Brain Drain - Ranking and Analysis with 2015 ACS data

Brain drain is a much talked about topic.There are various ways of measuring the brain drain. This article focuses on a simple view of the migration of residents with various education achievements. This article also explores ways of ranking USA states in the brain drain issue with migration and education attainment data obtained from Census' 2015 ACS (American Community Survey) five year PUMS data file.

* This article is aimed to review the 2015 ACS migration data for all states in the United States. For more detailed analysis about particular states, please follow the information given in Brain Drain for United States by States DIY 2010, 2015 ACS and try a DIY yourself. For Nebraska, please see Nebraska Brain Drain Migration and Ed. Attainment, 2015 United States ACS.

* Data update for other years
2016-2012 ACS 5-year PUMS data
2011-2007 ACS 5-year PUMS data
.

Continuing our previous explore of Census' ACS PUMS data: Population migration derived from ACS 2015 5-year PUMS dataset and Brain Drain - 2015 ACS State Migration for Working Age, we are going to bring in the education attainment element of the survey to this article. The education attainment levels considered are the PhD degree, the First Professional degree, the Master degree, the Bachelor degree, the Associate degree, some college experience, the High School Diploma, and the less-than-High-School level. 

For the scope, we are continue to confine ourselves to the working age (22-64) population while leaving spaces for discussion of other issues to later articles.

The main slides for this article is titled Brain Drain Rank and Analysis and have been posted at Tableau's public data site.

The story begins with the education attainment of each state in the United States of America. With the data, we can rank the states by the number of people at each education attainment level. Due to the overall population size, these rankings are dominated by large states like New York, Taxes, California, and Florida ... etc.

Ed. Attainment Head Count

  While total head counts are dominated by populated states, it is of interests to see the education attainments varies by states or regions. The second slide/sheet in the presentation allows user to look at just that. While head counts provides relative strength for each education attainment level, percents can provide better sense of quantification, which is what is presented in the third slide of the story.

Ed. Attainment Profile by Head Count

 

Ed. Attainment Profile by Per Cent

With the education attainment data been presented, we moved our focus to the net migration of working age population. The fourth slide of the story ranks the states in terms of net number of people migrated between states - the number of in-migration foreigner can be found in this article: Population migration derived from ACS 2015 5-year PUMS dataset. Observing the fourth slide, we see that both end of the net migration, again, is dominated by large states, which don't have to be, but it is what we observed.

Rank States By Net Migration Head Count

The ranking table by number of Net Migration is presented below, where the education levels were denoted as: Less Than High School Degree (LssHsDgr), High School Degree or Equivelent (HsDgrEqv), Some College But Less than Associate Degree (SomeCllg), Associate Degree (AssctDgr), Bachelor Degree (BchlrDgr), and Graduate Degrees (Grdts).

Looking at the table below, we can see that, at the graduate degree level, New York State lost the most (-16,947), with the Illinos come second (-9,089). Nebraska seems to do much better with  -1,173 and ranked 14 from the last.
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State	Ed. Attainment	HdCnt	Upper	Lower
California	1. LssHsDgr	-9,804	-7,536	-12,072
New York	1. LssHsDgr	-9,346	-7,507	-11,185
Alaska	1. LssHsDgr	-4,810	-3,818	-5,802
Illinois	1. LssHsDgr	-4,655	-2,875	-6,435
Massachusetts	1. LssHsDgr	-1,710	-563	-2,857
Alabama	1. LssHsDgr	-1,576	-209	-2,943
New Jersey	1. LssHsDgr	-1,274	55	-2,603
District of Columbia	1. LssHsDgr	-954	-407	-1,501
Hawaii	1. LssHsDgr	-699	-292	-1,106
Connecticut	1. LssHsDgr	-536	323	-1,395
Maine	1. LssHsDgr	-535	-64	-1,006
Idaho	1. LssHsDgr	-517	477	-1,511
Nebraska	1. LssHsDgr	-437	368	-1,242
New Hampshire	1. LssHsDgr	-313	346	-972
Montana	1. LssHsDgr	-288	123	-699
Vermont	1. LssHsDgr	-283	100	-666
Florida	1. LssHsDgr	-258	2,245	-2,761
North Carolina	1. LssHsDgr	-227	1,501	-1,955
Arizona	1. LssHsDgr	-163	1,403	-1,729
Iowa	1. LssHsDgr	-122	844	-1,088
Arkansas	1. LssHsDgr	-12	1,032	-1,056
South Carolina	1. LssHsDgr	23	1,306	-1,260
South Dakota	1. LssHsDgr	76	653	-501
New Mexico	1. LssHsDgr	89	1,052	-874
Wyoming	1. LssHsDgr	123	791	-545
Rhode Island	1. LssHsDgr	159	661	-343
Maryland	1. LssHsDgr	181	1,200	-838
Utah	1. LssHsDgr	249	1,048	-550
West Virginia	1. LssHsDgr	319	1,136	-498
North Dakota	1. LssHsDgr	445	984	-94
Nevada	1. LssHsDgr	608	1,869	-653
Delaware	1. LssHsDgr	801	1,297	305
Virginia	1. LssHsDgr	851	2,164	-462
Kansas	1. LssHsDgr	855	2,028	-318
Oregon	1. LssHsDgr	894	2,102	-314
Tennessee	1. LssHsDgr	908	2,365	-549
Indiana	1. LssHsDgr	929	2,225	-367
Colorado	1. LssHsDgr	1,006	2,315	-303
Michigan	1. LssHsDgr	1,069	2,225	-87
Washington	1. LssHsDgr	1,069	2,470	-332
Missouri	1. LssHsDgr	1,309	2,393	225
Kentucky	1. LssHsDgr	1,379	2,730	28
Louisiana	1. LssHsDgr	1,480	2,667	293
Wisconsin	1. LssHsDgr	1,757	2,742	772
Mississippi	1. LssHsDgr	1,796	3,015	577
Ohio	1. LssHsDgr	1,810	3,427	193
Pennsylvania	1. LssHsDgr	2,049	3,687	411
Minnesota	1. LssHsDgr	2,050	3,064	1,036
Oklahoma	1. LssHsDgr	2,622	3,955	1,289
Georgia	1. LssHsDgr	3,301	5,192	1,410
Texas	1. LssHsDgr	8,312	10,863	5,761
California	2. HsDgrEqv	-19,658	-15,233	-24,083
New York	2. HsDgrEqv	-19,550	-16,748	-22,352
Alaska	2. HsDgrEqv	-10,607	-9,148	-12,066
Illinois	2. HsDgrEqv	-6,589	-4,041	-9,137
New Jersey	2. HsDgrEqv	-4,588	-2,719	-6,457
Massachusetts	2. HsDgrEqv	-4,371	-2,602	-6,140
District of Columbia	2. HsDgrEqv	-3,231	-2,395	-4,067
Michigan	2. HsDgrEqv	-1,768	394	-3,930
Connecticut	2. HsDgrEqv	-1,695	-330	-3,060
Kansas	2. HsDgrEqv	-1,560	192	-3,312
Minnesota	2. HsDgrEqv	-1,243	453	-2,939
Maine	2. HsDgrEqv	-733	303	-1,769
Alabama	2. HsDgrEqv	-659	1,195	-2,513
Rhode Island	2. HsDgrEqv	-642	162	-1,446
Wisconsin	2. HsDgrEqv	-621	896	-2,138
Vermont	2. HsDgrEqv	-523	59	-1,105
Pennsylvania	2. HsDgrEqv	-228	2,227	-2,683
Montana	2. HsDgrEqv	-17	1,051	-1,085
Maryland	2. HsDgrEqv	15	2,117	-2,087
West Virginia	2. HsDgrEqv	139	1,739	-1,461
Hawaii	2. HsDgrEqv	190	1,502	-1,122
Mississippi	2. HsDgrEqv	206	1,816	-1,404
South Dakota	2. HsDgrEqv	370	1,201	-461
New Mexico	2. HsDgrEqv	474	1,825	-877
Nebraska	2. HsDgrEqv	516	1,826	-794
Iowa	2. HsDgrEqv	521	1,965	-923
Utah	2. HsDgrEqv	543	1,857	-771
Oklahoma	2. HsDgrEqv	667	2,978	-1,644
Arkansas	2. HsDgrEqv	670	2,452	-1,112
Wyoming	2. HsDgrEqv	694	1,858	-470
Virginia	2. HsDgrEqv	720	3,380	-1,940
Missouri	2. HsDgrEqv	766	2,907	-1,375
Louisiana	2. HsDgrEqv	922	2,997	-1,153
New Hampshire	2. HsDgrEqv	983	2,060	-94
Ohio	2. HsDgrEqv	994	3,363	-1,375
North Dakota	2. HsDgrEqv	1,184	2,413	-45
Delaware	2. HsDgrEqv	1,238	2,177	299
Indiana	2. HsDgrEqv	1,711	3,601	-179
Idaho	2. HsDgrEqv	1,885	3,297	473
Kentucky	2. HsDgrEqv	1,996	3,589	403
North Carolina	2. HsDgrEqv	2,033	5,348	-1,282
Arizona	2. HsDgrEqv	2,389	4,999	-221
Washington	2. HsDgrEqv	2,810	5,413	207
Colorado	2. HsDgrEqv	2,970	4,814	1,126
Oregon	2. HsDgrEqv	2,993	4,480	1,506
South Carolina	2. HsDgrEqv	3,252	5,405	1,099
Tennessee	2. HsDgrEqv	3,453	6,009	897
Nevada	2. HsDgrEqv	3,661	5,539	1,783
Georgia	2. HsDgrEqv	6,636	9,155	4,117
Texas	2. HsDgrEqv	10,069	13,880	6,258
Florida	2. HsDgrEqv	20,613	24,746	16,480
California	3. SomeCllg	-29,736	-25,522	-33,950
