research paper on income tax in india

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Tax structure and economic growth: a study of selected Indian states

• Yadawananda Neog   ORCID: orcid.org/0000-0002-3578-0460 1 &
• Achal Kumar Gaur 1  

Journal of Economic Structures volume  9 , Article number:  38 ( 2020 ) Cite this article

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The present study examines the long-run and short-run relationship between tax structure and state-level growth performance in India for the period 1991–2016. The analysis in this paper is based on the model of Acosta-Ormaechea and Yoo ( 2012 ), and for the verification of the relationship between taxation and economic growth the panel regression method is used. With the use of 14 Indian states data, Panel Pool mean group estimation indicates that income tax and commodity–service tax have negative effects whilst property and capital transaction tax have a significant positive effect on state economic growth. This study finds ‘U’ shape relationship between tax structure and growth performance. Based on the analysis, we conclude that for faster growth of Indian states, policymakers should give more focus on property taxes along with the reduction in income taxes.

1 Introduction

The study on the potential association between tax structure and growth performance has gathered a great deal of attention from policymakers, academicians and regulatory circles for several reasons. First, the developing and emerging economies require a large volume of tax revenues for the smooth and efficient functioning of the state at both the national and sub-national levels. Globalization has laid down the foundation for Goods and Service Tax (GST) in many developing countries (Mcnabb 2018 ). Due to competition, developing countries are also facing the difficulties to maintain existing tax revenues (Bird and Zolt 2011 ). Second, tax collection and structure of it create distortionary impacts in the economy through tax burden. Thus, the positive and negative impact of tax made the ‘tax–growth’ relationship more complex and the structure of taxation has a definite role in the development process of an economy.

In a budget constraint economy like India, investigation of tax–growth relationship enables us to formulate the suitable policy measure for the more inclusive and equitable growth process. The budget crisis is usually resolved through the cut-down of public spending or/and an increase in tax revenues (Macek 2014 ). Rapid reduction in spending or increase in taxes is harmful to long-run growth performance. Thus, the concern of the government lies with the problem of fiscal consolidation with sustainable growth performance where tax policies are vital.

Empirical evidence on the impact of tax structure on growth performance is not conclusive. India has adopted the Goods and Service Tax (GST) policy in 2017 intending to raise indirect tax collections and transform the indirect tax structure into a single market to avoid tax evasions and double taxation. GST is regarded as one of the major tax policy changes in independent India and economists are an optimist about its impact on revenue generations and growth performance. But this policy is not the only policy that shaped in independent India; other major policy changes also take place after independence. Footnote 1 Tax Reform Committee (TRC) report of 1991 regarded one of the productive and structured policy recommendations in the recent decade. At the state level, sales tax reform in the form of Value Added Tax (VAT) in 2005 becomes a fruitful policy initiative. However, the tax collections in both national and sub-national level are still low as compared to the international standards. Changes in tax policy also change in the tax structure in the economy and India witnessed these changes at both levels of governments. Recent studies proved that the changes in tax structure have decisive implication in the growth performance through work–leisure behaviour, investment decisions and overall productivity (Arnold et al. 2011 ; Gemmell et al. 2011 ; Macek 2014 ; Mdanat et al. 2018 ; Durusu-Ciftci 2018 ). In India, very few empirical studies are available which analyse the impact of these changes in tax structure on growth performance and this study will be first to investigate tax–growth nexus in India with the use of state-level data.

This analysis primarily concerned with tax structure rather than to tax levels (usually measured as a tax–GDP ratio). The main advantage of tax structure analysis is that it provides revenue-neutral tax policy changes which remove the difficulties related with the question of how aggregate tax revenue changes relates with expenditure changes (Arnold et al. 2011 ). The empirical results from linear panel regression suggest us that property and capital transection tax are positively affecting the state’s growth performance, where commodity and service tax effect negatively. However, the non-linear panel regression indicates that the positive effect is only visible for property taxes at a higher level where the negative effect of commodity and service taxes becomes positive after a threshold point. The effect of income tax is not significant in long run irrespective of panel regression models.

The structure of the paper is as follows: Sect.  2 deals with the theoretical framework and empirical literature, followed by a brief description of data and methodology in Sect.  3 . Empirical results and discussion are presented in Sect.  4 and our last Sect.  5 is for conclusions and recommendations.

2 Theoretical framework and empirical literature

Growth literature very recently acknowledges the role of taxation in the growth process of an economy. Until recently, growth models are more concerned with the steady-state process and exogenous changes. On the theoretical ground, taxation does not have any impact on growth (Myles 2000 ). Development of endogenous growth models creates the space for fiscal policy especially tax policy in determining the growth performance. Barro ( 1990 ), King and Rebello ( 1990 ) and Jones et al. ( 1993 ) were the pioneer in this regard. Tax level and tax structure have an impact on the saving behaviour of the household and investment in human capital. On the other hand, the firm also changes its investment decisions and innovations following tax policies (Johansson et al. 2008 ). These decisions and incentives in the accumulation of physical and human capital create the ‘Growth’ disparities amongst the countries and state economies.

