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on China |
| By: | Andres Rodriguez-Pose; Zhuoying You; ; |
| Abstract: | Artificial intelligence (AI) and robotics are revolutionising production, yet their potential to stimulate innovation and change innovation patterns remains underexplored. This paper examines whether AI and robotics can spearhead technological innovation, with a particular focus on their capacity to deliver where other policies have mostly failed: less developed cities and regions. We resort to OLS and IV-2SLS methods to probe the direct and moderating influences of AI and robotics on technological innovation across 270 Chinese cities. We further employ quantile regression analysis to assess their impacts on innovation in more and less innovative cities. The findings reveal that AI and robotics significantly promote technological innovation, with a pronounced impact in cities at or below the technological frontier. Additionally, the use of AI and robotics improves the returns of investment in science and technology (S&T) on technological innovation. AI and robotics moderating effects are often more pronounced in less innovative cities, meaning that AI and robotics are not just powerful instruments for the promotion of innovation but also effective mechanisms to reduce the yawning gap in regional innovation between Chinese innovation hubs and the rest of the country. |
| Keywords: | AI, robotics, China, technological innovation, territorial inequality |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2412&r=cna |
| By: | Jing Ding; Lei Jiang; Lucy Msall; Matthew J. Notowidigdo |
| Abstract: | In 2020, local governments in China began issuing digital coupons to stimulate spending in targeted categories such as restaurants and supermarkets. Using data from a large e-commerce platform and a bunching estimation approach, we find that the coupons caused large increases in spending of 3.1–3.3 yuan per yuan spent by the government. The large spending responses do not come from substitution away from non-targeted spending categories or from short-run intertemporal substitution. To rationalize these results, we develop a dynamic consumption model showing how coupons’ minimum spending thresholds create temporary notches that lead to large spending responses. |
| JEL: | E21 G50 H30 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32376&r=cna |
| By: | Zhang, Yunting; Zhao, Jin; Yu, Zhangsheng; Wang, Guanghai; Zhang, Jun; Jiang, Fan; Wu, Saishuang; Zhang, Yue; Zhang, Donglan; Chen, Xi |
| Abstract: | During the first wave of the COVID-19 pandemic, Shanghai implemented lockdown measures to stop transmission of the virus. Over 26 million residents, including 0.8 million children aged 3-6, were confined at home. This study leveraged a city-wide cohort of preschool children - the Shanghai Children's Health, Education and Lifestyle Evaluation, Preschool (SCHEDULE-P) - and used a quasi-experimental design to study the impact of lockdown on preschool children's mental health and changes in their home environment and lifestyles. Two cohorts - the pre-pandemic cohort and the pandemic cohort - were investigated and compared using the difference-in-differences approach. The Strengths and Difficulties Questionnaire was used to screen children who were at risk for mental health distress. The Index of Childcare Environment questionnaire was used to evaluate the quality and quantity of stimulation and support available to children in their family environment. Children's screen time, sleep duration, and household socioeconomic status were also queried. The results showed that having experienced lockdown and home confinement was associated with a 3.1% increase in the percentage of children at risk for mental health distress, was associated with 21.2 minutes/day longer screen time, 15.7 minutes/day longer sleep duration, and a less favorable family environment. Children of parents with lower levels of education were more likely to experience mental health challenges associated with the lockdown. |
| Keywords: | Lockdown, Preschoolers, Mental health, Home environment, Lifestyle, China |
| JEL: | I18 I12 H75 I28 C23 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:1430&r=cna |
| By: | Tianlei Huang (Peterson Institute for International Economics) |
| Abstract: | Expansionary fiscal policy helped China's economy grow in 2020, a year in which most economies contracted because of the COVID-19 pandemic. Amid a broader pivot to policy and regulatory tightening, fiscal support was withdrawn in 2021. In 2022, government budget turned expansionary to ensure economic stability ahead of the Communist Party Congress, but the execution fell short and fiscal policy ended up being weaker than planned. A recurrent problem during the pandemic, however, was that local governments did not fully spend their budgets. Aside from the sharp drop in local governments' land sale revenue in 2022, which dragged down their spending, it was also caused by local governments' failure to fully utilize their special bond quotas approved by the central government for capital investment. China's fiscal policy during the COVID-19 pandemic highlights four issues with implications for fiscal policy making. First, the government needs to avoid projecting unrealistically high land sale revenue in its budgets. Second, it needs to reconsider its problematic use of local-government special bond as a major fiscal stimulus instrument. Third, it needs to make sure its deficit, growth, and inflation targets are consistent. Last, Beijing needs to be more tolerant of higher fiscal deficits, at a minimum ensuring that overall fiscal spending grows at least as rapidly as nominal output. |
| Keywords: | China, public finance, government budget, fiscal deficit |
| JEL: | H61 H62 H77 P35 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:iie:wpaper:wp24-7&r=cna |
| By: | Francesca Guadagno (The Vienna Institute for International Economic Studies, wiiw); Robert Stehrer (The Vienna Institute for International Economic Studies, wiiw) |
| Abstract: | The energy-renewables ecosystem (ERES) plays a particularly important role in the green transition. This paper analyses its relevance in EU member states and the competitiveness for the EU27 as a whole vis-à-vis other global players and identifies structural dependencies and vulnerabilities. It does so by drawing on the Joint Research Centre’s FIGARO dataset and detailed trade data, and by developing a novel approach that adapts input-output indicators to the analysis of industrial ecosystems. A number of key findings emerge from our analysis. First, the ERES is particularly relevant in new member states, Austria and Germany. At the global level, the EU27 is the second most important exporter after China. Second, in 2020 the EU ecosystem was dependent on imports of coal and lignite from Russia, as well as on a variety of other products from China (including medium- and high-tech electronic products). Third, analysis on the basis of detailed trade data indicates that a few products in the ERES supply chain are delivered by only a handful of countries, which could indicate some vulnerability. Most of the partner countries supply some products that may be characterised as ‘risky’, but China is a main source of such products. |
