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February 14, 2025
in stock
1 min read

TDOC

Teladoc Health, Inc. (NYSE: TDOC) is a global leader in virtual healthcare services, offering a range of solutions including general medical consultations, mental health support, and chronic condition management.

Recent Financial Performance:

In 2023, Teladoc reported revenues of $2.60 billion, marking an 8.13% increase from the previous year. However, the company recorded a net loss of $220.37 million, which is a 98.39% reduction in losses compared to 2022.

Analyst Ratings and Price Targets:

As of February 14, 2025, the consensus among 20 analysts is a "Buy" rating for TDOC, with an average 12-month price target of $12.08, suggesting a potential downside of approximately 13% from the current price.

Recent Developments:

Acquisition of Catapult Health: On February 5, 2025, Teladoc announced the acquisition of Catapult Health for $65 million in cash. This move aims to enhance Teladoc's at-home diagnostics capabilities, supporting early detection and management of health conditions.
Collaboration with Amazon: In January 2025, Teladoc expanded its partnership with Amazon to integrate its virtual care services with Amazon's Health Benefits Connector, aiming to broaden access to Teladoc's cardiometabolic programs.

Investment Considerations:

While Teladoc continues to grow its revenue and expand through strategic acquisitions and partnerships, the company remains unprofitable. Analysts have set price targets ranging from $8.00 to $14.00, reflecting varied perspectives on the company's future performance.

Investors should monitor Teladoc's path to profitability, the integration of recent acquisitions, and the competitive landscape in the telehealth sector when considering an investment in TDOC.

February 14, 2025
in zh
1 min read

經濟繁榮與衰退的心理學 - 人類天生的跟隨者本能

經濟繁榮與衰退的循環在歷史上屢見不鮮。儘管我們從過去的教訓中學習，但市場仍然會經歷非理性的繁榮期，隨後又陷入痛苦的修正。這種現象的核心原因之一，是人類對於潮流的盲從傾向，缺乏批判性的思考。

經濟繁榮通常始於一個合理的論點。不論是 1990 年代末的互聯網革命、2000 年代初的房地產熱潮，還是當前的人工智慧浪潮，這些敘事最初都具有一定的價值。然而，當這些論點被投資者毫無保留地接受時，就會變得極其危險。一旦市場共識變得過於樂觀，就會助長投機行為，導致泡沫的形成。投資者在這股熱潮中往往會變得自滿，開始接受過度投機的行為為新常態。

這種盲目接受導致投資者忽視風險，將過去的市場走勢視為未來的保證，而不是關注基本面。結果就是，一旦現實無法滿足市場的樂觀預期，市場將迅速崩潰。繁榮轉為衰退，留下巨大的經濟損失和心理創傷。

令人困惑的是，為何許多受過專業訓練、具備理性分析能力的投資者，仍然會陷入這種群體行為的陷阱？答案可能深植於人類的心理，甚至與我們的演化歷史有關。

在大部分人類歷史中，我們的祖先以小型的狩獵採集部落為生存單位，他們的生存依賴於群體合作。在這樣的社會結構中，一個群體只能有一個領袖；如果領袖過多，將會導致內部衝突，進而威脅整個群體的穩定。因此，經過數千年的自然選擇，追隨者的比例遠遠超過領袖。那些頻繁挑戰權威的人，往往會被排擠，甚至被消滅。

歷史證明，領袖往往會為了權力不擇手段。許多國王為了鞏固自己的統治，不惜殺害競爭對手，甚至是親人。這種趨勢可能塑造了人類的基因，使得領袖型人格成為稀有特質，而大多數人則天生趨向於順從群體，尋求集體安全感，而非獨立行動。

如果這一假設成立，那麼不加批判地追隨潮流可能並不僅僅是一種後天習得的行為，而是深植於我們的 DNA。即使是極具智慧的人，也可能難以擺脫這種本能。認識到市場趨勢是一回事，但要抵抗群體心理的影響則是另一回事。

市場的運作機制反映了這種心理動力。獨立於群體思維之外需要極大的勇氣，尤其是在市場高漲時，與眾不同的投資者往往被視為「異類」。少數能夠逆勢操作的投資者，通常都要承受孤立的壓力。正如經濟學家凱恩斯（John Maynard Keynes）所說：「市場可以長時間保持非理性，而你可能撐不到市場恢復理性的一天。」

如果領導能力確實是罕見的基因特質，那麼這也能解釋為何市場中真正的逆勢投資者如此稀少。許多專業人士即便意識到自己只是在跟風，但卻無法改變這種行為模式。要能夠在市場瘋狂時保持冷靜，在市場低迷時保持信心，不僅需要專業知識，更需要與生俱來的心理素質。

