In the third quarter of 2025, faculty and students from the School of Economics, Shanghai University of Finance and Economics achieved fruitful academic research outcomes. More than 20 papers were published in well-known economic journals at home and abroad, including 13 papers in international Class II and above economics journals and 4 papers in domestic authoritative journals. Below is the overview and abstracts of these academic achievements.
Professor Meng Dawen from the Department of Microeconomics had his sole-authored paper Targeting Network Intervention with Social Norm published online in Journal of Economic Theory, an international Class I economics journal.
Targeting Network Intervention with Social NormJournal of Economic Theory Online, Volume 230
This paper discusses a network game in a local-average setup, where players' payoffs depend on the social norms they confront. I focus on an optimal targeting intervention problem, where a planner aims to maximize social welfare by altering individual characteristics ex ante. First, I decompose orthogonally the optimal intervention into the eigenspaces of the social welfare matrix. In what follows, I present the limit forms of the optimal intervention as the budget approaches infinity or zero. I then identify the errors arising from approximating the optimal intervention with its various limit forms. Finally, this paper explores the intervention problem under incomplete information, where the planner switches from mean to variance intervention based on the comparison of their unit costs and the aggregate budget size.
Associate Professor Zhang Jingyi from the Department of Macroeconomics and her doctoral student Lian Lili co-authored the paper Allocative implications of government investment in private sector, published online in Journal of Development Economics, an international Class I economics journal.
Allocative implications of government investment in private sectorJournal of Development Economics Online, Volume 179
In China, state owners make minority equity investments in private firms. We study the allocative implications of such government investments using a two-sector DSGE model with financial friction and idiosyncratic productivities. In our model, private owners are more productive than state owners but face tighter financial constraints. Equity investment by a state owner alleviates the private owner’s financial constraint but dampens its own productivity, consistent with Chinese firm-level data. Under this setup, only private owners with sufficiently high productivity accept such investment, while only state owners with sufficiently low productivity make such investment. As a result, expansion of such government investment improves capital allocation within each sector and across sectors. Our analysis shows that financial liberalizations, including liberalizing interest-rate controls and reducing the loan-to-value gap between sectors, stimulate private owners’ demand for such government investment but discourage state owners from making it, thus generating an ambiguous effect on aggregate productivity.
Assistant Professor Lin Yingqian from the Department of Econometrics co-authored the paper Identification and inference for semiparametric single index transformation models, formally published in Journal of Econometrics, an international Class I economics journal.
Identification and inference for semiparametric single index transformation modelsJournal of Econometrics Volume 251, Sep.2025
This paper considers a semiparametric single index model in which the dependent variable is subject to a nonparametric transformation. The model has the form G₀(Y)=g₀(Xᵀθ₀)+e, where X is a random vector of regressors, Y is the dependent variable and e is the random noise. The monotonic function G₀, the smooth function g₀ and the index vector θ₀ are all unknown. This model is quite general in the sense that it nests many popular regression models as special cases. We first propose identification strategies for the three unknown quantities, based on which estimators are then constructed. The kernel density weighted average derivative estimator of δ (proportional to θ₀) has a V-statistic representation and its asymptotic normality is established under the small bandwidth asymptotics. The kernel estimator of the transformation function G₀ is a functional of the conditional distribution estimator of Y given Xᵀθ₀ and is shown to be √n-consistent and asymptotically normal. The sieve estimator of g₀ is shown to enjoy the standard nonparametric asymptotic properties. A specification test for the single index structure and extension to allow for endogenous regressors are also developed. In addition, data-driven choices of the smoothing parameters are discussed. Simulation results illustrate the nice finite sample performance of the proposed estimators and specification test. An empirical application to studying the impact of family income on child achievement demonstrates the practical merits of the proposed model.
Professor Zhang Hanghui from the Department of Econometrics co-authored the paper Distribution regression with censored selection, formally published in Journal of Econometrics, an international Class I economics journal.
