--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  D:/stata17/Replication_paper/Logfile_forReg/Analysis_进一步讨论_产业结构变动差异.txt
  log type:  text
 opened on:  25 Jan 2026, 12:06:24

. 
. ***************
. * 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"

. 
. ***************aggregate into county-year industryRatio*************************
. * mainbiz_rev operating_rev sales_total
. rename years year

. 
. * 先改造合计（条件写在表达式里，避免 egen if 造成空洞）
. bysort countycode year: egen mainrev_manuf   = total(mainbiz_rev   * (is_manufacturing==1))

. bysort countycode year: egen operrev_manuf   = total(operating_rev * (is_manufacturing==1))

. bysort countycode year: egen sales_manuf     = total(sales_total   * (is_manufacturing==1))

. 
. bysort countycode year: egen mainrev_modsrv  = total(mainbiz_rev   * (is_modern_service==1))

. bysort countycode year: egen operrev_modsrv  = total(operating_rev * (is_modern_service==1))

. bysort countycode year: egen sales_modsrv    = total(sales_total   * (is_modern_service==1))

. 
. * 只保留每个县区-年份一行（现在每组每行都有相同的总和，不会丢）
. bysort countycode year: keep if _n==1
(1,048,610 observations deleted)

. 
. 
. * save aggregate variables
. keep prov policy_year policy countycode year mainrev_manuf mainrev_modsrv operrev_manuf operrev_modsrv sales_manuf sales_modsrv a0114

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

    Result                      Number of obs
    -----------------------------------------
    Not matched                         6,105
        from master                     5,941  (_merge==1)
        from using                        164  (_merge==2)

    Matched                            10,905  (_merge==3)
    -----------------------------------------

. keep if _merge == 3
(6,105 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
(10,905 real changes made)

. rename km distance

. rename km1 relativedistance

. rename bb citycluster

. rename a0114 industrycode2

. 
. * gen Y variables
. gen mainrev_modsrv2manuf = mainrev_modsrv/mainrev_manuf
(946 missing values generated)

. gen operrev_modsrv2manuf = operrev_modsrv/operrev_manuf
(942 missing values generated)

. gen sales_modsrv2manuf = sales_modsrv/sales_manuf
(948 missing values generated)

. 
. misstable sum  mainrev_modsrv2manuf operrev_modsrv2manuf sales_modsrv2manuf
                                                               Obs<.
                                                +------------------------------
               |                                | Unique
      Variable |     Obs=.     Obs>.     Obs<.  | values        Min         Max
  -------------+--------------------------------+------------------------------
  mainrev_mo~f |       946               9,959  |   >500          0      7557.4
  operrev_mo~f |       942               9,963  |   >500          0    985.3558
  sales_mods~f |       948               9,957  |   >500          0    5604.386
  -----------------------------------------------------------------------------

. winsor2 mainrev_modsrv2manuf operrev_modsrv2manuf sales_modsrv2manuf, replace cut(1 99)

. 
. * combine control variables
. rename year years

. rename countycode xx

. merge 1:1 xx years using "$DATA\sourcedata\区县控制变量_带Label.dta"

    Result                      Number of obs
    -----------------------------------------
    Not matched                        14,405
        from master                       186  (_merge==1)
        from using                     14,219  (_merge==2)

    Matched                            10,719  (_merge==3)
    -----------------------------------------

. keep if _merge == 3
(14,405 observations deleted)

. drop _merge

. rename years year

. rename xx countycode

. 
. gen gdpper = gdp/pophuji   // gdp/户籍人口
(800 missing values generated)

. gen industryratio = thirdgdp/secondgdp  //产业结构 = 三产/二产
(666 missing values generated)

. label var pophuji "户籍人口,表示人口规模"

. 
. ********************************************************************************
. *****************regression: 表5:产业结构变动的区位差异
. 
. replace distance = distance/1000  //变量缩放1000倍,以便统一小数点后3位数
(10,588 real changes made)

. 
. reghdfe mainrev_modsrv2manuf i.policy##c.distance , absorb(countycode year)  cluster(countycode)  //主营业务收入
(dropped 11 singleton observations)
(MWFE estimator converged in 5 iterations)
note: distance is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =      9,799
Absorbing 2 HDFE groups                           F(   2,   1782) =       4.37
Statistics robust to heteroskedasticity           Prob > F        =     0.0128
                                                  R-squared       =     0.6132
                                                  Adj R-squared   =     0.5269
                                                  Within R-sq.    =     0.0015
Number of clusters (countycode) =      1,783      Root MSE        =     0.9200