具有諷刺意味的是，最好的投資機會往往來自於逆向思維。正如股神巴菲特（Warren Buffett）所說：「當別人貪婪時要恐懼，當別人恐懼時要貪婪。」然而，歷史顯示，大多數投資者仍然會選擇相反的行動，在市場高峰時湧入，在市場崩盤時驚慌出逃。

如果我們接受人類天生具有追隨潮流的特質，那麼該如何避免成為市場狂熱的犧牲品？關鍵在於建立有紀律的投資決策框架：

批判性思考——始終對市場主流觀點保持質疑。潮流的流行並不代表它是正確的。
歷史視角——研究過去的市場繁榮與衰退，歷史往往會重演，這能提供寶貴的洞見。
獨立分析——專注於基本面，而非市場情緒。如果投資的唯一理由是「大家都在買」，那就是一個警訊。
情緒控制——頂尖的投資者能夠控制自己的情緒，市場由恐懼與貪婪驅動，但個人決策不必跟隨這些情緒。
逆向思維——當市場狂熱時保持冷靜，當市場恐慌時尋找機會。擁有與群體不同的觀點需要勇氣，但往往能帶來最佳的回報。

市場繁榮與衰退不僅僅是經濟現象，它深深植根於人類心理，甚至可能與我們的演化歷史息息相關。大多數人天生傾向於順從群體，而不是挑戰市場共識。這種從眾心理助長了市場的泡沫與崩潰。

理解這一點，對於投資者來說至關重要。雖然真正的市場領袖屈指可數，但透過有紀律的決策流程，可以降低盲目跟風的風險。歷史不會完全重演，但人性始終如一，這意味著市場的繁榮與衰退循環仍將繼續上演。

February 13, 2025
3 min read

Which Observability Tool Should You Choose? Coralogix vs. Dynatrace vs. ELK

As organizations scale their cloud infrastructure, the volume of logs, metrics, and traces generated grows exponentially. Observability platforms help manage this data efficiently, providing insights into system performance, security, and troubleshooting. Among the leading solutions are Coralogix, Dynatrace, and the ELK Stack (Elasticsearch, Logstash, Kibana), each offering unique features suited to different use cases.

Overview of the Three Platforms

Coralogix

Coralogix is a cloud-native observability platform that provides log analytics, metrics monitoring, tracing, and security insights in a unified solution. Unlike conventional log management tools that rely on indexed storage, Coralogix uses streaming analytics and machine learning to analyze logs in real time, optimizing costs and performance.

Dynatrace

Dynatrace is an enterprise-grade observability and application performance monitoring (APM) tool with AI-driven automation. It offers full-stack monitoring, user experience insights, and Davis AI for automated remediation, making it particularly suitable for large-scale enterprises that require deep automation.

ELK Stack

The ELK Stack (Elasticsearch, Logstash, and Kibana) is an open-source log and metrics aggregation platform that provides powerful analytics and visualization capabilities. It is highly customizable, making it a preferred choice for organizations that want full control over their observability stack and are willing to manage infrastructure themselves.

Feature Comparison

Feature	Coralogix	Dynatrace	ELK Stack
Data Storage Model	Streaming analytics (no indexing)	Indexed data storage	Indexed data storage
Cost Efficiency	Optimized with tiered storage	Higher cost due to indexing	Higher operational costs at scale
AI-Driven Insights	Machine learning anomaly detection	AI-powered automation (Davis AI)	Limited AI capabilities
Full-Stack Observability	Logs, metrics, traces, security	Logs, metrics, traces, APM, UX	Primarily logs and metrics
Automation & Remediation	Some automation capabilities	Advanced automation with AI	Requires custom scripts and third-party integrations
Security Features	SIEM capabilities	Built-in security monitoring	Security add-ons available but not native
Ease of Use	Developer-friendly, flexible	Enterprise-grade, guided approach	Requires configuration and infrastructure management

Key Takeaways

Cost Efficiency: Coralogix and ELK provide flexible storage solutions, while Dynatrace may have higher costs due to its extensive automation capabilities.
AI-Driven Insights: Dynatrace leads with Davis AI automation, Coralogix offers machine learning anomaly detection, while ELK relies on third-party tools for AI-driven insights.
Customization vs. Automation: ELK is the most customizable but requires manual setup. Dynatrace automates many observability tasks, and Coralogix balances flexibility and automation.
Security & Compliance: Coralogix has built-in SIEM capabilities, Dynatrace includes security monitoring, and ELK requires additional configurations for security compliance.

Use Cases

1. Cloud-Native Application Monitoring

Best suited for: Coralogix and Dynatrace
Why? Coralogix and Dynatrace offer real-time insights, while ELK may require additional configuration for cloud-native environments.

2. AI-Powered Automation and Self-Healing Systems

Best suited for: Dynatrace
Why? Dynatrace’s Davis AI enables automated issue detection and resolution.

