
基于工资的人力资本度量: 从微观个体到宏观总量
Wage-based Measurement of Human Capital: From Individual to Aggregate
从微观个体的人力资本到宏观经济体人力资本总量,不仅存在人力资本度量的内涵问题,同时受到不同技能劳动力加总方式的影响.本文从总量生产函数出发,基于体现劳动效率的个体劳动力工资的人力资本度量指标,构建了由个体层面到宏观层面相互融合的人力资本度量理论框架.在此基础上,本文深入研究三种基于工资的人力资本度量方法以及它们的内在联系,即效率单元、广义人力资本、及国际广泛使用的Jorgenson-Fraumeni(J-F)人力资本度量.效率单元主要通过相对工资体现不同技能层次劳动力的相对质量,广义人力资本在此基础上进一步体现不同技能劳动力间的相互作用.与相对工资的度量不同,J-F人力资本是以个人终身收入的实际值来度量,因此受到劳动力生命周期内人力资本动态变化的影响.然后我们利用中国数据计算了我国从1985年到2018年的相应三种人力资本度量结果并比较分析其动态差异,并且我们分别计算这三种人力资本度量指标对经济发展差异的要素解释力.研究发现,由于不同人力资本度量指标所反映的内涵不同,其描述的发展动态及对经济发展的解释程度存在明显差异.
Starting from the macroeconomic production function, we establish a theoretical framework of human capital measurement and form the linkage between individual and aggregate measures. Our focus is on the human capital measurement based on earnings. Because wage represents an individual's marginal productivity, wage-based measurement provides a comprehensive measure of human capital that cover all observed and unobserved aspects. We discuss three wage-based aggregate human capital measures, efficiency units, generalized division of labor (GDL) and the Jorgenson-Fraumeni lifetime income. The traditional measure of efficiency units reflects labor quantity and relative quality. Additionally, for efficient units, all labor is aggregated in a linear fashion, implicitly assuming perfect substitution among different skills. However, GDL aggregation takes into substitutability and complementarity of different skills with a given technology. The J-F human capital is based on lifetime-income but maintains the perfect substitution assumption in the aggregation. We further investigate the impact of individual life-time human capital dynamics on the J-F calculation and show that incorporating the dynamics will improve the J-F measurement. Using data from China, we calculate these three human capital measures from 1985 to 2018 and discuss their differences in dynamics. We further compare their importance in regional income disparity. The results show that the GDL human capital account for a much larger portion of income gap compared to the efficiency units. Additionally, J-F human capital also accounts for a large portion of income difference but appears to be falling below the GDL. Our results demonstrate that for aggregate human capital measurement, both the concepts and aggregations play an important role and will affect the study of human capital.
人力资本度量 / 效率单元 / 广义劳动力分类法 / J-F终身收入法 / 生命周期人力资本动态 {{custom_keyword}} /
measurement of human capital / efficient units / generalized division of labor / J-F lifetime income method / dynamic model of individual human capital {{custom_keyword}} /
表1 回归分析的变量说明与描述性统计 |
变量 | 样本量 | 均值 | 标准差 | 最小值 | 最大值 |
年龄 | 5413 | 41.5 | 8.457 | 19 | 60 |
女性=1 | 5413 | 0.386 | 0.487 | 0 | 1 |
初中及以下 | 5413 | 0.157 | 0.364 | 0 | 1 |
高中 | 5413 | 0.382 | 0.486 | 0 | 1 |
大专 | 5413 | 0.298 | 0.458 | 0 | 1 |
大学及以上 | 5413 | 0.148 | 0.355 | 0 | 1 |
经验 | 5413 | 23.36 | 9.613 | 0 | 44 |
在职培训=1 | 5413 | 0.313 | 0.464 | 0 | 1 |
年收入/元 | 5413 | 14308 | 7548 | 5340 | 57000 |
注: 数据来自CHIP2002年的城镇调查数据, 选取了就业身份是雇员, 签订的劳动合同性质是固定职工或者长期合同且在法定退休年龄内的样本. |