Distribution regression with censored selectionJournal of Econometrics Volume 251, Sep.2025
Chernozhukov, Fernández-Val, and Luo (2023, CFL (2023) hereafter) considered a distribution regression model subject to sample selection with a binary selection mechanism. In this paper, we show how to identify and estimate a semi-parametric distribution regression model subject to a censored selection rule. With censored selection, we do not need to impose the usual outcome exclusion restriction or exclusion of the level of the latent selection variable from the selection sorting function for model identification, unlike CFL (2023). We propose new semiparametric estimators and corresponding inference procedures for model parameters and related functional parameters. We apply our method to investigate wage inequality in the UK for the period 1978–2000 using the Family Expenditure Survey (FES) data. Our findings reveal that (i) the selection sorting exclusion and outcome exclusion restrictions imposed by CFL (2023) are rejected; (ii) there is negative selection into work at most quantile levels for females, but not for males; (iii) in contrast to CFL (2023), our selection sorting effect pattern does not offer clear evidence on assortative matching or glass ceiling in the UK labor market; (iv) the latent gender wage gaps after correcting for selection bias are about 25%–50% of CFL (2023)’s estimates, and are also significantly smaller than the observed wage gaps; (v) similar to CFL (2023), there exists some strong evidence on gender discrimination in the UK labor market.
Assistant Professor Luo Sai from the Department of Labor Economics co-authored the paper Curriculum, Political Participation, and Career Choice, published online in Review of Economics and Statistics, an international Class I economics journal.
Curriculum, Political Participation, and Career ChoiceReview of Economics and Statistics Online, Sep.2025
We examine the causal impact of ideological education on students’ political participation and career choices by exploiting China’s staggered rollout of a high school curriculum reform that emphasized political indoctrination. Using nationally representative survey data on college students that the authors collected, we find that exposure to the new curriculum increases the likelihood of joining the Chinese Communist Party by 14% and raises the probability of securing state-sector jobs after graduation by 15%. These results highlight the powerful role of ideological education in shaping students’ political alignments and career trajectories.
Professor Yao Lan from the Department of Microeconomics co-authored the paper Expectations-based reference-dependence and labor supply: Eliciting cabdrivers’ expectations in the field, formally published in Journal of Economic Behavior & Organization, an international Class II economics journal.
Expectations-based reference-dependence and labor supply: Eliciting cabdrivers’ expectations in the fieldJournal of Economic Behavior & Organization Volume 239, Nov.2025
This paper reports a field experiment on Shanghai cabdrivers’ labor supply, analyzing the data using an expectations-based reference-dependent model that allows daily income- and hours-targeting. Our main innovation is to elicit the cabdrivers’ income and hours expectations, twice a day. We find that expectations indeed affect labor supply in a way predicted by a reference-dependent model, and hours expectations have a stronger influence than income expectations. Both expectations are found to be correlated with their most recent historical average values. While income expectations do adjust within the day, hours expectations are sticky. The findings suggest that the targeting effect based on hours expectations plays a more important role than traditionally thought.
Associate Professor Lu Xiaohui and Professor Zhou Yahong from the Department of Econometrics co-authored the paper An Adaptive Kernel-Based Structural Change Test for Copulas, formally published in Journal of Business & Economic Statistics, an international Class II economics journal.
An Adaptive Kernel-Based Structural Change Test for CopulasJournal of Business & Economic Statistics Volume 43, Jul.2025
This article proposes a structural change test for copula models based on the kernel smoothing method. The proposed approach enables adaptable estimation of the dynamic marginal distributions, either parametrically or semi-parametrically. The test statistic is formulated via the weighted quadratic distance between the local smoothing copula and the empirical copula function, using pseudo-observations of marginal distributions. The test statistic is pivotal with an asymptotic standard Normal distribution, irrespective of the marginal distributions, parameters, and estimations, and is consistent against a wide range of smoothly transitioning structural changes as well as abrupt structural breaks for copula models. Monte Carlo simulations show that the test performs well in finite samples and outperforms existing tests in the case of periodic changes.
