metadata: authors: 0: author: Mulangu,Francis Muamba
1: author: Benlamine,Mokhtar
2: author: Keller,Michael
3: author: Nganou,Jean-Pascal
count: Africa,Latin America & Caribbean,World
docna: 0: docna: Disaggregated Impacts of Growth on
            Multidimensional Poverty: Does the Source of Growth Matter?
docty: Policy Research Working Paper
abstracts: cdata!: This paper presents comprehensive
            findings on the relationship between economic growth and
            poverty. Using a first-difference model applied to data from
            more than 80 countries spanning over 20 years, the paper
            investigates how changes in gross domestic product affect
            the Multidimensional Poverty Index and its subcomponents,
            considering variations in income level, region, and resource
            dependency. The analysis confirms that economic growth
            generally reduces the Multidimensional Poverty Index,
            although the magnitude of the effect varies significantly.
            It is less pronounced in low-income countries, Sub-Saharan
            Africa, Latin America and the Caribbean, and
            resource-dependent countries. The paper disaggregates gross
            domestic product growth by its dimensions, revealing that
            growth driven by total factor productivity, consumption, and
            sustainable growth significantly decreases the
            Multidimensional Poverty Index. In contrast, factors such as
            human capital development, capital deepening, investment,
            government spending, exports, and imports show ambiguous
            effects on the Multidimensional Poverty Index. These
            findings suggest that the effectiveness of these factors
            depends on country-level conditions. Given the clearer
            positive impact of total factor productivity, consumption,
            and sustainable growth on reducing multidimensional poverty,
            policy makers should prioritize strategies that promote
            these types of growth to fight poverty, especially in
            contexts where the effects of other growth contributors are
            uncertain or not well understood.
display_title: Disaggregated Impacts of Growth on
            Multidimensional Poverty: Does the Source of Growth Matter?
listing_relative_url: /research/2024/09/34395110/disaggregated-impacts-growth-multidimensional-poverty-source-growth-matter
topicv3: Economic Growth m1326241 471,Poverty m1326687 695
publishtoextweb_dt: 2024-09-25T00:00:00Z
dois: 10.1596/1813-9450-10930
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 51
author_keys: 0: author_key: 1286068
1: author_key: NA
2: author_key: 2122031
3: author_key: 167885
model_extractions: Policy Research Working Paper 10930 Disaggregated Impacts of Growth on Multidimensional Poverty Does the Source of Growth Matter?

_Francis Mulangu Mokhtar Benlamine Michael Keller Jean-Pascal Nganou_ Poverty and Equity Global Practice September 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10930

# **Abstract**

This paper presents comprehensive findings on the relationship between economic growth and poverty. Using a first-difference model applied to data from more than 80 countries spanning over 20 years, the paper investigates how changes in gross domestic product affect the Multidimensional Poverty Index and its subcomponents, considering variations in income level, region, and resource dependency. The analysis confirms that economic growth generally reduces the Multidimensional Poverty Index, although the magnitude of the effect varies significantly. It is less pronounced in low-income countries, Sub-Saharan Africa, Latin America and the Caribbean, and resource-dependent countries. The paper disaggregates gross domestic product growth by its dimensions, revealing that growth driven by total factor productivity, consumption, and sustainable growth significantly decreases the Multidimensional Poverty Index. In contrast, factors such as human capital development, capital deepening, investment, government spending, exports, and imports show ambiguous effects on the Multidimensional Poverty Index. These findings suggest that the effectiveness of these factors depends on country-level conditions. Given the clearer positive impact of total factor productivity, consumption, and sustainable growth on reducing multidimensional poverty, policy makers should prioritize strategies that promote these types of growth to fight poverty, especially in contexts where the effects of other growth contributors are uncertain or not well understood.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fmulangu@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Disaggregated Impacts of Growth on Multidimensional Poverty: Does the Source of Growth Matter?**<sup>**1**</sup>

Francis Mulangu, Mokhtar Benlamine, Michael Keller, and Jean-Pascal Nganou

**Keywords:** Multidimensional Poverty, Economic Growth, Sustainability, and Productivity **JEL classification:** I3, O1, Q54

- © 2024 The World Bank and International Monetary Fund.

> 1 The authors would like to extend their heartfelt gratitude to Daniel Mahler, Aly Sanoh, Steve Pennings, and Clarence Tsimpo Nkengne for their invaluable feedback and insightful comments on earlier versions of this paper. Their contributions have significantly enhanced the quality of this work. However, the views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank.
# **1. Introduction**

The discourse on whether and how economic growth translates into poverty reduction is a topic of significant importance and enduring debate in economic development. This debate extends far beyond academic circles, touching the core of policy making. A critical challenge is the ability to offer concrete guidance on prioritizing growth-oriented policies and identifying the specific nature of growth that effectively mitigates poverty. This ongoing discourse underscores the need for rigorous analysis and evidence to inform policy choices, particularly when considering the multiple facets of poverty beyond mere income metrics to encompass a broader range of deprivations affecting individual well-being.

While a substantial body of empirical evidence exists pointing to a generally negative correlation between economic growth and poverty, it is noteworthy that the lion's share of this literature has traditionally focused on income or monetary poverty. This narrow focus on money leaves a significant gap in our understanding of the dynamics between economic growth and multidimensional poverty—a form of poverty that considers various dimensions of deprivation, including education, health, and living standards (Alkire et al., 2021). The relationship between multidimensional poverty and economic growth remains relatively underexplored, with most studies merely qualitatively tracing the trajectories of GDP growth and multidimensional poverty at the national level without engaging in formal statistical analysis (e.g., Djossou et al. (2017), Tran et al. (2015)).

A handful of studies have ventured into examining the correlation between changes in GDP and shifts in composite indices of multidimensional poverty using cross-sectional data across different time points (Alkire et al., 2017; Burchi et al., 2019; Seth & Alkire, 2021). However, rigorous assessments of the relationship between economic growth and multidimensional poverty are scarce. To our knowledge, only two attempts have been made in this direction, notably by Santos et al. (2019) and Balasubramanian et al. (2023). Both employed unbalanced panel data and a firstdifference estimator (FDE) to uncover a negative correlation, albeit focusing on different indices of multidimensional poverty and study periods.

Our paper endeavors to contribute meaningfully to this relatively uncharted but essential literature. We begin our investigation by constructing a panel dataset that integrates Multidimensional Poverty Index (MPI) data with indicators of economic growth. Following the methodological footsteps of Santos et al. (2019) and Balasubramanian et al. (2023), we employ a first-difference model to elucidate the fundamental relationship between MPI and economic growth.