表2 在职培训及人力资本的提升 |
被解释变量ln(w) | 男 | 女 |
高中 | 0.1394***(6.1677) | 0.2257***(7.1854) |
大专 | 0.2950***(11.1139) | 0.3756***(10.4182) |
大学 | 0.4789***(15.4318) | 0.5953***(13.6393) |
经验 | 0.0180***(5.1350) | 0.0130***(2.9384) |
经验平方项 | -0.0001*(-1.7852) | -0.0001(-0.1662) |
在职培训 | 0.0444***(2.7322) | 0.0796***(3.9838) |
截距项 | 8.7873***(155.0435) | 8.7026***(154.9441) |
R2 | 0.1399 | 0.1475 |
N | 3310 | 2103 |
注: 1.此处使用的是OLS方法, 未考虑因遗漏能力等变量产生的内生性问题. 2.括号内数值为t检验值, ***、**和*分别代表 1%、5%和10%的显著性水平. |
表3 考虑在职培训对J-F计算结果的影响 |
教育程度 | 性别 | 年龄 | 终身收入(万元) | 差异(%)(个体-J-F)/J-F | |
个体人力资本动态模型计算方法 | J-F计算法 | ||||
高中 | 男 | 25 | 47.21 | 43.11 | 9.51 |
高中 | 男 | 45 | 22.75 | 20.59 | 10.49 |
大专 | 男 | 25 | 54.89 | 48.63 | 12.87 |
大专 | 男 | 45 | 26.66 | 23.39 | 13.98 |
大学 | 男 | 25 | 66.21 | 57.74 | 14.67 |
大学 | 男 | 45 | 32.24 | 27.84 | 15.80 |
高中 | 女 | 25 | 37.83 | 35.61 | 6.23 |
高中 | 女 | 45 | 14.31 | 13.34 | 7.27 |
大专 | 女 | 25 | 43.45 | 39.87 | 8.98 |
大专 | 女 | 45 | 16.47 | 14.95 | 10.17 |
大学 | 女 | 25 | 54.52 | 49.05 | 11.15 |
大学 | 女 | 45 | 20.67 | 18.41 | 12.28 |
注: 分别利用个体人力资本动态模型和J-F计算方法测算出各教育程度分男女、分年龄的个体终身收入, 然后以进入劳动力市场的年龄如25岁和全盛期的45岁进行对比分析. |
表4 1985–2018年基于不同度量的劳动力人力资本总量 |
年份 | 劳动力人口(千万) | 效率单元(千万) | GDL(千万) | J-F(1985价格, 千亿元) |
1985 | 58.14 | 59.81 | 72.90 | 178.75 |
1990 | 66.76 | 75.90 | 102.91 | 229.53 |
1995 | 70.69 | 87.44 | 141.87 | 231.32 |
2000 | 76.08 | 103.45 | 203.82 | 408.44 |
2005 | 77.65 | 115.11 | 340.98 | 609.06 |
2010 | 82.90 | 126.51 | 503.85 | 1068.48 |
2015 | 82.42 | 125.33 | 568.28 | 1514.07 |
2018 | 81.24 | 121.06 | 596.64 | 1854.84 |
注: 1. 所有数据均来自于《中国人力资本报告2020》. 网址为: 中央财经大学人力资本与劳动经济研究中心官方网站, http://humancapital.cufe.edu.cn/rlzbzsxm.htm; 中央财经大学-电子科技大学数据共享服务平台, http://cedcdata.cufe.edu.cn/cedc/metadata/list.html. 2. 广义人力资本基于李海峥和熊咸芳(2021)所估算的动态技能替代弹性值: 1985年至1994年3.513、1995年至2001年3.065、2002年至2016年2.549. 3. J-F计算结果基于1985年价格. |
表5 1985–2018分阶段劳动力人力资本年均增长率(%) |
时间段 | 总劳动力人力资本 | 劳动力人均人力资本 | ||||||
劳动力 | 效率单元 | GDL | J-F | 效率单元 | GDL | J-F | ||
1985–1994 | 2.08 | 4.03 | 6.12 | 3.63 | 1.93 | 3.98 | 1.47 | |
1995–2001 | 1.27 | 3.14 | 8.12 | 9.34 | 1.76 | 6.68 | 7.97 | |
2002–2009 | 0.83 | 2.06 | 10.60 | 10.15 | 1.26 | 9.74 | 9.22 | |
2010–2018 | -0.05 | -0.33 | 2.75 | 7.61 | -0.23 | 2.85 | 7.68 |
注: 各时间段年均增长率为各年相对上一年年增长率的简单算术平均值. |
表6 1985–2018年基于不同度量的劳动力人均人力资本 |
年份 | 效率单元(基础层级劳动力) | GDL(基础层级劳动力) | J-F(1985年价格, 千元) |
1985 | 0.98 | 1.19 | 30.79 |
1990 | 1.08 | 1.46 | 34.40 |
1995 | 1.18 | 1.91 | 32.72 |
2000 | 1.29 | 2.54 | 53.60 |
2005 | 1.40 | 4.14 | 78.39 |
2010 | 1.45 | 5.79 | 128.89 |
2015 | 1.46 | 6.62 | 183.75 |
2018 | 1.42 | 7.00 | 228.43 |
注: 1. 广义人力资本基于李海峥和熊咸芳(2021)所估算的动态技能替代弹性值: 1985年至1994年3.513、1995年至2001年3.065、2002年至2016年2.549. 2. J-F为基于1985年价格的实际劳动力人均人力资本.其数据及计算广义人力资本、效率单元的劳动力数量与工资数据均来自于《中国人力资本报告2020》. |
表7 1985–2018分阶段劳动力人力资本年均增长率(%) |
时间段 | GDL | 效率单元 | J-F人力资本 |
1985–1994 | 0.44 | 0.36 | 0.61 |
1995–2001 | 0.48 | 0.32 | 0.56 |
2002–2009 | 0.46 | 0.28 | 0.51 |
2010–2018 | 0.73 | 0.36 | 0.63 |
注: 1. 各时间段人力资本要素解释率为相应时间段内各年度人力资本要素解释率的简单算术平均值, 各年度人力资本要素解释率相加后除以该阶段所包括的年份数. 2. J-F人力资本数据为《中国人力资本报告2020》中的劳动力人均J-F人力资本各年度名义值; 省份GDP数据为国家统计局各省GDP名义值. |
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