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Enterprise Survey 2006-2017, Panel data

Peru, 2006 - 2017
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Reference ID
PER_2006-2017_ES-P_v01_M
Producer(s)
World Bank
Metadata
DDI/XML JSON
Created on
Dec 05, 2019
Last modified
Dec 05, 2019
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Total Annual Expenditure For Purchases Of Land And Buildings In Last Fiscal Yr (n5b)

Data file: Peru_2006_2010_2017

Overview

Valid: 1891
Invalid: 744
Minimum: -9
Maximum: 135000000
Type: Discrete
Decimal: 0
Start: 716
End: 724
Width: 9
Range: -9 - 135000000
Format: Numeric

Questions and instructions

Literal question
In fiscal year 2015, how much did this establishment spend on purchases of and buildings, including expansion and renovations of existing structures?
- land and buildings, including expansion and renovations of existing structures?
Categories
Value Category Cases
-9 Don't know (spontaneous) 97
5.1%
-8 12
0.6%
-7 1
0.1%
0 1394
73.7%
35 1
0.1%
4000 1
0.1%
4321 1
0.1%
8000 1
0.1%
10114 1
0.1%
12000 1
0.1%
15000 2
0.1%
18000 1
0.1%
20000 2
0.1%
24000 2
0.1%
25000 3
0.2%
27000 1
0.1%
30000 5
0.3%
32000 2
0.1%
36300 1
0.1%
40000 4
0.2%
42000 1
0.1%
47000 1
0.1%
48000 2
0.1%
50000 9
0.5%
52500 1
0.1%
58000 1
0.1%
60000 6
0.3%
61050 1
0.1%
69000 1
0.1%
70000 2
0.1%
74800 1
0.1%
75000 2
0.1%
76000 1
0.1%
77000 2
0.1%
80000 4
0.2%
86000 1
0.1%
90000 1
0.1%
95000 1
0.1%
97500 1
0.1%
98000 1
0.1%
100000 10
0.5%
104200 1
0.1%
104390 1
0.1%
105000 2
0.1%
110000 3
0.2%
113600 1
0.1%
120000 9
0.5%
122800 1
0.1%
140000 1
0.1%
144000 1
0.1%
145000 1
0.1%
150000 9
0.5%
151200 1
0.1%
157000 1
0.1%
165000 3
0.2%
168000 1
0.1%
170000 1
0.1%
180000 4
0.2%
186000 1
0.1%
188886 1
0.1%
195060 1
0.1%
200000 19
1%
210000 1
0.1%
230000 1
0.1%
240000 3
0.2%
250000 7
0.4%
260000 1
0.1%
270000 2
0.1%
284000 2
0.1%
286000 1
0.1%
300000 26
1.4%
320000 1
0.1%
324000 1
0.1%
325000 1
0.1%
330000 2
0.1%
335000 1
0.1%
350000 4
0.2%
359000 1
0.1%
360000 2
0.1%
371000 1
0.1%
395000 1
0.1%
396000 1
0.1%
400000 6
0.3%
410000 1
0.1%
420000 1
0.1%
427618 1
0.1%
448000 1
0.1%
450000 3
0.2%
480000 1
0.1%
500000 10
0.5%
504000 1
0.1%
520000 1
0.1%
562500 1
0.1%
577000 1
0.1%
580000 1
0.1%
600000 5
0.3%
606000 1
0.1%
650000 1
0.1%
652000 1
0.1%
670000 1
0.1%
700000 13
0.7%
750000 3
0.2%
792000 1
0.1%
800000 4
0.2%
801000 1
0.1%
858000 1
0.1%
870000 1
0.1%
900000 6
0.3%
911031 1
0.1%
1000000 12
0.6%
1010800 1
0.1%
1037000 1
0.1%
1050000 2
0.1%
1100000 1
0.1%
1200000 2
0.1%
1206046 1
0.1%
1300000 3
0.2%
1400000 2
0.1%
1420000 1
0.1%
1450000 1
0.1%
1485000 1
0.1%
1500000 4
0.2%
1600000 1
0.1%
1670000 1
0.1%
1675000 1
0.1%
1704000 1
0.1%
1800000 1
0.1%
1995182 1
0.1%
2000000 8
0.4%
2100000 1
0.1%
2450000 1
0.1%
2500000 2
0.1%
2640000 1
0.1%
2671900 1
0.1%
3000000 11
0.6%
3000100 1
0.1%
3200000 1
0.1%
3400000 1
0.1%
3500000 1
0.1%
3600000 1
0.1%
3750000 1
0.1%
4000000 2
0.1%
4260000 1
0.1%
4500000 3
0.2%
4620000 1
0.1%
5000000 6
0.3%
5025000 1
0.1%
5680000 1
0.1%
5800000 1
0.1%
6000000 6
0.3%
6167000 1
0.1%
7000000 1
0.1%
7392000 1
0.1%
7400000 1
0.1%
8000000 1
0.1%
8500000 1
0.1%
9000000 1
0.1%
9750000 1
0.1%
10000000 1
0.1%
10005000 1
0.1%
15000000 1
0.1%
17000000 1
0.1%
18000000 1
0.1%
19000000 1
0.1%
20000000 4
0.2%
24000000 1
0.1%
30000000 1
0.1%
32000000 1
0.1%
35000000 1
0.1%
40000000 1
0.1%
50000000 1
0.1%
135000000 1
0.1%
Sysmiss 744
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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