3. Cost-Effective Log Management

Best suited for: Coralogix and ELK Stack
Why? Coralogix optimizes costs with tiered data retention, while ELK provides open-source flexibility at the expense of management overhead.

4. Security and Compliance Requirements

Best suited for: Coralogix and Dynatrace
Why? Coralogix has SIEM capabilities, while Dynatrace provides security monitoring.

Choosing the Right Observability Tool

Each platform offers strengths depending on an organization’s needs:

Choose Coralogix if you need a cost-efficient, developer-friendly platform with real-time log streaming and SIEM capabilities.
Choose Dynatrace if your organization requires advanced AI-driven automation, APM, and a fully managed enterprise solution.
Choose ELK Stack if you want an open-source, highly customizable solution for log aggregation and analytics, and are willing to manage infrastructure.

Final Thoughts

Observability is critical to ensuring system performance, security, and cost-efficiency. Coralogix, Dynatrace, and ELK each provide distinct advantages, and the best choice depends on your organization's size, automation needs, and infrastructure management preferences.

February 13, 2025
in zh
2 min read

選擇哪種可觀察性工具？Coralogix vs. Dynatrace vs. ELK

隨著企業擴展其雲端基礎設施，日誌、指標和追蹤數據的產生量呈指數級增長。可觀察性平台幫助高效管理這些數據，提供系統性能、安全性和故障排除的深入見解。在眾多領先解決方案中，Coralogix、Dynatrace 和 ELK Stack (Elasticsearch, Logstash, Kibana) 各自提供了適合不同使用場景的獨特功能。

三大平台概覽

Coralogix

Coralogix 是一個 雲原生可觀察性平台，提供 日誌分析、指標監控、追蹤和安全性洞察 的綜合解決方案。與傳統依賴索引存儲的日誌管理工具不同，Coralogix 採用 流式分析 和機器學習來即時分析日誌，從而優化成本與效能。

Dynatrace

Dynatrace 是一款 企業級可觀察性和應用程式效能監控 (APM) 工具，擁有 AI 驅動的自動化功能。它提供 全棧監控、用戶體驗分析，以及 Davis AI 自動修復功能，特別適合需要深度自動化的大型企業。

ELK Stack

ELK Stack（Elasticsearch、Logstash 和 Kibana）是一個 開源日誌和指標聚合平台，提供強大的分析和可視化功能。由於其高度可自訂性，對於希望完全控制可觀察性架構並願意自行管理基礎設施的企業來說，是一個受歡迎的選擇。

功能比較

功能	Coralogix	Dynatrace	ELK Stack
數據存儲模型	流式分析（無索引）	索引數據存儲	索引數據存儲
成本效益	透過分層存儲優化	由於索引存儲成本較高	隨著規模增長，運營成本增加
AI 驅動的洞察	機器學習異常檢測	AI 自動化（Davis AI）	限制 AI 功能
全棧可觀察性	日誌、指標、追蹤、安全性	日誌、指標、追蹤、APM、用戶體驗	主要針對日誌與指標
自動化與修復	具備部分自動化能力	先進 AI 自動化	需要客製化腳本和第三方整合
安全功能	SIEM 能力	內建安全監控	需要額外的安全擴充模組
易用性	開發者友好，靈活	企業級，引導式使用體驗	需要額外配置和基礎架構管理

核心要點

成本效益：Coralogix 和 ELK 提供靈活的存儲解決方案，而 Dynatrace 由於擁有更強的自動化功能，可能會產生較高的成本。
AI 驅動的洞察：Dynatrace 擁有最強的 Davis AI 自動化，Coralogix 提供 機器學習異常檢測，而 ELK 則依賴第三方工具來提供 AI 驅動的洞察。
客製化 vs. 自動化：ELK 具備最高的可客製化能力，但需要手動配置，Dynatrace 提供許多自動化功能，而 Coralogix 則在靈活性與自動化之間取得平衡。
安全性與合規：Coralogix 內建 SIEM 能力，Dynatrace 具備 安全監控，而 ELK 需要額外配置來滿足安全需求。

使用場景

1. 雲原生應用監控

最佳選擇：Coralogix 和 Dynatrace
原因：這兩者都提供 即時可視化洞察，而 ELK 可能需要額外配置以適應雲端環境。

2. AI 驅動的自動化與自我修復系統

最佳選擇：Dynatrace
原因：Dynatrace 的 Davis AI 可自動偵測並修復問題。

3. 具成本效益的日誌管理

最佳選擇：Coralogix 和 ELK Stack
原因：Coralogix 透過 分層存儲優化成本，而 ELK 提供 開源靈活性，但需要更多管理。

4. 安全性與合規要求

最佳選擇：Coralogix 和 Dynatrace
原因：Coralogix 內建 SIEM，Dynatrace 則提供 內建安全監控。

如何選擇合適的可觀察性工具

每個平台各有優勢，適合不同的企業需求：

選擇 Coralogix，如果您需要 具成本效益、開發者友好的平台，並具備即時日誌流分析與 SIEM 能力。
選擇 Dynatrace，如果您的企業需要 高階 AI 自動化、APM，並希望擁有完整的企業級解決方案。
選擇 ELK Stack，如果您希望 使用開源、高度可客製化的日誌聚合與分析解決方案，並願意自行管理基礎設施。

