--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  D:/stata17/Replication_paper/Logfile_forReg/Analysis_benchmark2_现代服务业.txt
  log type:  text
 opened on:  25 Jan 2026, 12:02:28

. 
. ***************
. * Run
. ***************
. 
. use "$DATA\intermediate\forBenchmarkReg_pre.dta",clear

. 
. global y_var "lnmainrev lnoperrev lnsales lnemp_yearend "

. global x_var "policy policy_manuf policy_modsrv "

. global z_var "lnasset inv_intensity inv_to_sales leverage ppe_ratio current_assets_ratio roa ros profit_total"

. sum $y_var $z_var

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   lnmainrev |  1,034,854    10.62181    2.004435          0   20.58941
   lnoperrev |  1,043,854    10.62439    2.028006          0   20.59087
     lnsales |    784,265    10.37895    2.119739          0   19.44178
lnemp_year~d |    924,088    4.458913    1.518001          0   16.74697
     lnasset |    908,250     11.0118     2.22083          0   23.61651
-------------+---------------------------------------------------------
inv_intens~y |    846,403    21.58578     2210.01  -13528.09    1978975
inv_to_sales |    972,379    315.1202    24647.77       -9.9   1.39e+07
    leverage |    743,857    98.67503     8650.01    -129245    4706340
   ppe_ratio |    897,351    19.84756    675.3981  -8.720134   636066.7
current_as~o |    908,250    71.28058    30.43491  -15456.01   251.6501
-------------+---------------------------------------------------------
         roa |    907,443    392.7137    141801.8  -9.10e+07   3.66e+07
         ros |  1,043,784    2267.741     1506028  -2.56e+08   1.04e+09
profit_total |  1,065,444    29052.13     1283946  -7.61e+08   3.27e+08

. global controlVar " inv_to_sales leverage ppe_ratio ros profit_total "

. global mechanismVar "taxburden_due taxburden_due_vat taxburden_paid taxburden_paid_vat modern5 modern6 modern5_int modern6_int"

. 
. * gen X variables
. keep if industry_group==0 | industry_group==2
(664,859 observations deleted)

. drop D_modsrv

. gen D_modsrv = (industry_group == 2)

. gen double policy_modsrv = policy * D_modsrv    // 现代服务业相对对照服务业的政策效应

. label var policy_modsrv "Policy × Modern services (vs control services)"

. 
. *outlier process
. winsor2 $y_var $controlVar $mechanismVar, replace cut(1 99)

. 
. * combine citycluster and distance
. merge m:1 countycode using  "$DATA\sourcedata\distanceBetweenCounty.dta" 

    Result                      Number of obs
    -----------------------------------------
    Not matched                       116,621
        from master                   116,418  (_merge==1)
        from using                        203  (_merge==2)

    Matched                           284,179  (_merge==3)
    -----------------------------------------

. keep if _merge == 3
(116,621 observations deleted)

. drop _merge

. replace km = km - 1 //调整县区到所在中心县区的距离基准点为0，原有数据+ 1km，现在调整回去
(284,179 real changes made)

. rename km distance

. rename km1 relativedistance

. rename bb citycluster

. rename a0114 industrycode2

. 
. *----------------
. * regression
. *----------------
. xtset firm_id years

Panel variable: firm_id (unbalanced)
 Time variable: years, 2009 to 2014, but with gaps
         Delta: 1 unit

