China is the world's largest user of industrial robots. In 2016, sales of industrial robots in China reached 87,000 units, accounting for around 30 percent of the global market. To put this number in perspective, robot sales in all of Europe and the Americas in 2016 reached 97,300 units (according to data from the International Federation of Robotics). Between 2005 and 2016, the operational stock of industrial robots in China increased at an annual average rate of 38 percent. In this paper, we describe the adoption of robots by China's manufacturers using both aggregate industry-level and firm-level data, and we provide possible explanations from both the supply and demand sides for why robot use has risen so quickly in China. A key contribution of this paper is that we have collected some of the world's first data on firms' robot adoption behaviors with our China Employer-Employee Survey (CEES), which contains the first firm-level data that is representative of the entire Chinese manufacturing sector.

Using a recently constructed dataset that draws on the China Employer–Employee Survey, this paper provides new evidence on the earnings gap between rural migrant and urban manufacturing workers in the People's Republic of China. When we only control for province fixed effects, we find that rural migrant workers are paid 22.3% less per month and 32.2% less per hour than urban workers. We find that the gap in hourly earnings is larger than the gap in monthly earnings because rural migrant workers tend to work an average of 5.6% more hours per month than urban workers. Using these data, we also find that 87.4% of the monthly earnings gap and 73.9% of the hourly earnings gap can be attributed to differences in the individual characteristics and human capital levels of rural migrant and urban workers. Furthermore, we find that this unexplained earnings gap varies among different groups of workers. The earnings gap is much larger (i) for workers in state-owned enterprises than in nonstate-owned enterprises, (ii) for college-educated workers than workers with lower levels of educational attainment, and (iii) in Guangdong province than in Hubei province.

Drawing on data from a random sample of manufacturing firms collected in 2016 for the China Employer-Employee Survey (CEES), we examine differences in measures of productivity and financial returns between state-owned enterprises (SOEs) and private firms in China. The summary statistics show that labor productivity and total factor productivity are generally higher at SOEs than at private firms, but the productivity advantage of SOEs can mostly be explained by the higher levels of human capital of their workers, greater market power, and better management. Furthermore, SOEs’ advantage in productivity exists only in industries with higher SOE concentrations. In contrast, measures of financial returns—return on assets and return on equity—are lower for SOEs than for private firms. We believe that these results may reflect the fact that SOEs generally have easier access to capital, human capital, and markets than other types of firms in China.

Policymakers in developing countries have prioritized the mass expansion of vocational education and training (VET). Evidence suggests, however, that the quality of VET can be poor. One possible reason given by policymakers for this is a lack of resources per student. The goal of this study is to examine whether the quality of VET in developing countries increases by investing greater resources per student. To achieve this goal, we examine the impacts of attending model schools (which have far more resources per student) compared with non-model schools (which have fewer resources) on a range of student cognitive, non-cognitive, and behavioral outcomes. Using representative data from a survey of approximately 12,000 VET students from China, multivariate regression and propensity score matching analyses show that there are no significant benefits, in terms of student outcomes, from attending model vocational high schools, despite their substantially greater resources.

The demand for large-scale assessments in higher education, especially at an international scale, is growing. A major challenge of conducting these assessments, however, is that they require understanding and balancing the interests of multiple stakeholders (government officials, university administrators, and students) and also overcoming potential unwillingness of these stakeholders to participate. In this paper, we take the experience of the Study of Undergraduate Performance (SUPER) in conducting a large-scale international assessment as a case study. We discuss ways in which we mitigated perceived risks, built trust, and provided incentives to ensure the successful engagement of stakeholders during the study’s implementation.

We examine the effects of computer-based versus paper-based assessment of critical thinking skills, adapted from English (in the U.S.) to Chinese. Using data collected based on a random assignment between the two modes in multiple Chinese colleges, we investigate mode effects from multiple perspectives: mean scores, measurement precision, item functioning (i.e. item difficulty and discrimination), response behavior (i.e. test completion and item omission), and user perceptions. Our findings shed light on assessment and item properties that could be the sources of mode effects. At the test level, we find that the computer-based test is more difficult and more speeded than the paper-based test. We speculate that these differences are attributable to the test’s structure, its high demands on reading, and test-taking flexibility afforded under the paper testing mode. Item-level evaluation allows us to identify item characteristics that are prone to mode effects, including targeted cognitive skill, response type, and the amount of adaptation between modes. Implications for test design are discussed, and actionable design suggestions are offered with the goal of minimizing mode effect.

The wide-scale global movement of school education to remote instruction due to Covid-19 is unprecedented. The use of educational technology (EdTech) offers an alternative to in-person learning and reinforces social distancing, but there is limited evidence on whether and how EdTech affects academic outcomes. Recently, we conducted two large-scale randomized experiments, involving ~10,000 primary school students in China and Russia, to evaluate the effectiveness of EdTech as a substitute for traditional schooling. In China, we examined whether EdTech improves academic outcomes relative to paper-and-pencil workbook exercises of identical content. We found that EdTech was a perfect substitute for traditional learning. In Russia, we further explored how much EdTech can substitute for traditional learning. We found that EdTech substitutes only to a limited extent. The findings from these large-scale trials indicate that we need to be careful about using EdTech as a full-scale substitute for the traditional instruction received by schoolchildren.

