Economic Growth and Income Inequality in Iraq: An Assessment of the Impact of Public Revenues and Transfers on Income Redistribution

Economic Growth and Income Inequality in Iraq: An Assessment of the Impact of Public Revenues and Transfers on Income Redistribution | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Economic Growth and Income Inequality in Iraq: An Assessment of the Impact of Public Revenues and Transfers on Income Redistribution Hawre Latif Majeed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5805354/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Iraq's hydrocarbon wealth is a cornerstone of its Growth domestic product and government revenue, yet significant income inequality, as reflected by the Gini Index, poses persistent economic challenges. The unequal distribution of oil-derived income constrains public sector wages and investments, exacerbating social instability and impeding progress on the Human Development Index (HDI). Empirical analysis reveals a strong, consistent relationship between income inequality and developmental outcomes, with stationary trends indicating stable patterns over time. Addressing these disparities requires targeted interventions to promote sustainable growth. Reducing inequality and expanding opportunities for marginalized groups are essential. Leveraging information and communication technology to boost employment, alongside policies ensuring wage equity, labor inclusion, and social protection, can improve HDI outcomes. This study examines data spanning (1990 to 2024) to analyze Iraq's economic structure, employing robust empirical models that confirm these relationships. The analysis demonstrates high reliability, with no evidence of heteroskedasticity, serial correlation, or multicollinearity. Promoting equity across employment sectors and empowering collective bargaining are pivotal steps toward fostering a more inclusive and sustainable economic framework for Iraq. Hydrocarbon wealth Income inequality Human Development Index (HDI) Sustainable economic growth Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION Income inequality refers to the disparity in the distribution of income among members of a population. Income is defined as the disposable income available to a household within a specific fiscal year. It encompasses earnings, self-employment income, capital income, and public financial transfers; however, income taxes and social security contributions remitted by households are subtracted. Inequality is also characterized as the disparity between affluent and impoverished individuals, encompassing income inequality, wealth disparity, variations in wealth and income, or the wealth divide. This indicator is quantified using the Gini coefficient. This analysis is predicated upon a comparison of the accumulated proportions of the population with the cumulative proportions of income they receive (Qadir, & Abdullah, & Majeed, 2023 ). The relationship between income inequality and redistribution is intricate and multifaceted. On one hand, elevated levels of income inequality may engender social and economic challenges, including poverty, social unrest, and a decline in economic development (Granger et al., 2022). The relationship between Gross Domestic Product (GDP) and the Gini Index highlights the interplay between income levels and income distribution. While GDP measures the overall economic output and average income levels, the Gini Index captures the degree of income inequality within a population (Ali & Ghafar & Majeed, & Sabir, 2024). When GDP remains constant, a more unequal income distribution, as indicated by a higher Gini Index, tends to intensify the severity of poverty. Iraq's abundant hydrocarbon resources significantly contribute to its GDP, forming the backbone of government revenue through production and sales. However, the inequitable distribution of income derived from these resources, as indicated by the Gini Index, presents significant economic challenges (Qadir, B. M. & Mohammed, H. O & Majeed, H. L. 2021). Disparities in income allocation limit the government's ability to adequately compensate public sector employees and hinder investments in productive sectors, exacerbating economic instability and slowing progress in the Human Development Index (HDI). (Demirgüç-Kunt & Klapper, 2013 ). Addressing these disparities requires robust economic growth aimed at reducing poverty and fostering sustainable development. Bridging income inequality and expanding access to economic opportunities—especially for marginalized and educated individuals—are critical steps. Iraq's substantial natural resources, particularly its petroleum exports, have positioned it as one of the wealthiest nations in the Middle East, with a GDP of USD 264.18 billion in 2022 and a notable growth rate of 7.0%. However, the benefits of this economic prosperity are unevenly distributed, as reflected by a high Gini Index, signaling significant income inequality. A wealthy minority, often comprising the political elite and high-ranking state officials, enjoys substantial privileges and salaries far exceeding those of regular government employees, whose average net pay was approximately $ 580 per month in 2022 (Majeed, H. L, 2023 ). This disparity highlights the structural imbalances within Iraq's financial policies, particularly in the distribution of rights, privileges, and compensation (Saloom, T. M, 2024 ). Although Iraq's economy has rebounded following the severe recession of 2020 caused by the COVID-19 pandemic, the rebound has not been inclusive, with the benefits largely concentrated among the elite. Addressing these disparities through equitable policies and improved resource allocation is crucial for fostering sustainable development and improving Iraq's HDI, ensuring that economic growth translates into broader social and economic progress for all citizens (Saloom, T. M, 2024 ).. Iraq's oil GDP, which makes up a sizable amount of the country's GDP, increased by 12.1% in 2022 to 61% of GDP. However, non-oil GDP growth was constrained, in part because of the decline of agriculture and the stagnation of non-oil sectors. Each member of the Iraqi parliament, for example, receives a salary of 24 million Iraqi dinars each month, which, when converted to US dollars, comes to 16,200 dollars per month (Ahmed, Y. A., & Ibrahim, R. R. 2019 ). This means that the pay of a politician is 28 times higher than that of an average Iraqi employee (Majeed, H. L, 2022 ). The monthly remuneration of an Iraqi minister is 35 million Iraqi dinars, or $ 28,000, which is 58 times the compensation of a regular employee. However, after growing by USD 4,498 in 2020 and USD 5,044 in 2021, the latest economic spike propelled per capita GDP growth to 5.4% in 2022, hitting USD 6,265 (Ranis & Stewart & Samman, 2006). People use domestic credit to make investments. At 90 million Iraqi dinars, or $ 60,800 a month in US dollars, the president receives a salary that is 187 times higher than that of a regular employee. The prime minister and his aides were paid 60 million Iraqi dinar (about $ 40,500 USD) a month, which is 84 times the average employee's pay. The monthly remuneration of the head of Parliament and his aides is 55 million Iraqi dinars (37,100 US dollars), which is 77 times the pay of a regular employee (Majeed, H. L, 2022 ). Iraq's socioeconomic landscape reveals a stark disparity in income distribution, as reflected by the Gini Index, and highlights the broader challenges in improving the Human Development Index (HDI). Despite Iraq's notable GDP growth averaging 5.5% between 2005 and 2019, the rapid population increase curtailed GDP growth per capita to just 2.7%, limiting improvements in overall human development (Demirgüç-Kunt & Klapper, 2013 ). This economic imbalance is further deepened by the country's dependence on oil revenues, inadequate fiscal policies, and an overburdened public sector, where wages and pensions constitute a staggering 24% of GDP, (Žižek, S. Š., Mulej, M., & Hrast, N, 2023). Iraq's economic structure is heavily reliant on oil, which dominates the economy, contributing over 42% to GDP, accounting for 99% of exports, and providing 85% of the government budget. However, this dependence creates a skewed income distribution, as reflected in the high Gini Index, with the wealthiest 10% of the population receiving 32% of total income while the bottom 40% earn only 16.3%. Additionally, province-level disparities further underscore income inequality, with Baghdad's average monthly per capita income (IQD 426,800) being more than double that of Muthanna Province (IQD 170,100). These economic inequalities are compounded by high informality rates, particularly in sectors like agriculture (98.1%) and construction (99%), where marginalized groups, including women, are underrepresented (Majeed, H. L, 2022 ). The dominance of informal employment and the limited contribution of income and wealth taxes (5% of the budget) exacerbate these disparities, restricting opportunities for equitable growth. Iraq's Human Development Index (HDI) reflects these challenges, as income inequality, limited economic diversification, and underrepresentation in critical sectors hinder broader socioeconomic progress (Sharma, 2016 ). To achieve sustainable development, Iraq must diversify its economy, enhance formal employment opportunities, and implement equitable fiscal policies to address income disparities and improve HDI outcomes, (Lind, N, 2019 ). This uneven distribution exacerbates economic instability, restricts human development, and underscores the need for equitable policies to ensure sustainable growth. The remainder of the paper is structured as follows: Section 2 delves into the data, model specification, and methodological framework. Section 3 outlines the key findings and results. Section 4 offers actionable recommendations and strategic insights for policymakers. 2. DATA, MODEL SPECIFICATION AND METHODOLOGY 2.1 Data description This section describes the data used in the inquiry. Annual data from the World Income Database (WID) and the Central Bank of Iraq (CBI), two of the most comprehensive and reliable sources for tracking the historical evolution of income inequality, were used to calculate income disparity and redistribution trends. The WID provides detailed data on income distribution, inequality measures, and national accounts, while the CBI offers crucial information on economic indicators, including GDP, inflation rates, and fiscal policies specific to Iraq. Several scholars have relied on these databases to analyze trends in income inequality and its impacts on economic growth, including studies focused on the Iraq, Middle East and North Africa (MENA) region. These datasets, spanning multiple decades, allow for a robust analysis of the relationship between income inequality, human development, and economic performance in Iraq. The key variables examined in this study include: The analysis utilizes several key indicators to assess Iraq's socioeconomic dynamics: 1. Gross Domestic Product (GDP): This variable reflects Iraq's total economic output, capturing the overall performance of the national economy over time. It serves as a primary measure of economic growth and productivity. 2. Gini Index (GI): A metric for income inequality, the Gini coefficient ranges from 0 to 1, with 0 indicating perfect equality and 1 representing maximum inequality. This measure is critical for understanding the extent of income disparity within Iraq. 3. Human Development Index (HDI): A composite indicator that evaluates human development by factoring in life expectancy, education levels, and per capita income. The HDI provides a holistic view of the well-being and quality of life of Iraq’s population. The data spans from 1990 to 2024, encompassing various economic and political phases in Iraq, including periods of conflict, recovery, and economic stabilization. By analyzing this comprehensive dataset, the study seeks to uncover insights into the intricate relationships between economic growth, income inequality, and human development within Iraq's unique context. 2.2 Methodology The methodology employed in this study utilizes a combination of econometric techniques to analyze the relationship between GDP, income inequality (measured by the Gini Index), and human development (represented by the Human Development Index, HDI). The following steps outline the methodological framework: Descriptive Statistics: This preliminary analysis helps to explore the basic characteristics of the data, such as central tendencies (mean, median), variability (standard deviation), and distributions (skewness and kurtosis). Descriptive statistics offer a clear understanding of the data’s general features, which is essential for interpreting the relationships in subsequent analysis. Unit Root Testing: To ensure the stationarity of the variables, Augmented Dickey-Fuller (ADF) tests are employed. This step checks whether the time series data for GDP, Gini Index, and HDI contain unit roots. A unit root suggests that the series is non-stationary and requires differencing to achieve stationarity (Ahmed, Y. A., & Ibrahim, R. R. 2019). Cointegration Testing: After ensuring the stationarity of the variables, the next step is to examine the long-run relationships between GDP, income inequality, and human development. The Johansen cointegration test is applied to check if there is a stable, long-term equilibrium relationship among the variables. Autoregressive Distributed Lag (ARDL) Model: The ARDL model is used to estimate the short- and long-run relationships between the variables. This approach is particularly useful for analyzing the dynamics between variables that are integrated of different orders (i.e., some may be I(0) and others I(1)) and does not require all variables to be stationary at the same level. Diagnostic Checks: To validate the robustness of the model, several diagnostic tests are performed, including tests for heteroskedasticity (Breusch-Pagan-Godfrey test), serial correlation (Breusch-Godfrey LM test), and model specification (Ramsey RESET test). These tests ensure that the model is well-specified and reliable for drawing conclusions. 2.3 Econometric model The ARDL model is specified to examine the dynamic relationships between GDP, Gini Index, and HDI in Iraq. The ARDL model is particularly suitable for this study, as it can handle variables that are integrated of different orders and allows for both short- and long-run estimations. The general form of the ARDL model is: A basic econometric model would be: This is a simple linear model where economic growth is assumed to be linearly related to income inequality and human development. The lag lengths for each variable are determined based on the Akaike Information Criterion (AIC) or Schwarz Criterion, ensuring optimal model selection. The model will allow the study to assess both the immediate (short-run) and persistent (long-run) impacts of income inequality and human development on economic growth (GDP) in Iraq. 2.4 Empirical Results and Discussion In this section, we present and comprehensively discuss the empirical results derived from the application of the ARDL model, unit root tests, cointegration analysis, and the overall estimation of the dynamic relationships among economic growth (GDP), income inequality (Gini Index), and human development (HDI) in Iraq over the period from (1990 to 2024). The findings are systematically analyzed to capture both short-run and long-run effects, shedding light on the interplay between these key variables. Additionally, we explore the broader implications of these results for policy formulation, economic planning, and sustainable development in the Iraqi context. 