A large body of literature available on “Tax-Growth” relationship is mostly dedicated to cross-country settings (Martin and Fardmanesh 1990 ; Karras 1999 ; Myles 2000 ; Tosun and Abizadeh 2005 ; Johansson et al. 2008 ; Vartia et al. 2008 ; Arnold 2011 ; Szarowska 2013; Macek 2014 ; Stoilova 2017 ; Safi et al. 2017 ; Durusu-Ciftci 2018 ) that investigates the effect of tax policy on economic performance. Income and corporation taxes are the major tax instruments for the governments irrespective of the level of developments of a country. The formation of tax structure with these two taxes has many implications in the growth performance. The study made by Arnold et al. ( 2011 ), Macek ( 2014 ) and Dackehag and Hansson ( 2012 ) has explored the negative relation of income and corporation tax with growth performance. Vartia et al. ( 2008 ) find the negative impact of corporation tax for OECD countries. If we consider the average and marginal tax rate, marginal tax is very influential than to average tax rate in investment decisions and labour supply. Empirical studies prove that marginal tax has a negative relation with growth, which indicate raising of marginal tax rate is associated with compromises with growth performance (Padovano and Galli 2001 ; Lee and Gordon 2005 ; Poulson and Kaplani 2008 ). Studies also established that other type of taxes also has a significant impact on growth performance, like consumption tax (Johansson et al. 2008 ; Durusu-Ciftci 2018 ), GST and Payroll (Tosun and Abizadeh 2005 ), property tax (Xing 2011 ), labour tax (Szarowska 2014 ), sales tax (Ojede and Yamarik 2012 ), excise (Reynolds 2006 ), etc.

However, looking at the single country’s perspective, we find very little evidence on the same. Stockey and Rebelo ( 1995 ) with the use of the endogenous growth model study the role of tax reforms on U.S. growth performance. They have found that tax reforms have very minor implication with economic outcomes. There are several studies exist for US economy where they empirically try to establish the link between tax and growth. Atems ( 2015 ) finds the spatial spillover effect of income taxes on the growth of 48 contiguous states. On the other hand, Ojede and Yamarik ( 2012 ) have not found any kind of impact of income taxes on growth in these states. Their panel pool mean group estimation indicates that property and sales tax has detrimental consequences in development. With the use of data for the U.S. covering the period of 1912–2006, Barro and Redlick ( 2009 ) find that average marginal income taxes were halting the economic growth. However, they have provided an interesting argument that in wartime, the tax does not have any kind of relation with growth. In search of an answer to the question that whether corporate tax rise destroys jobs in the U.S., Ljungqvist and Smolyansky ( 2016 ) use firm-level data for the period 1970–2010. The main conclusion of the paper is that a rise in corporate tax is not good for employment and income and has very little impact on economic activity. Using the error correction model, Mdanat et al. ( 2018 ) find for Jordan that income tax, corporation tax and personal tax negatively impact the growth. They suggest that irrespective of tax collection, the prime focus of the government should be social justice of the people. Dladla and Khobai ( 2018 ) also find similar results for South Africa where income taxes are coming out to be negative. For the case of Italy, Federici and Parisi ( 2015 ) used the 880 firms’ data and results show that corporation tax is bad for investments with the consideration of both effective average and marginal taxes rates.

Looking at the literature, the empirical relationship of tax structure with growth performance is still unclear for India. This study attempts to fill the gap by examining the effect of tax policy on economic performance in an emerging economy such as India at the state level. Second, with the use of panel Pool Mean Group (PMG) estimator which assumes slope homogeneity in the long run and heterogeneity in the short run, we can incorporate the dynamic behaviour of the variables which will be new to tax structure–growth study in India. Third, the tax–growth nexus may show a non-linear relationship due to the threshold effect. We consider this non-linearity in our panel regression model which will be a contribution to the existing literature.

3 Data and methodology

To study the effect of tax policy on economic performance in India, we employed three models and included each tax instruments in the models separately to avoid the problem of Multicollinearity. Following the works of Arnold et al. ( 2011 ) and Acosta-Ormaechea and Yoo ( 2012 ), the tax structure is measured by the share of individual tax to the total state tax revenues. We investigate the tax–growth relationship with the following equation.

