ssaggregate: Create industry-level aggregates for shift-share IV
In kylebutts/ssaggregate: Create industry-level aggregates for shift-share IV
ssaggregate R Documentation
Create industry-level aggregates for shift-share IV
Description
ssaggregate converts "location-level" variables in a shift-share IV
dataset to a dataset of exposure-weighted "industry-level" aggregates,
as described in Borusyak, Hull, and Jaravel (2022).
Usage
ssaggregate(
data,
vars,
weights = NULL,
n,
shares = NULL,
s,
l = NULL,
t,
controls,
addmissing = F
)
Arguments
data
Data.frame of data
vars
RHS formula of variables, i.e. ~ y x ...
weights
String. Variable name indicating the weights used
n
String. Variable name indicating industry identifiers
shares
Data.frame that contains the shares dataset.
Each row should be uniquely indexed by the variables in l and n
(and t, when specified).
s
String. Variable name indicating name of exposure weight variable.
If using "wideformat", s should denote the stub name.
l
String. Variable name indicating location identifiers
t
String. Variable name indicating period identifiers
controls
RHS formula of control variables and fixed effects that will
be partialled out.
Formula following fixest::feols.
Fixed effects specified after "|".
addmissing
Logical. If true, creates "missing industry" observations
Details
There are two ways to specify ssaggregate, depending on whether the
industry exposure weights are saved in "long" format (unique rows for
industry x location) in a separate dataset shares or in "wide" format
(unique rows for location and columns for each industry) as part of df.
In general ssaggregate will execute faster with "long" exposure weights.
See the examples for proper syntax in both cases.
In the "long" case the dataset in memory must be uniquely identified by
the cross-sectional variables in l and, when applicable, the period
identifiers in t. The separate shares dataset is given by shares and
should be uniquely indexed by the variables in l and n (and t, when
specified). s should contain the name of the exposure weight variable,
and the two datasets should contain only matching values of l and t.
In the "wide" case the s option should contain the common stub of the
names of exposure weight variables, and n should contain the target
name of the shock identifier. For example, s = "share" should be
specified when the exposure variables are named share101, share102, etc.,
with 101 and 102 being values of the shock identifier in n. The string
option should be specified when the shock identifer is a string variable.
Missing values in any of the exposure weight variables are interpreted as
zeros. The dataset in memory may be a panel or repeated cross section,
with periods indexed by t.
In both cases there should be no missing values for the location-level
variables, conditional on any if and in sample restrictions. The resulting
industry-level dataset will contain exposure-weighted de-meaned averages of
the location-level variables, along with the average exposure weight s_n.
This dataset will be be indexed by the variables in n (and t, when
specified).
When the controls option is included the location-level variables are
first residualized by the control variables. Formula following
fixest::feols. Fixed effects specified after
"|".The transformation of variable y is also named y.
Including the addmissing option generates a "missing industry" observation,
with exposure weights equal to one minus the sum of a location's exposure
weights. Borusyak, Hull, and Jaravel (2022) recommend including this option
when the the sum of exposure weights varies across locations
(see Section 3.2). The missing industry observations will be identified by
NA in n.
Note that no information on industry shocks is used in the execution of
ssaggregate; once run, users can merge shocks and any industry-level
controls to the aggregated dataset. They can then estimate and validate
quasi-experimental shift-share IV regressions with other Stata procedures.
See Section 4 of Borusyak, Hull, and Jaravel (2022) for details and
below for examples of such procedures.
Value
A data.table of exposure-weighted "industry-level" aggregates, as
described in Borusyak, Hull, and Jaravel (2022).
Examples
Using ssaggregate
Using sepearate "long" share dataset
data("df")
data("shares")
industry = ssaggregate(
data = df,
shares = shares,
vars = ~ y + x + z + l_sh_routine33,
weights = "wei",
n = "sic87dd",
t = "year",
s = "ind_share",
l = "czone",
controls = ~ t2 + Lsh_manuf
)
head(industry)
#> sic87dd year s_n y x z l_sh_routine33
#> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2011 1990 5.991178e-03 0.9038660 -0.1129257 -0.15226680 -1.065246
#> 2: 2011 2000 4.729078e-03 0.5532335 0.3898516 -0.09367913 -1.178196
#> 3: 2015 1990 3.802834e-03 0.7228411 -0.4026864 0.08561252 -2.407878
#> 4: 2015 2000 4.638681e-03 -0.1133174 -0.1762856 -0.34968672 -2.320667
#> 5: 2021 1990 8.723289e-05 2.2034699 0.1183517 -0.03164776 -2.788844
#> 6: 2021 2000 4.436779e-05 0.3604907 0.2456597 -0.39783282 -1.195864
Using "wide" shares in dataset
data("df_wide")
industry_df = ssaggregate(
data = df_wide,
vars = ~ y + x + z + l_sh_routine33,
weights = "wei",
n = "sic87dd",
t = "year",
s = "ind_share",
controls = ~ t2 + Lsh_manuf
)
#> Warning: Invalid .internal.selfref detected and fixed by taking a (shallow) copy of the
#> data.table so that := can add this new column by reference. At an earlier point, this
#> data.table has been copied by R (or was created manually using structure() or similar).
