Human capital development and income inequality in Indonesia: Evidence from a nonlinear autoregressive distributed lag (NARDL) analysis

Goh Lim Thye; Law, Siong Hook; Trinugroho, Irwan Article Human capital development and income inequality in Indonesia: Evidence from a nonlinear autoregressive distributed lag (NARDL) analysis Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Goh Lim Thye; Law, Siong Hook; Trinugroho, Irwan (2022) : Human capital development and income inequality in Indonesia: Evidence from a nonlinear autoregressive distributed lag (NARDL) analysis, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 10, Iss. 1, pp. 1-18, https://doi.org/10.1080/23322039.2022.2129372 This Version is available at: https://hdl.handle.net/10419/303825 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Human capital development and income inequality in Indonesia: Evidence from a nonlinear autoregressive distributed lag (NARDL) analysis Goh Lim Thye, Siong Hook Law & Irwan Trinugroho To cite this article: Goh Lim Thye, Siong Hook Law & Irwan Trinugroho (2022) Human capital development and income inequality in Indonesia: Evidence from a nonlinear autoregressive distributed lag (NARDL) analysis, Cogent Economics & Finance, 10:1, 2129372, DOI: 10.1080/23322039.2022.2129372 To link to this article: https://doi.org/10.1080/23322039.2022.2129372 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 28 Oct 2022. Submit your article to this journal Article views: 3849 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 DEVELOPMENT ECONOMICS | RESEARCH ARTICLE Human capital development and income inequality in Indonesia: Evidence from a nonlinear autoregressive distributed lag (NARDL) analysis Goh Lim Thye 1 , Siong Hook Law 2 and Irwan Trinugroho 3 * Abstract: Indonesia ranks sixth globally in terms of wealth distribution inequality. Changes in human capital development may affect labor force efficiency and productivity as well as wages and income inequality levels. This study applies a nonlinear autoregressive distributed lag (NARDL) model to data from 1970 to 2019 to investigate the asymmetric impact of human capital development on income inequality in Indonesia. Our results provide significant evidence of the long-run asymmetric effects of human capital development on income inequality. More specifically, income inequality responded more significantly to increase in human capital development than to reduction. Hence, policymakers should establish inclusive lifelong learning systems that concentrate on skill enhancement, such as re-training and re-skilling, and technical and vocational training (TVET) systems to enhance a country’s human capital development. Subjects: Economics and Development; Econometrics; Development Economics Keywords: Human capital development; income inequality; asymmetric cointegration; Indonesia JEL CLASSIFICATION: D33; D63; J24 1. Introduction Although issues related to income inequality have been widely discussed in the mainstream media for some time and were included in the United Nations’ 17 Sustainable Development Goals established in 2015, the challenges of closing the income gap between upperand lower-income groups remain significant, and remedial policies remain ineffective. As of 2021, the richest 10% of the global population receives 52% of the total global income, while the poorest 50% receives only 8.5% (World Economic Forum, 2021). It has been discovered that the ongoing COVID-19 pandemic will exacerbate global income inequality and worsen income inequality in emerging and developing economies by 0.3 points in 2020. (World Bank, 2022). According to Oxfam’s (2022) report, Indonesia is now the world’s fourth most populous country and sixth most unequal in terms of wealth distribution. Income disparity in Indonesia has grown faster over the last two decades than in any other Southeast Asian country. Furthermore, the four richest men in Indonesia are said to be worth more than the combined wealth of the poorest 100 million people in Indonesia. Human capital development assumes continuous advancement in knowledge and skill competencies through continuing education, job training, and workshops. Not surprisingly, it is seen as a Thye et al., Cogent Economics & Finance (2022), 10: 2129372 https://doi.org/10.1080/23322039.2022.2129372 Page 1 of 17 Received: 24 August 2022 Accepted: 26 September 2022 *Corresponding author: Irwan Trinugroho, Faculty of Economics and Business, Universitas Sebelas Maret, Indonesia E-mail: [email protected] Reviewing editor: Aviral Tiwari, Finance and Economics, Rajagiri Business School, India Additional information is available at the end of the article © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. key factor for income growth, promotion of competitiveness, and higher employment, and is expected to have a curative effect on a nation’s level of income inequality (World Economic Forum, 