We begin the analysis by exploring the fundamental relationship between the MPI and economic growth, focusing on variations across different income groups and regions. This initial analysis replicates existing findings from the literature, providing a solid foundation for our research. Building upon this, our study contributes significantly to the literature by examining the subcomponents of MPI—namely education, health, and living standards—to identify any heterogeneity within these subcomponents. Furthermore, we investigate economic growth across various dimensions and components to provide deeper insights and distinguish our work from previous studies. In the second part of our study, we analyze the effects of economic growth by 2
examining factors such as labor productivity, growth of factors of production, and expenditure approach components, and introduce a novel method for measuring sustainable growth.

In the first part of this study, we identify a negative relationship between economic growth and the MPI, confirming prior research findings. Our analysis further indicates that the effect's magnitude varies notably across different income levels, regions, and degrees of resource dependency. Specifically, the reduction in MPI is less pronounced in low-income countries, as well as in the regions of Sub-Saharan Africa and Latin America and the Caribbean, and resourcedependent countries. Additionally, we find that while economic growth generally reduces the MPI components, these effects are often delayed.

In the second part of our research, we focus on different dimensions of economic growth and their impact on multidimensional poverty. Our findings show that not all growth dimensions contribute equally to MPI reduction. Notably, growth driven by total factor productivity (TFP), consumption, and sustainable practices consistently leads to significant declines in MPI, making these areas particularly effective in poverty reduction. Conversely, factors like human capital development, capital deepening, investment, government spending, exports, and imports show a statistically insignificant impact on MPI, suggesting that their effects may vary depending on country-specific conditions.

The rest of the paper proceeds as follows: Section 2 discusses the relative importance of multidimensional poverty for informing policies. Sections 3 and 4 explain the data and estimation strategy used to analyze the impact of growth on multidimensional poverty, respectively. Section 5 presents the results, followed by robustness checks in section 6. Finally, section 7 concludes.

3
# **2. Enhancing Policy Effectiveness through Multidimensional Poverty Measures**

Multidimensional poverty measures provide a broader perspective on human deprivation by encompassing various aspects such as health, education, and standard of living. Unlike monetary poverty measures that focus primarily on income, multidimensional indicators capture a wider range of deprivations, offering policy makers a more comprehensive understanding of the factors contributing to poverty. This approach allows for the design of integrated interventions that address the root causes of poverty across various dimensions. For example, policies aimed at reducing multidimensional poverty can simultaneously target issues such as access to health care, educational opportunities, and social inclusion, holistically tackling the interconnected aspects of poverty and improving overall well-being.

Furthermore, multidimensional poverty measures are particularly valuable for capturing the experiences of vulnerable and marginalized groups, who may face various forms of deprivation not fully represented by income-based measures alone. For instance, women, children, older people, and people with disabilities often face multidimensional forms of deprivation that incomebased measures struggle to capture (Alkire and Foster, 2011; UNDP, 2019; World Bank, 2011; UNICEF, 2016). By incorporating dimensions that, one way or another, touch gender inequality, intergenerational poverty, and social exclusion, multidimensional poverty indices provide a more inclusive representation of poverty, ensuring that the most vulnerable populations are not overlooked in policy discussions and resource allocation.

Research in development economics has highlighted that income-based poverty measures may not fully capture the complexity of human deprivation. Notably, Amartya Sen's work underscored the importance of considering multiple dimensions of well-being—such as health, education, and social inclusion—alongside income when assessing poverty (Sen, 1999). This broader perspective has led to the development of the MPI, which measures poverty across these various dimensions (Alkire, 2007). Studies using MPI suggest that traditional income-based poverty measures might underestimate the extent of poverty and overlook disparities in deprivation across different dimensions (Alkire et al., 2015).

Moreover, empirical evidence indicates that economic growth does not uniformly lead to reductions in monetary poverty, which underscores the value of a multidimensional approach to poverty measurement (Alkire et al., 2013). For instance, research by Cruz et al. (2015) demonstrated that economic growth can sometimes disproportionately benefit specific groups or regions, leaving others behind and leading to persistent monetary poverty. By considering multiple dimensions of deprivation, multidimensional poverty measures provide a more nuanced understanding of poverty dynamics. This allows policy makers to tailor interventions more effectively to meet the distinct needs of various population groups (Alkire et al., 2015).

Studies have demonstrated that multidimensional poverty measures are particularly effective in identifying vulnerable and marginalized populations whom income-based measures may not fully represent. For example, Alkire & Santos (2013) found that women and children are often disproportionately affected by multidimensional poverty, especially in access to education and health care. Similarly, Palmer et al. (2015) highlighted that people with disabilities face specific challenges that income-based measures may not capture, emphasizing the need to consider disability as a dimension of poverty. These findings support multidimensional measures as a complementary tool to provide a more comprehensive and inclusive poverty assessment.

4
The effect of economic growth on monetary poverty, especially in Sub-Saharan Africa, has been relatively modest (Wu et al., 2024). Despite efforts to address initial disparities in poverty levels, income, and inequality, the elasticity of poverty reduction to growth has remained low in this region. The limited pace of poverty reduction since 2000 has often been attributed to economic growth rather than major changes in income distribution. This situation has raised concerns about the effectiveness of growth-centric development strategies in significantly alleviating poverty (Aryeetey, 2015; McKey and Thorbecke, 2015).

However, focusing solely on monetary poverty may overlook the broader benefits that economic growth can provide, particularly in areas such as health care, education, and infrastructure. While income levels are a critical component of welfare, economic growth can also lead to improved access to essential services and opportunities that enhance overall well-being. These non-monetary benefits are particularly important in regions starting from a low base, such as many Sub-Saharan African countries. Thus, a comprehensive approach that considers the various dimensions of poverty and the multiple pathways through which growth can enhance welfare is crucial. This paper contributes to the ongoing debate by highlighting the importance of integrating multidimensional poverty measures with traditional monetary metrics to provide a fuller picture of welfare and development.

5
# **3. Data**

## _Multidimensional poverty index_

Our analysis uses the Multidimensional Poverty Index (MPI) and its indicators as dependent variables. The MPI, developed by the Oxford Poverty and Human Development Initiative (OPHI), provides a nuanced understanding of global poverty beyond income-based metrics. It incorporates a range of indicators reflecting various deprivations that individuals face across different aspects of life. This multidimensional approach aligns with the idea that poverty is not only about income, but also the inability to access essential services and enjoy a standard quality of life (Alkire et al., 2021). The MPI includes ten indicators grouped under three significant dimensions, as depicted in Table 1.