. 
. 
. 
. ********************************************************************************
. ****************附录表III1 Panel B: 现代服务业 descriptive statistics
. outreg2 using "$EXHIBIT/desc_PanelB.doc", replace sum(log) ///
>     keep(lnmainrev  lnsales policy_modsrv) dec(3)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       years |    284,179    2011.647    1.640383       2009       2014
       a0103 |    284,179    171.4395    48.00211        110        400
       a0106 |    284,179    31.59278    14.29869         11         65
       a0108 |    284,179    72897.17    117389.3       1301     500243
  countycode |    284,179    316322.3      143160     110101     659001
-------------+---------------------------------------------------------
       a0111 |    284,179    2008.457    4.052364       2002       2011
industryco~2 |    284,179    66.54733    10.04902         47         89
       a0116 |    284,151    668.9882    101.3005        470        899
       a0118 |    284,131    6690.932    1013.276       4700       8990
is_manufac~g |    284,179           0           0          0          0
-------------+---------------------------------------------------------
is_control~e |    284,179    .6553264    .4752626          0          1
is_modern_~e |    284,179    .3446736    .4752626          0          1
      is_reg |    284,179           1           0          1          1
total_assets |    237,458     2876567    1.11e+08   -1322054   1.81e+10
  total_liab |    186,291     2713473    9.67e+07   -3756390   1.40e+10
-------------+---------------------------------------------------------
   ln_equity |    256,368    9.456754    3.357664          0   22.22812
inv_intens~y |    197,374    17.40509    40.51568  -13528.09   1002.703
inv_to_sales |    229,147     4.14368    25.16319          0   231.1909
    leverage |    181,751     67.5735    49.25305          0   343.9646
   ppe_ratio |    228,241    11.85539    19.75867          0   89.74359
-------------+---------------------------------------------------------
current_as~o |    233,253    76.24457    30.87808  -6210.782   251.6501
         roa |    232,843    900.1268    254239.7  -9.10e+07   3.29e+07
         ros |    271,009   -10.00042    146.5961  -1266.961   198.4589
profit_total |    284,172    28899.23    107840.9    -104290     834598
        prov |    284,179    31.59278    14.29869         11         65
-------------+---------------------------------------------------------
 policy_year |    284,179    2013.233    .6614381       2012       2014
      policy |    284,179    .3164379    .4650868          0          1
industry_g~p |    284,179    .6893472    .9505251          0          2
     D_manuf |    284,179           0           0          0          0
 mainbiz_rev |    276,029    286114.3     4870513          0   8.75e+08
-------------+---------------------------------------------------------
operating_~v |    283,930      290535     4938774          0   8.76e+08
sales_dome~c |    284,179    28721.13    351578.7          0   4.14e+07
sales_export |    284,179     2870.61    294814.2      -1302   1.22e+08
 sales_total |    284,179    31591.74    463601.2          0   1.22e+08
 emp_yearend |    243,516    263.7511    5232.098          0    2008797
-------------+---------------------------------------------------------
     emp_avg |     36,417    307.6286    4161.697          0     450000
     firm_id |    284,179    518449.1      310665          1    1041372
     lnasset |    233,253    11.33187    2.584929          0   23.61651
   lnmainrev |    265,994    10.53119    2.045485   4.574711   15.14541
   lnoperrev |    271,040    10.52224     2.07342   4.248495   15.16469
-------------+---------------------------------------------------------
     lnsales |     87,385    9.007362    2.452031   1.791759   14.16483
lnemp_year~d |    241,145    4.016185    1.542728          0   8.037543
taxburden_~e |    271,009    .0759404    .2225887          0          2
taxbur~e_vat |    271,009    .0116065    .0286526          0   .1581191
taxburden_~d |    271,009    .0804917    .2690702          0   2.466651
-------------+---------------------------------------------------------
taxbur~d_vat |    270,853    .0092039    .0244422          0   .1415783
     modern5 |    284,179    1221.889    5332.103          0      41841
     modern6 |    284,179    1306.938    5699.002          0      44948
 modern5_int |    271,009    .0197685    .0821308          0   .6192675
 modern6_int |    271,009    .0204684    .0836979          0   .6276451
-------------+---------------------------------------------------------
    D_modsrv |    284,179    .3446736    .4752626          0          1
policy_mod~v |    284,179    .1297105    .3359852          0          1
    distance |    284,179    119.1963    158.1123          0   1063.613
 citycluster |    284,179    4.553004     3.78035          1         17
relativedi~e |    284,179    .5469327    .5988944   .0028171   3.945218

Following variable is string, not included:  
a0102  a0104  a0105  a0107  a0109  a0112  a0113  a0115  a0117  a0119  a0120  code_str  区县政府  Following variable has no observation, not included:  
distance_reviseGuangdong  distance_reviseGuangdong1  
D:/stata17/Replication_paper/Exhibits/desc_PanelB.doc
dir : seeout