2.5 Descriptive Statistics The descriptive statistics of the key variables—GDP, GINI Index, and HDI—are summarized as follows: Table (1): Descriptive statistics Statistics Growth Domestic Product Gini Index Human Development Index Mean 1.06E+11 106.3739 0.615618 Median 7.70E+10 85.59142 0.606500 Maximum 2.86E+11 254.6012 0.699000 Minimum 4.08E+08 0.831924 0.496000 Std. Dev. 9.03E+10 90.15459 0.059489 Probability 0.184018 0.151584 0.339430 Source: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12. The analysis of Iraq’s GDP, Gini Index, and Human Development Index (HDI) provides valuable insight into the nation’s economic and social landscape, especially regarding income inequality. The GDP, with its mean value of $106 billion and a notable range between $40.8 million and $286 billion, showcases the country's economic fluctuations, revealing both periods of growth and economic downturns. This volatility reflects Iraq’s reliance on oil exports and external market factors, which significantly affect the nation’s overall economic performance. The Gini Index, with a mean of 106.37, highlights the persistent income inequality in Iraq, as it indicates a large disparity in income distribution across the population. This suggests that while the country has substantial wealth, it is not evenly distributed, leading to a significant gap between the wealthiest and the poorest. The HDI remains relatively stable with a mean of 0.6156, indicating gradual but steady progress in improving the country’s human development indicators. Generally: · GDP: The mean GDP is approximately 1.06 × 10^11, with a maximum value of 2.86 × 10^11 and a minimum of 4.08 × 10^8. The standard deviation is 9.03 × 10^10, indicating significant variability in Iraq's GDP over the sample period. · GINI Index (Income Inequality): The mean GINI Index is 106.37, with a maximum of 254.60 and a minimum of 0.83. The high variability in the GINI index highlights fluctuations in income inequality in Iraq over time. · HDI (Human Development Index): The average HDI is 0.616, with a maximum of 0.699 and a minimum of 0.496. This suggests moderate human development over the period, with some improvements observed. These statistics indicate substantial fluctuations in both economic output and social indicators, which warrants further investigation into the underlying relationships. However, the relatively low HDI value points to the persistent challenges in education, healthcare, and living standards that contribute to the inequality experienced by a large portion of the population. These findings are critical in understanding the relationship between economic growth, income inequality, and human development in Iraq. While the GDP reflects overall economic performance, the Gini Index underscores the crucial issue of unequal wealth distribution, and the HDI reveals that social progress is not advancing at the same pace as economic growth. The observed trends and distributions, supported by statistical tests such as the Jarque-Bera, offer a clear indication that addressing income inequality will be key to improving both Iraq’s economic stability and the overall well-being of its population. The linkage between GDP growth and the Gini Index emphasizes the importance of inclusive economic policies that promote equitable income distribution, while the steady HDI suggests that there is a need for focused efforts to enhance education, healthcare, and other social indicators to improve living conditions for all Iraqis. 2.6 Unit Root and Stationarity Tests The Augmented Dickey-Fuller (ADF) tests were conducted to check the stationarity of the variables. The results are as follows: Table (2): Stationary – unit root test Variables Level/First Difference Exogenous ADF Statistic P-Value Stationarity LGDP Level Intercept -2.133001 0.2337 Non-stationary Level Intercept & Trend -4.513359 0.0054 Stationary with trend First Difference Intercept -10.63343 0.0000 Stationary First Difference Intercept & Trend -11.50365 0.0000 Stationary LGI Level Intercept -1.309553 0.6134 Non-stationary Level Intercept & Trend -4.980116 0.0017 Stationary with trend First Difference Intercept -15.02833 0.0000 Stationary First Difference Intercept & Trend -16.12745 0.0000 Stationary LHDI Level Intercept -0.833818 0.7961 Non-stationary Level Intercept & Trend -3.182457 0.1053 Non-stationary First Difference Intercept -6.090711 0.0000 Stationary First Difference Intercept & Trend -6.362300 0.0001 Stationary Source: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12. The unit root test results for Iraq’s economic data reveal that all variables—LGDP (GDP), LGI (Gini Index), and LHDI (Human Development Index)—are non-stationary at their levels, but they become stationary at the first difference, indicating they are I(1) variables. This is an important finding for econometric modeling, as it suggests that these variables exhibit long-term relationships, which can be analyzed further using techniques like cointegration analysis and error correction models. The cointegration tests conducted on Iraq’s economic data provide strong evidence of long-term connections between these variables, particularly between GDP, income inequality (Gini Index), and human development (HDI), suggesting that changes in one of these factors are likely to influence the others over time. The Trace Test results strongly support the presence of cointegrating relationships among GDP, Gini Index, HDI, and an external factor, with the test statistic for the null hypothesis of no cointegration significantly exceeding the critical values. The rejection of the null hypotheses further indicates that these variables share common long-term trends, which can be crucial for understanding the dynamics of income inequality in Iraq. Specifically, the Gini Index, a measure of income inequality, is tightly connected to both GDP growth and HDI improvements. The long-term relationships identified in the cointegration tests suggest that as Iraq's economy grows and develops, income inequality and human development are intertwined, reinforcing the need for policy interventions that address the underlying causes of inequality and support inclusive economic development. The fact that GDP growth, income inequality, and human development are cointegrated underscores the importance of sustainable economic growth that benefits all segments of society. Policymakers should consider these long-term relationships when designing economic policies to ensure that economic growth leads to reduced inequality and improved human development. Furthermore, the error correction models can help analyze the short-term adjustments in these variables, offering a more comprehensive understanding of the economic and social dynamics in Iraq. Generally · GDP: The test statistic for GDP at the level (without trend) is -2.13, which is not significant at the 5% level, suggesting a unit root at the level. However, the first difference of GDP is stationary with a test statistic of -10.63, indicating that GDP is integrated of order 1, I(1). · GINI Index: The GINI index is non-stationary at the level with a test statistic of -1.31 (p-value = 0.6134), but the first difference is stationary (test statistic = -15.03), confirming it is also I(1). · HDI: HDI at the level shows no evidence of stationarity (test statistic = -0.83, p-value = 0.7961), but it becomes stationary in the first difference (test statistic = -6.09), confirming that HDI is I(1). These results suggest that all variables are non-stationary at the level but become stationary after differencing, confirming their integration order as I(1). 2.7 The Johansen cointegration test Identifies long-term relationships among variables in a system, crucial for understanding their equilibrium dynamics. The test results provided analyze both Trace Statistics and Max-Eigen Statistics, each testing for the number of cointegrating equations (CEs) at a 5% significance level. Table (3): Johansen Cointegration Test Results Hypothesized No. of CE(s) Eigenvalue Trace Statistic 0.05 Critical Value Prob. Max-Eigen Statistic 0.05 Critical Value Prob. None * 0.896154 125.6612 40.17493 0.0000 67.94546 24.15921 0.0000 At most 1 * 0.671764 57.71577 24.27596 0.0000 33.42065 17.79730 0.0001 At most 2 * 0.484476 24.29512 12.32090 0.0003 19.87715 11.22480 0.0012 At most 3 * 0.136935 4.417974 4.129906 0.0422 4.417974 4.129906 0.0422 Source: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12. Generally 2.7.1 Trace Statistic Analysis · None: The trace statistic (125.6612) exceeds the critical value (40.17493) with a p-value of 0.0000, indicating at least one cointegrating equation exists. · At most 1: The trace statistic (57.71577) is also higher than the critical value (24.27596), with a p-value of 0.0000, suggesting a second cointegrating equation. · At most 2: The trace statistic (24.29512) surpasses the critical value (12.32090) at a p-value of 0.0003, supporting the presence of a third cointegrating equation. · At most 3: The trace statistic (4.417974) is slightly above the critical value (4.129906), with a p-value of 0.0422, indicating a fourth cointegrating equation at the 5% level. 2.7.2 Max-Eigen Statistic Analysis · None: The max-eigen statistic (67.94546) far exceeds the critical value (24.15921) with a p-value of 0.0000, confirming the presence of one cointegrating equation. · At most 1: The max-eigen statistic (33.42065) is higher than the critical value (17.79730), with a p-value of 0.0001, indicating a second cointegrating equation. · At most 2: The max-eigen statistic (19.87715) is above the critical value (11.22480), with a p-value of 0.0012, confirming the presence of a third cointegrating equation. · At most 3: The max-eigen statistic (4.417974) slightly exceeds the critical value (4.129906), with a p-value of 0.0422, suggesting the existence of a fourth cointegrating equation. 2.8 Interpretation Both Trace and Max-Eigen statistics consistently indicate the existence of up to four cointegrating equations at the 5% significance level. This demonstrates a strong long-term equilibrium relationship among the variables under study. The presence of multiple cointegrating equations suggests robust interdependencies, with each equation reflecting a distinct aspect of the relationships. 2.9 Implications The results imply that the variables are not only non-stationary individually but are also bound together in a long-term relationship. Policymakers and analysts can leverage these findings to design interventions that maintain equilibrium across the economic system. The high level of interconnection also underscores the importance of simultaneously addressing all key variables to ensure system stability. 2.9.1 Short-Run Dynamics In the short run, the ARDL model shows that the GINI index and HDI continue to have significant effects on GDP. The coefficients for these variables suggest that: Table (4): General Estimation Variable Coefficient Std. Error t-Statistic Prob.* GI -0.010071 0.002125 -4.739310 0.0002 LHDI(-2) 11.01465 3.197560 3.444704 0.0033 CRO -0.599341 0.094196 -6.362694 0.0000 C 50.14050 5.954468 8.420651 0.0000 R-squared 0.986933 Mean dependent var 25.02455 Adjusted R-squared 0.977133 S.D. dependent var 1.143978 S.E. of regression 0.172989 Akaike info criterion -0.369327 Sum squared resid 0.478806 Schwarz criterion 0.243598 Log likelihood 18.35525 Hannan-Quinn criter. -0.177367 F-statistic 100.7072 Durbin-Watson stat 1.696502 Prob(F-statistic) 0.000000 Source: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12. The ARDL model results for Iraq’s GDP (LGDP) reveal significant relationships between key economic indicators, particularly highlighting the role of income inequality (Gini Index - GI) in shaping the nation's economic performance. The Gini Index (GI) has a negative effect on GDP, with a coefficient of -0.010071, indicating that rising income inequality contributes to reduced economic growth. This finding is statistically significant, as evidenced by the t-statistic of -4.739310 and p-value of 0.0002, reinforcing the notion that disparities in income distribution hinder Iraq's GDP growth. In contrast, human development (LHDI), as measured by the log of HDI, positively impacts GDP. The coefficient for LHDI at lag 2 is 11.01465, with a t-statistic of 3.444704 and a p-value of 0.0033, suggesting that improving human development drives economic growth in Iraq. Moreover, external factors (CRO), which may include global economic conditions or political events, negatively affect Iraq’s GDP. The coefficient for CRO is -0.599341, with a t-statistic of -6.362694 and a p-value of 0.0000, confirming that such factors can detract from economic performance. The model's performance is robust, with an R-squared value of 0.986933, signifying that 98.7% of the variation in Iraq’s GDP is explained by the model. The adjusted R-squared of 0.977133 and the F-statistic of 100.7072 (with a p-value of 0.000000) further validate the model’s significance. Additionally, the Durbin-Watson statistic of 1.696502 indicates that there is no severe autocorrelation in the residuals. In conclusion, the ARDL model results indicate that income inequality has a detrimental impact on Iraq's economic growth , while human development fosters growth. External factors, however, have a negative influence on the country’s GDP. These findings highlight the need for policies that address income inequality, promote human development, and manage external economic shocks to ensure sustainable and equitable economic growth in Iraq. Generally: · Income inequality remains negatively associated with GDP in the short term, with a statistically significant coefficient of -0.0101 (p-value = 0.0002). This indicates that higher income disparity has an immediate dampening effect on economic growth. · The Human Development Index (HDI) contributes positively to GDP growth in the short run, with a coefficient of 11.01 (p-value = 0.0033), highlighting the critical role of improving life expectancy, education, and income in driving economic performance. · The short-run dynamics highlight the urgent need for policies to reduce income inequality and enhance human development, essential for immediate economic growth and long-term stability. 