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ERROR: type should be string, got "https://doi.org/10.1590/1980-549720230058\nRevista Brasileira de Epidemiologia\nwww.scielo.br/rbepid\nORIGINAL ARTICLE www.scielo.br/rbepid https://doi.org/10.1590/1980-549720230058\nRevista Brasileira de Epidemiologia Detection of sexually transmitted infections \namong transvestites and transsexual women \nin prison in the metropolitan region of \nRio de Janeiro, Brazil\nTestagem rápida de infecções sexualmente transmissíveis \nentre travestis e mulheres transexuais em situação de \nprisão na Região Metropolitana do Rio de Janeiro, Brasil\nCarlos Renato Alves-da-SilvaI,II\n, Claudia BonanI\n, \nSaint Clair dos Santos Gomes JuniorI\n, Rosilene Santarone VieiraI Testagem rápida de infecções sexualmente transmissíveis \nentre travestis e mulheres transexuais em situação de \nprisão na Região Metropolitana do Rio de Janeiro, Brasil\naI,II\n, Claudia BonanI\n, IFundação Oswaldo Cruz. Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira \n– Rio de Janeiro (RJ), Brazil. IIState Secretariat for Penitentiary Administration, LGBTI Health and Citizenship Support Division \n – Rio de Janeiro (RJ), Brazil. ABSTRACT Objective: To evaluate the seropositivity rate of rapid tests for HIV, syphilis and hepatitis B and C among transvestites and \ntransgender women (transfeminine persons) inmates in the metropolitan region of Rio de Janeiro, analyzing the results based on \nsociodemographic, prison profile and access to health technologies to prevent sexually transmitted infections (STIs). Methods: Cross-\nsectional census-type study carried out with transfeminine in eleven male prisons in Rio de Janeiro, between the months of April \nand June 2021. Results: The detection rates found were 34.4% for HIV, and 48.9% for syphilis, and 0.8% for type B and C hepatitis. Seropositivity for more than one infection was verified in 25.4% of participants, and HIV/syphilis was the most prevalent. An increase \nin the level of education (p=0.037) and having a steady partner in prison (p=0.041) were considered protective factors for STIs in \nthis population. Difficulties were identified in accessing STI prevention technologies, such as male condoms, lubricating gel, rapid \ntests, and prophylactic antiretroviral therapies for HIV. Conclusion: HIV and syphilis seropositivity rates were high, but within the \nprofile found in this population in other studies inside or outside prisons. The data found indicates the need to incorporate effective \nstrategies for access to health technologies for the prevention of STIs. The scarcity of scientific publications containing epidemiological \ndata on STIs in the transfeminine prison population limited deeper comparisons of the results obtained in this study. nder persons. Prisons. Syphilis. Hepatitis. HIV seropositivity. Keywords: Transgender persons. Prisons. Syphilis. Hepatitis. HIV seropositivity. CORRESPONDING AUTHOR: Carlos Renato Alves-da-Silva. Avenida Rui Barbosa, 716, Flamengo, CEP: 22250-020, Rio de Janeiro (RJ), Brasil. E-mail: carlos-renato. INTRODUCTION It is observed that transvestites \nand transsexual women, also defined as transfeminine \npeople, make up a target group for greater discrimination \nand violence, both due to the difficulties experienced in ed-\nucational establishments and the lack of opportunities in \nthe formal job market and due to their very existence, since \nthey live in the country with the highest transgender death \nrate in the world6. Some studies show the conditions of deprivation of lib-\nerty of transfeminine women, revealing scenarios of trans-\nphobic practices and violations of these people’s rights to \ncitizenship and health7,8, such as respect for the use of their \nsocial names, the use of clothes and accessories according \nto their gender, the guarantee of hormone treatment, ac-\ncess to and continuity of their education, among other pro-\nvisions described in the National Policy for Comprehensive \nHealth of the LGBT Population9 and in the legislation that \nprovides for the treatment of these people in the Brazilian \npenitentiary system. The spread of sexually transmitted infections (STIs) \nin prisons is one of the problems to be tackled by prison \nhealth teams, faced with environments with conditions \nconducive to unsafe sexual practices, as described in na-\ntional and international studies15-18. However, it is neces-\nsary to know and analyze this reality in order to promote \neffective actions to reduce the risks of STIs, such as com-\nbined strategies, in order to improve access to condoms, \nHIV prophylactic therapies, rapid STI testing regularly ev-\nery six months, among other actions recommended by the \nMinistry of Health (MoH)19. The National Penitentiary Department (Departamen-\nto Penitenciário Nacional – Depen) recently published a \npopulation survey, referring to the second half of 2022, \nrecording that, in Brazil, there were 830 thousand peo-\nple deprived of liberty (PDL), however, of this total, \n650 thousand were serving their criminal sentences in \nprison units and the rest of them were under house \narrest. This census also revealed that around 12,500 \nself-declared LGBTI people were imprisoned in Brazil-\nian prison units1, of which approximately 1,600 were \ntransvestites and transsexual women. The methodology \nadopted in this survey to quantify this population was \nbased on completing a questionnaire sent by Depen \nto the bodies of all federative units responsible for the \npenitentiary system. INTRODUCTION sexual women being deprived of liberty, occupying second \nplace in the national ranking, in absolute number, in custo-\ndy of the transfeminine population10. The majority of prison spaces in Brazil are reported due \nto prison overcrowding, inadequate physical structure, de-\nficiencies in human resources, and specialized materials \nfor health assistance and promotion, among other situa-\ntions mentioned in publications that highlighted the poten-\ntial of these spaces as human rights violators1,2. A technical document, published by the Ministry of \nWomen, Family and Human Rights, in 2020, containing the \nnational diagnosis of the criminal treatment of this popu-\nlation in Brazil, revealed that the imprisonment of trans-\nfeminine women in our country occurs, for the most part, \nin prisons aimed at male inmates and, in large part, at an \nalarming disproportion, with them being a minority of the \ntotal number of people imprisoned11. These conditions \ndrastically affect the overall health of these people, wheth-\ner due to possible psychological damage or harm to their \nphysical health. Imprisonment conditions of specific groups, such as \nwomen, the aged, indigenous people, foreigners, people \nwith disabilities, and people self-declared as lesbian, gay, \nbisexual, transgender, and intersex (LGBTI), are marked \nby the exacerbation of these violations, in the face of \nnon-compliance of the duty of care inherent to the vulner-\nabilities established by each specific group, as defined in \nsome national and international regulations3-5. Health care access and quality in the Brazilian pen-\nitentiary system is one of the greatest challenges to be \nfaced. The National Policy for Comprehensive Penitentiary \nHealth Care (Política Nacional de Atenção Integral à Saúde \nPenitenciária – Pnaisp) has emerged as a powerful manage-\nment strategy to resolve these obstacles in several Brazilian \nstates. This policy aims to guarantee access to comprehen-\nsive primary health care by PDL12,13. The implementation of \nPnaisp, in RJ, intensified, from January 2022, with the acces-\nsion of the capital of Rio de Janeiro, however, during the \nperiod if this study, only four units, of the total of 46 prison \nunits with outpatient care, had Pnaisp teams14. Social vulnerability among people from the LGBTI popu-\nlation is not homogeneous. https://doi.org/10.1590/1980-549720230058\nRevista Brasileira de Epidemiologia\nwww.scielo.br/rbepid\nORIGINAL ARTICLE silva@fiocruz.br HOR: Carlos Renato Alves-da-Silva. Avenida Rui Barbosa, 716, Flamengo, CEP: 22250-020, Rio de Janeiro (RJ), Brasil. E-mail: carlos-renato CONFLICT OF INTERESTS: nothing to declare HOW TO CITE THIS ARTICLE: Alves-da-Silva CR, Bonan C, Gomes Junior SCS, Vieira RS. Detection of sexually transmitted infections among transvestites and \ntranssexual women in prison in the metropolitan region of Rio de Janeiro, Brazil. Rev Bras Epidemiol. 2023; 26: e230058. https://doi.org/10.1590/1980-549720230058 This is an open article distributed under the CC-BY 4.0 license, which allows copying and redistribution of the material in any format and for any purpose as long as \nthe original authorship and publication credits are maintained. This is an open article distributed under the CC-BY 4.0 license, which allows copying and redistribution of the material in any format and for any purpose as long as \nthe original authorship and publication credits are maintained. Received on: 07/12/2023\nReviewed on: 09/27/2023\nAccepted on: 10/04/2023 Travestis e mulheres transexuais presas e as infecções sexualmente transmissíveis. Rev Bras Epidemiol. 2023; 26: e230058\n1 https://doi.org/10.1590/1980-549720230058 www.scielo.br/rbepid INTRODUCTION This study aimed to evaluate the isolated and com-\nbined seropositivity rate for HIV, syphilis, and type B and \nC hepatitis, through the use of rapid tests among trans-\nfeminines deprived of liberty, in the Metropolitan Region \nof Rio de Janeiro and to identify potential associated fac-\ntors, considering sociodemographic characteristics, pris-\non conditions and access to health technologies for the \nprevention of STIs, such as male condoms, lubricants, and \nrapid tests. Travestis e mulheres transexuais presas e as infecções sexualmente transmissíveis. Rev Bras Epidemiol. 2023; 26: e230058\n2 Study design This is a cross-sectional study with a quantitative ap-\nproach in a census format, that is, involving the entire \nself-declared adult transfeminine population deprived of \nliberty in the Metropolitan Region of Rio de Janeiro. The prison population in the state of Rio de Janeiro (RJ), \nrecorded in the same publication, revealed that around 48 \nthousand people were in RJ’s prisons, among which 579 de-\nclared themselves LGBTI, with 176 transvestites and trans- www.scielo.br/rbepid https://doi.org/10.1590/1980-549720230058 Participants Transfeminine PDL were previously selected, by con-\nsulting the Penitentiary Identification System (Sistema de \nIdentificação Penitenciária – Sipen) of the State Secretariat \nfor Penitentiary Administration (Secretaria de Estado de Ad-\nministração Penitenciária – Seap-RJ), a nominal database up-\ndated daily, through information about the people entering \nthe RJ penitentiary system. Access to this database was pos-\nsible, as one of the researchers is a public server pharmacist \nwho held the position of director of the LGBTI Health and \nCitizenship Support Division at Seap (RJ). The study also ad-\nopted the “snowball” method, to identify possible transfem-\ninines not included in Sipen20, when the selected participant \nreported the existence of transfeminines not registered in \nthis database, being subsequently invited to participate in \nthe research. All people who declared themselves trans-\nfeminine were included, using terms such as transvestite, \ntranny, transsexual woman, trans, transex, woman or any \nreference to transfeminine identity. Tran feminine women \nwho had been in prison for less than six months were ex-\ncluded, the time period recommended by the MoH for rap-\nid testing among transvestites and transsexual women in \nprison19. Interviewees who did not have the results of rapid \ntests in the last six months in their medical records and who \nexpressed opposition to carrying out rapid testing after the \ninterview were also excluded. Background Table 1. Socioeconomic, demographic, and prison \ncharacteristics of transfeminines deprived of liberty in \nthe Metropolitan Region of Rio de Janeiro, Brazil, 2021. Eleven male prisons that held transfeminine people \nduring the study period, with their respective locations, \ntotal prison population, number of transfeminine partici-\npants, and the percentage rate between participants and \nthe total prison population (Chart 1). It is clarified that, \nduring the study period, there were no transfeminine pris-\noners in female prison units and that the selected units are \nclassified as neutral, to which LGBTI people are sent in RJ. the Metropolitan Region of Rio de Janeiro, Brazil, 2021. N sample: 131 people. Characteristic – socioeconomic\nn\n%\nAge range (years)\n18–29\n85\n64.9\n30–39\n34\n25.9\n40 and over\n12\n9.2\nRace/color\nNon-white\n113\n86.3\nWhite\n18\n13.7\nEducation\nIncomplete elementary school \n65\n49.6\nComplete elementary school\n4\n3.0\nIncomplete High