Can trust make factories more productive? Using data from Chinese listed manufacturing firms between 2012 and 2021, we apply a Double/Debiased Machine Learning (DML) approach to obtain robust estimates of the policy effect of China’s Construction of Social Credit System (CSCS) pilot policy on labor productivity. We find that firms located in CSCS pilot cities experience significantly higher labor productivity. This gain is not driven by capital deepening or skill-biased labor substitution, but reflects genuine industrial upgrading. Further analysis reveals two key mechanisms: internally, the improved credit environment enhances governance by optimizing asset allocation; externally, it strengthens incentives through better access to subsidies and an improved business climate. We also provide evidence that social trust may exert moral restraint on managers and generate long-term incentives, while its productivity effects may vary across regions. These findings highlight the powerful role of informal institutions such as social trust in boosting manufacturing productivity and fostering sustainable economic growth.

This study examines the environmental and economic effects of heavy-haul railways (HHR) on regions located along the original road-transport routes. Taking China’s HHR capacity expansion for coal transport as a quasi-natural experiment, this study reveals that the shift in transportation mode significantly reduced multiple air pollutants along the replaced road-transport corridors, including PM2.5, PM10, PM1, and NO2. These improvements are primarily attributable to decreases in heavy-truck emissions and coal dust generated by road-based coal transportation, resulting in notable health benefits. Furthermore, despite the reduction of heavy truck transportation and related business, the regions near the original road routes experienced significant economic growth as evidenced by nightlight intensity, attracted more tourism and consumer services firms, and saw an influx of high skilled labor.

This study examines the industrial preferences of local governments in China through the lens of land allocation. We analyze industrial land transactions from 2007 to 2019, a period marked by both expanding industrial policy and intensifying land market competition. Using a border discontinuity design that compares land transactions near county borders, we separate local government preferences from firm demand. We find systematic industrial favoritism: localities offer deeper price discounts to industries aligned with their existing specialization. This favoritism persists over time, is more pronounced in industries with substantial unrealized agglomeration potential, and is stronger in regions with well-developed market institutions. We further show that local governments comply with upper-level industrial policy only when it aligns with their own specialization. Together, these findings point to a bottom-up process in which local governments use land allocation to attract firms that reinforce local industrial strengths through agglomeration spillovers.

This paper examines China’s Grain-for-Green Program, the world’s largest afforestation initiative aimed at converting steeply sloped cropland into forested areas, and evaluates its impact on environmental and economic sustainability. By utilizing a comprehensive dataset that integrates granular land-use information with county-level grain production data, the study reveals a ”win-win” outcome of the program: increased forest cover on steep terrains alongside enhanced local grain production. These dual benefits stem from the reallocation of agricultural activities from sloped areas to flatlands, as evidenced by the expansion of cultivation on more arable flat terrain. Two key policy mechanisms drive this reallocation: (i) central government subsidies that ease farmers’ credit constraints during the transition away from sloped cultivation, and (ii) local government initiatives to improve farmers’ access to flat, cultivable land. The findings underscore the importance of addressing underlying inefficiencies in resource allocation to achieve both environmental preservation and economic sustainability in environmental policy design.

Integrating intelligent technologies into corporate processes represents a transformative response to sustainable and responsible business practices. Despite its growing significance, the effects and mechanisms through which intelligent transformation impacts corporate environmental, social, and governance (ESG) performance remain insufficiently explored. Drawing on resource orchestration and dynamic capabilities theory, this study develops a theoretical framework to analyze how intelligent transformation empowers ESG improvement. Using a comprehensive dataset of Chinese A-share listed companies from 2009 to 2023, the empirical results confirm that intelligent transformation significantly enhances ESG performance. This improvement is realized through three key channels: enhancing information disclosure quality, fostering green innovation, and mitigating supply chain concentration. Furthermore, the effects are more pronounced among state-owned enterprises, technology- and capital-intensive corporations, corporations located in the eastern area of China, and those operating in highly marketized regions. A value chain analysis further reveals that intelligent transformation in research design, manufacturing, and marketing consistently drives ESG enhancements. These findings enrich the literature on intelligent transformation and provide actionable insights for corporations seeking to optimize their sustainability practices in an intelligence era.

