**Notice:** After doing some follow-up data cleaning, we have generated
better estimates. These estimates are not terribly different from our
initial estimates, but for the purposes of replication and in the
interests of transparency to the public, we report our revised
estimates:

**The Effects of Consolidation on Local Radio Advertising Prices
Population-Weighted Estimates**

**(T-statistics in Parentheses)**

  -------------------- -------------------------- ---------------------------
                       **Model 1 -- One-way       **Model 2 -- Two-way
                       fixed-effects**            fixed-effects**

                       Ln(Price of Radio          Ln(Price of Radio
                       Advertising during Morning Advertising during Morning
                       Drive-time)                Drive-time)

  Ln(Population)       -.15\* *- (1.75)*          -.16\* *- (1.84)*

  Ln(Real Income)      .48\*\*\* *(6.94)*         .46\*\*\* *(6.70)*

  Ln(Local HHI)        .02\*\* *(2.35)*           .04\*\*\* *(3.91)*

  Ln(National HHI)     -.06\*\*\* *(2.63)*        \-

  Ln(# of Owners)      -.04 *(1.46)*              -.02 *- (0.99)*

  Ln(Local-National)   -.02\*\*\* *- (5.11)*      -.02\*\*\* *- (4.96)*

  Ln(RealGDP)          3.31\*\*\* *(11.74)*       \-

  Constant             -16.87\*\*\* *-(10.19)*    -3.77\*\*\* *- (3.51)*

  F-statistic          169.04\*\*\*               171.87\*\*\*

  Adjusted R-squared   .97                        .97

  N                    1284                       1284
  -------------------- -------------------------- ---------------------------

(\*\*\* indicates significance at the 1% level, \*\* indicates
significance at the 5% level, \* indicates significance at the 10%
level)

The biggest change is that the coefficient on National HHI is now
negative and significant. However, we have limited confidence in this
particular finding, because, unlike the other variables, National HHI is
very sensitive to changes in population-weighting. We believe that the
estimate on National HHI may largely be driven by the high correlation
between National HHI and the Real GDP, and Real GDP may drive more of
the growth in advertising price in large population centers. We list
non-population-weighted results to illustrate our point:

**The Effects of Consolidation on Local Radio Advertising Prices**

**Non-Population-Weighted Estimates**

**(T-statistics in Parentheses)**

  -------------------- -------------------------- ---------------------------
                       **Model 1 -- One-way       **Model 2 -- Two-way
                       fixed-effects**            fixed-effects**

                       Ln(Price of Radio          Ln(Price of Radio
                       Advertising during Morning Advertising during Morning
                       Drive-time)                Drive-time)

  Ln(Population)       -.21 *- (1.49)*            -.22 *- (1.50)*

  Ln(Real Income)      .23\*\*\* *(7.25)*         .25\*\* *(2.28)*

  Ln(Local HHI)        .04\*\* *(2.38)*           .04\*\*\* *(2.91)*

  Ln(National HHI)     -.01 *(0.27)*              \-

  Ln(# of Owners)      -.02 *(0.60)*              -.02 *- (0.57)*

  Ln(Local-National)   -.01\*\* *- (2.24)*        -.01\*\* *- (2.25)*

  Ln(RealGDP)          2.30\*\*\* *(7.25)*        \-

  Constant             -16.87\*\*\* *-(10.19)*    -1.85 *- (1.09)*

  F-statistic          559.18\*\*\*               525.72\*\*\*

  Adjusted R-squared   .96                        .96

  N                    1284                       1284
  -------------------- -------------------------- ---------------------------

(\*\*\* indicates significance at the 1% level, \*\* indicates
significance at the 5% level, \* indicates significance at the 10%
level)

The coefficient on National HHI drops precipitously and is the only
concentration variable that loses its statistical significance in the
non-population-weighted regression. Notice that the coefficient on
log(RealGDP) is also lower, but still highly statistically significant.

We relate the instructions for study replication on the next two pages.

With the complete data set, which is contained in the Excel spreadsheet
CompleteDataRadio.xls, copy and paste directly from Excel to the editor
in Stata version 7.0. (You could also choose to import the file into
Stata.) See the references at the end of this piece and the redacted
Excel spreadsheet called RedactRadio.xls for information on data sources
and methodology used in creating variables.

To create real-dollar variables, use the following commands in Stata:

1: g RealGDP = (gdpfrombea)/(cpifrombls[^1])

2: g realamdrv = (cpmfor1849formorningdrivefrom[^2])/(cpifrombls)

3: g realincome = (percapitaincomeformsafrombea[^3])/(cpifrombls)

You have now converted your dollar variables from nominal to real
dollars. Note that we just divide by the CPI to do this.

To replicate our results, you now need logged variables. To generate
these, use the following commands in Stata.

4: g logRealGDP = ln(RealGDP)

5: g logrealamdrv = ln(realamdrv)

6: g logrealincome = ln(realincome)

7: g loglocalhhi = ln(localhhifrombia[^4])

8: g lognationalhhi = ln(nathhifrombia[^5])

9: g logpop = ln(populationfromsqad[^6])

10: g loglocalnational = ln(localnationalfrombia[^7])

11: g logown = ln(numberofownersfrombia[^8])

We also include a Stata data set, (if and only if you have the required
licenses for the relevant underlying data), called giveradio.dta, which
includes both the raw and transformed variables.

To the extent you read our paper, then you realize that we used
so-called "fixed-effects" to estimate our model[^9]. Fixed-effects are
easy to include in standard regression format using the xi: reg command
in Stata 7.0. We also used population weights. The first estimation used
one-way fixed effects with population weights.

The command is:

xi: reg logrealamdrv logrealincome logRealGDP logpop loglocalhhi logown
lognationalhhi loglocalnational i.msa
\[iweight=populationfromsqad/meanpopfromsqad\]

The second estimation is a two-way fixed-effects model. Because we're
including dummies for each time period to perform two-way fixed-effects
estimation, we cannot include lognationalhhi and logRealGDP, as these
are variables that only vary over time and do not vary across markets.
The command is:

xi: reg logrealamdrv logrealincome logpop loglocalhhi logown
loglocalnational i.msa i.date
\[iweight=populationfromsqad/meanpopfromsqad\]

In order to perform the non-population weighted regression that we've
also presented here, simply use the following commands:

One-way fixed-effects:

xi: reg logrealamdrv logrealincome logRealGDP logpop loglocalhhi logown
lognationalhhi loglocalnational i.msa, robust

Two-way fixed-effects:

xi: reg logrealamdrv logrealincome logpop loglocalhhi logown
loglocalnational i.msa i.date

References and Data Sources:

[^1]: \<http://www.bls.gov\>

[^2]: SQAD Spot Radio Database, 1995-2000 -

[^3]: Bureau of Economic Analysis, 2002, Regional Accounts Data, Local
    Area Personal Income
    <http://www.bea.doc.gov/bea/regional/reis/drill.cfm>

[^4]: BIA MasterAccess Database -- sum of squared local market shares
    for all owners in MSA

[^5]: BIA MasterAccess Database - **--** sum of squared national market
    share for all owners in nation

[^6]: SQAD Spot Radio Database, 1995-2000 -

[^7]: BIA MasterAccess Database -- each owner's local market share
    multiplied by each owner's national market share, summed for all
    owners in MSA

[^8]: BIA MasterAccess Database -- -- number of owners in MSA

[^9]: [Econometric Analysis of Panel Data]{.underline}, Badi H. Baltagi,
    John Wiley & Sons, 1995