选大学时,常被一条定律左右——所读学校名气有多大,毕业后的工资就有多高。但此定律是否正确,有待商榷。
华尔街日报通过对几千名在校大学进行分析并追踪其毕业后十年的状况,得出这一结论:对于某几个职业来说,名校学生的薪酬水平的确比高人一筹,然而在其他几个领域却没有丝毫区别。详情见以下数据:
Table 1.Average Annual Earnings by Major and Selectivity Type, 2003 Cross-Section
|
Top Selectivity |
Middle Selectivity |
Bottom Selectivity |
Overall |
Business |
72,704.88 |
62,229.69 |
56,136.24 |
63,390.44 |
Observations |
231 |
485 |
219 |
935 |
Engineering |
78,900.97 |
68,500.17 |
72,225.14 |
74,271.93 |
Observations |
287 |
205 |
71 |
563 |
Science |
65,432.43 |
61,645.53 |
59,227.33 |
63,024.08 |
Observations |
406 |
354 |
129 |
889 |
Social Science |
63,414.47 |
53,155.91 |
49,168.34 |
57,107.39 |
Observations |
548 |
505 |
184 |
1,237 |
Humanities |
57,056.57 |
49,831.24 |
44,562.64 |
52,042.74 |
Observations |
287 |
282 |
109 |
678 |
Other Major |
61,915.86 |
54,982.12 |
54,741.99 |
56,876.00 |
Observations |
533 |
944 |
421 |
1,898 |
Education |
48,858.99 |
42,049.48 |
39,397.44 |
42,771.33 |
Observations |
215 |
626 |
254 |
1,095 |
Table 2.Estimated Difference in Log Annual Earnings, 2003 Cross-Section
|
Top–Middle |
Top–Bottom |
Middle–Bottom |
Business
|
.120** |
.179** |
.059* |
(.034) |
(.040) |
(.034) |
|
Engineering
|
.077* |
?.036 |
?.112** |
(.040) |
(.055) |
(.056) |
|
Science
|
?.036 |
?.010 |
.025 |
(.031) |
(.042) |
(.041) |
|
Business
|
.105** |
.140** |
.034 |
(.026) |
(.036) |
(.035) |
|
Humanities
|
.055 |
.110** |
.055 |
(.036) |
(.047) |
(.045) |
|
Other Major
|
.046** |
.031 |
?.015 |
(.023) |
(.028) |
(.025) |
|
Education
|
.061* |
.086** |
.025 |
(.034) |
(.040) |
(.032) |
Table 3. College Major Distributions by Selectivity Type, 2003 Cross-Section
|
Top Selectivity |
Middle Selectivity |
Bottom Selectivity |
Business |
.092 |
.143 |
.158 |
Engineering |
.114 |
.060 |
.051 |
Science |
.162 |
.104 |
.093 |
Social Science |
.219 |
.148 |
.133 |
Humanities |
.114 |
.083 |
.079 |
Other Major |
.213 |
.278 |
.303 |
Education |
.086 |
.184 |
.183 |
Table 4. Percent of Colleges That Offer Particular Majors, by Selectivity Type
|
Business |
Engineering |
Education |
Top Selectivity |
80.61% |
62.42% |
76.36% |
Middle Selectivity |
80.95% |
51.11% |
82.54% |
Bottom Selectivity |
83.63% |
50.29% |
78.95% |
Table 5. Decomposition of Earnings Difference by Selectivity Type, 2003 Cross-Section
|
Difference in College Earnings Premium |
δq |
δp |
Top–Middle
|
.153** |
.096** |
.071** |
(.012) |
(.009) |
(.013) |
|
Top–Bottom
|
.200** |
.122** |
.067** |
(.015) |
(.013) |
(.021) |
|
Middle–Bottom
|
.063** |
.023** |
.035** |
(.014) |
(.007) |
(.014) |
通过跟踪调查并分析7300名大学生毕业十年以后的去向,将其专业分为以下几类:商学、工程学、社会科学、人文学科、教育学等,同时将大学分为三大类:包含了精英大学和高竞争性大学,中等录取要求型和低要求型。结果令人大吃了一惊:与科学、技术、工程、数学相关的专业,平均工资不会因大学类型的改变而发生很大变化。
为什么会出现此类情况?对于有潜力的雇员来说,在这些专业中就读的技能性学生通常优于名校毕业生,因为学校设置的课程是标准化的,学生们要学的专业知识也大致相同。因此,学生不必就读尽可能好的大学来确保未来的高工资。
对于其他领域来说,它们的工资远景会因学校的不同而发生很大变化。除去之前说的科学、技术、工程、数理专业,其他领域非常关心应聘者的毕业院校。
毕业后工资水平悬殊最大的是商科,重点大学毕业生要比次重点大学毕业生平均多挣12%,比一般大学毕业生多挣18%。同此,对于社会科学专业的毕业生来说,重点大学毕业生要比次重点大学毕业生平均多挣11%,比一般大学毕业生多挣14%。对于教育学专业的学生来说,工资差异百分比分别为6%和9%。在文科毕业生中,虽然重点大学的学生没比次重点大学的毕业生多挣多少,但是前者比一般大学的学生平均多挣11%。如此,你是否还执念名校?