School\n37\n28.3\nComplete High School \n19\n14.5\nComplete and incomplete higher education\n6\n4.6\nSexual orientation\nHeterosexual\n123\n93.9\nBisexual\n8\n5.3\nPaid activity before imprisonment\nWas not working\n23\n17.6\nSex worker\n55\n42.0\nFormal or informal employment (beauty, \nfashion, food, and others)\n53\n40.4\nUse of illicit substances before the current arrest\nYes\n106\n80.9\nNo\n25\n19.1\nCharacteristic – prison\nLength of stay in prison (years)\nUp to 1 \n59\n45.0\nMore than 1 \n72\n55.0\nProportion between transfeminines and men in the cell\n1/1 to 1/10\n20\n15.3\nOver 1/10\n111\n84.7\nSteady partner in prison\nYes\n77\n58.8\nNo\n54\n41.2\nNumber of partners in prison\nUp to 1\n22\n16.8\nMore than 1\n109\n83.2\nRegistered visitor\nYes\n65\n49.6\nNo\n66\n50.4\nLabor activity in prison\nYes\n23\n17.6\nNo\n108\n82.4\nEducation in prison\nYes\n26\n19.8\nNo\n105\n80.2 RESULTS 151 self-declared transfeminine people were identified, \nhowever 13 people had been in prison for less than six \nmonths and seven did not consent to rapid tests for the \nSTIs analyzed in this study. The database identified 92 of \nthe selected transfeminines (60.1%), the other interview-\nees were reached by the snowball method. Recruitment of \ninterviewees was carried out in 11 prison units (Chart 1), \nand it was found that four prison units had health teams, \nthrough the implementation of Pnaisp, and five prison \nunits had prison schools. Data relating to the sociodemo-\ngraphic and prison profile of the interviewees were collect-\ned from a sample composed of 131 transfeminines (Table \n1), demonstrating that 85 were young adults under the age \nof 29 (64.9%), and, not unlike the Brazilian prison popula-\ntion, the sample consisted of 113 black and brown women \n(86.3%). Education demonstrated that only 25 interviewees \nmanaged to complete high school education (19.1%) and 65 \ndid not complete elementary education (49.6%). The major-\nity, that is, 123 transfeminines, revealed that they were het-\nerosexual (93.9%). Prostitution was the most cited source \nof income for 55 of the interviewees (42.0%), and the use of \nillicit substances before imprisonment was revealed by 106 \ntransfeminines (80.9%). The situation inside prisons also \nrevealed that 72 interviewees had been in prison for more \nthan a year (55.0%) and 111 were incarcerated with cisgen-\nder and heterosexual men, in a proportion greater than \n1/10 (84.7%). It was revealed that 109 of the interviewees \nhad relationships with two or more partners throughout \ntheir imprisonment (83.2%) and, at the time of the inter-\nviews, 77 revealed that they were in a steady relationship \n(58.8%). Among those interviewed, 66 did not have regis-\ntered visitors (50.4%) and few were involved, or had been \ninvolved in resocializing practices, with records that 23 \nwere working or have worked in prisons (17.6%) and only \n26 had the opportunity to enter prison schools (19.8%). Data analysis Collected data were analyzed using the statistical pro-\ngram Jasp (Jeffrey’s Amazing Statistics Program) version \n0.17.1.0, evaluating percentage analyses of sociodemo-\ngraphic, prison and access to health technology variables, \nas well as the results for each rapid STI test evaluated. The variable called “positive result in at least one of the \nSTI tests” was created to evaluate associations with socio-\ndemographic variables, prison conditions, and access to \nhealth care technologies dichotomized to find statistical \nsignificance. The chi-square test was applied in all analyses, \nwith a statistical significance level of 5% (p<0.05). The results on access by transfeminines to male con-\ndoms, lubricants, and rapid testing for STIs (Table 2) \ndemonstrated that 29 had difficulty accessing male con-\ndoms (22.1%) and 95, lubricants (72.5%). It was found that \n37 subjects were on antiretroviral therapy (ART) for HIV \n(28.2%), thus, 94 health records were consulted to evalu-\nate the performance of rapid testing (71.8%), in which 41 Study variables The sociodemographic variables analyzed were: age \ngroup in years (from 18 to 29, from 30 to 39, over 40); race/\ncolor (white, non-white); education (incomplete and com-\nplete elementary school, incomplete and complete high \nschool, higher education); sexual orientation (heterosexu-\nal, bisexual); paid activity before prison (not working, sex \nworker, formal or other informal employment); and use \nof illicit substances before arrest (yes, no). The prison vari-\nables were: length of stay in prison (up to one year, over \none year); proportion between transfeminines and men in \nthe same cell (from 1/1 to 1/10, over 1/10); steady partner \nin prison (yes, no); number of partners in prison (up to one, \nmore than one); registered visitor (yes, no); work activity \nin prison (yes, no); schooling in prison (yes, no). The health \ncare technologies were: access to male condoms (yes, no); \naccess to lubricant (yes, no); carrying out rapid tests in the \nlast six months (yes, no); and rapid test positivity (yes, no). The variables analyzed were considered factors associated \nwith STIs, with some being used in some studies15,21. Data collect Data collect was carried out in the period between April \nand June 2021, using a structured questionnaire containing \nquestions with closed answers and selecting people who \ndeclared themselves transvestites or transsexual women. This information allowed to describe the sociodemographic \nand prison profiles, as well as access to health technologies \nused to prevent STIs: male condoms, lubricants, and rapid \ntesting. However, the variables related to length of stay and \nexistence of registered visitors were extracted from Sipen, \na strategy adopted to improve data accuracy. Data relating \nto the results of serological tests for HIV, syphilis, and hep-\natitis B and C in the last six months, as recommended by \nthe MoH, were extracted from the health records of the in-\nterviewees. When this information was not available, rapid \ntests were requested to be carried out by the prison unit’s \nhealth team, and no situation of rapid test shortages, inac-\ncessibility to health records or refusal by health profession- Travestis e mulheres transexuais presas e as infecções sexualmente transmissíveis. Rev Bras Epidemiol. 2023; 26: e230058\n3 www.scielo.br/rbepid https://doi.org/10.1590/1980-549720230058 36338520.2.0000.5269, authorized by the Criminal Execu-\ntions Court of the Court of Justice of the state of Rio de \nJaneiro and by the School of Penitentiary Management of \nSeap-RJ, through Process SEI-21/087/000987/2019. All eth-\nical and safety issues involved in research with PDL were \nobserved, including informed consents signed by all study \nparticipants. All interviewees with positive rapid test re-\nsults were assisted by the prison unit’s health teams, be-\ning included in institutional clinical protocols for the treat-\nment of STIs. als to carry out rapid tests were observed. These profes-\nsionals are responsible for inputting the results in medical \nrecords, as well as confirming the results, through confir-\nmatory laboratory tests, according to standardized opera-\ntional procedures established for each infection. The rapid \ntests recorded in the medical records and carried out after \nthe interviews were based on immunochromatography \ntechnology, using whole blood finger-prick test. All rapid tests were made available to prison units in Rio \nde Janeiro, through the State Secretariat of Health of Rio \nde Janeiro (Secretaria de Estado da Saúde do Rio de Janeiro \n– SES-RJ) and by the municipal Health Departments of the \nrespective cities where the prison units were located, and \nwhich had health teams hired by Pnaisp. s e mulheres transexuais presas e as infecções sexualmente transmissíveis. Rev Bras Epidemiol. 2023; 26: e230058\n4 Ethical aspects %\nPresídio Evaristo de Moraes*)\nRio de Janeiro\n3,600\n36\n1.0\nCadeia José Antônio Costa Barros† \nRio de Janeiro\n720\n11\n1.5\nInstituto Benjamim de Moraes Filho\nRio de Janeiro\n1,964\n17\n0.9\nPresídio Nelson Hungria \nRio de Janeiro\n830\n8\n1.0\nInstituto Plácido de Sá Carvalho*\nRio de Janeiro\n960\n10\n1.0\nPresídio Cotrim Neto† \nJaperi\n1,241\n6\n0.5\nPresídio Milton Dias Moreira\nJaperi\n1,725\n4\n0.2\nCadeia Tiago Silva Teles*,† \nSão Gonçalo\n1,303\n8\n0.6\nCadeia Juíza Patrícia Acioli* \nSão Gonçalo\n1,583\n8\n0.5\nPresídio Edgar Costa† \nNiterói\n524\n9\n1.7\nPresídio Romeiro Neto† \nMagé\n1,303\n14\n1.1\nTotal\n15,753\n131\n0,8\n\u0003\nSource: Seap-RJ, 2021. *existence of the Pnaisp health team; †no prison school. Name of prison unit\nMunicipality\nTotal PDL\nTrans. %\nPresídio Evaristo de Moraes*)\nRio de Janeiro\n3,600\n36\n1.0\nCadeia José Antônio Costa Barros† \nRio de Janeiro\n720\n11\n1.5\nInstituto Benjamim de Moraes Filho\nRio de Janeiro\n1,964\n17\n0.9\nPresídio Nelson Hungria \nRio de Janeiro\n830\n8\n1.0\nInstituto Plácido de Sá Carvalho*\nRio de Janeiro\n960\n10\n1.0\nPresídio Cotrim Neto† \nJaperi\n1,241\n6\n0.5\nPresídio Milton Dias Moreira\nJaperi\n1,725\n4\n0.2\nCadeia Tiago Silva Teles*,† \nSão Gonçalo\n1,303\n8\n0.6\nCadeia Juíza Patrícia Acioli* \nSão Gonçalo\n1,583\n8\n0.5\nPresídio Edgar Costa† \nNiterói\n524\n9\n1.7\nPresídio Romeiro Neto† \nMagé\n1,303\n14\n1.1\nTotal\n15,753\n131\n0,8\n\u0003\nSource: Seap-RJ, 2021. *existence of the Pnaisp health team; †no prison school. p\nJ,\n*existence of the Pnaisp health team; †no prison school. profile for hepatitis types B and C proved to be identical \nfor both infections, since, throughout this study, it was ver-\nified that two interviewees were already undergoing treat-\nment for these infections, one for hepatitis B (0.8%) and \none for hepatitis C (0.8%). No positive results were found \nin the 55 medical records with records of testing for hep-\natitis in the last six months (42.0%), as well as no positive \nresults in the 75 rapid tests carried out after the interviews \n(57.2%). The serological STIs profile through rapid testing \nof the interviewees revealed that 71 had positive results \nfor at least one of the four STIs tested in this study (54.2%). Ethical aspects The research was duly approved by the Research \nEthics Committee of the Fernandes Figueira National In-\nstitute of Women, Children and Adolescent Health (Insti-\ntuto Nacional de Saúde da Mulher, da Criança e do Adoles-\ncente Fernandes Figueira – IFF-Fiocruz), under CAAE No. Travestis e mulheres transexuais presas e as infecções sexualmente transmissíveis. Rev Bras Epidemiol. 2023; 26: e230058\n4 www.scielo.br/rbepid https://doi.org/10.1590/1980-549720230058 Chart 1. Map of the Metropolitan Region of Rio de Janeiro and the selected prison units with their respective population \ndata, 2021. Name of prison unit\nMunicipality\nTotal PDL\nTrans. %\nPresídio Evaristo de Moraes*)\nRio de Janeiro\n3,600\n36\n1.0\nCadeia José Antônio Costa Barros† \nRio de Janeiro\n720\n11\n1.5\nInstituto Benjamim de Moraes Filho\nRio de Janeiro\n1,964\n17\n0.9\nPresídio Nelson Hungria \nRio de Janeiro\n830\n8\n1.0\nInstituto Plácido de Sá Carvalho*\nRio de Janeiro\n960\n10\n1.0\nPresídio Cotrim Neto† \nJaperi\n1,241\n6\n0.5\nPresídio Milton Dias Moreira\nJaperi\n1,725\n4\n0.2\nCadeia Tiago Silva Teles*,† \nSão Gonçalo\n1,303\n8\n0.6\nCadeia Juíza Patrícia Acioli* \nSão Gonçalo\n1,583\n8\n0.5\nPresídio Edgar Costa† \nNiterói\n524\n9\n1.7\nPresídio Romeiro Neto† \nMagé\n1,303\n14\n1.1\nTotal\n15,753\n131\n0,8\n\u0003\nSource: Seap-RJ, 2021. *existence of the Pnaisp health team; †no prison school. Chart 1. Map of the Metropolitan Region of Rio de Janeiro and the selected prison units with their respective population \ndata, 2021. Chart 1. Map of the Metropolitan Region of Rio de Janeiro and the selected prison units with their respective population \ndata, 2021. Map of the Metropolitan Region of Rio de Janeiro and the selected prison units with their respective\n1 Chart 1. Map of the Metropolitan Region of Rio de Janeiro and the selected prison units with their respective population \ndata, 2021. Chart 1. Map of the Metropolitan Region of Rio de Janeiro and the selected prison units with their respective population \ndata, 2021. Name of prison unit\nMunicipality\nTotal PDL\nTrans. Travestis e mulheres transexuais presas e as infecções sexualmente transmissíveis. Rev Bras Epidemiol. 