2.9.2 Diagnostic Tests and Model Validation The diagnostic tests performed to check for heteroskedasticity, serial correlation, and functional form of the model confirm that the model is well-specified. Table (5): Bounds test Test Statistic Value Significance Level I (0) Critical Value I (1) Critical Value F-statistic 15.25583 10% 2.37 3.2 k (No. of Variables) 3 5% 2.79 3.67 2.5% 3.15 4.08 1% 3.65 4.66 Source: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12. The results of the F-Bounds Test in the analysis of Iraq’s economic data provide strong evidence of a long-run relationship between the variables, including GDP, Gini Index (GI), and Human Development Index (HDI). The F-statistic value of 15.25583 exceeds the upper bound critical values at both the 5% and 10% significance levels, suggesting the rejection of the null hypothesis that no long-run relationship exists among the variables. Specifically, the F-statistic surpasses the upper bound critical value of 3.67 at the 5% level, indicating that there is indeed a cointegrating relationship among GDP, income inequality (Gini Index), and human development (HDI). This finding points to a stable and long-term equilibrium relationship between these key economic indicators. The evidence of cointegration further emphasizes that income inequality, as reflected in the Gini Index, and human development, captured by the HDI, are tightly linked with the GDP of Iraq. Over the long term, changes in one of these variables—such as an increase in income inequality or improvements in human development—are likely to influence the others, highlighting the interdependence of these factors. The long-run equilibrium relationship identified by the F-Bounds Test supports the use of econometric models like ARDL (Autoregressive Distributed Lag) to explore these dynamic interactions further. This suggests that policymakers should consider the interconnectedness of economic growth, income inequality, and human development when crafting policies. For instance, reducing income inequality could potentially foster better human development outcomes and, in turn, support sustainable economic growth. Thus, the F-Bounds Test results underscore the importance of addressing income inequality in Iraq, as it plays a significant role in shaping the country's long-term economic trajectory. Generally The Breusch-Pagan-Godfrey test for heteroskedasticity and the Breusch-Godfrey LM test for serial correlation show no significant issues, indicating the robustness of the model. The Ramsey RESET test confirms that the functional form of the model is correctly specified. Table (6): Short and long run estimation Conditional Error Correction Regression (Short run) Variable Coefficient Std. Error t-Statistic Prob. C 50.14050 8.248479 6.078756 0.0000 GI -0.010167 0.002916 -3.486601 0.0030 LHDI 17.03940 4.607542 3.698153 0.0019 CRO -0.599341 0.161561 -3.709695 0.0019 Levels Equation Case 2: Restricted Constant and No Trend (long run) Variable Coefficient Std. Error t-Statistic Prob. GI -0.006986 0.000792 -8.818086 0.0000 LHDI 19.27591 0.903443 21.33604 0.0000 CRO -0.411806 0.032870 -12.52842 0.0000 C 34.45149 0.518683 66.42111 0.0000 Source: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12. The short-run and long-run estimations of the ARDL model for Iraq’s GDP (1990–2023) clearly illustrate the significant relationships between income inequality , human development , and external factors (CRO). In the short run , the results show that income inequality , as represented by the Gini Index (GI) , negatively impacts GDP with a coefficient of -0.010167 , indicating that rising inequality reduces the nation's economic output. This suggests that, in the short term, income inequality is a key deterrent to Iraq’s economic performance. In contrast, improvements in human development (LHDI) have a positive effect on GDP , with a coefficient of 17.03940 , emphasizing the role of social progress in fostering economic growth. However, the negative impact of external factors (CRO) on GDP , with a coefficient of -0.599341 , highlights how external shocks or instabilities continue to harm Iraq’s economic development. In analyzing the long-run dynamics, the Gini Index continues to show a negative relationship with GDP (coefficient = -0.006986), reinforcing the notion that income inequality suppresses economic growth over time. This evidence suggests that income inequality not only hinders economic performance in the short term but also presents significant long-term challenges to achieving sustainable economic growth in Iraq. On the other hand, human development (LHDI) exhibits a strong positive relationship with GDP in the long run (coefficient = 19.27591), underscoring the critical role of improving human development as a key driver of GDP growth. Additionally, the persistent negative effect of external factors (CRO) highlights the vulnerability of Iraq’s economy to external shocks, which continue to impede long-term economic stability and growth. These findings indicate that addressing income inequality , improving human development , and mitigating the adverse effects of external factors are vital for fostering sustainable economic growth in Iraq. Policymakers should prioritize reducing income inequality as it consistently emerges as a barrier to both short-term and long-term economic progress. Simultaneously, investment in human development is crucial for driving future growth. In the long run, reducing external shocks and stabilizing the external environment will also be key to ensuring Iraq’s economic resilience and development. The diagnostic tests conducted on the regression model analyzing Iraq's economic indicators—particularly GDP , income inequality (Gini Index) , and human development (HDI) —offer valuable insights into the reliability and robustness of the analysis. The Heteroskedasticity Test results, where p-values are greater than 0.05, suggest that there is no issue with the variance of residuals , meaning that the relationship between the variables remains stable and consistent across all levels. This stability is important when analyzing the dynamics of income inequality in Iraq, as it implies that the economic factors affecting inequality are not distorted by fluctuations in data variance. Similarly, the Serial Correlation Test shows no significant autocorrelation in the residuals, confirming that the model correctly captures the data’s structure over time without bias from previous error terms. This is especially relevant for understanding the long-term relationship between GDP and income inequality , as it indicates that changes in income inequality or human development can be interpreted as independent events that influence economic growth without being unduly affected by past trends. 2.9.3 Diagnostic Tests for Model Specification and Assumptions The section on "Diagnostic Tests for Model Specification and Assumptions" plays a pivotal role in validating the robustness and reliability of an econometric model. In econometrics, it is crucial to ensure that the model used is properly specified and that the assumptions behind it hold true, as violations can lead to biased or inconsistent results. Table (7): Model Diagnostic and Validation Tests Test Metric Value P- Value Decision Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 0.617315 0.7989 Fail to reject the null hypothesis; no evidence of heteroskedasticity. Obs*R-squared 9.177530 0.6877 Consistent with F-statistic, supports homoskedasticity. Scaled explained SS 2.987471 0.9956 Strong evidence against heteroskedasticity. Breusch-Godfrey Serial Correlation LM Test F-statistic 0.847523 0.3718 Fail to reject the null hypothesis; no evidence of serial correlation. Obs*R-squared 1.550915 0.2130 Supports the absence of serial correlation. Ramsey RESET Test t-statistic 0.030977 0.9755 Fail to reject the null hypothesis; no evidence of omitted variable bias or misspecification. F-statistic 0.000960 0.9755 Consistent with t-statistic; supports the model specification. Likelihood ratio 1.094069 0.9727 Confirms no specification error. Variance Inflation Factors (VIF) LGDP(-1) 1.094069 - Low VIF indicates no multicollinearity issues. LGI 4.860455 - Moderate VIF; acceptable multicollinearity level. HDI(-1) 5.400726 - Moderate multicollinearity, still acceptable. CRO(-1) 1.182023 - Low VIF; no multicollinearity issues. C (Constant) NA - High uncentered VIF is expected for constants, but not a concern for overall model performance. Source: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12. In terms of model specification , the Ramsey RESET Test assures that the model is correctly specified, ruling out the possibility of omitted variable bias. This is critical in the analysis of income inequality , as it ensures that all relevant factors—such as HDI , CRO , and other socio-economic variables—are appropriately accounted for, making the analysis more reliable and comprehensive. While multicollinearity is observed for a few variables like HDI and Gini Index , with VIFs slightly above 1, they do not pose significant issues for interpretation. The moderate multicollinearity observed means that the variables are related, which is expected when analyzing interconnected economic variables like income inequality and human development . However, these relationships are not strong enough to undermine the results. Finally, the forecasting reliability of the model is affirmed by the diagnostic tests, indicating that the short-run and long-run relationships between GDP , income inequality , and human development can be used for informed decision-making. The model's accuracy in forecasting economic trends will be essential for shaping policies that aim to reduce income inequality and foster sustainable economic growth in Iraq. These tests ensure that the findings regarding the negative impact of income inequality on GDP are not only statistically valid but can also provide a foundation for future policy interventions targeting socio-economic disparities. These diagnostic tests ensure that the model's short-run and long-run relationships between GDP, income inequality, and human development are valid, providing a solid foundation for informed policymaking. By confirming the model’s accuracy, we can be confident that the findings—such as the negative impact of income inequality on GDP—are robust and reliable, offering valuable guidance for future policy interventions aimed at reducing socio-economic disparities in Iraq. 3. RESULTS 3.1 Descriptive Statistics The descriptive analysis of the data for Iraq’s GDP, Gini Index (GI), and Human Development Index (HDI) reveals substantial variation across the variables. The GDP shows significant fluctuations, indicating a dynamic but unstable economic environment. The Gini Index suggests considerable income inequality, and HDI data indicates moderate levels of human development with some variability across time. 3.2 Unit Root and Cointegration Tests The Augmented Dickey-Fuller (ADF) tests indicate that all variables are non-stationary at their levels but become stationary after first differencing. Cointegration tests, using the Johansen method, confirm the presence of long-run relationships between GDP, GI, and HDI, suggesting that these variables are interrelated over time in the context of Iraq's economy. 3.3 ARDL Model Results The ARDL model indicates that income inequality (Gini Index) has a negative and significant effect on GDP, with higher inequality leading to lower economic growth. Conversely, the Human Development Index (HDI) has a positive relationship with GDP, suggesting that improvements in human development contribute to stronger economic performance. Political stability, as measured by security (CRO), also significantly impacts GDP, with instability hindering economic growth. 3.4 Policy Implications The findings underscore the need for policies aimed at reducing income inequality, investing in human capital, and fostering political stability. These factors are crucial for ensuring sustainable economic growth and development in Iraq. This streamlined summary focuses on four key results related to the Iraqi economy. Let me know if you'd like further refinement! 4. RECOMMENDATIONS Based on the findings from the analysis of the Iraqi economy, the following recommendations are proposed to enhance economic growth, reduce inequality, and improve human development: 4.1 Addressing Income Inequality Income inequality has a significant negative effect on economic growth in Iraq. To mitigate this, the government should implement progressive tax policies, enhance social welfare programs, and promote inclusive economic policies that ensure a more equitable distribution of wealth. Special attention should be given to rural areas and marginalized groups to provide more equal opportunities. 4.2 Investing in Human Capital Improving human development, as measured by HDI, has a positive impact on GDP. The government should increase investments in education, healthcare, and skills development to improve the human capital base of the nation. Programs aimed at improving access to quality education and healthcare for all citizens, particularly in underserved areas, should be prioritized. 4.3 Promoting Political Stability Political instability negatively affects economic growth. Therefore, fostering political stability through reforms that ensure transparent governance, the rule of law, and security is essential. Strengthening democratic institutions and ensuring the peaceful resolution of conflicts will help in creating a conducive environment for economic development. 4.4 Diversification of the Economy Iraq's economy is heavily reliant on oil revenues, which makes it vulnerable to fluctuations in global oil prices. A diversification strategy focusing on sectors such as agriculture, manufacturing, and services will reduce this dependence and provide a more stable economic foundation. Policies that encourage private sector growth, innovation, and entrepreneurship should be implemented to facilitate this transition. By adopting these recommendations, Iraq can improve its long-term economic growth trajectory, reduce inequality, and foster a more sustainable and inclusive development process. Declarations Author Contribution This research is based on my own expertise and findings.Unfortunately, academics in Iraq do not receive funding for their research. Data Availability describes the data used in the inquiry. Annual data from the World Income Database (WID) and the Central Bank of Iraq (CBI), two of the most comprehensive and reliable sources for tracking the historical evolution of income inequality, were used to calculate income disparity and redistribution trends. The WID provides detailed data on income distribution, inequality measures, and national accounts, while the CBI offers crucial information on economic indicators, including GDP, inflation rates, and fiscal policies specific to Iraq. Several scholars have relied on these databases to analyze trends in income inequality and its impacts on economic growth, including studies focused on the Iraq, Middle East and North Africa (MENA) region. These datasets, spanning multiple decades, allow for a robust analysis of the relationship between income inequality, human development, and economic performance in Iraq. References Ahmed, Y. A., & Ibrahim, R. R. (2019). The impact of FDI inflows and outflows on economic growth: an empirical study of some developed and developing countries. Journal of University of Raparin , 6 (1), 129-157. Ali, D., Ghafar, S., Majeed, H., & Sabir, B. (2024). Analysis of the role of bank policy and other variables on the efficiency of Economic in Iraq. Academic Journal of International University of Erbil , 1 (02), 29-48. Demirgüç-Kunt, A., & Klapper, L. (2013). Measuring financial inclusion: Explaining variation in use of financial services across and within countries. Brookings papers on economic activity , 2013 (1), 279-340. Lind, N. (2019). A development of the human development index. Social Indicators Research , 146 (3), 409-423. Majeed, H. L. (2022). International debt for the sake of local debt and the economic situation in Iraq. Eurasian Journal of Management & Social Sciences , 3 (3), 65-81. Majeed, H. L. (2023). Analyzing and Measuring the Impact of Exchange Rate Fluctuations on Economic Growth in Iraq for the Period (2004-2022. Journal of Kurdistani for Strategic Studies , (2). Qadir, B. M., Abdullah, S. Z., & Majeed, H. L. (2023). Diversification and economic growth in emerging economies: The Kurdistan of Iraq experience. Eurasian Journal of Management & Social Sciences , 4 (2), 33-60. Qadir, B. M., Mohammed, H. O., & Majeed, H. L. (2021). Evaluating the Petroleum Contracts of Kurdistan Region in the Surveying and Applying the Deloitte Data (A Comparative Review). International Journal of Multicultural and Multireligious Understanding , 8 (7), 236-244. Ranis, G., Stewart, F., & Samman, E. (2006). Human development: beyond the human development index. Journal of Human Development , 7 (3), 323-358. Saloom, T. M. (2024). Analysis of the Productivity of Educational Expenditures and Their Impact on Human Development in Iraq. Journal of Economics and Administrative Sciences , 30 (144), 386-396. Sharma, D. (2016). Nexus between financial inclusion and economic growth: Evidence from the emerging Indian economy. Journal of financial economic policy , 8 (1), 13-36. Žižek, S. Š., Mulej, M., & Hrast, N. (2023). Human Development Index. In The Palgrave Handbook of Global Sustainability (pp. 1303-1317). Cham: Springer International Publishing. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5805354","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":410219795,"identity":"d3042858-a915-4a17-a37d-4b297a6a1e58","order_by":0,"name":"Hawre Latif Majeed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACAwhlwcDAzPjwYQODBJCTAEaEtEgw8DAzGxs2JJCkhYHZTLIBrBSfeiAwZz9j9uEDg4S8PTszW+XMHxYM/Ow5BgwPynBrsezJMZ45g0HCsIeZme3mBqDDJHveGDAknMPjsAO5m5l5GCQYe5j5j918ANRicANoS2IbHi3n325m/sMgYQ+ypRCkxZ6glhtAW4DeTwRpYQQ5zECCoJb3nxl7DCSSew4zM0vOSJPgkTjzrOAAXr+cT0tm+FFhY9vef5jxY49NnRx/e/LGhz/whBhUI4LJAyIOMLAR0oIJyNAyCkbBKBgFwxYAAKsuR2zN80LgAAAAAElFTkSuQmCC","orcid":"","institution":"Kurdistan Technical Institute - Sulaimaniyah","correspondingAuthor":true,"prefix":"","firstName":"Hawre","middleName":"Latif","lastName":"Majeed","suffix":""}],"badges":[],"createdAt":"2025-01-10 16:53:14","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5805354/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5805354/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75961973,"identity":"db70e351-305e-459d-88a5-bd8f0dc21be4","added_by":"auto","created_at":"2025-02-11 03:06:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30928,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not provided with this version\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5805354/v1/2b5dd82387fc872c3d61e033.png"},{"id":75961992,"identity":"b4126090-6f25-44de-9fc6-d8a8e301d6f5","added_by":"auto","created_at":"2025-02-11 03:06:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24050,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not provided with this version\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5805354/v1/99167d19e834ed9d274e3d60.png"},{"id":75962537,"identity":"8d1388ad-6800-449e-9f1a-4155732cee30","added_by":"auto","created_at":"2025-02-11 03:14:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24931,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not provided with this version\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5805354/v1/03862020a20c6da79eafbbbc.png"},{"id":75961978,"identity":"5beee275-1dff-4920-93f8-1446cfa87003","added_by":"auto","created_at":"2025-02-11 03:06:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33286,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not provided with this version\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5805354/v1/4afde93ba03528529e9a9cf8.png"},{"id":75961979,"identity":"fc9ad15a-d949-4272-89f4-28d1d2700882","added_by":"auto","created_at":"2025-02-11 03:06:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45782,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not provided with this version\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5805354/v1/3840fcc5f7b416d283299d1f.png"},{"id":75963656,"identity":"fec055ed-5bc9-4266-9d72-24398d5d3574","added_by":"auto","created_at":"2025-02-11 03:30:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2379527,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5805354/v1/5a7d9766-5505-49d9-9988-4e11f36c42e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Economic Growth and Income Inequality in Iraq: An Assessment of the Impact of Public Revenues and Transfers on Income Redistribution","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eIncome inequality refers to the disparity in the distribution of income among members of a population. Income is defined as the disposable income available to a household within a specific fiscal year. It encompasses earnings, self-employment income, capital income, and public financial transfers; however, income taxes and social security contributions remitted by households are subtracted. Inequality is also characterized as the disparity between affluent and impoverished individuals, encompassing income inequality, wealth disparity, variations in wealth and income, or the wealth divide. This indicator is quantified using the Gini coefficient. This analysis is predicated upon a comparison of the accumulated proportions of the population with the cumulative proportions of income they receive (Qadir, \u0026amp; Abdullah, \u0026amp; Majeed, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relationship between income inequality and redistribution is intricate and multifaceted. On one hand, elevated levels of income inequality may engender social and economic challenges, including poverty, social unrest, and a decline in economic development (Granger et al., 2022). The relationship between Gross Domestic Product (GDP) and the Gini Index highlights the interplay between income levels and income distribution. While GDP measures the overall economic output and average income levels, the Gini Index captures the degree of income inequality within a population (Ali \u0026amp; Ghafar \u0026amp; Majeed, \u0026amp; Sabir, 2024). When GDP remains constant, a more unequal income distribution, as indicated by a higher Gini Index, tends to intensify the severity of poverty.\u003c/p\u003e \u003cp\u003eIraq's abundant hydrocarbon resources significantly contribute to its GDP, forming the backbone of government revenue through production and sales. However, the inequitable distribution of income derived from these resources, as indicated by the Gini Index, presents significant economic challenges (Qadir, B. M. \u0026amp; Mohammed, H. O \u0026amp; Majeed, H. L. 2021). Disparities in income allocation limit the government's ability to adequately compensate public sector employees and hinder investments in productive sectors, exacerbating economic instability and slowing progress in the Human Development Index (HDI). (Demirg\u0026uuml;\u0026ccedil;-Kunt \u0026amp; Klapper, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Addressing these disparities requires robust economic growth aimed at reducing poverty and fostering sustainable development. Bridging income inequality and expanding access to economic opportunities\u0026mdash;especially for marginalized and educated individuals\u0026mdash;are critical steps.\u003c/p\u003e \u003cp\u003eIraq's substantial natural resources, particularly its petroleum exports, have positioned it as one of the wealthiest nations in the Middle East, with a GDP of USD 264.18\u0026nbsp;billion in 2022 and a notable growth rate of 7.0%. However, the benefits of this economic prosperity are unevenly distributed, as reflected by a high Gini Index, signaling significant income inequality. A wealthy minority, often comprising the political elite and high-ranking state officials, enjoys substantial privileges and salaries far exceeding those of regular government employees, whose average net pay was approximately \u003cspan\u003e$\u003c/span\u003e580 per month in 2022 (Majeed, H. L, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This disparity highlights the structural imbalances within Iraq's financial policies, particularly in the distribution of rights, privileges, and compensation (Saloom, T. M, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough Iraq's economy has rebounded following the severe recession of 2020 caused by the COVID-19 pandemic, the rebound has not been inclusive, with the benefits largely concentrated among the elite. Addressing these disparities through equitable policies and improved resource allocation is crucial for fostering sustainable development and improving Iraq's HDI, ensuring that economic growth translates into broader social and economic progress for all citizens (Saloom, T. M, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)..\u003c/p\u003e \u003cp\u003eIraq's oil GDP, which makes up a sizable amount of the country's GDP, increased by 12.1% in 2022 to 61% of GDP. However, non-oil GDP growth was constrained, in part because of the decline of agriculture and the stagnation of non-oil sectors. Each member of the Iraqi parliament, for example, receives a salary of 24\u0026nbsp;million Iraqi dinars each month, which, when converted to US dollars, comes to 16,200 dollars per month (Ahmed, Y. A., \u0026amp; Ibrahim, R. R. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This means that the pay of a politician is 28 times higher than that of an average Iraqi employee (Majeed, H. L, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The monthly remuneration of an Iraqi minister is 35\u0026nbsp;million Iraqi dinars, or \u003cspan\u003e$\u003c/span\u003e28,000, which is 58 times the compensation of a regular employee. However, after growing by USD 4,498 in 2020 and USD 5,044 in 2021, the latest economic spike propelled per capita GDP growth to 5.4% in 2022, hitting USD 6,265 (Ranis \u0026amp; Stewart \u0026amp; Samman, 2006).\u003c/p\u003e \u003cp\u003ePeople use domestic credit to make investments. At 90\u0026nbsp;million Iraqi dinars, or \u003cspan\u003e$\u003c/span\u003e60,800 a month in US dollars, the president receives a salary that is 187 times higher than that of a regular employee. The prime minister and his aides were paid 60\u0026nbsp;million Iraqi dinar (about \u003cspan\u003e$\u003c/span\u003e40,500 USD) a month, which is 84 times the average employee's pay. The monthly remuneration of the head of Parliament and his aides is 55\u0026nbsp;million Iraqi dinars (37,100 US dollars), which is 77 times the pay of a regular employee (Majeed, H. L, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIraq's socioeconomic landscape reveals a stark disparity in income distribution, as reflected by the Gini Index, and highlights the broader challenges in improving the Human Development Index (HDI). Despite Iraq's notable GDP growth averaging 5.5% between 2005 and 2019, the rapid population increase curtailed GDP growth per capita to just 2.7%, limiting improvements in overall human development (Demirg\u0026uuml;\u0026ccedil;-Kunt \u0026amp; Klapper, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This economic imbalance is further deepened by the country's dependence on oil revenues, inadequate fiscal policies, and an overburdened public sector, where wages and pensions constitute a staggering 24% of GDP, (Žižek, S. Š., Mulej, M., \u0026amp; Hrast, N, 2023).\u003c/p\u003e \u003cp\u003eIraq's economic structure is heavily reliant on oil, which dominates the economy, contributing over 42% to GDP, accounting for 99% of exports, and providing 85% of the government budget. However, this dependence creates a skewed income distribution, as reflected in the high Gini Index, with the wealthiest 10% of the population receiving 32% of total income while the bottom 40% earn only 16.3%. Additionally, province-level disparities further underscore income inequality, with Baghdad's average monthly per capita income (IQD 426,800) being more than double that of Muthanna Province (IQD 170,100). These economic inequalities are compounded by high informality rates, particularly in sectors like agriculture (98.1%) and construction (99%), where marginalized groups, including women, are underrepresented (Majeed, H. L, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dominance of informal employment and the limited contribution of income and wealth taxes (5% of the budget) exacerbate these disparities, restricting opportunities for equitable growth. Iraq's Human Development Index (HDI) reflects these challenges, as income inequality, limited economic diversification, and underrepresentation in critical sectors hinder broader socioeconomic progress (Sharma, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To achieve sustainable development, Iraq must diversify its economy, enhance formal employment opportunities, and implement equitable fiscal policies to address income disparities and improve HDI outcomes, (Lind, N, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This uneven distribution exacerbates economic instability, restricts human development, and underscores the need for equitable policies to ensure sustainable growth.\u003c/p\u003e \u003cp\u003eThe remainder of the paper is structured as follows: Section 2 delves into the data, model specification, and methodological framework. Section 3 outlines the key findings and results. Section 4 offers actionable recommendations and strategic insights for policymakers.\u003c/p\u003e"},{"header":"2. DATA, MODEL SPECIFICATION AND METHODOLOGY","content":"\u003cp\u003e\u003cstrong\u003e2.1\u0026nbsp;Data description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis section describes the data used in the inquiry. Annual data from the World Income Database (WID) and the Central Bank of Iraq (CBI), two of the most comprehensive and reliable sources for tracking the historical evolution of income inequality, were used to calculate income disparity and redistribution trends. The WID provides detailed data on income distribution, inequality measures, and national accounts, while the CBI offers crucial information on economic indicators, including GDP, inflation rates, and fiscal policies specific to Iraq. Several scholars have relied on these databases to analyze trends in income inequality and its impacts on economic growth, including studies focused on the Iraq, Middle East and North Africa (MENA) region. These datasets, spanning multiple decades, allow for a robust analysis of the relationship between income inequality, human development, and economic performance in Iraq. \u003cstrong\u003eThe key variables examined in this study include:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis utilizes several key indicators to assess Iraq\u0026apos;s socioeconomic dynamics:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eGross Domestic Product (GDP):\u003c/strong\u003e This variable reflects Iraq\u0026apos;s total economic output, capturing the overall performance of the national economy over time. It serves as a primary measure of economic growth and productivity.