2017). Similarly, Becker (1962) and Rosen (1977) argued from a theoretical standpoint that individuals who advance their skills and competencies would benefit from a nation’s industrialisation. On the other hand, low-skilled workers will suffer or be laid off if they do not advance with the industrial revolution. As a result, human capital development would improve labor force efficiency and productivity, while also serving as a springboard for wage increase. Consequently, this has a direct impact on a country’s income distribution level. According to the World Bank, Indonesia’s Human Capital Index improved from 0.54 in 2020 to 0.53 in 2018, indicating that an Indonesian worker of the next generation would only be 54% as productive as they would be under the benchmark of complete education and full health. Thus, to accelerate investment in people for greater equity and economic growth in Indonesia, the Indonesian government has undertaken several initiatives to prioritise human capital development. These initiatives include allocating funds for various programs aimed at improving education, health, and social protection in the country (Holmemo, 2019). Furthermore, in 2019, Indonesian President Joko Widodo proposed a USD 178 billion budget for 2020 centered on education (The Star,), in addition to the USD 2.7 billion in loans provided by the Asian Development Bank (ADB) to fund the human capital development program (The Jakarta Post, 2020). Although the Indonesian government was an early adopter of the World Human Capital Project, many initiatives to improve human capital development have been implemented over the years (World Bank, 2019). A country’s income inequality level (as measured by the Gini index) remains significant. According to World Bank and the Standardised World Income Inequality Database (SWIID) reports, the Gini index remained significant at 46.50 in 2015 before rising to 46.60 in 2016 and 2017, before worsening to 47.40 in 2018 and 2019. However, while the inverse linkages between human capital development and income inequality are well founded, the empirical results have not been unanimous. Altgouhh and Lin (2007), Birchenall (2001), Shahpari and Davoudi (2014), Turnovsky (2011), and Chani et al. (2014) suggest that there is a significant correlation between human capital development and disparities in income distribution. Lee and Lee (2018) argue that educational equality and public policies that improve social benefits are the driving forces behind income inequality. Turnovsky (2011) found that human capital advancement has no impact on growth or income inequality. Chani et al. (2014) found that income inequality was caused by human capital inequality, but not vice versa. Nevertheless, these contrasting empirical studies continue to attract debate and call for further research. Moreover, most existing literature suggests that the relationship between human capital development and income distribution disparities is symmetrical (Behrman & Knowles, 1999; Jolliffe, 1998; Kajisa & Palanichamy, 2006). However, this study argues that the assumption of symmetrical bonding between human capital development and income inequality is inaccurate. Given that the level of human capital development directly reflects workers’ compensation, an increase in human capital development is expected to boost workers’ wages. However, as argued by the theory of stickiness in wage adjustment (Keynes, 1936), downward adjustment in wages caused by a reduction in human capital development may not be possible. In practical terms, any reduction in wages may be prohibited or made impossible by labor unions, labor laws, or even by minimum wage regulations laid down by the country’s labor law/National Wages Consultative Council Act. Hence, the adjustment of wages or income is not necessarily symmetrical to any variation in human capital development. Consequently, it is inaccurate to hypothesise that the impact of human capital development on income distribution is symmetrical. Therefore, a study on the asymmetric integration between human capital development and disparities in income distribution is required. Despite spending millions of dollars on various initiatives to improve Human Capital Development in Indonesia, income inequality remains significant. As a result, this study aims to Thye et al., Cogent Economics & Finance (2022), 10: 2129372 https://doi.org/10.1080/23322039.2022.2129372 Page 2 of 17 see if increasing human capital development reduces income inequality in Indonesia. Furthermore, by investigating the potential asymmetric impact of human capital development on income distribution disparities in Indonesia, this study seeks to fill a gap in the existing literature. More specifically, this study adopts the nonlinear autoregressive distributed lag (NARDL) model proposed by Shin et al. (2014) to highlight potential long-run asymmetries in the income inequality-human capital development nexus. The remainder of this paper is organised as follows. Section 2 presents the study background. Section 3 reviews the literature. Section 4 describes the data and methodology. Section 5 presents the estimation results and robustness checks. Finally, Section 6 presents the conclusion of the study as well as a discussion of the known limitations of this work and policy recommendations for future studies. 