. 
. 
.         
.         
. 
. ********************************************************************************
. ***************** 表1 Panle B: benchmark regression:staggered DID 
. 
. **************1)  Sun and Abraham (2021).— eventstudyinteract**************
. 
. *****************************Y: lnsales*******************************
. preserve

. * a) cohort variable
. gen cohort = . 
(284,179 missing values generated)

. replace cohort = 2012 if policy_year == 2012 & D_modsrv == 1
(18,130 real changes made)

. replace cohort = 2013 if policy_year == 2013 & D_modsrv == 1
(50,429 real changes made)

. replace cohort = 2014 if policy_year == 2014 & D_modsrv == 1
(29,390 real changes made)

. 
. * b) a dummy for the control group 
. gen never_treated = (cohort == .)

. 
. * c) dummies for event time ,excluding -1 .
. gen refy = years - cohort 
(186,230 missing values generated)

. tab refy, miss gen(devent)

       refy |      Freq.     Percent        Cum.
------------+-----------------------------------
         -5 |      4,192        1.48        1.48
         -4 |     11,205        3.94        5.42
         -3 |     14,174        4.99       10.41
         -2 |     13,857        4.88       15.28
         -1 |     17,660        6.21       21.50
          0 |     21,320        7.50       29.00
          1 |     12,763        4.49       33.49
          2 |      2,778        0.98       34.47
          . |    186,230       65.53      100.00
------------+-----------------------------------
      Total |    284,179      100.00

. des devent* 

Variable      Storage   Display    Value
    name         type    format    label      Variable label
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
devent1         byte    %8.0g                 refy== -5.0000
devent2         byte    %8.0g                 refy== -4.0000
devent3         byte    %8.0g                 refy== -3.0000
devent4         byte    %8.0g                 refy== -2.0000
devent5         byte    %8.0g                 refy== -1.0000
devent6         byte    %8.0g                 refy== 0.0000
devent7         byte    %8.0g                 refy== 1.0000
devent8         byte    %8.0g                 refy== 2.0000
devent9         byte    %8.0g                 refy== .

. drop devent5 devent9

. 
. * d) estimate event-study model
. eventstudyinteract lnsales devent* , cohort(cohort) control_cohort(never_treated) absorb(firm_id  years) vce(cluster prov#industrycode2)
(obs=54,796)

IW estimates for dynamic effects                        Number of obs = 77,545
Absorbing 2 HDFE groups                                 F(15, 910)    =  33.51
                                                        Prob > F      = 0.0000
                                                        R-squared     = 0.8438
                                                        Adj R-squared = 0.7635
                                                        Root MSE      = 1.1603
                   (Std. err. adjusted for 911 clusters in prov#industrycode2)
------------------------------------------------------------------------------
             |               Robust
     lnsales | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     devent1 |   .1203934   .1469154     0.82   0.413     -.167939    .4087258
     devent2 |     .13416   .1218277     1.10   0.271    -.1049358    .3732559
     devent3 |   .1006682   .1106416     0.91   0.363    -.1164742    .3178105
     devent4 |  -.1447725   .0994596    -1.46   0.146    -.3399694    .0504244
     devent6 |   .7100682   .1349142     5.26   0.000     .4452892    .9748473
     devent7 |   .6247788   .1951889     3.20   0.001     .2417061    1.007851
     devent8 |   .8677055   .2113995     4.10   0.000     .4528183    1.282593
------------------------------------------------------------------------------

. gen insample = e(sample)

. matrix b = e(b_iw)

. matrix V = e(V_iw)

. ereturn post b V 

. 
. 
. ********************************************************************************
. * e) draw event-study plot---******************附录图IV1 B**********************
. set scheme s1mono

. coefplot ///
>     (., keep(devent1 devent2 devent3 devent4 devent6 devent7 devent8)) ///
> ,   vertical ciopts(recast(rcap)) ///
>     xline(4.5, lpattern(dash) lcolor(red)) ///
>     yline(0, lpattern(solid) lcolor(gs8)) ///
>     addplot(scatteri 0 4.5, msymbol(O) mcolor(red) msize(medium)) ///
>     xlabel(1 "-5" 2 "-4" 3 "-3" 4 "-2" 5 "0" 6 "1" 7 "2") ///
>     xtitle("事件时间（相对期）") ///
>     ytitle("相对 -1 期的系数估计") ///
>     legend(off)