最終結論

可觀察性對於確保系統效能、安全性和成本效率至關重要。Coralogix、Dynatrace 和 ELK 各有獨特優勢，最佳選擇取決於您的組織規模、自動化需求和基礎架構管理偏好。

February 12, 2025
3 min read

Terragrunt - Simplifying Terraform Management

What is Terragrunt?

Terragrunt is a thin wrapper for Terraform that helps manage and reduce complexity in infrastructure-as-code (IaC) workflows. It streamlines Terraform configurations by promoting DRY (Don't Repeat Yourself) principles, enforcing best practices, and handling remote state management more efficiently.

Why Use Terragrunt?

Terraform is a powerful tool for managing cloud infrastructure, but as projects scale, managing multiple configurations, environments, and modules becomes cumbersome. This is where Terragrunt shines, offering:

Code Reusability: Avoid duplicating configurations across environments.
Remote State Management: Enforce consistent backend configurations.
Dependency Management: Ensure proper sequencing of module execution.
Workflow Simplification: Reduce boilerplate and enforce standards across teams.

Key Features of Terragrunt

1. DRY Terraform Configurations

Terragrunt enables hierarchical configuration using terragrunt.hcl files, allowing teams to store common configurations in a single place.

Example:

remote_state {
  backend = "s3"
  config = {
    bucket         = "my-terraform-state"
    key            = "state/terraform.tfstate"
    region         = "us-east-1"
    encrypt        = true
    dynamodb_table = "terraform-lock"
  }
}

This ensures every environment uses the same remote state configuration without copy-pasting it into every Terraform module.

2. Managing Multiple Environments

With Terragrunt, teams can manage multiple environments (e.g., dev, staging, prod) using a single configuration structure.

Directory Structure:

infra/
├── terragrunt.hcl  # Common configurations
├── dev/
│   ├── terragrunt.hcl  # Dev-specific configurations
│   ├── app/
│   ├── database/
├── prod/
    ├── terragrunt.hcl  # Prod-specific configurations
    ├── app/
    ├── database/

Each environment inherits from the root configuration but allows customization.

3. Handling Dependencies

Terragrunt helps orchestrate dependencies between modules. For example, an application module may depend on a database module.

Example:

dependency "database" {
  config_path = "../database"
}
inputs = {
  db_endpoint = dependency.database.outputs.db_endpoint
}

This ensures Terraform applies the database first before deploying the application.

4. Automatic Remote State Configuration

Instead of defining backend configurations manually in each Terraform module, Terragrunt centralizes them in terragrunt.hcl and applies them automatically.

How to Get Started with Terragrunt

1. Install Terragrunt

You can install Terragrunt using Homebrew (for macOS) or download it from the official releases:

brew install terragrunt

Or manually:

wget https://github.com/gruntwork-io/terragrunt/releases/latest/download/terragrunt_linux_amd64
chmod +x terragrunt
mv terragrunt /usr/local/bin/

2. Set Up Your Project

Define your Terraform modules.
Create terragrunt.hcl in each environment.
Configure remote state and dependencies.

3. Run Terragrunt Commands

Instead of running Terraform directly, use Terragrunt:

terragrunt run-all plan
terragrunt run-all apply

This executes Terraform across multiple modules while respecting dependencies.

Best Practices

Use a Consistent Folder Structure: Follow a predictable directory structure for environments and modules.
Leverage Inputs and Outputs: Pass variables between modules using Terragrunt dependencies.
Enforce Remote State: Prevent state drift by using a central backend.
Automate with CI/CD: Integrate Terragrunt with GitHub Actions, GitLab CI, or Jenkins.

Conclusion

Terragrunt enhances Terraform by simplifying configuration management, enforcing best practices, and streamlining workflows. It’s a must-have tool for DevOps teams managing large-scale infrastructure.

If you’re using Terraform extensively, consider adopting Terragrunt to improve efficiency and maintainability.