2. Background of the study With a total estimated population of 272,832,226 million, of which approximately 67.59% are in the labour force (15 to 64 years of age), income inequality in Indonesia has been on an upward trend since 1970. As highlighted in Figure 1, income inequality in Indonesia fluctuated from 41.5 to 42.8 from 1970 to 1992 before reaching 43.0 in 1993. The index worsened to 44.2 during the global financial crisis and has remained at 47.4 since 2018. According to the literature, factors attributed to the high-income inequality level in Indonesia include industrialisation and globalisation (Kanbur, 2015; Afandi et al., 2017), low education level (Chongvilaivan & Kim, 2015, Conteras et al., 2015), and increasing dominance of the financial sector (Hein & Detzer, 2015). On the other hand, the human capital index of Indonesia, which is based on the years of schooling and the number of populations returning to education, has recorded an upward trend over the years, which could be due to the country’s transformation from physical-based to knowledge-based human capital development (Holmemo, 2019). From Figure 2, it can be seen that the human capital development index has steadily increased since 1970. However, despite the general upward trend in human capital development, the progression was hit by a reverse trend after 2010, when the human capital development index dropped from a peak of 2.4168 index points in 2010 to 2.4021 index points in 2011 and continued to downtrend to 2.2882 in the year 2019. Interestingly, the progressive upward trend in Indonesia’s income inequality coincided with the downward trend in human capital development since 2010, suggesting that changes in human capital development may explain the level of income inequality in Indonesia. 3. Literature review The following section highlights previous literature concerning human capital development and income inequality issues. Liu and Wong (1981) applied the strict human capital model to analyse the impact of Singapore’s human capital development on income distribution based on a survey conducted Figure 1. Income inequality in Indonesia—1970-2019. Thye et al., Cogent Economics & Finance (2022), 10: 2129372 https://doi.org/10.1080/23322039.2022.2129372 Page 3 of 17 between June and August 1974. They concluded that rapid economic and educational development resulting in unequal incidences of educational opportunities and obsolescence of older skills acquired at school or on the job may have been factors that contributed to greater inequality. Sehrawat and Singh (2019) employed the nonlinear autoregressive distributed lag approach on Indian data from 1970 to 2016 and concluded that education expansion promoted income distribution parities, and economic growth, inflation, and trade openness discouraged income distribution parities. Lee and Lee (2018), who utilised cross-country data between 1980 and 2015, found that an equal distribution of educational availability contributed significantly to reducing income inequality. Educational expansion was a major factor in reducing educational inequality, and thus, income inequality. Shahabadi et al. (2018) investigated the effects of income inequality in Islamic countries between the years 1990–2013. They concluded that the upward enrolment rate in primary and secondary schools had a significant curative effect on income inequality and that the enrolment rate in universities worsened income inequality. In contrast, Hwang and Jung (2006), who applied cross-national evidence for 108 countries covering the period from 1947 to 1994, indicated that an upward enrolment rate at the secondary and tertiary levels of education improved income inequality. Chiu (1998) found that a higher level of aggregate human capital accumulated by an initial generation would improve the initial income distribution of all subsequent generations, signifying an overlapping-generations model with heterogeneity in income distribution and human capital. Utilising dynamic panel estimation techniques on a data range based on five-year intervals from 1980 to 2010, Coady and Dizioli (2018) found that inequality in schooling affects the income inequality level significantly. Specifically, income inequality and the average number of years of schooling for the older cohorts were positively correlated. In contrast, the relationship was inversely correlated with the number of years of schooling of the younger cohorts. Using quantile regression