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Hello! I'm Melany

I am an applied economist with substantial experience in data analysis as well as the application of causal inference methodologies in projects related to labor and development fields.

 

Outside academia, I worked at the Inter-American Development Bank, the World Bank, and Amazon (internship).

 

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Melany Gualavisi

Ph.D. Candidate in Economics

University of Illinois Urbana-Champaign

Email:

Address:

1407 West Gregory Dr.

Urbana, IL. 61801

EXPERIENCE

EXPERIENCE

Jun. 2022-Aug.2022

Economist-Intern

AMAZON

  • Estimated forward-looking models, leveraging machine learning methods (LASSO, Random Forest, Logistic regression) to support inventory, purchase, and production decisions.

  • Demonstrated exceptional learning skills of data analysis tools (SQL, Python), machine learning, analytical and writing.

2020-2022

WORLD BANK

Consultant

Conducted research related to education and poverty topics for developing countries.

  • Combined traditional data (survey and census data) with geospatial data to identify vulnerable population in Malawi to help targeting of social programs and policies.

  • Utilized solid machine learning (LASSO, XGBoost), data analysis, writing, and analytical skills.

  • Examined the implications of Venezuela's economic crisis on the educational sector from 2016-2019.

  • Highlighted the evolution of the returns to education over time and the intergenerational persistence in education.

2014-2017

Consultant

INTER-AMERICAN DEVELOPMENT BANK

  • Ensured effective management of Labor Markets and Social Security Division’s information system to enable the organization understand and comprehend all of the available information.

  • Updated and harmonized the labor market indicators of 21 countries using survey data. Harmonized and analyzed labor market indicators using surveys and public records.

  • Leveraged analytical inputs for several publications by conducting extensive data analysis; and coauthored articles.

RESEARCH

EDUCATION

Does Proximity to Coworkers Matters in Academia? Evidence from Relocation Events of Full Professors

with Marieke Kleemans and Rebecca Thornton

Despite the increase in virtual communication, physical proximity remains a fundamental way for people to connect, but how much does having experienced members in your network matter? This paper analyzes if physical proximity to experienced academics leads to different productivity paths of their networks in origin and destination institutions. Specifically, we examine the effects of relocation events of full professors on productivity and promotion outcomes of their networks using an event study design. Preliminary findings show that academics in the destination locations have better productivity paths compared to the ones in the origin institutions. Similarly, we find evidence of positive effects on promotion. We find that these effects are more important for men and for academics that experience relocation events earlier in their careers.

with Marieke Kleemans

This paper analyzes the effects of internal emigration on labor market outcomes in sending areas in Indonesia. To identify the impact, we apply an instrumental variable approach using labor demand shocks at destination areas to instrument emigration from origin areas. These shocks act as pull factors that generate exogenous variation in the number of emigrants that leave their origin locations, which allows us to estimate the causal effect of emigration on these areas. We find that individuals in origin locations with higher emigrant shares have lower income, especially in the formal sector. There are no effects on labor supply. These effects are present in low-skilled workers and in males. The results can be explained under a dual-sector labor market model that is based on two sectors that determine wages differently

Effects of emigration on Labor Market Outcomes in Sending Areas:
Evidence from Indonesia

Integrating Survey and Geospatial Data to Identify the Poor and Vulnerable: Evidence from Malawi

with David Newhouse

Generating timely data to identify the poorest villages in developing countries remains a fundamental challenge for existing data systems. This paper investigates the accuracy of four alternative methods for predicting a measure of village economic welfare for approximately 4,500 villages in ten poor Malawian districts: (1) Proxy Means Test scores calculated from the 2017 social registry, (2) the Meta Relative Wealth Index, (3) predictions derived from a standard household survey and publicly available geospatial indicators, and (4) predictions derived from a two-step approach that first predicts welfare into a hypothetical partial registry of approximately 450 villages, and then predicts welfare into the remaining villages using geospatial indicators.

SKILLS

SKILLS

Applied microeconometrics, Labor and Development Economics

Causal Inference

Data Analysis

Stata, Python, SQL, LaTeX

English (fluent), Spanish (native), Fench (basic)

PERSONAL

EXPERTISE

I'm from Ecuador

Ecuador is located in South America. It's a small country, but it will surprise you with its beauty! You can have mountains, beaches, and the Amazon in one place.

I used to climb mountains

I love mountains! Ecuador is a great place to practice mountaineering, so I used to climb mountains higher than 5000 meters every weekend. Also, I went to Cordillera Blanca in Peru and climbed Pisco and Vallunaraju

I love dancing

As a proud Latina, I love dancing salsa and bachata. I'm learning constantly new steps and styles.

CONTACT
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