浙江大学学报:人文社会科学版
浙江大學學報:人文社會科學版
절강대학학보:인문사회과학판
Journal of Zhejiang University(Humanities and Social Sciences)
2015年
2期
164~183
,共null页
产业结构调整 劳动力转移 劳动力结构 个体特征 面板数据 multinomial-logit模型 产业政策
產業結構調整 勞動力轉移 勞動力結構 箇體特徵 麵闆數據 multinomial-logit模型 產業政策
산업결구조정 노동력전이 노동력결구 개체특정 면판수거 multinomial-logit모형 산업정책
structural change; inter-sectoral labor flows; labor force composition; individualcharacteristics; panel data; multinomial-logit model; industry policies
根据家庭不变量信息将“城镇住户调查数据”匹配成长时期的个体层面面板数据后发现,不同产业的劳动者构成存在明显差异。统计结果证实,劳动者在产业间的转移概率与个体特征有关。理论模型进而说明,劳动者在产业间的转移决策是个体特征的函数,并可通过multinomial-logit模型估计。实证结论表明,性别和受教育程度是劳动者产业间转移的关键影响因素,当前工作性质还将影响已参加工作劳动者的转移决策,产业结构调整也将在需求面和供给面上引导劳动者在产业间转移。以上结论在更细致的行业层面上和不同时期内具有稳健性。对中国经济结构转型进行微观考察有助于为制定与劳动力结构相匹配的产业政策和选择不同的产业结构调整驱动力提供参考。
根據傢庭不變量信息將“城鎮住戶調查數據”匹配成長時期的箇體層麵麵闆數據後髮現,不同產業的勞動者構成存在明顯差異。統計結果證實,勞動者在產業間的轉移概率與箇體特徵有關。理論模型進而說明,勞動者在產業間的轉移決策是箇體特徵的函數,併可通過multinomial-logit模型估計。實證結論錶明,性彆和受教育程度是勞動者產業間轉移的關鍵影響因素,噹前工作性質還將影響已參加工作勞動者的轉移決策,產業結構調整也將在需求麵和供給麵上引導勞動者在產業間轉移。以上結論在更細緻的行業層麵上和不同時期內具有穩健性。對中國經濟結構轉型進行微觀攷察有助于為製定與勞動力結構相匹配的產業政策和選擇不同的產業結構調整驅動力提供參攷。
근거가정불변량신식장“성진주호조사수거”필배성장시기적개체층면면판수거후발현,불동산업적노동자구성존재명현차이。통계결과증실,노동자재산업간적전이개솔여개체특정유관。이론모형진이설명,노동자재산업간적전이결책시개체특정적함수,병가통과multinomial-logit모형고계。실증결론표명,성별화수교육정도시노동자산업간전이적관건영향인소,당전공작성질환장영향이삼가공작노동자적전이결책,산업결구조정야장재수구면화공급면상인도노동자재산업간전이。이상결론재경세치적행업층면상화불동시기내구유은건성。대중국경제결구전형진행미관고찰유조우위제정여노동력결구상필배적산업정책화선택불동적산업결구조정구동력제공삼고。
Multi-sector neoclassical growth models up to date still largely remain at a highly aggregate level in explaining structural changes and inter-sectoral labor movements. By assuming a homogeneous labor force, they tend to have a limitation in revealing how individual characteristics affect structural transformations. This paper intends to reexamine these theories at a micro level of individual workers. With the 1986 - 2009 Urban Household Survey Data of China's National Bureau of Statistics, we match a person over years using the invariant information of his or her family. We find substantial differences in labor force compositions across sectors: the primary sector has the largest share of workers with an educational attainment of junior high school or below and male workers, whereas the tertiary sector has the largest share of workers with higher education levels and females. But the age and work experience compositions do not differ much.
We establish a functional relationship between workers' individual characteristics and their sector-switch choices based on a theoretical model, and derive an empirically testable multinomial- logit model from the relationship. Individual characteristics are gender, age, educational attainment, danwei type, job type, and work experiences. We notice industrial characteristics can also affect workers' sector-switch choices, so we control each industry's labor share in SOE and collectively owned firms, productivity growth rate, and an additional fixed effect. We finally control the real GDP per capita of each worker's province according to structural change theories.
The multinomial-logit model is estimated by the panel data matched from Urban Household Surveys. We find that for people who have already been working, females tend to join the primary sector with a lower probability but the tertiary sector with a higher probability~ a higher education level and vocational education encourage workers to join the tertiary sector~ people with a white collar job also tend to leave the primary sector and join the tertiary sector~ work experiences exert a nonlinear effect, which is firstly negative and then positive, on the probability to join the tertiary sector. For people who start to work for the first time, gender, age and a college or above degree are key factors to determine their sector choices. In particular, females and college graduates are more likely to join the tertiary sector, while age have a nonlinear effect, which is also negative first and positive later, on this probability. As to industrial characteristics, we find that a higher share of SOE and collectively owned firms workers discourages people to leave the primary sector and join the tertiary sector, but these effects are partly mitigated by a productivity growth in the primary sector. In the meantime, a productivity growth in the secondary sector induces people to leave the primary or secondary sector for the tertiary sector, whereas a productivity growth in the tertiary sector does the opposite. Finally, consistent with structural change theories, higher income leads workers to leave the primary sector and join the tertiary sector. With robustness checks, we find these results valid at more detailed industry levels and in different periods. This study sheds light on making industrial policies compatible with the country's labor force composition and choosing the proper engine to facilitate the country's structural changes.