. 
. 
. * f) calculate aggregate effects
. keep if insample == 1 & D_modsrv == 1 & inlist(refy, 0,1,2)
(254,838 observations deleted)

. 
. tab refy, matcell(freq)

       refy |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     16,368       55.79       55.79
          1 |     10,578       36.05       91.84
          2 |      2,395        8.16      100.00
------------+-----------------------------------
      Total |     29,341      100.00

. matrix list freq

freq[3,1]
       c1
r1  16368
r2  10578
r3   2395

. 
. scalar N0 = freq[1,1]   // 第1行 = refy==0 的样本数

. scalar N1 = freq[2,1]   // 第2行 = refy==1 的样本数

. scalar N2 = freq[3,1]   // 第3行 = refy==2 的样本数

. display N0 N1 N2
16368105782395

. 
. scalar sumN = N0 + N1 + N2

. scalar w0 = N0/sumN

. scalar w1 = N1/sumN

. scalar w2 = N2/sumN

. display w0 w1 w2
.5578542.36051941.08162639

. 
. lincom w0*_b[devent6] + w1*_b[devent7] + w2*_b[devent8]

 ( 1)  .5578542*devent6 + .3605194*devent7 + .0816264*devent8 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6921871   .1518425     4.56   0.000     .3945812     .989793
------------------------------------------------------------------------------

. 
. 
. restore

. 
. 
. **************2) Callaway & Sant'Anna 2021 — csdid lnsales
. *****************************Y: lnsales*******************************
. preserve

. * a) cohort variable
. gen cohort = 0 

. replace cohort = 2012 if policy_year == 2012 & D_modsrv == 1
(18,130 real changes made)

. replace cohort = 2013 if policy_year == 2013 & D_modsrv == 1
(50,429 real changes made)

. replace cohort = 2014 if policy_year == 2014 & D_modsrv == 1
(29,390 real changes made)

. 
. tab cohort,miss

     cohort |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |    186,230       65.53       65.53
       2012 |     18,130        6.38       71.91
       2013 |     50,429       17.75       89.66
       2014 |     29,390       10.34      100.00
------------+-----------------------------------
      Total |    284,179      100.00

. 
. * ATT
. csdid lnsales , ivar(firm_id) time(years) gvar(cohort) agg(simple) 
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
...............
Difference-in-difference with Multiple Time Periods

                                                        Number of obs = 62,671
Outcome model  : regression adjustment
Treatment model: none
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .5879846   .0239437    24.56   0.000     .5410558    .6349134
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. restore

. 
. 
. 
. **************3)  Borusyak et al.(2023).— did_imputation finished
. 
. *****************************Y: lnsales*******************************
. preserve

. 
. gen time_treated = . 
(284,179 missing values generated)

. replace time_treated = 2012 if policy_year == 2012 & D_modsrv == 1
(18,130 real changes made)

. replace time_treated = 2013 if policy_year == 2013 & D_modsrv == 1
(50,429 real changes made)

. replace time_treated = 2014 if policy_year == 2014 & D_modsrv == 1
(29,390 real changes made)

. 
. egen prov_ind2_fe = group(prov industrycode2)

. 
. did_imputation lnsales firm_id years time_treated, fe(firm_id years)  autosample cluster(prov_ind2_fe)
Warning: part of the sample was dropped for the following coefficients because FE could not be imputed: tau.

                                                        Number of obs = 76,826
------------------------------------------------------------------------------
     lnsales | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         tau |   .7914353   .0655761    12.07   0.000     .6629085     .919962
------------------------------------------------------------------------------

. 
. restore

. 
. 
. 
. 
. 
. 
. ********************************************************************************
. ******************Robust Check : 替换被解释变量 附录表IV1  Panel B
. ******************(1) Sun and Abraham(2021)**************
. *****************************Y: lnmainrev*****************
. preserve

. * a) cohort variable
. gen cohort = . 
(284,179 missing values generated)

. replace cohort = 2012 if policy_year == 2012 & D_modsrv == 1
(18,130 real changes made)

. replace cohort = 2013 if policy_year == 2013 & D_modsrv == 1
(50,429 real changes made)

. replace cohort = 2014 if policy_year == 2014 & D_modsrv == 1
(29,390 real changes made)

. 
. * b) a dummy for the control group 
. gen never_treated = (cohort == .)