Here, Y it is the growth rate of Per capita net state domestic product (NSDP), SGI is the state gross investment as a percentage of state domestic product, TAX is one of the tax shares (Property, Commodity & Services and Income), Tax Burden Footnote 2 is the ratio of total tax revenues to state domestic product and ϵ is the error term. Per the work of Acosta-Ormaechea and Yoo ( 2012 ), this study is more concerned with the impact of tax structure on growth rate rather than level effect. In model 1, we include property tax share, and in model 2 and model 3, we incorporate commodity & service tax and income tax, respectively. By following the approach of Arnold et al. ( 2011 ), we include total tax burden as a control variable which will reduce the biases that may occur from correlation in between tax mix and tax burden. We also included Secondary Enrollment Rate as a proxy variable for human capital in our model, but the inconsistent and insignificant results make us drop the variable from the final estimation model.

In search of a possible non-linear relationship, we introduce a separate panel regression by introducing the square of each tax share into the models.

If the coefficient of α 3 significant and carries an opposite sign to α 2 , then we can conclude that there is a non-linear relationship exist.

In this study, we included 14 Indian states for the period 1991 to 2016 and excluded North-Eastern states due to their relatively small tax revenue collections. Data have been taken from the Centre for Monitoring Indian Economy (CMIE) and Handbook of Statistics on the Indian States, published by Reserve Bank of India. The states that are included in this study are Andhra Pradesh (undivided), Footnote 3 Assam, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir, Karnataka, Kerala, Maharashtra, Punjab, Tamil Nadu, Orissa, Rajasthan and West Bengal. All the states are included in model 1 and model 2. For model 3, due to the data availability, we include only seven states Footnote 4 namely Andhra Pradesh, Assam, Gujarat, Karnataka, Kerala, Maharashtra, and West Bengal.

The selection of the study period is primarily driven by the argument provided by Rao and Rao ( 2006 ) that after the market-oriented economic reform of 1991, more systematic and long-term goal-oriented tax reforms were initiated in state level for India. The economic reform also brings rapid growth in India and it becomes very interesting to look at the tax–growth nexus after the economic reform. The second restriction related to the use of long data span is the availability of data for each tax head for each of the states under this study.

3.1 Unit root

Pool Mean Group (PMG) specification is very fruitful and widely used model to capture the dynamic behaviour of policy variables. This model is very powerful as it can investigate both I (0) and I (1) variables in a single autoregressive distributive lag (ARDL) model setup. A necessary condition in the ARDL model is that the model cannot deal with the I(2) variables. Thus, the investigation of stationarity becomes a compulsion. We used popular panel unit root tests like LLC (Levin et al. 2002 ), the IPS (Im et al. 2003 ), the ADF-Fisher Chi square (Maddala and Wu 1999 ) and PP-Fisher Chi square (Choi 2001 ) in this study.

3.2 Panel PMG model

The Mean Group (MG) estimator was developed by Pesaran and Smith ( 1995 ) to solve the issue of bias related to heterogeneous slopes in dynamic panels. Traditional panel models like instrumental variables’ estimator of Anderson and Hsiao ( 1981 , 1982 ) and Arellano and Bond ( 1991 ) may produce inconsistent results in a dynamic panel framework (Pesaran et al. 1999 ). MG estimator takes the average value of every cross-section and provides the long-run estimate for ARDL or PMG. On the other hand, Pooled Mean Group (PMG) estimator developed by Pesaran et al. ( 1999 ) assumes slope homogeneity in the long run but heterogeneous slopes in the short run for cross-section units. Dynamic Fixed Effect (DFE) also works like PMG and restricts cointegrating vector to be equal across all panels and restricts the speed of adjustment to be equal.

Under these assumptions, PMG is more efficient estimator than to MG and DFE estimator. The prime requirement for PMG estimator is that T should be sufficiently large to N. Panel ARDL or PMG works through maximum likelihood. Our basic PMG begins with the following equation.

Here, x it is the vector explanatory variables and y i is the lag dependent variable. X it allows the inclusion of both I (0) and I (1) variables. State fixed effect is captured through μ i . Above equation can be re-parameterized to ARDL format.

ɸ i measures the state-specific speed of adjustment and known as Error Correction Term. Β i is the vector of long-run relationships and α ij and θ ij are the vectors of short-run dynamic relationships. Pesaran et al. ( 1999 ) did not provide any statistical test for checking long-run relationship but it can be concluded form sign and magnitude of Error Correction Term (ECT). If it is negative and less than − 2, a long-run relationship can be established.

4 Results and discussion

Panel unit root test results from Table 1 suggest that in the case of Model 1 & 2, the Growth rate of Per Capita Net State Domestic Product (PC-NSDP), Property tax and commodity taxes are stationary at level. Gross investment and total tax revenue share to GDP are stationary at the 1st difference in all models and income tax share is also stationary at the same order.