                              (Std. err. adjusted for 1,783 clusters in countycode)
-----------------------------------------------------------------------------------
                  |               Robust
mainrev_modsrv2~f | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
         1.policy |   .1775296   .0749653     2.37   0.018     .0305004    .3245587
         distance |          0  (omitted)
                  |
policy#c.distance |
               1  |  -.3463687   .1271251    -2.72   0.007    -.5956987   -.0970388
                  |
            _cons |   .2770333   .0138882    19.95   0.000     .2497943    .3042722
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  countycode |      1783        1783           0    *|
        year |         6           1           5     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store m1

. reghdfe operrev_modsrv2manuf i.policy##c.distance , absorb(countycode year) cluster(countycode)  //营业收入
(dropped 11 singleton observations)
(MWFE estimator converged in 5 iterations)
note: distance is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =      9,803
Absorbing 2 HDFE groups                           F(   2,   1782) =       4.49
Statistics robust to heteroskedasticity           Prob > F        =     0.0113
                                                  R-squared       =     0.6155
                                                  Adj R-squared   =     0.5296
                                                  Within R-sq.    =     0.0016
Number of clusters (countycode) =      1,783      Root MSE        =     0.8677

                              (Std. err. adjusted for 1,783 clusters in countycode)
-----------------------------------------------------------------------------------
                  |               Robust
operrev_modsrv2~f | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
         1.policy |   .1757596   .0723033     2.43   0.015     .0339515    .3175677
         distance |          0  (omitted)
                  |
policy#c.distance |
               1  |   -.341815   .1225163    -2.79   0.005    -.5821056   -.1015243
                  |
            _cons |    .267378   .0131443    20.34   0.000     .2415982    .2931579
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  countycode |      1783        1783           0    *|
        year |         6           1           5     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store m2

. reghdfe sales_modsrv2manuf i.policy##c.distance  , absorb(countycode year) cluster(countycode)   //销售额
(dropped 12 singleton observations)
(MWFE estimator converged in 5 iterations)
note: distance is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =      9,796
Absorbing 2 HDFE groups                           F(   2,   1781) =      19.73
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.5143
                                                  Adj R-squared   =     0.4058
                                                  Within R-sq.    =     0.0154
Number of clusters (countycode) =      1,782      Root MSE        =     0.2178

                              (Std. err. adjusted for 1,782 clusters in countycode)
-----------------------------------------------------------------------------------
                  |               Robust
sales_modsrv2ma~f | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
         1.policy |   .1213846   .0255319     4.75   0.000     .0713089    .1714602
         distance |          0  (omitted)
                  |
policy#c.distance |
               1  |  -.2883166   .0458953    -6.28   0.000    -.3783309   -.1983022
                  |
            _cons |    .041805   .0039759    10.51   0.000     .0340071    .0496028
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  countycode |      1782        1782           0    *|
        year |         6           1           5     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store m3

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

.         
. outreg2 [m2] using "$EXHIBIT/results.doc", append ///
>     ctitle("operrev_agg")          /// 列标题
>     se                       /// 报告括号内标准误
>     dec(3) nocons addtext(County FE, Yes, Year FE, Yes)
D:/stata17/Replication_paper/Exhibits/results.doc
dir : seeout

. 
. outreg2 [m3] using "$EXHIBIT/results.doc", append ///
>     ctitle("sales_agg")          /// 列标题
>     se                       /// 报告括号内标准误
>     dec(3) nocons addtext(County FE, Yes, Year FE, Yes)
D:/stata17/Replication_paper/Exhibits/results.doc
dir : seeout

. 
.         
. ********************************************************************************        
. ***************regression: 表6: 不同城市群间产业结构变动的差异  
. * compare different citycluster
. gen subsample = inlist(citycluster,1,4,5)

. //1 长三角  4 京津冀 5 粤港澳  subsample =1 ; else subsample == 0 :其他城市群
. 
. reghdfe mainrev_modsrv2manuf i.policy##c.distance  if subsample == 1 , absorb(countycode year) cluster(countycode) //主营业务收入
(dropped 2 singleton observations)
(MWFE estimator converged in 5 iterations)
note: distance is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =      2,663
Absorbing 2 HDFE groups                           F(   2,    499) =       2.42
Statistics robust to heteroskedasticity           Prob > F        =     0.0900
                                                  R-squared       =     0.6494
                                                  Adj R-squared   =     0.5671
                                                  Within R-sq.    =     0.0036
Number of clusters (countycode) =        500      Root MSE        =     1.0770