analysis on data ranging from 1966 to 1995 to investigate the impact of schooling level and school dispersion on the income distribution of Taiwan, Chu (2000) concluded that increases in the level of schooling or schooling dispersion and educational equality tended to improve the income distribution of the country. Foldvari and Leeuwen (2011), using two-stage least squares (2SLS) analysis of data from both OECD and non-OECD countries from 1960 to 2000, found that human capital was not a significant determinant of income inequality for non-OECD countries. However, a positive relationship was found in OECD countries. On the other hand, Rehme (2007) found that the effects of higher education on income inequality could not be explicitly determined, as increases in education first increased and then decreased growth and income inequality when measured by the Gini index. Similarly, Green (2007) concluded that the empirical evidence indicated that human capital policy did not create a good income redistribution policy. Using data from the China Urban Household Survey from 1992 to 2009, Feng Figure 2. Human capital index of Indonesia—1970-2017. Thye et al., Cogent Economics & Finance (2022), 10: 2129372 https://doi.org/10.1080/23322039.2022.2129372 Page 4 of 17 and Tang (2019) found that labor market factors and a falling marriage rate increased income inequality. However, changes in human capital level were not associated with income distribution. Similarly, Chani et al. (2014) investigated the causal relationship between human capital and income inequality in Pakistan using time series data from 1973 to 2009 and confirmed that income inequality caused human capital inequality, but that human capital inequality did not cause income inequality. In conclusion, although the preceding discussions provide some insightful information on the impact of human capital development on income inequality in their respective studies. There is still debate about whether a country’s human capital development can help to reduce income inequality. Furthermore, while existing literature has used a variety of estimation methods to investigate the impact of human capital development (for example, panel data analysis—Coady and Dizioli (2018); quantile regression—Chu (2000); two-stage least squares (2SLS)—Foldvari and Leeuwen (2011)), no study has examined the asymmetric link between income inequality and human capital development. As a result, it is not consistent with Keynes (1936) argument that wage adjustment can be sticky and asymmetric, which we argued would impact income distribution asymmetrically. Hence, this study intends to fill this gap in the literature by exploring the asymmetric impact of human capital development on the income inequality of Indonesia, the most populous nation in the ASEAN region. 4. Data and methodology 4.1. Asymmetric framework Based on our discussion in the previous sections, we argued that the asymmetric relationship between human capital development and income inequality could be explained through the human capitalwages model (recommended by Tchernis (2010)) and sticky wages model (recommended by Huo and Rios-Rull (2020)). The earlier model explained the connection between human capital and wages, whereas the latter highlighted the stickiness of wage adjustment. As a result of wage stickiness, income adjustment due to human capital development increases and decreases would not be symmetric, resulting in an asymmetrical impact on income inequality. Tchernis (2010) described an individual’s wage equation as follows: Wit ¼β1Eit þβ2Tit þβ3Xit þεit (1) Where W it represents an individual’s wages, E it denotes an individual’s market experience, T it represents seniority, and X it is a set of variables that affect current wages. Thus, according to Equation 1, if an individual improves their skill or educational attainment, X it is expected to rise, and thus wages will rise. However, Keynes (1936), Heckel et al. (2008), and Huo and Rios-Rull (2020) explained that while human capital development may have a direct impact on an individual’s wage and income, the impact may not be symmetrical due to sticky wages. As a result, the relationship between human capital development, income, and income inequality may be asymmetric. The hypothesis of sticky wages by Huo and Rios-Rull (2020) is as follows: n¼òn2w1 2w idi h i2w 2w1(2) Where n i is a continuum of differential labour varieties subject to i ∈ [0,1], and the union sets wages (wi), firms accept all wages as determined by the union. Thus, cost minimisation, given wages (w i ) and total labour (n), yields demand schedules for each labour variety, i as: ni¼wi w � � 2wn(3) Thye et al., Cogent Economics & Finance (2022), 10: 2129372 https://doi.org/10.1080/23322039.2022.2129372 Page 5 of 17 w is the aggregate wage index that subject to w¼òw1 2w idi � �1 1 2w which satisfies òwinidi¼wn Huo and Rios-Rull (2020) also