| Keywords: | green transition; energy-renewables ecosystem; linkages; dependencies; open strategic autonomy |
| JEL: | F10 F14 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:wii:rpaper:rr:473&r=cna |
| By: | Yi Lu; Jianguo Wang; Huihua Xie |
| Abstract: | This paper develops a generalized framework for identifying causal impacts in a reduced-form manner under kinked settings when agents can manipulate their choices around the threshold. The causal estimation using a bunching framework was initially developed by Diamond and Persson (2017) under notched settings. Many empirical applications of bunching designs involve kinked settings. We propose a model-free causal estimator in kinked settings with sharp bunching and then extend to the scenarios with diffuse bunching, misreporting, optimization frictions, and heterogeneity. The estimation method is mostly non-parametric and accounts for the interior response under kinked settings. Applying the proposed approach, we estimate how medical subsidies affect outpatient behaviors in China. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.09117&r=cna |
| By: | Kumar, Utkarsh; Ahmad, Wasim; Uddin, Gazi Salah |
| Abstract: | Exchange rate modeling has always fascinated researchers because of its complex macroeconomic dynamics. This study documents the exchange rate dynamics of major emerging economies after accounting for their macroeconomic cycles and explores the Bayesian Vector Error Correction Model (VECM) Markov Regime switching model, which uses time-varying transition probabilities. The main objective is to study the exchange rate dynamics of Brazil, Russia, India, China, and South Africa (BRICS) vis-à-vis the US dollar. The Bayesian setup uses two hierarchal shrinkage priors, the normal-gamma (NG) prior and the Litterman prior, for parameters' estimation. These shrinkage priors allow for a more comprehensive assessment of the regime-specific coefficients. The model performed well in differentiating between the two regimes for all currencies. The Russian ruble was identified to be the most depreciated currency, whereas the African Rand was the most appreciated. The evaluation of model features revealed that many regime-specific coefficients differed significantly from their common mean. A forecasting exercise was then performed for the out-of-sample period to assess the model's performance. A significant improvement was observed over the basic random walk (RW) model and the linear Bayesian vector autoregression (BVAR) model. |
| Keywords: | time-varying parameters; BRICS; cointegration; exchange rate forecasting; Markov switching |
| JEL: | F3 G3 |
| Date: | 2024–04–09 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:122816&r=cna |
| By: | Tan-Soo, Jie-Sheng (Lee Kuan Yew School of Public Policy, National University of Singapore); Finkelstein, Eric (Duke-NUS Medical School, Singapore); Qin, Ping (School of Applied Economics, Renmin University of China); Jeuland, Marc (Sanford School of Public Policy, Duke University, USA); Pattanayak, Subhrendu (Sanford School of Public Policy, Duke University, USA); Zhang, Xiaobing (School of Economics, Renmin University of China) |
| Abstract: | Due to worsening air quality across many cities in developing countries, there is an urgent need to consider more aggressive air pollution control measures. Valuation of the benefits of clean air is crucial for establishing the rationale for such policies, but is methodologically challenging, often expensive, and therefore remains limited. This study assesses the potential for more standardized and cost-effective measurement of the demand for air quality improvements, applying a contingent valuation procedure via online surveys, in three Asian megacities facing severe but varying pollution problems – Beijing, Delhi, and Jakarta. The study’s primary contribution is to demonstrate the viability of this approach, which significantly enhances comparability of valuations and their drivers across locations, and thereby has great potential for informing policy analysis and targeting of specific interventions. A second contribution is to supply sorely needed data on the benefits of clean air in these three particular Asian cities, which collectively have a population of about 50 million people. The annual willingness-to-pay for air quality to reach national standards is estimated to be US$150 in Jakarta (where average PM2.5 concentration, at 45µg/m3, exceeds national standards by the smallest amount, specifically a factor of 1.3), US$1845 in Beijing (PM2.5 at 58µg/m3, 1.7 times the standard), and US$1760 in Delhi (PM2.5 at 133µg/m3, 3.3 times the standard). The methods deployed could be applied more widely to construct a worldwide database of comparable air quality valuations. |
| Keywords: | Low and middle-income countries; air pollution; contingent valuation |
| JEL: | D00 |
| Date: | 2023–05–30 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:gunefd:2023_008&r=cna |
| By: | Jiawen Luo (School of Business Administration, South China University of Technology, Guangzhou 510640, China); Shengjie Fu (School of Business Administration, South China University of Technology, Guangzhou 510640, China); Oguzhan Cepni (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Ostim Technical University, Ankara, Turkiye); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | This study examines the impact of climate-related risks on the inflation rates of the United States, focusing on the overall Consumer Price Index (CPI) and its significant components, namely food and beverages and housing inflation. Employing quantile regression models and a comprehensive dataset spanning from January 1985 to September 2022, we analyze five specific climate risk factors alongside traditional macroeconomic predictors. Our findings indicate that models incorporating individual climate risks generally outperform those considering only macroeconomic factors. However, models combination strategies that integrate all five climate risk measures consistently deliver superior forecasting performance. Notably, the pronounced effect of climate risks on food inflation significantly contributes to the observed trends in the overall CPI, which is largely driven by this subcomponent. This research highlights the crucial role of climate factors in forecasting inflation, suggesting potential avenues for enhancing economic policy-making in light of evolving climate conditions. |
| Keywords: | Climate risks, US inflation, Dynamic quantile moving averaging, Forecasting |
| JEL: | C21 C22 C53 E31 Q54 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202420&r=cna |