We investigate political budget cycles in prefecture-level cities in China by leveraging crossprovince variation in the timing of the Provincial Congress of the Communist Party, which is the most significant event for promoting and reappointing prefecture leaders. Our findings reveal strong evidence of a cyclical pattern, characterized by an increase in government spending leading up to the Congress, followed by a decrease afterward. This trend is particularly pronounced for infrastructure spending, which is a crucial driver of economic growth in China. Additionally, we examine China's multi-layer government hierarchy and explore heterogeneity across prefectures. Our analysis shows that these cycles are especially evident when promotion opportunities for prefecture leaders are most prominent. In terms of revenue sources, we do not observe significant cyclical patterns for in-budget revenues; however, we do find evidence of cycles in revenues from land sales, a vital source of off-budget revenues. Our study contributes to the literature by examining both the size of government and the allocation of public funds in order to uncover distinct political budget cycles at the prefecture level in China and highlighting the vertical incentives within the multi-layer government hierarchy.

This paper investigates whether changing the seating arrangement in a classroom can facilitate positive spillovers from top-performing students to others,using a field experiment conducted in a Chinese high school.Among study groups with balanced abilities,the treatment altered the spatial distribution by assigning the two top students to seats in the spatial center of each group.In the reference groups where students were allowed to choose their own seating arrangements,the lowest performing were significantly less likely to sit next to a top student than they would be under a random assignment.The results suggest that,in the treated groups,there could be enhanced academic spillovers from the top students.The treatment especially benefited the two lowest performing students in science subjects.In contrast,the treatment exerted negative effects on the test scores of the two middle-performing students,due to a disruption mechanism.The results suggest that the spatial layout of a peer network can have a significant impact on learning outcomes.

This paper highlights the crucial role of land allocation mechanisms in fostering industrial agglomeration by examining China's 2007 industrial land market reform. By introducing transparency into the land-selling process, the reform facilitated more buyers to compete for land (as evidenced by increased land sale prices), enabling local governments to allocate land to the most suitable users. Utilizing comprehensive data sets that include information on initial local industrial structure, new industrial establishments, and industrial land transactions, the empirical analysis finds that the reform significantly increased the entry of firms from industries aligned with local specialization, particularly in regions that implemented the reform more strictly. Industries characterized by substantial unrealized agglomeration economies or highly localized spillover effects experienced amplified effects. A well-functioning capital market further enhanced the land market reform's impact. Supporting evidence demonstrates the reform's positive effect on economic growth (as evidenced by changes in nighttime luminosity), potentially through increasing local firms' TFP.

This paper examines the influence of housing wealth on fertility outcomes through a regression discontinuity design based on a 2006 Chinese housing-market policy. Our analysis reveals that the positive impact of this policy on housing wealth significantly enhances the likelihood of fertility by 7.3%. Our result implies that a 1% increase in housing wealth can raise the fertility rate by 0.18%. Furthermore, we observe that children born subsequent to the positive housing wealth shock exhibit improved health, not only at birth but also over the long term. Lastly, we present suggestive evidence suggesting that both parental pre-birth time allocation and parental health may help explain the documented positive effects of housing wealth on fertility rates.

This study aims to address debate in previous studies on whether AI has a positive or negative effect on carbon emission reduction. We used quantile regression and PSTR models to study the diverse impacts of AI on carbon emissions in 66 countries from 1993-2019. There were three main findings in this paper. First, the impact of AI on carbon emissions varies across countries, and its effect on carbon reduction is mainly found in high-carbon emission and high-income countries. Second, the industrial structure environment of different countries affects the role of AI in carbon reduction, with its marginal effect in limiting emissions decreasing with the rise of secondary industrial structures. Third, the impact of AI varies in countries based on their different demographic structures. The marginal effect of AI on carbon emission reduction increases in places with older populations. This study offers unique insight into the heterogeneous impact of AI on CO2 emissions. Our analysis confirms the importance of industrial and demographic structures in promoting carbon emission reduction. We provide effective policy recommendations for economic development and environmental governance.

