on:

July

1997-J

une

2013,

IVE

stim

ati

on

This

tabl

ere

port

sth

ere

sult

sfo

rth

efo

llow

ing

IVes

tim

ati

on

:

Return

i,t

=α

+β×Sticky j

+γ×Sticky j×Pub%

k,t

+δ×Pub%

k,t

+X′ i,t−

1×θ

+η k′+η t

+ε i,t,

i,j,k

,an

dt

den

ote

firm

,6-

an

d3-d

igit

NA

ICS

sect

ors

,an

dye

ar,

resp

ecti

vely

.S

eeeq

uati

on

(5)

for

the

des

crip

tion

of

the

IVappro

ach

.R

etu

rn(i

n

perc

ent)

isth

ean

nu

ali

zed

stoc

kre

turn

calc

ula

ted

from

Ju

lyof

yeart

toJ

un

eoft

+1.

The

sam

ple

isre

stri

cted

toco

mm

on

stoc

ks(l

iste

don

NY

SE

,

AM

EX

,an

dN

AS

AQ

)of

firm

sw

hose

hea

dqu

art

ers

are

base

din

the

Un

ited

Sta

tes.

Uti

liti

esan

dF

inan

cial

sect

ors

are

excl

uded

.Sticky j

isan

indic

ato

r

vari

abl

eeq

ual

to1

ifth

efr

equ

ency

of

pri

ceadju

stm

ent

(FP

A)

isbe

low

the

sam

ple

med

ian

,an

dze

rooth

erw

ise.

FP

Ais

the

freq

uen

cyof

pri

ceadju

stm

ent

at

the

gran

ula

rse

ctor

leve

l(s

eeT

abl

e1

for

adet

ail

eddes

crip

tion

).F

or

eachk

as

of

Ju

ne

of

yeart,Pub%

ism

easu

red

as

the

nu

mbe

rof

pro

du

ctin

dic

es

pu

blis

hed

byB

LS

as

afr

act

ion

of

the

maxi

mu

mn

um

ber

of

pro

du

cts

obs

erve

din

that

sect

or

(see

equ

ati

on

(3)

for

adet

ail

eddes

crip

tion

).HPub%

is

adu

mm

yva

riabl

eeq

ual

to1

ifPub%

isabo

veth

esa

mple

med

ian

,an

dze

rooth

erw

ise.X′ i,t−

1is

ase

tof

con

trol

vari

abl

es(s

eeT

abl

e7

for

adet

ail

ed

des

crip

tion

).A

llth

eva

riabl

esare

win

sori

zed

at

the

2.5

%le

vel.

Sta

ndard

erro

rsare

clu

ster

edat

the

3-d

igit

NA

ICS

leve

l.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Sti

cky

38.6

3*

38.6

3*

58.6

3***

58.6

3***

16.0

516.0

525.8

6**

25.8

6**

(1.7

6)

(1.7

5)

(2.8

0)

(2.6

7)

(1.4

4)

(1.4

1)

(2.4

3)

(2.2

1)

Sti

cky×

Pub%

-50.5

5*

-50.5

5*

-46.4

6***

-74.0

8***

-74.0

8***

-53.3

7***

(-1.9

0)

(-1.8

9)

(-3.0

4)

(-2.8

3)

(-2.7

0)

(-3.1

6)

Sti

cky×

HP

ub%

-23.3

6*

-23.3

6*

-21.5

8***

-34.5

9**

-34.5

9**

-24.7

3***

(-1.7

4)

(-1.7

0)

(-2.8

3)

(-2.4

9)

(-2.2

8)

(-3.0

5)

Pub%

26.7

026.7

016.4

040.1

8*

40.1

8*

23.4

3(1

.27)

(1.2

5)

(0.8

9)

(1.8

0)

(1.7

0)

(1.2

1)

HP

ub%

12.0

212.0

27.6

018.9

3*

18.9

3*

11.2

4(1

.20)

(1.2

1)

(0.9

2)

(1.9

1)

(1.8

8)

(1.3

3)

∆Pub%

2004

-25.3

3**

-25.3

3**

-34.0

6***

-34.0

6***

-26.6

1**

-26.6

1**

-36.0

3***

-36.0

3***

(-2.0

8)

(-2.0

1)

(-2.9

9)

(-2.9

2)

(-2.1

3)

(-2.0

2)

(-2.9

7)

(-2.8

6)

Siz

e-1

.63**

-1.6

3**

-1.7

5*

-1.6

4**

-1.6

4**

-1.7

5*

(-2.2

3)

(-2.2

0)

(-1.7

8)

(-2.2

1)

(-2.1

3)

(-1.7

7)

BM

5.9

3***

5.9

3***

7.4

1***

5.9

7***

5.9

7***

7.4

1***

(3.0

8)

(2.9

6)

(4.9

4)

(3.1

2)

(2.9

7)

(4.9

8)

Beta

1.4

11.4

11.2

41.4

51.4

51.1

6(0

.64)

(0.6

0)

(0.5

6)

(0.6

1)

(0.5

8)

(0.5

0)

Lev

-0.6

3-0

.63

0.9

5-0

.57

-0.5

70.9

5(-

0.7

3)

(-0.7

3)

(1.2

1)

(-0.6

5)

(-0.6

4)

(1.1

9)

CF

12.3

512.3

527.0

8***

12.1

012.1

027.2

4***

(1.4

2)

(1.3

1)

(3.0

1)

(1.3

5)

(1.2

7)

(3.0

6)

Turn

over

-19.8

1***

-19.8

1***

-23.6

9***

-20.1

2***

-20.1

2***

-23.9

0***

(-2.6

2)

(-3.3

5)

(-3.4

2)

(-2.7

6)

(-3.5

5)

(-3.5

7)

BD

Spre

ad

12.6

912.6

9-2

5.3

09.1

99.1

9-2

7.5

1(0

.20)

(0.1

9)

(-0.3

8)

(0.1

5)

(0.1

4)

(-0.4

2)

PC

M1.7

71.7

7-2

.30

2.0

22.0

2-2

.38

(0.3

0)

(0.2

6)

(-0.3

8)

(0.3

2)

(0.2

8)

(-0.3

9)

HP

HH

I-2

6.3

5***

-26.3

5***

-15.1

3*

-26.2

0***

-26.2

0***

-15.0

5*

(-3.8

2)

(-3.8

1)

(-1.9

6)

(-3.7

9)

(-3.8

0)

(-1.9

4)

Year

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

FF

48

FE

No

Yes

No

No

Yes

No

No

Yes

No

No

Yes

No

NA

ICS6

FE

No

No

Yes

No

No

Yes

No

No

Yes

No

No

Yes

N40,5

76

40,5

76

40,5

61

35,8

13

35,8

13

35,7

97

40,5

76

40,5

76

40,5

61

35,8

13

35,8

13

35,7

97

R2

0.0

20.0

20.0

20.0

30.0

30.0

30.0

20.0

20.0

20.0

30.0

30.0

3

t-st

ati

stic

sin

pare

nth

ese

s∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

51

Tab

le11

:P

an

el

Regre

ssio

ns

of

Annual

Sto

ckR

etu

rns

on

Pri

ceSti

ckin

ess

(conti

nuous

vari

able

)and

BL

SP

ublica

tion

(dum

my):

July

1997-J

une

2013

This

tabl

ere

port

sth

ere

sult

sfo

rth

efo

llow

ing

lin

ear

equ

ati

on

:

Return

i,t

=α

+β×Sticky%j

+γ×Sticky%j

+HPub%

k,t

+δ×HPub%

k,t

+X′ i,t−

1×θ

+η k′+η t

+ε i,t,

i,j,k

,an

dt

den

ote

firm

,6-

an

d3-d

igit

NA

ICS

sect

ors

,an

dye

ar,

resp

ecti

vely

.R

etu

rn(i

npe

rcen

t)is

the

an

nu

ali

zed

stoc

kre

turn

calc

ula

ted

from

Ju

ly

of

year

tto

Ju

ne

oft+

1.

The

sam

ple

isre

stri

cted

toco

mm

on

stoc

ks(l

iste

don

NY

SE

,A

ME

X,

an

dN

AS

AQ

)of

firm

sw

hose

hea

dqu

art

ers

are

base

d

inth

eU

nit

edS

tate

s.U

tili

ties

an

dF

inan

cial

sect

ors

are

excl

uded

.Sticky%

isF

PA

mu

ltip

lied

by-1

.F

PA

isth

efr

equ

ency

of

pri

ceadju

stm

ent

at

the

gran

ula

rse

ctor

leve

l(s

eeT

abl

e1

for

adet

ail

eddes

crip

tion

).F

or

each

3-d

igit

NA

ICS

sect

ork

as

of

Ju

ne

of

yeart,Pub%

ism

easu

red

as

the

nu

mbe

rof

pro

du

ctin

dic

espu

blis

hed

byB

LS

as

afr

act

ion

of

the

maxi

mu

mn

um

ber

of

pro

du

cts

obs

erve

din

that

sect

or

(see

equ

ati

on

(3)

for

adet

ail

eddes

crip

tion

).

X′ i,t−

1is

ase

tof

con

trol

vari

abl

es(s

eeT

abl

efo

ra

det

ail

eddes

crip

tion

).A

llth

eva

riabl

esare

win

sori

zed

at

the

2.5%

leve

l.S

tan

dard

erro

rsare

clu

ster

ed

at

the

3-d

igit

NA

ICS

leve

l.In

colu

mn

s(9

)-(1

4),

IVes

tim

ati

on

sare

perf

orm

ed.

See

equ

ati

on

(5)

for

the

des

crip

tion

of

the

IVappro

ach

.

OL

SIV

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

-FP

A-1

0.3

9-5

.07

71.5

0**

54.5

1*

71.3

0**

59.5

2**

109.3

587.0

4*

162.9

4106.8

7*

(-0.8

1)

(-0.5

2)

(2.0

7)

(1.9

7)

(2.5

5)

(2.1

9)

(0.9

9)

(1.8

7)

(1.3

7)

(1.8

8)

-FP

A×H

Pub%

-87.9

1**

-65.6

4**

-81.7

2***

-85.9

6***

-68.3

3***

-84.3

2***

-131.9

9-1

07.2

2**

-84.2

4**

-188.2

2-1

25.5

5**

-85.2

1**

(-2.6

0)

(-2.5

3)

(-2.7

4)

(-3.2

3)

(-2.8

1)

(-2.8

2)

(-1.0

3)

(-2.0

1)

(-2.1

4)

(-1.4

2)

(-2.0

5)

(-1.9

7)

HP

ub%

-8.5

6-6

.68

-9.6

1*

-6.0

0-7

.05

-9.3

1-2

7.7

4-2

2.5

4-2

0.4

7-3

8.5

5-2

5.0

3-1

8.3

9(-

1.0

5)

(-1.2

7)

(-1.8

0)

(-1.0

4)

(-1.4

2)

(-1.5

7)

(-0.9

7)

(-1.4

3)

(-1.5

4)

(-1.2

8)

(-1.4

3)

(-1.3

5)

∆Pub%

2004

-17.1

212.8

3-2

5.5

1*

6.7

2(-

1.0

3)

(0.5

4)

(-1.6

7)

(0.2

8)

Siz

e-1

.58**

-1.8

1**

-1.7

7*

-1.4

1*

-1.7

1**

-1.7

3*

(-2.1

3)

(-2.5

3)

