Post 1 (6/1):
The first post is centered on my research thesis, which explores how centrally imposed austerity policies affect different states in uneven ways. I'm interested in understanding whether some regions are hit harder than others and what factors shape these disparities.
In introductory economics classes, we are first taught about the tradeoff between equity and efficiency—how can we navigate the delicate balance between maximizing economic performance and ensuring a fair distribution of resources? This issue is the heart of major news headlines, as the new administration has signaled a clear shift in priorities. Look at the language: We've seen the rise of DOGE (The Department of Government Efficiency), followed by significant cuts to DEI programs (diversity, equity, and inclusion).
With the Trump administration in power, we are witnessing significant policy shifts—these actions fall under a broader agenda of aggressive cost-cutting. Targeting essential services like Medicaid and SNAP, these cuts dismantle the social safety net and disproportionately impact low-income families. The extremity of Trump's executive orders has spurred discourse and drawn overwhelming attention to the broader idea of "fiscal responsibility": How is this used to frame policies as necessary, masking their impact on communities' well-being?
The US's evolving domestic policy is a microcosm of austerity measures imposed by the Global North on the Global South through international institutions. The World Bank and International Monetary Fund (IMF) pride themselves on providing financial support for emerging economies, ranging from India to Brazil, measuring success through macroeconomic indicators (GDP, inflation, debt levels). By viewing the economy through a singular lens, they overlook the disconnect between fiscal goals and social well-being.
I believe research needs to focus more on the Global South, which is why my analysis centers on how austerity policies impact different states in varied ways. My project is broadly framed around the question: How do baseline structural vulnerabilities shape the long-term social and economic effects of austerity?
Within this question, I'm particularly interested in examining which indicators tend to "improve" under austerity (e.g., fiscal deficit) and which tend to deteriorate (e.g., public health or inequality). From there, I want to investigate how baseline development conditions shape the tradeoffs between fiscal and social outcomes post-IMF intervention. By tracing how a policy shock flows through existing systems and affects outcomes, I hope to highlight the importance of baseline development in determining long-term effects.
As the idea continues to evolve, I'm exploring different methods. Like other researchers, I want to start with a basic regression to simulate austerity and reflect on how structural disadvantages compound the effects. Later, I want to design a multilayer network model using differential equations and Monte Carlo simulations to quantify the cascading impact of social spending cuts on employment, income, and community stability.
I want to pursue a project that capitalizes on current discussions around equity and efficiency, designing a mathematical model that captures the complexity of this tradeoff. The model aims to describe the cascading effects of policy interventions on communities, disaggregating social systems into interconnected components to reveal the hidden consequences of economic decisions.
If a job training program is cut, several impacts occur: People lose jobs, families have less income, and children may receive less education. I want to leverage predictive mathematical frameworks to understand the long-term social and economic domino effects of efficiency-driven policy interventions, answering questions like 'What happens if food programs are cut by 5 percent?'
Although it is a hefty project, I'm excited to continue revising it to create an outcome that drives meaningful insights into the real-world impact of policy interventions. This will contribute to evidence-based policymaking that considers both economic performance and social resilience.
Post 2 (6/15):
At this point, the project has become more challenging — mainly because I’ve realized that India doesn’t have a centralized or easily accessible public data infrastructure. A lot of of the available data is either paywalled or fragmented across agencies, which has forced me to make several adjustments, such as reducing my sample size and narrowing my geographic focus.
This limitation has made me think more critically about access itself: what kinds of tools and datasets do policymakers actually have when designing interventions for different regions? How comprehensive is their picture of economic and social realities when data collection is inconsistent or incomplete?
The data cleaning process has also revealed deeper structural issues—many of the reported numbers and village names don’t align perfectly, and reconciling inconsistencies has taken significant effort. Moving forward, I want the project to produce something more actionable—for example, a scoring or ranking model that helps identify where data or structural gaps are most severe. This could make the research more policy-relevant and bridge the gap between theory and practical application.
Post 3 (6/30):
By the end of June, I’ve narrowed my analysis to four states across different regions, aiming to identify patterns through mathematical methods. However, one of the key challenges I’ve encountered is that my sample size is too small to yield statistically significant results. Because of this, I’ve shifted my approach toward a hybrid one—combining quantitative exploration with qualitative policy analysis.
Right now, I’m parsing through policy documents, government reports, and regional archives to understand how austerity measures were implemented within each state. This includes identifying who was in power, what political and economic conditions shaped their decision-making, and how those choices translated into tangible outcomes.
What’s becoming clear is that state-level differences—such as leadership style, administrative capacity, and local political dynamics—play a major role in how austerity policies are felt on the ground. Even with similar macroeconomic directives, the lived outcomes can vary dramatically depending on governance structures and social context.
In a way, this shift has clarified my project’s direction. Instead of chasing large-scale statistical significance, I want to demonstrate how contextual variation shapes the effectiveness of fiscal reforms. Integrating this qualitative layer will help ground the eventual model in real-world complexity—showing not just that austerity has uneven effects, but why those effects differ and what that means for equitable policymaking.