New York	3. SomeCllg	-19,769	-17,125	-22,413
Illinois	3. SomeCllg	-9,441	-6,802	-12,080
New Jersey	3. SomeCllg	-8,289	-6,392	-10,186
Alaska	3. SomeCllg	-7,887	-5,920	-9,854
Michigan	3. SomeCllg	-4,436	-2,106	-6,766
Ohio	3. SomeCllg	-1,566	1,209	-4,341
Maryland	3. SomeCllg	-1,349	429	-3,127
Minnesota	3. SomeCllg	-1,206	764	-3,176
Massachusetts	3. SomeCllg	-1,083	548	-2,714
Virginia	3. SomeCllg	-1,004	1,874	-3,882
Indiana	3. SomeCllg	-945	1,499	-3,389
New Hampshire	3. SomeCllg	-811	307	-1,929
New Mexico	3. SomeCllg	-788	784	-2,360
Connecticut	3. SomeCllg	-672	761	-2,105
District of Columbia	3. SomeCllg	-468	508	-1,444
Mississippi	3. SomeCllg	-394	1,190	-1,978
Kansas	3. SomeCllg	-391	1,523	-2,305
Rhode Island	3. SomeCllg	-302	447	-1,051
West Virginia	3. SomeCllg	-183	844	-1,210
Nebraska	3. SomeCllg	-157	1,246	-1,560
Vermont	3. SomeCllg	149	785	-487
Pennsylvania	3. SomeCllg	308	2,806	-2,190
Maine	3. SomeCllg	422	1,383	-539
Hawaii	3. SomeCllg	432	2,037	-1,173
Louisiana	3. SomeCllg	439	2,573	-1,695
Montana	3. SomeCllg	558	1,624	-508
Kentucky	3. SomeCllg	624	2,704	-1,456
Delaware	3. SomeCllg	659	1,515	-197
South Dakota	3. SomeCllg	802	1,771	-167
Wyoming	3. SomeCllg	878	1,965	-209
Iowa	3. SomeCllg	951	2,277	-375
Wisconsin	3. SomeCllg	1,036	3,056	-984
Idaho	3. SomeCllg	1,479	2,953	5
Oklahoma	3. SomeCllg	1,503	3,550	-544
Arkansas	3. SomeCllg	1,819	3,265	373
North Dakota	3. SomeCllg	1,872	3,002	742
Alabama	3. SomeCllg	1,990	3,925	55
Washington	3. SomeCllg	2,000	4,825	-825
Missouri	3. SomeCllg	2,278	4,558	-2
Utah	3. SomeCllg	2,764	4,601	927
North Carolina	3. SomeCllg	2,932	6,275	-411
Tennessee	3. SomeCllg	4,028	6,612	1,444
Nevada	3. SomeCllg	4,990	6,941	3,039
Georgia	3. SomeCllg	5,441	8,674	2,208
South Carolina	3. SomeCllg	5,750	8,247	3,253
Arizona	3. SomeCllg	6,580	9,419	3,741
Oregon	3. SomeCllg	7,490	9,575	5,405
Colorado	3. SomeCllg	8,222	10,737	5,707
Florida	3. SomeCllg	8,235	12,109	4,361
Texas	3. SomeCllg	14,246	18,081	10,411
New York	4. AssctDgr	-9,796	-8,106	-11,486
California	4. AssctDgr	-5,643	-3,372	-7,914
Illinois	4. AssctDgr	-4,374	-2,838	-5,910
Pennsylvania	4. AssctDgr	-3,287	-1,762	-4,812
Georgia	4. AssctDgr	-2,406	-706	-4,106
Alaska	4. AssctDgr	-2,216	-1,195	-3,237
Michigan	4. AssctDgr	-1,806	-662	-2,950
New Jersey	4. AssctDgr	-1,204	14	-2,422
Connecticut	4. AssctDgr	-1,164	-329	-1,999
Kansas	4. AssctDgr	-1,011	-161	-1,861
Vermont	4. AssctDgr	-946	-522	-1,370
Indiana	4. AssctDgr	-826	137	-1,789
Minnesota	4. AssctDgr	-825	589	-2,239
District of Columbia	4. AssctDgr	-612	-132	-1,092
New Mexico	4. AssctDgr	-528	188	-1,244
South Dakota	4. AssctDgr	-299	402	-1,000
Arkansas	4. AssctDgr	-266	416	-948
Louisiana	4. AssctDgr	-203	739	-1,145
Rhode Island	4. AssctDgr	-153	352	-658
Nebraska	4. AssctDgr	-119	740	-978
West Virginia	4. AssctDgr	-37	669	-743
Wyoming	4. AssctDgr	43	902	-816
Mississippi	4. AssctDgr	62	937	-813
Maine	4. AssctDgr	208	866	-450
Idaho	4. AssctDgr	225	1,045	-595
Montana	4. AssctDgr	277	1,004	-450
Hawaii	4. AssctDgr	341	1,442	-760
New Hampshire	4. AssctDgr	379	1,069	-311
Oklahoma	4. AssctDgr	381	1,333	-571
Iowa	4. AssctDgr	389	1,427	-649
Maryland	4. AssctDgr	479	1,802	-844
Massachusetts	4. AssctDgr	591	1,612	-430
North Dakota	4. AssctDgr	705	1,519	-109
Wisconsin	4. AssctDgr	717	2,076	-642
Kentucky	4. AssctDgr	728	1,823	-367
Nevada	4. AssctDgr	762	1,970	-446
Ohio	4. AssctDgr	782	2,037	-473
Virginia	4. AssctDgr	887	2,093	-319
Washington	4. AssctDgr	902	2,495	-691
Utah	4. AssctDgr	971	2,015	-73
Alabama	4. AssctDgr	972	2,229	-285
North Carolina	4. AssctDgr	1,013	2,626	-600
Arizona	4. AssctDgr	1,044	2,411	-323
Missouri	4. AssctDgr	1,118	2,460	-224
Delaware	4. AssctDgr	1,215	1,682	748
Tennessee	4. AssctDgr	1,323	2,801	-155
Oregon	4. AssctDgr	1,632	2,935	329
South Carolina	4. AssctDgr	2,176	3,337	1,015
Colorado	4. AssctDgr	2,509	4,164	854
Florida	4. AssctDgr	7,415	9,509	5,321
Texas	4. AssctDgr	7,475	9,743	5,207
New Jersey	5. BchlrDgr	-13,992	-10,691	-17,293
New York	5. BchlrDgr	-12,088	-8,361	-15,815
Ohio	5. BchlrDgr	-9,991	-6,961	-13,021
Pennsylvania	5. BchlrDgr	-9,433	-6,267	-12,599
Michigan	5. BchlrDgr	-8,544	-6,148	-10,940
Indiana	5. BchlrDgr	-6,434	-4,479	-8,389
Iowa	5. BchlrDgr	-5,519	-3,767	-7,271
Alaska	5. BchlrDgr	-5,355	-3,654	-7,056
Wisconsin	5. BchlrDgr	-5,240	-3,186	-7,294
Alabama	5. BchlrDgr	-3,609	-1,567	-5,651
Mississippi	5. BchlrDgr	-3,064	-1,621	-4,507
Illinois	5. BchlrDgr	-2,742	821	-6,305
Missouri	5. BchlrDgr	-2,558	-206	-4,910
Rhode Island	5. BchlrDgr	-2,484	-1,461	-3,507
Massachusetts	5. BchlrDgr	-2,405	230	-5,040
Utah	5. BchlrDgr	-2,266	-519	-4,013
Connecticut	5. BchlrDgr	-1,753	422	-3,928
Vermont	5. BchlrDgr	-1,690	-686	-2,694
New Mexico	5. BchlrDgr	-1,520	-146	-2,894
West Virginia	5. BchlrDgr	-1,464	-407	-2,521
Oklahoma	5. BchlrDgr	-1,222	540	-2,984
South Dakota	5. BchlrDgr	-1,208	-204	-2,212
Nebraska	5. BchlrDgr	-1,131	180	-2,442
Georgia	5. BchlrDgr	-686	2,649	-4,021
North Dakota	5. BchlrDgr	-573	695	-1,841
Montana	5. BchlrDgr	-436	781	-1,653
Kansas	5. BchlrDgr	-387	1,713	-2,487
Wyoming	5. BchlrDgr	-311	805	-1,427
Delaware	5. BchlrDgr	-256	731	-1,243
Maryland	5. BchlrDgr	-104	3,306	-3,514
District of Columbia	5. BchlrDgr	-44	1,281	-1,369
Kentucky	5. BchlrDgr	266	1,987	-1,455
Minnesota	5. BchlrDgr	361	3,019	-2,297
New Hampshire	5. BchlrDgr	388	1,767	-991
Idaho	5. BchlrDgr	581	2,127	-965
Hawaii	5. BchlrDgr	640	2,080	-800
Arkansas	5. BchlrDgr	929	2,407	-549
Maine	5. BchlrDgr	1,162	2,280	44
South Carolina	5. BchlrDgr	1,975	4,389	-439
Louisiana	5. BchlrDgr	2,217	4,084	350
Virginia	5. BchlrDgr	2,327	5,573	-919
Nevada	5. BchlrDgr	2,906	4,422	1,390
Tennessee	5. BchlrDgr	3,352	6,068	636
Oregon	5. BchlrDgr	4,491	6,671	2,311
Arizona	5. BchlrDgr	6,295	9,037	3,553
North Carolina	5. BchlrDgr	7,996	11,137	4,855
Washington	5. BchlrDgr	8,619	11,645	5,593
Florida	5. BchlrDgr	12,189	15,441	8,937
California	5. BchlrDgr	12,274	17,310	7,238
Colorado	5. BchlrDgr	13,560	16,546	10,574
Texas	5. BchlrDgr	25,981	29,291	22,671
New York	6. Grdts	-16,947	-13,557	-20,337
Illinois	6. Grdts	-9,089	-6,334	-11,844
Michigan	6. Grdts	-5,750	-3,869	-7,631
Pennsylvania	6. Grdts	-5,440	-3,402	-7,478
New Jersey	6. Grdts	-4,357	-2,426	-6,288
Ohio	6. Grdts	-4,193	-1,801	-6,585
Indiana	6. Grdts	-3,113	-1,688	-4,538
Georgia	6. Grdts	-2,772	-729	-4,815
Alaska	6. Grdts	-1,777	-792	-2,762
Oklahoma	6. Grdts	-1,676	-559	-2,793
Missouri	6. Grdts	-1,635	-101	-3,169
Massachusetts	6. Grdts	-1,624	493	-3,741
Rhode Island	6. Grdts	-1,517	-521	-2,513
Nebraska	6. Grdts	-1,173	-178	-2,168
Kentucky	6. Grdts	-1,155	-141	-2,169
District of Columbia	6. Grdts	-1,132	260	-2,524
West Virginia	6. Grdts	-1,064	-522	-1,606
Kansas	6. Grdts	-859	509	-2,227
Hawaii	6. Grdts	-628	386	-1,642
Connecticut	6. Grdts	-530	949	-2,009
Mississippi	6. Grdts	-494	295	-1,283
Iowa	6. Grdts	-449	687	-1,585
Utah	6. Grdts	-208	1,007	-1,423
Louisiana	6. Grdts	-193	1,010	-1,396
Wisconsin	6. Grdts	-156	1,227	-1,539
Arkansas	6. Grdts	-4	956	-964
South Dakota	6. Grdts	13	690	-664
Wyoming	6. Grdts	74	614	-466
Idaho	6. Grdts	173	1,033	-687
North Dakota	6. Grdts	259	1,054	-536
New Hampshire	6. Grdts	387	1,496	-722
Alabama	6. Grdts	499	1,768	-770
New Mexico	6. Grdts	519	1,618	-580
Delaware	6. Grdts	610	1,375	-155
Vermont	6. Grdts	722	1,595	-151
Maine	6. Grdts	976	2,060	-108
Tennessee	6. Grdts	1,055	2,737	-627
Montana	6. Grdts	1,117	1,975	259
Nevada	6. Grdts	1,611	2,608	614
Minnesota	6. Grdts	1,770	3,323	217
Arizona	6. Grdts	1,957	3,938	-24
South Carolina	6. Grdts	2,118	3,446	790
Maryland	6. Grdts	2,227	4,385	69
Virginia	6. Grdts	2,333	5,233	-567
Oregon	6. Grdts	3,033	4,553	1,513
North Carolina	6. Grdts	3,913	6,118	1,708
Colorado	6. Grdts	4,366	6,367	2,365
Florida	6. Grdts	6,642	9,331	3,953
Washington	6. Grdts	8,674	10,418	6,930
California	6. Grdts	9,390	12,819	5,961
Texas	6. Grdts	13,497	16,205	10,789