5 PMG model results

We have reported MG, PMG and DFE estimation in the Tables  2 and 3 . The Hausman test indicates that the PMG model is the best model for our data than to MG model. Negative and significant error correction terms in all the models show the long-run relationship in between variable. One major issue related to the tax–growth equation is the problem of endogeneity of the variables. As growth in per capita GDP is our dependent variables, there is a possibility that tax collections behave along with business cycles. Therefore, we tested the weak/strong exogeneity of the tax variables through the correlation analysis between business cycles and tax shares. Business cycles have been calculated using the Hodrick-Prescott (HP) Filter. We have found that all the tax instruments are very weakly related to the business cycles movement and thus, we conclude that variables are not truly endogenous.

The speed of adjustment in PMG model 1, 2 and 3 are 78.9%, 78.4% and 79.6%, respectively. For the sake of completeness, we have reported MG and DFE Footnote 5 model results also. But we are more concerned with the results of PMG estimator as Hausman test suggested that PMG is a better model than to MG. The sign of the property tax is positive and significant in the long run as well as in the short run. Results are in line with the findings of Acosta-Ormaechea and Yoo ( 2012 ). Property tax generally considered a good revenue source for state and municipal governments for providing economic and social services in the city. This tax is also able to establish cost–benefit linkages and feasible decisions for the citizens. The positive impact of property taxes indicates that the revenue generation and productive utilization of these revenues exceed the distortionary effect in these states. As we expected, the tax burden is negatively associated with growth performance in both long run and short run. The relationship is showing the distortionary effect of the tax collection in the state economy. In all models, gross investment enhancing the growth in per capita SDP in the long run. Signs are readily justified as enlargement of capital formation has a positive impact on output and employment which channelized to the development outcomes (Swan 1956 , Solow 1956 ).

Commodity and service taxes are negatively related to the growth in per-capita SDP in the long run as well as in short run and findings are similar to the work of Ojede and Yamarik ( 2012 ). Footnote 6 This tax now comes under the Goods & Services taxes, but in the pre-GST period, commodity and service taxes are reducing growth in per capita NSDP. Commodity taxes are indirect taxes and state own tax revenues mostly come from indirect taxes. As indirect taxes, it has certain disadvantages like inflationary pressure in the economy and regressive to the poor section of the society. Our results also support the same hypothesis that increased commodity tax share is harmful. In India, commodity and service tax includes central sale tax, state excise duty, vehicle tax, goods & passenger tax, electricity duty and entertainment tax. Central sale tax was imposed on interstate trade of commodities which is now transformed to Inter-State GST (IGST). According to Das ( 2017 ), if the IGST rate is high to the Revenue Neutral Rate, it will harm the aggregate demand in the economy through the reduction of disposable income. Heavy vehicle and passenger tax collections are creating an abysmal environment for industrial activities. The tax burden variable is also carrying a negative sign in both long run and short run and magnitude is very similar to model 1. Income tax share has become insignificant and positive in the long run and negative insignificant in the short run.

After examining the linear relationships, we extended our analysis to the examination of a non-linear relationship with the use of PMG estimation model. The result from Tables  4 and 5 indicates the existence of a non-linear relationship between tax structure and growth performance for Indian states. The linear coefficient for property taxes has now become negative and the square of it turns out to be positive. Thus, the property taxes show a ‘U’-shaped relationship with states’ growth performance which implies that a rise in property taxes is bad for growth initially and after a threshold point, it becomes growth enhancing. The threshold point for property taxes is 1.88 which indicates that more than 80.77% observation is more than to threshold point.

In the case of commodity and service taxes, both the linear and non-linear coefficients are significant with different signs. However, the coefficient magnitudes are abnormally large and this is due to the inclusion of both linear and quadratic terms into the single equation. The small commodity and service taxes are very bad for the state economy, whereas the large amount of it shows a positive relation. The threshold point for this tax is 4.45 which implies that 79.95% observation lies above the threshold. This is a very interesting result as high commodity and service taxes could lead to high inflation in the economy and high inflation regarded as atrocious for growth. Further investigation of these findings is highly recommendable. As like linear panel regression, the income tax shows no relation in our non-linear regression model also. However, the short-run coefficient for income tax is significant and shows a negative relationship. Income tax is considered to be distortionary tax to the economy in the presence of income and substitution effect (Kotlan 2011 ). Income tax mostly impacts the savings of the households and labour supply which is regarded as an engine of growth.

6 Conclusions and recommendations

In this study, we try to find out the long-run and short-run relationship between different tax structure and economic growth in states of India. Empirical evidence from linear regression suggests that the property tax enhancing growth and commodity & service taxes reduce it. The non-linear regression validates these findings for property taxes where high property taxes are good for growth. In the case of commodity & service taxes, the results become opposite after the threshold point and affecting the growth negatively. Interestingly, we do not find any significant impact of income taxes on growth in both linear and non-linear regressions in the long run.