\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eGini Index (GI):\u003c/strong\u003e A metric for income inequality, the Gini coefficient ranges from 0 to 1, with 0 indicating perfect equality and 1 representing maximum inequality. This measure is critical for understanding the extent of income disparity within Iraq.\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eHuman Development Index (HDI):\u003c/strong\u003e A composite indicator that evaluates human development by factoring in life expectancy, education levels, and per capita income. The HDI provides a holistic view of the well-being and quality of life of Iraq\u0026rsquo;s population.\u003c/p\u003e\n\u003cp\u003eThe data spans from 1990 to 2024, encompassing various economic and political phases in Iraq, including periods of conflict, recovery, and economic stabilization. By analyzing this comprehensive dataset, the study seeks to uncover insights into the intricate relationships between economic growth, income inequality, and human development within Iraq\u0026apos;s unique context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u0026nbsp;Methodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe methodology employed in this study utilizes a combination of econometric techniques to analyze the relationship between GDP, income inequality (measured by the Gini Index), and human development (represented by the Human Development Index, HDI). The following steps outline the methodological framework:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDescriptive Statistics: This preliminary analysis helps to explore the basic characteristics of the data, such as central tendencies (mean, median), variability (standard deviation), and distributions (skewness and kurtosis). Descriptive statistics offer a clear understanding of the data\u0026rsquo;s general features, which is essential for interpreting the relationships in subsequent analysis.\u003c/p\u003e\n\u003cp\u003eUnit Root Testing: To ensure the stationarity of the variables, Augmented Dickey-Fuller (ADF) tests are employed. This step checks whether the time series data for GDP, Gini Index, and HDI contain unit roots. A unit root suggests that the series is non-stationary and requires differencing to achieve stationarity\u0026nbsp;(Ahmed, Y. A., \u0026amp; Ibrahim, R. R. 2019).\u003c/p\u003e\n\u003cp\u003eCointegration Testing: After ensuring the stationarity of the variables, the next step is to examine the long-run relationships between GDP, income inequality, and human development. The Johansen cointegration test is applied to check if there is a stable, long-term equilibrium relationship among the variables.\u003c/p\u003e\n\u003cp\u003eAutoregressive Distributed Lag (ARDL) Model: The ARDL model is used to estimate the short- and long-run relationships between the variables. This approach is particularly useful for analyzing the dynamics between variables that are integrated of different orders (i.e., some may be I(0) and others I(1)) and does not require all variables to be stationary at the same level.\u003c/p\u003e\n\u003cp\u003eDiagnostic Checks: To validate the robustness of the model, several diagnostic tests are performed, including tests for heteroskedasticity (Breusch-Pagan-Godfrey test), serial correlation (Breusch-Godfrey LM test), and model specification (Ramsey RESET test). These tests ensure that the model is well-specified and reliable for drawing conclusions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3\u0026nbsp;Econometric model\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ARDL model is specified to examine the dynamic relationships between GDP, Gini Index, and HDI in Iraq. The ARDL model is particularly suitable for this study, as it can handle variables that are integrated of different orders and allows for both short- and long-run estimations. The general form of the ARDL model is:\u003cstrong\u003e\u0026nbsp;A basic econometric model would be:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg 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\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThis is a simple linear model where economic growth is assumed to be linearly related to income inequality and human development. The lag lengths for each variable are determined based on the Akaike Information Criterion (AIC) or Schwarz Criterion, ensuring optimal model selection. The model will allow the study to assess both the immediate (short-run) and persistent (long-run) impacts of income inequality and human development on economic growth (GDP) in Iraq.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4\u0026nbsp;Empirical Results and Discussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this section, we present and comprehensively discuss the empirical results derived from the application of the ARDL model, unit root tests, cointegration analysis, and the overall estimation of the dynamic relationships among economic growth (GDP), income inequality (Gini Index), and human development (HDI) in Iraq over the period from (1990 to 2024). The findings are systematically analyzed to capture both short-run and long-run effects, shedding light on the interplay between these key variables. Additionally, we explore the broader implications of these results for policy formulation, economic planning, and sustainable development in the Iraqi context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5\u0026nbsp;Descriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe descriptive statistics of the key variables\u0026mdash;GDP, GINI Index, and HDI\u0026mdash;are summarized as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (1): Descriptive statistics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8846%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrowth Domestic Product\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGini Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHuman Development Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.8846%;\"\u003e\n \u003cp\u003e\u0026nbsp;1.06E+11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u0026nbsp;106.3739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8077%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.615618\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Median\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.8846%;\"\u003e\n \u003cp\u003e\u0026nbsp;7.70E+10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u0026nbsp;85.59142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8077%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.606500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Maximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.8846%;\"\u003e\n \u003cp\u003e\u0026nbsp;2.86E+11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u0026nbsp;254.6012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8077%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.699000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Minimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.8846%;\"\u003e\n \u003cp\u003e\u0026nbsp;4.08E+08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.831924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8077%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.496000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Std. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.8846%;\"\u003e\n \u003cp\u003e\u0026nbsp;9.03E+10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u0026nbsp;90.15459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8077%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.059489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Probability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.8846%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.184018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.151584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8077%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.339430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of Iraq\u0026rsquo;s GDP, Gini Index, and Human Development Index (HDI) provides valuable insight into the nation\u0026rsquo;s economic and social landscape, especially regarding income inequality. The GDP, with its mean value of $106 billion and a notable range between $40.8 million and $286 billion, showcases the country\u0026apos;s economic fluctuations, revealing both periods of growth and economic downturns. This volatility reflects Iraq\u0026rsquo;s reliance on oil exports and external market factors, which significantly affect the nation\u0026rsquo;s overall economic performance. The Gini Index, with a mean of 106.37, highlights the persistent income inequality in Iraq, as it indicates a large disparity in income distribution across the population. This suggests that while the country has substantial wealth, it is not evenly distributed, leading to a significant gap between the wealthiest and the poorest. The HDI remains relatively stable with a mean of 0.6156, indicating gradual but steady progress in improving the country\u0026rsquo;s human development indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGenerally:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026middot; GDP: The mean GDP is approximately 1.06 \u0026times; 10^11, with a maximum value of 2.86 \u0026times; 10^11 and a minimum of 4.08 \u0026times; 10^8. The standard deviation is 9.03 \u0026times; 10^10, indicating significant variability in Iraq\u0026apos;s GDP over the sample period.\u003c/p\u003e\n\u003cp\u003e\u0026middot; GINI Index (Income Inequality): The mean GINI Index is 106.37, with a maximum of 254.60 and a minimum of 0.83. The high variability in the GINI index highlights fluctuations in income inequality in Iraq over time.\u003c/p\u003e\n\u003cp\u003e\u0026middot; HDI (Human Development Index): The average HDI is 0.616, with a maximum of 0.699 and a minimum of 0.496. This suggests moderate human development over the period, with some improvements observed.\u003c/p\u003e\n\u003cp\u003eThese statistics indicate substantial fluctuations in both economic output and social indicators, which warrants further investigation into the underlying relationships.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;However, the relatively low HDI value points to the persistent challenges in education, healthcare, and living standards that contribute to the inequality experienced by a large portion of the population.\u003c/p\u003e\n\u003cp\u003eThese findings are critical in understanding the relationship between economic growth, income inequality, and human development in Iraq. While the GDP reflects overall economic performance, the Gini Index underscores the crucial issue of unequal wealth distribution, and the HDI reveals that social progress is not advancing at the same pace as economic growth. The observed trends and distributions, supported by statistical tests such as the Jarque-Bera, offer a clear indication that addressing income inequality will be key to improving both Iraq\u0026rsquo;s economic stability and the overall well-being of its population. The linkage between GDP growth and the Gini Index emphasizes the importance of inclusive economic policies that promote equitable income distribution, while the steady HDI suggests that there is a need for focused efforts to enhance education, healthcare, and other social indicators to improve living conditions for all Iraqis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6\u0026nbsp;Unit Root and Stationarity Tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Augmented Dickey-Fuller (ADF) tests were conducted to check the stationarity of the variables. The results are as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (2): Stationary \u0026ndash; unit root test\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel/First Difference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2308%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExogenous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADF Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStationarity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLGDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9872%;\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-2.133001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.2337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eNon-stationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9872%;\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eIntercept \u0026amp; Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-4.513359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.0054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eStationary with trend\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9872%;\"\u003e\n \u003cp\u003eFirst Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-10.63343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9872%;\"\u003e\n \u003cp\u003eFirst Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eIntercept \u0026amp; Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-11.50365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLGI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9872%;\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-1.309553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.6134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eNon-stationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9872%;\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eIntercept \u0026amp; Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-4.980116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.0017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eStationary with trend\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9872%;\"\u003e\n \u003cp\u003eFirst Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-15.02833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9872%;\"\u003e\n \u003cp\u003eFirst Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eIntercept \u0026amp; Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-16.12745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLHDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9872%;\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-0.833818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.7961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eNon-stationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9872%;\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eIntercept \u0026amp; Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-3.182457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.1053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eNon-stationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9872%;\"\u003e\n \u003cp\u003eFirst Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-6.090711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9872%;\"\u003e\n \u003cp\u003eFirst Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eIntercept \u0026amp; Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-6.362300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe unit root test results for Iraq\u0026rsquo;s economic data reveal that all variables\u0026mdash;LGDP (GDP), LGI (Gini Index), and LHDI (Human Development Index)\u0026mdash;are non-stationary at their levels, but they become stationary at the first difference, indicating they are I(1) variables. This is an important finding for econometric modeling, as it suggests that these variables exhibit long-term relationships, which can be analyzed further using techniques like cointegration analysis and error correction models. The cointegration tests conducted on Iraq\u0026rsquo;s economic data provide strong evidence of long-term connections between these variables, particularly between GDP, income inequality (Gini Index), and human development (HDI), suggesting that changes in one of these factors are likely to influence the others over time.