2023; 26: e230058\n5 Ethical aspects Among these 71 transfeminines, it was found that 19 pre-\nsented more than one positive result in the rapid tests car-\nried out (26.8%), in which the most prevalent combined se- interviewees were tested for HIV (43.6%), among whom, \nthree were positive for the rapid HIV test (2.3%). Among \nthe 53 who underwent rapid testing after the interview, \nfive tested positive for HIV (9.4%). The total of these results \nrevealed that 45 of the transfeminines tested positive for \nHIV (34.4%). At the time of the interview, none were ob-\nserved undergoing treatment for syphilis, and the syphilis \ntesting profile revealed that 56 transfeminines had records \nof testing in the last six months (42.8%). The results of the \nrapid test for syphilis in the last six months from the date \nof the interview revealed that 56 were positive for this in-\nfection (69.6%), while 25 of them had positive results after \nthe interview (33.3%). Thus, among them, it was found that \n64 people were positive for syphilis (48.9%). The testing Travestis e mulheres transexuais presas e as infecções sexualmente transmissíveis. Rev Bras Epidemiol. 2023; 26: e230058\n5 www.scielo.br/rbepid https://doi.org/10.1590/1980-549720230058 ropositivity was HIV/syphilis, which accounted for 18 of the \ncases (94.7%). The associations made between the results \nof rapid tests and sociodemographic, prison characteristics \nand access to health care technologies used to prevent STIs \n(Table 3) demonstrated that, among the 46 transfeminine \nwomen over the age of 29, 31 had positive results for STIs \n(67.4%), compared to 85 aged between 18 and 29 years, \nin which there were 40 positive results (47.1%); (p=0.026). 44 positive tests for STIs were revealed among the 70 in-\nterviewees who only attended elementary school (62.8%), \ncompared to 27 positive results among the 61 who attend-\ned high school and higher education (44.3%); (p=0.033). Of \nthe 54 who declared not having a steady partner, 35 had \nmore positive results for STIs (64.8%), compared to the 36 \npositive results among the 77 who reported having emo-\ntional stability (46.8%); (p=0.041). Table 2. Access to health technologies associated with \nsexually transmitted infections and rapid test results \namong transfeminine women deprived of liberty in the \nMetropolitan Region of Rio de Janeiro, Brazil, 2021. g\np\ny\nMetropolitan Region of Rio de Janeiro, Brazil, 2021. Ethical aspects Characteristic\nn\n%\nAccess to male condoms\nYes\n29/131\n22.1\nNo \n102/131\n77.9\nAccess to lubricants\nYes\n95/131\n72.5\nNo \n36/131\n27.5\nCarrying out the rapid test for HIV\nNo, subject already undergoing treatment\n37/131\n28.2\nRegistered in the medical record, \nwith a positive result \n3/131\n2.3\nRegistered in the medical record, \nwith a negative result\n38/131\n29.0\nCarried out during the interview, \nnegative result\n48/131\n36.6\nCarried out during the interview, \npositive result\n5/131\n3.9\nCarrying out the rapid test for syphilis\nNo, subject already undergoing treatment\n0/131\n0.0\nRegistered in the medical record, \nwith a positive result \n39/131\n29.8\nRegistered in the medical record, \nwith a negative result\n17/131\n13.0\nCarried out during the interview, \nnegative result\n50/131\n38.1\nCarried out during the interview, positive result\n25/131\n19.1\nCarrying out the rapid test for hepatitis B\nNo, subject already undergoing treatment\n1/131\n0.8\nRegistered in the medical record, \nwith a positive result \n0/131\n0.0\nRegistered in the medical record, \nwith a negative result\n55/131\n42.0\nCarried out during the interview, negative result\n75/131\n57.2\nCarried out during the interview, positive result\n0/131\n0.0\nCarrying out the rapid test for hepatitis C\nNo, subject already undergoing treatment\n1/131\n0.8\nRegistered in the medical record, \nwith a positive result \n0/131\n0.0\nRegistered in the medical record, \nwith a negative result\n55/131\n42.0\nCarried out during the interview, negative result\n75/131\n57.2\nCarried out during the interview, positive result\n0/131\n0.0\nPositive result in at least one of the STI tests\nYes\n71/131\n54.2\nNo\n60/131\n45.8\nFrequency of positive results for STIs\nOnly one STI\n52/71\n73.2\nHIV/syphilis\n18/71\n25.4\nHIV/hepatitis B\n1/71\n1.4\nSTI: sexually transmitted infections; HIV: human immunodeficiency virus. Travestis e mulheres transexuais presas e as infecções sexualmente transmissíveis. Rev Bras Epidemiol. 2023; 26: e230058\n6 Travestis e mulheres transexuais presas e as infecções sexualmente transmissíveis. Rev Bras Epidemiol. 2023; 26: e230058\n7 DISCUSSION The observation that more than half of the medical re-\ncords checked did not have records of regular rapid test-\ning for STIs demonstrated the fragility of the health care \noffered to this population. The detection rate for HIV was \nsimilar when compared with other national studies with \nthis population outside prisons22,23 and with a study carried \nout in Mexico24. The majority of interviewees were young \nadults, black or brown, with low education, and with pros-\ntitution as their main source of income before imprison-\nment. A higher level of education, having a steady partner \nin prison, and younger age proved to be protective factors \nfor the STIs analyzed. The absence of records of rapid testing in the medical \nrecords can be translated into the difficulties in accessing \nhealth services reported in the various studies with the \ntransfeminine population, both due to the technical lack of \npreparation of the health professionals who assist these \npeople and the Brazilian penitentiary system itself, an en-\nvironment that enhances violations of the health rights of \nthe population deprived of liberty25,26. The HIV detection rate of 34.4% found in this study, \nwas similar to that found in 2012 in Mexico City24, where \n31.9% of incarcerated transfeminines tested positive for \nHIV in rapid tests. In the systematic review with meta-anal-\nysis published in 201827, which searched for studies on \nSTIs among key incarcerated populations, only two articles \nwere found, one carried out in the United States of America \nand the other in Argentina, signaling the scarcity of studies \nwith this population in the prison environment, as already \nrecorded in research published in 201928. The few epide-\nmiological data published on HIV and the lack of data for \nsyphilis and hepatitis B and C in the transfeminine popu-\nlation in prisons were limiting factors for comparative pur-\nposes. The sociodemographic and prison profile demon-\nstrated a known situation in relation to transfeminines in \nprison7,29,30, a reality composed of factors associated with https://doi.org/10.1590/1980-549720230058 www.scielo.br/rbepid Table 3. Association between the results of rapid testing for sexually transmitted infections and sociodemographic, \nprison characteristics and access to health technologies among transfeminine women deprived of liberty in the \nMetropolitan Region of Rio de Janeiro, Brazil, 2021. Table 3. REFERENCES trans necropolitics, such as early family separation, diffi-\nculties in remaining in school environments and accessing \nthe labor market, prostitution on the streets, surrounded \nby drugs and theft, facilitating conditions that lead them \nto prison or death in the broadest sense31. The conditions \nof incarceration observed in this study, in which transfem-\ninines share spaces with cisgender and heterosexual men, \nare comparable to those portrayed in a study carried out in \nthe state of São Paulo8 and in the national report published \nin 2020, situations favorable to sexual practices motivated \nby several factors, both due to economic favoritism and \nemotional relationships11. 1. Sánchez A, Toledo CRS, Camacho LAB, Larouze B. Mortalidade \ne causas de óbitos nas prisões do Rio de Janeiro, Brasil. Cad Saúde Pública 2021; 37(9): e00224920. http://dx.doi. org/10.1590/0102-311X00224920 2. Rangel FM, Bicalho PPG. Superlotação das prisões brasileiras: \noperador político da racionalidade contemporânea. Estud Psicol (Natal) 2016; 21(4): 415-23. https://doi. org/10.5935/1678-4669.20160040 3. Nota Técnica no 9/2020/DIAMGE/CGCAP/DIRPP/DEPEN/MJ: \ncustódia de pessoas LGBTI. Revista Brasileira Execução Penal \n2021; 2(2): 267-91. https://doi.org/10.1234/rbep.v2i2.395 The abandonment of this population in prisons occurs \nin similar proportions to cisgender women. As reported \nin the study carried out in 2019, around 60% of women \nincarcerated in Brazilian prisons did not receive visits from \nfamily members32. Family abandonment among transfem-\ninines begins, in most cases, before prison, a situation that \noften leads them to prostitution, which, despite being a \nrecognized profession in Brazil, cannot be the only fate for \nthese people6,33,34. 4. Rio de Janeiro (Estado). Resolução da Secretaria de Estado \nde Administração Penitenciária (SEAP) no 558, de 29 de \nmaio de 2015. Estabelece diretrizes e normativas para o \ntratamento da população LGBT no sistema penitenciário \ndo estado do Rio de Janeiro [Internet]. Diário Oficial do \nEstado do Rio de Janeiro de 03 de junho de 2015 [cited \non Jul 7, 2023]. Available at: https://www.ioerj.com.br/\nportal/modules/conteudoonline/view_pdf.php?ie=M-\njI1OTQ=&ip=MTQ=&s=YmYzNTM0MjdmMGNkNWUzN-\njhiZWUzM2YyNmMxYjM5ZTY=&directlink=1&Ori-\ngn=WebIndexer 4. Rio de Janeiro (Estado). Resolução da Secretaria de Estado \nde Administração Penitenciária (SEAP) no 558, de 29 de \nmaio de 2015. Estabelece diretrizes e normativas para o \ntratamento da população LGBT no sistema penitenciário \ndo estado do Rio de Janeiro [Internet]. Diário Oficial do \nEstado do Rio de Janeiro de 03 de junho de 2015 [cited \non Jul 7, 2023]. REFERENCES Available at: https://www.ioerj.com.br/\nportal/modules/conteudoonline/view_pdf.php?ie=M-\njI1OTQ=&ip=MTQ=&s=YmYzNTM0MjdmMGNkNWUzN-\njhiZWUzM2YyNmMxYjM5ZTY=&directlink=1&Ori-\ngn=WebIndexer Schooling was considered a protective factor for STIs, \nas demonstrated in other studies with the transfeminine \npopulation outside prisons35, however, the factors that in-\ndicated lower detection rates for these infections among \nyounger transfeminines and those with a steady partner \nrequire further studies to purposes of comparisons and \nextrapolations. The 5.1% refusal to undergo rapid STI tests \nobserved in this study was higher than that found in a \nstudy carried out in 2007 with cisgender women in prison \nin São Paulo, when 3% of them did not agree to partici-\npate in the tests36. It is important to highlight that, often, \nthe refusal to carry out rapid tests for STIs, especially for \nHIV, is directly related to the fear of receiving difficult news \nand the stigma of the repercussion of this result within the \nprison unit, whether among other transfeminine wom-\nen by pride and power37, or by the violence that can be \nperpetrated by their sexual partners. Ther fore, it is nec-\nessary for health teams to have strategies for an appro-\npriate approach, evaluating the psychological and phys-\nical consequences of carrying out rapid STI tests inside \nprisons. More than revealing epidemiological data, it is \nnecessary to understand social dynamics, knowledge, at-\ntitudes, and practices established within Brazilian prisons \nto adopt effective preventive measures aimed at mitigat-\ning the spread of STIs, which make transfeminine women \neven more vulnerable, as assessed in the systematic re-\nview published in 201938. It is essential that prison health \nteams can adopt combined prevention actions to combat \nthese diseases, through educational campaigns on mea-\nsures to prevent and treat these infections, regular rapid \ntesting and incorporation and promotion of access to pro-\nphylactic technologies to the risk of STIs. And, above all, \nthat these actions can accommodate the specific health \ndemands of this population in the prison reality. 5. Alamino FNP, Del Vecchio VA. Os princípios de Yogyakarta \ne a proteção de direitos fundamentais das minorias de \norientação sexual e de identidade de gênero. R Fac Dir Univ \nSão Paulo 2018; 113: 645-68. https://doi.org/10.11606/\nissn.2318-8235.v113i0p645-668 6. Benevides BG. Dossiê. Assassinatos e violências contra \ntravestis e transexuais brasileiras em 2022 [Internet]. Brasília: Associação Nacional de Travestis e Transexuais; \n2023 [cited on Jul 20, 2023]. Available at: https://antrabrasil. files.wordpress.com/2023/01/dossieantra2023.pdf 7. Ferreira GG. Violência, interseccionalidades e \nseletividade penal na experiência de travestis \npresas. Temporalis 2014; 14(27): 99-117. https://doi. DISCUSSION Association between the results of rapid testing for sexually transmitted infections and sociodemographic, \nprison characteristics and access to health technologies among transfeminine women deprived of liberty in the \nMetropolitan Region of Rio de Janeiro, Brazil, 2021. prison characteristics and access to health technologies among transfeminine women deprived of liberty in the \nMetropolitan Region of Rio de Janeiro, Brazil, 2021. Characteristic\nPositive result for STI\nN\nYes\nNo\np-value\nn\n%\nn\n%\nAge range (years)\n19–29\n85\n40\n47.1\n45\n52.9\n0.026\nOver 29\n46\n31\n67.4\n15\n32.6\nRace/color\nWhite\n113\n62\n54.9\n51\n45.1\n0.700\nNon-white\n18\n9\n50.0\n9\n50.0\nEducation\nElementary school\n70\n44\n62.8\n26\n37.2\n0.033\nHigh School and Higher education\n61\n27\n44.3\n34\n55.7\nSexual orientation\nHeterosexual\n123\n66\n53.7\n57\n46.3\n0.627\nBisexual\n8\n5\n62.5\n3\n37.5\nPaid activity before imprisonment\nSex worker\n55\n30\n54.5\n25\n45.5\n0.946\nFormal or other informal employment\n76\n41\n53.9\n35\n46.1\nUse of illicit substances before imprisonment\nYes\n106\n61\n57.5\n45\n42.5\n0.113\nNo\n25\n10\n40.0\n15\n60.0\nLength of stay in prison (years)\nUp to 1 \n59\n34\n57.6\n25\n42.4\n0.476\nMore than 1 \n72\n37\n51.4\n35\n48.6\nProportion between trans. and men in the cell\n1/1 to 1/10\n20\n11\n55.0\n9\n45.0\n0.938\nOver 1/10\n111\n60\n54.1\n51\n45.9\nSteady partner in prison\nYes\n77\n36\n46.8\n41\n53.2\n0.041\nNo\n54\n35\n64.8\n19\n35.2\nNumber of partners in prison\nUp to 1\n22\n13\n59.1\n9\n40.9\n0.614\nMore than 1\n109\n58\n53.2\n51\n46.8\nRegistered visitor\nYes\n65\n30\n46.2\n35\n53.8\n0.067\nNo\n66\n41\n62.1\n25\n37.9\nEducation in prison\nYes\n26\n15\n57.7\n11\n42.3\n0.690\nNo\n105\n56\n53.3\n49\n46.7\nAccess to male condoms\nYes\n102\n55\n53.9\n47\n46.1\n0.905\nNo \n29\n16\n55.2\n13\n44.8\nAccess to lubricants\nYes\n36\n16\n44.4\n20\n55.6\n0.168\nNo \n95\n55\n57.9\n40\n42.1\nN sample: 131 people. Bold indicates statistically significant p-values. STI: sexually transmitted infections. www.scielo.br/rbepid https://doi.org/10.1590/1980-549720230058 Travestis e mulheres transexuais presas e as infecções sexualmente transmissíveis. Rev Bras Epidemiol. 