As far as the total tax burden is concerned, negative relation with the growth performance is verified and results are in line with Arnold et al. ( 2011 ). The negative effect of commodity and service taxes in the short run is expected to be neutralized through the implementation of GST in India. Promotion of growth performance at the state level concerning income taxes is also very crucial. Income tax has a direct effect on individuals and their saving and investment behaviour. On the other side, tax revenues should be placed in productive investments. With the spending, the government can promote inclusive growth, equality and efficiency in the economy.

The most promising path emerged through this study for long-run growth performance in Indian states is to lower the total tax burden and shifting from income and commodity taxes to property tax for revenue generations. The conclusion may be debatable on various grounds as the studied variables do not take into account institutional quality, administrative efficiency in tax collection, fiscal balance and condition of the states and existence of informal sectors. Future research can be done to incorporate these issues.

Availability of data and materials

Dataset analysed in this study is available from the corresponding author on reasonable request.

One can see the writings of Rao and Rao ( 2006 ) for brief discussion.

This is the proxy for total tax burden in the economy with certain limitations. It does not include informal economy and expenditure policies.

Telangana state was established in 2014. We merged the data of Andhra Pradesh and Telangana to achieve aggregate data for undivided Andhra Pradesh.

Data for Income tax are available for ten states, but inclusion of these states made the model inconsistent due to huge fluctuations in tax revenue collections.

Most of the coefficients of PMG and DFE are in similar range and smaller than to MG estimator. This is due to MG estimator only takes the information of each state time series to estimate long-run and short-run coefficients.

They use sale tax, where our study takes aggregate revenue for commodity and services. However, inference can be drawn as sale tax and is one of the dominant contributors in total commodity and service tax revenue in India.

Abbreviations

Net state domestic product

Goods and service tax

Foreign direct investments

• Pool mean group

Dynamic fixed effect

Auto-regressive distributed lag

The organization for economic co-operation and development

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Neog, Y., Gaur, A.K. Tax structure and economic growth: a study of selected Indian states. Economic Structures 9 , 38 (2020). https://doi.org/10.1186/s40008-020-00215-3

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Alliance University, School of Law

Date Written: November 12, 2021

Taxation is the government's primary source of revenue, and the economic prosperity of every country is primarily determined by the tax structure it has established. A tax system that makes doing business simple and eliminates the possibility of tax evasion benefits a country's economy. On the other hand, on the one hand, there is a taxation framework that allows for tax evasion and on the other hand, there is a taxation structure that does not allow for easy tax evasion. The cost of conducting business inhibits the country's economic progress. Indian taxes have undergone numerous revisions, but it is still a long way from being an ideal taxation system. Many issues, such as tax evasion, reliance on indirect taxes, black money, and the development of a parallel economy, indicate that the Indian tax system will require considerable adjustments in the future to address all of these issues. The author of the following study paper makes an attempt to analyze the evolution of India's taxation system. The research article focuses on the importance of the Goods and Services Tax (GST), which was recently incorporated into the Indian tax framework, and how individuals rely more on indirect taxes than direct taxes.

Keywords: India Tax Structure, Evolution of Indian Tax structure, Direct Tax, Indirect Tax, GST

Suggested Citation: Suggested Citation

Anjali Tyagi (Contact Author)

Alliance university, school of law ( email ).

Bangalore India

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• DOI: 10.1016/0305-750X(95)00098-W
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Income Tax Compliance in India: An Empirical Analysis

• Arindam Das-Gupta , Radhika Lahiri , Dilip Mookherjee
• Published 1 December 1995
• World Development

50 Citations

Tax amnesties in india; an empirical evaluation, tax administration, taxpayer’s reciprocity and compliance in tanzania: empirical evidence from a survey, decomposing revenue effects of tax evasion, base broadening and tax rate reduction, tax reform and economic growth nexus in india: evidence from the cointegration and rolling-window causality, taxation base in developing countries, tax evasion as an outcome of organizational design, measuring tax administration effectiveness and its impact on tax revenue, determinants of tax evasion: a cross-country investigation, evading by any means vat enforcement and payroll tax evasion in china, factors affecting tax compliance behaviour in self assessment system, 14 references, inflation and the personal income tax, elasticity of non-corporate income tax in india, lessons of tax reform, inflation and tax evasion: an empirical analysis, income tax evasion: a theoretical analysis, the order of economic liberalization : financial control in the transition to a market economy, taxation and development, spurious regressions in econometrics, co-integration and error correction: representation, estimation and testing, related papers.

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TAX REFORMS IN INDIA : A CRITICAL ANALYSIS-

The present research paper examines the tax reforms undertaken in India in the areas of both direct

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Impact of Colonialism in E.M. Forster’s A Passage to India

Prof. alok chansoria, dr. shahewar syed.