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To mitigate the idea of populated states, one possible alternatives is to look at the percent of the population of a state that migrated. Since the percent give us a too small a number, in this article, we introduced the idea of per mil or per thousand, which is well explained in the Wikipedia article 'Per mille'. Presented in our fifth slide is the net migration for each state for every 1,000 residents. For example, for every 1,000 residents, Washington state shows that there are about about 2.1 people moved in with graduate degrees (including professional degree) and is ranked first in the chart.

Rank Net Migration; Per mil of State Population

The ranking table by number of Net Migration per 1,000 residents is presented below. After adjusting against population, New York State no longer ranked last at the gradate degree level. Alaska is now ranked last with -4.1 graduate degree holders leaving the state per 1,000 Alaska residents. With this adjustment, Nebraska now ranked 6th from the last with -1.1 graduate degree holders leaving the state per 1,000 Nebraska residents.
========

State	Ed. Attainment	per 1000 Rsd	Upper	Lower
Alaska	1. LssHsDgr	-11.1	-8.8	-13.4
District of Columbia	1. LssHsDgr	-2.3	-1.0	-3.6
Hawaii	1. LssHsDgr	-0.9	-0.4	-1.4
New York	1. LssHsDgr	-0.8	-0.7	-1.0
Vermont	1. LssHsDgr	-0.8	0.3	-1.9
Maine	1. LssHsDgr	-0.7	-0.1	-1.3
Illinois	1. LssHsDgr	-0.6	-0.4	-0.9
Idaho	1. LssHsDgr	-0.6	0.5	-1.7
Alabama	1. LssHsDgr	-0.6	-0.1	-1.1
Montana	1. LssHsDgr	-0.5	0.2	-1.2
California	1. LssHsDgr	-0.4	-0.3	-0.5
Massachusetts	1. LssHsDgr	-0.4	-0.1	-0.7
Nebraska	1. LssHsDgr	-0.4	0.4	-1.2
New Hampshire	1. LssHsDgr	-0.4	0.4	-1.3
Connecticut	1. LssHsDgr	-0.3	0.2	-0.7
New Jersey	1. LssHsDgr	-0.2	0.0	-0.5
Iowa	1. LssHsDgr	-0.1	0.5	-0.6
Arizona	1. LssHsDgr	0.0	0.4	-0.5
North Carolina	1. LssHsDgr	0.0	0.3	-0.4
Florida	1. LssHsDgr	0.0	0.2	-0.3
Arkansas	1. LssHsDgr	0.0	0.6	-0.6
South Carolina	1. LssHsDgr	0.0	0.5	-0.5
Maryland	1. LssHsDgr	0.1	0.3	-0.2
New Mexico	1. LssHsDgr	0.1	0.9	-0.8
Utah	1. LssHsDgr	0.2	0.7	-0.4
South Dakota	1. LssHsDgr	0.2	1.4	-1.1
Virginia	1. LssHsDgr	0.2	0.5	-0.1
Michigan	1. LssHsDgr	0.2	0.4	0.0
Tennessee	1. LssHsDgr	0.2	0.6	-0.1
Indiana	1. LssHsDgr	0.3	0.6	-0.1
Washington	1. LssHsDgr	0.3	0.6	-0.1
Rhode Island	1. LssHsDgr	0.3	1.1	-0.6
Ohio	1. LssHsDgr	0.3	0.5	0.0
Pennsylvania	1. LssHsDgr	0.3	0.5	0.1
West Virginia	1. LssHsDgr	0.3	1.1	-0.5
Colorado	1. LssHsDgr	0.3	0.7	-0.1
Wyoming	1. LssHsDgr	0.4	2.4	-1.6
Nevada	1. LssHsDgr	0.4	1.2	-0.4
Missouri	1. LssHsDgr	0.4	0.7	0.1
Oregon	1. LssHsDgr	0.4	0.9	-0.1
Kansas	1. LssHsDgr	0.5	1.3	-0.2
Wisconsin	1. LssHsDgr	0.5	0.8	0.2
Kentucky	1. LssHsDgr	0.6	1.1	0.0
Texas	1. LssHsDgr	0.6	0.7	0.4
Louisiana	1. LssHsDgr	0.6	1.0	0.1
Georgia	1. LssHsDgr	0.6	0.9	0.2
Minnesota	1. LssHsDgr	0.7	1.0	0.3
Mississippi	1. LssHsDgr	1.1	1.8	0.4
North Dakota	1. LssHsDgr	1.1	2.4	-0.2
Oklahoma	1. LssHsDgr	1.2	1.9	0.6
Delaware	1. LssHsDgr	1.5	2.5	0.6
Alaska	2. HsDgrEqv	-24.5	-21.1	-27.8
District of Columbia	2. HsDgrEqv	-7.8	-5.7	-9.8
New York	2. HsDgrEqv	-1.7	-1.5	-1.9
Vermont	2. HsDgrEqv	-1.5	0.2	-3.1
Massachusetts	2. HsDgrEqv	-1.1	-0.7	-1.6
Rhode Island	2. HsDgrEqv	-1.1	0.3	-2.4
Kansas	2. HsDgrEqv	-1.0	0.1	-2.1
Maine	2. HsDgrEqv	-1.0	0.4	-2.3
Illinois	2. HsDgrEqv	-0.9	-0.5	-1.2
New Jersey	2. HsDgrEqv	-0.9	-0.5	-1.3
California	2. HsDgrEqv	-0.9	-0.7	-1.1
Connecticut	2. HsDgrEqv	-0.8	-0.2	-1.5
Minnesota	2. HsDgrEqv	-0.4	0.1	-0.9
Michigan	2. HsDgrEqv	-0.3	0.1	-0.7
Alabama	2. HsDgrEqv	-0.2	0.4	-0.9
Wisconsin	2. HsDgrEqv	-0.2	0.3	-0.7
Pennsylvania	2. HsDgrEqv	0.0	0.3	-0.4
Montana	2. HsDgrEqv	0.0	1.9	-1.9
Maryland	2. HsDgrEqv	0.0	0.6	-0.6
Mississippi	2. HsDgrEqv	0.1	1.1	-0.9
West Virginia	2. HsDgrEqv	0.1	1.7	-1.4
Virginia	2. HsDgrEqv	0.1	0.7	-0.4
Ohio	2. HsDgrEqv	0.2	0.5	-0.2
Missouri	2. HsDgrEqv	0.2	0.9	-0.4
Hawaii	2. HsDgrEqv	0.2	1.9	-1.4
Iowa	2. HsDgrEqv	0.3	1.2	-0.5
Oklahoma	2. HsDgrEqv	0.3	1.4	-0.8
Louisiana	2. HsDgrEqv	0.4	1.1	-0.4
Utah	2. HsDgrEqv	0.4	1.2	-0.5
North Carolina	2. HsDgrEqv	0.4	1.0	-0.2
Arkansas	2. HsDgrEqv	0.4	1.5	-0.7
New Mexico	2. HsDgrEqv	0.4	1.6	-0.8
Indiana	2. HsDgrEqv	0.5	1.0	0.0
Nebraska	2. HsDgrEqv	0.5	1.8	-0.8
Arizona	2. HsDgrEqv	0.7	1.4	-0.1
Texas	2. HsDgrEqv	0.7	0.9	0.4
Washington	2. HsDgrEqv	0.7	1.3	0.1
Kentucky	2. HsDgrEqv	0.8	1.4	0.2
South Dakota	2. HsDgrEqv	0.8	2.6	-1.0
Tennessee	2. HsDgrEqv	0.9	1.6	0.2
Colorado	2. HsDgrEqv	1.0	1.6	0.4
Georgia	2. HsDgrEqv	1.2	1.6	0.7
South Carolina	2. HsDgrEqv	1.2	2.0	0.4
New Hampshire	2. HsDgrEqv	1.3	2.7	-0.1
Oregon	2. HsDgrEqv	1.3	2.0	0.7
Florida	2. HsDgrEqv	1.9	2.3	1.5
Wyoming	2. HsDgrEqv	2.1	5.6	-1.4
Idaho	2. HsDgrEqv	2.2	3.8	0.5
Nevada	2. HsDgrEqv	2.3	3.4	1.1
Delaware	2. HsDgrEqv	2.4	4.2	0.6
North Dakota	2. HsDgrEqv	2.9	6.0	-0.1
Alaska	3. SomeCllg	-18.2	-13.7	-22.7
New York	3. SomeCllg	-1.7	-1.5	-2.0
New Jersey	3. SomeCllg	-1.6	-1.2	-2.0
California	3. SomeCllg	-1.3	-1.2	-1.5
Illinois	3. SomeCllg	-1.3	-0.9	-1.6
District of Columbia	3. SomeCllg	-1.1	1.2	-3.5
New Hampshire	3. SomeCllg	-1.0	0.4	-2.5
Michigan	3. SomeCllg	-0.8	-0.4	-1.2
New Mexico	3. SomeCllg	-0.7	0.7	-2.1
Rhode Island	3. SomeCllg	-0.5	0.7	-1.7
Minnesota	3. SomeCllg	-0.4	0.2	-1.0
Maryland	3. SomeCllg	-0.4	0.1	-0.9
Connecticut	3. SomeCllg	-0.3	0.4	-1.0
Massachusetts	3. SomeCllg	-0.3	0.1	-0.7
Indiana	3. SomeCllg	-0.3	0.4	-0.9
Kansas	3. SomeCllg	-0.2	1.0	-1.4
Ohio	3. SomeCllg	-0.2	0.2	-0.7
Mississippi	3. SomeCllg	-0.2	0.7	-1.2
Virginia	3. SomeCllg	-0.2	0.4	-0.8
West Virginia	3. SomeCllg	-0.2	0.8	-1.2
Nebraska	3. SomeCllg	-0.2	1.2	-1.5
Pennsylvania	3. SomeCllg	0.0	0.4	-0.3
Louisiana	3. SomeCllg	0.2	1.0	-0.6
Kentucky	3. SomeCllg	0.2	1.1	-0.6
Wisconsin	3. SomeCllg	0.3	0.9	-0.3
Vermont	3. SomeCllg	0.4	2.2	-1.4
Washington	3. SomeCllg	0.5	1.2	-0.2
North Carolina	3. SomeCllg	0.5	1.1	-0.1
Hawaii	3. SomeCllg	0.5	2.5	-1.5
Maine	3. SomeCllg	0.6	1.8	-0.7
Iowa	3. SomeCllg	0.6	1.3	-0.2
Missouri	3. SomeCllg	0.7	1.3	0.0
Oklahoma	3. SomeCllg	0.7	1.7	-0.3
Alabama	3. SomeCllg	0.7	1.4	0.0
Florida	3. SomeCllg	0.8	1.1	0.4
Georgia	3. SomeCllg	0.9	1.5	0.4
Texas	3. SomeCllg	1.0	1.2	0.7
Montana	3. SomeCllg	1.0	2.9	-0.9
Tennessee	3. SomeCllg	1.1	1.8	0.4
Arkansas	3. SomeCllg	1.1	2.0	0.2
Delaware	3. SomeCllg	1.3	2.9	-0.4
Idaho	3. SomeCllg	1.7	3.4	0.0
South Dakota	3. SomeCllg	1.7	3.9	-0.4
Utah	3. SomeCllg	1.8	3.0	0.6
Arizona	3. SomeCllg	1.8	2.6	1.0
South Carolina	3. SomeCllg	2.1	3.1	1.2
Colorado	3. SomeCllg	2.7	3.5	1.8
Wyoming	3. SomeCllg	2.7	5.9	-0.6
Nevada	3. SomeCllg	3.1	4.3	1.9
Oregon	3. SomeCllg	3.3	4.2	2.4
North Dakota	3. SomeCllg	4.6	7.4	1.8
Alaska	4. AssctDgr	-5.1	-2.8	-7.5
Vermont	4. AssctDgr	-2.6	-1.5	-3.8
District of Columbia	4. AssctDgr	-1.5	-0.3	-2.6
New York	4. AssctDgr	-0.9	-0.7	-1.0
South Dakota	4. AssctDgr	-0.7	0.9	-2.2
Kansas	4. AssctDgr	-0.6	-0.1	-1.2
Illinois	4. AssctDgr	-0.6	-0.4	-0.8
Connecticut	4. AssctDgr	-0.6	-0.2	-1.0
New Mexico	4. AssctDgr	-0.5	0.2	-1.1
Pennsylvania	4. AssctDgr	-0.5	-0.2	-0.7
Georgia	4. AssctDgr	-0.4	-0.1	-0.7
Michigan	4. AssctDgr	-0.3	-0.1	-0.5
Minnesota	4. AssctDgr	-0.3	0.2	-0.7
California	4. AssctDgr	-0.3	-0.2	-0.4
Rhode Island	4. AssctDgr	-0.3	0.6	-1.1
New Jersey	4. AssctDgr	-0.2	0.0	-0.5
Indiana	4. AssctDgr	-0.2	0.0	-0.5
Arkansas	4. AssctDgr	-0.2	0.3	-0.6
Nebraska	4. AssctDgr	-0.1	0.7	-0.9
Louisiana	4. AssctDgr	-0.1	0.3	-0.4
West Virginia	4. AssctDgr	0.0	0.6	-0.7
Mississippi	4. AssctDgr	0.0	0.6	-0.5
Ohio	4. AssctDgr	0.1	0.3	-0.1
Wyoming	4. AssctDgr	0.1	2.7	-2.5
Maryland	4. AssctDgr	0.1	0.5	-0.2
Massachusetts	4. AssctDgr	0.2	0.4	-0.1
Oklahoma	4. AssctDgr	0.2	0.6	-0.3
North Carolina	4. AssctDgr	0.2	0.5	-0.1
Virginia	4. AssctDgr	0.2	0.4	-0.1
Wisconsin	4. AssctDgr	0.2	0.6	-0.2
Washington	4. AssctDgr	0.2	0.6	-0.2
Iowa	4. AssctDgr	0.2	0.8	-0.4
Idaho	4. AssctDgr	0.3	1.2	-0.7
Maine	4. AssctDgr	0.3	1.1	-0.6
Arizona	4. AssctDgr	0.3	0.7	-0.1
Kentucky	4. AssctDgr	0.3	0.7	-0.1
Missouri	4. AssctDgr	0.3	0.7	-0.1
Alabama	4. AssctDgr	0.4	0.8	-0.1
Tennessee	4. AssctDgr	0.4	0.8	0.0
Hawaii	4. AssctDgr	0.4	1.8	-0.9
Nevada	4. AssctDgr	0.5	1.2	-0.3
Montana	4. AssctDgr	0.5	1.8	-0.8
New Hampshire	4. AssctDgr	0.5	1.4	-0.4
Texas	4. AssctDgr	0.5	0.7	0.3
Utah	4. AssctDgr	0.6	1.3	0.0
Florida	4. AssctDgr	0.7	0.9	0.5
Oregon	4. AssctDgr	0.7	1.3	0.1
Colorado	4. AssctDgr	0.8	1.3	0.3
South Carolina	4. AssctDgr	0.8	1.2	0.4
North Dakota	4. AssctDgr	1.7	3.8	-0.3
Delaware	4. AssctDgr	2.3	3.2	1.4
Alaska	5. BchlrDgr	-12.4	-8.4	-16.3
Vermont	5. BchlrDgr	-4.7	-1.9	-7.5
Rhode Island	5. BchlrDgr	-4.1	-2.4	-5.8
Iowa	5. BchlrDgr	-3.2	-2.2	-4.3
New Jersey	5. BchlrDgr	-2.7	-2.1	-3.4
South Dakota	5. BchlrDgr	-2.6	-0.4	-4.8
Mississippi	5. BchlrDgr	-1.9	-1.0	-2.7
Indiana	5. BchlrDgr	-1.8	-1.2	-2.3
Wisconsin	5. BchlrDgr	-1.6	-1.0	-2.2
Ohio	5. BchlrDgr	-1.5	-1.1	-2.0
Michigan	5. BchlrDgr	-1.5	-1.1	-2.0
Utah	5. BchlrDgr	-1.5	-0.3	-2.6
North Dakota	5. BchlrDgr	-1.4	1.7	-4.6
West Virginia	5. BchlrDgr	-1.4	-0.4	-2.4
Alabama	5. BchlrDgr	-1.3	-0.6	-2.1
New Mexico	5. BchlrDgr	-1.3	-0.1	-2.5
Pennsylvania	5. BchlrDgr	-1.3	-0.9	-1.7
Nebraska	5. BchlrDgr	-1.1	0.2	-2.4
New York	5. BchlrDgr	-1.1	-0.7	-1.4
Wyoming	5. BchlrDgr	-0.9	2.4	-4.3
Connecticut	5. BchlrDgr	-0.9	0.2	-1.9
Montana	5. BchlrDgr	-0.8	1.4	-2.9
Missouri	5. BchlrDgr	-0.8	-0.1	-1.4
Massachusetts	5. BchlrDgr	-0.6	0.1	-1.3
Oklahoma	5. BchlrDgr	-0.6	0.3	-1.4
Delaware	5. BchlrDgr	-0.5	1.4	-2.4
Illinois	5. BchlrDgr	-0.4	0.1	-0.9
Kansas	5. BchlrDgr	-0.2	1.1	-1.6
Georgia	5. BchlrDgr	-0.1	0.5	-0.7
District of Columbia	5. BchlrDgr	-0.1	3.1	-3.3
Maryland	5. BchlrDgr	0.0	1.0	-1.0
Kentucky	5. BchlrDgr	0.1	0.8	-0.6
Minnesota	5. BchlrDgr	0.1	1.0	-0.7
Virginia	5. BchlrDgr	0.5	1.2	-0.2
New Hampshire	5. BchlrDgr	0.5	2.3	-1.3
California	5. BchlrDgr	0.6	0.8	0.3
Arkansas	5. BchlrDgr	0.6	1.5	-0.3
Idaho	5. BchlrDgr	0.7	2.4	-1.1
South Carolina	5. BchlrDgr	0.7	1.6	-0.2
Hawaii	5. BchlrDgr	0.8	2.6	-1.0
Louisiana	5. BchlrDgr	0.8	1.6	0.1
Tennessee	5. BchlrDgr	0.9	1.6	0.2
Florida	5. BchlrDgr	1.1	1.4	0.8
North Carolina	5. BchlrDgr	1.4	2.0	0.9
Maine	5. BchlrDgr	1.5	3.0	0.1
Arizona	5. BchlrDgr	1.7	2.5	1.0
Texas	5. BchlrDgr	1.7	2.0	1.5
Nevada	5. BchlrDgr	1.8	2.7	0.9
Oregon	5. BchlrDgr	2.0	2.9	1.0
Washington	5. BchlrDgr	2.1	2.9	1.4
Colorado	5. BchlrDgr	4.4	5.3	3.4
Alaska	6. Grdts	-4.1	-1.8	-6.4
District of Columbia	6. Grdts	-2.7	0.6	-6.1
Rhode Island	6. Grdts	-2.5	-0.9	-4.2
New York	6. Grdts	-1.5	-1.2	-1.8
Illinois	6. Grdts	-1.2	-0.9	-1.6
Nebraska	6. Grdts	-1.1	-0.2	-2.1
Michigan	6. Grdts	-1.0	-0.7	-1.4
West Virginia	6. Grdts	-1.0	-0.5	-1.5
Indiana	6. Grdts	-0.8	-0.5	-1.2
New Jersey	6. Grdts	-0.8	-0.5	-1.2
Oklahoma	6. Grdts	-0.8	-0.3	-1.3
Hawaii	6. Grdts	-0.8	0.5	-2.0
Pennsylvania	6. Grdts	-0.8	-0.5	-1.0
Ohio	6. Grdts	-0.6	-0.3	-1.0
Kansas	6. Grdts	-0.5	0.3	-1.4
Georgia	6. Grdts	-0.5	-0.1	-0.8
Missouri	6. Grdts	-0.5	0.0	-0.9
Kentucky	6. Grdts	-0.5	-0.1	-0.9
Massachusetts	6. Grdts	-0.4	0.1	-1.0
Mississippi	6. Grdts	-0.3	0.2	-0.8
Iowa	6. Grdts	-0.3	0.4	-0.9
Connecticut	6. Grdts	-0.3	0.5	-1.0
Utah	6. Grdts	-0.1	0.7	-0.9
Louisiana	6. Grdts	-0.1	0.4	-0.5
Wisconsin	6. Grdts	0.0	0.4	-0.5
Arkansas	6. Grdts	0.0	0.6	-0.6
South Dakota	6. Grdts	0.0	1.5	-1.4
Alabama	6. Grdts	0.2	0.6	-0.3
Idaho	6. Grdts	0.2	1.2	-0.8
Wyoming	6. Grdts	0.2	1.9	-1.4
Tennessee	6. Grdts	0.3	0.7	-0.2
California	6. Grdts	0.4	0.6	0.3
New Mexico	6. Grdts	0.5	1.4	-0.5
Virginia	6. Grdts	0.5	1.1	-0.1
New Hampshire	6. Grdts	0.5	1.9	-0.9
Arizona	6. Grdts	0.5	1.1	0.0
Minnesota	6. Grdts	0.6	1.1	0.1
Florida	6. Grdts	0.6	0.9	0.4
North Dakota	6. Grdts	0.6	2.6	-1.3
Maryland	6. Grdts	0.6	1.3	0.0
North Carolina	6. Grdts	0.7	1.1	0.3
South Carolina	6. Grdts	0.8	1.3	0.3
Texas	6. Grdts	0.9	1.1	0.7
Nevada	6. Grdts	1.0	1.6	0.4
Delaware	6. Grdts	1.2	2.6	-0.3
Maine	6. Grdts	1.3	2.7	-0.1
Oregon	6. Grdts	1.3	2.0	0.7
Colorado	6. Grdts	1.4	2.1	0.8
Montana	6. Grdts	2.0	3.5	0.5
Vermont	6. Grdts	2.0	4.4	-0.4
Washington	6. Grdts	2.1	2.6	1.7