(-1.7

5)

(-1.6

7)

(-2.0

5)

(-1.7

5)

BM

5.7

8***

6.9

8***

7.4

3***

6.0

9***

6.9

6***

7.3

6***

(2.8

6)

(3.8

9)

(4.9

1)

(3.0

8)

(3.7

1)

(4.9

6)

Beta

1.4

71.6

61.3

31.1

61.2

61.1

5(0

.73)

(0.7

7)

(0.6

2)

(0.5

7)

(0.5

2)

(0.5

2)

Lev

-0.7

90.0

80.9

2-0

.24

0.2

20.9

2(-

0.9

2)

(0.1

0)

(1.1

4)

(-0.2

3)

(0.2

5)

(1.1

6)

CF

11.7

622.3

2**

27.5

2***

12.7

222.0

6**

27.4

7***

(1.2

1)

(2.5

0)

(2.9

6)

(1.4

3)

(2.1

6)

(3.0

4)

Turn

over

-18.7

4**

-19.6

4**

-23.3

3***

-21.5

0**

-20.8

6***

-23.8

2***

(-2.2

6)

(-2.4

0)

(-3.2

2)

(-2.4

5)

(-3.0

4)

(-3.4

9)

BD

Spre

ad

21.8

7-8

.87

-22.9

04.9

2-1

2.4

6-2

2.3

2(0

.34)

(-0.1

2)

(-0.3

4)

(0.0

8)

(-0.1

7)

(-0.3

4)

PC

M1.6

31.8

9-2

.38

0.3

41.8

4-2

.34

(0.2

5)

(0.3

2)

(-0.3

9)

(0.0

5)

(0.2

4)

(-0.3

9)

HP

HH

I-2

6.2

9***

-21.3

3**

-15.5

2*

-25.3

0***

-20.8

3***

-15.1

6**

(-3.6

8)

(-2.6

1)

(-1.9

8)

(-3.5

9)

(-2.5

8)

(-1.9

7)

Const

ant

22.7

7***

26.3

2***

32.5

2***

31.9

0***

20.0

2***

51.6

0***

58.6

0***

44.5

1**

(6.0

1)

(5.3

2)

(3.8

9)

(4.7

8)

(2.8

9)

(3.8

7)

(4.1

8)

(2.2

8)

Year

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

FF

48

FE

No

Yes

No

Yes

No

No

Yes

No

No

Yes

No

No

Yes

No

NA

ICS6

FE

No

No

No

No

Yes

No

No

Yes

No

No

Yes

No

No

Yes

N41,6

66

41,6

66

40,6

34

40,6

34

40,6

34

35,8

62

35,8

62

35,8

62

40,5

76

40,5

76

40,5

61

35,8

13

35,8

13

35,7

97

R2

0.0

20.0

30.0

20.0

30.1

00.0

30.0

40.1

10.0

20.0

20.0

20.0

30.0

30.0

3

t-st

ati

stic

sin

pare

nth

ese

s∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

52

Table 12: Conditional CAPM Regressions

This table reports results for the conditional CAPM. In each June of year t, stocks are first assigned to two

baskets based on the frequency of price adjustment (FPA). FPA is the frequency of price adjustment at the

granular sector level (see Table 1 for a detailed description). The two FPA baskets are only sorted once

based on the median value of FPA at the granular sector level. Stocks are then independently assigned

to four baskets based on BLS publication (Pub%). For each k as of June of year t, Pub% is measured

as the number of product indices published by BLS in a month as a fraction of the maximum number of

products observed in that sector (see equation (3) for a detailed description). The four Pub% baskets are

annually rebalanced. The conditional CAPM is estimated on a rolling basis over the previous 12 months

as follows:

Rp,s = αp + βp ×Rm,s + εp,s,

where Rp,s is the portfolio excess return, αp (in percent) is a constant, and Rm,s is the excess return

of the CRSP value-weighted index.Newey and West (1987) standard errors are reported in parentheses.

Panels A and B report estimates on the sample period from July 1997 to June 2013. Panels C and D

report estimates on the sample period from July 1983 to June 2013.

P1 P2 P3 P4 P4-P1Panel A: β Estimates, 1997–2013

S 1.27*** 1.28*** 1.24*** 1.14*** -0.13***(32.34) (23.24) (27.81) (22.04) (-3.11)

F 1.04*** 1.16*** 1.39*** 1.20*** 0.16***(20.42) (22.13) (22.33) (22.13) (4.56)

S-F 0.23*** 0.12*** -0.16*** -0.06*(5.14) (3.77) (-5.46) (-1.85)

Panel B: α (in percent) , 1997–2013S -2.01*** -3.96*** -2.88*** -4.98*** -2.98***

(-4.42) (-7.71) (-9.88) (-12.04) (-10.95)

F -6.73*** -4.47*** -3.29*** -2.07*** 4.66***(-11.14) (-9.99) (-9.25) (-7.21) (7.12)

S-F 4.72*** 0.50 0.41 -2.92***(15.28) (1.56) (1.60) (-7.48)

Panel C: β, 1983–2013S 1.15*** 1.16*** 1.12*** 1.10*** -0.05

(29.52) (25.33) (25.97) (27.15) (-1.43)

F 0.98*** 1.10*** 1.22*** 1.08*** 0.09**(24.70) (25.99) (22.22) (22.72) (2.55)

S-F 0.17*** 0.05** -0.10*** 0.02(4.77) (2.05) (-3.82) (0.73)

Panel D: α (in percent), 1983–2013S -3.95*** -5.66*** -4.10*** -4.67*** -0.72*

(-9.23) (-12.12) (-14.52) (-14.02) (-1.74)

F -6.73*** -5.39*** -3.33*** -2.29*** 4.44***(-15.10) (-14.07) (-13.19) (-10.58) (9.73)

S-F 2.78*** -0.28 -0.77** -2.38***(7.40) (-0.95) (-2.75) (-6.86)

t-statistics in parentheses∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.0153

Tab

le13

:P

rice

Sti

ckin

ess

an

dM

an

agers

’D

isse

min

ati

on

of

Pri

vate

Info

rmati

on:

January

2002-D

ece

mb

er

2012

This

tabl

ere

port

sth

ere

sult

sfo

rth

efo

llow

ing

lin

ear

equ

ati

on

:

Discussionit

=α

+β×Sticky%j

+X′ it−1×θ

+η k′+η t

+ε it

Discussionit

isa

du

mm

yva

riabl

eth

at

equ

als

1if

man

age

rspro

vide

quan

tita

tive

info

rmati

on

when

dis

cuss

ing

the

futu

reof

firm

i′sin

pu

tco

sts

du

rin

g

the

earn

ings

con

fere

nce

call

hel

din

quart

ert,

an

dze

rooth

erw

ise.

The

sam

ple

isre

stri

cted

toco

mm

on

stoc

ks(l

iste

don

NY

SE

,A

ME

X,

an

dN

AS

AQ

)

of

firm

sw

hose

hea

dqu

art

ers

are

base

din

the

Un

ited

Sta

tes.

Uti

liti

esan

dF

inan

cial

sect

ors

are

excl

uded

.Sticky%

isF

PA

mu

ltip

lied

bya€

“1.

FP

Ais

the

freq

uen

cyof

pri

ceadju

stm

ent

at

the

gran

ula

rse

ctor

leve

l(s

eeT

abl

e1

for

adet

ail

eddes

crip

tion

).Sticky 1

0pctl

isa

du

mm

yva

riabl

eth

at

equ

als

1

for

the

firm

sin

the

bott

om

10%

of

the

dis

trib

uti

on

base

don

FP

A,

an

dze

rooth

erw

ise.Xit−1

isa

set

of

con

trol

vari

abl

esm

easu

red

as

of

the

begi

nn

ing

of

quart

ert,

incl

udin

gth

est

an

dard

dev

iati

on

of

an

aly

sts′

on

e-ye

ar-

ahea

dE

PS

fore

cast

(sca

led

byst

ock

pri

cela

gged

byon

em

on

th),

the

perc

enta

ges

of

inst

itu

tion

al

share

hold

ings

,th

elo

gari

thm

of

the

nu

mbe

rof

an

aly

sts

cove

rin

gth

efi

rm,

the

loga

rith

mof

the

nu

mbe

rof

part

icip

ati

ng

man

age

rs,

the

loga

rith

mof

the

nu

mbe

rof

part

icip

ati

ng

an

aly

sts,

Siz

e,B

M,

PC

M,

an

dH

PH

HI.

See

Tabl

e7

for

vari

abl

edefi

nit

ion

s.C

on

trol

vari

abl

esare

win

sori

zed

at

the

2.5%

leve

l.

All

Pre

senta

tion

Q&

A(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)

(11)

(12)

Sti

cky%

-0.2

1***

-0.1

1**

-0.1

9***

-0.0

9*

-0.1

0***

-0.0

7*

(-5.5

0)

(-2.0

7)

(-5.2

3)

(-1.6

8)

(-4.8

0)

(-1.8

8)

Sti

cky10%

-0.0

5**

-0.0

4***

-0.0

4**

-0.0

4***

-0.0

3**

-0.0

2**

(-2.1

6)

(-2.7

4)

(-2.4

4)

(-2.8

7)

(-2.2

0)

(-2.1

0)

Fore

cast

Dis

pers

ion

1.2

50.0

82.1

2*

0.2

01.0

9-0

.07

1.8

6*

0.0

10.7

20.5

41.0

8**

0.6

3*

(1.2

4)

(0.1

3)

(1.9

0)

(0.3

0)

(1.1

5)

(-0.1

0)

(1.7

8)

(0.0

1)

(1.5

2)

(1.5

2)

(2.0

4)

(1.7

6)

Inst

ituti

onal

Hold

ing

0.2

3***

0.1

1***

0.2

3***

0.1

1***

0.2

0***

0.1

1***

0.2

0***

0.1

2***

0.1

0***

0.0

4***

0.1

1***

0.0

4***

(8.4

9)

(4.0

8)

(8.7

6)

(4.1

5)

(7.6

6)

(4.0

4)

(7.9

0)

(4.1

2)

(7.3

9)

(2.9

5)

(7.5

6)

(3.1

0)

Ln(#

analy

st)

-0.0

6***

-0.0

3***

-0.0

5***

-0.0

3***

-0.0

4***

-0.0

2*

-0.0

3**

-0.0

2*

-0.0

3***

-0.0

1-0

.02**

-0.0

1(-

4.6

6)

(-2.6

8)

(-3.1

5)

(-2.6

9)

(-3.4

2)

(-1.7

3)

(-2.3

4)

(-1.7

4)

(-3.0

7)

(-0.7

2)

(-2.4

3)

(-0.7

6)

Ln

(#of

managers

)0.1

3***

0.1

3***

0.1

3***

0.1

3***

0.1

3***

0.1

4***

0.1

4***

0.1

4***

0.0

4***

0.0

5***

0.0

5***

0.0

5***

(9.6

5)

(12.7

6)

(9.8

8)

(13.0

4)

(9.6

3)

(11.9

2)

(9.6

5)

(12.0

8)

(4.8

6)

(5.9

1)

(4.9

8)

(5.9

8)

Ln

(#of

part

icip

ants

)0.1

1***

0.1

2***

0.1

1***

0.1

2***

0.0

7***

0.0

9***

0.0

7***

0.0

9***

0.0

9***

0.1

0***

0.0

9***

0.1

0***

(13.3

0)

