Tram Nguyen
PhD Candidate

Stanford University
Department of Economics
579 Serra Mall
Stanford, CA 94305

Curriculum Vitae

Development, Labor Economics

Expected Graduation Date:
June, 2018


Pascaline Dupas (Primary)

Marcel Fafchamps

Luigi Pistaferri


Spillover effects of a sector-specific minimum wage (Job Market Paper)
A sector-specific minimum wage, while designed to protect workers in one booming industry, might result in unintended consequences for other uncovered sectors. The garment sector in Cambodia is the only sector with a strongly enforced and monitored minimum wage. I study how changes in the garment minimum wage affect other low-skill, uncovered sectors. A labor search model predicts that, given the outside option channel for workers, the impact on the wage in the uncovered sector will be inverse-U shaped. Empirical results show that an increase in minimum wage compresses the female wage distributions in the garment sector. There is evidence for positive spillovers to non-garment, low-skill sector using a difference-in-difference framework. A substantial increase in minimum wage causes an increase in the average female wage in the low-skill sector. The spillovers are stronger for individuals who live closer to the garment factories or those in provinces where the garment sector is more present. As the minimum wage in the garment sector keeps increasing, the spillover effect diminishes and becomes negative but statistically insignificant.

The impact of garment industry on women welfare in Cambodia [Slides]
This paper examines the impact of the expansion of the garment industry in Cambodia in the 1990s on the education and marriage outcomes for young women. Using variation in the timing and locations of the entry of garment factories, I show that young girls who were under the age of fifteen when the factories first opened in their provinces obtain more years of schooling than those who were above 16 at the time. They also tend to delay marriage and childbirth until a later age. I rule out the income effect channel and suggest that the increase in education found are due to the returns to education mechanism. Garment factories provide new job opportunities for girls and induce them to obtain more education.

Bridging the gap Effects of reduced transportation costs on farm-gate price
One of the stark differences between developing and developed countries is the lack of physical infrastructure. This paper aims to answer whether infrastructure improvements and lower cost of transportation result in greater market integration and what are the impacts on prices at farm gates in the context of Vietnam Mekong Delta. In this region, the availability and conditions of transportations to and from the farms play an important role in determining producers' prices. Using bridge construction projects in the 2000s as exogenous treatments, I examine whether improved infrastructure reduces interregional price gaps and increases producers' prices. The impact could be either an increase in farm-gate prices of rice in beneficiary provinces due to reduced transportation cost, or a reduction of the price gap between provinces located across the bridge. I found no effect of reduced transportation cost on farm-gate prices, although there is evidence of farm diversification from less time-sensitive to more time-sensitive crops.

Correcting for misallocation using machine learning algorithm (with Adem Dugalic) [Poster]
We propose several algorithms to reduce the errors of classification created by using imperfect training sets. A classic example is misallocation of scarce funds to poor households due to unobserved income in combination with misreporting or corruption at the administrative level. Suppose there are several training examples in which the targets are misclassified at a certain rate. Since the errors are imperfectly correlated across training examples, wrongly classified observations in one sample will share common characteristics with observations that are differently classified in other samples. We use this insight to develop several machine learning algorithms to reduce the classification error. We apply the algorithms to the context of aid programs targeting poor households with limited knowledge of their true income and poverty ranking. In our mainline specification of the problem, our main algorithm makes a 37% improvement in the allocation of the funds.