# Demand and growth regimes of the BRICs countries – the national income and financial accounting decomposition approach and an autonomous demand-led growth perspective

Juan Manuel Campana, João Emboava Vaz, Eckhard Hein, Benjamin Jungmann

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Juan Manuel Campana, João Emboava Vaz, Eckhard Hein, Benjamin Jungmann. Demand and growth regimes of the BRICs countries – the national income and financial accounting decomposition approach and an autonomous demand-led growth perspective. European Journal of Economics and Economic Policies: Intervention, 2024, 21 (1), pp.17-41. 10.4337/ejeep.2023.0100. hal-05367613

HAL Id: hal-05367613

https://hal.science/hal-05367613v1

Submitted on 12 Feb 2026

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# Demand and growth regimes of the BRICs countries – the national income and financial accounting decomposition approach and an autonomous demand-led growth perspective

**Juan Manuel Campana**

Berlin School of Economics and Law, Germany
Université de Technologie de Compiègne, France
juanmacampana3@gmail.com

**João Emboava Vaz**

Berlin School of Economics and Law, Germany
Université de Technologie de Compiègne, France
University of the Witwatersrand, South Africa
Federal University of Rio de Janeiro, Brazil
joaojev8@gmail.com

**Eckhard Hein**

Institute for International Political Economy (IPE) Berlin, Berlin School of Economics and Law, Germany
eckhard.hein@hwr-berlin.de

**Benjamin Jungmann**

Institute for International Political Economy (IPE) Berlin, Berlin School of Economics and Law, Germany
Centre d'Economie de l'Université Paris Nord (CEPN), France
Benjamin.Jungmann@hwr-berlin.de

> We contribute to the recent debate in post-Keynesian economics (PKE), comparative political economy (CPE) and international political economy (IPE) on growth regimes. The paper presents an analysis of changes in demand and growth regimes in the BRICs countries, Brazil, Russia, India, and China, after the Global Financial Crisis and the Great Recession of 2007–2009. It discusses and applies two approaches: a first one based on national income and financial accounting decomposition and a second one based on the Sraffian Supermultiplier (SSM) growth model, distinguishing the dynamics of autonomous expenditure growth from those of the induced components of aggregate demand. It is argued that the SSM approach provides the bridge between the traditional approach based on national income and financial accounting decomposition and the analysis of growth drivers, both in PKE as well as in CPE and IPE. This is illustrated by pointing out some changes in the underlying political economy and economic policy growth drivers in each of the countries.

**Keywords:** demand and growth regimes, growth decomposition, autonomous demand-led growth

**JEL codes:** E02, E11, E12, P16, P51

Received 30 November 2022, accepted 21 April 2023

<figure><img src="image_3.png" /></figure>
# 1 INTRODUCTION

This paper is a contribution to the recent debate in post-Keynesian economics (PKE), comparative political economy (CPE) and international political economy (IPE) on growth regimes. It presents an analysis of changes in demand-led growth regimes in the BRIC countries, Brazil, Russia, India, and China, after the Global Financial Crisis and the Great Recession of 2007–2009. The paper discusses and applies two approaches, a first one based on national income and financial accounting decomposition and a second one, based on the Sraffian Supermultiplier (SSM) growth model, distinguishing the dynamics of autonomous expenditure growth from those of the induced components of aggregate demand.

As explained in more detail in Hein (2023b), the concept of demand and growth regimes in finance-dominated capitalism has been derived from the analysis of the macroeconomic channels of financialisation (via distribution, consumption, investment, and the capital account) from a PKE/Kaleckian distribution and growth theory perspective. Applying a national income and financial accounting decomposition approach, by looking at growth contributions of the main demand aggregates and financial balances of the macroeconomic sectors, allows one to cluster countries according to the sources and the financing of demand (Hein 2011, 2012). In the course of development of this approach, four regimes have been distinguished:<fnref n="1" /> the ‘debt-led private demand boom’ (DLPD), the ‘domestic demand-led’ (DDL), the ‘weakly export-led’ (WEL) and the ‘export-led mercantilist’ (ELM) regimes.<fnref n="2" />

As recently reviewed in Akcay et al. (2022) and Hein (2023a: ch. 8), several authors have identified changes in regimes towards ELM and DDL stabilised by government deficits for advanced capitalist economies after the 2007–2009 crises. No clear pattern could be observed for emerging capitalist economies. Rather, several countries seem to show a tendency towards DLPD and DDL regimes after the crises and contribute to the global complement to the ELM regime tendency of several advanced capitalist economies – and hence to persistent global current account imbalances. The recent analyses, by Akcay et al. (2022), Hein (2019) and Hein et al. (2021), have related these shifts of regimes, from DLPD towards DDL stabilised by government deficits or towards ELM, to the pre-crises private indebtedness dynamics, and the need for deleveraging, and to the fiscal policy responses. Hein et al. (2021) have included changes in the welfare models, and Hein/Martschin (2021) have analysed the role of the macroeconomic policy regime. Kohler/Stockhammer (2022), discarding the regime distinction for the post-crises period, have provided a systematic analysis of growth drivers for OECD countries, to explain the shift from the pre-crises to the post-crises patterns. They find a major role in the need for deleveraging generated by high private debt and the (lack of) expansionary deficit-financed fiscal policies. Jungmann (2023), however, could not support these findings for a set of emerging capitalist economies.

The PKE demand and growth regime approach has received some prominence in the CPE and IPE literature. In an attempt at overcoming the implications of the New Consensus Macroeconomics (NCM) as the supply-side-dominated macroeconomic backbone of much of the CPE research, in particular of the Varieties of Capitalism (VoC) approach (Carlin/Soskice 2009, 2015; Hall/Soskice 2001; Hope/Soskice 2016), Baccaro/Pontusson (2016, 2018) make use of the post-Keynesian (PK) categories of demand-led macroeconomic growth regimes, which they call ‘growth models’. Schedelik et al. (2021) suggest

<footnote n="1">1. For a review of studies making use of the national income and financial accounting decomposition approach, see e.g. Hein (2023a: ch. 8).</footnote>

<footnote n="2">2. See Hein (2012; 2014: ch. 10; 2023a: ch. 8) for derivations of these regimes making use of small-scale analytical Kaleckian distribution and growth models. For stock-flow-consistent numerical simulation models, see e.g. Belabed et al. (2018), Detzer (2018) and Prante et al. (2022).</footnote>
to apply the PKE-inspired growth regime or growth model approach to emerging capitalist economies, too, and Mertens et al. (2022) elaborate on such extensions. Meanwhile, this approach is now widely used in CPE and IPE and linked with different analyses of economic, political and social growth drivers (Baccaro et al. 2022). Furthermore, it has opened the floor for PKs to interact with CPE and IPE researchers and to provide solid macro-foundations for CPE and IPE research, on the one hand, and to enrich the PK regime analysis by a political economy dimension focusing on institutions and social coalitions supporting a specific regime, on the other hand.<fnref n="3" />

Whereas the previous analyses of sources and financing of demand in the national income and financial accounting decomposition approach, on the one hand, and the examinations of economic, social and political growth drivers, on the other hand, provide some important insight, a clear distinction between the different levels of analysis has been missing so far, which has led to some misunderstandings in the debates. Following Hein (2023b), we argue that, since located at different levels of analysis, the national income and financial accounting decomposition approach and the different analyses of growth drivers are not mutually exclusive or even contradictory, but can rather complement each other. However, a clear and systematic link between these two levels of analysis needs to be established. Inspired by the works of Freitas/Dweck (2013), Girardi/Pariboni (2016), Morlin et al. (2022) and Passos/Morlin (2022), we try to provide such a link by means of applying an autonomous demand-led growth perspective for the four BRICs countries (2001–2019) as well as comparing it with the results for the more traditional national income and financial accounting based decomposition, on the one hand, and providing some links with economic, social and political growth drivers, on the other hand.

According to the autonomous demand-led growth perspective of the SSM model, in the long run, growth is driven by autonomous demand growth, which is neither caused nor funded by current income and which does not add to productive capacity of the economy.<fnref n="4" /> The components of autonomous demand thus are exports, government expenditures, credit-financed consumption, and residential investment, whereas imports, income-financed consumption, and private investment are considered to be induced by current income.<fnref n="5" /> The respective propensities thus constitute the multiplier of autonomous demand. Such a systematic analysis of autonomous and induced components of aggregate demand and growth then provides the ground for an economic policy and political economy analysis of growth drivers by focusing on the components of autonomous demand and of the supermultiplier.