                                (Std. err. adjusted for 500 clusters in countycode)
-----------------------------------------------------------------------------------
                  |               Robust
mainrev_modsrv2~f | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
         1.policy |   .3522425   .1601544     2.20   0.028     .0375824    .6669025
         distance |          0  (omitted)
                  |
policy#c.distance |
               1  |  -.7342081   .4195175    -1.75   0.081    -1.558447    .0900303
                  |
            _cons |   .3827737   .0248378    15.41   0.000     .3339743    .4315732
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  countycode |       500         500           0    *|
        year |         6           1           5     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store mainrev1

. reghdfe mainrev_modsrv2manuf i.policy##c.distance  if subsample == 0 , absorb(countycode year) cluster(countycode) //主营业务收入
(dropped 9 singleton observations)
(MWFE estimator converged in 5 iterations)
note: distance is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =      7,136
Absorbing 2 HDFE groups                           F(   2,   1282) =       2.27
Statistics robust to heteroskedasticity           Prob > F        =     0.1034
                                                  R-squared       =     0.5898
                                                  Adj R-squared   =     0.4994
                                                  Within R-sq.    =     0.0008
Number of clusters (countycode) =      1,283      Root MSE        =     0.8515

                              (Std. err. adjusted for 1,283 clusters in countycode)
-----------------------------------------------------------------------------------
                  |               Robust
mainrev_modsrv2~f | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
         1.policy |   .0511622   .0545527     0.94   0.348    -.0558602    .1581847
         distance |          0  (omitted)
                  |
policy#c.distance |
               1  |  -.2711506   .1272611    -2.13   0.033    -.5208134   -.0214878
                  |
            _cons |   .2514354    .009666    26.01   0.000     .2324726    .2703983
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  countycode |      1283        1283           0    *|
        year |         6           1           5     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store mainrev0

. reghdfe operrev_modsrv2manuf i.policy##c.distance  if subsample == 1, absorb(countycode year) cluster(countycode) //营业收入
(dropped 2 singleton observations)
(MWFE estimator converged in 5 iterations)
note: distance is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =      2,664
Absorbing 2 HDFE groups                           F(   2,    499) =       2.74
Statistics robust to heteroskedasticity           Prob > F        =     0.0653
                                                  R-squared       =     0.6462
                                                  Adj R-squared   =     0.5632
                                                  Within R-sq.    =     0.0047
Number of clusters (countycode) =        500      Root MSE        =     1.0106

                                (Std. err. adjusted for 500 clusters in countycode)
-----------------------------------------------------------------------------------
                  |               Robust
operrev_modsrv2~f | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
         1.policy |   .3748235   .1601978     2.34   0.020     .0600782    .6895688
         distance |          0  (omitted)
                  |
policy#c.distance |
               1  |  -.7827315   .4145904    -1.89   0.060    -1.597289    .0318263
                  |
            _cons |   .3593335   .0238242    15.08   0.000     .3125253    .4061416
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  countycode |       500         500           0    *|
        year |         6           1           5     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store operrev1

. reghdfe operrev_modsrv2manuf i.policy##c.distance  if subsample == 0, absorb(countycode year) cluster(countycode) //营业收入
(dropped 9 singleton observations)
(MWFE estimator converged in 5 iterations)
note: distance is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =      7,139
Absorbing 2 HDFE groups                           F(   2,   1282) =       2.25
Statistics robust to heteroskedasticity           Prob > F        =     0.1062
                                                  R-squared       =     0.5972
                                                  Adj R-squared   =     0.5084
                                                  Within R-sq.    =     0.0008
Number of clusters (countycode) =      1,283      Root MSE        =     0.8051

                              (Std. err. adjusted for 1,283 clusters in countycode)
-----------------------------------------------------------------------------------
                  |               Robust
operrev_modsrv2~f | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
         1.policy |    .044446   .0524343     0.85   0.397    -.0584205    .1473125
         distance |          0  (omitted)
                  |
policy#c.distance |
               1  |  -.2541085   .1203176    -2.11   0.035    -.4901495   -.0180675
                  |
            _cons |   .2462403   .0092643    26.58   0.000     .2280655     .264415
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  countycode |      1283        1283           0    *|
        year |         6           1           5     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store operrev0