2023; 26: e230058\n8 REFERENCES org/10.22422/2238-1856.2014v14n27p99-117 8. Zamboni M. Travestis e transexuais privadas de liberdade: \na (des) construção de um sujeito de direitos. REA 2016; 2: \n15-23. 9. Brasil. Ministério da Saúde. Secretaria de Gestão Estratégica e \nParticipativa. Departamento de Apoio à Gestão Participativa. Política nacional de saúde integral de lésbicas, gays, \nbissexuais, travestis e transexuais. Brasília: Ministério da \nSaúde; 2013 10. Brasil. Ministério da Justiça e Segurança Pública. Departamento \nPenitenciário Nacional. Coordenação de Atenção às Mulheres \ne Grupos Específicos. Informação nº 95/2022/COAMGE/\nCGCAP/DIRPP/DEPEN. Mapeamento Nacional da População \nLGBTI [Internet]. [cited on Jul 18, 2023]. Available at: https://\nwww.gov.br/senappen/pt-br/servicos/sisdepen/relatorios/\npopulacao-carceraria/presos-lgbti/presos-lgbti-2022.pdf 11. Brasil. Ministério da Mulher, da Família e Direitos Humanos. Secretaria Nacional de Proteção Global. Departamento 11. Brasil. Ministério da Mulher, da Família e Direitos Humanos. Secretaria Nacional de Proteção Global. Departamento www.scielo.br/rbepid https://doi.org/10.1590/1980-549720230058 Healthcare (Basel) 2022; 10(12): 2380. https://doi:10.3390/\nhealthcare10122380 de Promoção dos Direitos de LGBT. LGBT nas prisões \nno Brasil: diagnóstico dos procedimentos institucionais \ne experiências de encarceramento [Internet]. Brasília: \nMinistério da Mulher, da Família e Direitos Humanos; \n2020 [cited on Jul 18, 2023]. Available at: https://www. gov.br/mdh/pt-br/assuntos/noticias/2020-2/fevereiro/\nTratamentopenaldepessoasLGBT.pdf de Promoção dos Direitos de LGBT. LGBT nas prisões \nno Brasil: diagnóstico dos procedimentos institucionais \ne experiências de encarceramento [Internet]. Brasília: \nMinistério da Mulher, da Família e Direitos Humanos; \n2020 [cited on Jul 18, 2023]. Available at: https://www. gov.br/mdh/pt-br/assuntos/noticias/2020-2/fevereiro/\nTratamentopenaldepessoasLGBT.pdf de Promoção dos Direitos de LGBT. LGBT nas prisões \nno Brasil: diagnóstico dos procedimentos institucionais \ne experiências de encarceramento [Internet]. Brasília: \nMinistério da Mulher, da Família e Direitos Humanos; \n2020 [cited on Jul 18, 2023]. Available at: https://www. gov.br/mdh/pt-br/assuntos/noticias/2020-2/fevereiro/\nTratamentopenaldepessoasLGBT.pdf 22. Santos PMR, Santos KC, Magalhães LS, Oliveira BR, Carneiro \nMAS, Souza MM, et al. Travestis and transexual women: \nwho are at higher risk for sexually transmitted infections? Rev Bras Epidemiol 2021; 24: e210017. https://doi. org/10.1590/1980-549720210017 12. Soares Filho MM. Política Nacional de Atenção Integral \nà Saúde das Pessoas Privadas de Liberdade no Sistema \nPrisional (PNAISP): um desafio para o Sistema Único de \nSaúde (SUS) brasileiro. In: Miranda AE, Rangel C, Costa-\nMoura R (orgs). Questões sobre a população prisional no \nBrasil: saúde, justiça e direitos humanos [Internet]. Vitória: \nUFES, Proex; 2016. p. 8-37. 23. Bastos FI, Bastos LS, Coutinho C, Toledo L, Mota JC, \nVelasco-de-Castro CA, et al. HIV, HCV, HBV, and syphilis \namong transgender women from Brazil: assessing \ndifferent methods to adjust infections rates of a hard-\nto-reach, sparse population. Medicine (Baltimore) \n2018; 97(suppl 1): S16-S24. https://doi.org/10.1097/\nmd.0000000000009447 13. Bartos MSH. REFERENCES Política Nacional de Atenção Integral à \nSaúde das Pessoas Privadas de Liberdade no Sistema \nPrisional: uma reflexão sob a ótica da intersetorialidade. Ciênc Saúde Coletiva 2023; 28(4): 1131-8. https://doi. org/10.1590/1413-81232023284.08962022 24. Colchero MA, Cortés-Ortiz MA, Romero-Martínez M, Vega H, \nGonzález A, Román R, et al. HIV prevalence, sociodemographic \ncharacteristics, and sexual behaviors among transwomen \nin Mexico City. Salud Publica Mex 2015; 57 Suppl 2: s99-\n106. https://doi.org/10.21149/spm.v57s2.7596 14. Prefeitura do Rio de Janeiro. Município do Rio de Janeiro \navança na cobertura de saúde no sistema prisional [Internet]. 2023 [cited on Sep 18, 2023]. Available at: https://prefeitura. rio/saude/municipio-do-rio-avanca-na-cobertura-de-saude-\nno-sistema-prisional/ 25. Rocon PC, Wandekoken KD, Barros MEB, Duarte MJO, Sodré \nF. Acesso à saúde pela população trans no Brasil: nas \nentrelinhas da revisão integrativa. Trab Edu Saúde 2020; 18(1): \ne0023469. http://dx.doi.org/10.1590/1981-7746-sol00234 26. Monteiro S, Brigeiro M. Experiências de acesso de mulheres \ntrans/travestis aos serviços de saúde: avanços, limites e \ntensões. Cad Saúde Pública 2019; 35(4): e00111318. https://\ndoi.org/10.1590/0102-311X00111318 15. Sousa KAA, Araújo TME, Teles AS, Rangel EML, Nery IS. Fatores \nassociados à prevalência do vírus da imunodeficiência em \npopulação privada de liberdade. Rev Esc Enferm USP 2017; 51: \ne03274. https://doi.org/10.1590/S1980-220X2016040903274 27. Wirtz AL, Yeh PT, Flath NL, Beyrer C, Dolan K. HIV and viral \nhepatitis among imprisoned key populations. Epidemiol Rev \n2018; 40(1): 12-26. https://doi.org/10.1093/epirev/mxy003 16. Machado F, Becker D, Oliveira CF, Possuelo LG, Renner \nJDP. Soroprevalência de infecção pelo HIV, hepatites B e \nC e sífilis em presidiários da região central do Rio Grande \ndo Sul, Brasil. Mundo Saúde 2019; 43(1): 117-28. htttps://\ndoi.org/10.15343/0104-7809.20194301117128 28. Rodrigues NG, Silva CH, Araújo IS. Visibilidade de pessoas \ntrans na produção científica brasileira. Reciis 2019; 13(3): \n658-70. https://doi.org/10.29397/reciis.v13i3.1723 17. Nasrullah M, Frazier E, Fagan J, Hardnett F, Skarbinski J. The \nassociation of recent incarceration and health outcomes \namong HIV-infected adults receiving care in the United States. Int J Prison Health 2016; 12(3): 135-44. https://10.1108/\nIJPH-04-2016-0010 29. Nascimento FEM. Agrupamento de travestis e transexuais \nencarceradas no Ceará, Brasil. Rev Estud Fem 2020; 28(1): \ne57687. https://doi.org/10.1590/1806-9584-2020v28n157687 30. Sanzovo NM. O lugar das trans na prisão. Belo Horizonte: \nEditora D’Plácido; 2020. 18. Singh GP, Lata S, Swu AK, Virk NS, Singh J, Thakkar S. A \nretrospective study to find prevalence of HIV, HCV and dual \nHIV-HCV infections in the prison inmates. J Family Med Prim \nCare 2022; 11(10): 6250-4. https://doi.org/10.4103/jfmpc. jfmpc_788_22 31. Caravaca-Morera JA, Padilha MI. Necropolítica \ntrans: diálogos sobre dispositivos de poder, morte e \ninvisibilização na contemporaneidade. 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Classificação \nbrasileira de ocupações [Internet]. Brasília: Ministério do \nTrabalho e Emprego; 2010 [cited on Jul 2, 2023]. Available at: \nhttps://portalfat.mte.gov.br/wp-content/uploads/2016/04/\nCBO2002_Liv3.pdf 21. Russoto Y, Micali C, Laganà N, Marino A, Campanella E, \nCelesia BM, et al. Diagnosis, treatment and prevention of \nHIV infections among detainees: a review of the literature. Travestis e mulheres transexuais presas e as infecções sexualmente transmissíveis. Rev Bras Epidemiol. 2023; 26: e230058 www.scielo.br/rbepid https://doi.org/10.1590/1980-549720230058 35. Ferreira ACG, Coelho LE, Jalil EM, Luz PM, Friedman RK, \nGuimarães MRC, et al. Transcendendo: a cohort study of \nHIV-infected and uninfected transgender women in Rio de \nJaneiro, Brazil. Transgend Health 2019; 4(1): 107-17. https://\ndoi:10.1089/trgh.2018.0063 37. Teixeira FB, Paulino DB, Raimondi GA, Crovato CAS, Prado \nMAM. Entre o segredo e as possibilidades do cuidado: (re)\npensando os silêncios em torno das narrativas das travestis \nsobre HIV/Aids. Sex, Salud Soc (Rio J) 2018; 29: 373-88. https://doi.org/10.1590/1984-6487.sess.2018.29.17.a 36. Strazza L, Massad E, Azevedo RS, Carvalho HB. Estudo de \ncomportamento associado à infecção pelo HIV e HCV em \ndetentas de um presídio de São Paulo, Brasil. Cad Saúde \nPública 2007; 23(1): 197-205. https://doi.org/10.1590/\nS0102-311X2007000100021 38. Brömdal A, Mullens AB, Phillips TM, Gow J. Experiences \nof transgender prisoners and their knowledge, attitudes, \nand practices regarding sexual behaviors and HIV/STIs: a \nsystematic review. Int J Transgend 2019; 20(1): 4-20. https://\ndoi.org/10.1080/15532739.2018.1538838 RESUMO Objetivo: Avaliar a taxa de soropositividade dos testes rápidos para HIV, sífilis e hepatite B e C entre travestis e mulheres transexuais \n(transfemininas) privadas de liberdade na Região Metropolitana do Rio de Janeiro, analisando os resultados diante do perfil \nsociodemográfico, prisional e acesso às tecnologias de saúde para prevenir infecções sexualmente transmissíveis (IST). REFERENCES Métodos: \nEstudo transversal do tipo censitário realizado com transfemininas em 11 prisões do Rio de Janeiro, entre os meses de abril e \njunho de 2021. Resultados: As taxas de soropositividade encontradas foram de 34,4% para o HIV, 48,9% para sífilis e 0,8% para as \nhepatites do tipo B e C. A soropositividade para mais de uma infecção foi verificada em 25,4% das participantes, e HIV/sífilis foi a mais \nprevalente. O aumento no nível de escolarização (p=0,037), e possuir parceiro fixo na prisão (p=0,041) foram considerados fatores \nde proteção para as IST nessa população. Foram identificadas dificuldades no acesso às tecnologias de prevenção contra IST, como \npreservativo masculino, gel lubrificante, testes rápidos e terapias antirretrovirais profiláticas para o HIV. Conclusão: As taxas de \nsoropositividade para o HIV e sífilis foram elevadas, mas no perfil encontrado nessa população em outros estudos dentro e fora das \nprisões. Os dados encontrados indicam a necessidade de incorporar estratégias efetivas para o acesso às tecnologias em saúde para \na prevenção das IST. A escassez de publicações científicas contendo dados epidemiológicos sobre IST na população transfeminina \nem situação de prisão limitou a realização de comparações mais profundas dos resultados obtidos neste estudo.i alavras-chave: Pessoas transgênero. Prisões. Sífilis. Hepatite. Soropositividade para HIV. ACKNOWLEGMENTS: We are grateful for the support of health professionals from Pnaisp and Seap-RJ, as well as the directors and criminal police officers of the \nprison units visited. We would like to thank the team from Health Management and the School of Penitentiary Management, and in particular, criminal police officer \nLuciana de Souza Resende, assigned to Seap’s Health and Citizenship Support Division for the LGBTI Population, and the transsexual woman activist Alessandra \nRamos Makkeda (in memoriam). AUTHORS’ CONTRIBUTIONS: Alves-da-Silva, CR: Project administration, Data curation, Writing – original draft, Investigation, Methodology. Bonan, C: \nConceptualization, Supervision, Validation. Gomes Junior, SCS: Formal analysis, Funding acquisition, Software. Vieira, RS: Writing – review & editing, Visualization. FUNDING: Research Incentive Program (Programa de Incentivo a Pesquisa – PIP) of Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes \nFigueira (IFF-Fiocruz). entive Program (Programa de Incentivo a Pesquisa – PIP) of Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernande Travestis e mulheres transexuais presas e as infecções sexualmente transmissíveis. Rev Bras Epidemiol. 2023; 26: e230058 10\n© 2023 | Epidemio is a publication of \n Associação Brasileira de Saúde Coletiva - ABRASCO"