The paper is organized as follows. In Section 2 we first apply the standard national income and financial accounting decomposition approach of demand and growth regime analysis to the BRICs countries for the two periods 2001–2010 and 2011–2019. Then we do the same for the SSM autonomous demand-led growth decomposition and we discuss the results. Section 3 then broadens the debate and provides some economic policy and political economy arguments driving the changes in autonomous growth dynamics and

<footnote n="3">3. For recent examples in this respect, see Behringer/van Treeck (2018, 2019), Stockhammer/Ali (2018), Stockhammer et al. (2016) and the contributions in the recent special issue of the *Review of Keynesian Economics*, 2022, 10(2).</footnote>

<footnote n="4">4. For the SSM approach based on the initial work of Serrano (1995), see the introduction and overviews by Freitas/Serrano (2015, 2017). For a comparative analysis of the SSM approach and other heterodox and orthodox growth models, see Hein (2023a: ch. 7).</footnote>

<footnote n="5">5. A classification of consumption out of public transfers as autonomous component could also be applied, which increases the weight of the public sector in autonomous expenditures, as shown by Haluska (2021) for Brazil and Labat/Summa (2024) for Spain.</footnote>
the components of the supermultiplier, and thus the changes in demand and growth regimes. Section 4 briefly summarises and concludes.

## 2 GROWTH DECOMPOSITIONS FOR THE BRICS COUNTRIES BEFORE AND AFTER THE 2007–2009 CRISES: A NATIONAL INCOME AND FINANCIAL ACCOUNTING-BASED APPROACH AND AN AUTONOMOUS DEMAND-LED GROWTH PERSPECTIVE

We analyse the BRICs countries from 2001 until 2019, the last year before the Covid-19 crisis. To address how the Global Financial Crisis might have affected growth, we divide our time series into two periods: (i) from 2001 to 2010 and (ii) from 2011 to 2019. The crisis years and 2010 are included in the first period, with the latter being considered a ‘rebound year’ for most countries. Although the results may vary slightly with alternative choices of periods, the main trends regarding regime shifts remain. The chosen periodization is also influenced by our interest in the post-crisis deceleration of growth observed for these (and other) countries.⁶

### 2.1 Demand and growth regimes in finance-dominated capitalism: the national income and financial accounting decomposition perspective

The analysis of demand and growth regimes in the BRICs countries applies the national income and financial accounting decomposition method first presented by Hein (2011) and further developed in other works of this PK research strand. The empirical exercise aims to identify the main sources of demand and their financing. The BRICs countries have been included in the multi-country analyses by Hein/Mundt (2012) and Akcay et al (2022), with different periods from ours. First, we implement a decomposition of real GDP growth (g) of each country by the growth contributions of their main demand aggregates: private consumption (C), government consumption (G), investment (I), exports (X) and imports (M), which make up net exports (NX).

$$g_t = \frac{dY_t}{Y_{t-1}} = \frac{dC_t}{Y_{t-1}} + \frac{dG_t}{Y_{t-1}} + \frac{dI_t}{Y_{t-1}} + \frac{dX_t}{Y_{t-1}} - \frac{dM_t}{Y_{t-1}} = \frac{dC_t}{Y_{t-1}} + \frac{dG_t}{Y_{t-1}} + \frac{dI_t}{Y_{t-1}} + \frac{dNX_t}{Y_{t-1}}. \quad (1)$$

The growth contributions should sum up to GDP growth. Then we analyze the financial balances of the main macroeconomic sectors. These are:

* the private sector financial balance ($FB_p = S - I$), as the difference between private saving (S) and private investment (I), and with the private household sector, the financial and non-financial corporate sectors as sub-sectors;

* the government sector financial balance ($FB_g = T - G$), as the difference between tax revenues and social security contributions (T) and government expenditures (G), and

* the external sector financial balance ($FB_e = M - X + FI^{net}$), as the difference of domestic imports (M) generating foreign sector revenues and domestic exports (X) which are equivalent to foreign sector expenditures. The external sector balance also

6. Alternative decompositions were conducted for the periods 2001–2007/2008–2019 and 2001–2009/2010–2019. While the regime classification for the first period is robust independent of the exact years, the identified regimes for the second period for Brazil, Russia and China may vary slightly depending on the choice of periods. This sensitivity results from the sign of the net export contributions to growth, which fluctuate around zero for these countries in these periods. However, the trends in regime development between the two periods remain the same.
includes the net revenues from the cross-border payments for factors of production, i.e. wages and capital incomes, as well as cross border transfers ($FI^{net}$), which may be positive or negative for the external sector, of course.

The sectoral financial balances of a country should sum up to zero, apart from statistical discrepancies, because a positive financial balance of one sector needs a respective negative financial balance of another sector – a creditor needs a debtor and vice versa:

$$FB_p + FB_G + FB_E = 0. \qquad (2)$$

Considering jointly the growth contribution decomposition and the sectoral financial balances, we can characterize each economy during the two periods and cluster them in the corresponding demand and growth regime under financialisation. Four regimes have been distinguished, which are characterized in Table 1.

Table 2 summarizes the main results of the methodology described above for our four countries and the two time periods. In general terms, we can see that there is no change of the DDL regime between periods in India, while Russia changes from WEL to ELM and China changes from ELM to WEL. Brazil presents a more difficult characterization with a change from DDL to WEL. We then turn to the autonomous demand-led growth decomposition, to better understand the economic demand drivers behind these changes.

<table><caption>Table 1 Demand and growth regimes</caption><tbody><tr><td>Export-led mercantilist (ELM)</td><td><ul><li>Positive financial balances of the private sector</li><li>Negative financial balances of the external sector</li><li>Positive nominal balance of goods and services</li><li>Positive growth contribution of net exports</li></ul></td></tr><tr><td>Weakly export-led (WEL)</td><td><p><u>Either</u></p><ul><li>Positive financial balances of the private sector</li><li>Negative financial balances of the external sector</li><li>Positive nominal balance of goods and services</li><li>Negative growth contribution of net exports</li></ul><p><u>or</u></p><ul><li>Negative but improving financial balances of domestic sectors</li><li>Positive but declining financial balances of the external sector</li><li>Negative but improving nominal net exports</li><li>Positive growth contribution of net exports</li></ul></td></tr><tr><td>Domestic demand-led (DDL)</td><td><ul><li>Positive or balanced financial balances of the private sector as a whole</li><li>Balanced or positive financial balances of the external sector</li><li>Growth is almost exclusively driven by domestic demand</li><li>Around zero growth contribution of net exports</li></ul></td></tr><tr><td>Debt-led private demand boom (DLPD)</td><td><ul><li>Negative or close to balance financial balances of the private sector</li><li>Positive financial balances of the external sector</li><li>Significant growth contributions of domestic demand and private consumption in particular</li><li>Negative growth contribution of net exports</li></ul></td></tr></tbody></table>

Source: Based on Dünhaupt/Hein (2019: 458).
<table><caption>Table 2 Demand and growth regimes in Brazil, Russia, India and China. Annual averages for the periods 2001–2010 and 2011–2019</caption><thead><tr><th rowspan="2"></th><th colspan="2">Brazil</th><th colspan="2">Russia</th><th colspan="2">India</th><th colspan="2">China</th></tr><tr><th>2001–2010</th><th>2011–2019</th><th>2001–2010</th><th>2011–2019</th><th>2001–2010</th><th>2011–2019</th><th>2001–2010</th><th>2011–2019</th></tr></thead><tbody><tr><td>Real GDP growth, percent</td><td>3.71</td><td>0.79</td><td>4.93</td><td>1.74</td><td>6.75</td><td>6.46</td><td>10.56</td><td>7.34</td></tr><tr><td colspan="9">Growth contributions by main demand aggregates, percentage points*</td></tr><tr><td>Domestic demand, including changes in inventories</td><td>3.84</td><td>0.76</td><td>5.81</td><td>1.57</td><td>7.36</td><td>6.31</td><td>10.13</td><td>7.40</td></tr><tr><td>Private consumption</td><td>2.24</td><td>0.92</td><td>3.72</td><td>1.20</td><td>3.28</td><td>3.86</td><td>3.37</td><td>3.05</td></tr><tr><td>Public consumption</td><td>0.61</td><td>0.11</td><td>0.33</td><td>0.11</td><td>0.64</td><td>0.62</td><td>1.55</td><td>1.34</td></tr><tr><td>Investment</td><td>0.93</td><td>-0.25</td><td>1.66</td><td>0.31</td><td>2.87</td><td>2.09</td><td>4.87</td><td>3.10</td></tr><tr><td>Inventories</td><td>0.06</td><td>-0.02</td><td>0.10</td><td>-0.05</td><td>0.56</td><td>-0.25</td><td>0.34</td><td>-0.09</td></tr><tr><td>Net exports of goods and services</td><td>-0.08</td><td>0.19</td><td>-0.85</td><td>0.27</td><td>-0.49</td><td>-0.03</td><td>0.48</td><td>-0.01</td></tr><tr><td>Exports</td><td>0.65</td><td>0.25</td><td>1.47</td><td>0.69</td><td>2.31</td><td>1.10</td><td>2.76</td><td>1.23</td></tr><tr><td>Imports</td><td>-0.73</td><td>-0.06</td><td>-2.31</td><td>-0.42</td><td>-2.81</td><td>-1.13</td><td>-2.28</td><td>-1.24</td></tr><tr><td>Balance of goods and services as share of nominal GDP, percent</td><td>1.01</td><td>-0.78</td><td>10.69</td><td>6.97</td><td>-2.95</td><td>-3.63</td><td>4.68</td><td>2.13</td></tr><tr><td colspan="9">Sectoral financial balances as share of nominal GDP, percent</td></tr><tr><td>Private sector</td><td>2.63</td><td>3.19</td><td>4.56</td><td>4.02</td><td>7.98</td><td>5.07</td><td>6.59</td><td>4.26</td></tr><tr><td>Public sector</td><td>-3.40</td><td>-6.01</td><td>2.60</td><td>-0.45</td><td>-8.74</td><td>-7.14</td><td>-1.40</td><td>-2.61</td></tr><tr><td>External sector</td><td>0.78</td><td>2.82</td><td>-7.16</td><td>-3.57</td><td>0.75</td><td>2.07</td><td>-5.19</td><td>-1.65</td></tr><tr><td>Demand and growth regime**</td><td>DDL</td><td>WEL</td><td>WEL</td><td>ELM</td><td>DDL</td><td>DDL</td><td>ELM</td><td>WEL</td></tr></tbody></table>