(15.7

2)

(11.8

0)

(15.5

5)

(7.6

3)

(9.4

7)

(6.9

8)

(9.4

0)

(20.4

9)

(18.8

0)

(21.4

1)

(19.0

0)

Siz

e0.0

2***

0.0

1***

0.0

3***

0.0

1***

0.0

2***

0.0

1**

0.0

2***

0.0

1**

0.0

2***

0.0

1***

0.0

2***

0.0

1***

(4.8

5)

(3.2

7)

(4.6

8)

(3.3

2)

(4.0

5)

(2.2

9)

(4.1

5)

(2.3

0)

(5.4

7)

(4.0

7)

(5.3

9)

(4.1

4)

BM

0.0

8***

0.0

5***

0.1

0***

0.0

6***

0.0

7***

0.0

5***

0.1

0***

0.0

6***

0.0

3***

0.0

2**

0.0

5***

0.0

2**

(4.6

3)

(4.4

3)

(4.8

8)

(4.7

3)

(4.8

0)

(3.9

8)

(5.1

5)

(4.2

3)

(2.6

6)

(2.1

9)

(3.0

6)

(2.4

1)

PC

M0.0

10.0

10.0

10.0

10.0

20.0

20.0

2*

0.0

2-0

.02

-0.0

1-0

.01

-0.0

1(0

.46)

(0.5

3)

(1.0

6)

(0.5

9)

(1.2

1)

(0.9

6)

(1.7

8)

(1.0

1)

(-1.0

7)

(-1.2

3)

(-0.8

1)

(-1.1

7)

HP

HH

I0.0

6*

-0.0

40.0

6-0

.04

0.0

5-0

.04

0.0

4-0

.03

0.0

4*

-0.0

30.0

4*

-0.0

2(1

.75)

(-1.5

2)

(1.4

3)

(-1.3

5)

(1.3

8)

(-1.2

9)

(1.1

4)

(-1.1

2)

(1.7

4)

(-1.3

2)

(1.6

6)

(-1.2

0)

Const

ant

-0.1

9***

-0.4

2***

-0.1

8***

-0.3

4***

-0.2

1***

-0.4

1***

-0.2

0***

-0.3

4***

-0.2

0***

-0.2

3***

-0.1

9***

-0.1

8***

(-4.4

4)

(-7.9

7)

(-4.0

2)

(-9.5

1)

(-4.9

0)

(-7.1

4)

(-4.6

1)

(-8.8

4)

(-8.1

9)

(-6.2

1)

(-6.5

6)

(-8.4

7)

FF

48

FE

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

Year

FE

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

N40069

40069

40069

40069

40069

40069

40069

40069

40069

40069

40069

40069

R-s

quare

d0.0

90.2

00.0

90.2

00.0

60.1

40.0

60.1

40.0

70.1

10.0

70.1

1

t-st

ati

stic

sin

pare

nth

ese

s∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

54

Online Appendix:

Sticky Prices and the Value of Public Information:Evidence from Financial Markets

Jin Xie Juanyi (Jenny) Xu

Not for Publication

A.1 A Partial-Equilibrium Model

In this section, we introduce Nimark (2008)’s partial-equilibrium model. We show how

public information shapes the Philips Curve at the granular sector level even if the Calvo

rate stays constant.

In an economy with idiosyncratic shocks, an agent has to form an expectation of other

agents’ action based on what she can observe directly. The expectation will be imperfect

if the collecting information is costly. Nimark (2008) applies this idea to the price-setting

problem when an idiosyncratic shock to a firm’s marginal cost occurs. More public

information or a lower cost of collecting information helps firms infer more accurately

the average economy wide shocks. The model predicts that output-price transparency

will reduce the size of the variance of the idiosyncratic component relative to that of the

average marginal cost innovation, which in turn reduces the persistence of inflation.

A firm cares about the sectoral price level because demand for its goods depends

on the relative price. But due to the presence of idiosyncratic shocks, firms cannot

infer the sectoral price level perfectly by observing their own marginal cost. The lagged

information then becomes an important source of information individual firms use to form

expectations about sectoral price. Given the existence of private measurement error, a

firm will not only fail to observe the average sectoral marginal cost but also continue

to remain uncertain about other firms’ expectation about its own expectation– whether

others know what others know about it. So the higher-order expectation will play a role

in determining the dynamics of inflation.

1

1.1 Industrial Structure and Optimal Price Setting

We assume each 6-digit NAICS sector j contains a continuum of monopolistic firms

distributed on a unit interval [0, 1] indexed by i. Each firm produces differentiated

goods. The customer combines these differentiated goods to produce a consumption good

for sector j. The production function for consumption at sector j at time t is given by

Xjt =

(∫ 1

0

Xjt (i)

λ−1λ di

) λλ−1

. (A.1)

Suppose the price for goods i in sector j is P jt (i) and the price index for consumption

goods at sector j is P jt . The demand for differentiated goods i is given by

Xjt (i) = (

P jt (i)

P jt

)−λXjt , (A.2)

where P jt (i) and Xj

t (i) are price and demand for good i in sector j. P jt and Xj

t are price

index and total demand for all goods produced by sector j, respectively. λ > 1 is the

elasticity of substitution across differentiated goods in sector j.

As in Calvo (1983), a constant probability 1 − ω exists that a firm will set its price

at any given period. The profit-maximization problem of firm i is as follows:

maxP jt (i)

Ejt (i)

∞∑k=0

(βω)k

[P jt (i)

P jt+k

Xjt+k(i)−MCj

t+k(i)Xjt+k(i)

], (A.3)

which is subject to the downward-sloping demand function (A.2). MCjt+k(i) is firm i’s real

marginal cost at time t. Solving the optimal pricing decision (A.3) gives us the optimal

price P j∗t (i) as follows:

P j,∗t (i) =

λ

λ− 1

∞∑k=0

(βω)kMCjt+k(i)X

jt+k(i)(P

jt+k)

λ

∞∑k=0

(βω)kXjt+k(i)(P

jt+k)

λ−1

. (A.4)

Log-linearizing equation (A.4) gives the optimal reset price of firm i in sector j at time t:

pj,∗t (i) = (1− βω)Ejt (i)

[∞∑k=0

(βω)i(pjt+k +mcjt+k(i))

], (A.5)

2

which is the discounted sum of firm i’s current and future nominal marginal cost.

Ejt (i)[·] ≡ E[· | Ijt (i)] is an expectation operator conditional on firm i’s information

set at time t. Note the sector j price level at time t is given by

pjt = θpjt−1 + (1− θ)pj,∗t , (A.6)

where pj,∗t is the average price chosen by firms in sector j resetting their price in period t:

pj,∗t =

∫pj,∗t (i)di. (A.7)

1.2 Marginal-Cost Shock

We assume that at time t, firm i in sector j is subject to a real marginal-cost shock mcjt(i).

The shock is composed of two parts: the sector-wide component mcjt and the firm-specific

idiosyncratic component µjt(i). Firm i only observes the sum of these two components,

but not mcjt (even with a lag) or µjt(i). That is, it only observes

mcjt(i) = mcjt + µjt(i), (A.8)

where µjt(i) is an idiosyncratic shock and follows µjt(i) ∼ N(0, σ2µ).

The sector-wide shock to the average marginal cost may come from cost inflation

such as an increase in oil price or wages. In this section, we can assume that it follows an

exogenous AR(1) process:

mcjt = ρmcjt−1 + νt, (A.9)

where νt ∼ N(0, σ2v) is an aggregate marginal cost shock that will affect the marginal cost

in sector j. For example, an oil-price shock or a wage-inflation cost-push shock will lead

to a persistent increase in the marginal cost in each sector.

1.3 Optimal Reset Price with Imperfect Common Knowledge

As shown in equation (A.5), firm i’s optimal price is set based on its time t information

set:

Ijt (i) ={mcjs(i), p

js−1, β, ω, σ

2µ, σ

2v | s ≤ t

}. (A.10)

3

The structural parameters{β, ω, σ2

µ, σ2v

}and the lagged sector price level pjs−1 are common

knowledge. Because the common and idiosyncratic components cannot be distinguished,

firm i cannot know with certainty what the sector-wide average marginal cost mcjt is. mcjt

matters for the optimal price of firm i because it determines the current price level. If the

process of mcjt is persistent, the current average marginal cost will be informative about

future marginal costs, and future price levels. To set the price of its goods optimally,

firm i has to form an expectation of mcjt , which means all firms solve a similar signal-

extraction problem before they set prices. Firms thus also form higher expectations,

that is, expectations of average expectations, and so on. By repeatedly substituting the

expressions of price level and the average reset price into equation (A.5), current sectoral

inflation can be written as a function of average higher-order expectations of current

marginal cost and future sectoral inflation as follows:

πjt = (1− ω)(1− βω)∞∑k=0

(1− ω)kmcj,(k)t|t + βω

∞∑k=0

(1− ω)kπj,(k+1)t+1|t , (A.11)

where the following notation for higher-order expectations was used:

x(0)t|t = xt, (A.11)

x(1)t|t =

∫E [xt | Is(j)] dj, (A.12)

x(2)t|t =

∫E[x

(1)t|s | Is(j)

]dj, (A.13)

x(k)t|t =

∫E[x

(k−1)t|s | Is(j)

]dj. (A.14)

As shown in (A.11), the idiosyncratic marginal-cost shock introduces private

information into the price-setting problem of firms and forces firms to form higher-order

expectations, about both marginal cost and future inflation.

If σ2µ = 0, no information uncertainty of any order exists and the Phillips curve

4

reduces to the standard New-Keynesian Phillips Curve:

πjt = βEtπjt+1 +

(1− ω)(1− βω)

ωmcjt . (A.15)

If σ2µ is very large relative to σ2

v , the firm will discard its own marginal cost as an

indicator of the economy-wide average. Instead, each firm will only use the common

observation of this lagged price level to form an imperfect expectation of the economy-

wide average marginal cost. In this setting, it can be shown that the observation of

the lagged price level perfectly reveals lagged average marginal cost. Because no other

source of information is available about the current average marginal cost, the first-order

expectation mcj,(1)t is simply given by ρmcjt−1. This structure is common knowledge and

implies some first-order uncertainty about average marginal costs, i.e., mcj,(1)t 6= mcjt but

no higher order uncertainty so that mcj,(k)t = mc

j,(1)t = ρmcjt−1 : k, l > 0. In this case, the

Phillips curve becomes

πjt = (1− ω)(1− βω)mct +(1− ω)(1− βω)

ω[(1− ρβ)−1 − θ]mcj,(1)

t|t (A.16)

The assumption of very large idiosyncratic marginal-cost shocks, and the fact that all

firms condition on the same information is common knowledge, made finding an analytical

expression for inflation possible. In the general case, when 0 < σ2µ < ∞, neither the

lagged price level nor the observation of a firm’s own marginal cost completely reveals the

average marginal cost or other firms’ estimates of it. Both the firm’s own marginal cost

and the lagged price level will then be needed to form optimal higher-order expectations

of marginal cost, and, due to the Calvo mechanism, higher-order expectations of future

inflation are formed. Unfortunately, when 0 < σ2µ < ∞, even the partial-equilibrium

model cannot be solved analytically, and we have to resort to numerical methods to solve

the model. As Nimark (2008) shows, when the ratio of the variance of the firm-specific

idiosyncratic shock to that of the sectoral marginal cost,σ2µ

σ2ν, decreases, the inflation inertia

also decreases.

