How many people actually live in Moldova? What is the extent of migration in the rural areas, and what is the population density? How many communities are depopulated, and what is the extent of this phenomenon? Which areas of the country are most affected by depopulation? How does this pattern change from month to month, and how did this change over the past 5 years? How to make local public investment decisions sensitive to population number if the censuses happen once in 10 years and results are published with delay? These are the questions that MiLab in Moldova is trying to answer through the Ghost Villages Project.
Official data on migration, both external and internal, is grossly underestimating the true extent of the phenomenon in Moldova. The most recent available data on population is the 2004 census, which no longer reflects the real situation on the ground. Since the 2014 census data will only be published in spring 2017 its relevance is already undermined by this significant delay and question marks hanging around the robustness of data collection at that time. The lack of updated, reliable, and disaggregated data on the actual population living in the country poses a challenge for policy making and targeted development interventions. Ultimately, we believe that access to this data will be very valuable for decision makers at the local and central levels, in such cases as sizing and directing local infrastructure projects and for informing the territorial administrative reform which is planned to be carried out in 2018.
The ultimate objective of the project is to map the actual population density in the rural areas of Moldova, spotting the “ghost villages” – the abandoned communities or parts of communities where households are empty. The mapping will be done using monthly data on power consumption at the household level as an indicator of whether the household is populated or not. Where possible, alternative source of data will be used to triangulate the findings and provide more granular insights. The project is run together with the NBS, and the tool will ultimately belong to the institution and will be hosted on their website.
We are also hoping for some additional insights. For instance, what if looking at the power consumption patterns of rural population gives us insights about income level fluctuation of the people? Can it then serve as a timelier and more frequent proxy than Household Budget Surveys? Or for instance, as initial data seem to confirm (spikes of energy consumption by some households during Christmas and Easter holidays), what if power consumption data gives us almost real-time information on circular migration flows? This clearly would give us insights on the best period to engage with migrants on decision making at local level? But what else?
As a first step, we are in the process of selecting a sample of 10 communities for testing the hypothesis that energy consumption data is a viable predictor of household inhabitance. Then, we will geocode the addressed in the energy consumption dataset and map them using the GIS maps. This will be followed by analysis of energy consumption patterns and estimation of minimum consumption threshold – a nominal value under which a household would be considered unpopulated. We will then send enumerators to the pilot villages in order to validate the results obtained, as well as refine the estimations, if necessary. Based on the data obtained we will create a grid map of population density. The last step will be scaling up the map for the entire country and integrating this data into the decision-making processes