Table 1: Dimensions, indicators, and weights of the MPI

|**Dimensions**<br>**(Weight)**|**Indicator(Weight)**|**Aperson is deprived of living in a household where...**|
|---|---|---|
|Health (1/3)|Nutrition (1/6)|Any person under 70 years of age for whom nutritional<br>information is available is undernourished.|
||Child mortality (1/6)|A child under 18 has died in the household in the five<br>years preceding the survey.|
|Education (1/3)|Years of schooling<br>(1/6)|No eligible household member has completed six years<br>of schooling.|
||School attendance (1/6)|Any school-aged child is not attending school up to the<br>age at which he/she would complete class 8.|
||Cooking fuel (1/18)|A household cooks using solid fuel, such as dung, crops,<br>shrubs, wood, charcoal, or coal.|
||Sanitation (1/18)|The household has unimproved or no sanitation facility,<br>or it is improved but shared with other households.|
|Living Standards|Drinking water (1/18)|The household’s source of drinking water is not safe, or<br>safe drinking water is a 30-minute or longer walk from<br>home, roundtrip.|
|(1/3)|Electricity (1/18)|The household has no electricity.|
||Housing (1/18)|The household has inadequate housing materials<br>in any of the three components: floor, roof, or walls.|
||Assets (1/18)|The household does not own more than one of<br>these assets: radio, TV, telephone, computer, animal cart,<br>bicycle, motorbike, or refrigerator, and does not own a<br>car or truck.|

Source: Alkire et al. (2021) To identify multidimensional poverty, each household is assessed against the ten indicators. For each indicator, if a household fails to meet the minimum standard, it is considered deprived. Each indicator is weighted equally within its dimension, and the dimensions have specific weights. A household is considered multidimensionally poor if the cumulative weighted score of its deprivations is 1/3 or higher. This threshold indicates that a household is deprived in at least onethird of the weighted indicators. The MPI value is calculated by multiplying the proportion of people identified as multidimensionally poor (incidence) by the average proportion of deprivations experienced by this poor group (intensity) (Alkire et al., 2021). While the MPI captures both the incidence and intensity of poverty, it does not measure the depth of poverty, unlike the adjusted poverty gap.

6
# _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
educational attainment, such as years of schooling, may emerge more gradually as the benefits of increased income permeate societal structures.

To explore these dynamics, we associate the change in multidimensional poverty with current and lagged GDP changes. For instance, the change in multidimensional poverty between 2012 and 2015 is linked to the change in GDP per capita during the same period, referred to as the current change in GDP. Additionally, we test for lagged impacts, where the change in GDP is defined as the change in the five years before the first survey or before the spell starts. In our example above, this would mean that the lagged effect is defined between 2007 and 2011. The lagged changes in GDP are consistently measured over the five years preceding the first survey. In contrast, the period for the current change in GDP varies depending on the interval between the surveys. Finally, we combine both current and lagged changes to assess the total (current + lagged) impact.

By testing different specifications, we aim to discern whether the effects of economic growth on multidimensional poverty are immediate or if they occur with a delay. This will enhance our understanding of the intricate ways economic changes influence various aspects of poverty.

# _Sample_

Our sample consists of 84 countries distributed across six world regions following OPHI’s region classification. Table 2 lists the six regions, details the number of countries and spells available for each region, and the first and last years for which we have MPI data.<sup>2</sup> Sub-Saharan Africa has the most representation, with 36 countries and 60 spells, whereas South Asia has the least representation, with five countries and seven spells. The number of observations per country ranges from two to four, and the length of each spell varies from one to twelve years, with an average spell length of five years. For further insights, Table A 2 in the appendix provides descriptive statistics for both the dependent and independent variables used in our analysis.

Table 2: Regions of the sample

|**Regions**|**Nr. of**<br>**countries**|**Nr. of spells**|**Firstyear**|**Lastyear**|
|---|---|---|---|---|
|Arab States|9|10|2006|2020|
|East Asia and the Pacific|9|12|2010|2022|
|Europe and Central Asia|12|16|2005|2019|
|Latin America and the Caribbean|13|22|2001|2021|
|South Asia|5|7|2006|2021|
|Sub-Saharan Africa|36|60|2000|2021|
|**Total**|**84**|**127**|**2000**|**2022**|

> 2 For a list of all countries by region refer to Table A 1 in the appendix.

8
# **4. Estimation Strategy**

Following the approach of Balasubramanian et al. (2023) and Santos et al. (2019), we adopted the first difference estimator (FDE) to investigate the impact of economic growth on multidimensional poverty. This method is particularly suited to address potential omitted variable biases by differencing out unobserved, time-invariant factors that might influence countries' economic growth and poverty levels.

In all three equations, ∆l stands for the change in the logarithm of the MPI or its components for country during period , capturing the annual proportional change in ii,tt llPP multidimensional poverty. ii tt The subscript denotes the period between two surveys, also known as the spell. The length of each spell can vary depending on when the surveys were conducted. The subscript refers to tt a lagged period, specifically the five years before the first survey in the spell. tt−1 In general, represents the change in the logarithm of GDP per capita or productivity. Specifically, in equation (1), ∆l l ∆ log ll same period or spell as for the dependent variable. This measures the immediate impact of GDP ii,tt<sup>covers the current change in GDP per capita, where tt is the</sup> per capita or productivity on poverty. In equation (2), we added ∆l l change in the logarithm of GDP per capita or productivity over a lagged five-year period. This ii,tt−1<sup>, which measures the</sup> recognizes the temporal lag between economic performance and its potential impact on poverty. Thus, in equation (2), we estimate both the current and the lagged impact of GDP per capita on the MPI separately. Finally, in equation (3), we combine the current changes in GDP (∆l l and the lagged changes in GDP (∆l l Variable represents a constant term, ii,tt−11 and<sup>) to calculate the total change in GDP (</sup> 2 are the coefficients of interest indicating the<sup>∆ll</sup> ii,tt,tt−1ii,tt<sup>). )</sup> elasticity of poverty with respect to GDP or productivity, and is the change in the error term. cc ββ ββ By employing this model, we directly estimate the growth elasticity of poverty—how changes in ii,tt ∆εε GDP per capita or productivity influence multidimensional poverty. This approach allows us to not only replicate the foundational analysis conducted by Balasubramanian et al. (2023) and Santos et al. (2019) but also extend our understanding by disaggregating the components of the MPI, economic growth, and productivity.

We want to emphasize that our estimates do not establish causal relationships, as we do not exploit any exogenous variation in economic growth or productivity. Consequently, our analysis primarily demonstrates correlations rather than causation. This limitation means that our findings are 9
susceptible to reverse causality and omitted variable bias, which should be considered when interpreting the results. Despite these limitations, we believe that the observed correlations likely reflect underlying causal relationships consistent with our economic understanding.