5

A.2 A Simple One-Sector GE Model

This economy consists of households that supply labor and consume goods, firms that

produce differentiated goods and set prices and a monetary-policy authority that sets

the nominal interest rate. Households are subject to economy-wide shocks to their

(dis)utility of supplying labor. The labor-supply shock is not directly observable by

firms but influences the marginal cost of production. In addition to the labor-supply

shock and the level of production, firms’ marginal costs are also affected by firm specific

wage-bargaining shocks, and firms cannot by direct observation distinguish between the

economy-wide labor supply shock and the idiosyncratic bargaining shock. So firms have to

form higher-order expectations of average marginal costs in order to set prices optimally.

For simplicity, we assume this economy contains only one sector.

2.1 The Model

The representative agent maximizes

E0

∞∑t=0

βt(C1−γt

1− γ− exp(λt)

N1+ϕt

1 + ϕ

),

where Nt is the aggregate labor supply in period t, and β is the discount factor. Ct is the

usual CES consumption aggregator,

Ct =

(∫ 1

0

Ct(i)ε−1ε dj

) εε−1

,

and λt is a shock to the disutility of supplying labor which is a sum of the persistence

component ξt and the transitory component ηt:

λt = ξt + ηt, ηt ∼ N(0, σ2η) .

The transitory component ηt of labor-supply shock λt prevents the lagged price level to

perfectly reveal the lagged value of the persistent component ξt. The persistent component

follows an AR(1) process:

ξt = ρξt−1 + υt, vt ∼ N(0, σ2v) .

6

Firm j produces the differentiated good Yt(i), using a linear technology with labor

as the sole input:

Yt(i) = Nt(i).

Because no capital exists, the aggregate consumption equals the aggregate production:

Yt = Ct.

The Euler equation of the representative household then implies the IS relation:

yt = Et [yt+1]− 1

γ[it − Et (πt+1)],

where the lower case denotes the log deviation from steady-state values of the

corresponding capital letter (expect for it). The monetary authority sets the nominal

interest rate following the Taylor rule:

it = φππt + φyyt.

The marginal cost of firm j is the real wage paid by firm j, which is determined by the

intratemporal optimality condition (labor-supply decision of households):

mct − γct − ϕnt − λt = 0,

and a firm-specific wage-bargaining shock εt(i). The bargaining shock introduces an

idiosyncratic component to firms’ marginal cost, and firm j’s marginal cost is

mct(i) = (γ + ϕ)yt + λt + εt(i).

In other words, firm i’s marginal cost is determined by aggregate output yt, the

labor-supply shock λt, and the idiosyncratic bargaining shock εt(i). The bargaining shock

is meant to capture, in a stylized way, the empirical finding that a significant part of the

variation in average wages at the firm level seems to be firm specific and uncorrelated

with industry-wide changes.

The timing of the model is as follows. First, the marginal-cost shock λt is realized.

7

Then, firms collect and process information about marginal cost. They also bargain over

marginal cost with households. So the marginal cost firm i pays is given by

mct(i) = (γ + ϕ)yt + ωt(i),

where ωt(i) = λt + εt(i). Firms cannot by direct observation distinguish between the

economy-wide shock to labor supply and the firm-specific bargaining shock, but only

observe the sum of the two, ωt(i), and the component dependent on output, (γ + ϕ)yt.

Firms set prices before production takes place, and their information set when setting the

price in period t is defined by

It(i) ={mct(i), ps−1, ys−1, β, θ, γ, ϕ, σ

2η, σ

2v , σ

2ε | s ≤ t

}.

2.2 Solving the Model

Due to Calvo-pricing, the price setting decision is forward looking, and firms therefore

need to form separate expectations (and higher-order expectations of the persistent labor-

supply-shock component ξt and the transitory component ηt. Define Φt = [ξt ηt]′ as the

labor-supply shock and firm j’s hierarchy of expectation of χt from order l to m be the

vector:

Φ(l:m)t|t ≡

[Φ

(l)t|t ,Φ

(l+1)t|t , · · ·Φ(m−1)

t|t ,Φ(m)t|t

].

The model is solved by an iterative version of the method of undetermined coefficients.

We conjecture that the hierarchy of labor-supply-shock expectations follows the VAR:

Φ(0:∞)t|t = MΦ

(0:∞)t−1|t−1 +NΓt,

where

Γt = [νt ηt]′ .

For a given M in the law of motion above, we can find output and inflation as

functions of the current state of the expectation hierarchy of the labor supply shock Φt.

We want a solution of the following form:

πt = KΦ(0:∞)t|t

8

yt = DΦ(0:∞)t|t

.

In the model, prices are set before output is realized, and because marginal cost

depends on aggregate output, firms have to form an expectation of aggregate output. We

can get firm i’s expectation of its own marginal cost:

E [mct(i) | It(i)] ≡ (γ + ϕ)E [yt | It(i)] + ωt(i).

Taking averages across firms yields an expression for the average expectation of firms’

own marginal cost:

mc(0)t = (γ + ϕ)y

(1)t + λt,

because∫ωt(i) = λt. Invoking common knowledge of rational expectations yields a

general expression for a k-order expectation of firms’ marginal cost:

mc(k)t = (γ + ϕ)y

(k+1)t + λ

(k)t .

Using the conjecture law of motion for the hierarchy of expectation, the expression for

inflation and higher order of marginal cost, we can write the Phillips curve as a function

of the expectation hierarchy of Φt. We get

KΦ(0:∞)t|t = (1− θ)(1− βθ)

∞∑k=0

(1− θ)k(DΦ(k:∞)t|t + 11x2Φ

(k)t|t ) + βθ

∞∑k=0

(1− θ)kKMΦ(k+1:∞)t|t .

Solving the model implies finding a fixed point for K , D, M , and N .

2.3 Equity Premium

Real dividend is given by

Dt =

∫ 1

0

(Pt(i)Yt(i)

Pt−MCt(i)Yt(i))di.

9

Note that due to the idiosyncratic shock, the marginal cost paid by individual firm i isMCt(i)Pt

, where

Yt(i) = (Pt(i)

Pt)−λCt.

Therefore, dividend can be expressed by

Dt =

∫ 1

0

[(Pt(i)

Pt)1−λYt −MCt(i)(

Pt(i)

Pt)−λYt)di (A.17)

= Yt[

∫ 1

0

(Pt(i)

Pt)1−λdi−

∫ 1

0

MCt(i)

MCtMCt(

Pt(i)

Pt)−λdi]

= Yt[

∫ 1

0

(Pt(i)

Pt)1−λdi−MCtWPDSt]

= Yt[1−1

µt],

where we define the combination of the wage and price-dispersion term as WPDSt =∫ 1

0MCt(i)MCt

(Pt(i)Pt

)−λdi, and µt is the markup, which is defined as

µt =Y realt

LIt=

Yt∫ 1

0MCt(i)Pt

(Pt(i)Pt

)−λYtdi=

1

MCt∫ 1

0MCt(i)MCt

(Pt(i)Pt

)−λdi= (MCt)

−1 1

WPDSt.

(A.18)

Y realt = Yt is the real output and LIt is the total real cost.1 Therefore, the log dividends

is

dt = yt + log(1− 1

µt). (A.19)

From Cochrane (2005), we first derive the log excess return of an asset over the

risk-free rate, rft+1. First , the price of an asset with a gross return Rt+1 is decided by

1 = Et[Mt,t+1Rt+1],

where Mt,t+1 is the stochastic discount factor to price this asset between t and t+1. Using

the assumption of log-normality, we get

1In the New Keyesian literature, the price dispersion is usually defined as∫ 1

0(Pt(i)Pt

)−λdi.

10

1 = exp[Et mt,t+1 + Et rt+1 +1

2vart (mt,t+1) +

1

2vart (rt+1) + covt(mt,t+1, rt+1)]

So we can get the log excess return of this asset over rft+1

Et rt+1 +1

2vart rt+1 − rft+1 = −covt(mt,t+1, rt+1).

Consider return of a claim to dividends in period t+ 1 with current price St , Rt+1 =Dt+1

St. Following Li and Palomino (2014) and Weber (2015), we can drive the expected

excess return of a claim to dividends:

Et rt+1 +1

2vart rt+1 − rft+1 = −covt(mt,t+1, rt+1)

= −covt(mt,t+1, dt+1)

Taking a first-order difference around the steady state markup, µ = λλ−1

, we have

log(1− 1

µt) ≈ log(1− 1

µ) +

1

µ− 1(log µt − log µ)

Then from A.19 and A.18, we can have

dt+1 = yt+1 + log(1− 1

µ) + (λ− 1)(log µt+1 − log µ)

= yt+1 + log(1− 1

µ) + (λ− 1)(−mct − wpdst+1).

So the expected excess return difference is

Et rt+1 +1

2vart rt+1 − rft+1 = −covt(mt,t+1, dt+1)

= −covt(mt,t+1, yt+1)− (λ− 1)covt(mt,t+1,−mct)

+(λ− 1)covt(mt,t+1, wpdst+1)

In our model,

11

Mt,t+1 = β(Ct+1

Ct)−γ.

So,

mt,t+1 = −γ(yt+1 − yt).

Also, from the labor-supply function,

−mct = −(γ + ϕ)yt+1 − λt+1.

In∫ 1

0MCt(i)MCt

(Pt(i)Pt

)−λdi, both MCt(i) and Pt(i) are affected by the idiosyncratic shock,

so we cannot get the wpdst+1 analytically. We will have to resort to numerical methods

to derive the expected excess return.

12

Tab

leA

.1:

Bevera

ge

and

Tobacc

oM

anufa

cturi

ng

This

tabl

eil

lust

rate

sth

egr

an

ula

rst

ruct

ure

of

the

Bev

erage

an

dT

oba

cco

Man

ufa

ctu

rin

gse

ctor.