\u003c/p\u003e\n\u003cp\u003eThe Trace Test results strongly support the presence of cointegrating relationships among GDP, Gini Index, HDI, and an external factor, with the test statistic for the null hypothesis of no cointegration significantly exceeding the critical values. The rejection of the null hypotheses further indicates that these variables share common long-term trends, which can be crucial for understanding the dynamics of income inequality in Iraq. Specifically, the Gini Index, a measure of income inequality, is tightly connected to both GDP growth and HDI improvements. The long-term relationships identified in the cointegration tests suggest that as Iraq\u0026apos;s economy grows and develops, income inequality and human development are intertwined, reinforcing the need for policy interventions that address the underlying causes of inequality and support inclusive economic development.\u003c/p\u003e\n\u003cp\u003eThe fact that GDP growth, income inequality, and human development are cointegrated underscores the importance of sustainable economic growth that benefits all segments of society. Policymakers should consider these long-term relationships when designing economic policies to ensure that economic growth leads to reduced inequality and improved human development. Furthermore, the error correction models can help analyze the short-term adjustments in these variables, offering a more comprehensive understanding of the economic and social dynamics in Iraq.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGenerally\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eGDP:\u003c/strong\u003e The test statistic for GDP at the level (without trend) is -2.13, which is not significant at the 5% level, suggesting a unit root at the level. However, the first difference of GDP is stationary with a test statistic of -10.63, indicating that GDP is integrated of order 1, I(1).\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eGINI Index:\u003c/strong\u003e The GINI index is non-stationary at the level with a test statistic of -1.31 (p-value = 0.6134), but the first difference is stationary (test statistic = -15.03), confirming it is also I(1).\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eHDI:\u003c/strong\u003e HDI at the level shows no evidence of stationarity (test statistic = -0.83, p-value = 0.7961), but it becomes stationary in the first difference (test statistic = -6.09), confirming that HDI is I(1).\u003c/p\u003e\n\u003cp\u003eThese results suggest that all variables are non-stationary at the level but become stationary after differencing, confirming their integration order as I(1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7\u0026nbsp;The Johansen cointegration test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentifies long-term relationships among variables in a system, crucial for understanding their equilibrium dynamics. The test results provided analyze both Trace Statistics and Max-Eigen Statistics, each testing for the number of cointegrating equations (CEs) at a 5% significance level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (3): Johansen Cointegration Test Results\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.2488%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesized No. of CE(s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEigenvalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrace Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05 Critical Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax-Eigen Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05 Critical Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2488%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.896154\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e125.6612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e40.17493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e67.94546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e24.15921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2488%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAt most 1 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.671764\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e57.71577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e24.27596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e33.42065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e17.79730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2488%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAt most 2 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.484476\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e24.29512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e12.32090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e19.87715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e11.22480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2488%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAt most 3 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.136935\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e4.417974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e4.129906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e0.0422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e4.417974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e4.129906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0385%;\"\u003e\n \u003cp\u003e0.0422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGenerally\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7.1\u0026nbsp; \u0026nbsp;\u0026nbsp;Trace Statistic Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026middot; None: The trace statistic (125.6612) exceeds the critical value (40.17493) with a p-value of 0.0000, indicating at least one cointegrating equation exists.\u003c/p\u003e\n\u003cp\u003e\u0026middot; At most 1: The trace statistic (57.71577) is also higher than the critical value (24.27596), with a p-value of 0.0000, suggesting a second cointegrating equation.\u003c/p\u003e\n\u003cp\u003e\u0026middot; At most 2: The trace statistic (24.29512) surpasses the critical value (12.32090) at a p-value of 0.0003, supporting the presence of a third cointegrating equation.\u003c/p\u003e\n\u003cp\u003e\u0026middot; At most 3: The trace statistic (4.417974) is slightly above the critical value (4.129906), with a p-value of 0.0422, indicating a fourth cointegrating equation at the 5% level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7.2\u0026nbsp; \u0026nbsp;\u0026nbsp;Max-Eigen Statistic Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026middot; None: The max-eigen statistic (67.94546) far exceeds the critical value (24.15921) with a p-value of 0.0000, confirming the presence of one cointegrating equation.\u003c/p\u003e\n\u003cp\u003e\u0026middot; At most 1: The max-eigen statistic (33.42065) is higher than the critical value (17.79730), with a p-value of 0.0001, indicating a second cointegrating equation.\u003c/p\u003e\n\u003cp\u003e\u0026middot; At most 2: The max-eigen statistic (19.87715) is above the critical value (11.22480), with a p-value of 0.0012, confirming the presence of a third cointegrating equation.\u003c/p\u003e\n\u003cp\u003e\u0026middot; At most 3: The max-eigen statistic (4.417974) slightly exceeds the critical value (4.129906), with a p-value of 0.0422, suggesting the existence of a fourth cointegrating equation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8\u0026nbsp;Interpretation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth Trace and Max-Eigen statistics consistently indicate the existence of up to four cointegrating equations at the 5% significance level. This demonstrates a strong long-term equilibrium relationship among the variables under study. The presence of multiple cointegrating equations suggests robust interdependencies, with each equation reflecting a distinct aspect of the relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9\u0026nbsp;Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results imply that the variables are not only non-stationary individually but are also bound together in a long-term relationship. Policymakers and analysts can leverage these findings to design interventions that maintain equilibrium across the economic system. The high level of interconnection also underscores the importance of simultaneously addressing all key variables to ensure system stability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9.1\u0026nbsp; \u0026nbsp;\u0026nbsp;Short-Run Dynamics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the short run, the ARDL model shows that the GINI index and HDI continue to have significant effects on GDP. The coefficients for these variables suggest that:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (4): General Estimation\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9003%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.328%;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb.*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9003%;\"\u003e\n \u003cp\u003e-0.010071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e0.002125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.328%;\"\u003e\n \u003cp\u003e-4.739310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLHDI(-2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9003%;\"\u003e\n \u003cp\u003e11.01465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e3.197560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.328%;\"\u003e\n \u003cp\u003e3.444704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e0.0033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9003%;\"\u003e\n \u003cp\u003e-0.599341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e0.094196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.328%;\"\u003e\n \u003cp\u003e-6.362694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9003%;\"\u003e\n \u003cp\u003e50.14050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e5.954468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.328%;\"\u003e\n \u003cp\u003e8.420651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR-squared\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9003%;\"\u003e\n \u003cp\u003e0.986933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 41.4791%;\"\u003e\n \u003cp\u003eMean dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e25.02455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R-squared\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9003%;\"\u003e\n \u003cp\u003e0.977133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 41.4791%;\"\u003e\n \u003cp\u003eS.D. dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e1.143978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.E. of regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9003%;\"\u003e\n \u003cp\u003e0.172989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 41.4791%;\"\u003e\n \u003cp\u003eAkaike info criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e-0.369327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum squared resid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9003%;\"\u003e\n \u003cp\u003e0.478806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 41.4791%;\"\u003e\n \u003cp\u003eSchwarz criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e0.243598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog likelihood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9003%;\"\u003e\n \u003cp\u003e18.35525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 41.4791%;\"\u003e\n \u003cp\u003eHannan-Quinn criter.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e-0.177367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9003%;\"\u003e\n \u003cp\u003e100.7072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 41.4791%;\"\u003e\n \u003cp\u003eDurbin-Watson stat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e1.696502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb(F-statistic)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 76.8489%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ARDL model results for Iraq\u0026rsquo;s GDP (LGDP) reveal significant relationships between key economic indicators, particularly highlighting the role of income inequality (Gini Index - GI) in shaping the nation\u0026apos;s economic performance. The Gini Index (GI) has a negative effect on GDP, with a coefficient of -0.010071, indicating that rising income inequality contributes to reduced economic growth. This finding is statistically significant, as evidenced by the t-statistic of -4.739310 and p-value of 0.0002, reinforcing the notion that disparities in income distribution hinder Iraq\u0026apos;s GDP growth.\u003c/p\u003e\n\u003cp\u003eIn contrast, human development (LHDI), as measured by the log of HDI, positively impacts GDP. The coefficient for LHDI at lag 2 is 11.01465, with a t-statistic of 3.444704 and a p-value of 0.0033, suggesting that improving human development drives economic growth in Iraq. Moreover, external factors (CRO), which may include global economic conditions or political events, negatively affect Iraq\u0026rsquo;s GDP. The coefficient for CRO is -0.599341, with a t-statistic of -6.362694 and a p-value of 0.0000, confirming that such factors can detract from economic performance.\u003c/p\u003e\n\u003cp\u003eThe model\u0026apos;s performance is robust, with an R-squared value of 0.986933, signifying that 98.7% of the variation in Iraq\u0026rsquo;s GDP is explained by the model. The adjusted R-squared of 0.977133 and the F-statistic of 100.7072 (with a p-value of 0.000000) further validate the model\u0026rsquo;s significance. Additionally, the Durbin-Watson statistic of 1.696502 indicates that there is no severe autocorrelation in the residuals.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the ARDL model results indicate that \u003cstrong\u003eincome inequality has a detrimental impact on Iraq\u0026apos;s economic growth\u003c/strong\u003e, while human development fosters growth. External factors, however, have a negative influence on the country\u0026rsquo;s GDP. These findings highlight the need for policies that address income inequality, promote human development, and manage external economic shocks to ensure sustainable and equitable economic growth in Iraq.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGenerally:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026middot; Income inequality remains negatively associated with GDP in the short term, with a statistically significant coefficient of -0.0101 (p-value = 0.0002). This indicates that higher income disparity has an immediate dampening effect on economic growth.