========
 
The idea of per mil or per thousand of residents do provide a sensible perception of the net migration situation for each state. However, since each state has different education attainment profile, it is also reasonable to take this information into consideration. One possible way of taking that into consideration is to calculate the per mil number of the education population instead of the total working age population. This idea is realized in the sixth slide of this presentation. For example, for Nebraska, if we look at the per mil number for graduate degree against the total working age population, Nebraska shows a net out migration of 1.1 people and ranked the 6th worst including the DC. However, if we look at the same net migration but against the graduate degree holder population, it shows a net out migration of 12.2 per thousand of graduate degree holders in Nebraska. Therefore, graduate degree holders in Nebraska is more likely to move out of the state of Nebraska.

Rank Net Migration; Per mil of Education Level Population

The ranking table by number of Net Migration per 1,000 degree holders is presented below:
========

State	Ed. Attainment	per 1000 Ed.Rsd	Upper	Lower
Alaska	1. LssHsDgr	-166.4	-132.1	-200.7
District of Columbia	1. LssHsDgr	-25.9	-11.1	-40.8
Hawaii	1. LssHsDgr	-13.2	-5.5	-20.9
Vermont	1. LssHsDgr	-12.5	4.4	-29.4
Maine	1. LssHsDgr	-11.6	-1.4	-21.9
Montana	1. LssHsDgr	-8.3	3.5	-20.1
New Hampshire	1. LssHsDgr	-6.7	7.4	-20.9
New York	1. LssHsDgr	-6.7	-5.4	-8.0
Idaho	1. LssHsDgr	-6.2	5.7	-18.0
Illinois	1. LssHsDgr	-6.1	-3.8	-8.5
Massachusetts	1. LssHsDgr	-5.4	-1.8	-9.0
Nebraska	1. LssHsDgr	-5.1	4.3	-14.4
Alabama	1. LssHsDgr	-4.3	-0.6	-8.0
Connecticut	1. LssHsDgr	-3.2	1.9	-8.3
New Jersey	1. LssHsDgr	-2.7	0.1	-5.5
California	1. LssHsDgr	-2.6	-2.0	-3.2
Iowa	1. LssHsDgr	-1.1	7.4	-9.5
North Carolina	1. LssHsDgr	-0.3	2.2	-2.9
Arizona	1. LssHsDgr	-0.3	2.8	-3.4
Florida	1. LssHsDgr	-0.2	1.8	-2.2
Arkansas	1. LssHsDgr	-0.1	5.0	-5.1
South Carolina	1. LssHsDgr	0.1	3.9	-3.8
New Mexico	1. LssHsDgr	0.5	6.2	-5.2
Maryland	1. LssHsDgr	0.6	3.9	-2.7
Virginia	1. LssHsDgr	1.9	4.8	-1.0
Utah	1. LssHsDgr	1.9	8.1	-4.2
Tennessee	1. LssHsDgr	2.1	5.5	-1.3
Michigan	1. LssHsDgr	2.2	4.6	-0.2
South Dakota	1. LssHsDgr	2.3	20.1	-15.4
Indiana	1. LssHsDgr	2.4	5.6	-0.9
Rhode Island	1. LssHsDgr	2.4	9.9	-5.1
West Virginia	1. LssHsDgr	2.6	9.1	-4.0
Nevada	1. LssHsDgr	2.6	7.9	-2.8
Washington	1. LssHsDgr	2.9	6.7	-0.9
Ohio	1. LssHsDgr	3.1	5.9	0.3
Pennsylvania	1. LssHsDgr	3.4	6.0	0.7
Texas	1. LssHsDgr	3.4	4.4	2.3
Colorado	1. LssHsDgr	3.6	8.4	-1.1
Louisiana	1. LssHsDgr	3.8	6.9	0.8
Missouri	1. LssHsDgr	4.0	7.3	0.7
Oregon	1. LssHsDgr	4.0	9.5	-1.4
Kentucky	1. LssHsDgr	4.3	8.6	0.1
Georgia	1. LssHsDgr	4.4	7.0	1.9
Wyoming	1. LssHsDgr	5.7	36.7	-25.3
Kansas	1. LssHsDgr	5.9	13.9	-2.2
Mississippi	1. LssHsDgr	7.3	12.2	2.3
Wisconsin	1. LssHsDgr	7.3	11.4	3.2
Oklahoma	1. LssHsDgr	10.3	15.6	5.1
Minnesota	1. LssHsDgr	10.6	15.9	5.4
Delaware	1. LssHsDgr	15.1	24.5	5.7
North Dakota	1. LssHsDgr	22.6	49.9	-4.8
Alaska	2. HsDgrEqv	-86.9	-74.9	-98.8
District of Columbia	2. HsDgrEqv	-46.5	-34.5	-58.6
New York	2. HsDgrEqv	-6.9	-5.9	-7.9
Vermont	2. HsDgrEqv	-4.9	0.6	-10.4
Massachusetts	2. HsDgrEqv	-4.8	-2.9	-6.7
California	2. HsDgrEqv	-4.3	-3.3	-5.2
Rhode Island	2. HsDgrEqv	-4.1	1.0	-9.3
Kansas	2. HsDgrEqv	-4.1	0.5	-8.6
Illinois	2. HsDgrEqv	-3.6	-2.2	-5.0
New Jersey	2. HsDgrEqv	-3.4	-2.0	-4.7
Connecticut	2. HsDgrEqv	-3.2	-0.6	-5.8
Maine	2. HsDgrEqv	-3.0	1.2	-7.2
Minnesota	2. HsDgrEqv	-1.7	0.6	-4.1
Michigan	2. HsDgrEqv	-1.1	0.3	-2.5
Alabama	2. HsDgrEqv	-0.8	1.5	-3.1
Wisconsin	2. HsDgrEqv	-0.7	1.0	-2.3
Montana	2. HsDgrEqv	-0.1	6.4	-6.6
Pennsylvania	2. HsDgrEqv	-0.1	0.9	-1.1
Maryland	2. HsDgrEqv	0.0	2.5	-2.4
West Virginia	2. HsDgrEqv	0.3	4.2	-3.5
Mississippi	2. HsDgrEqv	0.4	3.7	-2.9
Ohio	2. HsDgrEqv	0.5	1.6	-0.7
Virginia	2. HsDgrEqv	0.6	2.9	-1.7
Missouri	2. HsDgrEqv	0.8	2.9	-1.4
Hawaii	2. HsDgrEqv	0.9	6.9	-5.2
Oklahoma	2. HsDgrEqv	1.0	4.6	-2.5
Louisiana	2. HsDgrEqv	1.1	3.4	-1.3
Iowa	2. HsDgrEqv	1.1	4.1	-1.9
Arkansas	2. HsDgrEqv	1.2	4.4	-2.0
North Carolina	2. HsDgrEqv	1.4	3.8	-0.9
Indiana	2. HsDgrEqv	1.5	3.1	-0.2
Utah	2. HsDgrEqv	1.5	5.3	-2.2
New Mexico	2. HsDgrEqv	1.6	6.0	-2.9
Nebraska	2. HsDgrEqv	2.1	7.5	-3.3
Kentucky	2. HsDgrEqv	2.4	4.4	0.5
Texas	2. HsDgrEqv	2.7	3.7	1.7
South Dakota	2. HsDgrEqv	2.7	8.9	-3.4
Arizona	2. HsDgrEqv	2.8	5.8	-0.3
Tennessee	2. HsDgrEqv	2.9	5.0	0.7
Washington	2. HsDgrEqv	3.1	5.9	0.2
South Carolina	2. HsDgrEqv	4.2	6.9	1.4
Georgia	2. HsDgrEqv	4.2	5.8	2.6
Colorado	2. HsDgrEqv	4.6	7.4	1.7
New Hampshire	2. HsDgrEqv	4.6	9.7	-0.4
Oregon	2. HsDgrEqv	5.7	8.5	2.9
Florida	2. HsDgrEqv	6.6	7.9	5.3
Wyoming	2. HsDgrEqv	7.4	19.8	-5.0
Nevada	2. HsDgrEqv	8.0	12.1	3.9
Idaho	2. HsDgrEqv	8.0	14.0	2.0
Delaware	2. HsDgrEqv	8.0	14.1	1.9
North Dakota	2. HsDgrEqv	12.2	24.9	-0.5
Alaska	3. SomeCllg	-60.6	-45.5	-75.7
New York	3. SomeCllg	-9.7	-8.4	-11.0
New Jersey	3. SomeCllg	-8.7	-6.7	-10.7
District of Columbia	3. SomeCllg	-8.0	8.7	-24.7
California	3. SomeCllg	-5.7	-4.9	-6.6
Illinois	3. SomeCllg	-5.7	-4.1	-7.3
New Hampshire	3. SomeCllg	-5.2	2.0	-12.3
Michigan	3. SomeCllg	-3.1	-1.5	-4.7
New Mexico	3. SomeCllg	-2.7	2.7	-8.1
Rhode Island	3. SomeCllg	-2.4	3.5	-8.3
Maryland	3. SomeCllg	-1.9	0.6	-4.3
Connecticut	3. SomeCllg	-1.7	1.9	-5.4
Minnesota	3. SomeCllg	-1.7	1.1	-4.4
Massachusetts	3. SomeCllg	-1.6	0.8	-4.0
Indiana	3. SomeCllg	-1.1	1.8	-4.0
Ohio	3. SomeCllg	-1.1	0.8	-2.9
Virginia	3. SomeCllg	-1.0	1.8	-3.8
Mississippi	3. SomeCllg	-1.0	2.9	-4.8
Kansas	3. SomeCllg	-1.0	3.7	-5.6
West Virginia	3. SomeCllg	-0.8	3.9	-5.6
Nebraska	3. SomeCllg	-0.6	4.8	-6.1
Pennsylvania	3. SomeCllg	0.2	2.2	-1.7
Louisiana	3. SomeCllg	0.7	4.2	-2.8
Kentucky	3. SomeCllg	1.1	4.7	-2.5
Wisconsin	3. SomeCllg	1.4	4.1	-1.3
Washington	3. SomeCllg	2.0	4.7	-0.8
Vermont	3. SomeCllg	2.2	11.5	-7.1
Hawaii	3. SomeCllg	2.2	10.4	-6.0
North Carolina	3. SomeCllg	2.2	4.7	-0.3
Iowa	3. SomeCllg	2.4	5.8	-1.0
Maine	3. SomeCllg	2.5	8.3	-3.2
Missouri	3. SomeCllg	2.8	5.5	0.0
Oklahoma	3. SomeCllg	2.8	6.7	-1.0
Alabama	3. SomeCllg	3.0	6.0	0.1
