Tuesday, January 12, 2016

What does a million look like?

How many refugees could the USA settle quickly, distributed into communities across the country? This is not a proposal, just a back-of-the-envelope search for some sense of what’s do-able, if we were determined to do something.

Suppose the bishops caught fire and decided they were all going to move as fast as possible to settle refugees. They are all going to put refugee families in every parish. How many parishes are there?

The Official Catholic Directory for 2014 says there are 17,483 parishes in the country. So if every parish takes a family or a group – on average, of four people – that’s about 70,000 refugees settled.

Catholics are about 22% of the nation. So if Catholics can take 70,000, then other religious communities and organized civic groups (colleges, ethical societies), matching that, should be able to raise that by a factor of 5. All religious communities, and organized civic communities, should be able to take 350,000 refugees.  I mean, if people noticed that there’s a war going on, and acted as if wars can cause emergencies, then every parish (or other group) could take a family this afternoon. It would be a little messy for a few weeks – basements, couches, adjustments. But within a month, most people would be in living and dignified circumstances. Given a month to prepare, churches (and similar) could take 350,000 refugees, and provide hospitality that would not be embarrassing.

How about two families? Could your parish/church/synagogue/mosque/college/civic association take two families in a year? That would be a stretch, but is it do-able? There’s a war going on: can you help?

How about three? That would be a million refugees, fed and settled and befriended.

It’s not going to happen any time soon.

Me, I can’t handle a million. I can’t even keep track of all those zeroes: do not have a gut sense of the difference between one million and ten million. But could my parish, if convinced that there was an emergency, care for three families? Gulp. Yes, we can. In fact, we could do this without government help.

That’s what it would look like if we in the USA took our fair share.

Fair share #6 - using EU's approach

A fifth estimate of America’s fair share of Syrian refugees

The allocation plan used the European Union combines four factors: population, GDP (gross domestic product), unemployment, and the number of refugees welcomed in past five years.  Population and GDP are weighted equally. After those factors are combined to get an estimate of the nation’s fair share, there two correctives: unemployment and the history of welcoming refugees are correctives, that reduce a nation’s estimated fair share by up to 25%.

Please note that the EU estimates of fair share for relocating refugees started with a decision that Europe would take only 120,000, or 3% of the total number of Syrian refugees. That was a political decision, not based on any theory of justice.

The World Bank estimated, in 2014, that the GDP for the world was $77.9 trillion, and the GDP for the USA was $17.4 trillion, or 22%. I have not figured out how to use GDP as a means to figure out fair share of refugees. The European method started with the assumption that all EU members would share the burden; that assumption doesn’t work for the world. Syria, for example, will not share the load of refugees from Syria. But approximately: using GDP, the USA’s fair share is 22%, or 880,000 of the four million refugees from Syria.

Using population only, my estimate was that the fair share for the USA would be about 175,000.

Combining GDP and population estimates, weighted equally, the USA’s fair share would about 530,000. I don’t understand how to make the corrections for unemployment and history of welcome. But the largest possible correction would be to subtract a quarter of the total. So let’s take off the maximum until I figure out how to use their method more smoothly: 530,000 minus a quarter is a little under 400,000.

Very roughly, using the EU method of estimating fair share, the USA should take 400,000 Syrian refugees.

Now I have five estimates of the USA’s fair share of Syrian refugees:

1. AREA ONLY: 250,000
2. POPULATION ONLY: 175,000
3. ARABLE LAND ONLY: 750,000
4. “EMPTY BEDS”: 950,000
5. EU method: 400,000

Monday, January 11, 2016

Fair Share Estimate #5 – “EMPTY BEDS”

Fair Share Estimate #5 – “EMPTY BEDS”

The last chart aims for an estimate of “empty beds” (not a technical term).

The world’s average density is 463 people per arable square mile. Suppose, just to get a sense of what seems fair, we imagine schmearing the people in the world evenly throughout the arable land. (DO NOT TRY THIS! THIS IS A THOUGHT EXPERIMENT!) How many people would have to move to get from crowded spots to the world average density? And – the real question – how many people would move, and to where, to fill the sparsely populated land up to the global average?

Maybe we don’t want the whole world to have the same population density! Skip the maybe; we don’t. But if you estimate how many people it would take to move a relatively empty nation up to the global average, you get a measurement that may help provide numbers for a pursuit of a just immigration policy.

The chart lists 43 nations (the 25 largest by area, the 25 most populous, and the 25 with the most arable land).

The next column gives the population for each nation.

The third column has estimates for the “capacity” of each nation. For each nation, if the real population density of the nation were equal to the global average, what would the population be?  (Calculated in the previous chart, “fair share estimates #4 – capacity.”)

The fourth column estimates “empty beds.” That’s not a technical term. It’s the difference between the nation’s capacity and its actual population.

The fifth column shows empty beds in each nation as a percentage of the global total (about 1.8 billion). This percentage gives a reasonable estimate of just figures for how to divide the burdens of relocation in a global migration like the Syrian refugee crisis.

The final column applies the % of empty beds to the four million Syrian refugees.