10
# **5. Results**

In this section, we discuss the results of our estimations, focusing on the coefficients of interest, 1 and 2, from equations (1), (2), and (3). These coefficients measure the elasticity between poverty and economic growth. Elasticities reflect the responsiveness of the Multidimensional ββPoverty Index (MPI) to changes in economic growth. Therefore, ββ 1 and 2 indicate the percentage change in MPI resulting from a one percent change in economic growth. ββ ββ The first part of our analysis concentrates on the overall impact of economic growth on the MPI and its subcomponents, assessing variations by region, income level, and resource dependency. This section aims to validate findings from prior studies and contribute new insights regarding the disaggregated components of the MPI.

In the second part, we employ advanced growth measures to enhance our understanding of the relationship between economic growth and the MPI. This analysis broadens the existing literature by exploring different facets of growth, including labor productivity, factors of production growth, the expenditure approach, and sustainability considerations.

## _Economic growth and the MPI_

In the initial phase of our analysis, we focus on the MPI and its subcomponents. We begin by estimating the impact of GDP changes on the MPI, as presented in Table 3. Utilizing a firstdifference model, our analysis highlights how GDP changes affect variations in multidimensional poverty. This approach captures the dynamic relationship between economic growth and multidimensional poverty.

11
Table 3: MPI and GDP

|VARIABLES|(1)<br>MPI|(2)<br>MPI|(3)<br>MPI|(4)<br>MPI|(5)<br>MPI|(6)<br>MPI|
|---|---|---|---|---|---|---|
|Change in GDP (current)|-0.685***<br>(0.201)|-0.506**<br>(0.203)|||||
|Change in GDP (lagged)||-0.608**<br>(0.258)|||||
|Change in GDP (current +<br>lagged)|||-1.144***||-0.558*|-1.136***|
||||(0.225)||(0.317)|(0.224)|
|Change in GDP (following<br>Santos and|||||||
|Balasubramanian)||||-0.828***<br>(0.241)|||
|Initial level of MPI|||||0.001<br>(0.008)||
|Change in GDP (current +|||||||
|lagged) x Initial level of<br>MPI|||||0.197||
||||||(0.144)||
|Change in Gini (current)||||||-0.168<br>(0.756)|
|Constant|-0.052***|-0.041***|-0.038***|-0.047***|-0.037***|-0.039***|
||(0.006)|(0.008)|(0.008)|(0.011)|(0.014)|(0.008)|
|Observations|127|127|127|77|127|127|
|R-squared|0.068|0.106|0.134|0.089|0.170|0.135|

**Note:** Robust standard errors are presented in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

Model 1 investigates the elasticity of poverty with respect to economic growth, using the current change in GDP per capita as the independent variable. Our findings reveal a negative and significant elasticity of -0.685. This result implies that a 10% increase in GDP per capita is associated with a 6.85% reduction in the MPI.

In Model 2, we explore whether the impact of economic growth on MPI is lagged, as suggested by Santos et al. (2019) and Balasubramanian et al. (2023). To this end, we incorporate the lagged change in GDP over the five-year period preceding the first survey used to calculate the change in MPI. Our results indicate that the lagged change in GDP is also negative and significant, with a magnitude similar to the current change in GDP. Further analysis in Table 4 will demonstrate that specific subcomponents of the MPI account for the immediate impact while others are responsible for the lagged effect.

Model 3 combines the current and lagged impacts of changes in GDP on the MPI to estimate the total effect. With a coefficient of -1.144, our results suggest that a 1% increase in GDP correlates with a 1.144% decrease in the MPI. Comparatively, this effect is notably smaller than the impact of GDP on monetary poverty as identified in recent research by Wu et al. (2024). They reported a growth elasticity of monetary poverty of approximately 2.5, using the World Bank's $2.15-a-day poverty line. This discrepancy highlights that while economic growth does contribute to reductions in both monetary and multidimensional poverty, its effect is considerably more pronounced on 12
the former, underscoring the complexity of addressing the multidimensional aspects of poverty which include health, education, and living standards.

Our finding in model 3 aligns directionally with previous findings by Santos et al. (2019) and Balasubramanian et al. (2023), reinforcing the negative relationship between economic growth and multidimensional poverty. However, it is noteworthy that our estimates are approximately double those reported in the previous studies (Santos et al. (2019) estimated a coefficient of -0.56, and Balasubramanian et al. (2023) estimated a coefficient of -0.46).

The disparity between our findings and those of the other studies may be attributed to differences in the calculation of economic growth rates, the use of different MPI indicators, or variations in the sample. In Model 4, we apply the same economic growth variable as defined by Balasubramanian et al. (2023), who followed Santos et al. (2019). Their approach calculates economic growth as the average annual differences in logarithms, assuming a lag between the impact of growth on poverty.

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. Conversely, we associate the change in multidimensional poverty between 2010 and 2015 with the change in GDP per capita between 2005 and 2015.

Model 4 yields a smaller coefficient compared to Model 3. With a coefficient of -0.828, the impact in our sample, using the Balasubramanian/Santos growth rate, remains larger than the impacts found by Balasubramanian et al. (2023) and Santos et al. (2019). However, the reduction in magnitude indicates that solely including the lagged and smoothed change in GDP may overlook some of the effects. The remaining differences could be attributed to the aforementioned variations in the poverty index or sample differences.

Model 5 examines whether the initial poverty level affects our analysis. Given that elasticities can depend on the initial level of the poverty measure (Alkire et al., 2023), it is possible that countries at the higher or lower ends of the MPI distribution disproportionately influence the results compared to those in the middle. Including the initial poverty level as a control variable and interacting it with the change in GDP variable shows an insignificant coefficient for the initial poverty level itself and for the interaction with change in GDP. This shows that the povertygrowth elasticity does not depend on the initial level of poverty in our sample. For the remainder of our analysis, we choose not to control for the initial poverty level to maintain simplicity.

In Model 6, we control for the Gini coefficient to ensure that the observed effects of growth are not confounded by changes in the distribution of income (Balasubramanian et al., 2023; Santos et al., 2019). The Gini coefficient is insignificant, and the GDP coefficients remain unchanged. This result is in line with findings from Balasubramanian et al. (2023) and Santos et al. (2019). Consequently, we exclude this variable in the remaining analysis to keep the model as straightforward as possible while testing our results for robustness.<sup>3</sup>

> 3 We also tested the effects of the lagged change in the Gini coefficient, the combined current and lagged change in Gini, and the interaction between Gini and GDP change. In all cases, the coefficients related to Gini were found to be insignificant.

13
Next, in Table 4, we analyze the impact of economic growth on the individual components of the MPI to identify potential heterogeneity and determine which components drive the current and lagged impacts.<sup>4</sup> All significant coefficients follow the expected direction, indicating that economic growth reduces deprivation in each measured form.