Notes: *Contributions may not sum to the growth rate of real GDP because of rounding, approximation and not included statistical discrepancies in expenditure estimates of GDP. A statistical discrepancy usually arises in GDP measurement due to independent estimations by industrial origin and by expenditure categories. The inclusion of this discrepancy ensures GDP from the expenditure side equals GDP measured by the income or output approach (World Bank 2021). **Demand and growth regimes: export-led mercantilist (ELM), weakly export-led (WEL), domestic demand-led (DDL), debt-led private demand boom (DLPD).

Sources: IMF (2021a), IMF (2021b), World Bank (2021), authors' calculations and presentation.
## 2.2 Autonomous demand-led growth decomposition

To further investigate the dynamics of the sources of growth in the identified regimes, we apply a growth decomposition exercise based on the autonomous demand-led growth or SSM framework. We follow the methodology developed by Freitas/Dweck (2013) and recently applied by Morlin et al. (2022) to a set of developed countries. We distinguish between autonomous and induced components of demand in the following way:

$$Y_t = C_t + I_t + G_t + X_t - M_t \quad (3)$$

$$C_t = C_{H,t} + CC_t = cY_t + CC_t \quad (4)$$

$$I_t = I_{C,t} + I_{G,t} + I_{H,t} = bY_t + I_{G,t} + I_{H,t} \quad (5)$$

$$M_t = mY_t \quad (6)$$

$$Z_t = G_t + I_{G,t} + X_t + CC_t + I_{H,t} \quad (7)$$

$$Y_t = \frac{Z_t}{1 - c - h + m} = \alpha Z_t \quad (8)$$

$$\alpha = \frac{1}{1 - c + m - h} \quad (9)$$

Equation (3) presents the accounting decomposition of income (Y) by main demand aggregates. In equations (4) and (5) we distinguish between autonomous and induced components of demand regarding consumption (C) and investment (I). Household consumption out of disposable income ($C_H$) and corporate investment ($I_C$) are induced components, depending on the respective propensity to consume (c) and propensity to invest of private firms (h), whereas consumer credit-financed consumption (CC), general government investment ($I_G$), and households' residential investment ($I_H$) are considered to be autonomous. Imports (M), also an induced component of demand, is described in equation (6) by income and the propensity to import (m). Equation (7) presents the demand components (Z) considered autonomous from current income: apart from credit-financed consumption, residential and general government investment expenditures already mentioned, we have government consumption (G) and exports (X). Equation (8) indicates the long-term equilibrium income and equation (9) presents the supermultiplier (α) in terms of the parameters of the induced components of demand.

We base our empirical analysis on data from four international institutions: IMF (2021a, 2021b), World Bank (2021), BIS (2021), and OECD (2022), and on India's official national accounts data (MSPI 2021). For private consumption we distinguish between consumption out of disposable income and credit financed consumption. To do so, we used the BIS (2021) database on credit to households and applied the methodology used by Girardi/Pariboni (2016) to calculate real net flows of consumption credit by first-differencing end of period stocks of credit and deflating them by the GDP implicit price deflator (IMF 2021a; World Bank 2021). Due to data availability, we assume that credit to households is a good approximation for consumer credit, although it might contain some limitations.⁷

7. The BIS (2021) database points out that the series for India includes housing loans during one sub-period. However, the database might also include a proportion of housing loans for the other countries' series, implying a possible upward bias for consumer credit given that housing loans may have already been included as households' residential investment.
For investment we distinguish between general government, corporate, and household (residential) investment (IMF 2021a; MSP1 2021; OECD 2022; World Bank 2021).⁸ Given data limitations for the analysed countries, household investment is assumed as the approximation for household residential investment as an autonomous component of demand, because a substantial part of household investment is on dwellings. However, we are aware that part of household investment is induced investment in the capital stock of non-corporate business. State-owned enterprises' investment is not distinguished from corporate investment, as in Freitas/Dweck's (2013) original work. This results from both a lack of available data and a theoretical understanding that state-owned enterprises also are exposed to market competition pressures that induce investment decision, even if not in the same manner as with private corporations. Lastly, we employ [(C-CC)/GDP] as a proxy for the propensity to consume out of income (c), [(I-I<sub>G</sub>-I<sub>H</sub>)/GDP], the share of non-government and non-household investment to GDP, as a proxy for the corporate propensity to invest (b) and the share of imports in GDP [M/GDP] as a proxy for the propensity to import (m).⁹

As in Freitas/Dweck (2013), the decomposition formula is thus derived from national income accounting, but distinguishing between autonomous and induced components of aggregate demand, as shown in equations (3)–(9):

$$ g = \alpha(1) \frac{G(0)}{Y(0)} g_G + \alpha(1) \frac{I_G(0)}{Y(0)} g_{IG} + \alpha(1) \frac{X(0)}{Y(0)} g_X + \alpha(1) \frac{CC(0)}{Y(0)} g_{CC} \\ + \alpha(1) \frac{I_H(0)}{Y(0)} g_{IH} + \alpha(1) \frac{C(0)}{Y(0)} g_C + \alpha(1) \frac{M(0)}{Y(0)} g_M + \alpha(1) \frac{I_C(0)}{Y(0)} g_H \\ + \alpha(1) \frac{E(0)}{Y(0)} g_E. \quad (10) $$

The five first terms on the right-hand side of equation (10) represent the contributions of the autonomous expenditures and the next three terms represent the contributions of the induced components. For this last group, the contributions do not depend on the growth rates, but on the growth rates of their inducing parameters: the propensity to consume out of disposable income, the propensity to import, and the corporate propensity to invest, respectively. The last term represents the contribution of inventory changes on growth rates, which were included for the sum of the contributions to be equal to real GDP growth.¹⁰

8. For China, Brazil and Russia, we used data from OECD. This did not have the investment breakdown for India, and for Russia the time series was incomplete. We then complemented the series with national accounts data as further described in the Appendix.

9. As done by Girardi/Pariboni (2016). Following their contribution, we use the supermultiplier formula with the propensity to import instead of a domestic content parameter as in Freitas/Dweck (2013). Also, we use the propensity to consume instead of the propensity to save. As both approaches are just two forms of expression of the same mechanism, we chose this one for reasons of clarity. Furthermore, we do not attempt to statistically infer the referred propensities, which would not be possible with our dataset. We thus follow the SSM growth decomposition literature, on which our approach is based.

10. For the real growth rates to match contributions, the statistical discrepancy presented in the World Bank data was added to aggregate inventories changes. Different from Freitas/Dweck (2013), the time-series taken from the World Bank have the expenditures deflated by their own price deflator instead of the GDP deflator. By doing so we avoid the growth rates incorporating relative price changes (to the GDP deflator) and focus on volume changes. This is especially relevant for imports and exports, as relative price changes might be dominated by exchange rate dynamics.
The index (0) represents the data corresponding to the beginning of the period while (1) represents the end of the period. The supermultiplier is represented by $\alpha(1)$. We have calculated annual contributions to growth (with 0 representing the previous year and 1 representing the current year) and have taken averages of yearly contributions to represent the average contributions of each period. As should be clear, the growth contribution decomposition in equation (10) differs from the one in equation (1). Although both are derived from national income accounts, the contributions of the autonomous components in equation (10) also include the proportional induced demand contributions via the supermultiplier ($\alpha$).¹¹ As long as the inducements to consume, to invest and to import, and thus the supermultiplier, do not change, the growth contributions of the autonomous demand components will thus sum up to real GDP growth.

