Threshold Effects of Growth on Income in Kazakhstan

As income inequality and social disparities persist in Kazakhstan, the question arises: to what extent is the country’s economic growth inclusive? This paper aims to identify the growth thresholds at which positive macroeconomic developments begin to translate into sustainable increases in real household incomes. The study is based on data from 2000 to 2024, covering real GDP and decile-level income distribution. We apply Hansen’s threshold regression method to explore how different income groups respond to economic growth.

The findings reveal that the “trickle-down” effect of growth on household incomes is not automatic—it only becomes statistically significant after a certain growth rate is reached. For the poorest 10% of the population, real income growth only begins to show consistent gains when GDP growth exceeds 3.35% annually. Below this threshold, economic growth has no significant effect on their incomes.

In contrast, the richest 10% of the population benefit from growth starting at a slightly lower threshold of 3.30%, with noticeable positive effects even during periods of moderate growth. This means that even when growth is modest, the wealthiest segments of society tend to gain more, and sooner.

The average threshold for income growth across all households is also estimated at 3.3%, highlighting two key features: (1) the sluggish transmission of growth benefits to the broader population, and (2) the structural advantage of top-income deciles in reaping early and disproportionate gains from economic expansion.

These results suggest that Kazakhstan’s economic growth remains structurally non-inclusive and underline the need for a policy shift—particularly in areas of social welfare and fiscal redistribution.

Introduction

Since the early 2000s, Kazakhstan has experienced high levels of economic growth, largely driven by resource exports. However, this growth has not led to equally strong improvements in household well-being. Social inequality remains high, real income gains are uneven, and wealth disparities are growing. This raises an important question: does economic growth in Kazakhstan truly improve people’s living standards—and under what macroeconomic conditions?

This paper investigates the relationship between real GDP growth and changes in household incomes, with a focus on identifying growth thresholds that trigger inclusive outcomes. For the first time in Kazakhstan-focused research, we use Hansen’s threshold regression approach to quantify these critical turning points.

Literature Review

The relationship between economic growth and living standards has been widely studied in global economic literature. Early theories, such as Kuznets (1955), proposed that inequality initially rises during industrialization but declines as redistribution institutions develop.

More recent research focuses on the quality and inclusivenessof growth (Barro, 2000; Dollar & Kraay, 2002). Ravallion (2001) emphasizes that not all growth is equally beneficial for the poor—its impact depends heavily on a country’s economic structure, state capacity, and policy environment. Studies by Hanmer & Naschold (2000) and Bourguignon (2004) stress the importance of how fast and how reliably economic gains reach the lower-income segments.

Modern approaches, particularly in the context of developing or resource-dependent economies, emphasize threshold effects as a key analytical lens. Hansen (1999) introduced a method for estimating nonlinear relationships with unknown thresholds—an approach increasingly used to study growth’s impact on poverty and inequality (e.g., Loayza & Raddatz, 2010; Kakwani et al., 2016).

Despite this international progress, such techniques have not yet been widely applied in Kazakhstan. This study seeks to bridge that gap and provide a deeper understanding of the inclusivity—or lack thereof—of the country’s economic growth.

Data and Methodology

This study uses official data from the Bureau of National Statistics of Kazakhstan (BNS, ASPIR) covering the period from 2000 to 2024. The dataset includes the real GDP volume index, the real income index of the population, nominal incomes by decile groups, and the consumer price index (CPI). Since real income data by deciles are not published, we deflated the nominal income of the top and bottom deciles using the CPI to calculate real annual income growth rates for these groups.

Based on this, we constructed comparable time series for:

•⁠  ⁠The overall real income index of the population;

•⁠  ⁠Real income of the bottom 10% income group;

•⁠  ⁠Real income of the top 10% income group.

To capture possible nonlinear relationships between GDP growth and income changes, we applied Hansen’s (1999) threshold regression method, which allows for a single structural break. This approach helps identify the specific GDP growth rate at which the relationship between economic growth and income shifts in a statistically significant way.

Threshold Regression Model

The single-threshold model developed by Hansen (1999) detects the point at which the effect of economic growth on income becomes statistically different. The general form of the model is:

(1)

Where:

– is the dependent variable (real income growth),

– is the independent variable (GDP growth rate),

– is the unknown threshold, identified through residual minimization,

– are intercepts before and after the threshold,

– are slope coefficients before and after the threshold,

– is the error term.

The threshold is estimated by minimizing the sum of squared residuals.