Professor Sun Yan from the Department of Econometrics, together with doctoral students Tan Liming and Zhu Zhenyu, co-authored the paper Homogeneity Pursuit in Clustered Data Analysis When Cluster Sizes Are Small, published online in Journal of Business & Economic Statistics, an international Class II economics journal.
Homogeneity Pursuit in Clustered Data Analysis When Cluster Sizes Are SmallJournal of Business & Economic Statistics Online, Jul.2025
Clustered data analysis is an important topic in data science. A well established approach is to assume all clusters share the same unknown parameters of interest, and the difference between different clusters is formulated and accounted for by cluster effects. Whilst this approach works very well in many issues, such as exploring the global impact of an explanatory variable on the response variable, it does not provide much insight about individual attributes of each cluster. Assuming different clusters have completely different parameters would result in too many unknown parameters, which would lead to large variances of the final estimators. Following the idea of homogeneity pursuit proposed in Ke et al. (2015), various modelling approaches are proposed in recent literature to group the unknown parameters and explore the individual attributes in clustered data analysis. However, most of them are either difficult to implement or require each cluster to have reasonably big cluster size. In this paper, we propose a new approach, which is easy to implement and does not require any cluster to have big size, and establish its asymptotic properties without assuming the size of any cluster tends to infinity. We also conduct intensive simulation studies to show the approach works very well when sample size is finite. Finally, we apply the approach to a well known financial dataset to show its superiority in exploring individual attributes in clustered data analysis.
Professor Tang Qianfeng from the Department of Microeconomics and master’s student Zhu Zhan co-authored the paper Ekici’s reclaim-proof allocations revisited, published online in Journal of Mathematical Economics, an international Class II economics journal.
Ekici’s reclaim-proof allocations revisitedJournal of Mathematical Economics Online, Volume 121
We revisit the concept of reclaim-proof allocations proposed by Ekici (2013) for house allocation problems with existing tenants. As a concept of core, the definition of reclaim-proof allocations assumes that when a coalition blocks an allocation, an agent in the coalition is allowed to bring her allocated object into the coalition, even when it is privately owned by an outsider. We propose a new notion of core called the effectual core by restoring the feasibility of coalitional blocking in Ekici’s definition. Our main result shows that the effectual core, while by definition weaker than reclaim-proofness, is actually equivalent to it. Together with Ekici’s results, it is then immediate that an allocation is in the effectual core if and only if it is produced by the You request my house-I get your turn (YRMH-IGYT) mechanism (Abdulkadiroğlu and Sönmez, 1999) and if and only if it is a competitive allocation.
Lecturer Fan Xinyue co-authored the paper CPI Forecasting and Method Comparison Based on Macroeconomic Big Data, formally published in Journal of Management Science in China, a domestic Class A authoritative economics journal.
CPI Forecasting and Method Comparison Based on Macroeconomic Big DataJournal of Management Science in China, Issue 8, 2025
The arrival of the big-data era brings unprecedented opportunities and challenges to CPI forecasting. Making full use of high-dimensional data information and developing interpretable machine learning forecasting models are of great significance for both theoretical development and practical application. To this end, this study constructs a monthly macroeconomic database for China containing 239 variables across 9 categories and compares the CPI forecasting performance of 13 models including traditional time series models, regularized regressions, factor models and ensemble algorithms under large datasets. Furthermore, machine-learning derived algorithms are built based on the control-variable idea to interpret the results and analyze underlying mechanisms. The results show that random forest and XGBoost achieve favorable forecasting performance, especially with substantial advantages in medium- and long-term forecasting. Further analysis attributes their strengths to nonlinear model specifications and non-sparse variable processing: the former makes variable relationships in the model more consistent with reality, while the latter enables full exploitation of big-data information. Meanwhile, these two models identify autoregressive terms, prices, employment and other reasonably important variable categories for CPI prediction.
