Resources Science ›› 2016, Vol. 38 ›› Issue (11): 2095-2105.doi: 10.18402/resci.2016.11.08
• Orginal Article • Previous Articles Next Articles
XIONG Hu1, TIAN Ying2(), TIAN Li3
Received:
2015-12-14
Revised:
2016-08-30
Online:
2016-11-16
Published:
2016-11-16
XIONG Hu,TIAN Ying,TIAN Li. Empirical study on the impacts of the matching between human capital and industrial structure on investment efficiency[J].Resources Science, 2016, 38(11): 2095-2105.
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Table 1
The index system of human capital and industrial structure system"
子系统 | 指标类型 | 指标 | 单位 | 功效类型 |
---|---|---|---|---|
人力资本 | 教育 | 人口文化结构比例(未上学) 人口文化结构比例(小学) 人口文化结构比例(初中) 人口文化结构比例(高中) 人口文化结构比例(大专及以上) 生师比(小学) 生师比(普通中学) 生师比(普通高等学校) 教育经费 | % % % % % 教师人数=1 教师人数=1 教师人数=1 万元 | 负 正 正 正 正 负 负 负 正 |
健康 | 人口死亡率 每万人拥有病床数 每万人拥有卫生技术人员数 | % 张 人 | 负 正 正 | |
科研 | 研究与试验发展经费支出 研发经费强度 专利申请授权量 技术市场成交额 期刊总印数 | 亿元 % 件 亿元 万册 | 正 正 正 正 正 | |
产业结构 | 产值结构 | 第一产业产值占GDP比重 第二产业产值占GDP比重 第三产业产值占GDP比重 | % % % | 负 正 正 |
就业结构 | 第一产业就业人员占总就业人员比重 第二产业就业人员占总就业人员比重 第三产业就业人员占总就业人员比重 | % % % | 负 正 正 | |
产业结构合理化 | 泰尔指数 | 负 | ||
产业结构高级化 | 第三产业产值与第二产业产值之比 | % | 正 | |
产业结构高效化 | 第一产业劳动生产率 第二产业劳动生产率 第三产业劳动生产率 | 元/(人·年) 元/(人·年) 元/(人·年) | 正 正 正 | |
产业结构可持续 发展 | 单位生产总值能源消费量 单位工业增加值废水排放量 单位工业增加值废气排放量 | t标准煤/万元 t/万元 m3/元 | 负 负 负 |
Table 2
The matching degree between human capital and industrial structure of all provinces"
年份 | 1997 | 1998 | 2000 | 2002 | 2004 | 2006 | 2008 | 2010 | 2012 | 2013 | 均值 |
---|---|---|---|---|---|---|---|---|---|---|---|
北京 | 0.899 | 0.915 | 0.927 | 0.939 | 0.931 | 0.921 | 0.925 | 0.896 | 0.899 | 0.904 | 0.918 |
福建 | 0.381 | 0.342 | 0.385 | 0.418 | 0.409 | 0.422 | 0.405 | 0.476 | 0.493 | 0.466 | 0.421 |
广东 | 0.562 | 0.541 | 0.571 | 0.614 | 0.578 | 0.590 | 0.578 | 0.605 | 0.616 | 0.613 | 0.589 |
海南 | 0.441 | 0.412 | 0.375 | 0.412 | 0.418 | 0.365 | 0.360 | 0.373 | 0.432 | 0.436 | 0.399 |
河北 | 0.385 | 0.382 | 0.402 | 0.376 | 0.394 | 0.387 | 0.401 | 0.397 | 0.411 | 0.420 | 0.396 |
江苏 | 0.481 | 0.483 | 0.531 | 0.472 | 0.484 | 0.522 | 0.563 | 0.618 | 0.659 | 0.666 | 0.543 |
辽宁 | 0.583 | 0.574 | 0.573 | 0.576 | 0.564 | 0.561 | 0.534 | 0.548 | 0.599 | 0.615 | 0.571 |
山东 | 0.420 | 0.427 | 0.429 | 0.459 | 0.469 | 0.474 | 0.476 | 0.498 | 0.514 | 0.527 | 0.469 |
上海 | 0.724 | 0.722 | 0.769 | 0.758 | 0.749 | 0.752 | 0.740 | 0.793 | 0.750 | 0.734 | 0.755 |
天津 | 0.653 | 0.641 | 0.657 | 0.672 | 0.671 | 0.645 | 0.641 | 0.661 | 0.651 | 0.665 | 0.656 |
浙江 | 0.441 | 0.446 | 0.445 | 0.499 | 0.526 | 0.538 | 0.542 | 0.554 | 0.601 | 0.594 | 0.518 |
甘肃 | 0.596 | 0.612 | 0.555 | 0.575 | 0.589 | 0.576 | 0.597 | 0.541 | 0.554 | 0.560 | 0.577 |