\u003c/p\u003e\n\u003cp\u003e\u0026middot; The Human Development Index (HDI) contributes positively to GDP growth in the short run, with a coefficient of 11.01 (p-value = 0.0033), highlighting the critical role of improving life expectancy, education, and income in driving economic performance.\u003c/p\u003e\n\u003cp\u003e\u0026middot; The short-run dynamics highlight the urgent need for policies to reduce income inequality and enhance human development, essential for immediate economic growth and long-term stability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9.2 \u0026nbsp; \u0026nbsp;Diagnostic Tests and Model Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnostic tests performed to check for heteroskedasticity, serial correlation, and functional form of the model confirm that the model is well-specified.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (5): Bounds test\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificance Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI (0) Critical Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI (1) Critical Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e15.25583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ek (No. of Variables)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e2.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the F-Bounds Test in the analysis of Iraq\u0026rsquo;s economic data provide strong evidence of a long-run relationship between the variables, including GDP, Gini Index (GI), and Human Development Index (HDI). The F-statistic value of 15.25583 exceeds the upper bound critical values at both the 5% and 10% significance levels, suggesting the rejection of the null hypothesis that no long-run relationship exists among the variables. Specifically, the F-statistic surpasses the upper bound critical value of 3.67 at the 5% level, indicating that there is indeed a cointegrating relationship among GDP, income inequality (Gini Index), and human development (HDI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis finding points to a stable and long-term equilibrium relationship between these key economic indicators. The evidence of cointegration further emphasizes that income inequality, as reflected in the Gini Index, and human development, captured by the HDI, are tightly linked with the GDP of Iraq. Over the long term, changes in one of these variables\u0026mdash;such as an increase in income inequality or improvements in human development\u0026mdash;are likely to influence the others, highlighting the interdependence of these factors. The long-run equilibrium relationship identified by the F-Bounds Test supports the use of econometric models like ARDL (Autoregressive Distributed Lag) to explore these dynamic interactions further.\u003c/p\u003e\n\u003cp\u003eThis suggests that policymakers should consider the interconnectedness of economic growth, income inequality, and human development when crafting policies. For instance, reducing income inequality could potentially foster better human development outcomes and, in turn, support sustainable economic growth. Thus, the \u003cstrong\u003eF-Bounds Test\u003c/strong\u003e results underscore the importance of addressing \u003cstrong\u003eincome inequality\u003c/strong\u003e in Iraq, as it plays a significant role in shaping the country\u0026apos;s long-term economic trajectory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerally\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Breusch-Pagan-Godfrey test for heteroskedasticity and the Breusch-Godfrey LM test for serial correlation show no significant issues, indicating the robustness of the model. The Ramsey RESET test confirms that the functional form of the model is correctly specified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (6): Short and long run estimation\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConditional Error Correction Regression (Short run)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6688%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3119%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2926%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2572%;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6688%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3119%;\"\u003e\n \u003cp\u003e50.14050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2926%;\"\u003e\n \u003cp\u003e8.248479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2572%;\"\u003e\n \u003cp\u003e6.078756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6688%;\"\u003e\n \u003cp\u003eGI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3119%;\"\u003e\n \u003cp\u003e-0.010167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2926%;\"\u003e\n \u003cp\u003e0.002916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2572%;\"\u003e\n \u003cp\u003e-3.486601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e0.0030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6688%;\"\u003e\n \u003cp\u003eLHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3119%;\"\u003e\n \u003cp\u003e17.03940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2926%;\"\u003e\n \u003cp\u003e4.607542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2572%;\"\u003e\n \u003cp\u003e3.698153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6688%;\"\u003e\n \u003cp\u003eCRO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3119%;\"\u003e\n \u003cp\u003e-0.599341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2926%;\"\u003e\n \u003cp\u003e0.161561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2572%;\"\u003e\n \u003cp\u003e-3.709695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevels Equation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase 2: Restricted Constant and No Trend (long run)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6688%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3119%;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2926%;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2572%;\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6688%;\"\u003e\n \u003cp\u003eGI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3119%;\"\u003e\n \u003cp\u003e-0.006986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2926%;\"\u003e\n \u003cp\u003e0.000792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2572%;\"\u003e\n \u003cp\u003e-8.818086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6688%;\"\u003e\n \u003cp\u003eLHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3119%;\"\u003e\n \u003cp\u003e19.27591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2926%;\"\u003e\n \u003cp\u003e0.903443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2572%;\"\u003e\n \u003cp\u003e21.33604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6688%;\"\u003e\n \u003cp\u003eCRO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3119%;\"\u003e\n \u003cp\u003e-0.411806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2926%;\"\u003e\n \u003cp\u003e0.032870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2572%;\"\u003e\n \u003cp\u003e-12.52842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6688%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3119%;\"\u003e\n \u003cp\u003e34.45149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2926%;\"\u003e\n \u003cp\u003e0.518683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2572%;\"\u003e\n \u003cp\u003e66.42111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4695%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eshort-run and long-run estimations\u003c/strong\u003e of the ARDL model for Iraq\u0026rsquo;s GDP (1990\u0026ndash;2023) clearly illustrate the significant relationships between \u003cstrong\u003eincome inequality\u003c/strong\u003e, \u003cstrong\u003ehuman development\u003c/strong\u003e, and \u003cstrong\u003eexternal factors\u003c/strong\u003e (CRO). In the \u003cstrong\u003eshort run\u003c/strong\u003e, the results show that \u003cstrong\u003eincome inequality\u003c/strong\u003e, as represented by the \u003cstrong\u003eGini Index (GI)\u003c/strong\u003e, negatively impacts \u003cstrong\u003eGDP\u003c/strong\u003e with a coefficient of \u003cstrong\u003e-0.010167\u003c/strong\u003e, indicating that rising inequality reduces the nation\u0026apos;s economic output. This suggests that, in the short term, \u003cstrong\u003eincome inequality\u003c/strong\u003e is a key deterrent to Iraq\u0026rsquo;s economic performance. In contrast, improvements in \u003cstrong\u003ehuman development (LHDI)\u003c/strong\u003e have a positive effect on \u003cstrong\u003eGDP\u003c/strong\u003e, with a coefficient of \u003cstrong\u003e17.03940\u003c/strong\u003e, emphasizing the role of social progress in fostering economic growth. However, the negative impact of \u003cstrong\u003eexternal factors (CRO)\u003c/strong\u003e on \u003cstrong\u003eGDP\u003c/strong\u003e, with a coefficient of \u003cstrong\u003e-0.599341\u003c/strong\u003e, highlights how external shocks or instabilities continue to harm Iraq\u0026rsquo;s economic development.\u003c/p\u003e\n\u003cp\u003eIn analyzing the long-run dynamics, the Gini Index continues to show a negative relationship with GDP (coefficient = -0.006986), reinforcing the notion that income inequality suppresses economic growth over time. This evidence suggests that income inequality not only hinders economic performance in the short term but also presents significant long-term challenges to achieving sustainable economic growth in Iraq. On the other hand, human development (LHDI) exhibits a strong positive relationship with GDP in the long run (coefficient = 19.27591), underscoring the critical role of improving human development as a key driver of GDP growth. Additionally, the persistent negative effect of external factors (CRO) highlights the vulnerability of Iraq\u0026rsquo;s economy to external shocks, which continue to impede long-term economic stability and growth.\u003c/p\u003e\n\u003cp\u003eThese findings indicate that addressing \u003cstrong\u003eincome inequality\u003c/strong\u003e, improving \u003cstrong\u003ehuman development\u003c/strong\u003e, and mitigating the adverse effects of \u003cstrong\u003eexternal factors\u003c/strong\u003e are vital for fostering sustainable economic growth in Iraq.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePolicymakers should prioritize \u003cstrong\u003ereducing income inequality\u003c/strong\u003e as it consistently emerges as a barrier to both short-term and long-term economic progress. Simultaneously, investment in \u003cstrong\u003ehuman development\u003c/strong\u003e is crucial for driving future growth. In the long run, reducing \u003cstrong\u003eexternal shocks\u003c/strong\u003e and stabilizing the external environment will also be key to ensuring Iraq\u0026rsquo;s economic resilience and development.\u003c/p\u003e\n\u003cp\u003eThe diagnostic tests conducted on the regression model analyzing Iraq\u0026apos;s economic indicators\u0026mdash;particularly \u003cstrong\u003eGDP\u003c/strong\u003e, \u003cstrong\u003eincome inequality (Gini Index)\u003c/strong\u003e, and \u003cstrong\u003ehuman development (HDI)\u003c/strong\u003e\u0026mdash;offer valuable insights into the reliability and robustness of the analysis. The \u003cstrong\u003eHeteroskedasticity Test\u003c/strong\u003e results, where p-values are greater than 0.05, suggest that there is no issue with the \u003cstrong\u003evariance of residuals\u003c/strong\u003e, meaning that the relationship between the variables remains stable and consistent across all levels. This stability is important when analyzing the dynamics of \u003cstrong\u003eincome inequality\u003c/strong\u003e in Iraq, as it implies that the economic factors affecting inequality are not distorted by fluctuations in data variance.\u003c/p\u003e\n\u003cp\u003eSimilarly, the \u003cstrong\u003eSerial Correlation Test\u003c/strong\u003e shows no significant autocorrelation in the residuals, confirming that the model correctly captures the data\u0026rsquo;s structure over time without bias from previous error terms. This is especially relevant for understanding the \u003cstrong\u003elong-term relationship\u003c/strong\u003e between \u003cstrong\u003eGDP\u003c/strong\u003e and \u003cstrong\u003eincome inequality\u003c/strong\u003e, as it indicates that changes in \u003cstrong\u003eincome inequality\u003c/strong\u003e or \u003cstrong\u003ehuman development\u003c/strong\u003e can be interpreted as independent events that influence \u003cstrong\u003eeconomic growth\u003c/strong\u003e without being unduly affected by past trends.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9.3\u0026nbsp; \u0026nbsp;\u0026nbsp;Diagnostic Tests for Model Specification and Assumptions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe section on \u0026quot;Diagnostic Tests for Model Specification and Assumptions\u0026quot; plays a pivotal role in validating the robustness and reliability of an econometric model. In econometrics, it is crucial to ensure that the model used is properly specified and that the assumptions behind it hold true, as violations can lead to biased or inconsistent results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (7): Model Diagnostic and Validation Tests\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP- Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDecision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeteroskedasticity Test: Breusch-Pagan-Godfrey\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.617315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.7989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003eFail to reject the null hypothesis; no evidence of heteroskedasticity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eObs*R-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e9.177530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.6877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003eConsistent with F-statistic, supports homoskedasticity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eScaled explained SS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e2.987471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.9956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003eStrong evidence against heteroskedasticity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreusch-Godfrey Serial Correlation LM Test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.847523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.3718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003eFail to reject the null hypothesis; no evidence of serial correlation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eObs*R-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e1.550915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.2130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003eSupports the absence of serial correlation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRamsey RESET Test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003et-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.030977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.9755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003eFail to reject the null hypothesis; no evidence of omitted variable bias or misspecification.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.000960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.9755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003eConsistent with t-statistic; supports the model specification.