E.M. Forster’s A Passage to India (1924) is a seminal novel that critiques British colonialism in India during the early 20 th century. This article delves into the far-reaching impact of colonialism on the novel’s themes, characters, and narrative structure. Through the characters of Dr. Aziz, Fielding, and Adela, the novel highlights the complexities of cultural exchange, the fragility of human relationships, and the distortions of colonial discourse. The analysis reveals how Forster’s work continues to resonate with contemporary discussions on imperialism, cultural identity, and post colonialism offering valuable insights into the enduring legacy of colonialism.            

Author Biographies

Chairman Board of Studies in English, European Languages

Rani Durgawati Vishwavidyalaya

Jabalpur, Madhya Pradesh, India

Hawabagh College

Bhabha, H.K. The Location of Culture. Routledge, 1984.

Forster, E.M. A Passage to India. Kalyani Publishers, 2005. (All textual citations are taken from this edition)

Leavis, F.R. “E.M. Forster.” Forster: A Collection of Critical Essays, edited by Malcolm Bradbury, Prentice Hall of India Private Ltd., 1966.

Parry, Benita, “A Passage to More than India.” Forster: A Collection of Critical Essays, edited by Malcolm Bradbury, Prentice-Hall of India Private Ltd., 1966.

---. Delusions and Discoveries: India in the British Imagination, 1880-1930. Verso, 1972.

Said, Edward W. Culture and Imperialism. Chatto and Windus, 1993.

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Copyright (c) 2023 Prof. Alok Chansoria, Dr. Shahewar Syed

This work is licensed under a Creative Commons Attribution 4.0 International License .

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After ex-employee accused it of AI recklessness, OpenAI publishes paper to show it is tackling AI risks

Openai has published a research paper that is aimed at making the advanced functionalities of gpt-4 more accessible and comprehensible to its users..

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• OpenAI has published a research paper that makes the working of GPT4 more accessible
• The company wants to show it is serious about tackling AI risks
• This comes days after ex-OpenAI employee accused the company of being reckless with AI development

Earlier this week, a former OpenAI employee accused the company of being reckless about AI development . On Thursday, OpenAI released a research paper that shows how the workings of an AI model, GPT-4 in particular, can be reverse engineered. Basically, the company wants to make AI models more easily explainable, so that people know that the company is serious about tackling AI risks. Of course, OpenAI says that the new tool is aimed at making the advanced functionalities of GPT-4 more accessible and comprehensible to researchers, developers, and enthusiasts in the field of AI.

The primary objective of this new tool is to demystify the inner workings of GPT-4 by breaking down its complex model into understandable segments. AI tools like GPT-4 are sophisticated and complex models due to their capability of generating human-like responses. This capability also often leaves users puzzled about how it arrives at specific outputs. The new tool by OpenAI is basically aimed at extracting meaningful concepts from the vast and intricate structure of GPT-4, making it easier to understand and use.

The research paper details sparse autoencoders and explores a technique for creating efficient representations of data. Sparse autoencoders are a type of neural network that aim to learn compressed, useful features from input data while promoting sparsity in their activation patterns. This means only a small number of neurons are active at once, which enhances the model's ability to identify important structures in the data. The paper discusses various methods to enforce sparsity and demonstrates how these techniques can improve performance in tasks like image recognition and data reconstruction.

OpenAI claims that this tool will enhance the usability of GPT-4. It says developers and researchers can now explore the model's outputs more effectively, enabling them to fine-tune applications and create more accurate and reliable AI systems.

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The state of AI in 2023: Generative AI’s breakout year

You have reached a page with older survey data. please see our 2024 survey results here ..

The latest annual McKinsey Global Survey  on the current state of AI confirms the explosive growth of generative AI (gen AI) tools . Less than a year after many of these tools debuted, one-third of our survey respondents say their organizations are using gen AI regularly in at least one business function. Amid recent advances, AI has risen from a topic relegated to tech employees to a focus of company leaders: nearly one-quarter of surveyed C-suite executives say they are personally using gen AI tools for work, and more than one-quarter of respondents from companies using AI say gen AI is already on their boards’ agendas. What’s more, 40 percent of respondents say their organizations will increase their investment in AI overall because of advances in gen AI. The findings show that these are still early days for managing gen AI–related risks, with less than half of respondents saying their organizations are mitigating even the risk they consider most relevant: inaccuracy.

The organizations that have already embedded AI capabilities have been the first to explore gen AI’s potential, and those seeing the most value from more traditional AI capabilities—a group we call AI high performers—are already outpacing others in their adoption of gen AI tools. 1 We define AI high performers as organizations that, according to respondents, attribute at least 20 percent of their EBIT to AI adoption.

The expected business disruption from gen AI is significant, and respondents predict meaningful changes to their workforces. They anticipate workforce cuts in certain areas and large reskilling efforts to address shifting talent needs. Yet while the use of gen AI might spur the adoption of other AI tools, we see few meaningful increases in organizations’ adoption of these technologies. The percent of organizations adopting any AI tools has held steady since 2022, and adoption remains concentrated within a small number of business functions.