ERROR: type should be string, got "https://doi.org/10.1007/s43253-023-00101-1\nReview of Evolutionary Political Economy (2023) 4:349–386 https://doi.org/10.1007/s43253-023-00101-1\nReview of Evolutionary Political Economy (2023) 4:349–386 ORIGINAL PAPER Growth drivers in emerging capitalist economies: building \nblocks for a post‑Keynesian analysis and an empirical \nexploration of the years before and after the Global \nFinancial Crisis Benjamin Jungmann1,2 Received: 17 February 2022 / Accepted: 22 May 2023 \n© The Author(s) 2023\n/ Published online: 3 July 2023 Extended author information available on the last page of the article *\t Benjamin Jungmann \n\t\nBenjamin.jungmann@hwr-berlin.de *\t Benjamin Jungmann \n\t\nBenjamin.jungmann@hwr-berlin.de\nExtended author information available on the last page of the article *\t Benjamin Jungmann Benjamin.jungmann@hwr-berlin.de Keywords  Growth model · Growth driver · Financialisation · Emerging economies · \nPost-Keynesian economics · Comparative political economy Abstract This paper contributes to the growth models debate by expanding the concept of growth \ndrivers to emerging capitalist economies (ECEs). Conceptually, the paper synthesizes \ngrowth drivers with a growth model operationalization based on GDP growth contributions \nand financial sector balances. Drawing on post-Keynesian and structuralist economics, as \nwell as, empirical studies, seven growth drivers for ECEs are reviewed: income distribution, \nprice and non-price competitiveness, commodity prices, private debt, foreign direct \ninvestment (FDI) and fiscal policy. Descriptive data for these drivers are presented for 19 \nregionally grouped ECEs between 2000 and 2019. On average, Asian ECEs exhibit higher \ngrowth rates, stable real exchange rates, high and increasing non-price competitiveness and \nhigh private debt levels. Latin American countries show comparatively lower growth rates, \nhigh but decreasing income inequality, unstable exchange rates and relatively expansionary \nfiscal policy after the Global Financial Crisis (GFC). Central and Eastern European \ncountries generally display medium to high growth rates, lower income inequality, high \nnon-price competitiveness, a substantial FDI stock and, after the GFC, real depreciations \nand contractionary fiscal policy. The assessment of cross-country growth drivers via \nbivariate coefficients reveals limited robust results, except for non-price competitiveness, \nwhich emerges as a significant driver. Additionally, we find indications that private debt \nand expansionary fiscal policy became more important for growth in ECEs after the \nGFC. This is in line with the emergence of domestic demand- and private debt-led growth \nmodels in ECEs following the GFC in the course of private deleveraging and austerity \npolicies in developed capitalist economies. Keywords  Growth model · Growth driver · Financialisation · Emerging economies · \nPost-Keynesian economics · Comparative political economy JEL classification  E11 · E12 · E65 · F62 · F65 JEL classification  E11 · E12 · E65 · F62 · F65 012341 456789)\n3 456789)\n3 350 B. Jungmann 1  Originally, post-Keynesian authors used the term “demand and growth regimes” or “macroeconomic \nregimes” while “growth models” originated within CPE. If not otherwise specified, we will use these \nterms interchangeably throughout this article. Irrespective of the term, the concept should not be con-\nfused with the distinction between wage-led and profit-led demand that is going to be introduced in 2.1 \nand works at a different level of analysis.\n2  Stockhammer (2022a) provides an overview on the post-Keynesian fundamentals of the growth models \napproach.\n3  ECEs have featured in some large-scale analysis by post-Keynesian authors without considering their \nspecific economic properties and role in the international growth model constellation (e.g. Dodig et al. \n2016; Hein and Mundt 2012). The term ECEs refers to economies with a capitalist mode of production \nthat feature some but not all of the characteristics of DCEs, e.g. in terms of financial and trade integra-\ntion into world markets or sectoral composition of the economy. f\n2  Stockhammer (2022a) provides an overview on the post-Keynesian fundamentals of the growth mode\napproach.\n3 3  ECEs have featured in some large-scale analysis by post-Keynesian authors without considering their \nspecific economic properties and role in the international growth model constellation (e.g. Dodig et al. \n2016; Hein and Mundt 2012). The term ECEs refers to economies with a capitalist mode of production \nthat feature some but not all of the characteristics of DCEs, e.g. in terms of financial and trade integra-\ntion into world markets or sectoral composition of the economy. 1  Originally, post-Keynesian authors used the term “demand and growth regimes” or “macroeconomic \nregimes” while “growth models” originated within CPE. If not otherwise specified, we will use these \nterms interchangeably throughout this article. Irrespective of the term, the concept should not be con-\nfused with the distinction between wage-led and profit-led demand that is going to be introduced in 2.1 \nand works at a different level of analysis.\n2 Stockhammer (2022a) provides an overview on the post Keynesian fundamentals of the growth models 1  Introduction (2023), on the other hand, look at commodity-driven growth models \nin general and highlight the pro-cyclical effects on these models exerted by capi-\ntal flows and commodity price swings. Mertens et al. (2022) investigate the growth \nmodels of eight ECEs and the social bloc dynamics of four. They add the category \nof an investment-led growth model and make the case that large ECEs may exhibit \nmultiple, regionally different growth models. This is echoed by Tan and Conran \n(2022) who argue that the rise of China was driven by a hybrid system that com-\nprised two growth models: an export-led and a state-led investment one. Akcay et al. (2022) conduct an analysis of growth models in eight large ECEs. In the post-GFC context when DCEs became overall more export-led, they find that \nECEs have not followed the trajectory of DCEs and instead switched to or continued \npursuing domestic demand-led models (India, Argentina and Brazil), private debt-\nled ones (Turkey and South Africa) or decreased their export-led stance (China). Hence, ECEs, together with domestic demand-led DCE like the US and UK, have \ncontributed to the necessary global counterpart to export-led mercantilist DCEs with \nhigh current account surpluses. Furthermore, Akcay et al. look into factors driving \nthese changes. They find that, in some ECEs, improved income distribution helped \nto stabilize income-financed domestic demand while, on the other hand, further \nincreases in financialisation boosted debt-financed private demand. In a similar way, \nCampana et al. (2024) complement their growth model analysis of Brazil, China, \nIndia and Russia by examining factors that drive export growth, income distribution \nand political conflict underlying fiscal policy.4 li\nIn this paper, we take up on the issue of what drives aggregate demand and \nultimately growth in ECEs. In this context, the contribution this paper attempts is \ntwofold. First, on a conceptual level, we address the criticism raised by Kohler and \nStockhammer (2022) that growth model operationalization, which relies solely on \nthe growth contributions of aggregate demand components and financial sector \nbalances, does not provide meaningful insights into the causal drivers of growth. Therefore, they argue for a growth model operationalization based on growth drivers \nwhich are “factors that are not themselves part of aggregate income but influence \nthe growth of its components” (Kohler and Stockhammer 2022, p. 1319). 1  Introduction In the post-Keynesian literature, growth models have been operationalized using the \nGDP growth contributions of aggregate demand – consumption, investment, government \nexpenditure and net exports – and the sectoral financial balances of the macroeconomic \nsectors – households, firms, the government and the external sector, found in national \nincome and financial accounts (Hein 2011, 2012). This decomposition gives insights into \nthe demand sources of growth and their financing at a macroeconomic level. Using this \nmethod to study the macroeconomics of financialisation, yielded the known constellation \nof export-led versus private debt-led growth models prior to the Global Financial Crisis \n(GFC) of 2007–09 (Stockhammer 2015). Meanwhile, this concept has been applied to \nthe post-GFC constellation (Akcay et al. 2022; Dodig et al. 2016; Hein 2019; Dünhaupt \nand Hein 2019; Hein and Martschin 2020; Hein et al. 2021). Furthermore, commenced \nby Baccaro and Pontusson (2016), the ‘growth models approach’ gained prominence \nwithin Comparative Political Economy (CPE).1 This approach marks a shift within \nComparative Capitalism from the Varieties of Capitalism (VoC) approach based on \nNew Consensus Macroeconomics with supply-side determined long-run equilibria \nto post-Keynesian based demand-focused approaches. By doing so, issues of demand \ngeneration, instability, economic policies and international interdependencies move \ncentre stage.2 While this approach has mostly been applied to developed capitalist economies \n(DCEs) (e.g. Hall 2018; Johnston and Regan 2018; Hein 2019; Hein and Martschin \n2020; Hein et al. 2021; Van Doorslaer and Vermeiren 2021; Hassel and Palier 2021; \nBaccaro et  al. 2022 and the contributions therein), attempts have been made to \nextend it to emerging capitalist economies (ECEs).3 Schedelik et al. (2021) examine \nthe growth trajectories of India, Brazil and China. They recommend the growth \nmodels approach to study ECEs while emphasizing the importance of retaining \ninstitutionalist aspects from the VoC literature. Nölke et  al. (2022) compare the \ndifferent growth trajectories of India and Brazil. Looking at VoC-categories and \napplying a growth models approach analysing demand formation, distribution and \nthe dominant social bloc, they conclude that the latter is better suited to explain the \ndivergence of the two countries. Morgan et al. (2021) examine the tension between \ninstitutional embeddedness and politics and its impact on growth model change by \nanalysing Brazil’s experience between 2002 and 2018. 1 3 Growth drivers in emerging capitalist economies: building… 351 Relatedly, Sierra (2022) investigates the underlying factors contributing to the \npersistence of commodity-driven growth models in Latin American countries. Sche-\ndelik et  al. 4  Campana et al. (2024) conduct a growth decomposition based on the Sraffian supermultiplier, distin-\nguishing autonomous demand components and their contributions from that of induced components. \nWithin the growth models strand, this method was first revitalized by Morlin et al. (2022) examining the \nUnited States, Germany, Japan and Sweden. Passos and Morlin (2022) applied this method to growth \nmodels in Latin America. 1  Introduction However, \ninstead of arguing replacing one by the other, we make the case for a conceptual \nsynthesis and present a set of potential growth drivers in ECEs. Building on post-\nKeynesian and structuralist economics as well as empirical studies on ECEs, seven \ngrowth drivers for ECEs are reviewed: income distribution, price and non-price \ncompetitiveness, commodity prices, private debt, foreign direct investment (FDI) \nand fiscal policy. 1 3 352 B. Jungmann Second, we explore these growth drivers empirically for 19 ECEs.5 We pro-\nvide descriptive data on each growth driver for these ECEs in a regional grouping \nfrom 2000 and 2019. Furthermore, to investigate which growth drivers determined \ngrowth on a cross-country level in the years before and after the GFC, we establish \nbivariate coefficients. For the descriptive data, we find that the Asian ECEs exhibit \non average higher growth rates, stable real exchange rates (RERs), higher and ris-\ning non-price competitiveness and high private debt levels. In Latin America, we \nfind comparatively low growth rates, high levels of income inequality that are abat-\ning, unstable RERs and relatively expansionary fiscal stances after the GFC. The \ncountries of Central and Eastern Europe (CEE), often with the exception of Russia, \nexhibit medium to high growth rates, comparatively low levels of income inequal-\nity, high non-price competiveness and a high FDI stock. Moreover, their RERs were \nrather stable, particularly, after the GFC when they achieved overall depreciations \nwhile their fiscal policy stances became less expansionary. In terms of cross-country \ngrowth drivers derived from bivariate coefficients, our results remain sparse with \nthe exception of non-price competitiveness that drove cross-country growth. Finally, \nin line with the descriptive findings of overall higher private debt and expansionary \nfiscal policy stances after the GFC, we find indications of both factors becoming \nmore relevant as cross-country growth drivers during that time. This is in line with \nthe emergence, respectively, persistence of domestic demand- and private debt-led \ngrowth models in ECEs following the GFC in the course of private deleveraging and \nausterity policies in DCEs (Akcay et al. 2022). The remainder is structured as follows: Section 2 places the concept of growth \ndrivers within growth model operationalization and reviews seven possible growth \ndrivers for ECEs. 5  Our sample encompasses the Latin American ECEs of Argentina, Brazil, Chile, Colombia and Mex-\nico; the Asian ECEs of China, Indonesia, India, Korea, Malaysia and Thailand; the Central and Eastern \nEuropean ECEs of the Czech Republic, Hungary, Poland and Russia; the Middle Eastern ECEs of Israel, \nSaudi Arabia and Turkey; and South Africa. The sample was largely determined by data availability. 