This paper studies how administrative border adjustments influence individual firm productivity and local economic development. Exploiting a novel quasi-natural experiment conducted since the 1990s in China, our empirical analysis finds that district border adjustments (DBAs) significantly enhance the TFP of manufacturing firms in the adjusted districts. Firms located in the border streets of the districts benefit the most. We investigate the mechanism and find that DBA improves firms’ productivity by enhancing agglomeration economies of industry clusters, reducing firms’ management costs and alleviating their financial constraints, which results in district-level industry specialization and more efficient capital allocation within the district. We also find that DBAs have a significantly positive effect on the overall economic development of border streets as measured by nightlights.

Intelligent transformation is a typical feature of Industry 4.0. To investigate the causal relationship between financial development and intelligent transformation, we empirically investigate the relationship between the samples using panel data from 69 countries from 1993 to 2020, and address potential endogeneity using 2SLS regression. The results show that financial development has significant, positive, and heterogeneous effects on the intelligent transformation of manufacturing. The contributions of financial development are more pronounced in non-advanced, low levels of manufacturing technology, high manufacturing dependence, and bank-dominated economies. Finally, financial development promotes transformation through three channels: innovation, investment, and knowledge spillover effects. The findings would not only contribute to the related literature but also enable policymakers to better understand the value of financial development to the intelligent transformation of industries, especially for developing economies and economies with a high dependence on manufacturing.

This study explores the relationship between AI applications and firm stock liquidity. We measure the AI applications of Chinese listed firms based on text analytics on annual reports from 2007 to 2020. Our results show that AI applications increase stock liquidity, and the effect of AI on increasing stock liquidity is more significant in SOEs and high-tech firms. In addition, AI increases stock liquidity by enhancing market attention rather than directly improving firm performance.