Table of Contents

• It’s early days still, but use of gen AI is already widespread
• Leading companies are already ahead with gen AI
• AI-related talent needs shift, and AI’s workforce effects are expected to be substantial
• With all eyes on gen AI, AI adoption and impact remain steady

About the research

1. it’s early days still, but use of gen ai is already widespread.

The findings from the survey—which was in the field in mid-April 2023—show that, despite gen AI’s nascent public availability, experimentation with the tools  is already relatively common, and respondents expect the new capabilities to transform their industries. Gen AI has captured interest across the business population: individuals across regions, industries, and seniority levels are using gen AI for work and outside of work. Seventy-nine percent of all respondents say they’ve had at least some exposure to gen AI, either for work or outside of work, and 22 percent say they are regularly using it in their own work. While reported use is quite similar across seniority levels, it is highest among respondents working in the technology sector and those in North America.

Organizations, too, are now commonly using gen AI. One-third of all respondents say their organizations are already regularly using generative AI in at least one function—meaning that 60 percent of organizations with reported AI adoption are using gen AI. What’s more, 40 percent of those reporting AI adoption at their organizations say their companies expect to invest more in AI overall thanks to generative AI, and 28 percent say generative AI use is already on their board’s agenda. The most commonly reported business functions using these newer tools are the same as those in which AI use is most common overall: marketing and sales, product and service development, and service operations, such as customer care and back-office support. This suggests that organizations are pursuing these new tools where the most value is. In our previous research , these three areas, along with software engineering, showed the potential to deliver about 75 percent of the total annual value from generative AI use cases.

In these early days, expectations for gen AI’s impact are high : three-quarters of all respondents expect gen AI to cause significant or disruptive change in the nature of their industry’s competition in the next three years. Survey respondents working in the technology and financial-services industries are the most likely to expect disruptive change from gen AI. Our previous research shows  that, while all industries are indeed likely to see some degree of disruption, the level of impact is likely to vary. 2 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. Industries relying most heavily on knowledge work are likely to see more disruption—and potentially reap more value. While our estimates suggest that tech companies, unsurprisingly, are poised to see the highest impact from gen AI—adding value equivalent to as much as 9 percent of global industry revenue—knowledge-based industries such as banking (up to 5 percent), pharmaceuticals and medical products (also up to 5 percent), and education (up to 4 percent) could experience significant effects as well. By contrast, manufacturing-based industries, such as aerospace, automotives, and advanced electronics, could experience less disruptive effects. This stands in contrast to the impact of previous technology waves that affected manufacturing the most and is due to gen AI’s strengths in language-based activities, as opposed to those requiring physical labor.

Responses show many organizations not yet addressing potential risks from gen AI

According to the survey, few companies seem fully prepared for the widespread use of gen AI—or the business risks these tools may bring. Just 21 percent of respondents reporting AI adoption say their organizations have established policies governing employees’ use of gen AI technologies in their work. And when we asked specifically about the risks of adopting gen AI, few respondents say their companies are mitigating the most commonly cited risk with gen AI: inaccuracy. Respondents cite inaccuracy more frequently than both cybersecurity and regulatory compliance, which were the most common risks from AI overall in previous surveys. Just 32 percent say they’re mitigating inaccuracy, a smaller percentage than the 38 percent who say they mitigate cybersecurity risks. Interestingly, this figure is significantly lower than the percentage of respondents who reported mitigating AI-related cybersecurity last year (51 percent). Overall, much as we’ve seen in previous years, most respondents say their organizations are not addressing AI-related risks.

2. Leading companies are already ahead with gen AI

The survey results show that AI high performers—that is, organizations where respondents say at least 20 percent of EBIT in 2022 was attributable to AI use—are going all in on artificial intelligence, both with gen AI and more traditional AI capabilities. These organizations that achieve significant value from AI are already using gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management. When looking at all AI capabilities—including more traditional machine learning capabilities, robotic process automation, and chatbots—AI high performers also are much more likely than others to use AI in product and service development, for uses such as product-development-cycle optimization, adding new features to existing products, and creating new AI-based products. These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization.

AI high performers are much more likely than others to use AI in product and service development.

Another difference from their peers: high performers’ gen AI efforts are less oriented toward cost reduction, which is a top priority at other organizations. Respondents from AI high performers are twice as likely as others to say their organizations’ top objective for gen AI is to create entirely new businesses or sources of revenue—and they’re most likely to cite the increase in the value of existing offerings through new AI-based features.