NA

ICS

secto

rSecto

rnam

eP

roduct

code

Pro

duct

nam

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ce

312—

Bevera

ge

&to

bacco

mfg

312—

Bevera

ge

&to

bacco

mfg

200312

3121–

Bevera

ge

mfg

3121–

Bevera

ge

mfg

198412

31211-

Soft

dri

nk

&ic

em

fg31211-

Soft

dri

nk

&ic

em

fg200312

312111

Soft

dri

nk

manufa

ctu

ring

312111

Soft

dri

nk

manufa

ctu

ring

198106

312111

Soft

dri

nk

manufa

ctu

ring

3121112

Soft

dri

nks,

bott

led,

canned,

gla

ss,

pla

stic

,carb

onate

d201112

312111

Soft

dri

nk

manufa

ctu

ring

312111A

Soft

dri

nks,

non-c

arb

onate

d200012

312111

Soft

dri

nk

manufa

ctu

ring

312111A

3Soft

dri

nks,

non-c

arb

onate

d,

all

oth

er

typ

es

200706

312111

Soft

dri

nk

manufa

ctu

ring

312111A

4Soft

dri

nks,

non-c

arb

onate

d,

fruit

dri

nks,

cockta

ils,

ades

201112

312111

Soft

dri

nk

manufa

ctu

ring

312111M

Mis

cellaneous

receip

ts198106

312111

Soft

dri

nk

manufa

ctu

ring

312111M

MM

iscellaneous

receip

ts198106

312111

Soft

dri

nk

manufa

ctu

ring

312111P

Pri

mary

pro

ducts

198106

312111

Soft

dri

nk

manufa

ctu

ring

312111S

Secondary

pro

ducts

198106

312112

Bott

led

wate

rm

anufa

ctu

ring

312112

Bott

led

wate

rm

anufa

ctu

ring

200312

312112

Bott

led

wate

rm

anufa

ctu

ring

3121120

Bott

led

wate

r200012

312112

Bott

led

wate

rm

anufa

ctu

ring

312112P

Pri

mary

pro

ducts

200312

312113

Ice

manufa

ctu

ring

312113

Ice

manufa

ctu

ring

198512

312113

Ice

manufa

ctu

ring

3121130

Ice

200312

312113

Ice

manufa

ctu

ring

312113M

Mis

cellaneous

receip

ts199808

312113

Ice

manufa

ctu

ring

312113P

Pri

mary

pro

ducts

198512

312113

Ice

manufa

ctu

ring

312113S1

Secondary

pro

ducts

200912

31212-

Bre

weri

es

31212-

Bre

weri

es

200312

312120

Bre

weri

es

312120

Bre

weri

es

198206

312120

Bre

weri

es

3121201

Canned

beer

and

ale

case

goods

198206

312120

Bre

weri

es

3121204Z

Bott

led

beer

and

ale

case

goods

198206

312120

Bre

weri

es

3121207

Beer

and

ale

inbarr

els

and

kegs

198206

312120

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weri

es

3121209

All

oth

er

malt

bevera

ges

and

bre

win

gpro

ducts

198206

312120

Bre

weri

es

312120M

Mis

cellaneous

receip

ts200212

312120

Bre

weri

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312120P

Pri

mary

pro

ducts

198206

312120

Bre

weri

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312120S

Secondary

pro

ducts

200312

31213-

Win

eri

es

31213-

Win

eri

es

200312

312130

Win

eri

es

312130

Win

eri

es

198312

312130

Win

eri

es

3121300

Win

es,

bra

ndy,

and

bra

ndy

spir

its

199812

312130

Win

eri

es

31213008

Win

es,

whit

e,

red

&ro

segra

pe,

and

oth

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fruit

,14

perc

ent

or

less

alc

ohol

201112

312130

Win

eri

es

3121300811

Win

es,

whit

egra

pe,

14

perc

ent

or

less

alc

ohol

conte

nt

198312

312130

Win

eri

es

3121300821

Win

es,

red

gra

pe,

14

perc

ent

or

less

alc

ohol

conte

nt

198312

312130

Win

eri

es

3121300831

Win

es,

rose

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&oth

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fruit

s&

berr

ies,

14

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201112

312130

Win

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31213009

Win

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dess

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ent,

and

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312130

Win

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3121300911

Win

es,

dess

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,exclu

din

gsp

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s201112

312130

Win

eri

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3121300921

Win

es,

eff

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ent,

inclu

din

gsp

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ling

(carb

onate

d),

win

ecoole

rs201112

312130

Win

eri

es

3121300B

Oth

er

win

es,

bra

ndy,

and

bra

ndy

spir

its

200606

312130

Win

eri

es

312130M

Mis

cellaneous

receip

ts199112

312130

Win

eri

es

312130P

Pri

mary

pro

ducts

198312

31214-

Dis

tilleri

es

31214-

Dis

tilleri

es

200312

312140

Dis

tilleri

es

312140

Dis

tilleri

es

198306

312140

Dis

tilleri

es

3121402

Dis

till

ed

liquor,

except

bra

ndy

199807

312140

Dis

tilleri

es

31214022

Dis

tilled

whis

key,

all

oth

er

dis

tilled

liquor,

exclu

din

gbra

ndy

201112

312140

Dis

tilleri

es

3121404

Bott

led

liquor,

except

bra

ndy

198306

312140

Dis

tilleri

es

3121404B

Bott

led

whis

key

201206

312140

Dis

tilleri

es

3121404C

Bott

led

gin

,vodka,

rum

,cord

ials

,cockta

ils,

and

sim

ilia

rcom

pounds

201206

312140

Dis

tilleri

es

3121404D

All

oth

er

bott

led

liquor,

except

bra

ndy

201206

312140

Dis

tilleri

es

312140M

Mis

cellaneous

receip

ts198308

312140

Dis

tilleri

es

312140M

MM

iscellaneous

receip

ts198308

312140

Dis

tilleri

es

312140P

Pri

mary

pro

ducts

198306

312140

Dis

tilleri

es

312140S

Secondary

pro

ducts

199712

312140

Dis

tilleri

es

312140SS

Secondary

pro

ducts

198306

3122–

Tobacco

mfg

3122–

Tobacco

mfg

198412

31223-

Tobacco

manufa

ctu

ring

31223-

Tobacco

manufa

ctu

ring

198212

312230

Tobacco

manufa

ctu

ring

312230

Tobacco

manufa

ctu

ring

201112

312230

Tobacco

manufa

ctu

ring

3122300

Tobacco

stem

min

gand

redry

ing

198406

312230

Tobacco

manufa

ctu

ring

31223001

Tobacco,

unst

em

med

leaf,

redri

ed

befo

repackin

g200312

312230

Tobacco

manufa

ctu

ring

31223004

Tobacco,

stem

med

198406

312230

Tobacco

manufa

ctu

ring

3122301

Cig

are

ttes,

inclu

din

gnonto

bacco

198212

312230

Tobacco

manufa

ctu

ring

3122309

Oth

er

tobacco

pro

ducts

200312

312230

Tobacco

manufa

ctu

ring

31223091

Cig

ars

198212

312230

Tobacco

manufa

ctu

ring

31223094

Chew

ing

and

smokin

gto

bacco

198212

312230

Tobacco

manufa

ctu

ring

31223097

Reconst

itute

dto

bacco,

pro

cess

ed

sheet

and

hom

ogeniz

ed

198906

312230

Tobacco

manufa

ctu

ring

312230P

Pri

mary

pro

ducts

201112

13

Table A.2: An Example of Concordance between NAICS and SIC

This table illustrates the concordance between the 2002 North American Industry Classification System

(NAICS) and the 1987 Standard Industrial Classification System (SIC) in the Producer Price Index

(PPI) program complied by the Bureau of Labor Statistics (BLS). Panel A presents the concordance for

the Petroleum and Coal Products Manufacturing sector. Panel B presents the concordance for the Textile

Mills sector.

Panel A: Petroleum and Coal Products Manufacturing

NAICS Product Name SIC324110 Petroleum refineries 2911324110P Primary products 2911P3241101 Gasoline, including finished base stocks and blending agents 29111324110111 Aviation gasoline (except jet fuel) incl finished base stocks & blending agents 291111132411013 Motor gasoline, including finished base stocks and blending agents 291113324110134 Regular gasoline 2911134324110135 Mid-premium gasoline 2911135324110136 Premium gasoline 29111363241102 Jet fuel 291123241103 Kerosene, except jet fuel 291133241104 Light fuel oils 29114324110411 Home heating oil and other distillates, NEC 2911411324110413 Diesel fuel 29114133241105 Heavy fuel oils, including No. 5, No. 6, heavy diesel, gas enrichment oils, etc. 291153241107 Lubricating oil and greases, made in a refinery 291173241108 Unfinished oils and lubricating oil base stock 291183241109 Asphalt 29119324110A Liquefied refinery gases, including other aliphatics(feed stock and other uses) 2911A324110D Other finished petroleum products, including waxes 2911D324110SM Secondary and miscellaneous products 2911SM324110M Miscellaneous receipts 2911M324110S Secondary products 2911S

Panel B: Textile Mills

NAICS Product Name SIC313311 Broadwoven fabric finishing mills313311P Primary products3133111 Finished cotton broadwoven fabrics (not finished in weaving mills) 226173133113 Job or commission finishing of cotton broadwoven fabrics 226193133115 Finished manmade fiber & silk broadwoven fabrics (not finished in weaving mills) 226283133117 Job or commission finishing of manmade fiber and silk broadwoven fabrics 226293133119 Finished broadwoven wool fabrics and felts (not finished in weaving mills)313311SM Secondary products and miscellaneous receipts313311M Miscellaneous receipts313311S Secondary products313312 Textile/fabric finishing (exc broadwoven) mills313312P Primary products3133121 Finished fabrics (except broadwoven) and other finished textiles313312SM Secondary products and miscellaneous receipts313312M Miscellaneous receipts313312S Secondary products313320 Fabric coating mills 2295313320P Primary products 2295P3133201 Vinyl coated fabrics, including expanded vinyl coated 229523133203 Rubber coated fabrics3133205131 Pyroxylin and polyurethane coated fabrics 22953163133205491 Other coated or laminated fabrics, excluding rubberized fabrics 2295322313320SM Secondary products and miscellaneous receipts 2295SM313320M Miscellaneous receipts 2295M313320S Secondary products 2295S

14

Table A.3: Expansion of BLS Publication Coverage across Sectors

This table reports descriptive statistics for the coverage expansion in January 2004 by the Office of

Publications at BLS across 3-digit NAICS sectors. ∆Pub%2004j is the difference between Pub%jsjs in

January 2004 and November 2003 for a 3-digit NAICS sector k. For each k, Pub% is measured as the

number of product indices published by BLS in a month as a fraction of the maximum number of products

observed in that sector (see equation (3) for a detailed description).

Sector Name NAICS ∆Pub%2004

Forestry and Logging 113 0.00Oil and Gas Extraction 211 0.00Mining (except Oil and Gas) 212 0.04Support Activities for Mining 213 0.18Utilities 221 0.23Construction of Buildings 236Specialty Trade Contractors 238Food Manufacturing 311 0.08Beverage and Tobacco Product Manufacturing 312 0.19Textile Mills 313 0.18Textile Product Mills 314 0.26Apparel Manufacturing 315 0.23Leather and Allied Product Manufacturing 316 0.19Wood Product Manufacturing 321 0.45Paper Manufacturing 322 0.22Printing and Related Support Activities 323 0.26Petroleum and Coal Products Manufacturing 324 0.02Chemical Manufacturing 325 0.10Plastics and Rubber Products Manufacturing 326 0.20Nonmetallic Mineral Product Manufacturing 327 0.12Primary Metal Manufacturing 331 0.21Fabricated Metal Product Manufacturing 332 0.12Machinery Manufacturing 333 0.12Computer and Electronic Product Manufacturing 334 0.18Electrical Equipment, Appliance, and Component Manufacturing 335 0.18Transportation Equipment Manufacturing 336 0.27Furniture and Related Product Manufacturing 337 0.24Miscellaneous Manufacturing 339 0.08Merchant Wholesalers, Durable Goods 423Merchant Wholesalers, Nondurable Goods 424Wholesale Electronic Markets and Agents and Brokers 425Recyclable Materials 429 -0.03Motor Vehicle and Parts Dealers 441 0.38Furniture and Home Furnishings Stores 442 0.40Electronics and Appliance Stores 443 0.23Building Material and Garden Equipment and Supplies Dealers 444 0.61Food and Beverage Stores 445 0.19Health and Personal Care Stores 446 0.17Gasoline Stations 447 0.47Clothing and Clothing Accessories Stores 448 0.31Sporting Goods, Hobby, Musical Instrument, and Book Stores 451 0.26General Merchandise Stores 452 0.73Miscellaneous Store Retailers 453 0.27Nonstore Retailers 454 0.39Air Transportation 481 0.07Rail Transportation 482 0.00Water Transportation 483 0.65Truck Transportation 484 0.56Pipeline Transportation 486 0.33Support Activities for Transportation 488 0.33Postal Service 491 0.00Couriers and Messengers 492 0.60Warehousing and Storage 493 0.25Publishing Industries (except Internet) 511 0.21Broadcasting (except Internet) 515 0.33Telecommunications 517 0.13Data Processing, Hosting, and Related Services 518 0.25Other Information Services 519Credit Intermediation and Related Activities 522Securities, Commodity Contracts, and Other Financial Investments and Related Activities 523 0.42Insurance Carriers and Related Activities 524 0.09Real Estate 531 0.50Rental and Leasing Services 532 0.33Professional, Scientific, and Technical Services 541 0.15Administrative and Support Services 561 0.12Waste Management and Remediation Services 562 0.50Educational Services 611Ambulatory Health Care Services 621 0.08Hospitals 622 0.11Nursing and Residential Care Facilities 623 0.35Amusement, Gambling, and Recreation Industries 713Accommodation 721 0.54Repair and Maintenance 811Administration of Environmental Quality Programs 924 0.09