Professor Zhou Yahong from the Department of Econometrics and undergraduate student Zhu Xiandong co-authored the paper How Does the Development of Data Elements Reshape Anti-Money Laundering Risks?, formally published in Insurance Studies, a domestic Class B authoritative economics journal.
How Does the Development of Data Elements Reshape Anti-Money Laundering Risks?Insurance Studies, Issue 7, 2025
Money laundering undermines the stability of the financial system and fuels crime, corruption and terrorist activities, exerting negative impacts on national economic security and international reputation. Along with the development of the digital economy, anti-money laundering risks faced by obligated institutions (including financial institutions and designated non-financial institutions) and regulators have evolved. Within a Stackelberg game framework, this paper discusses how data elements — a critical underpinning of digital economy development — affect the behaviors of money launderers, obligated institutions and regulators. Based on panel data from 60 countries and regions covering 2013 to 2023, it empirically tests the impact of data-element development on anti-money laundering risks. The study finds that the strategic choices of relevant stakeholders in anti-money laundering are related to the relative marginal contribution of data elements. The development of data elements reduces anti-money laundering risks, mainly through higher digital payment coverage and improved digital capacity of regulators. Greater digital payment coverage and digital regulatory transformation raise the transparency of financial transactions and strengthen regulators’ monitoring and analytical capacity, enabling obligated institutions and regulators to monitor and track capital flows more effectively. Heterogeneity tests show that compared with high-income countries, data-element development reduces anti-money laundering risks more significantly in non-high-income countries. This research carries theoretical and policy value for understanding how data-element development reshapes anti-money laundering risks, increasing investment in the development and utilization of data elements, further advancing digital transformation of regulators, and improving the effectiveness of anti-money laundering work.
Doctoral student Song Yang and Professor Chen Yuanyuan from the Department of Labor Economics co-authored the paper The Spillover of Employment Pressure onto Marriage and Fertility Behavior: The Extra Penalty of Human Capital Mismatch, formally published in Nankai Economic Studies, a domestic Class B authoritative economics journal.
The Spillover of Employment Pressure onto Marriage and Fertility Behavior: The Extra Penalty of Human Capital MismatchNankai Economic Studies, Issue 6, 2025
Late marriage, delayed childbearing and low fertility within families have become bottlenecks for population development in China. Clarifying the deep-rooted drivers behind marriage and fertility behavior is essential for promoting high-quality population development and advancing Chinese-style modernization. Using panel data from the China Family Panel Studies for 2010–2020, this paper depicts the distribution of human capital mismatch across industries and occupations in China and empirically examines the penalty effect of human capital mismatch on marriage and fertility as well as its mechanisms. The results show that human capital mismatch significantly lowers individuals’ marriage and fertility rates, postpones marriage and childbearing timing, and reduces the number of children born. Delayed marriage serves as the core driver of declining fertility. This effect shows no significant gender difference; women do not choose to return to family roles because of workplace human capital mismatch. Falling individual income and rising job uncertainty constitute the main channels through which human capital mismatch negatively affects marriage and fertility outcomes.
Assistant Researcher Lang Xuhua and Professor Mao Peihua from the Department of Political Economy co-authored the paper Leading the Transformation and Upgrading of Manufacturing through Service-Oriented Manufacturing, formally published on the Theory Page of Wenhui Daily.
Leading the Transformation and Upgrading of Manufacturing through Service-Oriented ManufacturingWenhui Daily, Theory Page
As a new industrial form integrating manufacturing and services, service-oriented manufacturing promotes the shift of manufacturing from product-centered to human-centered operations and has become an important direction for the transformation and upgrading of China’s manufacturing sector. Accelerating the development of service-oriented manufacturing is a practical measure for China to effectively enhance the core competitiveness of manufacturing firms based on a deep understanding of the development laws of industrial capital. It is also a strategic option for advancing new industrialization and building a stronger manufacturing nation.
