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The children of high-income parents often become high-income adults, while their low-income peers often become low-income adults. Education plays a central role in this intergenerational income persistence. Because education-based inequalities grew in recent decades, many scholars predicted that intergenerational income persistence would increase. However, previous research suggests that it remained stable across recent cohorts. We address this puzzle. Analyzing National Longitudinal Surveys of Youth data, we find that growing educational inequality by parental income, along with rising economic returns to education, increased intergenerational persistence, as scholars expected. However, two countervailing trends offset this increase. The expansion of higher education reduced persistence, because completing college helps low-income children become high-income adults. Yet, this reduction in persistence was far from enough to offset the increase in persistence associated with growing educational inequality and rising educational returns. Intergenerational persistence would have increased if not for another change: within educational groups, parental income became less predictive of adult income. New methodological tools underlie these findings, tools that quantify, for the first time, education's full force in intergenerational income persistence. These findings suggest that to reduce intergenerational persistence, educational policies should focus less on how many people complete college and more on who completes college.

With increasing demands in real-time or near real-time remotely sensed imagery applications in such as military deployments, quick response to terrorist attacks and disaster rescue, the on-board geometric calibration problem has attracted the attention of many scientists in recent years. This paper presents an on-board geometric calibration method for linear CCD sensor arrays using FPGA chips. The proposed method mainly consists of four modules&mdash ; Input Data, Coefficient Calculation, Adjustment Computation and Comparison&mdash ; in which the parallel computations for building the observation equations and least squares adjustment, are implemented using FPGA chips, for which a decomposed matrix inversion method is presented. A Xilinx Virtex-7 FPGA VC707 chip is selected and the MOMS-2P data used for inflight geometric calibration from DLR (Kö ; ln, Germany), are employed for validation and analysis. The experimental results demonstrated that: (1) When the widths of floating-point data from 44-bit to 64-bit are adopted, the FPGA resources, including the utilizations of FF, LUT, memory LUT, I/O and DSP48, are consumed at a fast increasing rate ; thus, a 50-bit data width is recommended for FPGA-based geometric calibration. (2) Increasing number of ground control points (GCPs) does not significantly consume the FPGA resources, six GCPs is therefore recommended for geometric calibration. (3) The FPGA-based geometric calibration can reach approximately 24 times faster speed than the PC-based one does. (4) The accuracy from the proposed FPGA-based method is almost similar to the one from the inflight calibration if the calibration model and GCPs number are the same.

The molten salt reactor (MSR) is one of the six advanced reactor concepts selected by Generation IV International Forum (GIF) because of its inherent safety and the promising capabilities of TRU transmutation and Th-U breeding. In this study, a three-dimensional thermal-hydraulic model (3DTH) is developed for evaluating the steady-state performance of the graphite-moderated channel type MSR. The coupled code is developed by exchanging the power distribution, temperature, and fuel density distribution between SCALE and 3DTH. Firstly, the thermal-hydraulic model of the coupled code is validated by RELAP5 code. Then, the mass flow distribution, temperature field, keff, and power density distribution for a conceptual design of the 2MWt experimental molten salt reactor are calculated and analyzed by the coupled code under both normal operating situation and the central fuel assembly partly blocked situation. The simulated results are conductive to facilitate the understanding of the steady behavior of the graphite-moderated channel type MSR.

AbstractWhile causal inference has become front and center in empirical political science, we know little about how to analyze causality with latent outcomes, such as political values, beliefs, and attitudes. In this article, we develop a framework for defining, identifying, and estimating the causal effect of an observed treatment on a latent outcome, which we call the latent treatment effect (LTE). We describe a set of assumptions that allow us to identify the LTE and propose a hierarchical item response model to estimate it. We highlight an often overlooked exclusion restriction assumption, which states that treatment status should not affect the observed indicators other than through the latent outcome. A simulation study shows that the hierarchical approach offers unbiased estimates of the LTE under the identification and modeling assumptions, whereas conventional two‐step approaches are biased. We illustrate our proposed methodology using data from two published experimental studies.

This article reviews recent advances in causal inference relevant to sociology. We focus on a selective subset of contributions aligning with four broad topics: causal effect identification and estimation in general, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference. We describe how machine learning, as an estimation strategy, can be effectively combined with causal inference, which has been traditionally concerned with identification. The incorporation of machine learning in causal inference enables researchers to better address potential biases in estimating causal effects and uncover heterogeneous causal effects. Uncovering sources of effect heterogeneity is key for generalizing to populations beyond those under study. While sociology has long emphasized the importance of causal mechanisms, historical and life-cycle variation, and social contexts involving network interactions, recent conceptual and computational advances facilitate more principled estimation of causal effects under these settings. We encourage sociologists to incorporate these insights into their empirical research.

Land expropriation, where peasants' property rights are encroached by the state, has been recognized as a primary source of social dissension in rural China. Since the end of the last century, the Administrative Litigation Law (ALL) has provided people with a legal weapon to defend themselves against violations by state power. Drawing on the theory of relative deprivation, this paper proposes that peasants are more likely to sue the state when they feel deprived. To examine this hypothesis, we first present a case study to depict the causal process and then use quantitative research to improve the external validity of our findings. We created a novel and unique database of prefecture-level administrative litigations and relative deprivation for Poisson regression analysis. The quantitative results prove that the more peasants feel relatively deprived, the more likely they are to sue the state. Furthermore, the positive effect of relative deprivation on administrative litigations has become more significant over time, implying peasants' growing awareness of legal resistance. This paper concludes that a critical step towards eliminating social inequity and maintaining social stability in rural China is to reduce the relative deprivation of peasants by, for example, allowing them to share in land value appreciation in the process of urbanization. (China Q/GIGA)