Figure 1 shows a general common variation of total real GDP growth and autonomous demand growth for the four countries, which is indicated by the high correlation coefficients

<figure><img src="image_4.png" /></figure>

Sources: BIS (2021), IMF (2021a), MSPI (2021), OECD (2022), World Bank (2021), authors' calculations and presentation.

<caption>Figure 1 Correlation between autonomous demand and GDP growth rates in the BRICs countries, 2001–2019</caption>

11. This implies, for example, that the growth contributions of exports in Tables 2 and 3 will generally not be the same, since the one in Table 3 also includes the proportional impact via the induced components of demand.
between the two variables for at least three countries, 0.69 for Brazil, 0.9 for Russia and 0.6 for China, while India shows a somewhat lower correlation coefficient (0.36).

The composition of autonomous demand in the four countries is quite heterogeneous, as shown in Figure 2. Consumer credit and residential investment play a bigger role in India and China, with their volatility contributing to the higher volatility in total autonomous demand in these two countries. Brazil has a smaller share of exports in GDP and is thus less open than the other economies. It also displays the lowest share of autonomous demand in GDP. China has the highest share exceeding 60 per cent of GDP in most of the years.

Figure 3 shows the evolution of the calculated supermultipliers ($\alpha$), and we see that is has declined in Brazil, India and China during the first period until 2007 and then recovered somewhat towards the end of this period until 2010, in the course of the 2007–2009 crisis and the following recovery. In Russia, the supermultiplier has remained rather stable in this period, only rising during the 2007–2009 crisis. In our second period, 2011–2019, all the countries have witnessed a tendency of the supermultiplier to rise, again except for Russia where it has remained rather stable.

As is also obvious from Table 3, which summarizes the decomposition of the autonomous demand and the supermultiplier components growth averages, we have an inverse relationship between the development of the supermultiplier and real GDP growth rates for all the countries in the second period. While the supermultiplier showed a tendency to rise in this period, real GDP growth rates have been much lower than in the first period.

<figure><img src="image_5.png" /></figure>

Sources: BIS (2021), IMF (2021a), MSPI (2021), OECD (2022), World Bank (2021), authors' calculations and presentation.

<caption>Figure 2 Autonomous demand components, as percentage of real GDP, in the BRICs countries, 2001–2019</caption>
<figure><img src="image_6.png" /></figure>

*Sources:* BIS (2021), IMF (2021a), MSPI (2021), OECD (2022), World Bank (2021), authors' calculations and presentation.

<caption>**Figure 3 Evolution of the supermultipliers in the BRICs countries, 2001–2019**</caption>

This fall thus cannot be attributed to the induced components of demand, but rather to the fall in the growth contributions of autonomous demand and its components. In Brazil, the autonomous demand growth contribution has fallen to almost zero in the second period, and in the other three countries it has more than halved. Except for Russia, the growth contributions of the induced components have risen significantly, turning from negative values in the first period to positive values in the second. In Russia, however, they have fallen as well.

## 2.3 The national income and financial accounting perspective in light of the autonomous demand-led growth decomposition

The results regarding autonomous growth contributions presented in Table 3 can now be used to illuminate the demand and growth regimes changes in Table 2, derived from the national income and financial accounting decomposition approach.

As analysed in Section 2.1, Brazil experienced a regime change from a DDL to a WEL regime. From Table 3, and also from Table 2, we can see that this shift did not come from an increase in the growth contributions of exports. The shift in regimes was rather driven by an even larger decline in the growth contribution of autonomous government expenditures, in comparison to the deceleration in export growth contributions. According to Table 3, in the first period, government consumption and investment contributed together 1.9 percentage points to real GDP growth on average, while in the second period the contraction of these expenditures ‘contributed’ minus 0.1 percentage points to real GDP growth. Exports contributed 1.5 percentage points in the first period and 0.6 percentage points in the second period, while the induced growth contribution of imports increased, based on a reversal of the increase in the propensity to import in the first period
<caption>Table 3 Autonomous demand-led growth decomposition for the BRICs countries. Average annual growth of real GDP, autonomous and induced components of demand, in percent, 2001–2010 and 2011–2019</caption>

A. Contributions to growth of autonomous and induced demand components

<table><thead><tr><th rowspan="2"></th><th colspan="2">Brazil</th><th colspan="2">Russia</th><th colspan="2">India</th><th colspan="2">China</th></tr><tr><th>2001–2010</th><th>2011–2019</th><th>2001–2010</th><th>2011–2019</th><th>2001–2010</th><th>2011–2019</th><th>2001–2010</th><th>2011–2019</th></tr></thead><tbody><tr><td>GDP</td><td>3.71</td><td>0.79</td><td>4.93</td><td>1.74</td><td>6.75</td><td>6.46</td><td>10.56</td><td>7.34</td></tr><tr><td>Total Z</td><td>4.07</td><td>0.24</td><td>3.94</td><td>1.82</td><td>8.63</td><td>4.13</td><td>13.00</td><td>6.36</td></tr><tr><td>Autonomous components growth contribution</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>G</td><td>1.42</td><td>0.25</td><td>0.51</td><td>0.18</td><td>1.13</td><td>1.13</td><td>2.66</td><td>2.13</td></tr><tr><td>I<sub>G</sub></td><td>0.45</td><td>-0.34</td><td>0.43</td><td>0.29</td><td>0.52</td><td>0.47</td><td>0.98</td><td>0.53</td></tr><tr><td>X</td><td>1.50</td><td>0.60</td><td>2.27</td><td>1.22</td><td>4.17</td><td>1.83</td><td>4.84</td><td>1.94</td></tr><tr><td>I<sub>H</sub></td><td>0.30</td><td>0.02</td><td>0.46</td><td>-0.20</td><td>1.88</td><td>0.97</td><td>3.10</td><td>1.05</td></tr><tr><td>CC</td><td>0.41</td><td>-0.29</td><td>0.27</td><td>0.33</td><td>0.94</td><td>-0.27</td><td>1.42</td><td>0.71</td></tr><tr><td>Total Induced</td><td>-0.33</td><td>0.98</td><td>1.14</td><td>0.23</td><td>-2.58</td><td>2.47</td><td>-2.90</td><td>1.21</td></tr><tr><td>Induced components growth contribution</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C<sub>H</sub></td><td>0.05</td><td>1.38</td><td>2.41</td><td>0.02</td><td>-1.48</td><td>1.05</td><td>-2.21</td><td>0.49</td></tr><tr><td>M</td><td>-0.85</td><td>0.13</td><td>-1.91</td><td>0.29</td><td>-2.43</td><td>1.05</td><td>-1.08</td><td>0.16</td></tr><tr><td>I<sub>C</sub></td><td>0.46</td><td>-0.53</td><td>0.64</td><td>-0.08</td><td>1.32</td><td>0.38</td><td>0.40</td><td>0.55</td></tr><tr><td>Inventories</td><td>-0.03</td><td>-0.43</td><td>-0.16</td><td>-0.31</td><td>0.71</td><td>-0.14</td><td>0.46</td><td>-0.23</td></tr><tr><td>Total</td><td>3.71</td><td>0.79</td><td>4.93</td><td>1.74</td><td>6.75</td><td>6.46</td><td>10.56</td><td>7.34</td></tr></tbody></table>
<table><caption>B. Sectoral contributions to growth</caption><thead><tr><th rowspan="2"></th><th colspan="2">Brazil</th><th colspan="2">Russia</th><th colspan="2">India</th><th colspan="2">China</th></tr><tr><th>2001–2010</th><th>2011–2019</th><th>2001–2010</th><th>2011–2019</th><th>2001–2010</th><th>2011–2019</th><th>2001–2010</th><th>2011–2019</th></tr></thead><tbody><tr><td><strong>GDP</strong></td><td>3.71</td><td>0.79</td><td>4.93</td><td>1.74</td><td>6.75</td><td>6.46</td><td>10.56</td><td>7.34</td></tr><tr><td><strong>Domestic Sector</strong></td><td>3.06</td><td>0.06</td><td>4.57</td><td>0.23</td><td>5.01</td><td>3.58</td><td>6.80</td><td>5.23</td></tr><tr><td>Private Sector</td><td>1.19</td><td>0.15</td><td>3.63</td><td>-0.23</td><td>3.37</td><td>1.99</td><td>3.17</td><td>2.57</td></tr><tr><td>C<sub>H</sub></td><td>0.05</td><td>1.38</td><td>2.41</td><td>0.02</td><td>-1.48</td><td>1.05</td><td>-2.21</td><td>0.49</td></tr><tr><td>I<sub>C</sub></td><td>0.46</td><td>-0.53</td><td>0.64</td><td>-0.08</td><td>1.32</td><td>0.38</td><td>0.40</td><td>0.55</td></tr><tr><td>CC</td><td>0.41</td><td>-0.29</td><td>0.27</td><td>0.33</td><td>0.94</td><td>-0.27</td><td>1.42</td><td>0.71</td></tr><tr><td>I<sub>H</sub></td><td>0.30</td><td>0.02</td><td>0.46</td><td>-0.20</td><td>1.88</td><td>0.97</td><td>3.10</td><td>1.05</td></tr><tr><td>Inventories</td><td>-0.03</td><td>-0.43</td><td>-0.16</td><td>-0.31</td><td>0.71</td><td>-0.14</td><td>0.46</td><td>-0.23</td></tr><tr><td><strong>Public Sector</strong></td><td>1.87</td><td>-0.09</td><td>0.94</td><td>0.47</td><td>1.64</td><td>1.59</td><td>3.64</td><td>2.66</td></tr><tr><td>G</td><td>1.42</td><td>0.25</td><td>0.51</td><td>0.18</td><td>1.13</td><td>1.13</td><td>2.66</td><td>2.13</td></tr><tr><td>I<sub>G</sub></td><td>0.45</td><td>-0.34</td><td>0.43</td><td>0.29</td><td>0.52</td><td>0.47</td><td>0.98</td><td>0.53</td></tr><tr><td><strong>External Sector</strong></td><td>0.65</td><td>0.73</td><td>0.36</td><td>1.51</td><td>1.74</td><td>2.87</td><td>3.76</td><td>2.11</td></tr><tr><td>X</td><td>1.50</td><td>0.60</td><td>2.27</td><td>1.22</td><td>4.17</td><td>1.83</td><td>4.84</td><td>1.94</td></tr><tr><td>M</td><td>-0.85</td><td>0.13</td><td>-1.91</td><td>0.29</td><td>-2.43</td><td>1.05</td><td>-1.08</td><td>0.16</td></tr><tr><td><strong>Total</strong></td><td>3.71</td><td>0.79</td><td>4.93</td><td>1.74</td><td>6.75</td><td>6.46</td><td>10.56</td><td>7.34</td></tr></tbody></table>