February 12, 2025
in zh
2 min read

Terragrunt 介紹：簡化 Terraform 管理

什麼是 Terragrunt？

Terragrunt 是 Terraform 的輕量級封裝工具，旨在幫助管理和降低基礎設施即代碼（IaC）工作流程的複雜性。它透過推動 DRY（Don't Repeat Yourself，不要重複自己）原則、強制執行最佳實踐以及更高效地處理遠端狀態管理來簡化 Terraform 配置。

為什麼要使用 Terragrunt？

Terraform 是管理雲端基礎設施的強大工具，但隨著專案規模的擴大，管理多個配置、環境和模組變得更加繁瑣。這就是 Terragrunt 發揮作用的地方，它提供：

代碼重用：避免在不同環境間重複配置。
遠端狀態管理：強制執行一致的後端配置。
依賴管理：確保模組執行的正確順序。
簡化工作流程：減少樣板代碼並在團隊間強制執行標準。

Terragrunt 的關鍵功能

1. DRY Terraform 配置

Terragrunt 透過 terragrunt.hcl 文件實現層次化配置，使團隊能夠在單一位置存儲通用配置。

範例：

remote_state {
  backend = "s3"
  config = {
    bucket         = "my-terraform-state"
    key            = "state/terraform.tfstate"
    region         = "us-east-1"
    encrypt        = true
    dynamodb_table = "terraform-lock"
  }
}

這可確保每個環境使用相同的遠端狀態配置，而無需在每個 Terraform 模組中重複配置。

2. 管理多個環境

透過 Terragrunt，團隊可以使用單一配置結構來管理多個環境（例如 dev、staging、prod）。

目錄結構：

infra/
├── terragrunt.hcl  # 通用配置
├── dev/
│   ├── terragrunt.hcl  # 開發環境配置
│   ├── app/
│   ├── database/
├── prod/
    ├── terragrunt.hcl  # 生產環境配置
    ├── app/
    ├── database/

每個環境都繼承根目錄的配置，但允許進一步自定義。

3. 處理依賴關係

Terragrunt 幫助管理模組之間的依賴關係。例如，一個應用模組可能依賴於資料庫模組。

範例：

dependency "database" {
  config_path = "../database"
}
inputs = {
  db_endpoint = dependency.database.outputs.db_endpoint
}

這確保 Terraform 在部署應用程式之前先應用資料庫配置。

4. 自動化遠端狀態配置

Terragrunt 可將後端配置集中在 terragrunt.hcl，並自動應用它們，而無需手動在每個 Terraform 模組中定義後端。

如何開始使用 Terragrunt

1. 安裝 Terragrunt

你可以使用 Homebrew（適用於 macOS）安裝 Terragrunt，或從官方發布頁面下載：

brew install terragrunt

或手動安裝：

wget https://github.com/gruntwork-io/terragrunt/releases/latest/download/terragrunt_linux_amd64
chmod +x terragrunt
mv terragrunt /usr/local/bin/

2. 設置專案

定義 Terraform 模組。
在每個環境中創建 terragrunt.hcl。
配置遠端狀態和依賴關係。

3. 執行 Terragrunt 命令

與直接執行 Terraform 不同，使用 Terragrunt 來運行：

terragrunt run-all plan
terragrunt run-all apply

這將在多個模組間執行 Terraform，並確保依賴關係的正確執行順序。

最佳實踐

使用一致的目錄結構：為環境和模組遵循可預測的目錄結構。
利用輸入和輸出：透過 Terragrunt 依賴關係在模組間傳遞變數。
強制執行遠端狀態：使用集中化後端防止狀態漂移。
與 CI/CD 自動化集成：將 Terragrunt 整合至 GitHub Actions、GitLab CI 或 Jenkins。

結論

Terragrunt 透過簡化配置管理、強制執行最佳實踐並精簡工作流程來增強 Terraform。對於管理大規模基礎設施的 DevOps 團隊來說，這是一款不可或缺的工具。

如果你正在大規模使用 Terraform，考慮採用 Terragrunt 來提高效率和可維護性。

February 11, 2025
5 min read

Impact of Donald Trump’s Tariff Policy on the Phillips Curve

The Phillips Curve is one of the most fundamental concepts in macroeconomics, illustrating the inverse relationship between inflation and unemployment. Named after economist A.W. Phillips, this curve has been widely studied, debated, and modified over time, especially in the face of changing economic conditions.

In this blog post, we’ll explore: - The origins of the Phillips Curve - Its theoretical implications - The breakdown of the original relationship - Modern perspectives on inflation and unemployment - The impact of Donald Trump's 2025 tariff policy on the Phillips Curve

1. The Origins of the Phillips Curve

In 1958, New Zealand-born economist A.W. Phillips published a paper analyzing data from the United Kingdom (1861–1957) and found a negative correlation between wage inflation and unemployment. The idea was simple: - When unemployment is low, employers must compete for workers, driving up wages. - When unemployment is high, job seekers are abundant, reducing the pressure on wages.

Later, economists like Paul Samuelson and Robert Solow expanded this idea to show a direct link between inflation (general price level increases) and unemployment.

This suggested a trade-off between inflation and unemployment: lower unemployment came at the cost of higher inflation, and vice versa.