. reghdfe sales_modsrv2manuf i.policy##c.distance  if subsample == 1 , absorb(countycode year) cluster(countycode) //销售额
(dropped 2 singleton observations)
(MWFE estimator converged in 5 iterations)
note: distance is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =      2,664
Absorbing 2 HDFE groups                           F(   2,    499) =      10.34
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.5701
                                                  Adj R-squared   =     0.4693
                                                  Within R-sq.    =     0.0451
Number of clusters (countycode) =        500      Root MSE        =     0.2617

                                (Std. err. adjusted for 500 clusters in countycode)
-----------------------------------------------------------------------------------
                  |               Robust
sales_modsrv2ma~f | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
         1.policy |   .2798824    .061999     4.51   0.000     .1580711    .4016937
         distance |          0  (omitted)
                  |
policy#c.distance |
               1  |  -.7203066   .1613725    -4.46   0.000     -1.03736   -.4032533
                  |
            _cons |   .0537381    .007102     7.57   0.000     .0397846    .0676916
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  countycode |       500         500           0    *|
        year |         6           1           5     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store sales1

. reghdfe sales_modsrv2manuf i.policy##c.distance  if subsample == 0 , absorb(countycode year) cluster(countycode) //销售额
(dropped 10 singleton observations)
(MWFE estimator converged in 5 iterations)
note: distance is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =      7,132
Absorbing 2 HDFE groups                           F(   2,   1281) =      10.80
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4803
                                                  Adj R-squared   =     0.3657
                                                  Within R-sq.    =     0.0082
Number of clusters (countycode) =      1,282      Root MSE        =     0.1968

                              (Std. err. adjusted for 1,282 clusters in countycode)
-----------------------------------------------------------------------------------
                  |               Robust
sales_modsrv2ma~f | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
         1.policy |   .0530009   .0252123     2.10   0.036      .003539    .1024629
         distance |          0  (omitted)
                  |
policy#c.distance |
               1  |  -.1941805   .0418615    -4.64   0.000    -.2763051   -.1120559
                  |
            _cons |   .0411717   .0043802     9.40   0.000     .0325786    .0497648
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  countycode |      1282        1282           0    *|
        year |         6           1           5     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store sales0

. 
. outreg2 [mainrev1] using "$EXHIBIT/results.doc", replace ///
>     ctitle("mainrev1")          /// 列标题
>     se                       /// 报告括号内标准误
>     dec(3) nocons addtext(County FE, Yes, Year FE, Yes)
D:/stata17/Replication_paper/Exhibits/results.doc
dir : seeout

.         
. outreg2 [mainrev0] using "$EXHIBIT/results.doc", append ///
>     ctitle("mainrev0")          /// 列标题
>     se                       /// 报告括号内标准误
>     dec(3) nocons addtext(County FE, Yes, Year FE, Yes)
D:/stata17/Replication_paper/Exhibits/results.doc
dir : seeout

.         
. outreg2 [operrev1] using "$EXHIBIT/results.doc", append ///
>     ctitle("operrev1")          /// 列标题
>     se                       /// 报告括号内标准误
>     dec(3) nocons addtext(County FE, Yes, Year FE, Yes)
D:/stata17/Replication_paper/Exhibits/results.doc
dir : seeout

.         
. outreg2 [operrev0] using "$EXHIBIT/results.doc", append ///
>     ctitle("operrev0")          /// 列标题
>     se                       /// 报告括号内标准误
>     dec(3) nocons addtext(County FE, Yes, Year FE, Yes)
D:/stata17/Replication_paper/Exhibits/results.doc
dir : seeout

. 
. outreg2 [sales1] using "$EXHIBIT/results.doc", append ///
>     ctitle("sales1")          /// 列标题
>     se                       /// 报告括号内标准误
>     dec(3) nocons addtext(County FE, Yes, Year FE, Yes)
D:/stata17/Replication_paper/Exhibits/results.doc
dir : seeout

.         
. outreg2 [sales0] using "$EXHIBIT/results.doc", append ///
>     ctitle("sales0")          /// 列标题
>     se                       /// 报告括号内标准误
>     dec(3) nocons addtext(County FE, Yes, Year FE, Yes)
D:/stata17/Replication_paper/Exhibits/results.doc
dir : seeout

. 
. 
. 
. *****************
. * close log txt
. *****************
. 
. log close
      name:  <unnamed>
       log:  D:/stata17/Replication_paper/Logfile_forReg/Analysis_进一步讨论_产业结构变动差异.txt
  log type:  text
 closed on:  25 Jan 2026, 12:06:31
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