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How China's Rural Health Program Lifted Incomes: Evidence from 800 Million Beneficiaries
No full textThis study evaluates the economic impact of the New Rural Cooperative Medical Scheme in China, the world's largest rural public health program, covering over 800 million rural residents. Using longitudinal survey data from villages that gained access to the program in different years, we find that the program improved the probability of being in good health by 4.4% to 8.2% across age groups. For the average participating household, per capita income increased by 20.3% over a decade, driven primarily by greater off-farm labor participation and higher wages, alongside significant agricultural income growth. The aggregate income gains were six times the government's program investments. These effects can be replicated by a structural model that characterizes the health investments and labor allocation of utility-maximizing rural residents. Counterfactual analyses based on the structural model suggest that China could further increase the program's benefits by raising the reimbursement rate up to 0.8 (but not beyond). Additionally, eliminating the current cross-province reimbursement constraints would further boost income gains by 18.7%
Tradeoffs over Rate Cycles: Activity, Inflation and the Price Level
No full textCentral banks often face tradeoffs in how their monetary policy decisions impact economic activity (including employment), inflation and the price level. This paper assesses how these tradeoffs have evolved over time and varied across countries, with a focus on understanding the post-pandemic adjustment. To make these comparisons, we compile a cross-country, historical database of “rate cycles” (i.e., easing and tightening phases for monetary policy) for 24 advanced economies from 1970 through 2024. This allows us to quantify the characteristics of interest rate adjustments and corresponding macroeconomic outcomes and tradeoffs. We also calculate Sacrifice Ratios (output losses per inflation reduction) and document a historically low “sacrifice” during the post-pandemic tightening. This popular measure, however, ignores adjustments in the price level—which increased by more after the pandemic than over the past four decades. A series of regressions and simulations suggest monetary policy (and particularly the timing and aggressiveness of rate hikes) play a meaningful role in explaining these tradeoffs and how adjustments occur during tightening phases. Central bank credibility is the one measure we assess that corresponds to only positive outcomes and no difficult tradeoffs
Nato expansion: An open door policy?
No full textRussia’s 2022 invasion of Ukraine shook the world’s security architecture and ultimately led to Finland and Sweden officially joining NATO in 2023 and 2024 respectively. A key question which arises then, is what determines NATO membership? Is there an open door policy or are accession decisions based on geopolitics? This paper develops a predictive model assessing the probability of joining NATO for several European countries. The model is based on logis- tic regression and shows that the most important determinants of NATO membership are past geopolitics such as EU and USSR memberships. Less important factors include the strength of economy, political stability and geography. Using a sample from 1979 to 2020, the model predicts that Sweden and Finland were highly likely to join NATO, while the probability of Ukraine’s accession is low
Profit-enhancing emissions taxes in near-zero-emissions industries
No full textMotivated by the recent global trend of net-zero-emissions environmental regulations, we investigate the relationship between emissions tax rates and firm profits in oligopolies. Our result indicates that when the resulting emission levels are approximately zero, a marginal increase in the tax rate enhances firms' profits except in monopoly markets. This finding suggests that firms might not resist a further increase in environmental tax if the target emissions level is sufficiently low. Moreover, we present parametric numerical examples suggesting that the profit-enhancing range is large and not limited to near-zero emissions
High Tech and Innovative Emerging Industries and Pakistan's Policies and Regulations towards Adaptation in the light of China’s Strategies of Reverse Engineering
No full textThe evolution of innovation dates back to ancient civilizations and continues to shape modern economies through high-tech advancements. Reverse engineering—a process of deconstructing and enhancing technologies—has been instrumental in industrial growth worldwide, notably in countries like China and Japan. Pakistan’s potential in leveraging reverse engineering remains underutilized, hindered by outdated infrastructure, inadequate R&D investments, weak institutional frameworks, and fragmented policies. Initiatives like STZs and the Digital Pakistan Policy offer promise but suffer from misaligned execution. This study underscores the transformative potential of reverse engineering in Pakistan’s defense, agriculture, pharmaceuticals, and renewable energy sectors. By fostering academia-industry-government collaboration, improving infrastructure, and adopting global best practices, Pakistan can bridge its technological gaps, enhance export competitiveness, and reduce its import dependency. A robust reverse engineering strategy will catalyze innovation, strengthen industrial output, and pave the way for long-term economic sustainability and self-reliance
Assessment of quality and efficiency in higher education system. Empirical study for the EU countries
No full textHigh quality and efficient education are fundamental to a country's development. As a result, the continuous assessment of the quality and efficiency of education remains a subject of constant debate. This has led to an increased interest in developing evaluation methods that are as reliable as possible. In this paper, we assess the quality and efficiency of higher education by constructing composite quality and efficiency indices, using various statistical methods for European Union's countries for the year 2022. For the construction of the composite quality index, we considered nine variables, then we used principal component analysis (PCA) to determine the importance of each variable, whereas the weighting method was applied in order to extract the factor loading coefficients of the score matrix. To construct the composite efficiency index, eight variables were analysed and we applied stochastic frontier analysis (SFA), which estimated a production frontier and measured the random inefficiency of production units. Inefficiency scores were obtained for each country and were combined with the outputs considered in the analysis to provide an overview of the efficiency of decision-making units (DMUs). These results were then correlated with the number of universities included in the international top rankings using the Spearman coefficient. Our findings reveal a positive correlation between the two composite indices and the number of universities featured in these rankings for each country analysed. This confirms that the analysed variables provide insight into the quality and efficiency of higher education system in these countries, which could increase the number of universities included in the international rankings