广西 | 0.485 | 0.488 | 0.495 | 0.500 | 0.530 | 0.527 | 0.504 | 0.522 | 0.494 | 0.501 | 0.508 |
贵州 | 0.387 | 0.368 | 0.353 | 0.421 | 0.440 | 0.411 | 0.441 | 0.414 | 0.375 | 0.431 | 0.403 |
内蒙古 | 0.679 | 0.691 | 0.647 | 0.667 | 0.671 | 0.702 | 0.680 | 0.683 | 0.723 | 0.670 | 0.681 |
宁夏 | 0.568 | 0.570 | 0.553 | 0.585 | 0.593 | 0.595 | 0.599 | 0.557 | 0.575 | 0.580 | 0.577 |
青海 | 0.514 | 0.545 | 0.537 | 0.568 | 0.515 | 0.528 | 0.524 | 0.528 | 0.502 | 0.547 | 0.536 |
陕西 | 0.780 | 0.772 | 0.783 | 0.747 | 0.755 | 0.721 | 0.711 | 0.702 | 0.754 | 0.791 | 0.747 |
四川 | 0.701 | 0.716 | 0.674 | 0.691 | 0.684 | 0.677 | 0.658 | 0.650 | 0.694 | 0.725 | 0.684 |
新疆 | 0.723 | 0.720 | 0.676 | 0.711 | 0.720 | 0.719 | 0.689 | 0.642 | 0.659 | 0.652 | 0.693 |
云南 | 0.573 | 0.611 | 0.552 | 0.545 | 0.549 | 0.549 | 0.537 | 0.516 | 0.507 | 0.508 | 0.547 |
重庆 | 0.589 | 0.609 | 0.687 | 0.677 | 0.667 | 0.707 | 0.683 | 0.674 | 0.673 | 0.675 | 0.672 |
安徽 | 0.506 | 0.493 | 0.573 | 0.560 | 0.578 | 0.596 | 0.637 | 0.581 | 0.649 | 0.643 | 0.577 |
河南 | 0.596 | 0.564 | 0.534 | 0.522 | 0.531 | 0.571 | 0.595 | 0.596 | 0.611 | 0.595 | 0.572 |
黑龙江 | 0.812 | 0.776 | 0.744 | 0.739 | 0.716 | 0.722 | 0.713 | 0.725 | 0.717 | 0.717 | 0.737 |
湖北 | 0.676 | 0.688 | 0.668 | 0.702 | 0.666 | 0.677 | 0.673 | 0.652 | 0.704 | 0.706 | 0.679 |
湖南 | 0.585 | 0.584 | 0.613 | 0.630 | 0.628 | 0.632 | 0.653 | 0.609 | 0.664 | 0.652 | 0.624 |
吉林 | 0.772 | 0.776 | 0.788 | 0.761 | 0.748 | 0.742 | 0.715 | 0.701 | 0.700 | 0.685 | 0.737 |
江西 | 0.486 | 0.469 | 0.496 | 0.484 | 0.499 | 0.473 | 0.555 | 0.473 | 0.487 | 0.497 | 0.504 |
山西 | 0.691 | 0.669 | 0.669 | 0.658 | 0.656 | 0.670 | 0.676 | 0.716 | 0.671 | 0.659 | 0.675 |
Table 3
The regression results of panel data model"
变量 | OLS | FE | RE | FE-2SLS | RE-2SLS |
---|---|---|---|---|---|
match | 3.739*** | 1.562 | 2.619* | 1.719* | 3.065*** |
(0.758) | (1.884) | (1.548) | (1.043) | (1.010) | |
human | -0.528** | 0.452 | -0.322 | 0.697* | -0.154 |
(0.261) | (0.743) | (0.511) | (0.414) | (0.378) | |
match×human | -1.842*** | -1.121 | -1.511** | -1.158** | -1.725*** |
(0.362) | (0.966) | (0.757) | (0.521) | (0.491) | |
gdp | -0.191*** | -0.132 | -0.224** | -0.518*** | -0.405*** |
(0.057) | (0.228) | (0.114) | (0.120) | (0.076) | |
market | 0.331*** | 0.330*** | 0.273*** | 0.385*** | 0.310*** |
(0.060) | (0.086) | (0.086) | (0.054) | (0.056) | |
traffic | -0.087*** | 0.039 | -0.029 | 0.082* | 0.011 |
(0.018) | (0.089) | (0.062) | (0.048) | (0.037) | |
labor | 0.340*** | -0.036 | 0.367*** | -0.118 | 0.495*** |
(0.057) | (0.197) | (0.118) | (0.123) | (0.075) | |
open | 0.053** | 0.200*** | 0.129*** | 0.175*** | 0.141*** |