According to this reasonable sketch, the fair share of four million Syrian refugees for the USA would be a little under a million.

# by size
Nation
population
capacity
 "empty beds"
% empty beds
fair share
3
USA
321,368,864
       763,978,706.00
       442,609,842
24.6%
                 983,577
1
RUSSIA
142,423,773
       543,693,492.00
       401,269,719
22.3%
                 891,710
6
AUSTRALIA
22,751,014
       216,916,889.00
       194,165,875
10.8%
                 431,480
2
CANADA
35,099,836
       192,410,299.00
       157,310,463
8.7%
                 349,579
46
UKRAINE
44,429,471
       150,378,233.00
       105,948,762
5.9%
                 235,442
9
KAZAKHSTAN
18,157,122
       102,350,317.00
          84,193,195
4.7%
                 187,096
8
ARGENTINA
43,431,886
       127,088,870.00
          83,656,984
4.6%
                 185,904
5
BRAZIL
204,259,812
       271,334,668.00
          67,074,856
3.7%
                 149,055
22
NIGER
18,045,729
         67,034,992.00
          48,989,263
2.7%
                 108,865
43
FRANCE
66,553,766
         99,157,006.00
          32,603,240
1.8%
                    72,452
37
TURKEY
79,414,269
       106,380,732.00
          26,966,463
1.5%
                    59,925
16
SUDAN
36,108,853
         62,782,800.00
          26,673,947
1.5%
                    59,275
70
POLAND
38,562,189
         56,738,335.00
          18,176,146
1.0%
                    40,391
52
SPAIN
48,146,134
         62,864,288.00
          14,718,154
0.8%
                    32,707
25
SOUTH AFRICA
53,675,563
         68,342,967.00
          14,667,404
0.8%
                    32,594
18
IRAN
81,824,270
         90,562,800.00
            8,738,530
0.5%
                    19,419
21
CHAD
11,631,456
         16,324,454.00
            4,692,998
0.3%
                    10,429
19
MONGOLIA
2,992,908
            5,470,808.00
            2,477,900
0.1%
                      5,506
17
LIBYA
6,411,776
            8,390,949.00
            1,979,173
0.1%
                      4,398
24
MALI
16,955,536
         17,408,800.00
                453,264
0.0%
                      1,007
12
GREENLAND
57,733
                    4,630.00
                (53,103)
0.0%
                       (118)
51
THAILAND
67,976,405
         65,255,683.00
          (2,720,722)
-0.2%
                    (6,046)
23
ANGOLA
19,625,353
         15,296,594.00
          (4,328,759)
-0.2%
                    (9,619)
10
ALGERIA
39,542,166
         34,956,963.00
          (4,585,203)
-0.3%
                 (10,189)
14
MEXICO
121,736,809
       112,720,591.00
          (9,016,218)
-0.5%
                 (20,036)
40
BURMA
56,320,206
         45,436,505.00
 (10,883,701.00)
-0.6%
                 (24,186)
13
Saudi Arabia
27,752,316
         16,621,700.00
       (11,130,616)
-0.6%
                 (24,735)
20
PERU
30,444,999
         13,334,400.00
       (17,110,599)
-1.0%
                 (38,024)
72
ITALY
61,855,120
         35,952,413.00
 (25,902,707.00)
-1.4%
                 (57,562)
63
GERMANY
80,854,408
         53,568,174.00
       (27,286,234)
-1.5%
                 (60,636)
80
United Kingdom
64,088,222
         25,984,023.00
 (38,104,199.00)
-2.1%
                 (84,676)
32
NIGERIA
181,562,056
       139,240,768.00
       (42,321,288)
-2.4%
                 (94,047)
27
ETHIOPIA
99,465,819
         51,893,040.00
       (47,572,779)
-2.6%
               (105,717)
11
CONGO, DR
79,375,136
         30,026,939.00
       (49,348,197)
-2.7%
               (109,663)
66
VIETNAM
94,348,835
         30,339,464.00
 (64,009,371.00)
-3.6%
               (142,243)
73
PHILIPPINES
100,998,376
         26,229,876.00
 (74,768,500.00)
-4.2%
               (166,152)
30
EGYPT
88,487,396
         13,458,021.00
 (75,029,375.00)
-4.2%
               (166,732)
62
JAPAN
126,919,659
         20,196,060.00
     (106,723,599)
-5.9%
               (237,164)
36
PAKISTAN
199,085,847
         88,117,697.00
     (110,968,150)
-6.2%
               (246,596)
95
BANGLADESH
168,957,745
         35,044,470.00
     (133,913,275)
-7.4%
               (297,585)
15
INDONESIA
255,993,674
         93,274,128.00
     (162,719,546)
-9.0%
               (361,599)
7
INDIA
1,251,695,584
       672,188,030.00
     (579,507,554)
-32.2%
           (1,287,795)
4
CHINA
1,367,485,388
       641,674,015.00
     (725,811,373)
-40.3%
           (1,612,914)


Fair Share Estimate #4 – REAL POPULATION DENSITY

Fair Share Estimate #4 – REAL POPULATION DENSITY

The chart provides one way to estimate what is fair when the nations of the world try to figure out how to help Syrian refugees. How many should go where? This is NOT A POLICY PROPOSAL! This is a sketch to help think.