Florida	3. SomeCllg	3.5	5.1	1.8
Montana	3. SomeCllg	3.7	10.7	-3.3
Texas	3. SomeCllg	4.0	5.1	2.9
Georgia	3. SomeCllg	4.2	6.6	1.7
Arkansas	3. SomeCllg	4.6	8.3	0.9
Tennessee	3. SomeCllg	4.8	7.9	1.7
Delaware	3. SomeCllg	5.9	13.7	-1.8
Idaho	3. SomeCllg	6.0	12.0	0.0
Utah	3. SomeCllg	6.2	10.3	2.1
Arizona	3. SomeCllg	6.8	9.8	3.9
South Dakota	3. SomeCllg	7.4	16.4	-1.5
South Carolina	3. SomeCllg	9.3	13.4	5.3
Wyoming	3. SomeCllg	9.5	21.2	-2.3
Colorado	3. SomeCllg	11.3	14.7	7.8
Nevada	3. SomeCllg	11.5	16.0	7.0
Oregon	3. SomeCllg	12.0	15.4	8.7
North Dakota	3. SomeCllg	18.0	28.9	7.1
Alaska	4. AssctDgr	-62.9	-33.9	-91.9
District of Columbia	4. AssctDgr	-47.6	-10.3	-84.9
Vermont	4. AssctDgr	-30.9	-17.1	-44.7
New York	4. AssctDgr	-9.2	-7.6	-10.8
Connecticut	4. AssctDgr	-7.2	-2.0	-12.4
Kansas	4. AssctDgr	-7.1	-1.1	-13.0
Illinois	4. AssctDgr	-7.0	-4.5	-9.4
Georgia	4. AssctDgr	-5.5	-1.6	-9.4
South Dakota	4. AssctDgr	-5.3	7.1	-17.7
New Mexico	4. AssctDgr	-5.3	1.9	-12.5
Pennsylvania	4. AssctDgr	-5.0	-2.7	-7.3
New Jersey	4. AssctDgr	-3.3	0.0	-6.7
Michigan	4. AssctDgr	-3.3	-1.2	-5.4
California	4. AssctDgr	-3.2	-1.9	-4.5
Rhode Island	4. AssctDgr	-2.8	6.5	-12.1
Indiana	4. AssctDgr	-2.5	0.4	-5.4
Arkansas	4. AssctDgr	-2.3	3.6	-8.2
Minnesota	4. AssctDgr	-2.2	1.6	-6.0
Louisiana	4. AssctDgr	-1.3	4.6	-7.1
Nebraska	4. AssctDgr	-1.0	6.3	-8.4
West Virginia	4. AssctDgr	-0.5	8.5	-9.4
Mississippi	4. AssctDgr	0.4	5.8	-5.0
Wyoming	4. AssctDgr	1.1	24.0	-21.7
Ohio	4. AssctDgr	1.3	3.4	-0.8
Iowa	4. AssctDgr	1.7	6.4	-2.9
North Carolina	4. AssctDgr	1.9	4.9	-1.1
Massachusetts	4. AssctDgr	1.9	5.2	-1.4
Wisconsin	4. AssctDgr	1.9	5.6	-1.7
Maryland	4. AssctDgr	2.0	7.4	-3.5
Washington	4. AssctDgr	2.1	5.8	-1.6
Oklahoma	4. AssctDgr	2.2	7.7	-3.3
Virginia	4. AssctDgr	2.4	5.6	-0.9
Maine	4. AssctDgr	2.6	10.7	-5.5
Idaho	4. AssctDgr	2.6	12.1	-6.9
Arizona	4. AssctDgr	3.2	7.4	-1.0
Kentucky	4. AssctDgr	3.4	8.6	-1.7
Hawaii	4. AssctDgr	3.7	15.8	-8.3
Missouri	4. AssctDgr	3.9	8.6	-0.8
Alabama	4. AssctDgr	4.1	9.5	-1.2
New Hampshire	4. AssctDgr	4.8	13.6	-3.9
Tennessee	4. AssctDgr	4.9	10.5	-0.6
Montana	4. AssctDgr	5.2	18.9	-8.5
Nevada	4. AssctDgr	5.8	15.1	-3.4
Utah	4. AssctDgr	6.0	12.4	-0.4
Florida	4. AssctDgr	6.3	8.1	4.5
Texas	4. AssctDgr	7.1	9.2	4.9
Oregon	4. AssctDgr	8.0	14.3	1.6
South Carolina	4. AssctDgr	8.5	13.0	4.0
Colorado	4. AssctDgr	9.2	15.2	3.1
North Dakota	4. AssctDgr	11.3	24.4	-1.8
Delaware	4. AssctDgr	29.3	40.6	18.0
Alaska	5. BchlrDgr	-70.8	-48.3	-93.3
Vermont	5. BchlrDgr	-19.7	-8.0	-31.4
Rhode Island	5. BchlrDgr	-19.4	-11.4	-27.3
Iowa	5. BchlrDgr	-16.0	-10.9	-21.1
Mississippi	5. BchlrDgr	-13.6	-7.2	-19.9
South Dakota	5. BchlrDgr	-12.7	-2.1	-23.2
New Jersey	5. BchlrDgr	-10.8	-8.3	-13.4
West Virginia	5. BchlrDgr	-10.7	-3.0	-18.3
Indiana	5. BchlrDgr	-10.1	-7.0	-13.2
New Mexico	5. BchlrDgr	-9.0	-0.9	-17.2
Ohio	5. BchlrDgr	-8.5	-5.9	-11.0
Michigan	5. BchlrDgr	-8.5	-6.1	-10.8
Alabama	5. BchlrDgr	-8.4	-3.6	-13.1
Wisconsin	5. BchlrDgr	-7.9	-4.8	-11.0
Utah	5. BchlrDgr	-7.2	-1.6	-12.7
Pennsylvania	5. BchlrDgr	-6.6	-4.4	-8.8
North Dakota	5. BchlrDgr	-6.2	7.6	-20.1
Wyoming	5. BchlrDgr	-5.4	13.9	-24.6
Nebraska	5. BchlrDgr	-4.9	0.8	-10.6
New York	5. BchlrDgr	-4.8	-3.3	-6.3
Missouri	5. BchlrDgr	-4.0	-0.3	-7.8
Montana	5. BchlrDgr	-3.8	6.8	-14.4
Connecticut	5. BchlrDgr	-3.7	0.9	-8.2
Oklahoma	5. BchlrDgr	-3.3	1.5	-8.1
Delaware	5. BchlrDgr	-2.5	7.2	-12.3
Massachusetts	5. BchlrDgr	-2.4	0.2	-5.0
Illinois	5. BchlrDgr	-1.7	0.5	-3.9
Kansas	5. BchlrDgr	-1.1	5.0	-7.3
Georgia	5. BchlrDgr	-0.6	2.4	-3.7
District of Columbia	5. BchlrDgr	-0.4	11.1	-11.9
Maryland	5. BchlrDgr	-0.1	4.3	-4.5
Minnesota	5. BchlrDgr	0.5	4.0	-3.0
Kentucky	5. BchlrDgr	0.7	5.5	-4.1
New Hampshire	5. BchlrDgr	2.1	9.7	-5.4
Virginia	5. BchlrDgr	2.1	5.1	-0.8
California	5. BchlrDgr	2.7	3.8	1.6
Hawaii	5. BchlrDgr	3.7	12.0	-4.6
Idaho	5. BchlrDgr	3.7	13.7	-6.2
Arkansas	5. BchlrDgr	3.9	10.0	-2.3
South Carolina	5. BchlrDgr	4.2	9.4	-0.9
Tennessee	5. BchlrDgr	5.2	9.5	1.0
Louisiana	5. BchlrDgr	5.3	9.8	0.8
Florida	5. BchlrDgr	6.1	7.7	4.4
North Carolina	5. BchlrDgr	7.3	10.1	4.4
Maine	5. BchlrDgr	7.5	14.8	0.3
Texas	5. BchlrDgr	9.2	10.4	8.1
Washington	5. BchlrDgr	9.8	13.2	6.4
Arizona	5. BchlrDgr	9.8	14.1	5.5
Oregon	5. BchlrDgr	10.0	14.8	5.1
Nevada	5. BchlrDgr	11.9	18.0	5.7
Colorado	5. BchlrDgr	17.4	21.2	13.6
Alaska	6. Grdts	-43.0	-19.2	-66.8
Rhode Island	6. Grdts	-20.9	-7.2	-34.7
West Virginia	6. Grdts	-13.7	-6.7	-20.7
Nebraska	6. Grdts	-12.2	-1.9	-22.5
Oklahoma	6. Grdts	-10.8	-3.6	-18.0
Indiana	6. Grdts	-10.6	-5.8	-15.5
Michigan	6. Grdts	-10.5	-7.0	-13.9
New York	6. Grdts	-10.4	-8.3	-12.4
Illinois	6. Grdts	-10.2	-7.1	-13.2
District of Columbia	6. Grdts	-9.1	2.1	-20.3
Hawaii	6. Grdts	-8.3	5.1	-21.7
Ohio	6. Grdts	-6.9	-2.9	-10.8
Pennsylvania	6. Grdts	-6.8	-4.2	-9.3
New Jersey	6. Grdts	-6.2	-3.4	-8.9
Kentucky	6. Grdts	-5.2	-0.6	-9.8
Kansas	6. Grdts	-5.1	3.0	-13.3
Missouri	6. Grdts	-4.9	-0.3	-9.4
Georgia	6. Grdts	-4.8	-1.3	-8.3
Mississippi	6. Grdts	-4.3	2.6	-11.3
Iowa	6. Grdts	-3.1	4.8	-11.0
Massachusetts	6. Grdts	-2.4	0.7	-5.6
Connecticut	6. Grdts	-1.6	2.9	-6.1
Utah	6. Grdts	-1.5	7.2	-10.2
Louisiana	6. Grdts	-1.1	5.5	-7.6
Wisconsin	6. Grdts	-0.5	4.2	-5.2
Arkansas	6. Grdts	0.0	8.4	-8.4
South Dakota	6. Grdts	0.4	21.4	-20.6
Alabama	6. Grdts	2.3	8.0	-3.5
Idaho	6. Grdts	2.6	15.8	-10.5
Wyoming	6. Grdts	2.7	22.8	-17.3
Virginia	6. Grdts	3.3	7.3	-0.8
Tennessee	6. Grdts	3.3	8.5	-1.9
Maryland	6. Grdts	3.9	7.7	0.1
New Hampshire	6. Grdts	4.0	15.4	-7.4
California	6. Grdts	4.0	5.5	2.5
New Mexico	6. Grdts	4.5	14.2	-5.1
Minnesota	6. Grdts	5.3	9.9	0.6
Arizona	6. Grdts	6.0	12.1	-0.1
Florida	6. Grdts	6.9	9.6	4.1
North Carolina	6. Grdts	7.5	11.7	3.3
North Dakota	6. Grdts	9.0	36.4	-18.5
South Carolina	6. Grdts	9.4	15.3	3.5
Delaware	6. Grdts	10.3	23.2	-2.6
Texas	6. Grdts	10.5	12.6	8.4
Colorado	6. Grdts	11.0	16.1	6.0
Oregon	6. Grdts	12.7	19.1	6.4
Maine	6. Grdts	13.9	29.2	-1.5
Nevada	6. Grdts	14.5	23.5	5.5
Vermont	6. Grdts	15.8	34.9	-3.3
Washington	6. Grdts	19.5	23.4	15.5
Montana	6. Grdts	23.1	40.9	5.4

========

The last slide of the presentation allows user to compare selected states.