#> Avoid names<- and attr<- which in R currently (and oddly) may copy the whole data.table.
#> Use set* syntax instead to avoid copying: ?set, ?setnames and ?setattr. If this message
#> doesn't help, please report your use case to the data.table issue tracker so the root cause
#> can be fixed or this message improved.
head(industry_df)
#> sic87dd year s_n y x z l_sh_routine33
#> <char> <num> <num> <num> <num> <num> <num>
#> 1: 2011 1990 5.991178e-03 0.9038660 -0.1129257 -0.15226680 -1.065246
#> 2: 2011 2000 4.729078e-03 0.5532335 0.3898516 -0.09367913 -1.178196
#> 3: 2015 1990 3.802834e-03 0.7228411 -0.4026864 0.08561252 -2.407878
#> 4: 2015 2000 4.638681e-03 -0.1133174 -0.1762856 -0.34968672 -2.320667
#> 5: 2021 1990 8.723289e-05 2.2034699 0.1183517 -0.03164776 -2.788844
#> 6: 2021 2000 4.436779e-05 0.3604907 0.2456597 -0.39783282 -1.195864
Including the "missing industry"
data("df")
data("shares")
industry_df = ssaggregate(
data = df,
shares = shares,
vars = ~ y + x + z + l_sh_routine33,
weights = "wei",
n = "sic87dd",
t = "year",
s = "ind_share",
l = "czone",
controls = ~ t2 + Lsh_manuf,
addmissing = TRUE
)
head(industry_df)
#> sic87dd year s_n y x z l_sh_routine33
#> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2011 1990 1.456902e-03 0.9038660 -0.1129257 -0.15226680 -1.065246
#> 2: 2011 2000 1.149991e-03 0.5532335 0.3898516 -0.09367913 -1.178196
#> 3: 2015 1990 9.247522e-04 0.7228411 -0.4026864 0.08561252 -2.407878
#> 4: 2015 2000 1.128009e-03 -0.1133174 -0.1762856 -0.34968672 -2.320667
#> 5: 2021 1990 2.121282e-05 2.2034699 0.1183517 -0.03164776 -2.788844
#> 6: 2021 2000 1.078912e-05 0.3604907 0.2456597 -0.39783282 -1.195864
After aggregation, shocks and any shock-level controls can be merged on to
the new dataset. For example, after the previous command a user could run
industry_df[is.na(sic87dd), sic87dd := 0]
data("shocks")
data("industries")
industry_df = merge(industry_df, shocks, by=c("sic87dd", "year"), all.x=T)
industry_df = merge(industry_df, industries, by=c("sic87dd"), all.x=T)
industry_df[is.na(g), let(g = 0, year = 0, sic3 = 0)]
Running the shock-level IV regression
Basic shift-share IV
fixest::feols(y ~ year | 0 | x ~ g, data = industry_df,
weights = ~ s_n, vcov = "hc1")
#> TSLS estimation, Dep. Var.: y, Endo.: x, Instr.: g
#> Second stage: Dep. Var.: y
#> Observations: 796
#> Standard-errors: Heteroskedasticity-robust
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.000088298 0.024946 0.003540 9.9718e-01
#> fit_x -0.464302730 0.087633 -5.298243 1.5162e-07 ***
#> year -0.000000182 0.000021 -0.008516 9.9321e-01
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 0.009783 Adj. R2: 0.149066
#> F-test (1st stage), x: stat = 160.1 , p < 2.2e-16 , on 1 and 793 DoF.
#> Wu-Hausman: stat = 1.02823, p = 0.310884, on 1 and 792 DoF.
