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maryomololaundefined

LIPEDE Omolola Mary,Nigeria,GPSPD

@maryomolola
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Recent Best Controversial

  • Demographic Dividend (DD)
    maryomololaundefined maryomolola

    Can Africa gain demography dividend??? Demographic Dividend is the benefits an economy obtained from its youthful population. What areas do you think should be invested in to enjoy DD?


  • Cleaning the database
    maryomololaundefined maryomolola

    Thank you for this useful piece on imputation techniques in panel data analysis. I appreciate the simplicity, especially in walking us through single and multiple imputation in Stata.

    From a micro-econometric context, where we typically handle household or individual-level data that corresponds to REAL INDIVIDUALS (and this is me definitely boasting about micro-econometrics, unapologetically...lol) and situations; handling missing data becomes even more crucial. In these contexts, missing values are not just a statistical inconvenience; they may reflect real-world limitations like survey fatigue, distrust, or access issues, which, if not properly accounted for, can lead to biased results and ill-informed policy recommendations.

    In my own research on household surveys (e.g., displacement issues, health outcomes, labour force data, educational attainment), multiple imputation methods such as MICE are particularly handy. They allow us to include the uncertainty of missingness, especially when variables are inter-related to one another; e.g., income, labour market status, and levels of education. That being said, I would also caution that the MAR (Missing At Random) assumption needs to be rigorously theoretically justified. Some micro dataset missingness can be NMAR (Not Missing At Random); e.g., income nonresponse among the wealthiest households.

    Another practical point: it is essential to combine multiple imputation with rigorous model diagnostics, for instance, checking convergence of imputations or comparing observed and imputed data distributions to achieve credibility, especially if findings will inform local or national-level interventions.

    Looking forward to hearing how other individuals are applying these methods to community-focused datasets. Always happy to chat this!


  • Precision Agriculture
    maryomololaundefined maryomolola

    ALI Ibrahim Houssen,Djibouti,ACS This is wonder Ibrahim! Thank you for sharing. I totally agree with you with you that we need to leverage AI in agriculture and automate agricultural activities. In Nigeria, there are young people already automating and using technology to farm - Agrictech. We have young people going beyond using soil to plant to soilless agriculture (I have visited one of such farms; and it is a wonder). We have farms using automated irrigation system - we just need to scale and build the capacity of other people in the agriculture sector (who by the way are mostly elderly people). More so, taking Nigeria as a case study - we likewise need to start discussing how to leverage technology to have a warehouse that can preserve agricultural produce and strengthen supply chain. Wastage is another big issue in the agricultural sector in Africa. Importantly, beyond the agricultural activities, we need to discuss how to up the value chain game!


  • Cleaning the database
    maryomololaundefined maryomolola

    LOMPO Aguima Aime Bernard,Burkina Faso,SPORD Thank you for acknowledging my comment, I cannot agree less that it is always enriching to bridge methodological discussions across micro and macro contexts. Particularly, I have been trying my hands on how to infuse both macro and micro datasets together in research and it has been a rewarding (and honestly, stressful) journey.


  • Organizational survival during Hard Times
    maryomololaundefined maryomolola

    SYAHUKA Hilda, Uganda, DOA Heeeiiii! This is a tough one and a brilliant question. In a world swinging from pandemics to political volatility, like Trump’s executive orders, resilience has become the currency of relevance. One practical approach African governments and institutions can adopt is adaptive strategy rooted in real-time data and local context. The key is agility: building feedback loops into governance, investing in human capital, and forging regional value chains that don’t crumble under global shocks. It's less about having a rigid plan and more about being structurally prepared to pivot, learn, and act fast. Staying relevant isn’t about predicting the next storm, it’s about dancing in the rain with strategy, data, and people-centred solutions.

    I wrote a piece on my LinkedIn page specifically for what the USAID freeze means for nonprofit organisations - you can find it here: https://www.linkedin.com/posts/omolola-ak-fasasi-nee-lipede_the-shut-down-of-usaid-has-ripples-effect-activity-7304795664163479553-ERG5?utm_source=share&utm_medium=member_desktop&rcm=ACoAACQVAUUBk8tEGZ4TqBp_prRrXJ9q7tUusv8

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