Warwick open day- A few economic insights

Introductory university lecture- The impact of tariff's on trade:

A really nice lecture that showcased a different approach to research. I found the talk quite engaging and learned a lot from it. I hope to apply this type of research in my own work moving forward.



So, lets get started, what was the talk about? And why was it interesting?

There are two types of work that are carried out at universities: empirical and theoretical work. The former focuses more on data and creating models, whereas the latter consists of more assumptions. With data sets being increasingly more available, empirical work is a more common form of research. 

Primarily, the focus of the lecture was on the US-China Trade war. With the 2025 trade war being quite recent, and therefore having a lesser so amount of data to work on, the researcher looked into the 2018/2019 trade war and hoped to make some parallels. 

Figure one: The levels of tariffs between US and China

Figure one shows an increase in tariffs from both sides reaching an apogee, by June 2020. Tariffs increase the cost of imports for American buyers which hopes to see an increase in domestic production. The professor decided to question the impact of these increased tariffs: could this mean increased trade diversions? And, decided to look into Mexico. You may wonder: why Mexico? Well, there are two main reasons for choosing the country; its close to the US and also produces goods, such as steel and machinery, that are similar to China. 

Figure 2: American demand of imports from China and Mexico

The graph shows that in 2018 that as Chinese imports decrease, Mexican exports increase. The small difference between the volume of imports suggests that other countries also benefited from these increase in tariffs. 

Figure 3: Time lags

Event Study: there are suggestions that these implications of imports were not immediate, and that it takes a few months before the affects of increasing tariffs materialises. I will later evaluate this in greater depth. 

The research process really tries to look for causation. The professor really looked into whether, as a result of increased tariff, were exports more competitive? As a result of greater production was there greater employment (derived demand). Did Mexican wages start rising? Were there some people that benefitted more than others (Yes, those on lower incomes, younger generations and women did)? All of this, of course, was backed up by analysing huge amount of data sets and making regressive econometric models.

One particularly nice consequence of the increase in protectionism was a reduction in inequality- Gini coefficient reduced from 0.46 to 0.44. Although, some could say that this was a marginal decrease. 

The next step was to see if there were similarities that could be taken to an account when looking at the recent trade war. Yes, but there were three main flaws:

• Other countries than China are being affected by tariffs(the 10% blanket tariff)
• Mexico experienced a range of tariffs on their goods and services
• Volatile trade policies by trump- you never know when he will implement his next radical policy

There are also three main flaws that are worth considering when looking into Mexico as a starting point:

• The shadow, hidden economy accounts for about 24% of Mexico's GDP
• Sounds so cliche but correlation doesnt not equal causation, other countries also benefited such as Vietnam. In 2019, Vietnam’s exports to the U.S. surged by over 35%, one of the fastest growth rates globally.
• Time lags could lead to misleading interpretations. Short-term data may not fully capture the extent of trade diversion or labor market effects. This means that misleading conclusions if not considering the lag structure of trade and production responses.
• Could this growth actually be due to trade diversions OR did Mexico become increasingly more competitive globally?

The case study highlights the power and, well,  limitations of using empirical research to unpack complex global events like trade wars. While Mexico seemed like a logical substitute for China, a deeper analysis reveals how messy real-world data can be. Hidden economies, policy volatility, and parallel shifts in countries like Vietnam all remind us that economic outcomes are rarely driven by a single factor.

More importantly, the study shows that trade policy doesn't just move goods—it redistributes opportunity. By tracing impacts on wages and inequality, the research shifts the focus from national trade balances to who actually benefits. It’s a reminder that behind every data point is a human story—and that’s where the real insight lies.

A very nice conversation with a lecturer- Dr Bhaskar Chakravorty:

I had the incredible opportunity to sit down with Dr. Bhaskar Chakravorty for an enlightening conversation that spanned several critical issues in development and inequality. His deep insights into the causes of inequality and the mechanisms for addressing it offered a thought-provoking perspective that blended academic rigor with real-world relevance.

We began by discussing how inequality arises, and Dr. Chakravorty outlined three core reasons.

1. Geographical Location: The first factor he emphasized was geography. He pointed out how some regions are naturally advantaged; by access to trade routes, fertile land, or temperate climates, while others are landlocked, arid, or vulnerable to natural disasters. These disparities, though seemingly geographical, have deep economic implications. For instance, countries with better access to ports or natural resources often have an easier time integrating into global trade, thereby accelerating development.
2. Institutional History: The second major factor he discussed was the nature of institutions—whether they are extractive or inclusive. Extractive institutions, typically found in countries with colonial legacies, concentrate power and wealth in the hands of a few. Inclusive institutions, on the other hand, are participatory and spread benefits more broadly across society. Dr. Chakravorty cited examples from India, where regions with more democratic and transparent institutions tend to fare better economically than those with historically exploitative governance structures.
3. Socioeconomic Stratification: Although not labeled as a separate point, Dr. Chakravorty also touched on how social structures, such as caste and class systems, further entrench inequality over time, often overlapping with both geographical and institutional disparities.

When it came to solutions, Dr. Chakravorty was clear: state intervention is essential.

• In regions where the shadow economy is large(i.e., where much of the economic activity goes unreported and untaxed) he emphasized the importance of formalisation. For instance, he spoke about a rickshaw driver in India who primarily deals in cash. This kind of informal economic activity, while essential for survival, ultimately limits tax revenue, social security contributions, and access to public welfare programs. Without being part of the formal system, such workers remain invisible to the mechanisms meant to protect and support them.
• He also pointed out that improving welfare systems(healthcare, education, income support) can significantly reduce inequality. He illustrated this with a striking comparison between India and the UK. Despite both countries having similar Gini coefficients before tax, the post-tax Gini coefficient in the UK is far lower, highlighting the power of progressive taxation and redistribution through government programs.

Dr. Chakravorty also explained a new Lorenz Curve, a modified version that includes unemployment data, showing not only the unequal spread of income but also the extent of economic exclusion. This version provided a more nuanced understanding of inequality: not just who earns how much, but who gets left out entirely.

Our conversation naturally shifted to a classic debate in development economics: Is economic growth more important for reducing inequality, or should the focus be on redistribution through transfer payments? Dr. Chakravorty offered a balanced view, arguing that the two are not mutually exclusive. Economic growth is necessary to expand the total wealth of a nation, but without equitable distribution, that wealth often gets concentrated. On the other hand, redistribution policies alone cannot compensate for a lack of growth. The ideal approach, he emphasized, is a combination of both: stimulating growth while ensuring that its benefits are shared broadly.

It was an amazing conversation that provided a lot of insights and valuable points to inspire new research directions. I'm really grateful for the opportunity to be part of it.