# _Other variables_

The independent variables encompass GDP per capita, labor productivity per worker, and several distinct methodologies for disaggregating GDP and labor productivity. GDP per capita data, measured in US dollars at constant 2015 prices, was sourced from the World Development Indicators (World Bank, 2024).

Productivity variables are drawn from the Cross-Country Database of Productivity developed by Dieppe (2021). The first productivity variable is labor productivity, defined as the amount of output produced per worker and calculated as GDP divided by the number of employed people.

Labor productivity growth is further decomposed into three components: human capital growth, capital deepening growth, and total factor productivity (TFP) growth. Human capital growth measures how improvements in worker skills and capabilities through education, training, and health improvements increase productivity. Capital deepening measures changes in the capital-toworker ratio, and TFP measures the efficiency with which factor inputs are combined. TFP often serves as a proxy for technological progress in growth accounting exercises (Dieppe, 2021).

We further disentangled the components of economic growth from an expenditure perspective and calculated per capita values for consumption, (public and private) investment, government spending, exports, and imports measured in constant 2015 U.S. dollars. These measures were derived from the World Development Indicators database.

Moreover, we delineated economic growth into sustainable and unsustainable components, adopting the methodology outlined by Mahler (2021). The unsustainable part of national income was quantified as the aggregate of natural resource depletion, the economic costs attributable to CO2 and particulate emissions, and the consumption of fixed capital. Conversely, the sustainable portion was determined by subtracting these costs from the total national income, represented as Gross National Income (GNI).

# _Data structure_

The data is organized into spells, as defined by Cox (2007). These spells represent the intervals between two survey years, with the length of each spell determined by the time elapsed between the surveys. Within this framework, the dependent and most independent variables are computed as the average annual change in the logarithm of the variable for each spell, representing the yearly (compound) proportional change.

Santos et al. (2019) and Balasubramanian et al. (2023) argue that there is a time lag between changes in GDP per capita and variations in multidimensional poverty, suggesting that the impacts of economic growth on poverty do not manifest instantaneously but rather unfold over time. Consequently, their multidimensional poverty estimate for a particular year is associated with the average GDP per capita over the five years preceding that year. For instance, the change in multidimensional poverty between 2010 and 2015 is linked to the average GDP per capita change between 2005–2009 and 2010–2014.

Our approach builds on this understanding by adjusting the data structure to account for the complexity of the MPI, which comprises ten subcomponents. This complexity leads us to propose that the timing of economic growth's impact on the MPI likely varies across its different components. For example, improvements in income from economic growth might immediately enhance nutrition levels as households gain the ability to purchase food. In contrast, effects on 7