Land use regulations have been pursued by many governments around the world. When making land development policies for cities that feature high population density and limited land supply, one question looms large: where and how much to build across different localities of a city? To answer this question, it is essential to understand how land development policies shape (or reshape) the spatial distribution of population and economic activities within a city because such internal urban structure has fundamental and persistent impacts on urban economic development and welfare (Lucas and Rossi-Hansberg, 2002; Rossi-Hansberg, 2004). Ahlfeldt, Redding, Sturm, and Wolf (2015) and Heblich, Redding and Sturm (2020) use a spatial GE framework to demonstrate how historic shocks to urban transportation networks reshaped the urban structure of Berlin and London respectively and in turn influenced their economic development and urban growth. However, there has been few attempts to study the impact of land use regulations (especially heterogeneous land development policies within a city) on the spatial distributions of urban population and economic activities and the consequent welfare implications. This paper aims to fill the gap.Our study faces two main challenges: On the one hand, high building density can accommodate more residents and employment, which will through agglomeration economies further enhance local amenities and attract even more population and employment. On the other hand, high residential and workplace employment densities are likely to generate crowdedness effect and lengthen commuting time and cost. While the former issue has been studied in the literature, the latter one has seldom been addressed in a spatial equilibrium framework. In order to conduct a comprehensive welfare evaluation of land use regulation policies, it is crucial to incorporate the above two forces into a unified framework. This paper develops a spatial general equilibrium model that incorporates both the positive externality of agglomeration economies and the negative externality of commuting congestion. We then calibrate the model. Using it as workhorse, we carry out counterfactual analyses to evaluate the effects of various land regulation policies on urban internal structure and welfare. As a sensitivity check, under the present land use regulations, we find that our predicted equilibrium outcomes can fit the current data quite well, much better than the model without the commuting crowdedness effect. This paper focuses on Shanghai specifically, the most populous city in China. Unlike other cities in market economy countries, the city government of Shanghai has been regulating housing supply across various localities in Shanghai, mainly through limited land supply and building density (floor-to-area ratio) constraints. According to Shanghai’s 2017-2035 master plan, by 2035 the whole city is going to build up 270 million square meter residential floor space in addition to the current 1.05 billion square meter total floor space. Where to build up these new floor space? The plan also delineates the spatial boundaries of one core urban area around the city center and 14 sub-centers surrounding the core urban area. Those center or sub-center areas may enjoy the priority of land development or redevelopment. However, our data suggests that the fundamental (exogenous) local living and production amenities in those center /or subcenters are not necessarily higher than other tracts in Shanghai. Moreover, employment densities in those areas are already high which alerts commuting congestion. Given this, we try various alternative ways of allocating the new floor space across different tracts in Shanghai and evaluate their corresponding equilibrium outcomes in our counterfactual analyses.We first construct a comprehensive data set based on various data resources that contains information on the residential and workplace employment, housing development densities and prices, land use regulations, wages, and bilateral travel flows and times at the tract level (jiedao or zhen in Chinese) within the city of Shanghai. Using this data, we estimate and calibrate the parameters of the model, including commuting cost parameter with respect to travel time, worker utility dispersion parameter, the values of tract-level fundamental production and residential amenities, and congestion elasticities with respect to both residential and workplace employment densities. Next, using the calibrated and estimated model parameters, we conduct counterfactual analysis to evaluate alternative ways of allocating the additional floor space to different locations of the city. The analysis allows us to identify the contributions of various mechanisms to the improvement of welfare resulting from building up more floor space, including agglomeration externalities, commuting costs, and housing costs. Our counterfactual analyses generate three sets of findings. First, allocating floor space to locations with more advantageous local fundamentals (e.g., transport connections, schools, hospitals, green space) can generate welfare gains. Relative to the initial welfare level which is our benchmark and is normalized to one, if we re-allocate the initial floor space to each tract according its own amenity levels, the welfare gain is 70%. This is consistent with the argument of Sturm et. al (2022). Informed by the above result, in all of the following counterfactuals, we allocate the new floor space across tracts according to each tract's fundamental amenities in the respective targeted areas to be developed. Second, compact development can generate a higher utility level than spreading-out development. Specifically, we divide the city into several 10 km-wide belts: 20-30km, 30-40km, 40-50km, and 50-60km. We experiment with allocating all the new floor space to each different 10 km-wide belts respectively. We find that they all generate higher welfare gains (around 12% relative to the initial level) than spreading out the new floor space all over the city which generates a welfare gain of 9.58%. This is because compact development can generate more supply of housing within a smaller geographical scale and in turn facilitate the rising of agglomeration economies without increasing the housing price. Thirdly, decentralized development works better that centralized ones for Shanghai. Among all the 10 km-wide belts along the radius from the city center, the one that is nearest to the city fringe (i.e., the 50-60km belt) is the best location for new housing development. Allocating new floor space to this area according to the values of local fundamentals can generate a welfare gain of 15.42%, relative to the initial level. Within this belt there locate several contiguous tracts that have fundamental amenities above the city average. Meanwhile, they are sparsely populated now. So they have great potential to accommodate more population and firms in the future.By contrast, allocating new floor space to either 0-10km or 10-20km belts works poorly. Further, if we focus on developing the government-specified core urban area (i.e., the 35 tracts near the city center), we can get a welfare gain of 12% relative to the initial level, which is also significantly lower than the above more decentralized development pattern. Why? This is because the core is already very crowded. If increasing building density here, the positive agglomeration effect will be dominated by the negative commuting crowdedness effect. If we decentralize the new floor space to the sub-centers (Counterfactual C8), we can get a welfare gain of 14.12%, relative to the initial level. The wage gain from agglomeration effect is the strongest, which is not surprising since those subcenters have high productivity and amenities. However, the higher housing price somehow drags down the overall welfare gain. In sum, our findings suggest that a more decentralized yet compact land development according to individual tract amenities seem to be a sensible way to build up Shanghai in the future.