It’s simply a list of the 25 largest nations, the 25 nations with the most arable land, and the 25 nations with the largest populations. It gives the REAL POPULATION DENSITY of each nation as of 2014 (CIA World Factbook).

“Real population density” requires a little effort to understand. But consider Antarctica. There aren’t a lot of people at the South Pole. In 2005, there was the largest crowd ever wintering over there: 86 people! With five million square miles to share, that’s over 50,000 square miles apiece. But there’s not a lot of food, not a lot of shelter, not much light for months. So everybody was hanging out at one dinky little station, and it was crowded.

“Real population density,” also called “physiological density,” refers to the population on the arable land. Population is not a useful measure by itself; nor is land by itself; nor is arable land by itself. But real population density is an effort to put these factors together. The measurement has an awful history; it is used in population control presentations all the time. So if you use it, use it carefully.

There are two charts below. The first chart below shows the real population density of 43 nations (the largest, the most populous, and those with the most arable land). Real population density is population divided by arable land. The chart shows population, arable land, real population density, and real density as a percentage of the global average. The final column is “capacity,” the theoretical population of that nation is the real population density matched the global average (arable land X the global average of 463).

# by size
Nation
arable
population
Real density
% of average
 capacity
3
USA
1,650,062
321,368,864
195
42.1%
           763,978,706
7
INDIA
1,451,810
1,251,695,584
862
186.2%
           672,188,030
4
CHINA
1,385,905
1,367,485,388
987
213.1%
           641,674,015
1
RUSSIA
1,174,284
142,423,773
121
26.2%
           543,693,492
5
BRAZIL
586,036
204,259,812
349
75.3%
           271,334,668
6
AUSTRALIA
468,503
22,751,014
49
10.5%
           216,916,889
2
CANADA
415,573
35,099,836
84
18.2%
           192,410,299
46
UKRAINE
324,791
44,429,471
137
29.5%
           150,378,233
32
NIGERIA
300,736
181,562,056
604
130.4%
           139,240,768
8
ARGENTINA
274,490
43,431,886
158
34.2%
           127,088,870
14
MEXICO
243,457
121,736,809
500
108.0%
           112,720,591
37
TURKEY
229,764
79,414,269
346
74.7%
           106,380,732
9
KAZAKHSTAN
221,059
18,157,122
82
17.7%
           102,350,317
43
FRANCE
214,162
66,553,766
311
67.1%
              99,157,006
15
INDONESIA
201,456
255,993,674
1271
274.5%
              93,274,128
18
IRAN
195,600
81,824,270
418
90.4%
              90,562,800
36
PAKISTAN
190,319
199,085,847
1046
225.9%
              88,117,697
25
SOUTH AFRICA
147,609
53,675,563
364
78.5%
              68,342,967
22
NIGER
144,784
18,045,729
125
26.9%
              67,034,992
51
THAILAND
140,941
67,976,405
482
104.2%
              65,255,683
52
SPAIN
135,776
48,146,134
355
76.6%
              62,864,288
16
SUDAN
135,600
36,108,853
266
57.5%
              62,782,800
70
POLAND
122,545
38,562,189
315
68.0%
              56,738,335
63
GERMANY
115,698
80,854,408
699
150.9%
              53,568,174
27
ETHIOPIA
112,080
99,465,819
887
191.7%
              51,893,040
40
BURMA
98,135
56,320,206
574
124.0%
              45,436,505
72
ITALY
77,651
61,855,120
797
172.0%
              35,952,413
95
BANGLADESH
75,690
168,957,745
2232
482.1%
              35,044,470
10
ALGERIA
75,501
39,542,166
524
113.1%
              34,956,963
66
VIETNAM
65,528
94,348,835
1440
311.0%
              30,339,464
11
CONGO, DR
64,853
79,375,136
1224
264.3%
              30,026,939
73
PHILIPPINES
56,652
100,998,376
1783
385.1%
              26,229,876
80
United Kingdom
56,121
64,088,222
1142
246.6%
              25,984,023
62
JAPAN
43,620
126,919,659
2910
628.4%
              20,196,060
24
MALI
37,600
16,955,536
451
97.4%
              17,408,800
13
Saudi Arabia
35,900
27,752,316
773
167.0%
              16,621,700
21
CHAD
35,258
11,631,456
330
71.3%
              16,324,454
23
ANGOLA
33,038
19,625,353
594
128.3%
              15,296,594
30
EGYPT
29,067
88,487,396
3044
657.5%
              13,458,021
20
PERU
28,800
30,444,999
1057
228.3%
              13,334,400
17
LIBYA
18,123
6,411,776
354
76.4%
                8,390,949
19
MONGOLIA
11,816
2,992,908
253
54.7%
                5,470,808
12
GREENLAND
10
57,733
5773
1246.9%
                        4,630