Conditional shift-share IV with clustered standard errors
fixest::feols(y ~ year | 0 | x ~ g, data = industry_df[g < 45, ],
weights = ~ s_n, cluster = ~ sic3)
#> TSLS estimation, Dep. Var.: y, Endo.: x, Instr.: g
#> Second stage: Dep. Var.: y
#> Observations: 757
#> Standard-errors: Clustered (sic3)
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.00007456 0.000081 0.918778 3.5987e-01
#> fit_x -0.59570752 0.141843 -4.199764 4.8623e-05 ***
#> year -0.00000135 0.000019 -0.070177 9.4416e-01
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 0.009944 Adj. R2: 0.153545
#> F-test (1st stage), x: stat = 236.6 , p < 2.2e-16 , on 1 and 754 DoF.
#> Wu-Hausman: stat = 3.958e-5, p = 0.994982, on 1 and 753 DoF.
Shift-share reduced form regression (y on z)
fixest::feols(y ~ year | 0 | z ~ g, data = industry_df,
weights = ~ s_n, vcov = "hc1")
#> TSLS estimation, Dep. Var.: y, Endo.: z, Instr.: g
#> Second stage: Dep. Var.: y
#> Observations: 796
#> Standard-errors: Heteroskedasticity-robust
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.000103048 0.029114 0.003540 0.99717677
#> fit_z -0.318593633 0.067554 -4.716113 0.00000284 ***
#> year -0.000000212 0.000023 -0.009319 0.99256692
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 0.009811 Adj. R2: 0.144315
#> F-test (1st stage), z: stat = 377.3, p < 2.2e-16 , on 1 and 793 DoF.
#> Wu-Hausman: stat = 18.5, p = 1.958e-5, on 1 and 792 DoF.
Shock-level balance check
fixest::feols(l_sh_routine33 ~ g + year, data = industry_df,
weights = ~ s_n, vcov = "hc1")
#> OLS estimation, Dep. Var.: l_sh_routine33
#> Observations: 796
#> Standard-errors: Heteroskedasticity-robust
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.00002335 0.009037 0.002583 0.99794
#> g -0.00244245 0.001645 -1.484810 0.13799
#> year 0.00000898 0.000043 0.208076 0.83522
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 0.018808 Adj. R2: -2.71e-4
See Borusyak, Hull, and Jaravel (2022) for other examples of shock-level
analyses and guidance on specifying and validating a quasi-experimental
shift-share IV.
kylebutts/ssaggregate documentation built on Nov. 21, 2022, 10:11 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| ssaggregate | R Documentation |
Create industry-level aggregates for shift-share IV
Description
ssaggregate converts "location-level" variables in a shift-share IV dataset to a dataset of exposure-weighted "industry-level" aggregates, as described in Borusyak, Hull, and Jaravel (2022).
Usage
ssaggregate( data, vars, weights = NULL, n, shares = NULL, s, l = NULL, t, controls, addmissing = F )
Arguments
data |
Data.frame of data |
vars |
RHS formula of variables, i.e. |
weights |
String. Variable name indicating the weights used |
n |
String. Variable name indicating industry identifiers |
shares |
Data.frame that contains the shares dataset.
Each row should be uniquely indexed by the variables in |
s |
String. Variable name indicating name of exposure weight variable.
If using "wideformat", |
l |
String. Variable name indicating location identifiers |
t |
String. Variable name indicating period identifiers |
controls |
RHS formula of control variables and fixed effects that will
be partialled out.
Formula following |
addmissing |
Logical. If true, creates "missing industry" observations |
Details
There are two ways to specify ssaggregate, depending on whether the
industry exposure weights are saved in "long" format (unique rows for
industry x location) in a separate dataset shares or in "wide" format
(unique rows for location and columns for each industry) as part of df.
In general ssaggregate will execute faster with "long" exposure weights.
See the examples for proper syntax in both cases.
In the "long" case the dataset in memory must be uniquely identified by
the cross-sectional variables in l and, when applicable, the period
identifiers in t. The separate shares dataset is given by shares and
should be uniquely indexed by the variables in l and n (and t, when
specified). s should contain the name of the exposure weight variable,
and the two datasets should contain only matching values of l and t.
In the "wide" case the s option should contain the common stub of the
names of exposure weight variables, and n should contain the target
name of the shock identifier. For example, s = "share" should be
specified when the exposure variables are named share101, share102, etc.,
with 101 and 102 being values of the shock identifier in n. The string
option should be specified when the shock identifer is a string variable.
Missing values in any of the exposure weight variables are interpreted as
zeros. The dataset in memory may be a panel or repeated cross section,
with periods indexed by t.