Table 4: MPI components and GDP

|VARIABLES|(1)<br>Nutrition|(2)<br>Child<br>mortality|(3)<br>Years of<br>schooling|(4)<br>School<br>attendance|(5)<br>Cooking<br>fuel|
|---|---|---|---|---|---|
|Change in GDP (current)|-0.390|-0.153|-0.365|-0.762**|-0.920|
||(0.325)|(0.299)|(0.382)|(0.296)|(0.775)|
|Change in GDP (lagged)|-0.644*|-0.837*|-0.191|0.222|-1.022***|
||(0.361)|(0.483)|(0.480)|(0.416)|(0.358)|
|Constant|-0.035***|-0.055***|-0.055***|-0.051***|-0.026|
||(0.010)|(0.011)|(0.015)|(0.011)|(0.026)|
|Observations|114|117|125|126|122|
|R-squared|0.052|0.044|0.008|0.041|0.078|
||(6)|(7)<br>Drinking|(8)|(9)|(10)|
|VARIABLES|Sanitation|water|Electricity|Housing|Assets|
|Change in GDP (current)|-1.805**|-0.502|-0.580|-0.948|-1.142***|
||(0.818)|(0.516)|(0.711)|(0.724)|(0.436)|
|Change in GDP (lagged)|-1.150**|-1.765***|-2.340**|-1.291***|-0.757*|
||(0.477)|(0.609)|(0.929)|(0.478)|(0.438)|
|Constant|-0.005|-0.046***|-0.043**|-0.030|-0.065***|
||(0.026)|(0.016)|(0.021)|(0.023)|(0.015)|
|Observations|126|124|118|122|124|
|R-squared|0.164|0.121|0.115|0.087|0.086|

**Note:** Robust standard errors are presented in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

The health components, specifically nutrition and child mortality, are presented in Models 1 and 2 of Table 3. Both components exhibit a negative lagged response to economic growth, indicating that higher economic growth reduces nutritional deprivation and lowers child mortality rates, albeit with a delay.

Economic growth only weakly influences the education components of the MPI (Models 3 and 4). Years of schooling show no significant coefficient, while school attendance is only significant for the current change in GDP. This suggests that economic growth immediately impacts the decision to enroll or withdraw a child from school, whereas years of schooling is a more rigid measure that requires considerable time to change. Even the lagged coefficient for years of schooling is insignificant, underscoring its rigidity. This rigidity is expected, as the years of schooling indicator includes all household members aged ten and older, encompassing adults. Consequently, economic growth is unlikely to significantly alter the educational attainment of older

> 4 For a table with the combined current and lagged impact refer to Table A 3 in the appendix.

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individuals, such as those aged 30 or 40, implying that this variable changes only gradually over time.

Lagged changes in GDP significantly and negatively influence all living standards components of the MPI. It seems that higher economic growth reduces deprivation in terms of living standards with a delay. Current changes in GDP only affects sanitation and assets with a negative sign, i.e. higher economic growth reduces sanitation and asset deprivations instantly.

Overall, the components of the MPI are predominantly and negatively influenced by economic growth in a lagged manner, indicating that economic growth reduces most forms of deprivation, albeit with a delay. Only three components—school attendance, sanitation, and assets—are influenced by current changes in GDP and are, therefore, likely responsible for the significant coefficients observed in Models 1 and 2 of Table 3.

Next, we examine the relationship between economic growth and its impact on the MPI across different income groups, as shown in Table 5. To address the issue of small sample sizes, which could compromise the robustness of our findings if each income group was analyzed separately, we employ a methodological approach that interacts the GDP growth variable with income group dummies. This technique enables us to utilize the full sample for our analysis without significantly increasing the degrees of freedom.

To interpret the interaction approach, it is essential to consider both the coefficient of economic growth and the coefficient of the interaction term. The total effect for an income group is captured by the sum of these two coefficients. This further means that a negative coefficient for the interaction term indicates that economic growth is more effective at reducing the MPI, while a positive coefficient suggests that economic growth is less effective or may even exacerbate the MPI.

The only significant interaction is found in Model 1 for the low-income dummy (Table 5). The positive interaction term indicates a reduction in the overall poverty-reducing effect for lowincome countries. In other words, the same economic growth reduces poverty less in low-income countries compared to lower-middle and upper-middle-income countries.

Further analysis of the individual components of the MPI (Table A 4) reveals that low-income countries notably lag in leveraging economic growth to improve nutrition, sanitation, drinking water, and housing. Meanwhile, lower-middle-income countries fall behind in cooking fuel, sanitation, and drinking water. Conversely, upper-middle-income countries perform better in transforming economic growth to enhance cooking fuel, sanitation, drinking water, and housing conditions.

Next, we examine whether economic growth helps to reduce poverty differently in countries that depend heavily on natural resources. This investigation is driven by concerns that resourcedependent countries might suffer from the "resource curse," which could lead to slower economic growth, increased corruption, deterioration of the tax base, and inefficient resource use (Keller, 2020, 2022; Leite & Weidmann, 1999; Sachs & Warner, 1995), which could counteract poverty alleviation.

For our analysis, we added a variable to Model 4 in Table 5 to identify countries considered resource-dependent—defined as those where natural resource rents exceed 10% of GDP on average over the last five years (Keller, 2020). We then explored how this resource dependency interacts with economic growth in affecting the MPI.

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The findings present two main points. First, the resource dependency indicator by itself does not significantly affect MPI, indicating that resource-dependent countries, on average, do not differ in their level of poverty from other countries. Second, the interaction between economic growth and resource dependency is significant and positive. This suggests that economic growth has a less substantial impact on reducing poverty in resource-dependent countries compared to those that are not resource dependent.

This result reveals an additional concern for policy makers: the already slow economic growth in resource-dependent countries further translates into less effective poverty reduction, amplifying the urgency of addressing the unique obstacles presented by natural resources.

Table 5: MPI and GDP by income groups and resource dependency

|VARIABLES<br>Dummy|(1)<br>MPI<br>low income|(2)<br>MPI<br>lower-<br>middle|(3)<br>MPI<br>upper-<br>middle|(4)<br>MPI<br>Resource<br>dependent|
|---|---|---|---|---|
|Change in GDP (current + lagged)|-1.261***|-1.171***|-1.003***|-1.504***|
||(0.251)|(0.341)|(0.210)|(0.283)|
|Dummy|0.026**|-0.036**|0.005|-0.013|
||(0.013)|(0.015)|(0.026)|(0.014)|
|Dummy x change in GDP|0.727**|0.172|-0.479|1.003***|
||(0.339)|(0.410)|(0.481)|(0.379)|
|Constant|-0.049***|-0.024**|-0.040***|-0.031**|
||(0.012)|(0.012)|(0.006)|(0.012)|
|Observations|126|126|126|125|
|R-squared|0.251|0.204|0.151|0.163|

**Note:** Robust standard errors are presented in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

Wu et al. (2024) identified a different poverty-growth elasticity for Sub-Saharan Africa using income poverty, prompting us to examine the relationship between economic growth and its impact on the MPI across different world regions. To address the issue of small sample sizes, we employ the same approach as for the income groups (see above) by interacting individual region dummies with changes in GDP.