assumed that every representative household consists of a continuum of workers with different labour variety i but enjoys the same consumption level. Thus, the household utility functions are as follows: E0∑1 t¼0βtu ct ð Þ òvðni;t  �di  �� � (4) To maximise the utility of the agents, the opportunity to reset the wage occurs with probability1— θw. The union’s problem, according to Huo and Rios-Rull (2020), will be: max w� i;t E∑1 k¼0βθw ð Þkulctþk ð Þ w� i;t ptþkv ni;tþk  � � �� � (5) is subject to ni;tþk¼w� i;t wtþk � � 2w ntþk(6) and the first order condition of Equation (4) is as follows: Et∑1 k¼0βθw ð Þkni;tþkulctþk ð Þ w� i;t ptþk�w �w1 vlni;tþk  � ulctþk ð Þ !" #( )¼0 (7) Huo and Rios-Rull (2020) asserted that because wages are sticky, Equation (7) may imply an optimal supply of labour where ,i;t<ni;tt hus violating the labour supply constraint. As a result of the preceding, we argued that the impact of human capital development on inequality would be asymmetric. 4.2. The model Additionally, from the existing literature, the consumer price index (CPI), the real gross domestic product per capita (RGDPC), employment, trade openness, and urbanisation are often linked to the level of income and income distribution (Li & Zou, 2002; Mah, 2013; Siami-Namini & Hudson, 2019). Hence, the rate of inflation, real GDP per capita, employment, trade openness, and urbanisation are included as control variables in the following base model. As a result, the following is the basic model used to investigate the impact of human capital development on income inequality in Indonesia: IE ¼fðHC;CPI;RGDPC;EMP;TO;UBRÞ(8) where IE stands for income inequality, HC stands for human capital, CPI stands for consumer price index, RGDPC stands for real GDP per capita, EMP stands for employment, and TO and UBR stand for trade openness and urbanisation rate, respectively. Equation (9) depicts the ARDL bounds cointegration test model. where IE represents income inequality, HC is the human capital index, CPI refers to the consumer price index, RGDPC represents the real GDP per capita, EMP represents the number of people employed, and TO and UBR represent the level of trade openness and urbanisation rate, respectively. The ARDL bound cointegration test model is shown in Equation (9). The empirical model is based on the asymmetric cointegration approach proposed by Shin et al. (2014). This method employs the nonlinear autoregressive distributed lag cointegration (NARDL) Thye et al., Cogent Economics & Finance (2022), 10: 2129372 https://doi.org/10.1080/23322039.2022.2129372 Page 6 of 17 approach to capture both longand short-run asymmetries between human capital development and income inequality, in which the human capital development (HC) variable is decomposed into two partial sum processes that accumulate in positive and negative changes. The asymmetric long-run equation is as follows: IEt¼α0þα1HCþ tþα2HCtþα3CPItþα4RGDPCtþα5EMPtþα6TOtþα7UBRtþεt(9) where IE is the variable for income inequality; HC is the variable for human capital development; α o , α 1 , α 2, α3;α4;α5;α6and α7 are the vectors of the long-run parameters to be estimated. HCþ tand HCt on the other hand, are the partial sums of the positive and negative changes in the HC variable. HCþ t¼∑t i¼1ΔHCþ t¼∑t i¼1max ΔHCi;0ð Þ (10) and HCt¼∑t i¼1ΔHCt¼∑t i¼1min ΔHCi;0ð Þ (11) where HCt ¼HC0þHCþ tþHCt(12) According to the above formulation, the long-run relationships between income inequality (IE) and human capital development (HC) are α1 and α2, respectively where α1 captures the long-run relationship between income inequality and increases in human capital development and α2 captures the long-run relationship between income inequality and decreases in human capital development. By default, Equation (12) indicates that the current value of the human capital development (HC t ) variable is given by the sum of its initial value (HC 0 ) and partial sums of positive and negative HCt  �. The empirical long-run Equation (8) in the autoregressive distributed lag (ARDL) model proposed by Shin et al. (2014) is as follows: ΔIEt¼β0þβ1IEt1þβ2HCþ t1þβ3HCt1þβ4CPIt1þβ5RGDPCt1þβ6EMPt1þ β7TOt1þβ8UBRt1þ∑p i¼1φiΔIEtiþ∑q i¼0ðθþ iHCþ tiþθiHCtiÞþ∑r i¼0γiΔCPIt1þ ∑s i¼0δiΔRGDPCt1þ∑w i¼0πiΔEMPt1þ∑y i¼0φiΔTOt1þ∑z i¼0τiΔUBRt1þμt (13) All variables were defined as previously described, with the addition of the lag orders p, q, r, s, w, y, and z. The long-run parameters in Equation (8) were calculated using Equation (13), namely,  β2=β1¼α1 and β3=β1¼α2. Furthermore, ∑q i¼0θþ i measured the short-run impact of human capital development (HC) increase in income inequality (IE), whereas ∑q i¼0θi measured the short-run impact of human capital development (HC) decrease in income inequality (IE). The following steps were used to implement the nonlinear ARDL (NARDL) analysis. First, the NARDL model, like the ARDL error-correction model developed by Pesaran et