15

Table A.4: Triple Difference: BLS Publication, Price Stickiness, and Borrow-ing Costs (Interactions): January 1997-December 2012, OLS Regression

This table reports the results for estimating the following linear equation:

Spreadqjs = α+ β1 × Stickyj + β2 ×Bondis + Pub%ks + γ × Stickyj ×Bondis × Pub%ks+

δ1 × Stickyj ×Bondis + δ2 × Stickyj ×Bondjsw + δ3 × Pub%ks +X ′i,t−1 × θ + ηi + ηt + εqjs,

The Great Recession period (December 2007 to June 2009) is excluded. q, i, j, k, s, and t denote

borrowing transaction, firm, 6- and 3-digit NAICS sectors, month, and year, respectively. The sample

is restricted to common stocks (listed on NYSE, AMEX, and NASAQ) of firms whose headquarters are

based in the United States. Utilities and Financial sectors are excluded. Spread is the difference between

the bond, or syndicated loan, interest rate and the risk-free rate. Sticky is an indicator variable equal to

1 if the frequency of price adjustment (FPA) is below the sample median, and zero otherwise. FPA is the

frequency of price adjustment at the granular sector level (see Table 1 for a detailed description). Bond is

an indicator variable equal to 1 if the firm borrows a public bond, and zero if the firm borrows a loan. For

each k, Pub% is measured as the number of product indices published by BLS in a month as a fraction

of the maximum number of products observed in that sector (see equation (3) for a detailed description).

HPub% is a dummy variable that equals 1 if Pub% is above the sample median, and zero otherwise.X ′i,t−1is a vector of additional controls (see Table 2 for a detailed description). Standard errors are clustered at

the 3-digit NAICS level.

(1) (2) (3) (4)Sticky -10.41 -6.95

(-0.59) (-1.50)Bond 110.65*** 119.34*** 111.01*** 119.47***

(10.11) (10.74) (10.26) (10.86)Pub% -16.08 18.22

(-0.79) (1.14)HPub% -17.82* -2.75

(-1.99) (-0.33)Sticky × Bond 109.33*** 122.09*** 11.03 12.90

(3.24) (3.57) (0.95) (1.05)Sticky × Pub% 13.83 -10.02

(0.69) (-0.47)Sticky × HPub% 12.22* -0.71

(2.00) (-0.11)Sticky × Bond × Pub% -164.01*** -176.80***

(-4.08) (-4.28)Sticky × Bond × HPub% -49.89*** -48.33***

(-4.52) (-4.31)Total Vol 2638.56*** 2337.91*** 2629.81*** 2331.42***

(13.55) (11.64) (13.79) (11.94)Profitability -176.92*** -151.94*** -174.59*** -149.53***

(-5.26) (-3.83) (-5.28) (-3.80)Size -24.51*** -22.36*** -24.55*** -22.63***

(-15.09) (-4.32) (-15.29) (-4.50)BM 4.83*** 1.09 4.70*** 1.01

(2.82) (0.53) (2.78) (0.48)Intangibility 18.16 2.20 15.89 -0.62

(1.23) (0.10) (1.07) (-0.03)Lev 150.58*** 78.91*** 150.89*** 79.28***

(11.50) (5.47) (11.73) (5.44)PCM -15.22 -16.31 -15.84 -17.22

(-0.58) (-1.27) (-0.61) (-1.35)HP HHI 6.67 16.01* 7.05 16.99*

(0.72) (1.84) (0.79) (1.98)Constant 308.00*** 304.03*** 305.09*** 315.57***

(12.13) (6.75) (13.89) (7.95)Year FE Yes Yes Yes YesHP 100 FE Yes Yes Yes YesFirm FE No No No NoN 10685 10685 10685 10685adj. R2 0.50 0.66 0.50 0.66

t-statistics in parentheses∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01

16

Tab

leA

.5:

BL

SP

ublica

tion

,P

rice

Sti

ckin

ess,

and

Loa

nC

ontr

act

Ter

ms:

Jan

uar

y19

97-D

ecem

ber

2012

,O

LS

Reg

ress

ion

This

tabl

ere

port

sth

ere

sult

sfo

res

tim

ati

ng

the

foll

ow

ing

lin

ear

equ

ati

on

:

Contract

term

s qjs

=α

+β×Sticky j

+γ×Sticky j×Pub%

ks

+δ×Pub%

ks

+X′ i,t−

1×θ

+η i

+η t

+ε qjs,

The

Gre

at

Rec

essi

on

peri

od(D

ecem

ber

2007

toJ

un

e2009)

isex

clu

ded

.U

tili

ties

an

dF

inan

cial

sect

ors

are

excl

uded

.q,i,j,k

,s,

an

dt

den

ote

dea

l,

firm

,6-

an

d3-d

igit

NA

ICS

sect

ors

,m

on

th,

an

dye

ar,

resp

ecti

vely

.L

oan

con

tract

term

sare

des

crib

edas

foll

ow

s.O

wn

pis

the

ow

ner

ship

of

the

lead

ban

kin

each

dea

l.M

aty

isth

en

atu

ral

loga

rith

mof

loan

matu

rity

.D

ivd

isan

indic

ato

req

ual

to1

ifth

edea

lhas

aco

ven

an

tof

div

iden

dre

stri

ctio

n,

an

d

zero

oth

erw

ise.

Capx

isan

indic

ato

req

ual

to1

ifth

edea

lhas

aco

ven

an

tof

capit

al

expe

ndit

ure

rest

rict

ion

,an

dze

rooth

erw

ise.

Per

eis

an

indic

ato

r

equ

al

to1

ifth

edea

lhas

ape

rform

an

ceco

ven

an

tre

stri

ctio

n,

an

dze

rooth

erw

ise.

Per

form

an

ceco

ven

an

tsare

those

rely

on

inco

me

state

men

t(c

ash

-flow

state

men

t)in

form

ati

on

.C

apl

isan

indic

ato

req

ual

to1

ifth

edea

lhas

aca

pit

al

cove

nan

tre

stri

ctio

n,

an

dze

rooth

erw

ise.

Capit

al

cove

nan

tsare

those

that

rely

on

bala

nce

-sh

eet

info

rmati

on

.S

tick

yis

an

indic

ato

rva

riabl

eeq

ual

to1

ifth

efr

equ

ency

of

pri

ceadju

stm

ent

(FP

A)

isbe

low

the

sam

ple

med

ian

,

an

dze

rooth

erw

ise.

FP

Ais

the

freq

uen

cyof

pri

ceadju

stm

ent

at

the

gran

ula

rse

ctor

leve

l(s

eeT

abl

e1

for

adet

ail

eddes

crip

tion

).B

on

dis

an

indic

ato

r

vari

abl

eeq

ua

lto

1if

the

firm

borr

ow

sa

pu

blic

bon

d,

an

dze

roif

the

firm

borr

ow

sa

loan

.X′ i,t−

1is

ave

ctor

of

addit

ion

al

con

trols

(see

Tabl

e2

for

a

det

ail

eddes

crip

tion

).C

olu

mn

s(1

)-

(6)

pre

sen

tre

sult

sfo

rO

LS

regr

essi

on

s,an

dco

lum

ns

(7)

a€

“(1

2)

pre

sen

tre

sult

sfo

rIV

esti

mati

on

.S

eeeq

uati

on

(5)

for

the

des

crip

tion

of

the

IVappro

ach

.

OL

SIV

Ow

np

Maty

Div

dC

apx

Fin

lC

ap

lO

wn

pM

aty

Div

dC

apx

Fin

lC

ap

l(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)

(11)

(12)

Sti

cky×

Pu

b%

0.0

7-0

.13

-0.0

20.0

2-0

.06

-0.0

20.0

6-0

.19*

-0.0

9-0

.01

-0.0

7-0

.06

(1.0

5)

(-0.9

2)

(-0.1

5)

(0.3

9)

(-0.7

4)

(-0.2

0)

(0.9

9)

(-1.8

3)

(-0.9

2)

(-0.2

4)

(-0.8

0)

(-0.6

3)

Pu

b%

0.0

10.0

3-0

.09

-0.0

8*

-0.0

1-0

.02

0.0

1-0

.01

-0.2

0**

-0.1

3**

-0.1

0-0

.12

(0.1

9)

(0.2

3)

(-0.8

7)

(-1.7

4)

(-0.0

9)

(-0.1

6)

(0.2

2)

(-0.0

9)

(-2.2

6)

(-2.4

6)

(-1.2

0)

(-0.9

6)

Pro

fita

bilit

y0.0

2-0

.03

-0.3

3***

-0.2

2**

0.2

9***

0.2

0*

0.0

2-0

.02

-0.3

3***

-0.2

2***

0.3

0***

0.2

0**

(0.1

0)

(-0.1

2)

(-2.7

1)

(-2.3

2)

(4.2

6)

(1.9

5)

(0.1

4)

(-0.1

4)

(-3.1

2)

(-2.7

0)

(5.0

5)

(2.3

2)

Siz

e-0

.06***

-0.0

4*

-0.0

2-0

.02*

-0.0

2-0

.01

-0.0

6***

-0.0

4**

-0.0

2*

-0.0

2**

-0.0

2-0

.01

(-3.3

0)

(-1.7

3)

(-1.4

8)

(-1.8

2)

(-1.0

2)

(-0.7

2)

(-4.5

7)

(-2.1

3)

(-1.8

5)

(-2.2

5)

(-1.2

8)

(-0.9

2)

BM

-0.0

0-0

.01

0.0

0-0

.00

0.0

1-0

.01

-0.0

0-0

.01

0.0

0-0

.00

0.0

1-0

.01

(-0.0

3)

(-0.6

4)

(0.2

8)

(-0.0

8)

(0.9

0)

(-1.1

5)

(-0.0

4)

(-0.8

0)

(0.2

8)

(-0.1

2)

(1.0

2)

(-1.3

8)

Inta

ngib

ilit

y-0

.15*

-0.0

9-0

.12

-0.1

1**

-0.1

0-0

.00

-0.1

5***

-0.0

8-0

.12*

-0.1

1**

-0.1

00.0

0(-

1.8

7)

(-0.8

3)

(-1.5

1)

(-2.2

0)

(-1.2

3)

(-0.0

5)

(-2.6

1)