Notes: Statistical discrepancy added as part of inventory changes in World Bank data. See Appendix for the methodology on consumer credit and residential investment calculations.

Sources: BIS (2021), IMF (2021a), MSP1 (2021), OECD (2022), World Bank (2021), authors' calculations and presentation.
towards a slight decline in the second period. Given a growth rate of only 0.8 percent in the second period, weakened export growth played the major role in sustaining growth in Brazil while the domestic autonomous growth drivers saw an even more significant decline. The WEL regime is thus a result of the dramatic decline in domestic autonomous demand growth.

As shown in Table 2, Russia changed from a WEL to an ELM regime. From Tables 2 and 3, it can be seen that export growth remained the biggest contributor to growth in the country in both periods, however with growth contributions roughly halved in the second period. The biggest impact towards ELM came from the reversal of the rise of the propensity to import from the first period towards a decline in the second period, which meant a significant rise of the growth contributions of imports in the second period. As can be seen in Table 3, while imports contributed negatively to growth in the first period, this changed to slightly positive contributions in the second period. Simultaneously, the rise in the propensity to consume out of income, which had been a major driver of growth towards the end of the first period, came to a halt and did not contribute to growth anymore in the second period.

India was the only country that did not show any demand and growth regime change from the first to the second period, remaining DDL according to Table 2. Whereas Table 2 does not display any shifts in the structure of growth contributions, Table 3 reveals some major changes, which, however, balance each other and thus do not change the nature of the demand and growth regime. Autonomous growth contributions more than halved from the first to the second period, driven by a significant fall in the growth contributions of exports and private households' credit financed consumption and residential investment, while the growth contributions of government consumption and investment remained constant. But this was compensated by a reversal of the falling household propensity to consume out of income from the first period towards a rise in the second period, as well as by a reversal of the rising inducement to import from the first period to a falling tendency in the second, leading to significant positive growth contributions of both induced components of demand.

China changed from an ELM regime in the first period towards a WEL regime in the second with falling net exports and current account surpluses (Table 2). From Table 3 it can be seen that this shift was mainly driven by a significant decline in the autonomous growth contribution of exports. However, this was accompanied by a reversal of the rising propensity to import from the first period towards a falling propensity in the second period, which meant positive growth contributions of induced imports. But the growth contribution of the external sector declined considerably and almost halved, whereas the contributions of the domestic sector only fell slightly. The public sector almost sustained its autonomous growth contributions, and the fall in the growth contributions of private households' autonomous credit financed consumption and residential investment was compensated for by a reversal of the falling trend of the propensity to consume out of income in the first period, which rose in the second period and led to positive growth contributions of induced consumption. China's change of regime was thus driven both by lower autonomous export growth and by a relative improvement of domestic demand overall, both autonomous and induced.

## 3 POLITICAL ECONOMY OF AUTONOMOUS DEMAND COMPONENTS AND THE SUPERMULTIPLIER IN THE BRICS COUNTRIES

Explaining the development of autonomous and induced demand components requires the analysis of political economy and structural features. Hence, as stressed by Morlin et al.
(2022), the SSM approach lends itself to be complemented by CPE and IPE concepts. But with the analysis of growth drivers and the macroeconomic policy regime approach (see Introduction), PKE concepts are also available to examine the determinants of the autonomous demand components and the supermultiplier. However, it is beyond the scope of this paper to provide a comprehensive analysis for the four countries. Instead, we outline some developments to explain the dynamics shown in Table 3. Inspired by analyses of growth drivers (Kohler/Stockhammer 2022; Jungmann 2023) and macroeconomic policy regimes (Hein/Martschin 2021), we will consider price and non-price competitiveness, commodity prices, international demand dynamics, and distributional trends in combination with some relevant (CPE and IPE) country case studies. This analysis therefore does not claim to be exhaustive, but rather suggests a combination of methods and thus points to avenues for further research.

A general trend in the BRICs countries is the deceleration of autonomous export growth in the second period. Exports are driven by price and non-price competitiveness, commodity prices and foreign demand. The general deceleration of exports can be attributed to the loss in dynamism of foreign demand after the Global Financial Crisis. In terms of growth regimes, this loss in demand dynamics can be linked to formerly DLPD economies in Europe becoming ELM or WEL at lower growth rates (Hein et al. 2021; Kohler/Stockhammer 2022). Otherwise put, the external demand provided by DLPD core capitalist economies prior to the Global Financial Crisis was only insufficiently substituted thereafter. This development occurred due to ‘stagnation policies’ in European countries in the form of austerity and contractionary fiscal policies (Hein 2016, 2018). Hence core capitalist economies and their policy choices drive global demand and thus export growth in emerging capitalist economies to a considerable extent (Matthijs 2022).

While this fall in foreign demand growth was felt across the BRICs countries, country-specific factors explain the divergence in the post-crisis period. In Brazil, export growth also suffered from reduced non-price competitiveness, measured by the economic complexity index (ECI), and a fall in commodity prices, reflected in the commodity export price index (CEPI) (Figure 4). The commodity export boom and the concomitant real appreciation of the Brazilian real may have exacerbated the loss in technological sophistication as described by the phenomenon of ‘Dutch Disease’ (Bresser-Pereira 2008). Figure 5 illustrates this structural change: the weight of raw materials in Brazil’s export basket increased at the expense of manufactured goods. Looking at the Brazilian international price competitiveness, proxied by the real effective exchange rate (REER),<fnref n="12" /> we see that the REER exhibits a high degree of volatility (Figure 4). The Brazilian REER movements can be attributed to the development of its commodity prices as Figure 4 indicates. Moreover, capital entering and leaving Brazil have added to the sudden currency swings (Kaltenbrunner/Painceira 2015).

The deceleration of Brazil’s export growth was by no means compensated by increased public autonomous demand (Table 3). In fact, as argued by Serrano/Summa (2015) and indicated in Table 3, the fall of Brazil’s domestic demand growth contributions, public demand in particular, was more crucial for its faltering growth than the deterioration of its exports. Serrano/Summa (2015: 803) argue that '[the] sharp fall in domestic demand [was] a result of deliberate policy decisions made by the government and was not necessary, i.e., it was not made in response to some external constraint such as a balance-of-payments problem'. Rather, the fiscal consolidations starting in 2011 and the full-blown turn to austerity in 2015 resulted from the pressure exerted by the owning class on the

<footnote n="12">12. An increase (decrease) in the REER indicates a loss (increase) in price competitiveness.</footnote>
<figure><img src="image_7.png" /></figure>

*Notes*: REER: real effective exchange rate; CEPI: commodity export price index; ECI: economic complexity index.

*Sources*: BIS (2022), Gruss and Kebhaj (2019) and OEC (2022), authors' calculations and presentation.