Over the last forty years, China has experienced extraordinary growth under output market reforms, but the growth rates are now tapering off. Reforms in factor markets and city governance have been much slower and are viewed as having the potential to yield considerable efficiency gains. In this paper, we explore this possibility, tackling the key issues of local political manipulation of land markets and objectives of local leaders, constraints on the local budgetary process to finance infrastructure and capital market favoritism of certain cities. We use a structural general equilibrium model with trade and migration frictions, based on prefecture level data. We model the political process of land misallocation within cities which drives up housing prices and estimate city-by-city local leaders’ preferences over economic performance versus residents’ welfare. Counterfactual analysis shows that equalizing capital prices across cities, changing the political scorecard for city leaders to reward just maximization of local consumer welfare, and relaxing local budget constraints together increase welfare of consumers and returns to capital by 13.7% and 2.25% respectively. Housing prices would decline in almost all cities; and the reforms would reduce the current excessive, often showcase investment in local public infrastructure by 49% nationally. These reforms would significantly reduce the population of favored cities with low capital costs like Tianjin and Beijing and raise the population of cities with high costs of capital and low local-leader weights on consumer welfare like Shenzhen and Dongguan.

Land allocation has played an essential role in China’s industrial development. In the past, most industrial land was transacted through negotiations that usually lacked transparency and competition. Since 2007, the central government has required industrial land to be sold via public auction. This paper studies the land transaction mechanisms operating before and after the 2007 reform and how it has affected allocation efficiency. It analyzes three comprehensive data sets, which contain information on new industrial establishments, land transactions, and nighttime lights. The analysis shows, in sharp contrast to the pre-reform period, more land was allocated to industries that enjoyed a local comparative advantage during the post-reform period; this effect is more pronounced in areas that implemented the reform more stringently. These findings suggest that the reform has facilitated firms to cluster spatially to obtain the benefits associated with agglomeration economies both within and across industries and led to a more efficient geographic distribution of industries. The paper also finds suggestive evidence that the reform has increased local output, which is consistent with the theory.

随着中国城市服务业比重的提高,商业用地作为服务业的载体在城市土地开发中的重要性日益凸显.本文的理论分析表明,因为商业用地能对周边住宅用地持续产生正向溢出效应,地方政府会更多地将商业用地通过挂牌出让,以降低其出让价格,实现更高的长期价值.基于土地交易微观数据的实证结果表明,商业用地的挂牌比例比住宅用地高7.9％.机制分析表明,商业用地对住宅用地出让价格有正向的溢出作用,并且该溢出效应会持续五年以上.商业地块的溢出效应越大,城市政府越倾向于将其挂牌出让.这一系列结果体现了城市政府在市场化开发商业地产时的策略选择,是"有为政府"对"有效市场"的反馈和能动.

This paper develops a theoretical framework to study the critical role that politics play in shaping the spatial dimension of China's urbanization and the related welfare implications. Utilizing a large data set of residential land transactions matched with city leaders in 200 Chinese cities from 2000 through 2011, the empirical analysis finds that a 1 standard deviation increase in the career-incentive measure leads to 9 additional kilometers of outward expansion, a 23% increase relative to the sample average. It also finds some suggestive evidence pointing to the distortionary impacts of overly strong incentives of city leaders on spatial expansion, consistent with the theory.

This paper proposes a method for estimating the extent to which density regulation causes the actual housing supply per land unit to deviate from the unconstrained amount, i.e., the impact of density regulation on the intensive margin of housing supply. To overcome the challenges that both the housing supply per land unit (which is a combination of quantity and quality measures) and its unconstrained amount are unobserved to the researchers, we extend the framework of Epple, Gordon and Sieg (2010) by explicitly allowing substitution between the quantity and quality inputs in the housing production function and by imposing a regulatory upper limit on the use of the quantity input. We show theoretically that the land share of the housing value can be used as a proxy for the stringency of the density regulation. We apply this method in our investigation of the stringency of floor-to-area ratio (FAR) regulation in urban China at the land-parcel level and find that the regulation imposes a significant binding constraint on the housing supply per land unit. We then explore the spatial and temporal variations of FAR regulation stringency. We also find that the FAR regulation stringency intensified the housing price appreciation that occurred during the economic stimulus period after 2008.