Table 6 presents the results by region for the MPI and highlights three regions: Latin America and the Caribbean, South Asia, and Sub-Saharan Africa. In the case of Sub-Saharan Africa, our findings align with Wu et al. (2024) in that, the growth-poverty relationship is smaller for non-monetary poverty, similar to their findings for monetary poverty. However, we also observe that this reduced relationship holds for Latin America and the Caribbean. Conversely, the opposite is true for South Asia. This region appears to leverage economic growth more effectively in reducing poverty than the rest of the sample.

When examining the subcomponents of the MPI, as detailed in Table A 5, we observe additional nuances. The Arab states, for example, exhibit poor performance in reducing child mortality. However, they excel in providing all living standard components of the MPI compared to the rest of the sample. The East Asia and Pacific region shows superior performance only regarding assets. The negative MPI result for Latin America and the Caribbean, shown in Table 6, is driven by poor performance in the subcomponents of years of schooling, housing, and assets. South Asia, on the other hand, outperforms the sample in the subcomponents of nutrition, years of schooling, and 16
electricity. Lastly, Sub-Saharan Africa encounters significant difficulties with nutrition, cooking fuel, sanitation, drinking water, electricity, and housing.

Table 6: MPI and GDP by region

|VARIABLES|(1)<br>_MPI_|(2)<br>_MPI_|(3)<br>_MPI_|(4)<br>_MPI_|(5)<br>_MPI_|(6)<br>_MPI_|
|---|---|---|---|---|---|---|
|Region|_Arab_<br>_states_|_East Asia_<br>_and the_<br>_Pacific_|_Europe_<br>_and_<br>_Central_<br>_Asia_|_Latin_<br>_America_<br>_and the_<br>_Caribbean_|_South_<br>_Asia_|_Sub-_<br>_Saharan_<br>_Africa_|
|Change in GDP|||||||
|(current + lagged)|-1.221***|-0.843***|-0.989***|-1.290***|-1.138***|-1.245***|
||(0.257)|(0.212)|(0.254)|(0.237)|(0.238)|(0.319)|
|Region dummy|-0.018|-0.005|0.062|-0.037**|0.047**|0.012|
||(0.011)|(0.028)|(0.155)|(0.018)|(0.022)|(0.017)|
|Region dummy x<br>change in GDP|-0.778|-0.674|-1.987|1.044*|-1.043*|1.011***|
||(0.473)|(0.564)|(3.474)|(0.596)|(0.542)|(0.374)|
|Constant|-0.033***|-0.042***|-0.040***|-0.032***|-0.038***|-0.049***|
||(0.010)|(0.008)|(0.006)|(0.009)|(0.008)|(0.016)|
|Observations|127|127|127|127|127|127|
|R-squared|0.153|0.164|0.157|0.152|0.136|0.230|

**Note:** Robust standard errors are presented in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

To summarize the first part of the analyses, our results confirmed that economic growth consistently reduces the MPI, consistent with the findings from Balasubramanian et al. (2023) and Santos et al. (2019). The effect varies significantly across different income groups, regions, and levels of resource dependency. Specifically, the impact is less pronounced in low-income countries, Sub-Saharan Africa, Latin America, and the Caribbean, and in resource-dependent countries. Additionally, while economic growth generally leads to a reduction in MPI components, these effects often appear with a temporal lag.

# _Advanced growth measures and the MPI_

In the second part of our analysis, we focus on how economic growth influences the MPI. In this section, we measure economic growth in various ways: 1) as labor productivity, 2) as factors of production growth, 3) as expenditure approach components, and 4) from a sustainability perspective (Table 7).

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Table 7: MPI and growth

|VARIABLES|(1)<br>MPI|(2)<br>MPI|(3)<br>MPI|(4)<br>MPI|
|---|---|---|---|---|
|Change in labour productivity|-0.828**<br>(0.332)||||
|Change in TFP||-1.390***<br>(0.434)|||
|Change in physical capital||-0.638<br>(0.536)|||
|Change in human capital||-0.467<br>(0.712)|||
|Change in consumption|||-1.711***<br>(0.421)||
|Change in investment|||-0.081<br>(0.192)||
|Change in government spending|||0.154<br>(0.148)||
|Change in export|||-0.120||
||||(0.225)||
|Change in import|||0.299<br>(0.422)||
|Sustainable change in GNI||||-0.911***<br>(0.305)|
|Unsustainable change in GNI||||-0.328**<br>(0.156)|
|Constant|-0.049***|-0.054***|-0.031**|-0.038***|
||(0.011)|(0.015)|(0.013)|(0.012)|
|Observations|91|65|89|80|
|R-squared|0.065|0.119|0.168|0.129|

**Note:** Robust standard errors are presented in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

In model 1 of Table 7, we analyze the relationship between changes in labor productivity and the MPI. Labor productivity, characterized as the ratio of an economy's GDP to its number of workers, is a crucial indicator of economic efficiency and capacity. We find that improvements in labor productivity are inversely related to the MPI. This finding echoes the negative correlation observed between economic growth and multidimensional poverty, albeit with a slightly less pronounced effect.

Furthermore, we observe a consistent trend when dissecting the impact of labor productivity improvements across the specific components of the MPI—namely health, education, and living standards. The coefficients suggest that increases in labor productivity contribute to positive outcomes in most dimensions of poverty, much like the effects attributed to economic growth (Table A 6 in the appendix).

Delving deeper into the relationship between economic growth and multidimensional poverty, we dissect economic growth into factors of production such as total factor productivity (TFP), capital deepening, and human capital, each potentially influencing multidimensional poverty differently. TFP, which measures the efficiency of all inputs in the production process, can directly enhance economic output without additional inputs, potentially improving employment opportunities and 18
wages and reducing poverty. Human capital, reflecting the education and skills of the workforce, theoretically reduces poverty by increasing employability and earning potential. Capital deepening, involving increased capital per worker, is expected to boost productivity and wages but requires complementary skills and technologies to reduce poverty effectively.

