Research Analyst, Economics Department, MOM

The Economics Department (ED) in the Ministry of Manpower advances evidence-based and data-driven policymaking through robust labour research, analysis and analytics. ED undertakes economic surveillance and forecasting, ex-ante policy analyses, ex-post impact evaluations, applied economic research and advanced analytics to inform manpower policies that shape Singapore’s labour market, workplaces and retirement outcomes. Research Analysts work closely with economists, data analysts, policymakers and operational divisions to generate evidence-based insights that support policy formulation, implementation and evaluation. ED’s work spans key areas such as labour market dynamics, productivity and human capital development, wages and inequality, labour security and retirement adequacy, and labour market policies and regulations. ED is organised into three sub-units: • Economic Research Unit (ERU) Supports manpower policies that optimise workforce allocation, strengthen human capital and drive business outcomes. • Socio-Economic Research Unit (SRU) Supports manpower policies that strengthen workplace outcomes, improve inclusivity and enhance retirement adequacy. • Analytics Research Unit (ARU) Supports manpower policies by leveraging big data and advanced analytics to uncover deeper labour market insights, strengthen policy design and enhance operational effectiveness. Responsibilities • Conducting policy-relevant research and analysis Analyse a diverse range of manpower issues, including workforce allocation, foreign and local manpower policies, retirement adequacy, lower-wage workers, vulnerable workforce segments, workplace outcomes and business impacts. • Supporting economic surveillance and forecasting Contribute to ED’s monitoring of labour market developments, workforce trends and economic indicators to support timely policy assessments and decision-making. • Undertaking ex-ante and ex-post policy analysis Assess the likely impact of proposed policies before implementation, and evaluate the actual impact of policies and programmes after implementation. • Applying quantitative and empirical methods Use econometric, statistical, data analytics and modelling techniques to derive insights from administrative data, surveys and other datasets. • Building domain expertise in labour economics Develop specialist knowledge of labour economics, public policy, empirical methods, data sources, analytical frameworks and relevant academic literature. • Collaborating across Government and with external researchers Work with researchers, economists, policymakers and operational teams within and outside MOM to support policy thinking and strengthen evidence-based policymaking. • Communicating insights clearly and effectively Translate complex research findings into clear, policy-relevant insights for senior management, policymakers and other stakeholders. Requirements We are looking for candidates who are intellectually curious, analytically rigorous and passionate about using research to improve public policy. You should have: • A degree, Master’s or PhD in Economics, Statistics, Data Analytics, Public Policy, or another quantitative discipline. • Strong analytical and critical thinking skills, with the ability to conduct rigorous research and draw policy-relevant conclusions. • Strong quantitative skills, including experience in econometrics, statistical analysis, forecasting, policy modelling, simulations and/or impact evaluation. • Proficiency in statistical or programming software such as Stata, R or Python. • A strong interest in empirical research and public policy. • Good written and verbal communication skills, with the ability to explain complex ideas clearly and logically. • The ability to work collaboratively with researchers, policymakers and stakeholders across different domains. • A willingness to learn new methods, data sources and policy areas. Candidates with experience in labour economics, applied microeconomics, programme evaluation, causal inference, data science and/or machine learning will have an advantage.