As we’ve seen in previous years , these high-performing organizations invest much more than others in AI: respondents from AI high performers are more than five times more likely than others to say they spend more than 20 percent of their digital budgets on AI. They also use AI capabilities more broadly throughout the organization. Respondents from high performers are much more likely than others to say that their organizations have adopted AI in four or more business functions and that they have embedded a higher number of AI capabilities. For example, respondents from high performers more often report embedding knowledge graphs in at least one product or business function process, in addition to gen AI and related natural-language capabilities.

While AI high performers are not immune to the challenges of capturing value from AI, the results suggest that the difficulties they face reflect their relative AI maturity, while others struggle with the more foundational, strategic elements of AI adoption. Respondents at AI high performers most often point to models and tools, such as monitoring model performance in production and retraining models as needed over time, as their top challenge. By comparison, other respondents cite strategy issues, such as setting a clearly defined AI vision that is linked with business value or finding sufficient resources.

The findings offer further evidence that even high performers haven’t mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so. For example, just 35 percent of respondents at AI high performers report that where possible, their organizations assemble existing components, rather than reinvent them, but that’s a much larger share than the 19 percent of respondents from other organizations who report that practice.

Many specialized MLOps technologies and practices  may be needed to adopt some of the more transformative uses cases that gen AI applications can deliver—and do so as safely as possible. Live-model operations is one such area, where monitoring systems and setting up instant alerts to enable rapid issue resolution can keep gen AI systems in check. High performers stand out in this respect but have room to grow: one-quarter of respondents from these organizations say their entire system is monitored and equipped with instant alerts, compared with just 12 percent of other respondents.

3. AI-related talent needs shift, and AI’s workforce effects are expected to be substantial

Our latest survey results show changes in the roles that organizations are filling to support their AI ambitions. In the past year, organizations using AI most often hired data engineers, machine learning engineers, and Al data scientists—all roles that respondents commonly reported hiring in the previous survey. But a much smaller share of respondents report hiring AI-related-software engineers—the most-hired role last year—than in the previous survey (28 percent in the latest survey, down from 39 percent). Roles in prompt engineering have recently emerged, as the need for that skill set rises alongside gen AI adoption, with 7 percent of respondents whose organizations have adopted AI reporting those hires in the past year.

The findings suggest that hiring for AI-related roles remains a challenge but has become somewhat easier over the past year, which could reflect the spate of layoffs at technology companies from late 2022 through the first half of 2023. Smaller shares of respondents than in the previous survey report difficulty hiring for roles such as AI data scientists, data engineers, and data-visualization specialists, though responses suggest that hiring machine learning engineers and AI product owners remains as much of a challenge as in the previous year.

Looking ahead to the next three years, respondents predict that the adoption of AI will reshape many roles in the workforce. Generally, they expect more employees to be reskilled than to be separated. Nearly four in ten respondents reporting AI adoption expect more than 20 percent of their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of their workforces will decrease by more than 20 percent.

Looking specifically at gen AI’s predicted impact, service operations is the only function in which most respondents expect to see a decrease in workforce size at their organizations. This finding generally aligns with what our recent research  suggests: while the emergence of gen AI increased our estimate of the percentage of worker activities that could be automated (60 to 70 percent, up from 50 percent), this doesn’t necessarily translate into the automation of an entire role.

AI high performers are expected to conduct much higher levels of reskilling than other companies are. Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforces over the next three years as a result of AI adoption.

4. With all eyes on gen AI, AI adoption and impact remain steady

While the use of gen AI tools is spreading rapidly, the survey data doesn’t show that these newer tools are propelling organizations’ overall AI adoption. The share of organizations that have adopted AI overall remains steady, at least for the moment, with 55 percent of respondents reporting that their organizations have adopted AI. Less than a third of respondents continue to say that their organizations have adopted AI in more than one business function, suggesting that AI use remains limited in scope. Product and service development and service operations continue to be the two business functions in which respondents most often report AI adoption, as was true in the previous four surveys. And overall, just 23 percent of respondents say at least 5 percent of their organizations’ EBIT last year was attributable to their use of AI—essentially flat with the previous survey—suggesting there is much more room to capture value.

Organizations continue to see returns in the business areas in which they are using AI, and they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. And looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years.

The online survey was in the field April 11 to 21, 2023, and garnered responses from 1,684 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 913 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

The survey content and analysis were developed by Michael Chui , a partner at the McKinsey Global Institute and a partner in McKinsey’s Bay Area office, where Lareina Yee is a senior partner; Bryce Hall , an associate partner in the Washington, DC, office; and senior partners Alex Singla and Alexander Sukharevsky , global leaders of QuantumBlack, AI by McKinsey, based in the Chicago and London offices, respectively.

They wish to thank Shivani Gupta, Abhisek Jena, Begum Ortaoglu, Barr Seitz, and Li Zhang for their contributions to this work.

This article was edited by Heather Hanselman, an editor in the Atlanta office.

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