(2)

To test whether the threshold is statistically significant, Hansen suggests using:
•⁠ ⁠Bootstrap confidence intervals,
•⁠ ⁠Sup-Wald or Sup-F statistics.
The null hypothesis of a linear relationship is rejected if the simulated bootstrap Sup-F statistic exceeds the critical value.

(3)

Empirical Results
We used the bootstrap Sup-F test to determine whether there is a statistically significant structural break in the regression relationship. This involved multiple iterations using bootstrapped residuals, calculating test statistics and corresponding p-values.

Note: The Sup-F statistic shows how much better the threshold model explains the data compared to a simple linear model. A higher value indicates a significant change in the slope coefficients before and after the threshold.
In all models, the GDP growth threshold was found to be statistically significant, confirming the presence of a structural nonlinearity in how economic growth translates into real income gains. However, the nature of this nonlinearity varies across income groups, shedding light on the asymmetry in the “trickle-down” effect of growth.

For the General Population
For the overall real income index, the threshold was identified at 3.30% annual GDP growth. Below this level, economic growth has no statistically significant impact on household incomes. This suggests that moderate macroeconomic growth does not translate into tangible income gains for the majority of the population.
However, once the threshold is crossed, the effect becomes both strong and statistically significant:
•⁠ ⁠The coefficient rises to 1.92,
•⁠ ⁠Model explanatory power increases dramatically, with R² jumping from 0.17 to 0.66.
This indicates a momentum effect—when GDP growth exceeds the threshold, the economy begins to exert a systematic and predictable influence on household income growth.

Bottom Decile (10% Least Well-Off)
The most pronounced threshold effect is observed in the model for the bottom decile. For the poorest 10% of the population, the GDP growth threshold is identified at 3.35%. Below this level, income growth shows virtually no responseto changes in GDP. The regression coefficient is just 0.12, statistically insignificant (p = 0.81), and the model’s explanatory power is extremely weak (R² = 0.06). This suggests a complete absence of “trickle-down” effects under conditions of low or moderate economic activity. In practical terms, the incomes of the poorest households remain inert and do not improve even as the economy grows modestly.
However, once GDP growth surpasses the 3.35% threshold, the picture changes dramatically. The coefficient jumps to 2.41, becomes statistically significant (p = 0.001), and the R²rises sharply to 0.71. This contrast highlights the vulnerability of low-income groups to the business cycle—only in periods of strong economic expansion do their incomes begin to grow, and when they do, the growth is substantial. This may indicate a catch-up effect, where the poorest segments experience accelerated gains—but only once the broader economy has reached a high enough growth rate.

Top Decile (10% Most Well-Off)
For the top 10% of the population, the threshold is the same as that of the overall income index: 3.30%. However, the pattern of response is fundamentally different. Even beforethe threshold is reached, GDP growth already has a positive and statistically significant impact on their incomes. This suggests that affluent groups begin to benefit from economic growth early on—even during modest expansions—likely due to their stronger ties to export industries, financial markets, and corporate profits.
After the threshold, the impact becomes even more pronounced: the regression coefficient increases to 2.18 (p < 0.001) and the R² rises to 0.74. This trajectory reflects a reinforcing growth effect favoring upper-income groups, confirming the hypothesis that the wealthiest benefit first and most from economic expansion, while lower-income groups catch up only later, if at all.

The presence of a 3.3–3.35% GDP growth threshold shows that economic gains do not reach households—especially the poor—during low-growth periods. The findings of this study suggest that Kazakhstan’s economic growth is not fully inclusive. By using threshold regression analysis, we identified critical GDP growth levels beyond which real income gains begin to materialize for different segments of the population.
Crucially, the poorest households require a higher level of economic activity before their incomes begin to grow. In a context of anticipated growth volatility, this highlights the need for fiscal and institutional measures to reduce vulnerability and ensure income support for the least well-off. Social policies should not rely solely on the promise of growth but must actively intervene to close the gap in how that growth is shared.

References
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3.⁠ ⁠Hanmer, L., & Naschold, F. (2000). Attaining the International Development Targets: Will Growth Be Enough? Development Policy Review, 18(1), 11–36. https://doi.org/10.1111/1467-7679.00098
4.⁠ ⁠Hansen, B. E. (1999). Threshold Effects in Non-Dynamic Panels: Estimation, Testing, and Inference. Journal of Econometrics, 93(2), 345–368. https://doi.org/10.1016/S0304-4076(99)00025-1
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