Our investigation, as presented in model 2 of Table 7, places a significant emphasis on the role of TFP. The data compellingly indicate that advancements in TFP are closely linked with reductions in the MPI. This finding highlights the crucial role of technological progress and efficiency improvements in diminishing multidimensional poverty's various facets. Notably, the positive impact of TFP extends across all health, living standards, and the years of schooling dimensions of the MPI (Table A 7 in the appendix).

The insignificant results for human and physical capital growth are surprising, as these factors are typically expected to reduce poverty. One possible explanation, though speculative, is that the benefits of human capital and capital deepening may be concentrated among higher-income groups, not reaching the poorest segments of society. Additionally, if capital deepening is associated with automation and labor-saving technologies, it might reduce the demand for lowskilled labor, thereby failing to alleviate poverty among the least skilled workers.

Overall, the insignificant coefficients for human capital and capital deepening do not necessarily indicate a lack of relationship between these variables and poverty. It may be that in some countries the effect is positive, while in others it is negative, resulting in an overall average zero effect in our sample. This further suggests that underlying factors determine the direction of the effect. Further research could focus on identifying these factors, as it is beyond the scope of this study, to better understand the conditions under which human capital and capital deepening influence poverty.

Since only TFP significantly and unambiguously reduces multidimensional poverty, policies could focus on fostering technological development, innovation, and efficiency across industries. For example, policies advocated to improve TFP include tax incentives, subsidies, or direct funding to foster research and development, strengthening intellectual property rights aiming to incentivize private sector investment in innovation, and education and training programs tailored to the evolving needs of industries to help workers adapt to and benefit from new technologies. Note that these policies are not exhaustive and, more importantly, we are not testing them in this study; hence, their mention here should only be seen as a motivation for future research.

Next, we evaluate the impact of economic growth on MPI from the expenditure perspective of GDP. According to the expenditure approach, GDP equals consumption, (public and private) investment, government spending, and exports minus imports. For each expenditure category, we calculated the per capita value and determined the average annual change in the logarithm of the variable.

Generally, we would expect that growth in each expenditure component reduces poverty. Consumption growth, for instance, is directly associated with improved living standards. Public and private investment in physical capital and infrastructure can improve productivity and access to services. Efficient government spending can directly impact poverty through social welfare programs and public services targeting the poor. Export growth might generate employment opportunities, potentially reducing poverty. Similarly, imports can reduce costs and increase access to various goods, potentially enhancing welfare, provided that price stability is maintained.

However, situations exist in which growth in one or more expenditure components can lead to an increase in poverty. For example, while consumption growth can improve living standards, it might 19
also lead to increased inequality or unsustainable debt levels, exacerbating poverty. Investment in physical capital and infrastructure, as well as export growth, for example due to natural resource projects, although beneficial for productivity and employment, could displace communities or cause environmental degradation, negatively impacting poverty levels. Inefficient or corrupt government spending can divert resources away from those in need. Lastly, a surge in imports, while reducing costs, might harm domestic industries and lead to job losses, thereby increasing poverty.

Model 3 of Table 7 shows the results for the impact of each expenditure component on multidimensional poverty. The only significant coefficient comes from consumption. Consumption growth reduces multidimensional poverty, while changes in investment, government spending, exports, and imports do not significantly impact the MPI. The subcomponents of the MPI, shown in Table A 8 in the appendix, reveal a similar result, namely that predominantly changes in consumption reduce poverty for almost all subcomponents, except for years of schooling.

The main results from Model 3 indicate that consumption growth unambiguously reduces poverty, while the remaining expenditure components do not show a significant overall impact. It is important to note that this does not imply that government spending, investment, or trade have no impact on poverty; rather, the total impact in our sample averages out to zero. This suggests that in some countries, for example effective government spending reduces poverty, whereas in others, wasteful spending or corruption exacerbates it. For example, countries rich in natural resources such as oil and gas often experience significant increases in investment, government spending, and export due to new resource projects. However, resource-rich countries with poor institutional quality frequently fall victim to the resource curse, characterized by higher corruption, waste, and mismanagement, which negatively impacts poverty (Bhattacharyya & Hodler, 2010; Keller, 2020; Robinson & Torvik, 2005).

The example of resource-rich countries illustrates that underlying factors, such as institutional quality, can determine whether growth in government spending, investment, exports, and imports reduces or increases poverty. Identifying these factors is beyond the scope of this paper but could serve as a motivation for further research. For policy makers, our results suggest that, in case of doubt, it is safer to promote consumption-increasing policies to reduce poverty rather than policies aimed at increasing government spending, investment, exports, or imports, especially when the underlying factors are unknown.

Economic growth can be split into sustainable and unsustainable components, each with distinct implications for multidimensional poverty. Following Mahler (2021), the unsustainable part of national income can be defined as the sum of the value of depletion of natural resources, the cost of damage to national income due to CO2 and particulate emissions, and the consumption of fixed capital. The sustainable part is the residual of total national income (GNI).

Sustainable growth reflects income generated through means that do not compromise future economic potential. This growth is presumed to be inclusive and enduring, likely supporting longterm living standards and social welfare improvements. On the other hand, unsustainable growth includes elements like the depletion of natural resources and the consumption of fixed capital, which may initially stimulate economic activity and reduce poverty but potentially undermine future welfare and environmental sustainability.

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Model 4 of Table 7 presents the results with sustainable and unsustainable changes in GNI as independent variable. Both coefficients are negative and significant, indicating that both forms of growth reduce multidimensional poverty. However, comparing the magnitudes reveals that sustainable growth outperforms unsustainable growth by a factor of approximately three. This result aligns with Mahler's (2021) findings, who used monetary variables in his analysis. The MPI subcomponents generally follow a similar pattern, showing a solid poverty-reducing effect through sustainable growth and a smaller or even insignificant effect through unsustainable growth (Table A 9 in the appendix).

To summarize the second part of the analysis, our findings indicate that not all dimensions of economic growth equally contribute to the reduction of multidimensional poverty. Specifically, economic growth propelled by advancements in TFP, consumption growth, and sustainable growth consistently and significantly decreases the MPI in comparison to other contributors examined.

Conversely, other contributors such as human capital development, capital deepening, investment, government spending, exports, and imports do not exhibit a statistically significant impact on the MPI. This lack of significance suggests an ambiguous effect; in some cases, these contributors may reduce poverty, while in others they may not, resulting in an overall average effect close to zero. We propose that the direction and magnitude of these effects are contingent upon country-specific underlying factors. Thus, further research is warranted to elucidate the conditions under which human capital, capital deepening, investment, government spending, exports, and imports can effectively reduce poverty.