Deep Impulse Response Functions for Macroeconomic Dynamics: A Hybrid LSTM-Wavelet Approach Compared to an ANN-Wavelet and VECM Models
No full textThis study presents a novel hybrid framework that integrated Long Short-Term Memory (LSTM) networks with Daubechies wavelet transforms to estimate Deep Impulse Response Functions (DIRF) for monthly macroeconomic time series, across five economies: Brazil, Egypt, Indonesia, United States, and the United Kingdom. Eight key variables, yield curve latent factors (LEVEL, SLOPE, CURVATURE), foreign exchange rates, equity indices, central bank policy rates, GDP growth rates, and inflation rates, were modeled using the proposed LSTM-Wavelet approach, and were compared against an ANN-Wavelet hybrid, and a traditional Vector Error Correction Model (VECM). The LSTM-Wavelet model achieved a superior overall median R2, outperforming the ANN-Wavelet and VECM. The approach excelled in capturing nonlinear dynamics and temporal dependencies for variables such as equity indices, policy rates, GDP, and inflation. Db4 was superior for capturing short and medium-term patterns in macroeconomic variables like GDP, EQUITY, and FX, cause its shorter filter and moderate smoothing excelled at isolating cyclical patterns in noisy, volatile data. Cumulative DIRFs revealed consistent cross variable dynamics e.g., yield curve shocks propagated to equity, FX, policy rates, GDP, and inflation, in line with economic theory. These findings underscored the hybrid model’s ability to capture non-linearity, multiscale interactions in macroeconomic data, offering valuable insights for forecasting and policy analysis
Chemins périlleux : la migration dangereuse des Éthiopiens vers l'Afrique du Sud
No full textSince the 1990s, Ethiopian youths and adults—primarily from the country’s southern and central regions—have been migrating to South Africa via the "southern route." Over the past 25 years, this male-dominated migration flow has grown increasingly irregular, relying on human smugglers and multiple transit countries. The Ethiopian immigrant population in South Africa has expanded significantly, with shifts in the demographics of migrants, including age, ethnicity, place of origin, gender, and socioeconomic status. Rural youth have increasingly joined this migration stream, and more women are now migrating for marriage. Migration brokers play a pivotal role in facilitating irregular migration from Ethiopia to South Africa. Upon arrival, most Ethiopian immigrants engage in the informal economy and remain socially segregated, with language barriers hindering integration. The profile of migrants has diversified in recent years, now including teenagers, college graduates, and civil servants. Concurrently, the smuggling and settlement processes have evolved, particularly due to stricter border controls—exacerbated by factors such as the COVID-19 pandemic—which have altered smuggling dynamics and exacerbated inequalities among Ethiopian migrants in South Africa. Social networks sustain this migration trend, fuelled by narratives of financial success shared by early migrants through remittances, material goods sent back home, and social media. Labour market demands shape migrant profiles, with varying skill levels (low-skilled, unskilled, high-skilled) and gendered labour roles influencing migration patterns. Religion, particularly evangelical Christianity, also plays a significant role, framing migration as a divine blessing, shaping risk perceptions, and providing spiritual support in navigating the challenges of settlement. Aspirations for economic advancement and self-improvement drive many migrants, often leading them into precarious journeys facilitated by smuggling networks operating from Hosanna (the capital of Hadiya Zone) and Nairobi. Corruption among law enforcement agencies further enables this transnational smuggling industry. However, rising xenophobia in South Africa and stricter enforcement in transit countries like Kenya, Tanzania, and Malawi have reduced migration along this route since 2015. Unauthorized Ethiopian migrants in South Africa face stigmatization. They are, often being perceived as criminals, informal economy operators, or threats to local employment opportunities. This perception exacerbates their marginalization and limits their integration into South African society
Decarbonizing the Building Sector: The Integrated Role of ESG Indicators
No full textThis work tests the relationship of the building sector's carbon dioxide (CO₂) emissions with a set of environmental, social, and governance (ESG) indicators in an international panel of countries. Using machine learning approaches alongside traditional econometric techniques, the work identifies strong predictors of emissions intensity in the nature of scientific productivity, healthcare infrastructure, and good governance. The findings indicate higher scientific productivity and better government governance are associated with reduced CO₂ emissions from building stocks, while the effectiveness of government and R&D expenditures are found to be associated with higher emission rates, possibly due to the broader urban infrastructures of the developed nations. With the help of clustering as well as permutation-based measures of importance, the work establishes the complex dynamics
interlinking development, knowledge creation, and environmental efficiency. The result provides practical indications for policymakers who aim to harmonize the national ESG policies with the
targets of decarbonization in the built space
On a Definition of Trend
No full textSeveral reasons explain the absence of a precise, complete and widely accepted definition of trend for economic time series, and the existence of two major disparate models is one of the most important. A recent operational proposal tried to overcome this difficulty resorting to a statistical test with good asymptotic properties against both those alternatives. However, this proposal may be criticized because it rests on a tool for inductive, not deductive, inference. Besides criticizing this recent definition, drawing heavily on previous ones, the paper provides a new proposal, more complete, containing several necessary but no sufficient condition(s)