Farms: Data set of farm accountancy data

In sfadv: Advanced Methods for Stochastic Frontier Analysis

Description

The Farms data frame contains simulated data for 2,500 dairy farms.

Usage

Farms

Format

This data frame contains the following 14 variables:

farm_output	Farm total output, in constant Euros.
agri_land	Farm agricultural area, in hectares.
tot_lab	Total labour used on the farm, in hours.
tot_asset	Total assets of the farm (excluding land), in constant Euros.
LFA	Dummy variable indicating whether the farm is located within a
	Less Favoured Area (1) or not (0).
hired_lab	Ratio of the farm hired labour to total labour (tot_lab).
rented_land	Ratio of the farm rented land to agricultural area (agri_land).
debt_asset	Ratio of the farm total debts to total assets.
costs	Costs of variable inputs used on the farm, in constant Euros.
subs	Amount of the farm production subsidies received per hectare of agricultural area
	(agri_land), in constant Euros.
region	Factor variable representing the administrative region of the farm.
milkprice	Average farm milk price per ton of milk, in constant Euros.
price_ind	National yearly price index of variable inputs.
T	Time trend.

Source

Simulated farmers' accountancy data

Examples

head(Farms)
str(Farms)
summary(Farms)
  
lm.output <- lm(farm_output ~ agri_land + tot_lab + tot_asset + costs, data = Farms)
summary(lm.output)

Example output

* Please cite the 'sfadv' package as:
  Desjeux Y. and Latruffe L. (2017). sfadv: Advanced Methods for Stochastic Frontier Analyses. R package version 1.0.0. 
  URL: https://CRAN.R-project.org/package=sfadv.

See also: citation("sfadv")

* For any questions, suggestions, or comments on the 'sfadv' package, please make use of 'tracker' facilities at:
  https://r-forge.r-project.org/projects/sfadv/
  farm_output agri_land tot_lab tot_asset LFA  hired_lab rented_land debt_asset
1    43958.54      9.00    3560 181420.71   1 0.00000000  0.05555556 0.01348549
2    52324.55     27.00    2160 193926.19   1 0.00000000  0.00000000 0.04202163
3    17341.65     17.68    2500  73600.96   1 0.00000000  0.47341629 0.00000000
4    27213.65      9.50    3300 106802.94   1 0.04545455  0.42105263 0.00000000
5    31741.35      7.23    3103 136659.00   0 0.00000000  0.00000000 0.00000000
6    25592.54      6.75    5815 106816.83   1 0.00000000  0.00000000 0.00000000
     costs     subs region milkprice price_ind T
1 26208.02 47.72433      B  348.3645  77.98881 1
2 36395.01  0.00000      A  379.7166  77.98881 1
3  8907.77  0.00000      C  303.9909  77.98881 1
4 13699.00  0.00000      C  339.0067  77.98881 1
5 20486.55  0.00000      A  367.5100  77.98881 1
6 10279.47  0.00000      A  321.8801  77.98881 1
'data.frame':	2500 obs. of  14 variables:
 $ farm_output: num  43959 52325 17342 27214 31741 ...
 $ agri_land  : num  9 27 17.68 9.5 7.23 ...
 $ tot_lab    : num  3560 2160 2500 3300 3103 ...
 $ tot_asset  : num  181421 193926 73601 106803 136659 ...
 $ LFA        : num  1 1 1 1 0 1 0 1 1 1 ...
 $ hired_lab  : num  0 0 0 0.0455 0 ...
 $ rented_land: num  0.0556 0 0.4734 0.4211 0 ...
 $ debt_asset : num  0.0135 0.042 0 0 0 ...
 $ costs      : num  26208 36395 8908 13699 20487 ...
 $ subs       : num  47.7 0 0 0 0 ...
 $ region     : Factor w/ 5 levels "A","B","C","D",..: 2 1 3 3 1 1 1 1 3 3 ...
 $ milkprice  : num  348 380 304 339 368 ...
 $ price_ind  : num  78 78 78 78 78 ...
 $ T          : num  1 1 1 1 1 1 1 1 1 1 ...
  farm_output       agri_land         tot_lab        tot_asset      
 Min.   :  7496   Min.   :  0.80   Min.   :  720   Min.   :   8223  
 1st Qu.: 29808   1st Qu.:  9.23   1st Qu.: 2350   1st Qu.:  92320  
 Median : 48824   Median : 13.43   Median : 3512   Median : 133172  
 Mean   : 69159   Mean   : 17.57   Mean   : 3616   Mean   : 174078  
 3rd Qu.: 80212   3rd Qu.: 20.88   3rd Qu.: 4380   3rd Qu.: 204696  
 Max.   :780623   Max.   :250.00   Max.   :17123   Max.   :1947669  
      LFA         hired_lab        rented_land       debt_asset     
 Min.   :0.00   Min.   :0.00000   Min.   :0.0000   Min.   :0.00000  
 1st Qu.:1.00   1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.00000  
 Median :1.00   Median :0.00000   Median :0.1126   Median :0.00000  
 Mean   :0.84   Mean   :0.02089   Mean   :0.2939   Mean   :0.04534  
 3rd Qu.:1.00   3rd Qu.:0.00000   3rd Qu.:0.5556   3rd Qu.:0.04498  
 Max.   :1.00   Max.   :1.00000   Max.   :1.0000   Max.   :0.91040  
     costs             subs          region     milkprice       price_ind     
 Min.   :  2473   Min.   :    0.00   A:1036   Min.   :173.1   Min.   : 77.99  
 1st Qu.: 18978   1st Qu.:    0.00   B: 480   1st Qu.:296.1   1st Qu.: 86.03  
 Median : 28980   Median :   65.67   C: 421   Median :314.8   Median : 88.22  
 Mean   : 41716   Mean   :  221.00   D: 459   Mean   :315.1   Mean   : 90.92  
 3rd Qu.: 47815   3rd Qu.:  212.78   E: 104   3rd Qu.:331.1   3rd Qu.: 94.72  
 Max.   :581728   Max.   :16859.69            Max.   :602.2   Max.   :111.80  
       T         
 Min.   : 1.000  
 1st Qu.: 4.000  
 Median : 7.000  
 Mean   : 7.658  
 3rd Qu.:11.000  
 Max.   :15.000  

Call:
lm(formula = farm_output ~ agri_land + tot_lab + tot_asset + 
    costs, data = Farms)

Residuals:
    Min      1Q  Median      3Q     Max 
-126508   -8525    -895    7226  164436 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.211e+04  1.022e+03 -11.850  < 2e-16 ***
agri_land    2.132e+02  2.559e+01   8.335  < 2e-16 ***
tot_lab      1.622e+00  2.957e-01   5.485 4.54e-08 ***
tot_asset    1.374e-01  3.689e-03  37.252  < 2e-16 ***
costs        1.144e+00  1.273e-02  89.864  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 19760 on 2495 degrees of freedom
Multiple R-squared:  0.9208,	Adjusted R-squared:  0.9207 
F-statistic:  7253 on 4 and 2495 DF,  p-value: < 2.2e-16

sfadv documentation built on May 2, 2019, 5:25 p.m.