Lelo Nxumalo

In the fast-paced world of economic policy and financial markets, waiting for official GDP statistics can feel like an eternity. With quarterly GDP data often released weeks after the quarter ends, economists and policymakers need better tools to understand economic conditions in real-time. Enter GDP nowcasting  – a technique that uses machine learning to predict current-quarter GDP growth using timely economic indicators. What is GDP Nowcasting? GDP nowcasting bridges the gap between economic theory and practical decision-making by providing near real-time estimates of economic growth. Unlike traditional forecasting that predicts future values, nowcasting focuses on estimating the current state of the economy using available high-frequency data. The approach leverages the fact that while GDP data comes with a lag, many related economic indicators are available much sooner – employment figures, industrial production, retail sales, and consumer sentiment surveys are all published...

The debate over minimum wage policy has raged for decades, with economists, policymakers, and business leaders offering sharply different predictions about its effects on employment. Critics warn that raising the minimum wage will force employers to cut jobs, while supporters argue that higher wages boost worker productivity and spending power. But what does the actual data tell us. Using a comprehensive difference-in-differences analysis and Federal Reserve Economic Data covering 43 U.S. states from 2012-2020 of the "Fight for $15" movement between 2012 and 2020, I provide some evidence about how minimum wage increases actually affect employment in the real world. The Perfect Natural Experiment The period from 2012 to 2020 provided economists with an ideal "natural experiment" to study minimum wage effects. Here's why this timeframe was perfect for analysis: Federal Stability : The federal minimum wage remained frozen at $7.25 per hour since 2009, creating ...

Understanding labor market transitions is important for policymakers and researchers in developing countries. Changes in economic activity status (employed, unemployed, out of labor force) vary significantly from one country to another and also within countries from one socio-economic group to another. Below I summarize analysis from the latest Ghana Annual Household Income and Expenditure Survey ( AHIES ) published by the Ghana Statistical Service. The AHIES is a panel survey that has followed over 10,000 individuals since 2022Q1 to assess changes in their livelihoods. I am currently analyzing trends and show here what has happened to economic activity between Q2 and Q3.  Transitions in Ghana labor force status, 2022Q2 - 2022Q3 Source : Own analysis using AHIES from Ghana Statistical Service. Note : Left hand side is 2022Q2 and right side is 2022Q3 81 percent of working age individuals (15-64-years) who were  employed  in 2022Q2 were still employed in 2022Q3; 7 perc...

What determines earnings in Ghana? The Human Capital model postulates that the log of earnings of an individual is a function of that individual's productive characteristics. These individual characteristics help explain the marginal productivity and the returns to them. [1] In Mincer (1974) this model was formalized as in equation (1): In equation (1), lnY t is the log of earnings in year t , Educ is years of schooling, Exp is years of cumulative work experience, and X is a vector of other variables. We ran this model for Ghana using GLSS data with X including the variables shown in table 1. We build on Gundersen (2016) in specifying the model used in this analysis. [2] We find that, conditional on age and age squared (as a proxy for experience), sex, parents’ education, occupation, public versus private sector employment, and marital status, an additional year of education boosts annual earnings by 5.7 percent . Experience has a statistically positive marginal effect on an...

In upcoming research, my colleague and I posit that the skill content of a worker’s current occupation is a high dimension piece of information that can function as a job market signal, particularly for low wage, non-post-secondary educated workers. Using the 2013 Skills Towards Employability and Productivity (STEP) Survey data for Ghana, we construct a skill vector for every occupation consisting of a skill measure incorporating routine versus nonroutine; and manual versus cognitive intensity. We then create an occupation relatedness measure across all occupations at the 3-digit ISCO level. The fact that many jobs in different industries share common skills but differ substantially in wages suggests that there may be incomplete information in the labor market and potential pathways for low educated workers to become skilled through alternative routes (STARs). The figure below illustrates the resulting relatedness plot of occupations with node size denoting employment shares and node c...

My colleague and I just had our study published . The study discusses the association between household exposure to negative shocks and social safety net program participation in Ghana. To examine this issue, we link data from high-resolution geospatial maps of drought and flood risks to government administrative data on safety net program beneficiaries at the district level. We find that drought risk is positively associated with household participation in selected, main public social safety net programs. (The corresponding evidence for flood risk is weaker). We interpret the finding to be a result of pre-shock program coverage of drought-prone areas, in part achieved indirectly through the intentional targeting of poor areas by the programs.