(0.022) | (0.057) | (0.047) | (0.033) | (0.029) | |
urban | 0.070 | 0.547*** | 0.284* | 0.587*** | 0.325*** |
(0.065) | (0.186) | (0.166) | (0.095) | (0.089) | |
gov | -0.001 | -0.023 | 0.117** | -0.030 | 0.113* |
(0.055) | (0.063) | (0.058) | (0.068) | (0.065) | |
T | -0.035*** | -0.066** | -0.045*** | -0.023* | -0.036*** |
(0.007) | (0.025) | (0.006) | (0.015) | (0.009) | |
样本量 | 510 | 510 | 510 | 480 | 480 |
F检验 | 0.000 | ||||
LM检验1 | 0.000 | ||||
LM检验2(识别不足) | 0.000 | 0.000 | |||
Wald F(弱识别) | 0.000 | 0.000 | |||
Hausman检验1 | 0.000 | 0.000 | |||
Hausman检验2 | 0.000 |
Table 4
The regression results of stabile tests"
变量 | OLS(1) | FE(2) | RE(3) | FE-2SLS(4) | RE-2SLS(5) | SYS-GMM(6) | SYS-GMM(7) |
---|---|---|---|---|---|---|---|
match | 3.280*** | 1.568 | 1.668 | 2.176 | 2.416** | 2.386*** | 1.717** |
(0.823) | (2.377) | (1.827) | (1.418) | (1.183) | (0.923) | (0.728) | |
human | -0.280 | 0.080 | -0.071 | -0.0001 | -0.046 | 0.094 | 0.124 |
(0.276) | (0.855) | (0.607) | (0.534) | (0.415) | (0.288) | (0.250) | |
match×human | -1.634*** | -1.188 | -1.020 | -1.431** | -1.380** | -1.217*** | -0.860** |
(0.399) | (1.240) | (0.917) | (0.732) | (0.582) | (0.449) | (0.349) | |
gdp | -0.213*** | -0.091 | -0.288** | -0.649*** | -0.458*** | 5.284*** | 5.551*** |
(0.063) | (0.312) | (0.115) | (0.165) | (0.083) | (1.010) | (0.781) | |
market | 0.259*** | 0.203 | 0.199 | 0.305*** | 0.286*** | 0.029 | -0.002 |
(0.084) | (0.129) | (0.126) | (0.087) | (0.084) | (0.074) | (0.063) | |
traffic | -0.107*** | -0.055 | -0.105* | -0.020 | -0.081** | -0.040 | -0.035 |
(0.202) | (0.078) | (0.055) | (0.050) | (0.036) | (0.030) | (0.029) | |
labor | 0.355*** | 0.276 | 0.450*** | 0.172 | 0.585*** | 0.482*** | 0.471*** |
(0.063) | (0.197) | (0.110) | (0.160) | (0.082) | (0.090) | (0.076) | |
open | 0.062** | 0.210*** | 0.132*** | 0.174*** | 0.141*** | 0.109*** | 0.107*** |
(0.025) | (0.063) | (0.039) | (0.041) | (0.033) | (0.029) | (0.027) | |
urban | 0.049 | 0.377** | 0.248* | 0.445*** | 0.307*** | 0.160 | 0.173* |
(0.070) | (0.163) | (0.139) | (0.119) | (0.096) | (0.120) | (0.103) | |
gov | -0.124** | -0.064 | -0.044 | -0.072 | -0.004 | -0.027 | -0.065 |
(0.059) | (0.077) | (0.062) | (0.071) | (0.065) | (0.056) | (0.052) | |
T | -0.020 | -0.043 | -0.012 | 0.012 | -0.007 | -0.016* | -0.013* |
(0.009) | (0.041) | (0.010) | (0.022) | (0.010) | (0.008) | (0.007) | |
L.icor | 0.286*** | 0.282*** | |||||
(0.068) | (0.061) | ||||||
L.gdp | -5.656*** | -5.904*** | |||||
(0.988) | (0.768) | ||||||
样本量 | 390 | 390 | 390 | 360 | 360 | 360 | 480 |
AR(2)检验 | 0.240 | 0.765 | |||||
Hansen检验 | 1.000 | 1.000 |
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