. 
. * c) dummies for event time ,excluding -1 .
. gen refy = years - cohort 
(186,230 missing values generated)

. tab refy, miss gen(devent)

       refy |      Freq.     Percent        Cum.
------------+-----------------------------------
         -5 |      4,192        1.48        1.48
         -4 |     11,205        3.94        5.42
         -3 |     14,174        4.99       10.41
         -2 |     13,857        4.88       15.28
         -1 |     17,660        6.21       21.50
          0 |     21,320        7.50       29.00
          1 |     12,763        4.49       33.49
          2 |      2,778        0.98       34.47
          . |    186,230       65.53      100.00
------------+-----------------------------------
      Total |    284,179      100.00

. des devent* 

Variable      Storage   Display    Value
    name         type    format    label      Variable label
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
devent1         byte    %8.0g                 refy== -5.0000
devent2         byte    %8.0g                 refy== -4.0000
devent3         byte    %8.0g                 refy== -3.0000
devent4         byte    %8.0g                 refy== -2.0000
devent5         byte    %8.0g                 refy== -1.0000
devent6         byte    %8.0g                 refy== 0.0000
devent7         byte    %8.0g                 refy== 1.0000
devent8         byte    %8.0g                 refy== 2.0000
devent9         byte    %8.0g                 refy== .

. drop devent5 devent9

. 
. * d) estimate event-study model
. quietly eventstudyinteract lnmainrev devent* , cohort(cohort) control_cohort(never_treated) absorb(firm_id  years) vce(cluster firm_id)

. gen insample = e(sample)

. matrix b = e(b_iw)

. matrix V = e(V_iw)

. ereturn post b V 

. 
. * e) calculate aggregate effects
. keep if insample == 1 & D_modsrv == 1 & inlist(refy, 0,1,2)
(250,232 observations deleted)

. 
. tab refy, matcell(freq)

       refy |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     19,544       57.57       57.57
          1 |     11,771       34.67       92.25
          2 |      2,632        7.75      100.00
------------+-----------------------------------
      Total |     33,947      100.00

. matrix list freq

freq[3,1]
       c1
r1  19544
r2  11771
r3   2632

. 
. scalar N0 = freq[1,1]   // 第1行 = refy==0 的样本数

. scalar N1 = freq[2,1]   // 第2行 = refy==1 的样本数

. scalar N2 = freq[3,1]   // 第3行 = refy==2 的样本数

. display N0 N1 N2
19544117712632

. 
. scalar sumN = N0 + N1 + N2

. scalar w0 = N0/sumN

. scalar w1 = N1/sumN

. scalar w2 = N2/sumN

. display w0 w1 w2
.57572098.3467464.07753262

. 
. lincom w0*_b[devent6] + w1*_b[devent7] + w2*_b[devent8]

 ( 1)  .575721*devent6 + .3467464*devent7 + .0775326*devent8 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0372677   .0092956     4.01   0.000     .0190487    .0554867
------------------------------------------------------------------------------

. 
. restore

. 
. 
. 
. ***********************(2)Callaway & Sant'Anna 2021 — csdid**************
. *****************************Y: lnmainrev*******************************
. preserve

. * a) cohort variable
. gen cohort = 0 

. replace cohort = 2012 if policy_year == 2012 & D_modsrv == 1
(18,130 real changes made)

. replace cohort = 2013 if policy_year == 2013 & D_modsrv == 1
(50,429 real changes made)

. replace cohort = 2014 if policy_year == 2014 & D_modsrv == 1
(29,390 real changes made)

. 
. tab cohort,miss

     cohort |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |    186,230       65.53       65.53
       2012 |     18,130        6.38       71.91
       2013 |     50,429       17.75       89.66
       2014 |     29,390       10.34      100.00
------------+-----------------------------------
      Total |    284,179      100.00

. 
. * ATT
. csdid lnmainrev , ivar(firm_id) time(years) gvar(cohort) agg(simple) 
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
...............
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 229,637
Outcome model  : regression adjustment
Treatment model: none
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .0253646   .0095056     2.67   0.008      .006734    .0439952
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. restore

