Assalomu aleykum, Dear Mr. Zohidov, Dr. Khakimov, 

Dear Distinguished Guests and Dear Colleagues! 

It is a great honor to join you at this crucial round table, where we gather to address one of the most pressing challenges of our time. Eradicating poverty is a common goal of human society, isn’t it?

Poverty is a complex issue, one that we, as a global community, have pledged to tackle head-on. 2030 Agenda place poverty reduction at the very core of our collective mission, aiming to ultimately eradicate poverty around the world.

As we gather today, we recognize that poverty has layers of complexity and causes ripple effects throughout society. It affects education, economies, and the health and well-being of individuals, families, and entire communities. To truly understand poverty, we must delve deeper into these complexities to design more effective, targeted strategies to combat it.

UNDP has set an ambitious goal – by 2025, we aim to help 100 million people escape multidimensional poverty, empowering them to realize their full potential. 

The Multidimensional Poverty Index (MPI) has proved to be an indispensable tool for assessing socio-economic conditions in many countries. UNDP recommends that policy makers buy into the norm of constructing, harmonizing and monitoring MPIs, to complement other approaches for identifying deprived households.

In Uzbekistan, the recent findings from the pilot national MPI report have given us a clearer picture of the situation.

Approximately 18.4% of the adult population — around 4.2 million people — are identified as multidimensionally poor. The report also highlights regional disparities, with areas like the Republic of Karakalpakstan, Namangan, and Tashkent regions having the highest proportions of multidimensionally poor individuals.

Additionally, we see significant differences between rural and urban areas, with rural communities facing a higher incidence of poverty at 23.2%, compared to 13.9% in urban settings.

Now that Uzbekistan has a tool to measure multidimensional poverty, it is time to harness this knowledge for policymaking. UNDP stands ready to share our global expertise in using the MPI to inform policy and support the next phase of poverty reduction efforts.

The MPI is not just a tool for measurement — it is a strategic guide for action. We strongly recommend that policymakers embrace the MPI to better identify deprived households, allocate resources more effectively, and target the sectors and regions that need the most support. The MPI can help us ensure that no one is left behind.

Beyond just computing national MPIs, we urge the use of the MPI as a counterfactual simulation tool.

MPI can be a powerful tool for analyzing how social and economic shocks impact poverty levels. For instance, simulations can predict how policies like cash transfers (positive shocks) or events like the COVID-19 pandemic (negative shocks) affect poverty.

First, by calculating the likelihood of certain households facing deprivation in certain areas, a more targeted and effective approach to reducing poverty can be developed. This method replaces random guesses with a data-driven model, helping to focus efforts where they are needed most.

Second, by simulating possible future events, governments can see how specific occurrences might influence MPI indicators. This approach takes into account deeper factors that impact these indicators, beyond just changes in the economy.

Finally, using optimization techniques like linear programming, this methodology helps governments allocate resources in the most effective way. By focusing efforts on areas with high deprivation, the goal is to combat poverty by using the least amount of resources.

This strategy, which is guided by a 'poverty heat map,' ensures that resources are directed toward areas with the highest need and greatest impact potential.

Countries across the globe are increasingly incorporating MPI into their national strategies. For example, Spain has included MPI in its action plan for the 2030 Agenda, while the United Kingdom has used microsimulation models to identify policy packages that are both effective and sustainable in meeting poverty reduction targets. We can learn from these examples and adapt them to the unique context of Uzbekistan.

Undoubtedly, monetary-poverty measures have strong merit in terms of data availability, population coverage, and popular and political acceptance. Rather than replacing the monetary-poverty measures, we suggest tailoring MPI as a complementary tool for policy making.

We live in an interconnected world where global challenges are growing more complex and interlinked. Yet, at the same time, our institutions often struggle to keep pace with these challenges. 

We face global gridlock, exacerbated by polarization and barriers to international cooperation. But there is hope — we have the tools, knowledge, and technologies to address poverty and inequality. 

To do so, we must capitalize on global cooperation and reimagine our approaches to development.  In our increasingly interdependent world, global public goods—such as climate resilience, digital access, and financial innovations—are critical to our shared future. 

It is time to rethink international finance to complement traditional development assistance, and to embrace the data-driven tools and financial mechanisms now at our disposal.

As we move forward, let us draw on the best of our cooperative capacities, our human ingenuity, and our shared commitment to eradicating poverty. 

Rahmat!