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

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