<caption>Figure 4 Growth rates of REER and CEPI and ECI level in the BRICs countries, 2001–2019</caption>

government with the aim of stopping the process of rising wages, creating unemployment and curtailing workers' bargaining power. In this they were supported by the middle class, which was angered by the rising costs of low-skilled services (Serrano/Summa 2022). These groups' dominance over economic policy was consolidated after the impeachment of the then president Dilma Rousseff in 2016 when labour rights and fiscal expenditure reforms depressed real wages and public spending further. In addition, in the course of lawfare actions, corruption scandals were uncovered, which led to the disruption of informal networks between state and business elites. This had a potentially negative impact on public and private investments, as these networks formed the basis for investment decisions, especially in the construction and oil sectors (Nölke et al. 2022). Growth was stabilised to some extent in the second period for Brazil by the increase of consumption out of disposable income. We would expect such development to stem from a redistribution of income towards wages and lower income households. As Table 4 indicates, however, income inequality in Brazil increased in the second period, reflected by the rise in the Gini coefficient (see also Carvalho 2018: ch. 5). Thus the increase in the propensity to consume out of disposable income can be attributed to the fall of credit-financed
<figure><img src="image_8.png" /></figure>

Sources: World Bank (2021, 2022) and IMF (2021b), authors’ calculations and presentation.

<caption>Figure 5 Exports by product group in the BRICs countries, 2001–2019</caption>

<table><caption>Table 4 Distribution trends</caption><thead><tr><th>Country</th><th>Period</th><th>Wage share*</th><th>Gini coefficient of disposable income</th></tr></thead><tbody><tr><td rowspan="2">Brazil</td><td>2001–2010</td><td>+</td><td>—</td></tr><tr><td>2011–2019</td><td>+</td><td>+</td></tr><tr><td rowspan="2">Russia</td><td>2001–2010</td><td>+</td><td>—</td></tr><tr><td>2011–2019</td><td>—</td><td>—</td></tr><tr><td rowspan="2">India</td><td>2001–2010</td><td>—</td><td>+</td></tr><tr><td>2011–2019</td><td>—</td><td>+</td></tr><tr><td rowspan="2">China</td><td>2001–2010</td><td>—</td><td>+</td></tr><tr><td>2011–2019</td><td>+</td><td>—</td></tr></tbody></table>

Notes: Distribution indicators refer to the changes within the period, ‘+’ indicates an increase, ‘—’ a decrease, ‘0’ no change. An increase (decrease) in the Gini coefficient indicates a rise (fall) in personal income inequality. Wage shares are adjusted.

*Due to data availability, the first period for the wage share only covers the years 2004–2010.

Sources: ILO (2022), Solt (2020), authors’ calculations and presentation.
consumption (Table 3). This is in line with the sharp fall in consumer credit after 2012 and public credit after 2015 (Carvalho 2018: ch. 3).

Russia recorded an even more dynamic increase in its commodity export prices than Brazil during the first period. In the second period, its commodity export prices also fell more sharply (Figure 4). Similar to Brazil, during the post-crisis period, Russia witnessed a decline in its non-price competitiveness. Both developments are reflected in the structure of Russia's export basket. While Russia's exports already mainly relied on raw materials in the early 2000s, this reliance persisted and even increased slightly thereafter (Figure 5). This made the Russian exports heavily reliant on commodity exports and less sensitive to general improvements in its price competitiveness. Furthermore, the overall increased price competitiveness of Russia in the post-crisis period (Jungmann 2023), were by no means associated with a stable REER (Figure 4). Moreover, in 2014, Russia experienced sharp depreciations that led to a severe financial and economic crisis. The falling commodity prices were the main contributing factor to this crisis and their effect was aggravated by the economic sanctions on Russia in the course of its annexation of Crimea (Dreger et al. 2016). What attenuated the depressing growth effects from an autonomous demand-led growth perspective was the fall in the propensity to import. This may simply reflect the loss in purchasing power due to lower growth and a weaker currency. Moreover, this could also be a consequence of Russian counter-sanctions on agricultural imports, which have led to an increase in Russia's agricultural production (Liefert et al. 2019).

The weak Russian export growth in the second period was not countered by public demand. While the autonomous public demand components were little dynamic during the first period, they continued to decline slightly in the second one. As a reaction to the 2014 events, the Russian government incurred public deficits in three consecutive years. In 2016, however, despite dismal growth rates, a policy turn towards austerity was taken. Consequently the public deficit fell in 2017 and turned into a surplus in 2018 and 2019 (Tooze 2022). This fiscal austerity has been framed as part of Russia's 'fortress' strategy, which aims at protecting the current account surplus in spite of falling commodity prices. This could be interpreted as an economic strategy that aims at avoiding balance-of-payment problems through internal balancing in the event of external pressure. However, in the Russian case, geopolitical considerations, i.e. the shielding from expected sanctions, drove this policy significantly (Smid 2021). Finally, in the second period, the propensity to consume no longer contributed to growth. This can be attributed to the declining wage share that had risen in the previous period (Table 4).

The stability of India's growth and its regime stems from the constancy of the growth contributions of public consumption and investment demand. This constancy was achieved although India, similar to Brazil, experienced corruption scandals in the 2010s and subsequent shifts in its electoral landscape. Nonetheless the informal networks of business and political elites governing economic growth remained largely intact and no ideological ruptures occurred in the area of development policy (Nölke et al. 2022). In addition to this political factor, Nassif et al. (2016) argue that India was able to pursue more expansionary macroeconomic policies compared to Brazil and Russia (and South Africa) because it was less financially integrated internationally due to remaining capital flows regulations, which reduced external vulnerability and allowed for a more discretionary use of policies.¹³

13. However, with India running persistent current account deficits, it is far from certain that its external vulnerability will remain low and its scope for expansionary policy high. The scope for expansionary macroeconomic policies in developing and emerging economies depends positively on low current account deficits or surpluses, a competitive exchange rate, regulation of capital flows, sufficient foreign exchange reserves, and low levels of short-term external debt (Ocampo 2011: 21).
The reduced degree of India's external vulnerability becomes apparent in its REER, which is significantly less volatile than that of Brazil and Russia (Figure 4). Equally important in explaining India's stable growth, apart from the autonomous components, is the rise in its induced components – consumption from disposable income and the propensity to import. As reflected in Table 4, India saw a decrease in its wage share and an increase in personal income inequality. Hence the rise in the propensity to consume out of disposable income may be due to the decrease in consumer credit-financed consumption and residential investment (Table 3). The fall in the propensity to import during the second period could stem from a lower import content of India's exports due to a form of import substitution and technological levelling-up, as the significant increase in India's ECI during the second period indicates (Figure 4). Correspondingly the share of consumer and capital goods in the Indian export basket rose (Figure 5).

As argued in Section 2.3, the shift in China to a less export-led demand and growth regime, at lower but still comparatively high growth rates, can be attributed to lower export growth and a relative improvement of autonomous and induced domestic demand. Within the induced components, the growth contributions of consumption out of disposable income rose, what can be attributed to the higher wage share and decreased personal income inequality (Table 4). The consistency of public demand in spite of falling export growth can be ascribed to the lower levels of external vulnerability in China stemming from its persistent current account surpluses, ample foreign exchange reserves and regulation of capital flows (Nassif et al. 2016). The stability of public demand and the increasing importance of consumption, both of which promote domestic demand, are consistent with China's new 'dual circulation' development paradigm (Yifu/Wang 2022). This new paradigm aims 'to shift the focus from foreign to domestic circulation as the major driving force for China's sustainable development, and to emphasize the importance of a positive reciprocal relationship between domestic and international economic circulations' (Yifu/Wang 2022: 303). Nonetheless the pivot towards 'dual circulation' does not mean the abandonment of export growth. Securing export growth while fostering consumption demand may prove tricky, however, if exports are driven by price competitiveness and consumption demand is not debt-financed. What is hence required are exports driven by non-price competitiveness. In line with this, China's ECI consistently increased (Figure 4), and raw materials play a negligible role in its export basket (Figure 5). China's improved technological sophistication may also explain the positive contribution to growth of the falling import propensity in the second period, because such a rise in sophistication decreases the import content of exports.

In sum, all countries suffered from the loss in dynamism in foreign demand after the Global Financial Crisis, partly due to the stagnation policies in the capitalist core. The lower levels of export growth in Brazil and Russia during the second period reflect their pronounced dependency on commodity exports, losses in non-price competitiveness, and volatile exchange rates. India and China exhibit export baskets dominated by manufactured goods, improvements in non-price competitiveness and less volatile exchange rates. Furthermore, China and India show considerably more stable growth rates in their public autonomous demand components than Brazil and Russia. We have argued that these differences rather stem from country-specific political economy factors than from economic necessities such as balance of payment-constraints.

