Decrease the number of zeros in the coefficients

[Yurlady Chaverra-Palacios]

Hello everyone

I am working on a paper. I am asking for your advice on how to re-scale the data and decrease the number of zeros in the coefficients.
Attached is the regression table

Table 2: Cocoa Agricultural Productivity
Dependent Variable: [lnYield (ton/he)]
- - - OLS, Municipality and Year Fixed Effect - - -

Variables	(1)	(2)	(3)
Ln(Credit for Investment)	0.00452***	0.00164**	0.00137*
	(0.000546)	(0.000618)	(0.000623)
Ln(Fiscal Revenue)	-0.00847*	0.00206	-0.00774
	(0.00343)	(0.00744)	(0.00868)
Land use	-0.000196	-0.00758	-0.00637
	(0.00271)	(0.00834)	(0.00831)
Labor	0.0139***	0.0357***	0.0339***
	(0.00173)	(0.00525)	(0.00827)
Aqueduct Coverage	-0.000514**	0.0000916	0.000176
	(0.000163)	(0.000161)	(0.000159)
Energy Coverage	0.00186***	0.000800*	0.000664
	(0.000279)	(0.000362)	(0.000367)
Constant	-1.596***	-2.918***	-2.726***
	(0.0957)	(0.294)	(0.514)
Municipality FE	No	Yes	Yes
Year FE	No	No	Yes
N	6424	6424	6424
R-sq	0.052	0.616	0.623
OLS Robust Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Thanks!

[Andrew Musau]

This forum is for learning how to use the forum software. Next time, post your question on the General Forum. You need to divide your variables by a positive constant. For logged variables, you may want to do this before taking logs. Here is an example:

sysuse auto, clear
regress mpg price weight disp
foreach var in price weight displacement{
    replace `var'= `var'/10000
}
regress mpg price weight disp

Res.:

. regress mpg price weight disp

      Source |       SS           df       MS      Number of obs   =        74
-------------+----------------------------------   F(3, 70)        =     44.23
       Model |  1599.61376         3  533.204588   Prob > F        =    0.0000
    Residual |  843.845694        70  12.0549385   R-squared       =    0.6547
-------------+----------------------------------   Adj R-squared   =    0.6399
       Total |  2443.45946        73  33.4720474   Root MSE        =     3.472

------------------------------------------------------------------------------
         mpg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       price |  -.0000966   .0001636    -0.59   0.557     -.000423    .0002297
      weight |  -.0063909    .001209    -5.29   0.000    -.0088022   -.0039796
displacement |   .0054824    .009921     0.55   0.582    -.0143044    .0252693
       _cons |   40.10848   2.029845    19.76   0.000     36.06008    44.15687
------------------------------------------------------------------------------

.
. foreach var in price weight displacement{
  2.
.     replace `var'= `var'/10000
  3.
. }
variable price was int now float
(74 real changes made)
variable weight was int now float
(74 real changes made)
variable displacement was int now float
(74 real changes made)

.
. regress mpg price weight disp

      Source |       SS           df       MS      Number of obs   =        74
-------------+----------------------------------   F(3, 70)        =     44.23
       Model |  1599.61377         3  533.204591   Prob > F        =    0.0000
    Residual |  843.845688        70  12.0549384   R-squared       =    0.6547
-------------+----------------------------------   Adj R-squared   =    0.6399
       Total |  2443.45946        73  33.4720474   Root MSE        =     3.472

------------------------------------------------------------------------------
         mpg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       price |  -.9662414   1.636186    -0.59   0.557    -4.229512     2.29703
      weight |  -63.90924   12.09013    -5.29   0.000    -88.02225   -39.79623
displacement |   54.82433   99.21035     0.55   0.582    -143.0445    252.6931
       _cons |   40.10848   2.029845    19.76   0.000     36.06008    44.15687
------------------------------------------------------------------------------

.