. 
. 
. *************************(3) Borusyak et al.(2023).— did_imputation****************
. *****************************Y: lnmainrev  *********************
. preserve

. 
. gen time_treated = . 
(284,179 missing values generated)

. replace time_treated = 2012 if policy_year == 2012 & D_modsrv == 1
(18,130 real changes made)

. replace time_treated = 2013 if policy_year == 2013 & D_modsrv == 1
(50,429 real changes made)

. replace time_treated = 2014 if policy_year == 2014 & D_modsrv == 1
(29,390 real changes made)

. 
. egen prov_ind2_fe = group(prov industrycode2)

. 
. did_imputation lnmainrev firm_id years time_treated, fe(firm_id years)  ///
>    autosample cluster(firm_id)
Warning: part of the sample was dropped for the following coefficients because FE could not be imputed: tau.

                                                       Number of obs = 263,371
------------------------------------------------------------------------------
   lnmainrev | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         tau |   .0414594   .0093141     4.45   0.000      .023204    .0597148
------------------------------------------------------------------------------

. restore

. 
. 
. 
. 
. 
. 
. 
. 
. 
. ********************************************************************************
. **********************表3 第(2)列: 现代服务业在位企业扩张的区位差异
. *distance 交互项: 到中心地区的地理距离
. preserve

. 
. misstable sum distance
(variables nonmissing or string)

. sum distance,d

                          distance
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs             284,179
25%     6.662768              0       Sum of wgt.     284,179

50%     40.00977                      Mean           119.1963
                        Largest       Std. dev.      158.1123
75%     176.4002       1063.613
90%     352.2917       1063.613       Variance       24999.51
95%     440.3301       1063.613       Skewness       1.743656
99%     629.8052       1063.613       Kurtosis       6.378902

. replace distance = distance/1000  //变量缩放1000倍,以便统一小数点后3位数
(254,452 real changes made)

. *total
. reghdfe lnmainrev i.policy_modsrv##c.distance  , absorb(firm_id prov#years citycluster#years ) cluster(prov#industrycode2) //主营业务收入
(dropped 12970 singleton observations)
(MWFE estimator converged in 135 iterations)

HDFE Linear regression                            Number of obs   =    253,024
Absorbing 3 HDFE groups                           F(   3,   1052) =      11.36
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8573
                                                  Adj R-squared   =     0.8013
                                                  Within R-sq.    =     0.0009
Number of clusters (prov#industrycode2) =      1,053Root MSE      =     0.9073

                             (Std. err. adjusted for 1,053 clusters in prov#industrycode2)
------------------------------------------------------------------------------------------
                         |               Robust
               lnmainrev | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------------+----------------------------------------------------------------
         1.policy_modsrv |   .0811649   .0251827     3.22   0.001     .0317509     .130579
                distance |  -2.687418   .6283127    -4.28   0.000    -3.920307    -1.45453
                         |
policy_modsrv#c.distance |
                      1  |  -.0495918   .1301575    -0.38   0.703    -.3049896     .205806
                         |
                   _cons |   10.85437   .0734145   147.85   0.000     10.71031    10.99842
------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------------+
         Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------------+---------------------------------------|
             firm_id |     71165           0       71165     |
          prov#years |       174           3         171     |
   citycluster#years |       102          84          18    ?|
-------------------------------------------------------------+
? = number of redundant parameters may be higher

. estimates store m1

. 
. outreg2 [m1] using "$EXHIBIT/results.doc", replace ///
>     ctitle("lnmainrev")          /// 列标题
>     se                       /// 报告括号内标准误
>     dec(3) nocons addtext(Firm FE, Yes, Province#Year FE, Yes, CityCluster#Year,Yes)                  
D:/stata17/Replication_paper/Exhibits/results.doc
dir : seeout

. 
. 
. restore

. 
. 
. 
. 
. *****************
. * close log txt
. *****************
. 
. log close
      name:  <unnamed>
       log:  D:/stata17/Replication_paper/Logfile_forReg/Analysis_benchmark2_现代服务业.txt
  log type:  text
 closed on:  25 Jan 2026, 12:06:17
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
