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限制个人职业发展的究竟是“穷爸富爸”还是你的性别 | Springer Open |
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论文标题：How long is the shadow? The relationships of family background to selected adult outcomes: results from PIAAC
期刊：
作者：Henry Braun
发表时间：2018/05/26
数字识别码：10.1186/s40536-018-0058-x
原文链接：
微信链接：
原文作者：Henry Braun
家庭对一个人生活的影响有多大？它会决定我们的学业，职业和收入吗？如果真的有影响，这些来自家庭优劣的影响又是如何代代相传的呢？最近在Large-scale Assessments in Education上发表的一个新研究试图解答这些问题。
家庭背景与个人成年后的成就之间的相互关系一直是一个有趣的话题，因为这关乎到代与代之间的传承，不管是好的传承还是坏的传承。本研究试图（1）量化家庭背景（主要观察父母的受教育水平和家中的藏书量）对个人未来职业成就（以其在国民收入分布中所处的收入分位为指标）的影响；（2）记录其他因素对这些关系的影响程度；所谓的其他因素包括认知能力，教育程度和职业类别。同时我们研究了年龄和性别对以上相同指标的影响。
我们的数据来自21个参与第一轮国际成人能力评估项目（PIAAC, 2012）的国家。PIAAC是在技术平台上进行的家庭调查。调查通过概率抽样选定成人参与者，首先在不同技能层面进行评估，然后再通过广泛的背景调查问卷进行评估。
我们发现在所有参加调查的国家中，家庭背景与收入密切相关，但这种联系很大程度上受认知能力和教育程度的影响。意料之中的是，家庭背景与认知能力和教育程度高度相关。并且如我们所预期的，更强的认知能力和更高的教育程度与收入正相关。
在所有国家中，性别和收入有很强的联系，而且这种强度居然基本上不受其他因素的影响。具体来说，即使考虑了年龄，全职的职业，认知能力，教育水平和职业类别等因素，女性占据国民收入分布最高分位的可能性仍比男性低。总的来说，在对其他因素进行适当控制的情况下，比起男性，女性更有可能处于国民收入分布里的最低分位。性别带来的劣势幅度因国家而异，日本的程度最大，爱尔兰的程度最小。较低的教育程度带来的劣势也是巨大的，并且在多数国家里大于性别相关的劣势。
鉴于PIAAC是一种横向研究，我们不能从这些模式里得出因果关系，但是各国之间的一致现象说明这个问题值得深究。当某个国家要把这些研究结果应用到政策上时，需要了解本国经济活动的历史走势，一贯的政策，并且要考虑文化和人口组成等因素。所以，要想充分利用这些发现，我们有必要根据不同国家的情况进行更深入的纵向追踪研究。
摘要：
Background
Ongoing interest in the relationships between family background and adult outcomes is motivated by concerns regarding the intergenerational transmission of advantage/disadvantage. Currently all countries are far from achieving the ideal that all individuals, irrespective of their starting points or their demographic characteristics, are able to accumulate sufficient human capital so that they can achieve success in the workplace and fulfill their responsibilities as family members and as citizens. This study quantifies the length of the shadow cast by family background and personal characteristics on an individual’s prospects in the labor market. It also examines the extent to which these relationships are mediated by factors more proximal to labor market entry.
Methods
This study uses data for 21 OECD countries from the first round of PIAAC. It employs descriptive statistics, correlations and logistic regression. Two dichotomous variables are derived from each country’s national annual income distribution: Q1 = 1 if the individual’s income is in the first (lower) quartile and Q4 = 1 if the individual works full-time and whose income is in the fourth (upper) quartile. For each country, a nested sequence of logistic regression models are fit to ascertain the role of more proximal factors in mediating the impact of family background and demographic characteristics on these labor market outcomes.
Results
The patterns of relationships are qualitatively similar across the 21 countries, although the estimated associations vary greatly in strength. Parental education accounts for substantial proportions of the variation in respondents’ Educational Attainment and Cognitive Skills. In most countries, children born to parents with lower levels of education have less than a fifty–fifty chance of exceeding that level. Family background is strongly associated with income, but the relationship is largely mediated by Educational Attainment and Cognitive Skills. Females and younger individuals have much higher odds of being in the lower quartile and much lower odds of reaching the upper quartile, even after adjusting for other variables. The magnitudes of these adjusted odds are concerning.
Conclusions
Family background and gender cast a long shadow on individuals’ life prospects. Countries vary greatly in their success in mitigating these disadvantages. Formulating effective policies will depend on understanding a complex set of dynamics that surely differ among countries.
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期刊介绍：
() is a joint publication of the International Association for the Evaluation of Educational Achievement (IEA) and Educational Testing Service (ETS). The articles in this journal contribute to the science of large-scale assessments, help disseminate state-of-the-art information about empirical research using these databases and make the results available to policy makers and researchers around the world.
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