# 4 CONCLUSIONS

In this paper we have presented an analysis of changes in demand-led growth regimes in the BRICs countries, Brazil, Russia, India and China, after the Global Financial Crisis and
the Great Recession of 2007–2009. We have applied two approaches, the first based on national income and financial accounting decomposition and the second, based on the SSM growth model, distinguishing the dynamics of autonomous expenditure growth from those of the induced components of aggregate demand. We have argued that the SSM approach provides a bridge between the previous analyses of growth regimes according to the sources and the financing of demand based on national income and financial accounting, on the one side, and of the examination of economic, social and political growth drivers, on the other side. Inspired by the approach of Freitas/Dweck (2013), Girardi/Pariboni (2016), Morlin et al. (2022) and Passos/Morlin (2022), we have established such a link by means of applying an autonomous demand-led growth perspective to the four BRICs countries and comparing it with the results for the more traditional national income and financial accounting based decomposition, on the one hand, and providing some considerations on changes in growth drivers, on the other hand. We have examined the changes in growth contributions of the autonomous demand components, government consumption and investment expenditures, exports, credit-financed consumption and households’ residential investment, as well as the changes in the components of the supermultiplier, the inducements to consume, to invest and to import, for the four BRICs countries for the period 2001–2010 and the period 2011–2019. This has provided an understanding of the forces which have moved Brazil from a DDL to a WEL regime, Russia from a WEL to an ELM regime and China from an ELM to a WEL regime, and explained why India has maintained a DDL regime. Finally, we have provided some political economy and economic policy drivers of the key changes in the autonomous growth components of each of the country, without claiming to be comprehensive in this respect.

## ACKNOWLEDGEMENTS

Early versions of this paper were presented at the annual conference of the European Association for Evolutionary Political Economy (EAEPE) in September 2022 in Naples, Italy, and at the ‘Frontiers in Growth Regimes Research’ workshop of the IPE Berlin in October 2022. We are most grateful to the participants for helpful comments and suggestions, in particular to Riccardo Pariboni, Ricardo Summa, Ryan Woodgate and the referees of this journal. Of course, remaining errors are exclusively ours.

## REFERENCES

Akcay, Ü., Hein, E., Jungmann, B. (2022): Financialisation and macroeconomic regimes in emerging capitalist countries before and after the Great Recession, in: *International Journal of Political Economy*, 51(2), 77–100.

Baccaro, L., Blyth, M., Pontusson, J. (eds) (2022): *Diminishing Returns: The New Politics of Growth and Stagnation*, New York: Oxford University Press.

Baccaro, L., Pontusson, J. (2016): Rethinking comparative political economy: the growth model perspective, in: *Politics & Society*, 44(2), 175–207.

Baccaro, L., Pontusson, J. (2018): Comparative political economy and varieties of macroeconomics, Max Planck Institute for the Study of Societies, Cologne, MPIfG Discussion Paper, No 18/10.

Behringer, J., van Treeck, T. (2018): Income distribution and the current account, in: *Journal of International Economics*, 114, 238–254.

Behringer, J., van Treeck, T. (2019): Income distribution and growth models: a sectoral balances approach, in: *Politics & Society*, 47(3), 303–332.
Belabed, C., Theobald, T., van Treeck, T. (2018): Income distribution and current account imbalances, in: *Cambridge Journal of Economics*, 42(1), 47–94.

BIS (Bank for International Settlements) (2021): Credit to the non-financial sector, URL: https://www.bis.org/statistics/totcredit.htm?m=6_380_669 (accessed 27 December 2021).

BIS (2022): Effective exchange rate indices, URL: https://www.bis.org/statistics/eer.htm (accessed 24 June 2022).

Bresser-Pereira, L.C. (2008): The Dutch disease and its neutralization: a Ricardian approach, in: *Brazilian Journal of Political Economy*, 28(1), 47–71.

Carlin, W., Soskice, D. (2009). Teaching intermediate macroeconomics using the 3-equation model, in: Fontana, G., Setterfield, M. (eds), *Macroeconomic Theory and Macroeconomic Pedagogy*, London: Palgrave Macmillan.

Carlin, W., Soskice, D. (2015): *Macroeconomics: Institutions, Instability, and the Financial System*, Oxford, UK: Oxford University Press.

Carvalho, L. (2018): *Valsa brasileira: do boom ao caos económico*, Editora Todavia SA.

Detzer, D. (2018): Inequality, emulation and debt: the occurrence of different growth regimes in the age of financialisation in a stock-flow consistent model, in: *Journal of Post Keynesian Economics*, 41(2), 284–315.

Dreger, C., Kholodilin, K.A., Ulbricht, D., Fidrmuc, J. (2016): Between the hammer and the anvil: the impact of economic sanctions and oil prices on Russia’s ruble, in: *Journal of Comparative Economics*, 44(2), 295–308.

Dünhaupt, P., Hein, E. (2019): Finanzialisation, distribution, and macroeconomic regimes before and after the crisis: a post-Keynesian view on Denmark, Estonia, and Latvia, in: *Journal of Baltic Studies*, 50(4), 435–465.

Freitas, F., Dweck, E. (2013): The pattern of economic growth of the Brazilian economy 1970–2005: a demand-led growth perspective, in: Levrero, E., Palumbo, A. and Stirati, A. (eds), *Sraffa and the Reconstruction of Economic Theory*, Volume Two, London: Palgrave Macmillan.

Freitas, F., Serrano, F. (2015): Growth rate and level effects, the stability of the adjustment of capacity to demand and the Sraffian supermultiplier, in: *Review of Political Economy*, 27(3), 258–281.

Freitas, F., Serrano, F. (2017): The Sraffian supermultiplier as an alternative closure for heterodox growth theory, in: *European Journal of Economics and Economic Policies: Intervention*, 14(1), 70–91.

Girardi, D., Pariboni, R. (2016): Long-run effective demand in the US economy: an empirical test of the Sraffian supermultiplier model, in: *Review of Political Economy*, 28(4), 523–544.

Gruss, B., Kebhaj, S. (2019): Commodity terms of trade a new database, IMF Working Paper, No 19/21, URL: https://www.imf.org/en/Publications/WP/Issues/2019/01/24/Commodity-Terms-of-Trade-A-New-Database-46522 (accessed 24 June 2022).

Hall, P.A., Soskice, D. (2001): *Varieties of Capitalism: The Institutional Foundations of Comparative Advantage*, Oxford, UK: Oxford University Press.

Haluska, G. (2021): A desaceleração e a recessão econômica no Brasil entre 2011 e 2019 analisada a partir do modelo do Supermultiplicador Sraffiano, in: Anais do 14o Encontro Internacional da Associação Keynesiana Brasileira: o Brasil em um mundo em transformação, UFABC, URL: https://www.even3.com.br/anais/akb2021/375494-a-desaceleracao-e-a-recessao-economica-no-brasil-entre-2011-e-2019-analisada-a-partir-do-modelo-do-supermultiplic/ (accessed 13 November 2022).

Hein, E. (2011): Redistribution, global imbalances and the financial and economic crisis – the case for a Keynesian New Deal, in: *International Journal of Labour Research*, 3(1), 51–73.

Hein, E. (2012): *The Maroeconomics of Finance-dominated Capitalism – and Its Crisis*, Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing.

Hein, E. (2014): *Distribution and Growth after Keynes: A Post-Keynesian Guide*, Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing.

Hein, E. (2016): Secular stagnation or stagnation policy? A post-Steindlian view, in: *European Journal of Economics and Economic Policies: Intervention*, 13(2), 160–171.

Hein, E. (2018): Stagnation policy in the Eurozone and economic policy alternatives: a Steindlian/neo-Kaleckian perspective, in: *Wirtschaft und Gesellschaft*, 44(3), 315–348.
Hein, E. (2019): Financialisation and tendencies towards stagnation: the role of macroeconomic regime changes in the course of and after the financial and economic crisis 2007-9, in: *Cambridge Journal of Economics*, 43(4), 975-999.

Hein, E. (2023a): *Macroeconomics after Kalecki and Keynes: Post-Keynesian Foundations*, Cheltenham and Northampton, MA, USA: Edward Elgar Publishing.

Hein, E. (2023b): Varieties of demand and growth regimes – post-Keynesian foundations, in: *European Journal of Economics and Economic Policies: Intervention*, 20(3), 410–443.

Hein, E., Martschin, J. (2020): The Eurozone in crisis – a Kaleckian macroeconomic regime and policy perspective, in: *Review of Political Economy*, 32(4), 563–588.

Hein, E., Martschin, J. (2021): Demand and growth regimes in finance-dominated capitalism and the role of the macroeconomic policy regime: a post-Keynesian comparative study on France, Germany, Italy and Spain before and after the Great Financial Crisis and the Great Recession, in: *Review of Evolutionary Political Economy*, 2(3), 493–527.

Hein, E., Mundt, M., (2012): Financialisation and the requirements and potentials for wage-led recovery: a review focussing on the G20, ILO Working Paper, Conditions of Work and Employment Branch, Issue 37.

Hein, E., Paternesi Meloni, W., Tridico, P. (2021): Welfare models and demand-led growth regimes before and after the financial and economic crisis, in: *Review of International Political Economy*, 28(5), 1196-1223.

Hope, D., Soskice, D. (2016): Growth models, varieties of capitalism and macroeconomics, in: *Politics & Society*, 44(2), 209–226.