The wide-scale global movement of school education to remote instruction due to Covid-19 is unprecedented. The use of educational technology (EdTech) offers an alternative to in-person learning and reinforces social distancing, but there is limited evidence on whether and how EdTech affects academic outcomes, and that limited evidence is mixed.1,2 For example, previous studies examine performance of students in online courses and generally find that they do not perform as well as in traditional courses. On the other hand, recent large-scale evaluations of supplemental computer-assisted learning programs show large positive effects on test scores. One concern, however, is that EdTech is often evaluated as a supplemental after-school program instead of as a direct substitute for traditional learning. Supplemental programs inherently have an advantage in that provide more time learning material.

Recently, we conducted two large-scale randomized experiments, involving ~10,000 primary school students in China and Russia, to evaluate the effectiveness of EdTech as a substitute for traditional schooling.3,4 In both, we focused on whether and how EdTech can substitute for in-person instruction (being careful to control for time on task). In China, we examined whether EdTech improves academic outcomes relative to paper-and-pencil workbook exercises of identical content. We followed students ages 9–13 for several months over the academic year. When we examined the impacts of each supplemental program we found that EdTech and workbook exercise sessions of equal time and content outside of school hours had the same effect on standardized math test scores and grades in math classes. As such, EdTech appeared to be a perfect substitute for traditional learning.

In Russia, we built on these findings by further exploring how much EdTech can substitute for traditional learning. We examined whether providing students ages 9–11 with no EdTech, a base level of EdTech (~45 min per week), and a doubling of that level of EdTech can improve standardized test scores and grades. We found that EdTech can substitute for traditional learning only to a limited extent. There is a diminishing marginal rate of substitution for traditional learning from doubling the amount of EdTech use (that is, when we double the amount of EdTech used we do not find that test scores performance doubles). We find that additional time on EdTech even decreases schoolchildren’s motivation and engagement in subject material.

The findings from the large-scale trials indicate that we need to be careful about using EdTech as a full-scale substitute for the traditional instruction received by schoolchildren. There are two general takeaways: First, to a certain extent, EdTech can successfully substitute for traditional learning. Second, there are limits on how much EdTech may be beneficial. Admittedly, we need to be careful about extrapolating from the smaller amount of technology substitution in our experiments to the full-scale substitution in the face of the coronavirus pandemic. However, these studies may offer important lessons. For example, a balanced approach to learning in which schoolchildren intermingle work on electronic devices and work with traditional materials might be optimal. Schools could mail workbooks to students or recommend that students print out exercises to break up the amount of continuous time schoolchildren spend on devices. This might keep students engaged throughout the day and avoid problems associated with removing the structure of classroom schedules. Schools and families can devise creative remote learning solutions that include a combination of EdTech and more traditional forms of learning. Activities such as reading books, running at-home experiments, and art projects can also be used to break up extensive use of technology in remote instruction.

In some accountability regimes, teachers pay more attention to higher achieving students at the expense of lower achieving students. The overall goal of this study is to examine, in this type of accountability regime, the impacts of a pay-for-percentile type scheme in which incentives exist for all students but which are larger for improving the achievement of lower achieving students. Analyzing data from a large-scale randomized experiment in rural China, we find that incentives improve average achievement by 0.10 SDs and the achievement of low-achieving students by 0.15 SDs. We find parallel changes in teacher behavior and curricular coverage. Taken together, the results demonstrate that incentive schemes can effectively address teacher neglect of low-achieving students.

Purpose: Although China has instituted compulsory education through Grade 9, it is still unclear whether students are, in fact, staying in school. In this paper, the authors use a multi-year (2003–2011) longitudinal survey data set on rural households in 102–130 villages across 30 provinces in China to examine the extent to which students still drop out of school prior to finishing compulsory education.

Design/methodology/approach: To examine the correlates of dropping out, the study uses ordinary least squares and multivariate probit models.

Findings: Dropout rate from junior high school was still high (14%) in 2011, even though it fell across the study period. There was heterogeneity in the measured dropout rate. There was great variation among different regions, and especially among different villages. In all, 10% of the sample villages showed extremely high rates during the study period and actually rose over time. Household characteristics associated with poverty and the opportunity cost of staying in school were significantly and negatively correlated with the completion of nine years of schooling.

Research limitations/implications: The findings of this study suggest that China needs to take additional steps to overcome the barriers keeping children from completing nine years of schooling if they hope to either achieve their goal of having all children complete nine years of school or extend compulsory schooling to the end of twelfth grade.

Originality/value: The authors seek to measure the prevalence of both compulsory education rates of dropouts and rates of completion in China. The study examines the correlates of dropping out at the lower secondary schooling level as a way of understanding what types of students (from what types of villages) are not complying with national schooling regulations. To overcome the methodological shortcomings of previous research on dropout in China, the study uses a nationally representative, longitudinal data set based on household surveys collected between 2003 and 2011.