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eLikelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e1.094069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.9727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003eConfirms no specification error.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariance Inflation Factors (VIF)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eLGDP(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e1.094069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003eLow VIF indicates no multicollinearity issues.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eLGI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e4.860455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003eModerate VIF; acceptable multicollinearity level.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eHDI(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e5.400726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003eModerate multicollinearity, still acceptable.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eCRO(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e1.182023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003eLow VIF; no multicollinearity issues.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eC (Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6923%;\"\u003e\n \u003cp\u003eHigh uncentered VIF is expected for constants, but not a concern for overall model performance.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Prepared by researchers based on annual data for the period (1990-2023) using the program E-views 12.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn terms of \u003cstrong\u003emodel specification\u003c/strong\u003e, the \u003cstrong\u003eRamsey RESET Test\u003c/strong\u003e assures that the model is correctly specified, ruling out the possibility of omitted variable bias. This is critical in the analysis of \u003cstrong\u003eincome inequality\u003c/strong\u003e, as it ensures that all relevant factors\u0026mdash;such as \u003cstrong\u003eHDI\u003c/strong\u003e, \u003cstrong\u003eCRO\u003c/strong\u003e, and other socio-economic variables\u0026mdash;are appropriately accounted for, making the analysis more reliable and comprehensive.\u003c/p\u003e\n\u003cp\u003eWhile \u003cstrong\u003emulticollinearity\u003c/strong\u003e is observed for a few variables like \u003cstrong\u003eHDI\u003c/strong\u003e and \u003cstrong\u003eGini Index\u003c/strong\u003e, with \u003cstrong\u003eVIFs\u003c/strong\u003e slightly above 1, they do not pose significant issues for interpretation. The \u003cstrong\u003emoderate multicollinearity\u003c/strong\u003e observed means that the variables are related, which is expected when analyzing interconnected economic variables like \u003cstrong\u003eincome inequality\u003c/strong\u003e and \u003cstrong\u003ehuman development\u003c/strong\u003e. However, these relationships are not strong enough to undermine the results.\u003c/p\u003e\n\u003cp\u003eFinally, the \u003cstrong\u003eforecasting reliability\u003c/strong\u003e of the model is affirmed by the diagnostic tests, indicating that the \u003cstrong\u003eshort-run and long-run relationships\u003c/strong\u003e between \u003cstrong\u003eGDP\u003c/strong\u003e, \u003cstrong\u003eincome inequality\u003c/strong\u003e, and \u003cstrong\u003ehuman development\u003c/strong\u003e can be used for informed decision-making. The model\u0026apos;s accuracy in forecasting economic trends will be essential for shaping policies that aim to reduce \u003cstrong\u003eincome inequality\u003c/strong\u003e and foster \u003cstrong\u003esustainable economic growth\u003c/strong\u003e in Iraq. These tests ensure that the findings regarding the negative impact of \u003cstrong\u003eincome inequality\u003c/strong\u003e on \u003cstrong\u003eGDP\u003c/strong\u003e are not only statistically valid but can also provide a foundation for future policy interventions targeting socio-economic disparities.\u003c/p\u003e\n\u003cp\u003eThese diagnostic tests ensure that the model\u0026apos;s short-run and long-run relationships between GDP, income inequality, and human development are valid, providing a solid foundation for informed policymaking. By confirming the model\u0026rsquo;s accuracy, we can be confident that the findings\u0026mdash;such as the negative impact of income inequality on GDP\u0026mdash;are robust and reliable, offering valuable guidance for future policy interventions aimed at reducing socio-economic disparities in Iraq.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eThe descriptive analysis of the data for Iraq\u0026rsquo;s GDP, Gini Index (GI), and Human Development Index (HDI) reveals substantial variation across the variables. The GDP shows significant fluctuations, indicating a dynamic but unstable economic environment. The Gini Index suggests considerable income inequality, and HDI data indicates moderate levels of human development with some variability across time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Unit Root and Cointegration Tests\u003c/h2\u003e \u003cp\u003eThe Augmented Dickey-Fuller (ADF) tests indicate that all variables are non-stationary at their levels but become stationary after first differencing. Cointegration tests, using the Johansen method, confirm the presence of long-run relationships between GDP, GI, and HDI, suggesting that these variables are interrelated over time in the context of Iraq's economy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3 ARDL Model Results\u003c/h2\u003e \u003cp\u003eThe ARDL model indicates that income inequality (Gini Index) has a negative and significant effect on GDP, with higher inequality leading to lower economic growth. Conversely, the Human Development Index (HDI) has a positive relationship with GDP, suggesting that improvements in human development contribute to stronger economic performance. Political stability, as measured by security (CRO), also significantly impacts GDP, with instability hindering economic growth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Policy Implications\u003c/h2\u003e \u003cp\u003eThe findings underscore the need for policies aimed at reducing income inequality, investing in human capital, and fostering political stability. These factors are crucial for ensuring sustainable economic growth and development in Iraq.\u003c/p\u003e \u003cp\u003eThis streamlined summary focuses on four key results related to the Iraqi economy. Let me know if you'd like further refinement!\u003c/p\u003e \u003c/div\u003e"},{"header":"4. RECOMMENDATIONS","content":"\u003cp\u003eBased on the findings from the analysis of the Iraqi economy, the following recommendations are proposed to enhance economic growth, reduce inequality, and improve human development:\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Addressing Income Inequality\u003c/h2\u003e \u003cp\u003eIncome inequality has a significant negative effect on economic growth in Iraq. To mitigate this, the government should implement progressive tax policies, enhance social welfare programs, and promote inclusive economic policies that ensure a more equitable distribution of wealth. Special attention should be given to rural areas and marginalized groups to provide more equal opportunities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Investing in Human Capital\u003c/h2\u003e \u003cp\u003eImproving human development, as measured by HDI, has a positive impact on GDP. The government should increase investments in education, healthcare, and skills development to improve the human capital base of the nation. Programs aimed at improving access to quality education and healthcare for all citizens, particularly in underserved areas, should be prioritized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Promoting Political Stability\u003c/h2\u003e \u003cp\u003ePolitical instability negatively affects economic growth. Therefore, fostering political stability through reforms that ensure transparent governance, the rule of law, and security is essential. Strengthening democratic institutions and ensuring the peaceful resolution of conflicts will help in creating a conducive environment for economic development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Diversification of the Economy\u003c/h2\u003e \u003cp\u003eIraq's economy is heavily reliant on oil revenues, which makes it vulnerable to fluctuations in global oil prices. A diversification strategy focusing on sectors such as agriculture, manufacturing, and services will reduce this dependence and provide a more stable economic foundation. Policies that encourage private sector growth, innovation, and entrepreneurship should be implemented to facilitate this transition.\u003c/p\u003e \u003cp\u003eBy adopting these recommendations, Iraq can improve its long-term economic growth trajectory, reduce inequality, and foster a more sustainable and inclusive development process.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThis research is based on my own expertise and findings.Unfortunately, academics in Iraq do not receive funding for their research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003edescribes the data used in the inquiry. Annual data from the World Income Database (WID) and the Central Bank of Iraq (CBI), two of the most comprehensive and reliable sources for tracking the historical evolution of income inequality, were used to calculate income disparity and redistribution trends. The WID provides detailed data on income distribution, inequality measures, and national accounts, while the CBI offers crucial information on economic indicators, including GDP, inflation rates, and fiscal policies specific to Iraq. Several scholars have relied on these databases to analyze trends in income inequality and its impacts on economic growth, including studies focused on the Iraq, Middle East and North Africa (MENA) region. These datasets, spanning multiple decades, allow for a robust analysis of the relationship between income inequality, human development, and economic performance in Iraq.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhmed, Y. A., \u0026amp; Ibrahim, R. R. (2019). The impact of FDI inflows and outflows on economic growth: an empirical study of some developed and developing countries. \u003cem\u003eJournal of University of Raparin\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 129-157.\u003c/li\u003e\n \u003cli\u003eAli, D., Ghafar, S., Majeed, H., \u0026amp; Sabir, B. (2024). Analysis of the role of bank policy and other variables on the efficiency of Economic in Iraq. \u003cem\u003eAcademic Journal of International University of Erbil\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(02), 29-48.\u003c/li\u003e\n \u003cli\u003eDemirg\u0026uuml;\u0026ccedil;-Kunt, A., \u0026amp; Klapper, L. (2013). Measuring financial inclusion: Explaining variation in use of financial services across and within countries. \u003cem\u003eBrookings papers on economic activity\u003c/em\u003e, \u003cem\u003e2013\u003c/em\u003e(1), 279-340.\u003c/li\u003e\n \u003cli\u003eLind, N. (2019). 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Diversification and economic growth in emerging economies: The Kurdistan of Iraq experience. \u003cem\u003eEurasian Journal of Management \u0026amp; Social Sciences\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(2), 33-60.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eQadir, B. M., Mohammed, H. O., \u0026amp; Majeed, H. L. (2021). Evaluating the Petroleum Contracts of Kurdistan Region in the Surveying and Applying the Deloitte Data (A Comparative Review). \u003cem\u003eInternational Journal of Multicultural and Multireligious Understanding\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(7), 236-244.\u003c/li\u003e\n \u003cli\u003eRanis, G., Stewart, F., \u0026amp; Samman, E. (2006). Human development: beyond the human development index. \u003cem\u003eJournal of Human Development\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(3), 323-358.\u003c/li\u003e\n \u003cli\u003eSaloom, T. M. (2024). Analysis of the Productivity of Educational Expenditures and Their Impact on Human Development in Iraq. \u003cem\u003eJournal of Economics and Administrative Sciences\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(144), 386-396.\u003c/li\u003e\n \u003cli\u003eSharma, D. (2016). Nexus between financial inclusion and economic growth: Evidence from the emerging Indian economy. \u003cem\u003eJournal of financial economic policy\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 13-36.\u003c/li\u003e\n \u003cli\u003eŽižek, S. \u0026Scaron;., Mulej, M., \u0026amp; Hrast, N. (2023). Human Development Index. In \u003cem\u003eThe Palgrave Handbook of Global Sustainability\u003c/em\u003e (pp. 1303-1317). Cham: Springer International Publishing.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hydrocarbon wealth, Income inequality, Human Development Index (HDI), Sustainable economic growth","lastPublishedDoi":"10.21203/rs.3.rs-5805354/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5805354/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIraq's hydrocarbon wealth is a cornerstone of its Growth domestic product and government revenue, yet significant income inequality, as reflected by the Gini Index, poses persistent economic challenges. The unequal distribution of oil-derived income constrains public sector wages and investments, exacerbating social instability and impeding progress on the Human Development Index (HDI). Empirical analysis reveals a strong, consistent relationship between income inequality and developmental outcomes, with stationary trends indicating stable patterns over time.\u003c/p\u003e \u003cp\u003eAddressing these disparities requires targeted interventions to promote sustainable growth. Reducing inequality and expanding opportunities for marginalized groups are essential. Leveraging information and communication technology to boost employment, alongside policies ensuring wage equity, labor inclusion, and social protection, can improve HDI outcomes. This study examines data spanning (1990 to 2024) to analyze Iraq's economic structure, employing robust empirical models that confirm these relationships.\u003c/p\u003e \u003cp\u003eThe analysis demonstrates high reliability, with no evidence of heteroskedasticity, serial correlation, or multicollinearity. Promoting equity across employment sectors and empowering collective bargaining are pivotal steps toward fostering a more inclusive and sustainable economic framework for Iraq.\u003c/p\u003e","manuscriptTitle":"Economic Growth and Income Inequality in Iraq: An Assessment of the Impact of Public Revenues and Transfers on Income Redistribution","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-11 03:06:37","doi":"10.21203/rs.3.rs-5805354/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d1419d0-c2a7-49fc-83ef-2d0433649d56","owner":[],"postedDate":"February 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-11T03:06:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-11 03:06:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5805354","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5805354","identity":"rs-5805354","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}