ILO (2022): ILOSTAT Database, URL: https://ilostat.ilo.org/data (accessed 17 July 2022).

IMF (2021a): International Financial Statistics, URL: https://data.imf.org/ (accessed 21 December 2021).

IMF (2021b): World Economic Outlook October 2021, URL: https://www.imf.org/en/Publications/WEO/weo-database/2021/October (accessed 21 December 2021).

Jungmann, B. (2023): Growth drivers in emerging capitalist economies: building blocks for a post-Keynesian analysis and an empirical exploration of the years before and after the Global Financial Crisis, in: *Review of Evolutionary Political Economy*, 4(2), 349–386.

Kaltenbrunner, A., Painceira, J.P. (2015): Developing countries’ changing nature of financial integration and new forms of external vulnerability: The Brazilian experience, in: *Cambridge Journal of Economics*, 39(5), 1281–1306.

Kohler, K., Stockhammer, E. (2022): Growing differently? Financial cycles, austerity, and competitiveness in growth models since the Global Financial Crisis, in: *Review of International Political Economy*, 29(4), 1314–1341.

Labat, H., Summa, R. (2024): A supermultiplier demand-led growth accounting analysis applied to the Spanish economy (1998-2019), in: *European Journal of Economics and Economic Policies: Intervention*, 21(1), 42–72.

Liefert, W.M., Liefert, O., Seeley, R., Lee, T. (2019): The effect of Russia’s economic crisis and import ban on its agricultural and food sector, in: *Journal of Eurasian Studies*, 10(2), 119–135.

Matthijs, M. (2022): Hegemonic leadership is what states make of it: reading Kindleberger in Washington and Berlin, in: *Review of International Political Economy*, 29(2), 371–398.

Mertens, D., Nölke, A., May, C., Schedelik, M., ten Brink, T., Gomes, A. (2022): Moving the center: adapting the toolbox of growth model research to emerging capitalist economies, Institute for International Political Economy (IPE) Working Paper, No 188/2021.

Morlin, G.S., Passos, N., Pariboni, R. (2022): Growth theory and the growth model perspective: insights from the supermultiplier, in: *Review of Political Economy*, advance access, doi: https://doi.org/10.1080/09538259.2022.2092998.

MSPI (2021): Ministry of Statistics and Programme Implementation – India, URL: https://www.mospi.gov.in/ (accessed 27 December 2021).

Nassif, A., Feijo, C., Araújo, E. (2016): The BRICS’s economic growth performance before and after the international financial crisis, in: *International Journal of Political Economy*, 45(4), 294–314.

Nölke, A., May, C., Mertens, D., Schedelik, M. (2022): Elephant limps, but jaguar stumbles: unpacking the divergence of state capitalism in Brazil and India through theories of capitalist diversity, in: *Competition & Change*, 26(3–4), 311–333.
Ocampo, J.A. (2011): Macroeconomy for development: countercyclical policies and production sector transformation, in: *Cepal Review*, 104, 7-35.

OEC (2022): Economic Complexity Rankings, URL: https://oec.world/en/rankings/eci/hs6/hs96 (accessed 24 June 2022).

OECD (2022): Investment by Sector, URL: https://data.oecd.org/gdp/investment-by-sector.htm (accessed 1 April 2022),

Passos, N., Morlin, G. (2022): Growth models and comparative political economy in Latin America, in: *Revue de la Régulation*, 33(2), doi: https://doi.org/10.4000/regulation.21444.

Prante, F.J., Hein, E., Bramucci, A. (2022): Varieties and interdependencies of demand and growth regimes in finance-dominated capitalism: a post-Keynesian two-country stock-flow consistent simulation approach, in: *Review of Keynesian Economics*, 10(2), 262-288.

Schedelik, M., Nölke, A., Mertens, D., May, C. (2021): Comparative capitalism, growth models and emerging markets: the development of the field, *New Political Economy*, 26(4), 514-526.

Serrano, F. (1995): Long-period effective demand and the Sraffian supermultiplier, in: *Contributions to Political Economy*, 14(1), 67-90.

Serrano, F., Summa, R. (2015): Aggregate demand and the slowdown of Brazilian economic growth in 2011-2014, in: *Nova Economia*, 25(spe), 803-833.

Serrano, F., Summa, R. (2022): Distributive conflict and the end of Brazilian economy's 'Brief Golden Age', Institute for International Political Economy (IPE) Working Paper, No 186/2022.

Smid, T. (2021): Russia's Fortress Strategy is not for free, Atradius, URL: https://group.atradius.com/publications/economic-research/russia-fortress-strategy-is-not-for-free.html.

Solt, F. (2020): Measuring income inequality across countries and over time: the standardized World Income Inequality Database, in: *Social Science Quarterly*, 101(3), 1183-1199.

Stockhammer, E., Durand, C., List, L. (2016): European growth models and working class restructuring: an international post-Keynesian political economy perspective, in: *Environment and Planning A*, 48(9), 1804-1828.

Stockhammer, E., Ali, S.M. (2018): Varieties of capitalism and post-Keynesian economics on Euro crisis, in: *Wirtschaft und Gesellschaft*, 44(3), 349-370.

Tooze, A. (2022): Chartbook #91: What if Putin's war regime turns to MMT? [Substack newsletter], URL: https://adamtooze.substack.com/p/chartbook-91-what-if-putins-war-regime (accessed 15 July 2022).

World Bank (2021): World Bank Data, URL: https://data.worldbank.org/ (accessed 20 December 2021).

World Bank (2022): World Integrated Trade Solution. Trade stats - country at a glance, URL: https://wits.worldbank.org/datadownload.aspx?lang=en (accessed 19 July 2022).

Yifu, L.J., Wang, X. (2022): Dual circulation: a new structural economics view of development, in: *Journal of Chinese Economic and Business Studies*, 20(4), 303-322.
# APPENDIX

**Consumer Credit (CC)** time-series are taken from the BIS database of Credit to the non-financial sector (BIS 2021) as ‘Credit to Households and NPISHs from all sectors at Market value, adjusted for breaks’. We followed the methodology from Girardi/Pariboni (2016) to calculate real net flows: we took end-of-period stocks in each year (monthly series) and transformed the series in first differences to obtain net flows. Finally, we deflated these net flows using the GDP implicit price deflators (IMF 2021a; World Bank 2021).

Not all countries had complete data for the period. For China, we assumed the share of household credit to private non-financial sector credit remained stable from 2001 until 2006 (first available year). For India, the available data starts in 2007, likely affected by the global financial bubble. We assume for the prior years a stable share of household credit to private non-financial sector credit equal to the average of 2007–2019. The credit to households series for India during the years before 2012 in the BIS data includes housing loans, which is not specified for the other series but might also be included. The presence of housing loans in the series might produce an upward bias in consumer credit given that this might be already included in the series of household investment. Nevertheless, we believe it was the best available proxy for consumer credit and this distortion should not substantially change our main results for a descriptive analysis.

**General Government Investment ($I_G$)** series were constructed using different sources. For Brazil, Russia and China we applied the coefficients of Investment by sector as percentage of Gross Fixed Capital Formation (GFCF) from the OECD Data base (OECD 2022) to data of Total Gross Fixed Capital Formation at constant prices from the World Bank and the IMF (IMF 2021a; World Bank 2021). For Russia, the OECD source did not have the complete period. We calculated the coefficients for the period 2001–2010, assuming the average proportion of government investment to total investment for the period 2011–2019 remained constant.

For India, we used the series General Government Gross Fixed Capital Formation (GG GFCF) for the period 2011–2019 from National Accounts data (MSPI 2021) and for the previous years between 2001–2010 we assumed the ratio between GG GFCF and Gross Capital Formation of the public sector remained stable to the level of 2011.

**Household Investment ($I_H$)** time-series were constructed using different sources. For Brazil, Russia and China, we followed the same procedure as for Government Investment. We applied the coefficients of Investment by sector as percentage of Gross Fixed Capital Formation (GFCF) from the OECD Data base (OECD 2021) to data of Total Gross Fixed Capital Formation at constant prices from the World Bank and the IMF (IMF 2021a; World Bank 2021). Again, for Russia the source did not have the complete period, so we calculated the coefficients for the period 2001–2010 assuming that the average of the period 2011–2019 remained constant.

For India, we used the series Household Gross Fixed Capital Formation (HH GFCF) for the period 2011–2019 from National Accounts data (MSPI 2021) and for the previous years between 2001–2010 we assumed the ratio between HH GFCF and Household Gross Capital Formation remained stable at the value of 2011.
**Household consumption out of disposable income (C<sub>H</sub>)** was calculated subtracting
Consumer Credit (CC) from Total consumption (C).

**Corporate investment (I<sub>C</sub>)** was calculated subtracting Government Investment (I<sub>G</sub>) and
Household Investment (I<sub>H</sub>) from Total investment (I).