1  Introduction Section 3 presents, first, descriptive data of each growth driver \nfor 19 ECEs between 2000 and 2019 and, second, bivariate coefficients relat-\ning the development in growth drivers to GDP growth for the pre- and post-GFC \nperiod. Section 4 summarizes and discusses the empirical observations. Section 5 \nconcludes. 2  \u0007Building blocks for a post‑Keynesian analysis of growth drivers \nin emerging capitalist economies In their paper, Kohler and Stockhammer (2022) have questioned the usefulness of the \ncommon post-Keynesian method of growth model operationalization for the post-GFC \nperiod. This method of growth decomposition operationalizes growth models based \non the GDP growth contributions of aggregate demand and sectoral financial balances \n(e.g. Hein 2011, 2012, 2019). According to Kohler and Stockhammer (2022), the \nassessment of the formerly private debt-led Southern European peripheral economies 1 3 Growth drivers in emerging capitalist economies: building… 353 as export-led in the post-GFC years is misleading, as their export-led characteristics \nrather stem from private deleveraging and fiscal austerity depressing imports than \nfrom sustained export growth. Studies that identify these economies as export-led from \na growth decomposition standpoint also cite private deleveraging and fiscal austerity \nas driving this development (Dodig et al. 2016; Hein and Martschin 2020; Hein et al. 2021). Still, Kohler and Stockhammer (2022) argue growth models should instead be \nunderstood through their growth drivers, as they offer information on why or why not \ndemand components grew. The drivers considered by the authors are financial cycles, \nfiscal policy and international price as well as non-price competitiveness. In this \nperspective, the Southern European peripheral growth models underwent a debt-driven \ndepression following their pre-GFC debt-driven growth due to the cyclical nature of \nfinance-driven growth, exacerbated by contractionary fiscal policy. ii\nWe acknowledge the limitations of using GDP growth decompositions as the \nsole basis for growth model operationalization. While these decompositions \nidentify the most dynamic aggregate demand components, they do not explain \nwhy these components grew. However, one should not throw the baby out with \nthe bath water. One merit of growth model operationalization via national income \nand financial accounting is that they consider sectoral financial balances, which \nillustrate the financing of aggregate demand within and across economies, \nindicating the sustainability of and the interdependencies between growth models. This is particularly important as analysing national capitalisms in their totality \nand interdependence rather than in isolation sets the growth models approach \napart from VoC (Schedelik et  al. 2021, p. 518). And although the export-led \nversus private debt-led dichotomy came to an end with the GFC, international \ninterdependencies between growth models persist. After the GFC, debt-led \nprivate demand growth models have ceased to exist among DCEs while export-led \nones prevailed and increased in number. 2  \u0007Building blocks for a post‑Keynesian analysis of growth drivers \nin emerging capitalist economies Domestic demand-led DCEs and ECEs \nwith high public deficits and debt-led private demand ECEs have become the \ncounterpart for export-led DCEs (Hein et al. 2021; Akcay et al. 2022). In general, \nnational income and financial accounts will always provide relevant information \non macroeconomic developments, even if they cannot in themselves provide causal \nexplanations for demand and growth. Applicants of this method are well aware \nof this and complement their growth model analyses therefore with indicators of \ndistribution and financialisation (e.g. Hein 2011, 2019; Hein and Mundt 2012; \nAkcay et  al. 2022), link it to welfare models (Hein et  al. 2021) or embed it in \na comprehensive analysis of macroeconomic policies (Hein and Martschin 2021) \n– factors that can be considered growth drivers. We advocate hence for the synthesis of growth decomposition and growth driver \nanalysis because both approaches have their merits and inform each other. For once, \ngrowth models rely on growth drivers to derive demand and growth. And seemingly \nsimilar growth models from a growth decomposition standpoint may differ \nsignificantly depending on their growth drivers, e.g. in terms of sustainability and \ncyclicality. On the other hand, different growth drivers may become more relevant \ndepending on the international growth model constellation, e.g. an economy may \nsee it easier to grow based on growth drivers that stimulate exports within an \ninternational growth model constellation that supplies sufficient external demand 1 3 354 B. Jungmann while in the face of depressed external demand growth drivers that stimulate \ndomestic demand become more relevant (Akcay et al. 2022). while in the face of depressed external demand growth drivers that stimulate \ndomestic demand become more relevant (Akcay et al. 2022). In what follows, we will set out the building blocks for an analysis of growth \ndrivers in ECEs.6 Building on post-Keynesian and structuralist economics as well \nas empirical studies on ECEs, seven growth drivers for ECEs are reviewed: income \ndistribution, price and non-price competitiveness, commodity prices, private debt, \nFDI and fiscal policy. Arguing for the synthesis of growth model operationalization \nvia national income and financial accounting with that via growth drivers, we point \nout how different growth drivers are expected to shape growth models. We will refer \nhere to three types of growth models common in the post-Keynesian literature (e.g. Hein 2011, 2019; Hein et al. 2021; Akcay et al. 6  For a more general account on how to employ post-Keynesian economics and economic structuralism \nto analyse growth models in ECEs and developing economies see Stockhammer (2022b).\n7  A fourth category found in the literature is that of a weakly export-led growth model. Such a model \nshares some but not all features of an export-led mercantilist model and features of the other two mod-\nels, e.g. it may exhibit positive growth contributions by net exports with a negative current account. We \nhence view it as an intermediate growth model which we for now exclude from the conceptual argument \nlinking growth models and drivers. Further growth models are possible, particularly as the presented \nones were derived from an analysis of DCEs. For example, Mertens et al. (2022) added an investment-led \nmodel, which for our purposes can be considered as a form of a domestic demand-led one. 2  \u0007Building blocks for a post‑Keynesian analysis of growth drivers \nin emerging capitalist economies 2022): 1) a domestic demand-led \ngrowth model is characterized by positive or balanced financial balances of the pri-\nvate sector and a balanced or negative current account. Correspondingly, net exports \ndo not contribute to growth which almost exclusively stems from domestic demand; \n2) an export-led mercantilist growth model exhibits positive financial balances of the \nprivate sector and a positive current account with positive net exports contributing \nto growth; 3) a debt-led private demand (boom) growth model is characterized by \nnegative or close to balance financial balances of the private sector, in particular, of \nthe household sector and a negative current account. Growth is driven by domes-\ntic demand, particularly, private demand and net exports contribute negatively to \ngrowth (Hein 2019, p. 980).7 2.1  \u0007Income distribution: rising wage share and reduced income inequality Based on post-Kaleckian distribution and growth models, we review changes in \nthe income distribution as a possible driver of growth (Bhaduri and Marglin 1990). In this framework, economies are either classified as wage-led if their demand \nand growth is positively affected by an increasing wage share or as profit-led in \nthe opposite case. Whether an economy exhibits a wage-led or profit-led demand \nregime rests on structural properties such as the different propensities to consume, \nthe responsiveness of investment to demand and profitability, the price sensitivity \nof net exports and the relevance of the respective aggregate demand component for \ntotal demand (Lavoie and Stockhammer 2013). Determining an economy’s demand \nregime is an empirical task: More often than not, domestic demand is found to be \nwage-led. This is due to the positive effect of an increased wage share on consump-\ntion because of higher propensities to consume out of wage income than out of profit 1 1 3 Growth drivers in emerging capitalist economies: building… 355 income. Meanwhile, the effect of changes in income distribution on investment is \noften found to be insignificant. For total demand, profit-led cases are more likely \ndue to a positive effect of the profit share on net exports. A total profit-led demand \nregime requires the rise in price competitiveness due to a lower wage share and the \nrespective increase in net exports to outweigh the depressing effects on consumption \nout of wages. In this context, ECEs are more likely to exhibit total profit-led demand \nregimes due to their relative openness and price sensitive exports, especially, if they \nare small (see Table 1 and Hein (2014, pp. 302–303)). In any case, as stressed by \nLavoie and Stockhammer (2013), the identified regime type neither implies that the \nfunctional income distribution developed accordingly nor that policies were applied \nto achieve such development; for example, a wage-led economy can exhibit a rising \nprofit share due to pro-capital policies. i\nParticularly due to rising wage inequality, the exclusive focus on the functional income \ndistribution has been questioned. Hein and Prante (2020) review the different Kaleckian \ngrowth models accounting for wage inequality: Some models distinguish direct from \nindirect/overhead labour, thereby, in the short run, the wage share becomes endogenous \nto economic activity in an inverse way, making demand appear profit-led when in fact \nthe causality is reversed (e.g. Lavoie 2009). 8  We focus here on increases in the wage share and reduced income inequality as profit-led regimes rest \non the expansionary effects of price competitiveness, which will be reviewed as a growth driver of its \nown in the next section. 2.1  \u0007Income distribution: rising wage share and reduced income inequality Alternatively, models split profits and wages \nbetween workers who own part of the capital stock and capitalists who receive wages \nin their function as managers. These models yield expansionary effects from increased \nworkers’ wage share irrespective of the demand regime due to workers’ lower propensity \nto save. Thus, higher workers’ wage shares increase the probability of wage-led demand as \nthe overall propensity to save of wage income falls (Palley 2017). Another type of model \nincorporates persistence in basic consumption needs and interdependent consumption \npatterns, where lower-income ranks mimic the consumption behaviour of higher ranks. This leads to increased consumption and private debt ratios in response to higher profit \nshares and income inequality (e.g. Kapeller and Schütz 2015). Hence, according to these \nmodels, growth models based on private debt would be unlikely to occur with increases in \nwage shares and decreases in income inequality. We conclude that growth driven by increasing wage shares and reduced income \ninequality are conducive to domestic demand-led regimes.8 Domestic demand-led \nregimes have most of their growth stemming from domestic demand components, \nprivate consumption being usually the biggest. Since domestic demand is over-\nwhelmingly found to be wage-led, it will be boosted by increases in the wage share \nand reduction in income inequality due to the higher propensities to consume out of \nwages and low income households. 