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    Home / Central Data Catalog / ENTERPRISE_SURVEYS / CHL_2006_ES_V01_M_WB / variable [F1]
enterprise_surveys

Enterprise Survey 2006

Chile, 2006
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Reference ID
CHL_2006_ES_v01_M_WB
DOI
https://doi.org/10.48529/ej13-bz23
Producer(s)
World Bank
Collection(s)
Enterprise Surveys
Metadata
DDI/XML JSON
Created on
Mar 24, 2011
Last modified
Sep 26, 2013
Page views
40261
Downloads
3671
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  • Chile-2006--full
    data-1

Hypothetical cost to purchase its own machinery vehicles and equipment in use (Ln7b)

Data file: Chile-2006--full data-1

Overview

Valid: 640
Invalid: 377
Type: Discrete
Decimal: 0
Start: 1300
End: 1311
Width: 12
Range: -9 - 400000000
Format: Numeric

Questions and instructions

Literal question
If this establishment had to hypothetically purchase its own land and buildings, and machinery and equipment in use now, as they are in their current condition, how much would it cost to purchase each of the following?
Land and buildings
Categories
Value Category Cases
-9 138
21.6%
0 15
2.3%
120000 1
0.2%
4000000 2
0.3%
6000000 1
0.2%
7000000 1
0.2%
8000000 1
0.2%
10000000 1
0.2%
10400000 1
0.2%
13000000 1
0.2%
15000000 1
0.2%
20000000 2
0.3%
25000000 1
0.2%
30000000 10
1.6%
35000000 1
0.2%
40000000 9
1.4%
45000000 6
0.9%
50000000 14
2.2%
60000000 9
1.4%
65000000 1
0.2%
66000000 1
0.2%
70000000 7
1.1%
76579822 1
0.2%
80000000 21
3.3%
90000000 7
1.1%
90316000 1
0.2%
94000000 1
0.2%
95000000 1
0.2%
100000000 49
7.7%
103000000 1
0.2%
110000000 3
0.5%
115000000 1
0.2%
120000000 14
2.2%
130000000 5
0.8%
140000000 5
0.8%
150000000 24
3.8%
160000000 2
0.3%
170000000 4
0.6%
180000000 7
1.1%
190000000 1
0.2%
200000000 43
6.7%
205000000 1
0.2%
210000000 1
0.2%
230000000 1
0.2%
240000000 1
0.2%
250000000 12
1.9%
260000000 2
0.3%
272000000 1
0.2%
280000000 1
0.2%
300000000 20
3.1%
310000000 1
0.2%
320000000 2
0.3%
335000000 1
0.2%
350000000 4
0.6%
360000000 1
0.2%
380000000 1
0.2%
400000000 19
3%
412500000 1
0.2%
420000000 1
0.2%
450000000 6
0.9%
451000000 1
0.2%
460000000 1
0.2%
480000000 1
0.2%
500000000 17
2.7%
534000000 1
0.2%
542000000 1
0.2%
550000000 2
0.3%
580000000 1
0.2%
600000000 17
2.7%
620000000 1
0.2%
640000000 1
0.2%
690000000 1
0.2%
700000000 4
0.6%
750000000 3
0.5%
797000000 1
0.2%
800000000 7
1.1%
810000000 1
0.2%
850000000 2
0.3%
868000000 1
0.2%
900000000 2
0.3%
1000000000 10
1.6%
1080000000 2
0.3%
1094000000 1
0.2%
1100000000 1
0.2%
1200000000 3
0.5%
1400000000 1
0.2%
1500000000 2
0.3%
1600000000 3
0.5%
1800000000 4
0.6%
2000000000 4
0.6%
2100000000 2
0.3%
2124000000 1
0.2%
2160000000 1
0.2%
2200000000 2
0.3%
2300000000 1
0.2%
2360000000 1
0.2%
2400000000 2
0.3%
2500000000 2
0.3%
2600000000 1
0.2%
2650000000 1
0.2%
2700000000 2
0.3%
2800000000 2
0.3%
2858000000 1
0.2%
3000000000 3
0.5%
3108000000 1
0.2%
3200000000 1
0.2%
3300000000 1
0.2%
3560000000 1
0.2%
3600000000 1
0.2%
3660000000 1
0.2%
4000000000 2
0.3%
4400000000 1
0.2%
4600000000 1
0.2%
5000000000 2
0.3%
5100000000 1
0.2%
7000000000 1
0.2%
7200000000 2
0.3%
8000000000 1
0.2%
10000000000 3
0.5%
12000000000 2
0.3%
13000000000 1
0.2%
14000000000 1
0.2%
15000000000 1
0.2%
25000000000 1
0.2%
100000000000 1
0.2%
216972000000 1
0.2%
477000000000 1
0.2%
Sysmiss 377
Warning: these figures indicate the number of cases found in the data file. They cannot be interpreted as summary statistics of the population of interest.
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