Given the more definitive impact of TFP, consumption, and sustainable growth on diminishing multidimensional poverty, it is advisable for policy makers to prioritize strategies that foster these specific forms of growth, particularly in contexts where the influence of other growth contributors is uncertain or poorly understood.

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# **6. Robustness Checks**

To ensure the integrity of our findings, we conducted robustness checks, mainly focusing on the potential influence of changes in inequality, applying alternative methodologies, and testing for outliers.

In the first robustness test, we controlled for changes in inequality, measured by the Gini coefficient, across all model specifications used in our analysis. This adjustment aimed to assess whether variations in income distribution might influence the relationship between economic growth and multidimensional poverty.

The analysis demonstrates that the coefficients for changes in the Gini coefficient are statistically insignificant in nearly all cases. Importantly, including this inequality measure does not modify the significant coefficients for changes in GDP. This outcome indicates that our main findings—that economic growth contributes to reductions in multidimensional poverty—are not affected by fluctuations in income inequality.

Table A 10 in the appendix provides a comprehensive view of these robustness checks, detailing the effects of changes in GDP on the MPI and its components, with adjustments for changes in inequality.<sup>5</sup> To further validate the robustness of our results, we explored alternative methodological approaches, starting with a simple Ordinary Least Squares (OLS) regression using levels instead of changes to estimate the correlation between the MPI and GDP per capita. While our primary first difference model is also an OLS regression, it employs changes in variables rather than levels, effectively canceling out time-invariant effects, which is why we prefer it. In this robustness check, the OLS regression incorporates the variables' levels. Consequently, although we do not expect the coefficients to exactly mirror those from the first difference model, we anticipate that the direction and significance of the coefficients will align.

The findings from the level OLS regression are indeed in harmony with those from the first difference model; most coefficients are similar in direction and significance, reinforcing the negative correlation between economic growth and multidimensional poverty observed in our primary analysis. Table A 11 in the appendix presents the results of this robustness check, detailing the relationship between GDP per capita and the MPI and its components.<sup>6</sup> Continuing our robustness checks, we evaluated the suitability of a non-linear model to account for potential biases related to the initial poverty level. Given that this relationship may not adhere to a linear model, as discussed in Alkire et al. (2023), a model capable of handling logistic distributions is recommended. To this end, we employed beta regression, a technique well-suited for continuous dependent variables constrained within a range of 0 to 1 (Cribari-Neto & Zeileis, 2010).

While our preference remains with the first difference model—primarily due to its ability to control for country-specific fixed effects, which is challenging in beta regression due to our sample size limitations—we implemented beta regression to ensure our findings were not biased by the linear

> 5 Further results using the remaining independent variables of the analysis are not reported due to space reasons and can be obtained upon request.

> 6 Further results using the remaining independent variables of the analysis are not reported due to space reasons and can be obtained upon request.

22
assumptions of our primary model. In this robustness check, we regressed on the levels of variables, similar to the OLS approach.

The results from the beta regression are consistent with those from the first difference model; most coefficients are negative, and the key coefficients remain significant. This consistency across different modeling approaches underscores the robustness of our findings, demonstrating that our initial results are not an artifact of the linear modeling approach. Detailed outcomes of this analysis are presented in Table A 12 in the appendix, which showcases the relationships between GDP per capita and the MPI and its components.<sup>7</sup> For the final robustness check, we conducted an analysis to account for potential outliers that could disproportionately influence our findings. This involved re-running all specifications of our analysis while sequentially excluding one country at a time (Pennings, 2021).

The coefficients and confidence intervals obtained from this robustness check are presented in Figure B 1 in Appendix B. The figure illustrates the relationships between GDP per capita and the MPI, along with its individual components.<sup>8</sup> The results of this robustness check confirm the stability of our findings, as no single country was found to have a significant impact on the overall results.

> 7 Further results using the remaining independent variables of the analysis are not reported due to space reasons and can be obtained upon request.

> 8 Further results using the remaining independent variables of the analysis are not reported due to space reasons and can be obtained upon request.

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# **7. Conclusion**

This paper has explored the intricate dynamics between economic growth and multidimensional poverty. This concept goes beyond income-based measures to include other dimensions of deprivation, such as health, education, and living standards. Our analysis demonstrates a significant negative correlation between economic growth and the Multidimensional Poverty Index (MPI), suggesting that multidimensional poverty decreases as economies expand. However, this relationship is not uniform; it varies significantly across income groups, world regions, resource dependency, and growth dimension.

In the first part of our analysis, we confirmed that economic growth has a consistent povertyreducing effect on the MPI, aligning with findings from previous studies. However, the magnitude of this effect varies significantly across different income groups, regions, and levels of resource dependency. Specifically, the impact of economic growth on reducing MPI is less pronounced in low-income countries, regions such as Sub-Saharan Africa and Latin America and the Caribbean, and in countries identified as resource dependent. This underscores the importance of considering regional and income-specific contexts when designing and implementing economic growth policies aimed at poverty reduction. Furthermore, our analysis reveals that the components of the MPI are generally negatively influenced by economic growth, indicating a reduction in multidimensional poverty. However, these effects often manifest with a temporal lag, highlighting the complex and sometimes delayed nature of poverty reduction through economic growth.

In the second part of our analysis, we explored the diverse dimensions of economic growth and their respective impacts on multidimensional poverty. Our findings reveal that these dimensions do not contribute equally to the reduction of the MPI. Specifically, growth driven by advancements in total factor productivity (TFP), consumption growth, and sustainable growth shows a consistent and significant decrease in MPI, standing out as particularly effective in reducing multidimensional poverty. In contrast, other contributors to economic growth—such as human capital development, capital deepening, investment, government spending, exports, and imports—did not demonstrate a statistically significant impact on MPI. This variability suggests that these factors may have an ambiguous effect on poverty reduction, sometimes contributing positively and other times not, resulting in an overall neutral impact. We suggest that the direction and magnitude of these effects are influenced by specific country-level factors, such as institutional frameworks, economic structures, and social policies. Therefore, further research is needed to understand better the conditions under which human capital, capital deepening, investment, government spending, exports, and imports can effectively reduce poverty.

Given the clearer positive impact of TFP, consumption, and sustainable growth on reducing multidimensional poverty, policy makers should prioritize strategies that promote these types of growth. This is especially important in contexts where the effects of other growth contributors are uncertain or not well understood, ensuring that efforts to stimulate economic growth also lead to substantial poverty reduction.

In conclusion, while economic growth is a powerful tool for reducing multidimensional poverty, its effectiveness depends on the nature of the growth and the specific economic context. Future research should continue to explore these dynamics to inform more nuanced and effective poverty reduction strategies, ensuring that economic policies are growth-oriented but also inclusive and sustainable.

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