2. The Breakdown of the Original Phillips Curve

While the Phillips Curve held up well in the 1950s and 1960s, it broke down in the 1970s, largely due to the phenomenon of stagflation—the coexistence of high inflation and high unemployment.

This was unexpected based on the original theory. The 1970s oil shocks, combined with changes in monetary and fiscal policies, led to: - Rising inflation due to supply shocks (e.g., oil prices) - Persistent unemployment despite inflation

The rational expectations theory, developed by economists like Robert Lucas, and the natural rate hypothesis, introduced by Milton Friedman and Edmund Phelps, provided alternative explanations. They argued that: - In the long run, unemployment is determined by structural factors (such as labor market policies and productivity) rather than inflation. - If people expect high inflation, businesses and workers adjust their behavior, neutralizing the short-term trade-off.

This led to the expectations-augmented Phillips Curve, where inflation expectations play a crucial role.

3. Modern Perspectives on the Phillips Curve

A Flattening Curve?

In recent years, economists have debated whether the Phillips Curve has become flatter, meaning inflation and unemployment are now less correlated. Possible reasons include: - Globalization: Wages and prices are influenced by global rather than local conditions. - Technological advancements: Automation and digitalization change labor market dynamics. - Anchored inflation expectations: Central banks' credibility in managing inflation has led to stable inflation expectations, weakening the trade-off.

Phillips Curve in the Post-Pandemic Era

The COVID-19 pandemic and its aftermath introduced new challenges: - Supply chain disruptions led to inflation despite high unemployment in 2020. - Labor shortages and wage pressures in 2021–2023 fueled inflation, even as unemployment fell.

Central banks, particularly the Federal Reserve, now focus on a data-driven approach, considering multiple factors beyond the traditional Phillips Curve.

4. The Impact of Donald Trump’s 2025 Tariff Policy on the Phillips Curve

In 2025, current U.S. President Donald Trump announced new tariff policies aimed at reducing trade deficits and promoting domestic manufacturing. These policies include higher tariffs on Chinese, Mexican, Canada imports, significantly impacting global trade.

How Do Tariffs Affect Inflation and Unemployment?

Higher Inflation Due to Increased Import Costs
Tariffs act as a tax on imported goods, raising the prices of products like electronics, vehicles, and consumer goods.
This cost increase gets passed on to consumers, fueling cost-push inflation—where inflation rises due to higher production costs rather than increased demand.
According to NPR, American consumers are likely to see increased costs for various goods as a result of these tariffs.
Unemployment Effects Depend on Industry
Some domestic industries may benefit as tariffs make foreign goods more expensive, encouraging local production and hiring.
However, other industries that rely on imported components may struggle with higher costs, leading to layoffs or slower job growth.
Export-oriented industries may face retaliatory tariffs from other countries, hurting American manufacturers and reducing jobs.
A Brookings Institution analysis suggests that tariffs could negatively impact all three economies involved—Mexico, Canada, and the U.S..

How Does This Influence the Phillips Curve?

Short-Term Impact: The trade-off between inflation and unemployment may become more pronounced. Higher inflation from tariffs could coincide with higher unemployment in affected industries, moving the economy towards stagflation-like conditions.
Long-Term Impact: If businesses adapt by reshoring production and investing in automation, structural changes could flatten the Phillips Curve further, reducing the inflation-unemployment link.
Policy Response: The Federal Reserve may need to tighten monetary policy (raise interest rates) to combat inflation, which could slow economic growth and increase unemployment. S&P Global Ratings has indicated that these tariffs might necessitate revisions to the U.S. economic forecast.

Overall, Trump’s tariff policies in 2025 could disrupt the traditional Phillips Curve relationship, making it harder to predict the inflation-unemployment trade-off.

5. Policy Implications

The Phillips Curve has shaped economic policies for decades. Here’s how: - Monetary Policy: Central banks use interest rates to balance inflation and unemployment, though they now acknowledge inflation is influenced by supply-side factors too. - Fiscal Policy: Governments use stimulus programs to boost employment but must be cautious of inflationary pressures. - Trade Policy: Tariffs, supply chain dynamics, and globalization must be factored into inflation and labor market policies. - Labor Market Policies: Investments in education, training, and labor mobility can reduce the natural rate of unemployment.

Conclusion

While the Phillips Curve remains a useful framework, its traditional inverse relationship between inflation and unemployment is no longer as straightforward. Structural changes in the economy, globalization, and inflation expectations all influence modern policymaking.

Donald Trump’s 2025 tariff policies further complicate the Phillips Curve, introducing supply-side inflation pressures while affecting employment dynamics across industries. Policymakers must now consider a broader set of factors, including trade policy, supply chains, and monetary interventions, when managing inflation and unemployment.