2.2  \u0007Price competitiveness As outlined in the previous section, increases in price competitiveness, i.e. real \ndepreciations, increase demand if the rise in net exports it triggers outweighs the 8  We focus here on increases in the wage share and reduced income inequality as profit-led regimes rest \non the expansionary effects of price competitiveness, which will be reviewed as a growth driver of its \nown in the next section. 1 3 B. Jungmann 356 able 1   Overview of studies investigating ECEs demand regimes\nCountry\nDomestic demand\nTotal demand\nWage-led\nProfit-led\nWage-led\nProfit-led\nArgentina\nOnaran and Galanis (2012): 1970–\n2007; Alarco (2016): 1950–2012\nAlarco (2016): 1950–2012; Oyvat \net al. (2020): 1972–2007\nOnaran and Galanis (2012): \n1970–2007\nBrazil\nAlarco (2016): 1950–2012; Tomio \n(2020): 1956–2008; Araújo and \nGala (2012): 1960–2008\nAlarco (2016): 1950–2012; Tomio \n(2020): 1956–2008\nAraújo and Gala (2012): 1960–2008; \nde Jesus et al. (2018): 1970–2008\nChile\nAlarco (2016): 1950–2012\nAlarco (2016): 1950–2012; Oyvat \net al. (2020): 1967–1994\nChina\nOnaran and Galanis (2012): \n1978–2007; Jetin and Reyes Ortiz \n(2020): 1982–2016\nWang (2009, Chapter 3): 1993–\n2007; Molero-Simarro (2015): \n1978–2007\nJetin and Reyes Ortiz (2020): \n1982–2016\nOnaran and Galanis (2012): 1970–\n2007; Wang (2009, Chapter 3): \n1993–2007; Molero-Simarro (2015): \n1978–2007\nColombia\nAlarco (2016): 1950–2012\nAlarco (2016): 1950–2012; Loaiza \net al. (2017): 1970–2011\nOyvat et al. (2020): 1967–2011; \nCharpe et al. (2014): 1970–2010\nndia\nOnaran and Galanis (2012): \n1970–2007\nOnaran and Galanis (2012): \n1970–2007; Oyvat et al. (2020): \n1964–2011\nKohli (2018) finds it for 1981–2012 to be either wage- or profit-led depend-\ning on the source of the distributional change\nndonesia\nOyvat et al. (2020): 1971–2011\nKorea\nOnaran and Galanis (2012): 1970–\n2007; Kurt (2018): 1970–2011; \nJoo et al. (2020): 1982–2018\nOnaran and Galanis (2012): 1970–\n2007; Onaran and Stockhammer \n(2005): 1970–2000; Oyvat et al. (2020): 1964–2011; Joo et al. (2020): 1982–2018\nKurt (2018): 1970–2011\nMalaysia\nOyvat et al. (2020): 1972–2011 1 3\nTable 1   Overview of studies investigating ECEs demand regimes\nCountry\nDomestic demand\nTotal demand\nWage-led\nProfit-led\nWage-led\nProfit-led\nArgentina\nOnaran and Galanis (2012): 1970–\n2007; Alarco (2016): 1950–2012\nAlarco (2016): 1950–2012; Oyvat \net al. (2020): 1972–2007\nOnaran and Galanis (2012): \n1970–2007\nBrazil\nAlarco (2016): 1950–2012; Tomio \n(2020): 1956–2008; Araújo and \nGala (2012): 1960–2008\nAlarco (2016): 1950–2012; Tomio \n(2020): 1956–2008\nAraújo and Gala (2012): 1960–2008; \nde Jesus et al. (2018): 1970–2008\nChile\nAlarco (2016): 1950–2012\nAlarco (2016): 1950–2012; Oyvat \net al. 2.2  \u0007Price competitiveness (2020): 1967–1994\nChina\nOnaran and Galanis (2012): \n1978–2007; Jetin and Reyes Ortiz \n(2020): 1982–2016\nWang (2009, Chapter 3): 1993–\n2007; Molero-Simarro (2015): \n1978–2007\nJetin and Reyes Ortiz (2020): \n1982–2016\nOnaran and Galanis (2012): 1970–\n2007; Wang (2009, Chapter 3): \n1993–2007; Molero-Simarro (2015): \n1978–2007\nColombia\nAlarco (2016): 1950–2012\nAlarco (2016): 1950–2012; Loaiza \net al. (2017): 1970–2011\nOyvat et al. (2020): 1967–2011; \nCharpe et al. (2014): 1970–2010\nIndia\nOnaran and Galanis (2012): \n1970–2007\nOnaran and Galanis (2012): \n1970–2007; Oyvat et al. (2020): \n1964–2011\nKohli (2018) finds it for 1981–2012 to be either wage- or profit-led depend-\ning on the source of the distributional change\nIndonesia\nOyvat et al. (2020): 1971–2011\nKorea\nOnaran and Galanis (2012): 1970–\n2007; Kurt (2018): 1970–2011; \nJoo et al. (2020): 1982–2018\nOnaran and Galanis (2012): 1970–\n2007; Onaran and Stockhammer \n(2005): 1970–2000; Oyvat et al. (2020): 1964–2011; Joo et al. (2020): 1982–2018\nKurt (2018): 1970–2011\nMalaysia\nOyvat et al. (2020): 1972–2011 Growth drivers in emerging capitalist economies: building… 357 g g\np\ng\nSource: Jiménez (2020), Akcay et al. (2022) and own extension\nNo studies on the demand regimes of the Czech Republic, Hungary, Israel, Poland, Russia and Saudi Arabia\nTable 1   (continued)\nCountry\nDomestic demand\nTotal demand\nWage-led\nProfit-led\nWage-led\nProfit-led\nMexico\nOnaran and Galanis (2012): 1970–\n2007; Alarco (2016): 1950–2012\nAlarco (2016): 1950–2012\nOnaran and Galanis (2012): \n1970–2007; Oyvat et al. (2020): \n1972–2009; Charpe et al. (2014): \n1970–2011\nSouth Africa\nOnaran and Galanis (2012): \n1970–2007\nOyvat et al. (2020): 1972–2007; \nStrauss and Isaacs (2016): \n1970–2013\nOnaran and Galanis (2012): \n1970–2007\nThailand\nJetin and Kurt (2016): 1970–2011\nJetin and Kurt (2016): 1970–2011\nTurkey\nOnaran and Galanis (2012): 1970–\n2007; Yılmaz (2015): 1987–2006\nOnaran and Galanis (2012): 1970–\n2007; Onaran and Stockhammer \n(2005): 1963–1997; Oyvat et al. (2020): 1964–2009;\nYılmaz (2015): 1987–2006 1\nSource: Jiménez (2020), Akcay et al. (2022) and own extension\nNo studies on the demand regimes of the Czech Republic, Hungary, Israel, Poland, Russia and Saudi Arabia\nTable 1   (continued)\nCountry\nDomestic demand\nTotal demand\nWage-led\nProfit-led\nWage-led\nProfit-led\nMexico\nOnaran and Galanis (2012): 1970–\n2007; Alarco (2016): 1950–2012\nAlarco (2016): 1950–2012\nOnaran and Galanis (2012): \n1970–2007; Oyvat et al. (2020): \n1972–2009; Charpe et al. (2014): \n1970–2011\nSouth Africa\nOnaran and Galanis (2012): \n1970–2007\nOyvat et al. 2.2  \u0007Price competitiveness (2020): 1972–2007; \nStrauss and Isaacs (2016): \n1970–2013\nOnaran and Galanis (2012): \n1970–2007\nThailand\nJetin and Kurt (2016): 1970–2011\nJetin and Kurt (2016): 1970–2011\nTurkey\nOnaran and Galanis (2012): 1970–\n2007; Yılmaz (2015): 1987–2006\nOnaran and Galanis (2012): 1970–\n2007; Onaran and Stockhammer \n(2005): 1963–1997; Oyvat et al. (2020): 1964–2009;\nYılmaz (2015): 1987–2006 Source: Jiménez (2020), Akcay et al. (2022) and own extension\nNo studies on the demand regimes of the Czech Republic, Hungary, Israel, Poland, Russia and Saudi Arabia\nTable 1   (continued)\nCountry\nDomestic demand\nTotal demand\nWage-led\nProfit-led\nWage-led\nProfit-led\nMexico\nOnaran and Galanis (2012): 1970–\n2007; Alarco (2016): 1950–2012\nAlarco (2016): 1950–2012\nOnaran and Galanis (2012): \n1970–2007; Oyvat et al. (2020): \n1972–2009; Charpe et al. (2014): \n1970–2011\nSouth Africa\nOnaran and Galanis (2012): \n1970–2007\nOyvat et al. (2020): 1972–2007; \nStrauss and Isaacs (2016): \n1970–2013\nOnaran and Galanis (2012): \n1970–2007\nThailand\nJetin and Kurt (2016): 1970–2011\nJetin and Kurt (2016): 1970–2011\nTurkey\nOnaran and Galanis (2012): 1970–\n2007; Yılmaz (2015): 1987–2006\nOnaran and Galanis (2012): 1970–\n2007; Onaran and Stockhammer \n(2005): 1963–1997; Oyvat et al. (2020): 1964–2009;\nYılmaz (2015): 1987–2006 1 3 358 B. Jungmann domestic demand-depressing effects caused by the redistribution towards profits \n(Hein 2014, Chapter 7). Besides distributional issues, negative balance sheet effects \ncan also impede the expansionary effects of increased price competitiveness. This \nis because real depreciations increase the cost of external debt, which in turn can \nconstrain demand (e.g. Krugman 1999). Besides these potentially negative effects, \nthere exists an extensive body of literature stressing the positive effects of price \ncompetitiveness, proxied as a depreciated or low RER, particularly in developing \neconomies and ECEs (see Rapetti (2020) for an overview). Within this literature, \nprice competitiveness fosters growth via the ‘tradable-led growth channel’. This \nchannel stresses the role of ‘modern tradable activities’ and the associated structural \ntransformation towards higher productivity activities. The channel rests broadly \non three elements: higher level of productivity from modern tradable activities, \nan increase in overall productivity through structural change towards modern \ntradables and a low and stable RER that corrects for market failures and promotes \naccumulation in these sectors (Rapetti 2020, p. 36). Conversely, an appreciated RER \nmay avert such favourable structural transformation and depress growth.f In sum, increased price competitiveness may affect growth negatively through negative \nbalance sheet and distributional effects. On the other hand, it may boost growth via net \nexports and through the tradable-led growth-channel. 9  The indicator is derived through the export basket. Each export basket is classified according to the \nubiquity and diversity of its components—the more diverse and non-ubiquitous its export basket, the \nhigher a country’s ECI. 2.2  \u0007Price competitiveness Due to its positive effect on net \nexports and associated negative effects on consumption demand, price competitiveness as \na growth driver will be conducive for export-led growth models. 10  However, as we will argue further below, export-led growth models may also be based on commod-\nity prices. Thus export-led growth models from a growth decomposition perspective do not necessarily \nrequire non-price (or price) competitiveness. 2.3  \u0007Non‑price competitiveness The previously outlined tradable-led growth-channel bears resemblance with the \nliterature that stresses the importance of technological capabilities, i.e. non-price \ncompetitiveness. The importance of non-price factors can be derived from Thirlwall’s \n(1979) law according to which growth in an open economy is constrained by the ratio \nbetween the growth rate of exports and the income elasticity of demand for imports. The growth rate of exports can be decomposed into the rest of the world’s income \nelasticity of demand for the home country’s exports and the rate of growth of the rest \nof the world’s income. Thus, the growth rate depends on two income elasticities, both \ndetermined by technological capabilities (McCombie 1989). The more technological \ncapabilities a country has, the more complex and differentiated the products it can \nproduce. More sophisticated goods are characterized by higher income elasticities \nof demand for exports and lower income elasticity of demand for imports and hence \nassociated with higher growth rates (Gouvêa and Lima 2010). The importance of \nincome elasticities of exports and imports also has a long tradition in economic \nstructuralism (Ocampo and Parra 2006). Hidalgo and Hausmann (2009) have introduced the economic complexity index \n(ECI) to quantify the technological capabilities of an economy.9 Structuralist 1 3 Growth drivers in emerging capitalist economies: building… 359 scholars applied the ECI to vindicate their reasoning on the pivotal role of tech-\nnological capabilities in fostering economic growth and development via increas-\ning returns to scale and positive externalities (Gala et al. 2018). For DCEs, Gräbner \net al. (2020) show the crucial role of technological capabilities and their relation \nto growth models in explaining the divergences within the Eurozone. They argue \nthat export-led growth models require a certain degree of technological capabilities, \nwhile in the absence of these capabilities economies might tend to develop private \ndebt-led growth models.10 Likewise, the macroeconomic policy regime approach \n(Hein and Martschin 2021) and the analysis of growth drivers (Kohler and Stock-\nhammer 2022) also suggest the importance of non-price competitiveness for DCEs. As non-price competitiveness mainly boosts exports it is conducive to export-led \ngrowth models. But non-price competitiveness does not necessarily depress private \nconsumption as price competitiveness likely will. Non-price competitiveness can \nthus also feature in rather domestic demand-led growth models as current account \nsurpluses are not inevitable. 11  Schedelik et al. (2023) emphasize that commodity price-driven growth models are affected not only \nby the pro-cyclical behaviour of commodity prices but also, simultaneously, by that of capital flows. In \ntheir analysis, Campana et al. (2024) highlight the specific role of commodities in the growth models of \nBrazil and Russia. Similarly, Passos and Morlin (2022) and Sierra (2022) examine commodity exports as \na salient feature of Latin American growth models.\n12  During asset price booms, rising real estate prices increase household wealth, boost residential invest-\nment and allow real estate to be collateralized for credit-financed consumption (Stockhammer and Wil-\ndauer 2016). 