Net Migration; Per mil of Education Level Population

A mind of STEM graduates

 

With a higher education panel, error was caught by STEM graduates, but the reputation  worth less to nothing.

Many things went by in life without consciously been recorded or memorized. With my mind in education and STEM, a recent event is worth noting.

An office draft that is to be published were routed internally and to external experts for review. A section of the document presented the following information:

State and Local Appropriation Per FTE student

Data Source: State Higher Education Executive Officers Association (SHEEO), State Higher Education Finance (SHEF) Report: FY2014

Cost of living adjustment (COLA)
Enrollment mix index(EMI)

(adjusted)  State and local appropriations per FTE student = ([Educational appropriations]/[FTE enrollment...])/[COLA/EMI]
...

The section, basically, describes how much money the state or local government spend on (per FTE, Full-Time Equivalent) student with adjusting factors of COLA and EMI.

When given the above information, for most or all reviewers, there isn't much to suspect the accuracy of the information - for one, the source is authoritative, for two, the formula involves more items/numbers than they want to see. Then, the third, there could be doubts if they could understand the methodology behind the calculation.These kinds of scene happened often. A report/publication often is a mash of information from other sources with limited originality. Lots of times, entities lack the talent and confidence in devising and defending their own methodologies.

STEM graduates, due to their training, confidences in handling complicated situation are high. It became more likely for them to take extra steps to try to seriously understand the limits and implications of a methodology. In other words, the ability of STEM graduates are peers to those that devised the methodology.

With their training, the first thing went through STEM graduates' mind is what [COLA/EMI] trying to show? From the syntax convention used, it seems that square brackets were used to denote items. However, due to their desire of unambiguity, they will either questioning the use of square brackets in '[COLA/EMI]' or questioning what COLA/EMI trying to show - as '/' can mean divide in mathematics but can mean something totally different in casual writings.


In order to answer the requisition mind, quick Googling revealed the info on EMI:
1. Integrated Postsecondary Education Data System (IPEDS) data were usedto develop a national average cost per fall FTE for each of the Carnegie
Classifications of institutions. This calculation used financial information from
FY 2011 and fall 2010 FTE data.
2. The proportion of each state’s FTE in each of the Carnegie Classifications was
calculated for fall 2010, and then multiplied by the national average cost per
FTE in FY 2011 for each respective classification. For each state, the products
for each Classification were summed, which yields the state’s enrollment mix
unit cost for the year.

With the mathematical training and the understanding of the purpose of the formula, it is clear that the EMI have to be divided by the '[Educational appropriation]'. At this point, the question is if the authoritative source made the mistakes? A STEM graduate knows what is right and what is wrong and would not afraid to challenge the authoritarians - after all, there is no authoritarians in the science - it is a matter of truth.

In this particular case, the source did it right. The question is then, what we did wrong and how should it be presented correctly. The turn of this event is that, as expected, among these specialists, all but one notice the error, none but two cares the voice of a STEM graduate.

If this is happened in the higher education, how about the general public? Do you really think our STEM push can be successful while, in most people's mind, STEM graduates do not worth the attention.

Methodology: Higher Education-Workforce Pipeline IPEDS CIP SOC crosswalk

Original Article

To Be Completed!

** If you are using Microsoft Internet Explorer or Google Chrom browser, you would not be able to read the formulas in this article. These formulas were written in MathML, a W3C standard, and can be viewed in FireFox.

This methodology is much improved over my previous methodology.

In essence, the number of graduates can potentially be hired for a program with CIP code C is:
$$\text{Potentially\_Hired}\left(C\right)=(G_c+\epsilon )\sum _{S_c}{O}_{s_c}\left[\frac{1}{\sum _{C_{S_c}}(G_{C_{S_c}}+\epsilon )}\right]$$
Where $G_x$ is the number of graduates for CIP x, $O_z$ is the number of job opening for a given SOC z, $S_x$ is the SOC S related to a CIP x, $C_z$ is the CIP C related to an SOC z, and $\epsilon$ is a very small number, which play important role when $G_x$ is zero.

Following are drafts and is to be ignored for Now!
Number of graduates for CIP c:

Number of job open for SOC s:
$O_s$
SOC related to a CIP c:
$S_c$
CIP related to an SOC s:
$C_s$
$G_{S_{C_s}}$
$\sum _{-1}^{\mathrm{+1}}$
$\sqrt{{(x_1-x_2)}^2+{(y_1-y_2)}^2}$

An exciting discovery ... Yeh !!

An exciting discovery!

I started this blog to provide my analysis to the Internet society and, especially, as my thanks to wonderful people who contribute contents freely to the Internet society. Every time I am on the Internet reading personal websites, blogs, or the Wikipedia, I appreciate their volunteer works. These individuals may not volunteer in any physical form, but their contribution to the human society is of no less. A while back, I thought about creating some kind of website that can recognize these wonderful people for their volunteer works. Unfortunately, I haven't have time to move forward on the project.

Now my discovery. The other day, I was surprised to find that one of my post is actually referenced by a book. I considered that a great honor and totally flattered. But I would like to attribute this to all the people who volunteered their time and made our life better and easier.

 

Evaluate Nebraska's Education-Workforce Pipeline - CIP SOC Crosswalk

The methodology employed by this article is a major improvement over the previous model in that suggested increase or decrease of award production will have no side effects on related fields!

With the P20 initiatives bobbling up all over the United States, the idea of education pipeline has extended from education to the workforce supply. The idea of linking higher education production to economic prosperity has found its way into legislatures.

The idea of aligning higher education production with the workforce demand isn't new. A crosswalk system that linking the field of study to appropriate occupation was last updated in March 2012 by National Center for Education Statistics (NCES) and the Bureau of Labor Statistics (BLS). The crosswalk, also known as CIP to SOC crosswalk, had been used in various workforce supply studies. However, because the complexity of the crosswalk, most of the studies limited their use to few focused fields of interest.

While the CIP to SOC crosswalk is a useful tool, limitations must be observed. The guiding principle behind the development summarized it well:

• “A CIP-SOC relationship must indicate a “direct” relationship, that is, programs in the CIP category are preparation directly for entry into and performance in jobs in the SOC category. The programs satisfy requirements for entry and/or prepare individuals to meet licensure or certification requirements to work in the occupation.”

An example can provide some clarity to the principle. In the nursing field, the registered nurse program (CIP 513801 Associate degree) is crosswalked to the registered nurse (RN) occupation (SOC 291141 Registered Nurse) and not the Licensed Practical Nurse (LPN) occupation (SOC 292061) even though the registered nurse can certainly worked in the LPN occupation. When interpret the result of this work, please keep this in mind, the result is based on 'appropriate' mapping and not the 'all possible mapping'. In the case of Registered Nurse, State of Nebraska can decide to produce more Registered Nurses to fill the LPN occupation. But that probably isn't the most efficient approach.

This article presents a general approach that is applied to all field of studies. The approach is applied to the state of Nebraska data set but can be easily applied to other states.

The data employed are the 2012-22 long term occupation projection data from Nebraska's Department of Labor and the 2013 degree awarded data from IPEDS' (Integrated Postsecondary Education Data System) completion survey.

While the methodology possessed some very desirable characteristics, like all researches, the data employed played vital role in the outcome. During the development, the job opening advertise data available from Nebraska Department of Labor's website were examined. The data for occupation like 'bus driver, School or Special Client' seems to be OK. The data for hot occupations like nurse or computer related positions post some challenges. For one, there were a lot of entries posted by staffing companies or recruiters and, by reading through some of the entries, some of them are simply phantom entries that mimic the real positions. For the nursing jobs, multi-leveled job listings are common. For example, a position can be advertized as RN/LPN, which begged the question of which category this position is counted under. With these observations, the result presented is based on Labor's projection data which can easily be replaced with more accurate data if such data is available.

The methodology employed by this article is different from that of our previous articles. This new approach eliminated couple of the mentioned limitations of previous methodology. Noticeably, when increase or reduce the number of graduates for a given field following the suggestion of this article would not interfere with the number suggested by this article for other fields. This is a major improvement over the previous model in which additional analysis is needed to make sure no side effect occurred in other related fields.

At the heart of this new methodology is the assumption of equal chance of employment, which means that all graduates that can be walked to an occupation, they all have the same chance of getting hired. If supported by real-life data, this factor can be modified to provide better result.

The top 10 education programs that could be targeted to produce more graduates are:

CIP	Title	Dgr Level	Job Open	UnFilled
490205	Truck and Bus Driver/Commercial Vehicle Operator and Instructor.	LessAssct	1,024	(953)
513999	Practical Nursing, Vocational Nursing and Nursing Assistants, Other.	LessAssct	468	(456)
513801	Registered Nursing/Registered Nurse.	Associate	702	(292)
520301	Accounting.	Bachelor	467	(156)
110101	Computer and Information Sciences, General.	Bachelor	259	(113)
520801	Finance, General.	Bachelor	300	(55)
460302	Electrician.	LessAssct	154	(96)
120401	Cosmetology/Cosmetologist, General.	LessAssct	126	(84)
110701	Computer Science.	Bachelor	121	(75)
480501	Machine Tool Technology/Machinist.	LessAssct	144	(65)
460303	Lineworker.	LessAssct	71	(56)

The top 10 eduction programs that could consider reducing graduates are:

CIP	Title	Dgr Level	Job Open	Over Supplied
540101	History, General.	Bachelor	22	163
513801	Registered Nursing/Registered Nurse.	Master	11	180
230101	English Language and Literature, General.	Bachelor	27	198
510912	Physician Assistant.	Master	43	245
450101	Social Sciences, General.	Bachelor	41	255
130301	Curriculum and Instruction.	Master	13	398
131202	Elementary Education and Teaching.	Bachelor	276	404
260101	Biology/Biological Sciences, General.	Bachelor	70	436
520201	Business Administration and Management, General.	Bachelor	368	588
520101	Business/Commerce, General.	Bachelor	386	618

Now Microsoft has spoken, Microsoft, Yahoo, and Google CEO...

Let truth speaks for itself...

Microsoft CEO Satya Nadella
Yahoo CEO Marissa Mayer
Google CEO Larry Page
How about Apple's Tim Cook?

ACT score distribution (2013) in charts

Introduction:

While working on our project, we find the need to visualize the ACT score distribution. ACT published the score distribution annually for National and for each state. What is missing is charts that help visualizing the distribution.

Below, we presented the National ACT score distributions for the class of 2013.Please note the math distribution has a very distinctive shape.

The main scores:

Figure 1 - ACT English Score Distribution (2013)

Figure 2 - ACT Math Score Distribution (2013)

Figure 3 - ACT Reading Score Distribution (2013)

Figure 4 - ACT Science Score Distribution (2013)

Figure 5 - ACT Composite Score Distribution (2013)

In addition to the main score, ACT also published distributions for sub-scores that constituted the main score.

Sub-Scores - ACT English Score Distribution (2013):

Figure 6 - English (Usage/Mechanics) Score Distribution (2013)

Figure 7 - English (Rhetorical Skills) Score Distribution (2013)

Sub-Scores - ACT Math Score Distribution (2013):

Figure 8 - Math (Pre-Algebra) Score Distribution (2013)

Figure 9 - Math (Algebra/Coordinate Geometry) Score Distribution (2013)

 

Figure 10 - Math (Plane Geometry/Trigonometry) Score Distribution (2013)

Sub-Scores - ACT Reading Score Distribution (2013):

Figure 11 - Reading (Social Science) Score Distribution (2013)

Figure 12 - Reading (Art/Literature) Score Distribution (2013)

Distance picked, peer institution selected, the nearest IPEDS

Other Peer Institution Selection Article

** If you are using Microsoft Internet Explorer or Google Chrom browser, you would not be able to read the formulas in this article. These formulas were written in MathML, a W3C standard, and can be viewed in FireFox.

Summary
The goal of this article is to provide mathematical insights into the selection of nearest peer institutions. The discussion begins with a general review of distance in mathematics and extended the idea to the measurement of nearness. Examples were used to demonstrate the importance of properly selected distance function.

The idea of peer
In many fields of study, it is a useful practice to tag objects that are similar to a particular object, the Anchor, as peers. The similarity can customarily be measured by the shortness of distance. The smaller the distance, the more similar an object is to the Anchor and the more likely an object to be selected as a peer of the Anchor.

Distances in Mathematics
In mathematics a metric or distance function is a function that defines the distance between two elements/objects (see Wikipedia article). However, as the abstract nature of the mathematics, the 'set theory' set criteria on behavior of the distance function but left the explicit definition of the distance to the case of application since the explicit definition is irrelevant in the content of the set theory.