In both cases there should be no missing values for the location-level
variables, conditional on any if and in sample restrictions. The resulting
industry-level dataset will contain exposure-weighted de-meaned averages of
the location-level variables, along with the average exposure weight s_n.
This dataset will be be indexed by the variables in n (and t, when
specified).
When the controls option is included the location-level variables are
first residualized by the control variables. Formula following
fixest::feols. Fixed effects specified after
"|".The transformation of variable y is also named y.
Including the addmissing option generates a "missing industry" observation,
with exposure weights equal to one minus the sum of a location's exposure
weights. Borusyak, Hull, and Jaravel (2022) recommend including this option
when the the sum of exposure weights varies across locations
(see Section 3.2). The missing industry observations will be identified by
NA in n.
Note that no information on industry shocks is used in the execution of ssaggregate; once run, users can merge shocks and any industry-level controls to the aggregated dataset. They can then estimate and validate quasi-experimental shift-share IV regressions with other Stata procedures. See Section 4 of Borusyak, Hull, and Jaravel (2022) for details and below for examples of such procedures.
Value
A data.table of exposure-weighted "industry-level" aggregates, as
described in Borusyak, Hull, and Jaravel (2022).
Examples
Using ssaggregate
Using sepearate "long" share dataset
data("df")
data("shares")
industry = ssaggregate(
data = df,
shares = shares,
vars = ~ y + x + z + l_sh_routine33,
weights = "wei",
n = "sic87dd",
t = "year",
s = "ind_share",
l = "czone",
controls = ~ t2 + Lsh_manuf
)
head(industry)
#> sic87dd year s_n y x z l_sh_routine33
#> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2011 1990 5.991178e-03 0.9038660 -0.1129257 -0.15226680 -1.065246
#> 2: 2011 2000 4.729078e-03 0.5532335 0.3898516 -0.09367913 -1.178196
#> 3: 2015 1990 3.802834e-03 0.7228411 -0.4026864 0.08561252 -2.407878
#> 4: 2015 2000 4.638681e-03 -0.1133174 -0.1762856 -0.34968672 -2.320667
#> 5: 2021 1990 8.723289e-05 2.2034699 0.1183517 -0.03164776 -2.788844
#> 6: 2021 2000 4.436779e-05 0.3604907 0.2456597 -0.39783282 -1.195864
Using "wide" shares in dataset
data("df_wide")
industry_df = ssaggregate(
data = df_wide,
vars = ~ y + x + z + l_sh_routine33,
weights = "wei",
n = "sic87dd",
t = "year",
s = "ind_share",
controls = ~ t2 + Lsh_manuf
)
#> Warning: Invalid .internal.selfref detected and fixed by taking a (shallow) copy of the
#> data.table so that := can add this new column by reference. At an earlier point, this
#> data.table has been copied by R (or was created manually using structure() or similar).
#> Avoid names<- and attr<- which in R currently (and oddly) may copy the whole data.table.
#> Use set* syntax instead to avoid copying: ?set, ?setnames and ?setattr. If this message
#> doesn't help, please report your use case to the data.table issue tracker so the root cause
#> can be fixed or this message improved.