(-0.9

7)

(-1.6

9)

(-2.5

4)

(-1.4

5)

(0.0

0)

Lev

-0.0

6-0

.04

-0.0

10.1

1*

-0.0

5-0

.10

-0.0

6-0

.05

-0.0

10.1

1**

-0.0

5-0

.10

(-0.9

9)

(-0.5

9)

(-0.1

6)

(1.8

2)

(-1.2

3)

(-1.2

1)

(-1.3

9)

(-0.7

2)

(-0.1

9)

(2.1

3)

(-1.4

5)

(-1.4

0)

PC

M-0

.05

0.2

40.1

2-0

.00

-0.0

10.0

1-0

.05

0.2

40.1

2*

0.0

0-0

.01

0.0

1(-

0.6

7)

(1.3

6)

(1.4

1)

(-0.0

0)

(-0.3

5)

(0.1

3)

(-0.9

4)

(1.6

1)

(1.6

7)

(0.0

1)

(-0.4

1)

(0.1

6)

HP

HH

I-0

.00

-0.0

6-0

.02

-0.0

1-0

.01

-0.0

4-0

.00

-0.0

7-0

.03

-0.0

1-0

.02

-0.0

5(-

0.0

1)

(-1.0

6)

(-0.8

2)

(-0.2

5)

(-0.3

6)

(-0.9

7)

(-0.0

2)

(-1.2

6)

(-1.0

7)

(-0.3

3)

(-0.4

3)

(-1.1

9)

Con

stant

0.7

1***

4.2

9***

0.6

2***

0.4

8***

0.3

7**

0.7

7***

(3.4

2)

(17.3

6)

(3.1

9)

(3.0

0)

(2.5

1)

(4.5

0)

Yea

rF

EY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esF

irm

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

N3078

9319

9920

9920

9920

9920

2317

8485

9079

9079

9079

9079

ad

j.R

20.6

70.3

00.2

80.3

40.3

80.2

7

t-st

ati

stic

sin

pare

nth

eses

∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

17

Tab

leA

.6:

BL

SP

ublica

tion,

Pri

ceSti

ckin

ess

,and

Borr

ow

ing

Cost

s(c

ross

-sect

ional

regre

ssio

n):

January

1997–

Dece

mb

er

2012,

OL

SR

egre

ssio

n

This

tabl

ere

port

sth

ere

sult

sfo

res

tim

ati

ng

the

foll

ow

ing

lin

ear

equ

ati

on

:

Spreadi

=α

+β×Bondi+γ×Bondi×Pub%

k+δ×Pub%

k+X′ i×θ

+η j

+ε i,

The

Gre

at

Rec

essi

on

peri

od(D

ecem

ber

2007

toJ

un

e2009)

isex

clu

ded

.U

tili

ties

an

dF

inan

cial

sect

ors

are

excl

uded

.T

he

data

are

coll

apse

dto

asi

ngl

e

cross

sect

ion

base

don

firm

an

dis

suan

cety

pe(i

.e.,

bon

dan

dlo

an

).i,j,

an

dk

den

ote

firm

,6-d

igit

,an

d3-d

igit

NA

ICS

sect

ors

,re

spec

tive

ly.

Sti

cky-

an

dfl

exib

le-p

rice

-firm

subs

am

ple

sare

form

edba

sed

on

whet

her

the

freq

uen

cyof

pri

ceadju

stm

ent

(FP

A)

isbe

low

or

abo

veth

esa

mple

med

ian

.F

PA

is

mea

sure

dat

the

gran

ula

rse

ctor

leve

l(s

eeT

abl

e1

for

adet

ail

eddes

crip

tion

).S

pre

ad

isth

ediff

eren

cebe

twee

nth

ebo

nd,

or

syn

dic

ate

dlo

an

,in

tere

stra

te

an

dth

eri

sk-f

ree

rate

.B

on

dis

an

indic

ato

rva

riabl

eeq

ual

to1if

afi

rmbo

rrow

sa

pu

blic

bon

d,

an

dze

roif

afi

rmbo

rrow

sa

loan

.Pub%

ism

easu

red

as

the

nu

mbe

rof

pro

du

ctin

dic

espu

blis

hed

byB

LS

ina

mon

thas

afr

act

ion

of

the

maxi

mu

mn

um

ber

of

pro

du

cts

obs

erve

din

that

sect

or

(see

equ

ati

on

(3)

for

adet

ail

eddes

crip

tion

).HPub%

isa

du

mm

yth

at

equ

als

1ifPub%

isabo

veth

esa

mple

med

ian

,an

dze

rooth

erw

ise.X′ i,t−

1is

ave

ctor

of

addit

ion

al

con

trols

(see

Tabl

e2

for

adet

ail

eddes

crip

tion

).S

tan

dard

erro

rsare

clu

ster

edat

the

3-d

igit

NA

ICS

leve

l.

Sti

cky

Fle

xib

leS

tick

yF

lexib

le(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)B

on

d249.4

6***

274.9

9***

153.8

7**

152.6

0*

180.7

0***

184.8

0***

173.4

1***

169.5

3***

(4.8

8)

(5.2

1)

(2.0

7)

(2.0

1)

(9.5

0)

(10.2

0)

(9.4

0)

(9.5

4)

Bon

d×

Pu

b%

-123.1

8*

-161.6

6**

20.2

118.2

1(-

1.9

5)

(-2.4

9)

(0.2

0)

(0.1

7)

Pu

b%

164.1

2***

228.4

5***

178.6

4***

217.8

5***

(3.6

0)

(5.1

6)

(4.4

0)

(5.2

4)

Bon

d×

HP

ub

%-3

4.9

6**

-45.0

3***

-4.1

2-1

.71

(-2.3

4)

(-3.1

9)

(-0.1

7)

(-0.0

7)

HP

ub

%25.2

5***

33.8

0***

39.4

0***

42.5

5***

(5.6

8)

(7.0

2)

(4.7

6)

(4.9

1)

Tota

lV

ol

1891.4

3***

2348.4

9***

1680.8

9***

2063.2

9***

(9.7

7)

(3.6

9)

(8.9

4)

(3.0

6)

Pro

fita

bilit

y-3

40.1

7***

-240.9

2***

-358.6

0***

-243.4

8***

-336.3

0***

-246.8

7***

-371.9

4***

-271.0

7***

(-5.9

3)

(-3.9

3)

(-3.6

6)

(-3.4

1)

(-5.8

3)

(-4.0

6)

(-3.8

6)

(-3.9

5)

Ln

(Sale

s)-3

7.6

0***

-30.1

5***

-31.1

2***

-23.8

7***

-36.9

7***

-30.1

2***

-29.7

0***

-23.0

1***

(-14.8

1)

(-13.0

3)

(-10.4

4)

(-6.3

9)

(-13.9

8)

(-11.7

9)

(-10.5

2)

(-6.3

1)

BM

7.4

010.9

7**

5.9

55.7

66.5

69.4

6**

6.2

8*

6.2

4**

(1.6

2)

(2.6

3)

(1.5

7)

(1.6

6)

(1.4

3)

(2.2

0)

(1.9

4)

(2.1

3)

Inta

ngib

ilit

y21.6

434.8

83.4

615.6

330.2

745.2

4**

8.2

620.5

9(0

.96)

(1.6

0)

(0.0

7)

(0.3

1)

(1.4

1)

(2.1

2)

(0.1

8)

(0.4

6)

Lev

128.0

6***

127.3

0***

151.9

8***

137.6

7***

122.5

9***

119.9

5***

146.5

7***

132.3

8***

(5.8

9)

(6.2

3)

(6.4

9)

(5.4

9)

(5.6

3)

(5.7

8)

(6.6

6)

(5.8

2)

PC

M62.5

050.2

654.8

950.5

961.2

250.0

253.9

449.5

2(1

.20)

(1.1

5)

(1.1

6)

(1.3

1)

(1.2

1)

(1.1

7)

(1.1

2)

(1.2

4)

HP

HH

I0.6

5-8

.20

27.4

411.7

34.1

0-2

.57

28.9

114.9

3(0

.06)

(-0.7

0)

(0.5

3)

(0.2

5)

(0.4

0)

(-0.2

5)

(0.5

6)

(0.3

1)

Con

stant

283.3

1***

115.6

7***

214.1

1***

61.1

9380.9

5***

265.9

6***

312.7

0***

199.6

4***

(7.9

9)

(3.3

4)

(7.2

5)

(1.0

7)

(31.3

0)

(18.1

1)

(14.7

5)

(3.9

3)

NA

ICS

6F

EY

esY

esY

esY

esY

esY

esY

esY

esN

1973

1973

1046

1046

1973

1973

1046

1046

ad

jR

20.4

80.5

10.4

90.5

30.4

70.5

10.4

80.5

1

t-st

ati

stic

sin

pare

nth

eses

∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

18

Tab

leA

.7:

BL

SP

ublica

tion,

Pri

ceSti

ckin

ess

,and

Sto

ck-R

etu

rnV

ola

tili

ty:

July

1997-J

une

2013

This

tabl

ere

port

sth

ere

sult

sfo

rth

efo

llow

ing

lin

ear

equ

ati

on

:

Voli,t+

1=α

+β×

(Sticky j

)+γ×

(Sticky j

)×Pub%

kt

+δ×Pub%

kt

+θ 1×Voli,t

+X′ it−1×θ 2

++η k

+η t

+ε jt,

See

equ

ati

on

(5)

for

the

des

crip

tion

of

the

IVappro

ach

.i,j,k

,an

dt

den

ote

firm

,6-

an

d3-d

igit

NA

ICS

sect

ors

,an

dye

ar,

resp

ecti

vely

.T

he

sam

ple

is

rest

rict

edto

com

mon

stoc

ks(l

iste

don

NY

SE

,A

ME

X,

an

dN

AS

AQ

)of

firm

sw

hose

hea

dqu

art

ers

are

base

din

the

Un

ited

Sta

tes.

Uti

liti

esan

dF

inan

cial

sect

ors

are

excl

uded

.V

ol

isth

est

an

dard

dev

iati

on

of

dail

yst

ock

retu

rns

calc

ula

ted

from

Ju

lyof

each

year

tto

Ju

ne

of

t+1.

We

set

the

vola

tili

tyto

mis

sin

gif

we

have

less

than

60

dail

yre

turn

obs

erva

tion

s.T

ota

lV

ol

isca

lcu

late

du

sin

gra

wre

turn

s,an

dId

ioV

ol

isca

lcu

late

du

sin

gre

sidu

al

retu

rns

esti

mate

dfr

om

CA

PM

mod

el.

Sti

cky

isan

indic

ato

rva

riabl

eeq

ual

to1

ifth

efr

equ

ency

of

pri

ceadju

stm

ent

(FP

A)

isbe

low

the

sam

ple

med

ian

,an

d

zero

oth

erw

ise.