What do you think? Is the Phillips Curve still relevant in today’s economy? Let’s discuss in the comments! 🚀

February 11, 2025
in zh
1 min read

唐納德·特朗普2025年關稅政策對菲利普斯曲線的影響

菲利普斯曲線（Phillips Curve） 是宏觀經濟學中最基本的概念之一，顯示通貨膨脹與失業率之間的反向關係。這條曲線以經濟學家 A.W. Phillips 的名字命名，多年來一直是學術界研究、討論和修改的對象，特別是在經濟環境變化的背景下。

在本篇文章中，我們將探討： - 菲利普斯曲線的起源 - 其理論含義 - 原始關係的崩潰 - 現代對通貨膨脹與失業的觀點 - 唐納德·特朗普2025年關稅政策對菲利普斯曲線的影響

1. 菲利普斯曲線的起源

1958年，新西蘭經濟學家 A.W. Phillips 發表了一篇論文，分析了 1861年至1957年英國的數據，發現工資通脹與失業率之間呈負相關。其核心觀點如下： - 當失業率低時，雇主必須相互競爭以吸引勞動力，導致工資上升。 - 當失業率高時，求職者眾多，企業無需支付高薪來吸引員工，工資壓力降低。

後來，經濟學家 保羅·薩繆爾森（Paul Samuelson） 和 羅伯特·索洛（Robert Solow） 擴展了這一概念，顯示通貨膨脹與失業率之間也存在直接聯繫。

這表明，經濟體在一定程度上面臨著通貨膨脹與失業之間的權衡關係：降低失業率往往會導致更高的通膨，反之亦然。

2. 菲利普斯曲線的崩潰

儘管菲利普斯曲線在1950年代和1960年代表現良好，但在1970年代卻出現了異常，即所謂的 停滯性通貨膨脹（Stagflation）——高通膨與高失業率並存。

這一現象與原始理論預測不符。1970年代的石油危機，加上貨幣和財政政策的變化，導致： - 由於供應衝擊（如油價上漲），通貨膨脹攀升。 - 儘管物價上升，失業率卻未下降。

理性預期理論（Rational Expectations Theory），由經濟學家 羅伯特·盧卡斯（Robert Lucas） 提出，以及 自然失業率假說（Natural Rate Hypothesis），由 米爾頓·弗里德曼（Milton Friedman） 和 埃德蒙·費爾普斯（Edmund Phelps） 提出，為這一現象提供了解釋。他們認為： - 長期來看，失業率由結構性因素（如勞動力市場政策和生產率）決定，而非通膨。 - 如果人們預期通膨將上升，企業和勞動者將調整行為，使短期內通膨和失業的關係失效。

這導致了預期擴展菲利普斯曲線（Expectations-Augmented Phillips Curve），強調通膨預期在決定經濟行為中的關鍵作用。

3. 現代對菲利普斯曲線的觀點

菲利普斯曲線正在變得更平坦？

近年來，經濟學家對於菲利普斯曲線是否變得更平坦展開討論，即通膨和失業率之間的關聯性是否變弱。可能的原因包括： - 全球化：工資和物價受到全球供應鏈的影響，而非單一國家的經濟狀況。 - 技術進步：自動化和數字化改變了勞動市場的動態。 - 通膨預期穩定：中央銀行成功管理通膨預期，使得傳統的通膨—失業關係減弱。

後疫情時代的菲利普斯曲線

新冠疫情及其影響帶來了新的挑戰： - 供應鏈中斷導致了即使在高失業率時期（如2020年）也出現通膨。 - 2021-2023年間的勞動力短缺與工資壓力推高了通膨，即使失業率下降。

4. 唐納德·特朗普2025年關稅政策對菲利普斯曲線的影響

2025年，美國現任總統唐納德·特朗普宣布了新的關稅政策，旨在減少貿易逆差並促進本土製造業發展。這些政策包括對中國、墨西哥和加拿大的進口產品提高關稅，對全球貿易產生重大影響。

關稅如何影響通膨與失業率？

進口成本上升導致通膨加劇
關稅如同對進口商品徵收的稅費，提高了電子產品、汽車等商品的價格。
這些成本轉嫁給消費者，推動成本推動型通膨（Cost-Push Inflation）。
NPR 報導指出，這些關稅將導致美國消費者支付更高的商品價格。
對失業率的影響取決於行業
本土產業可能受益，因為關稅提高了外國商品的成本，刺激本土生產。
依賴進口零件的產業可能遭受打擊，導致裁員或增長放緩。
布魯金斯學會（Brookings Institution）分析認為，這些關稅可能會對美國、墨西哥和加拿大三方經濟造成負面影響。

結論

菲利普斯曲線仍然是一個有用的經濟分析框架，但其通膨與失業率之間的傳統反向關係已不再明確。全球化、技術變革和政策選擇使得這一關係變得更加複雜。

特朗普的2025年關稅政策進一步擾亂了菲利普斯曲線，推動通膨的同時可能影響就業市場。政策制定者需要綜合考慮貿易政策、供應鏈、貨幣政策等因素，以平衡通膨和就業。

你認為菲利普斯曲線在當今經濟環境下仍然適用嗎？歡迎留言討論！🚀

February 10, 2025
3 min read

Impact of Donald Trump's 2025 Tariff Policy on the IS-LM Model

The IS-LM (Investment-Savings & Liquidity preference-Money supply) model is a fundamental macroeconomic tool used to analyze the interaction between the goods market and the money market. Developed by John Hicks in 1937 as an interpretation of John Maynard Keynes' General Theory of Employment, Interest, and Money, this model remains relevant in understanding short-term economic fluctuations and policy implications.