For cases were the elements are Euclidean geometry points, the distances are commonly defined as:  $\sqrt{{(x_1-x_2)}^2+{(y_1-y_2)}^2}$

However, this is not the only permissible definition for distance.


Distance in a case of study
The lack of explicit definition of distance for a case of study call into question of the distance between the mathematics and the real world.

However, contrary to most believes, mathematics is not isolated in its own abstract world, plenty of mathematical branches grow out of real world problems. For example, the criteria for the distance function are abstracted qualities of the common distance definition of the Euclidean geometry.

The absent of the definition, in essence, offer the opportunity to select a sensible definition for the case of study.

In the land of peer selection, properties are constantly associated with objects and distances are customary defined through properties.

In the case of higher-education-institution objects, possible properties are fall enrollment headcounts, percent of male enrollment, total revenue, ... etc, like those collected by IPEDS ( Integrated Postsecondary Education Data System) survey.

Distance of Interest for this article
Of all the possible choice of distance definitions, the following are of demonstrative interest. The subscript i denoted various properties while the X denoted the Anchor object in the set and x a different object. The D denote the distance

1. Sum of the square of the difference
   $D=\sum _i{d}_i=\sum _i{(x_i-X_i)}^2$
2. Sum of the Ranking of difference
   $D=\sum _i{d}_i=\sum _i$ Ranking of $|x_i-X_i|$
   where the minimum value has a rank of 1 and the next smallest has a rank of 2 ... etc.
3. Sum of the square of the percent difference
   $D=\sum _i{d}_i=\sum _i{(\frac{x_i}{X_i}-1)}^2$
4. Sum of the absolute value of the percent difference
   $D=\sum _i{d}_i=\sum _i|\frac{x_i}{X_i}-1|$

Fabricated demonstrative data
For the sack of demonstration, higher education institution IPEDS like objects were considered. The Anchor institution, My Inst, alone with three other institutions and their fabricated property values were listed in Table 1. Comparisons of these institution were presented in Figure 1. Data are fabricated to demonstrate that a good methodology would not depend on data to produce reasonable result. With Figure 1, it is clearly shown that Inst-1 is the institution that most similar to My Inst, the Anchor institution, followed by Inst-2 and Inst-3.

Table 1 - demonstrative data

Inst	Enrollment	% Men	Revenue
Institution 1	1900	63%	105,000
Institution 2	2050	90%	101,000
Institution 3	1970	35%	104,000
My Institution	2000	60%	100,000

Figure 1 - comparing institutions (click to see the picture)

Distance evaluated with each illustrative definition

Table 2 - Sum of the square of the difference

Inst	Enrollment	Percent Men	Revenue	Distance
Inst 1	10000	0.0009	25,000,000	25,010,000.0
Inst 2	2500	0.09	1,000,000	1,002,500.1
Inst 3	900	0.0625	16,000,000	16,000,900.1

With the 'sum of the square of the difference' approach, the similarity ranking is in the order of Inst-2, Inst-3, and followed by Inst 1. Table 2 demonstrated that, in this model, the property having larger value would overshadow differences in other properties. It is, therefore, important to scale properties to a compatible matter.t


Table 3 - Sum of the Ranking of difference

Inst	Enrollment	Percent Men	Revenue	Distance
Inst 1	3	1	3	7
Inst 2	2	3	1	6
Inst 3	1	2	2	5

The 'Sum of the Ranking' practice considered the Inst-3 as the most similar peer with Inst-2 and Inst-1 following. The problem with this approach may not be obvious. Couple of points can be made, if observe carefully. First of all is the misrepresentation of the true differences with category like integers, which by itself can't even avoid rounding errors. Using ordering also create problem in that distances between adjacent values are been replaced by 1.

Table 4 - Sum of the square of the percent difference

Inst	Enrollment	% Men	Revenue	Distance
Inst 1	0.3%	0.3%	0.3%	0.8%
Inst 2	0.1%	25.0%	0.0%	25.1%
Inst 3	0.0%	17.4%	0.2%	17.5%

Inst-1 is ranked as the most likely followed by Inst-3 and Inst-2 in the 'Sum of the square of the percent difference' process. 

Under this approach, differences are represented by the percent difference from the Anchor with no categorization attempted. Another benefit of this approach is the straightforward approach and the ease of explanation to audiences. Weighting to each property, as will be discussed later, is also plain to see and easy to identify.

Lesson learned
Properties' value varied in magnitudes, invoking values directly undermined the difference in properties with smaller magnitude. The employ of ranking could over-shadow the difference in value and, in effect, assigned a difference of 1 for all adjacent values.

The square vs. the absolute value (definition 3 vs. definition 4) 
The fact that ${\left(\sum _i{A}_i\right)}^2\succ \sum _i{A_i}^2$ implied that the squared method would favor multiple smaller differences than a single larger difference while the absolute value method will weight small differences and single bigger difference equally. Visually, the squared method made sense.

Weighting properties
Once standardize to the 'sum of the square of the percent difference', weighting can easily be done by multiples to the 'square of the percent difference' before the sum.

* While customized distance can be used in Nearest Neighbor analysis, Nearest Neighbor represent a specific topic in the cluster analysis.

Doctor's degree by field of study, race, foreign, and gender

The field of choice for US doctor awardees are similar regard less of race. Non-resident alien accounted for almost 30% of the supply for higher education instructors!

Observing of doctoral degree conferred in the United States provided us a unique look into the US higher education system. It provided implications for our future higher education system. For one, the doctors are the main source of future college instructors, and secondly, they are future leaders of our higher education institutions. They are also the main research talent that will continue to move the country forward. Lawyers also play important roles in the social justice of our society, especially in protecting minorities' legal rights.

The CL education center has just released a set of data processing reports based on the IPEDS (Integrated Postsecondary Education Data System) 2011-12 completer survey - the analysis can be found under the section: United State's Doctor Production.

IPEDS survey classifies Doctors into 3 groups: The Research Doctors, the Professional Practice Doctors, and the Other Doctors. For non-legal, and non-medical field, the research doctors are the main source for college instructors. The professional practice doctors are mainly the lawyers, and the medical doctors, but also includes doctors that are mainly prepared for specialized career practices like ...

Total Doctor Degrees Awarded by Field of Study (CIP)

Figure 1, Doctors' field of study for each race (click to view the whole chart)

The major field/CIP of interested are list in Table 1 below.

Table 1, major field of interest for each race (multiple and unknown race not displayed)

CIP	Title	Native	AsianPI	Black	Hispanic	White	Alien
11	Computer and Info. Sci.	0.3%	0.8%	0.4%	0.2%	0.5%	4.2%
13	Education	8.6%	1.9%	15.9%	6.6%	5.8%	3.4%
14	Engineering	1.6%	3.7%	1.4%	2.0%	2.4%	24.8%
22	Legal Prof. and Studies	37.5%	21.2%	29.1%	38.8%	30.5%	5.7%
26	Bio and Biomedical Sci.	3.1%	4.1%	2.6%	3.7%	4.0%	11.2%
40	Physical Sciences	1.3%	1.5%	0.9%	1.5%	2.4%	11.3%
42	Psychology	3.5%	1.7%	3.9%	5.5%	4.0%	1.8%
45	Social Science	1.1%	1.0%	1.3%	1.5%	1.7%	5.9%
51	Health Professions related	36.3%	58.7%	32.3%	33.3%	39.4%	10.1%

Figure 1 and Table 1 revealed that, for US citizen, almost all races are favoring Legal, and Health Professions roughly equally, except Asian Pacific Islander are skewed more toward Health Professions. Beside the point made above, in general, all races exhibit similar field-of-study pattern. The major disparities in the chart come from the non-residence alien, where the dominated field is the engineering. Their domination on physical science and biology is also noticeable.


Total Doctor Degrees Awarded by Race
Table 2, total doctor degree awarded by race

Native	AsianPI	Black	Hispanic	White	Alien	Unknown	Mult.Rc
0.5%	9.5%	6.1%	5.5%	58.8%	11.5%	7.3%	0.7%

Total Research Doctors Awarded by Race
Table 3, total research doctors awarded by race

Native	AsianPI	Black	Hispanic	White	Alien	Unknown	Mult.Rc
0.4%	5.3%	6.3%	4.1%	49.2%	27.3%	6.8%	0.6%

The research doctor degree, which mainly removed the lawyers and medical doctors from the total doctor count, awarded to non-residence alien comprise a whooping 27% of all the research doctor degree awarded. Research doctors are the major supply of teachers for our higher education system.

Table 4, research doctors awarded by fields(CIP) and by race

CIP	Title	Native	AsianPI	Black	Hispanic	White	Alien	Unknown	Mult.Rc
1	Agriculture and Operation	0.7%	2.5%	1.3%	4.1%	34.5%	52.0%	4.5%	0.4%
3	Nature Resource and Conservation	0.4%	3.4%	2.5%	3.4%	56.7%	27.4%	5.6%	0.5%
4	Architecture	0.0%	17.8%	4.0%	3.5%	29.2%	37.1%	6.4%	2.0%
5	Area, Ethnic, Cultural, Gender Studies	2.2%	8.6%	11.9%	5.0%	44.2%	16.9%	10.4%	0.7%
9	Communication, Journalism	0.7%	2.6%	5.7%	2.1%	56.3%	25.3%	6.9%	0.3%
10	Communication Technologies	0.0%	0.0%	0.0%	0.0%	0.0%	100.0%	0.0%	0.0%
11	Computer and Info Sci.	0.2%	8.3%	2.0%	1.2%	33.0%	50.4%	4.7%	0.2%
12	Personal and Culinary Services
13	Education	0.8%	3.1%	16.6%	6.1%	58.3%	7.0%	7.5%	0.6%
14	Engineering	0.2%	7.0%	1.7%	2.2%	28.2%	56.2%	4.2%	0.4%
15	Engineering Technologies	0.0%	5.5%	1.8%	1.8%	49.1%	34.5%	7.3%	0.0%
16	Foreign Languages, Literatures	0.1%	3.6%	1.7%	6.6%	47.0%	32.4%	8.4%	0.3%
19	Family, Consumer Sci.	0.3%	3.4%	9.1%	2.2%	57.2%	25.3%	2.2%	0.3%
22	Legal Prof. and studies	0.0%	1.6%	0.5%	2.1%	11.0%	77.0%	7.9%	0.0%
23	English, Literature	0.4%	3.4%	4.0%	3.8%	70.8%	8.5%	7.9%	1.1%
24	Liberal Arts, Sci. General Studies	0.0%	4.5%	3.4%	1.1%	56.8%	12.5%	20.5%	1.1%
25	Library Sci.	0.0%	2.0%	2.0%	2.0%	48.0%	42.0%	4.0%	0.0%
26	Bio. Biomedical Sci.	0.3%	8.2%	3.3%	4.3%	49.8%	27.2%	6.3%	0.6%
27	Math. And Statistics	0.3%	5.7%	1.4%	1.8%	39.4%	46.5%	4.7%	0.1%
28	Military Sci. Leadership
29	Military Tech.	0.0%	0.0%	0.0%	0.0%	83.3%	16.7%	0.0%	0.0%
30	Interdisciplinary Studies	0.5%	5.3%	10.0%	4.8%	52.3%	20.2%	5.6%	1.4%
31	Recreation, Leisure Studies	0.4%	1.2%	7.0%	2.1%	62.8%	24.8%	1.2%	0.4%
32	Basic, Remedial Education
33	Citizenship Activities
34	Health Knowledge and Skill
35	Social Skills
36	Leisure, Recreational Activities
37	Self-Improvement
38	Philosophy and Religious Studies	0.1%	2.6%	5.4%	2.9%	62.5%	18.1%	7.5%	1.0%
39	Theology, Religious Vocations	0.0%	4.7%	10.8%	3.3%	61.0%	12.6%	7.3%	0.2%
40	Physical Sciences	0.2%	4.6%	1.7%	2.6%	43.7%	40.6%	6.2%	0.5%
41	Science Tech.	0.0%	33.3%	0.0%	0.0%	66.7%	0.0%	0.0%	0.0%
42	Psychology	0.6%	4.7%	6.7%	8.0%	63.1%	6.9%	9.0%	0.9%
43	Protective Services	0.0%	3.8%	11.5%	3.1%	63.4%	12.2%	5.3%	0.8%
44	Public Administration	0.4%	4.7%	15.1%	5.3%	47.2%	17.0%	9.7%	0.7%
45	Social Science	0.3%	4.5%	3.8%	3.9%	47.4%	32.2%	7.2%	0.7%
46	Construction Trades
47	Mechanic, Repair Tech.
48	Precision Production
49	Materials Transportation
50	Performing Arts	0.4%	6.1%	1.9%	3.5%	57.8%	22.1%	7.8%	0.5%
51	Health Professions	0.3%	5.9%	7.7%	3.3%	60.9%	13.3%	8.1%	0.5%
52	Business, Marketing	0.4%	5.2%	12.5%	4.4%	41.2%	26.0%	10.2%	0.2%
53	Secondary Diplomas
54	History	0.5%	2.6%	4.5%	4.5%	64.7%	12.3%	9.6%	1.2%
60	Residency Programs

Figure 4 further breaks down the research doctors by field of study (CIP) and by race. The engineering stand out. 56% of the engineering doctor degrees were awarded to non-resident alien. Other non-resident alien heavy fields include Agriculture(52%),
Architecture(37%), Computer Sci.(50%), Engineering Tech(35%), Foreign Languages(32%), Library Sci.(42%), Math. And Statistics(47%), Physical Sciences(41%), Social Science(32%). All of these fields displayed a non-resident alien rate that is higher than the 27.3%, the average rate held by non-resident alien.