FP

Ais

the

freq

uen

cyof

pri

ceadju

stm

ent

at

the

gran

ula

rse

ctor

leve

l(s

eeT

abl

e1

for

adet

ail

eddes

crip

tion

).F

or

each

3-d

igit

NA

ICS

sect

or

kas

of

Ju

ne

of

yeart,Pub%

ism

easu

red

as

the

nu

mbe

rof

pro

du

ctin

dic

espu

blis

hed

byB

LS

as

afr

act

ion

of

the

maxi

mu

mn

um

ber

of

pro

du

cts

obs

erve

din

that

sect

or

(see

equ

ati

on

(3)

for

adet

ail

eddes

crip

tion

).X′ it−1

isa

set

of

con

trol

vari

abl

es(s

eeT

abl

efo

ra

det

ail

eddes

crip

tion

).A

llth

e

vari

abl

esare

win

sori

zed

at

the

2.5%

leve

l.S

tan

dard

erro

rsare

clu

ster

edat

the

3-d

igit

NA

ICS

leve

l.In

colu

mn

s(9

)-(1

4),

IVes

tim

ati

on

sare

perf

orm

ed.

See

equ

ati

on

(5)

for

the

des

crip

tion

of

the

IVappro

ach

.

OL

SIV

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Sti

cky

0.0

06***

0.0

05***

0.0

12***

0.0

11***

(3.5

9)

(3.7

0)

(4.2

4)

(4.7

0)

Sti

cky×

Pub%

-0.0

08***

-0.0

09***

-0.0

11***

-0.0

07***

-0.0

08***

-0.0

09***

-0.0

15***

-0.0

13***

-0.0

14***

-0.0

14***

-0.0

12***

-0.0

13***

(-3.7

9)

(-3.5

4)

(-2.7

3)

(-4.0

1)

(-3.7

6)

(-2.7

7)

(-4.4

8)

(-3.6

6)

(-2.9

1)

(-4.9

8)

(-3.9

4)

(-3.1

4)

Pub%

0.0

04***

0.0

05***

0.0

06**

0.0

04***

0.0

05***

0.0

06***

-0.0

02

-0.0

04***

-0.0

03*

-0.0

03*

-0.0

05***

-0.0

05***

(2.9

8)

(2.8

3)

(2.4

4)

(3.0

5)

(3.1

2)

(2.6

9)

(-1.2

9)

(-2.8

7)

(-1.7

6)

(-1.6

7)

(-3.4

1)

(-2.6

0)

∆Pub%

2004

-0.0

01

-0.0

02

(-0.5

4)

(-0.7

3)

Tota

lV

ol

0.3

81***

0.3

53***

0.1

14***

0.2

98***

0.2

76***

0.0

87***

(15.2

7)

(14.3

9)

(5.2

5)

(12.3

4)

(11.2

8)

(4.6

1)

Idio

Vol

0.3

69***

0.3

39***

0.0

91***

0.3

44***

0.3

17***

0.1

06***

(14.5

5)

(13.8

0)

(4.3

7)

(13.1

7)

(12.5

4)

(5.4

8)

Siz

e-0

.002***

-0.0

02***

-0.0

03***

-0.0

02***

-0.0

02***

-0.0

04***

-0.0

02***

-0.0

02***

-0.0

03***

-0.0

03***

-0.0

03***

-0.0

04***

(-16.6

5)

(-15.3

5)

(-6.5

7)

(-21.1

1)

(-21.7

1)

(-11.2

2)

(-21.1

1)

(-19.4

2)

(-4.9

9)

(-21.1

6)

(-22.7

3)

(-8.7

3)

BM

0.0

00

0.0

00*

0.0

01***

0.0

00

0.0

00

0.0

01***

-0.0

01***

-0.0

01***

-0.0

01***

-0.0

01***

-0.0

01***

-0.0

01***

(1.3

5)

(1.7

0)

(4.7

8)

(0.9

0)

(1.2

9)

(4.3

5)

(-10.1

0)

(-6.2

1)

(-2.6

9)

(-10.2

8)

(-6.8

8)

(-4.1

3)

Beta

0.0

00

0.0

00

0.0

01***

-0.0

00

-0.0

00

0.0

01***

-0.0

01**

-0.0

01***

-0.0

01**

-0.0

01***

-0.0

02***

-0.0

01***

(1.1

4)

(0.7

8)

(4.4

2)

(-1.4

0)

(-1.3

5)

(3.5

5)

(-2.2

7)

(-2.7

4)

(-2.4

8)

(-4.2

8)

(-5.1

7)

(-4.0

9)

Lev

0.0

00***

0.0

00***

-0.0

00

0.0

00***

0.0

00***

0.0

00

0.0

00

0.0

00

-0.0

00***

0.0

00

0.0

00

-0.0

00**

(2.7

6)

(2.6

9)

(-0.1

2)

(2.9

5)

(2.8

6)

(0.1

3)

(1.1

8)

(1.0

2)

(-2.8

0)

(1.5

2)

(1.4

2)

(-2.3

9)

CF

-0.0

07***

-0.0

07***

-0.0

02***

-0.0

07***

-0.0

07***

-0.0

02***

-0.0

05***

-0.0

05***

0.0

02

-0.0

05***

-0.0

05***

0.0

02

(-15.7

6)

(-13.7

2)

(-3.3

2)

(-15.8

5)

(-13.8

2)

(-3.1

1)

(-6.6

0)

(-6.0

8)

(1.2

3)

(-6.3

9)

(-5.8

0)

(1.6

1)

Turn

over

-0.0

01

-0.0

01

0.0

00

-0.0

03**

-0.0

03**

-0.0

02

0.0

01

0.0

00

0.0

01

-0.0

03

-0.0

03

-0.0

03

(-0.3

9)

(-0.7

5)

(0.0

2)

(-2.0

9)

(-2.5

3)

(-1.3

3)

(0.2

6)

(0.0

7)

(0.1

6)

(-1.2

6)

(-1.4

8)

(-1.1

5)

BD

Spre

ad

0.2

30***

0.2

27***

0.1

99***

0.2

28***

0.2

25***

0.1

93***

0.2

59***

0.2

57***

0.2

37***

0.2

31***

0.2

29***

0.2

09***

(27.8

9)

(28.2

5)

(28.6

6)

(25.9

3)

(26.3

1)

(26.0

4)

(19.7

7)

(25.9

9)

(28.5

6)

(19.8

9)

(25.4

8)

(27.7

5)

PC

M-0

.000

-0.0

01***

-0.0

01***

-0.0

00

-0.0

00**

-0.0

01**

-0.0

01***

-0.0

01***

-0.0

02***

-0.0

01***

-0.0

01***

-0.0

02***

(-1.6

4)

(-3.2

7)

(-3.7

5)

(-1.1

9)

(-2.6

4)

(-2.3

0)

(-5.1

1)

(-7.3

9)

(-6.0

7)

(-6.3

3)

(-5.5

7)

(-4.4

6)

HP

HH

I-0

.001*

-0.0

00

-0.0

00

-0.0

01

-0.0

00

-0.0

00

-0.0

01

-0.0

01

-0.0

02*

-0.0

01

-0.0

01*

-0.0

02*

(-1.7

1)

(-0.5

1)

(-0.6

2)

(-1.4

1)

(-0.2

3)

(-0.7

7)

(-1.5

9)

(-1.6

1)

(-1.6

6)

(-1.5

8)

(-1.6

8)

(-1.8

2)

Const

ant

0.0

26***

0.0

33***

0.0

55***

0.0

34***

0.0

40***

0.0

68***

(10.1

4)

(10.5

0)

(9.3

0)

(14.4

0)

(14.0

2)

(14.0

7)

Year

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

FF

48

FE

Yes

No

No

Yes

No

No

Yes

No

No

Yes

No

No

NA

ICS6

FE

No

Yes

No

No

Yes

No

No

Yes

No

No

Yes

No

Fir

mF

EN

oN

oY

es

No

No

Yes

No

No

Yes

No

No

Yes

N32515

32515

32515

32515

32515

32515

32470

32449

31849

32470

32449

31849

adj.R

20.7

12

0.7

16

0.7

59

0.7

20

0.7

24

0.7

70

t-st

ati

stic

sin

pare

nth

ese

s∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

19

Table A.8: Panel Regressions of Annual Stock Returns on Price Stickiness(dummy) and BLS Publication: July 1983-June 2013, IV Estimation

This table reports the results for the following IV estimation:

Returnit = α+ β × Stickyj + γ × Stickyj × Pub%kt + δ × Pub%kt +X ′it−1 × θ + ηk′ + ηt + εit,

i, j, k, and t denote firm, 6- and 3-digit NAICS sectors, and year, respectively. The sample is restricted

to common stocks (listed on NYSE, AMEX, and NASAQ) of firms whose headquarters are based in the

United States. Utilities and Financial sectors are excluded. Return (in percent) is the annualized stock

return calculated from July of year t to June of t+1. Stickyj is an indicator variable equal to 1 if the

frequency of price adjustment (FPA) is below the sample median, and zero otherwise. FPA is the frequency

of price adjustment at the granular sector level (see Table 1 for a detailed description). For each k as of

June of year t, Pub% is measured as the number of product indices published by BLS as a fraction of the

maximum number of products observed in that sector (see equation (3) for detailed description). HPub%

is a dummy variable that equals 1 if Pub% is above the sample median, and zero otherwise. X ′it−1 is a

set of control variables (see Table 7 for a detailed description). All the variables are winsorized at the

2.5% level. Standard errors are clustered at the 3-digit NAICS level.

(1) (2) (3) (4) (5) (6)Sticky 14.21 14.21 2.39 2.39

(0.98) (0.99) (0.38) (0.40)Sticky × Pub% -23.13 -23.13 -35.04**

(-1.23) (-1.23) (-2.55)Sticky × HPub% -8.92 -8.92 -16.47**

(-1.07) (-1.08) (-2.43)Pub% 41.12 41.12 11.04

(1.40) (1.46) (0.48)HPub% 22.12 22.12 5.69

(1.48) (1.59) (0.42)∆ Pub%2004 -32.09*** -32.09*** -33.42*** -33.42***

(-2.66) (-2.65) (-2.82) (-2.62)Size -1.24 -1.24 -0.79 -1.20 -1.20 -0.81

(-1.51) (-1.49) (-0.88) (-1.46) (-1.44) (-0.91)BM 6.07*** 6.07*** 7.77*** 6.27*** 6.27*** 7.73***

(3.04) (3.14) (4.78) (3.27) (3.33) (4.87)β 4.15* 4.15* 2.76* 4.02* 4.02* 2.81*

(1.94) (1.83) (1.77) (1.80) (1.71) (1.74)Lev -1.59 -1.59 -0.37 -1.61 -1.61 -0.37

(-1.43) (-1.48) (-0.42) (-1.44) (-1.50) (-0.42)CF 13.72 13.72 27.14*** 13.98 13.98 27.44***

(1.38) (1.29) (3.18) (1.37) (1.28) (3.28)Turnover -8.98 -8.98 -25.16*** -9.36 -9.36 -24.99***

(-1.05) (-1.14) (-3.07) (-1.09) (-1.22) (-3.02)BD Spread 88.48 88.48 53.95 90.21 90.21 53.59

(1.48) (1.36) (0.89) (1.51) (1.38) (0.88)PCM 3.34 3.34 -5.14 3.02 3.02 -5.21

(0.40) (0.36) (-0.77) (0.35) (0.32) (-0.78)HHI -70.22 -70.22 21.93 -74.95 -74.95 30.43

(-1.27) (-1.21) (0.42) (-1.37) (-1.33) (0.59)Year FE Yes Yes Yes Yes Yes YesFF48 FE No Yes No No Yes NoNAICS6 FE No No Yes No No YesN 70,264 70,264 70,256 70,264 70,264 70,256R2 0.02 0.02 0.02 0.02 0.02 0.02

t-statistics in parentheses∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01

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