Components of the IS-LM Model

The IS-LM model consists of two curves:

The IS Curve (Investment-Savings)
Represents equilibrium in the goods market.
Derived from the Keynesian Cross model, it shows combinations of interest rates and output where aggregate demand equals aggregate output.
Downward sloping: Lower interest rates stimulate investment and increase output.
The LM Curve (Liquidity preference-Money supply)
Represents equilibrium in the money market.
Derived from the supply and demand for money, it shows combinations of interest rates and output where money supply equals money demand.
Upward sloping: Higher output increases money demand, raising interest rates.

Equilibrium in the IS-LM Model

The intersection of the IS and LM curves determines the equilibrium level of output (GDP) and interest rates in the economy. This equilibrium reflects the balance between the goods and money markets.

Shocks and Policy Implications

Fiscal Policy (Government Spending & Taxation)
Expansionary fiscal policy (e.g., increased government spending or tax cuts) shifts the IS curve rightward, increasing output and interest rates.
Contractionary fiscal policy (e.g., reduced spending or tax hikes) shifts the IS curve leftward, decreasing output and interest rates.
Monetary Policy (Money Supply & Interest Rates)
Expansionary monetary policy (e.g., increasing the money supply) shifts the LM curve rightward, lowering interest rates and increasing output.
Contractionary monetary policy (e.g., reducing the money supply) shifts the LM curve leftward, raising interest rates and decreasing output.

Impact of Donald Trump's 2025 Tariff Policy on the IS-LM Model

Donald Trump’s 2025 tariff policies have significant implications for the IS-LM model, particularly through their effects on trade, investment, and inflation.

Reduction in Net Exports (NX) → Leftward Shift of IS Curve
Tariffs on imports reduce foreign goods' competitiveness but also invite retaliatory tariffs, reducing U.S. exports.
Since net exports are part of aggregate demand (Y = C + I + G + NX), a decline in NX lowers GDP, shifting the IS curve leftward.
Increased Uncertainty & Lower Business Investment → Leftward Shift of IS Curve
Uncertainty in trade policy discourages business investment, especially in industries reliant on global supply chains.
Reduced investment lowers aggregate demand, further reinforcing the IS curve’s leftward shift.
Higher Domestic Prices (Inflationary Pressure) → Upward Shift of LM Curve
Tariffs increase the costs of imported goods, causing cost-push inflation.
The Federal Reserve may respond with tighter monetary policy (higher interest rates) to combat inflation, shifting the LM curve leftward.
Fed Policy Response to Slowdown → Potential Rightward LM Shift
If economic slowdown dominates over inflation, the Fed might ease monetary policy, increasing the money supply and shifting the LM curve rightward to counteract the IS curve’s leftward shift.
Final Outcome: Stagflationary Effects?
If inflation dominates, the LM curve shifts leftward, reducing output and increasing interest rates (stagflation).
If economic slowdown dominates, monetary easing shifts the LM curve rightward, mitigating output losses.

Limitations of the IS-LM Model

Despite its utility, the IS-LM model has limitations:

Assumes a fixed price level: It does not incorporate inflation dynamics.
Neglects international trade: It is primarily a closed-economy model.
Simplifies financial markets: Modern economies have more complex financial interactions than the basic money market framework assumed here.

Conclusion

The IS-LM model remains a valuable framework for understanding short-run macroeconomic fluctuations and policy responses. While it has limitations, it provides foundational insights into how fiscal and monetary policies influence output and interest rates. Donald Trump's 2025 tariff policies illustrate how trade policy interacts with macroeconomic forces, shifting both IS and LM curves in ways that policymakers must carefully navigate.

February 10, 2025
in zh
1 min read

唐納·川普2025年關稅政策對IS-LM模型的影響

IS-LM（投資-儲蓄 & 流動性偏好-貨幣供給）模型是一個基本的宏觀經濟工具，用於分析商品市場與貨幣市場之間的相互作用。該模型由約翰·希克斯（John Hicks）於1937年發展，作為對約翰·梅納德·凱恩斯（John Maynard Keynes）《就業、利息和貨幣通論》的一種詮釋。至今，它仍然在理解短期經濟波動和政策影響方面發揮著重要作用。