Total Research Doctors Awarded by Gender
Men(51%) and women(49%) held roughly the same share of all the research doctor degrees awarded by US institutions. The lowest fields with women recipients are Military Tech(17%), Theology(19%), Computer(20%), and Engineering(22%).

Professional Practice Doctor Degrees Awarded by Gender
In this category, men(53%) held a slightly larger share in the Legal Profession while women(57%) hold larger share in the Medical Doctor field.

Salaries for Professor, Instructor and Graduate Assistant - an IPEDS derivation

** If you are using Microsoft Internet Explorer or Google Chrom browser, you would not be able to read the formulas in this article. These formulas were written in MathML, a W3C standard, and can be viewed in FireFox.

For the 2012-13 IPEDS (Integrated Postsecondary Education Data System) data collection year, National Center for Education Statistics (NCES) changed the information it collected through its Human Resource component. This change in data collection dictates how the average salary for Full-Time instructional Faculty can be calculated.

This article intended to provide a comparison of the new and the old way of calculating the average salary. The discussion is intentionally simplified in order to demonstrate the conceptual differences.

Prior to 2012-13 data collection, headcount numbers and salary outlays were collected for faculty with 9- or 10-month contract and 11- or 12-month contract for each gender and rank. So, for each gender and rank, there are basically 4 numbers: Total Salary Outlay for faculty with 9- or 10-month contract (S₉), Total Headcount for faculty with 9- or 10-month contract (H₉), Total Salary Outlay for faculty with 11- or 12-month contract (S₁₁), and Total Headcount for faculty with 11- or 12-month contract (H₁₁). The suggested way (by NCES) to calculate the annual 9-month average salary for each gender-rank combination is given by:

  $\frac{S_9+\frac{9}{11}{S}_{11}}{H_9+H_{11}}$

, which, in essence, is the average of the monthly salary times 9.

Beginning 2012-13 data collection year, for each gender-rank combination, 5 numbers are collected: the Total Headcount for faculty with 9-month contract (H₉), the Total Headcount for faculty with 10-month contract (H₁₀), the Total Headcount for faculty with 11-month contract (H₁₁), the Total Headcount for faculty with 12-month contract (H₁₂), and the Total Salary Outlay for faculty with all four contract length (S₉+S₁₀+S₁₁+S₁₂). The suggested way (by NCES) to calculate the annual 9-month average salary for each gender-rank combination is given by:

$\frac{S_9+S_{10}+S_{11}+S_{12}}{9H_9+10H_{10}+11H_{11}+12H_{12}}x9$

, which, in essence, is the total salary outlay distributed into the total number of manpower-month.

Logically, the methodology changes begged the explanation of the differences between these two methods.

For simplicity, case with only 9-month and 11-month faculties are considered. Under this condition, the 2012-13 method reduced to:

  $\frac{S_9+S_{11}}{9H_9+11H_{11}}x9$.

By carrying out the difference of the new and old methods, we arrived at:

$\frac{\frac{2}{11}{S}_{11}-\frac{2}{9}x\left(\frac{S_9+\frac{9}{11}{S}_{11}}{H_9+H_{11}}\right)x{H}_{11}}{9H_9+11H_{11}}x9$.

The difference indicates if the new number is higher or lower than the old number and by how much. The value represented by the parenthesis is that of the old method - the average monthly salary times 9 month. By multiplying it by two over nine and times the number of faculty with 11-month contract, the result represents the amount of money needed to bring the 11-month faculties' average salary to that of the old method for the two months (9-11). The leading term in the numerator indicates the two month allocation from the total salary outlay for the 11-month faculties. The net value of the numerator is, therefore, the amount of money that can be used the raise or lower the value of the new method apart from the average monthly salary of the old method. By solving the inequality equation:

$\frac{2}{11}{S}_{11}-\frac{2}{9}x\left(\frac{S_9+\frac{9}{11}{S}_{11}}{H_9+H_{11}}\right)x{H}_{11}>0$
$\mathrm{ }$
, it can be proofed that higher monthly salary for the 11-month faculties would result in higher value for the 2012-13 formula than the older formula and the reverse is also true.

By consideration above and by making the same assumption NCES had made in the past (i.e. assuming all faculty with 9- or 10-month contracts are actually 9-month contract and that all faculty with 11- or 12-month contract are actually 11-month contract), it is possible to apply the new method to the pre 2012-13 data with predictable discrepancy.

Even though NCES had used the 9- and 11-month assumption in the past, the new 2012-13 data can be used to gauge if that assumption is a valid one. For example, the 2012-13 data revealed that majority of Nebraska's colleges are either have 9-month contracts or 12-month contracts. There are some 10-month contracts, but the 11-month contracts are nearly none. With these observation, the following formulas is a better estimate for the pre 2012-13 data:
$\frac{S_9+\frac{9}{12}{S}_{12}}{H_9+H_{12}}$ 

At the same time, by discounting the minorities, the following formula can be applied to all years:
$\frac{S_9+S_{12}}{9H_9+12H_{12}}x9$

This would show the effects and differences caused by the new formula and also provide a ( reasonably ) compatible trend from the past to current.

On Obama’s Bold Plan to Reshape American Higher Education

Original Article

Summary goes here!

A step finally taken, which I have advocated for a long time.

Jest few points about things the author of the article like to see to be included in Obama's plan.

On 'Nobel Prize winner wants to get in the Physics 101 business' should get more weight:

• Personally, I don't think a Researcher is necessary a better teacher. They may have their own way of understanding things, it does not necessary mean those understanding are on all levels and that they can transfer those idea effectively especially to students that may have totally different mindsets.
• If there is going to have an objective measurements of outcome, why do we try to add some un-objective impurity to the formula? This is more like going back to the old system where the established (regional accredited) are carrying more weights.
• By providing this loophole to encourage high level researcher to teach low level courses, aren't we promoting inefficiency? I am not saying there would have no benefits to the students, but I am saying that this by-law is not needed. If they can provide extra benefits to lower level courses, those benefits should be build into the objective measurement formula and, therefore, it will show if the benefits is there and, would, therefore, render the by law unnecessary.

A song for the Ed community

Educate future generation and change the world!

 Gem like you, Gem like you, here is a song for you.

Give me a song, give me a song, give me is the name of the song.

Give me the word, give me the word, give me has taken the world.
Game boring girl, game boring girl, give me has taken them all.
Bragging boy, bragging boy, give me is their whole.

Boring game, bragging game, give me is all their game.
The give me will never end their game.

Gem like you, gem like you, may the give me never got you.
Gem like you, gem like you, may the ring never got you.

May the ring never replace you. May the ring never replace a gem like you.





- Of cause, there is a story behind it.

 

Summary of the IPEDS data release schedule

The Best Known Date for IPEDS data release!

IPEDS stand for Integrated Postsecondary Education Data System and is a set of survey conducted by the National Center for Education Statistics (NCES), a division of the United State Education Department(US ED).

All the IPEDS survey data can be obtained through the IPEDS data center. IPEDS survey data are published in various quality control levels. The most recent guide line are covered in the document: IPEDS Data Release Procedure. In essence, beginning with data submitted in the 2011-12 survey year, data will be released in 3 phases: preliminary, provisional and final, geared toward national, state and institutional level analysis. In tech terms, these are what they mean:

• Preliminary - Edited but unimputed for non-responding school
• Provisional - Fully Edited with Imputing for non-responding institutions
• Final - Includes Edited updates from PYR - were referred to as 'revised data'

However, to track down when each set of data were published, it is not an easy task. One source of this kind of information is the 'This Week in IPEDS' news letter/email. The archive for these news letters can be found at IPEDS news site. However, even with the news letter, sometimes it is still hard to pinpoint the date a certain data set is made available at the IPEDS data center.

The following info is compiled from the news letters mentioned above. The only thing can be said about the date is these are the 'best known date'. Procedures to derive these info are detailed in this document.

Survey Year 2010-11

Survey Year	Survey	Preliminary*	Provisional	Final
2010-11	IC	2/8/2011	9/20/2011	4/27/2012
2010-11	C	2/8/2011	9/20/2011	4/27/2012
2010-11	E12	2/8/2011	9/20/2011	4/27/2012
2010-11	HR	-	11/22/2011	Target ?/?/2013
2010-11	SFA	-	-	4/27/2012
2010-11	EF	-	-	-
2010-11	F	-	-	-
2010-11	GR	-	-	-

Survey Year 2011-12

Survey Year	Survey	Preliminary*	Provisional	Final
2011-12	IC	-	9/26/2012	Target ?/?/2013
2011-12	C	-	9/26/2012	Target ?/?/2013
2011-12	E12	-	9/26/2012	Target ?/?/2013
2011-12	HR	-	9/26/2012	Target ?/?/2013
2011-12	SFA	-	9/26/2012	Target ?/?/2013
2011-12	EF	10/9/2012	Target ?/?/2012	Target ?/?/2013
2011-12	F	10/9/2012	Target ?/?/2012	Target ?/?/2013
2011-12	GR	10/9/2012	Target ?/?/2012	Target ?/?/2013

* Preliminary data will not be accessible once the provisional data is made available.

IC -Institutional Characteristics
C - The Completion Survey
E12 - 12-month Enrollment Survey
HR - Human Resource Survey
SFA - Student Financial Aid Survey
EF - Fall Enrollment Survey
GR - The Graduation Rate Survey

* A personal note: It take about 5 months for preliminary release and about 7-11 months for provisional release.

Homeland: Immigration in America Refugees

Original Article
 

Keep that spirit up - not that the America is a Great country, but that how lucky we are and how to light the spirits of our youth and best our efforts.

My first reaction after watching the show is that this is why America is a Great country - the heart of American people.

It has been a long time, I totally appreciated the America Dream - You will get there as long as you work hard for it - the opportunity. Just imaging how these refugees have to go through to survive in America and compare with our kids that growing up in America. Shouldn't our kids appreciate how fortunate they are - as the old saying goes: Counting your blessings - my feel is that a lot of us have lost that and that big heart. A lot of us seems to forget how fortunate we are and what we should have done our best to advance the world.

Have we done our best? With tones of fortune after the World War II, have we not worked our best to educate our children to keep that spirits up? Or have we been blamed other countries for products and jobs we can do with our ancient education?

America is a Great country, don't let that fade away!

Please keep that spirit up - not that the America is a Great country, but that how lucky we are and how to light the spirits of our youth and best our efforts.

It’s time to drop the college-for-all crusade

Original Article

Summary goes here!

Personally, I would attribute some of these kind of  bad American policies to the generosity of American and some to the idea of 'Political Correctness'.

As a foreign born American, I am totally impressed by the generosity of American and, at times, puzzled and resisted. Growing up, I took nothing for granted even though I have supporting parents. I was educated, taught and self-studied, that begging is of no dignity and god only help people that help themselves. Personally, I have been summon all these under the 'responsibility'.

As to the 'Political Correctness', for one thing, I would like to remind the reader that OPPORTUNITIES is all it should be considered. Given the opportunity, in the sense of promote responsibility, it is up to the people to work hard to get what they want.

More practically, we should realized that not all people were born equal, intellectually. The best a society can hopeful is to have everyone do their best. To have people reach their best is to teach them being responsible. To teach responsibility, the operation of the system and the society must, by itself, promote responsibility. For example, to entitle to education as a right, students must demonstrate their wiliness to put in efforts in studying. For a normal people, this can simply be a requirement to reach certain testing scores. This is nothing new, what is new is the infusion of the idea of responsibility.

In theory, I am willing to support the idea of free education for all those who did their best. On the other hand, since resources are limited, we should support those who will benefit the society the most. With the full support of the society, I am visioning the building of a system where the freely educated scholars recognizing the support from and the responsibility to the society. Formula may need be sought to build a practical system, the idea is to promote responsibility as the core value of education.

For people reaching their best but were not able to be the best, this is where the society come in to benefit the constituent of the whole society.

Can Financial Aid Improve Student Success at Louisiana’s Community Colleges?

Original Article

Summary goes here!

An old saying: "Help people who helped themselves."

Financial Aid can help if students were matured enough to realize the value of the extra time they got and put it into study. If not, I don't see how the extra money can improve students' success. It may even distract them from the study to enjoy other things in life.

This is a good demonstration of why building the sense responsibility is the number one task in education. Any attempt to improve the education outcome without promoting students' sense of responsibility is a wrong approach. You can hire personal coaches that following student around to improve his/her education. But does the society have the responsibility to support people this way? Will this ever been sustainable?

The histories of a lot of Federal Aid programs have shown that a lot of people will take advantages of these systems in undesirable ways and a lot of these programs were later re-tweaked or added responsibility strings to it.