al. (2001), does not allow I(2) variables. The presence of the I(2) variables invalidates the computed F-statistics for the cointegration test. As a result, the Augmented Dickey-Fuller (ADF) and Philips-Perron (PP) unit root tests were performed to ensure that all variables were either I(0) or I(1) (1). The Kwiatkowski– Phillips–Schmidt–Shin (KPSS) test was used to validate the results of the ADF and PP unit root tests. Second, the nonlinear error correction model was run under the NARDL model using a two-step least-squares estimation to obtain the optimum lags of the NARDL model. Fourth, the boundsThye et al., Cogent Economics & Finance (2022), 10: 2129372 https://doi.org/10.1080/23322039.2022.2129372 Page 7 of 17 Our findings confirm the long-run asymmetric effects of human capital development on income inequality in Indonesia thus is in parallel with Ucal et al. (2016) findings that . In particular, an increase in human capital development tends to improve the level of income inequality in the long run with a larger deviation, whereas a decrease in human capital development tends to impact Indonesia’s income inequality in the short run with a larger deviation. Consequently, human capital development appears to be an effective tool for influencing Indonesia’s level of income inequality. Furthermore, inflation, real GDP per capita, and trade openness exacerbate income inequality in Indonesia. Our findings support Adhi’s (2015) claim that rich elites and foreign corporations control the majority of the Indonesian economy, thereby worsening income inequality in the country. According to our findings, increased human capital development reduces income inequality in Indonesia. Similarly, the United Nations (2013) report highlighted that social programs implemented by several Latin American countries targeting human capital development through education and health services, cash transfers, and labor market reforms have played a significant role in reducing regional income inequalities. Therefore, to improve human capital development, Indonesian policymakers should establish inclusive lifelong learning systems that focus on skill enhancement, such as re-training and re-skilling and technical and vocational training (TVET). Second, with inflation expected to worsen Indonesia’s income inequality, policymakers should consider implementing a cash transfer program, such as food vouchers, to assist vulnerable groups. Furthermore, policymakers should ensure that adequate infrastructure and employment opportunities are available in cities to allow those struggling in the suburbs to relocate to cities eventually. Finally, a more effective progressive tax system should be implemented to tax top income earners to close the current income gap between the rich and the poor. This study has only scratched the surface of Indonesia’s complex human capital development and income inequality issues. More research into the demographics of human capital in Indonesia is needed to allow policymakers to tailor policies to specific groups, whether they are high-, middle-, or lowincome. Finally, while this study contributed to the literature by demonstrating that human capital development is asymmetrically related to income inequality, our findings must be viewed in light of some limitations. First, because this study relied on secondary data, it may be overly generalised and may have overlooked Indonesia’s within-region income inequality level. Second, because this study only applied to Indonesia, it provides no evidence that the asymmetrical cointegration between human capital development and income inequality applies to other countries. As a result, future studies should consider broadening their scope to include both within-country and between-country research. Funding This work was supported by the Universitas Sebelas Maret grant no. 255/UN27.22/PM.01.01/2022. Author details Goh Lim Thye 1 Siong Hook Law 2 Irwan Trinugroho 3 E-mail: [email protected] 1 Department of Economics, Universiti Malaya, Kuala Lumpur, Malaysia. 2 Department of Economics, Universiti Putra Malaysia, Seri Kembangan, Malaysia. 3 Faculty of Economics and Business and Business and Center for Fintech and Banking, Universitas Sebelas Maret, Kota Surakarta, Indonesia. Disclosure statement No potential conflict of interest was reported by the author(s). Citation information Cite this article as: Human capital development and income inequality in Indonesia: Evidence from a nonlinear autoregressive distributed lag (NARDL) analysis, Goh Lim Thye, Siong Hook Law & Irwan Trinugroho, Cogent Economics & Finance (2022), 10: 2129372. References Adhi, A. (2015). 80 Persen Industri Indonesia Disebut Dikuasai Swasta. SURYA.co.id. https://surabaya.tribun news.com/2015/03/03/80-persen-industri-indonesiadisebut-dikuasai-swasta Afandi, A., Rantung, V. P., & Marashdeh, H. (2017). 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