head(industry_df)
#> sic87dd year s_n y x z l_sh_routine33
#> <char> <num> <num> <num> <num> <num> <num>
#> 1: 2011 1990 5.991178e-03 0.9038660 -0.1129257 -0.15226680 -1.065246
#> 2: 2011 2000 4.729078e-03 0.5532335 0.3898516 -0.09367913 -1.178196
#> 3: 2015 1990 3.802834e-03 0.7228411 -0.4026864 0.08561252 -2.407878
#> 4: 2015 2000 4.638681e-03 -0.1133174 -0.1762856 -0.34968672 -2.320667
#> 5: 2021 1990 8.723289e-05 2.2034699 0.1183517 -0.03164776 -2.788844
#> 6: 2021 2000 4.436779e-05 0.3604907 0.2456597 -0.39783282 -1.195864
Including the "missing industry"
data("df")
data("shares")
industry_df = ssaggregate(
data = df,
shares = shares,
vars = ~ y + x + z + l_sh_routine33,
weights = "wei",
n = "sic87dd",
t = "year",
s = "ind_share",
l = "czone",
controls = ~ t2 + Lsh_manuf,
addmissing = TRUE
)
head(industry_df)
#> sic87dd year s_n y x z l_sh_routine33
#> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2011 1990 1.456902e-03 0.9038660 -0.1129257 -0.15226680 -1.065246
#> 2: 2011 2000 1.149991e-03 0.5532335 0.3898516 -0.09367913 -1.178196
#> 3: 2015 1990 9.247522e-04 0.7228411 -0.4026864 0.08561252 -2.407878
#> 4: 2015 2000 1.128009e-03 -0.1133174 -0.1762856 -0.34968672 -2.320667
#> 5: 2021 1990 2.121282e-05 2.2034699 0.1183517 -0.03164776 -2.788844
#> 6: 2021 2000 1.078912e-05 0.3604907 0.2456597 -0.39783282 -1.195864
After aggregation, shocks and any shock-level controls can be merged on to the new dataset. For example, after the previous command a user could run
industry_df[is.na(sic87dd), sic87dd := 0]
data("shocks")
data("industries")
industry_df = merge(industry_df, shocks, by=c("sic87dd", "year"), all.x=T)
industry_df = merge(industry_df, industries, by=c("sic87dd"), all.x=T)
industry_df[is.na(g), let(g = 0, year = 0, sic3 = 0)]
Running the shock-level IV regression
Basic shift-share IV
fixest::feols(y ~ year | 0 | x ~ g, data = industry_df,
weights = ~ s_n, vcov = "hc1")
#> TSLS estimation, Dep. Var.: y, Endo.: x, Instr.: g
#> Second stage: Dep. Var.: y
#> Observations: 796
#> Standard-errors: Heteroskedasticity-robust
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.000088298 0.024946 0.003540 9.9718e-01
#> fit_x -0.464302730 0.087633 -5.298243 1.5162e-07 ***
#> year -0.000000182 0.000021 -0.008516 9.9321e-01
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 0.009783 Adj. R2: 0.149066
#> F-test (1st stage), x: stat = 160.1 , p < 2.2e-16 , on 1 and 793 DoF.
#> Wu-Hausman: stat = 1.02823, p = 0.310884, on 1 and 792 DoF.
Conditional shift-share IV with clustered standard errors
fixest::feols(y ~ year | 0 | x ~ g, data = industry_df[g < 45, ],
weights = ~ s_n, cluster = ~ sic3)
#> TSLS estimation, Dep. Var.: y, Endo.: x, Instr.: g
#> Second stage: Dep. Var.: y
#> Observations: 757
#> Standard-errors: Clustered (sic3)
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.00007456 0.000081 0.918778 3.5987e-01
#> fit_x -0.59570752 0.141843 -4.199764 4.8623e-05 ***
#> year -0.00000135 0.000019 -0.070177 9.4416e-01
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 0.009944 Adj. R2: 0.153545
#> F-test (1st stage), x: stat = 236.6 , p < 2.2e-16 , on 1 and 754 DoF.
#> Wu-Hausman: stat = 3.958e-5, p = 0.994982, on 1 and 753 DoF.
Shift-share reduced form regression (y on z)
fixest::feols(y ~ year | 0 | z ~ g, data = industry_df,
weights = ~ s_n, vcov = "hc1")
#> TSLS estimation, Dep. Var.: y, Endo.: z, Instr.: g
#> Second stage: Dep. Var.: y
#> Observations: 796
#> Standard-errors: Heteroskedasticity-robust
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.000103048 0.029114 0.003540 0.99717677
#> fit_z -0.318593633 0.067554 -4.716113 0.00000284 ***
#> year -0.000000212 0.000023 -0.009319 0.99256692
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 0.009811 Adj. R2: 0.144315
#> F-test (1st stage), z: stat = 377.3, p < 2.2e-16 , on 1 and 793 DoF.
#> Wu-Hausman: stat = 18.5, p = 1.958e-5, on 1 and 792 DoF.
Shock-level balance check
fixest::feols(l_sh_routine33 ~ g + year, data = industry_df,
weights = ~ s_n, vcov = "hc1")
#> OLS estimation, Dep. Var.: l_sh_routine33
#> Observations: 796
#> Standard-errors: Heteroskedasticity-robust
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.00002335 0.009037 0.002583 0.99794
#> g -0.00244245 0.001645 -1.484810 0.13799
#> year 0.00000898 0.000043 0.208076 0.83522
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 0.018808 Adj. R2: -2.71e-4
See Borusyak, Hull, and Jaravel (2022) for other examples of shock-level analyses and guidance on specifying and validating a quasi-experimental shift-share IV.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.