2.4  \u0007Commodity prices Price and non-price competitiveness drive manufactured exports. However, ECEs’ \nexport basket are often characterized by a large share of commodities. UNCTAD \n(2021, p. 14) classifies around 55 per cent of ECEs as commodity dependent, \nmeaning their shareof commodities in total exports exceeds 60 per cent. For DCEs, \nthis value is only at 28 per cent, justifying the neglect of commodityprices as a \npotential growth driver for this country group by Kohler and Stockhammer (2022). Commodity prices, with the exception of oil, are essentially demand-driven and \nfollow a cyclical movement over 20–70 years, leading to the notion of ‘commodity \nsuper-cycles’ (Erten and Ocampo 2013). Furthermore, the financialisation of \ncommodities also contributes to commodity prices’ cyclicality (Pollin and Heintz \n2011). Investigating the effects of these cycles on economic activity, Fernández \net al. (2020) find that commodity super cycles play a determining role for aggregate \noutput in small and open DCEs and ECEs.f Besides the positive effects on external demand and income, rising commodity \nprices might also increase growth through the increased financial resilience of \nthe economy as the rising current account surplus allows for the accumulation of \ninternational reserves that can be used to cushion the detrimental effects of financial \nhavoc. Moreover, a favourable development of the terms of trade can allow for \nincreased imports for production purposes (Menezes and Souza 2019). However, \nif not counteracted, the increased export volume can lead to real appreciation with \ndetrimental effects on manufacturing industries’ price competitiveness leading to \npremature de-industrialization – as described by the concept of ‘Dutch disease’ \n(Bresser-Pereira 2008). Furthermore, as stressed by structuralist, reliance on 1 3 360 B. Jungmann commodity exports comes with deteriorating terms of trade and adverse effects on \nthe growth trajectory (Ocampo and Parra 2006). As commodity prices boost export demand, growth driven by commodity prices \nwill likely be export-led. An export-led commodity prices-driven growth model will \nexhibit different characteristics than export-led models driven by price or non-price \ncompetitiveness. If not counteracted rising commodity exports are mutually exclu-\nsive to price competitiveness in the short term with detrimental effects for non-price \ncompetitiveness in the longer term. Moreover, growth models based on commodity \nprices will exhibit the cyclical features of these prices and declining terms of trade \naffect the long-term prospects of growth negatively.11 2.5  \u0007Private debt: financialisation, financial development and cycles This stems from the depressing effects of \nfinancialisation on real corporate investment and an increased use of NFC debt for \nfinancial activities and payouts (Davis 2017). Likewise, for some ECEs, studies find \nrising NFC indebtedness to be associated with heightened involvement of NFCs in \nfinancial activities, increased holding of liquid assets and financial payouts at the \nexpense of real investment (Bonizzi 2013, p. 89, for an overview). At the same time, \ngrowth model analysis on ECEs indicates that investment demand plays a more \nprominent role there, leading Mertens et al. (2022) to add the category of an invest-\nment-led growth model. Hence, we take into account the possibility of NFC debt as the base of real invest-\nment, thereby driving growth. In general, finance occupies an important position \nwithin post-Keynesian economics as creditworthy firms can take out loans, irre-\nspective of prior savings, to finance investment and start production (Lavoie 2014, \nChapter 4). Correspondingly, post-Keynesian authors advocate for a financial sector \nsophisticated enough to provide necessary finance for NFC (Priewe and Herr 2005, \nChapter 4). However, this advocacy shall not be mistaken for full-blown financial \nliberalization which is deemed harmful for growth and development (e.g. Arestis \n2006). Increasing indebtedness in ECEs is hence not necessarily negatively connoted in \nthe context of growth. Rather, one has to distinguish favourable expansion of pri-\nvate debt associated with the built-up of productive capacities from unfavourable \nexpansions. Unfavourable expansions of private debt do not lead to the build-up of \nproductive capacities and include debt-financed household consumption, residential \ninvestment and debt-financed financial activities and financial payouts by NFCs. iii\nIn all cases, debt expansion follows cyclical dynamics as stressed by Kohler \nand Stockhammer (2022) by referring to the Financial Instability Hypothesis \n(Minsky 1977). During a boom, asset prices rise, credit expands and financial \npositions become increasingly risky. This built-up fragility eventually leads to \nbusts marked by falling asset prices, deleveraging and reduced spending. On top \nof theses domestic endogenous dynamics, ECEs’ cycles may be characterized \nby their subordinate integration into financial markets. This subordination stems \nfrom the inferior position their currencies occupy in the international currency \nhierarchy as international investors assign a lower liquidity premium to their \ncurrencies compared to key currencies. To attract capital inflows, ECEs must \ncompensate for the lower liquidity premium with higher interest rates, which \nin turn adversely affect corporate investment (Bortz and Kaltenbrunner 2018). 2.5  \u0007Private debt: financialisation, financial development and cycles In debt-led private demand growth models in DCEs, like in the US, UK and south-\nern Europe, wealth-based and debt-financed private consumption and residential \ninvestment drove demand and growth (Stockhammer 2015).12 AAlso ECEs followed \ndebt-led private demand growth models, namely Mexico and Hungary before the \nGFC, Turkey afterwards, and South Africa in both periods (Akcay et al. 2022, p. 83). But private debt also played a significant role in ECEs where no debt-led private \ndemand growth model was identified. Akcay et al. (2022, p. 89–91) argue that grow-\ning private debt contributed to the Chinese growth model becoming less export-led \nafter the GFC. In what follows, we will examine the role of private debt as a growth \ndriver in ECEs distinguishing between household debt, on the one hand, and debt of \nnon-financial corporations (NFCs) on the other. For NFC debt, we distinguish finan-\ncial sector development conducive to long-term growth from construction booms \nand debt used for financial activities. Finally, we emphasize the cyclicality of growth \ndriven by private debt in the context of EMEs’ subordinate integration into global \nfinancial markets. i\nFinancialisation in ECEs has come with increased indebtedness of households. Following the GFC, household debt growth accelerated in several ECEs (Karwowski \nand Stockhammer 2017, p. 76) Country- or region-specific accounts of this rise can \nbe found in Karwowski (2012) and Ashman et al. (2011) for South Africa; Chang \n(2016) for South Korea; Rethel (2010) for Malaysia; dos Santos (2013) for Bra-\nzil, Mexico, Poland and Turkey; Karacimen (2016) for Turkey; and Gabor (2010) \nfor Central and Eastern Europe. Notwithstanding the variegated manifestations of \nhousing financialisation among ECEs, e.g. in terms of mortgage debt to GDP and \nhomeownership rates (Fernandez and Aalbers 2020), the listed studies and reports 3 Growth drivers in emerging capitalist economies: building… 361 on China (Bird 2020) indicate that the increase in household debt in ECEs is largely \ndriven by housing-related financing needs. Household debt as a growth driver \nshould then come with increased residential investment and rising house prices. Despite their rise, household debt-to-GDP levels in ECEs are still relatively \nlow compared to those in private debt-led DCEs. The levels of NFC debt in ECEs, \nhowever, are much closer to those in DCEs (Karwowski and Stockhammer 2017, \npp. 75–76). The growth model literature on DCEs disregards NFC debt for real \ninvestment as a potential growth driver. 2.5  \u0007Private debt: financialisation, financial development and cycles Furthermore, ECEs that rely on capital inflows face the problem of their debt \nbeing of short maturity and denominated in foreign currency, making them \nadditionally vulnerable to exchange rate movements (Arestis and Glickman 1 3 3 3 362 B. Jungmann 2002). Concomitant balance sheet effects amplify the amplitude of the business \ncycle: during the boom, pro-cyclical capital flows to ECEs and exchange rate \nappreciations increase the net worth of firms with foreign currency denominated \ndebt boosting their investment. Conversely, during the bust, negative balance \nsheet effects amplify the contraction (Kohler 2019).13l f\nCapital flows to ECEs are not only pro-cyclical with respect to their domestic \neconomic activity. They are also partly determined by the decisions of institutional \ninvestors in DCEs. Liquidity considerations and global factors, such as the monetary \npolicy in DCEs, lead investors to withdraw or invest capital in ECEs (Bonizzi 2017). The respective cyclical and secular movement in capital flows, asset prices and \ncredit growth has become known as the global financial cycle (Rey 2015). In this \ncontext, ECEs’ capital inflows increase during expansionary and decrease during \nrestrictive US monetary policy stances (Bräuning and Ivashina 2020; Ahmed and \nZlate 2014). Otherwise put, financial cycles in financially globally integrated ECEs \nare not only pro-cyclical with respect to domestic endogenous factors but also to \ninternational ones.f The different characteristics of private debt as a growth driver described \nabove have correspondingly different implications for the growth model. Rising \nNFC debt in the context of financial sector development will boost investment \nand hence be conducive for a domestic demand-led model. Thereby productive \ncapacities are built up by which price and non-price competitiveness may rise \neventually and drive exports. While rising NFC debt for financing financial \nactivities and payouts is expected to have negative effects on demand and growth, \ndebt-financed household consumption and residential investment foster the \nemergence of a debt-led private demand growth model. If private debt expansion \nis based on capital inflows, appreciations are likely to occur. This will have \ndetrimental effects on price competitiveness and on non-price competitiveness \neventually, as explained in the respective sections. In all cases of private debt-\ndriven growth, cyclicality will prevail. The amplitude of the cycle and its trigger \nmay depend on the type and degree of international financial integration. The \nseverity of the growth downturn during the financial bust will depend on the \nexistence of alternative growth drivers. 13  Additionally to the effects on corporate balance sheets, capital flows to subordinated financially inte-\ngrated ECEs may fuel household indebtedness, as shown by Kaltenbrunner and Painceira (2018) for the \ncase of Brazil. 2.5  \u0007Private debt: financialisation, financial development and cycles While the post-GFC period saw growth \ndeceleration in all formerly private debt-driven countries, these were more \nsevere in peripheral Europe where fiscal policy was not utilized to cushion the \ndebt-driven stagnation like it was in the Anglo-Saxon countries (Kohler and \nStockhammer 2022; Hein et al. 2021). 2.6  \u0007Vertical foreign direct investment: tax havens and export platforms Vertical FDI already featured prominently in the second VoC generation. Nölke and \nVliegenthart (2009) coined the term ‘dependent market economies’ for the CEE 3 Growth drivers in emerging capitalist economies: building… 363 countries of Poland, Hungary, the Czech Republic and Slovakia. These economies are \nembedded into complex manufacturing value chains offering a skilled but relatively \ncheap labour force. The necessary capital is largely provided by multinational \ncorporations (MNCs) via FDI, making these economies dependent on decisions taken \nby hierarchically organised foreign corporations. In the growth models literature, CPE scholars invented the category of ‘FDI-led \ngrowth models’ for the CEE countries, the Baltic States and Ireland (Bohle 2018; \nBohle and Regan 2021; Regan and Brazys 2018). There, FDI-led growth models are \n“particular cases of export-oriented growth, because the major exporting firms are \nforeign-owned” (Bohle and Regan 2021, p. 82). Ownership is hence the key difference \nbetween FDI-led growth models and common export-led economies such as Germany. These FDI-led economies grow by attracting FDI that leads to the build-up of \nproductive capacities and consequently to rising external demand for domestically \nproduced goods. The main difference among FDI-led countries being then the sectors \nto which they receive their FDI (Bohle and Regan 2021). Applying a post-Keynesian \nperspective, Woodgate (20212023) questions this distinction. According to Woodgate, \nFDI-led economies not only differ in terms of ownership from export-led economies \nbut also in their national accounts. While both types have positive net exports, FDI-led \nones exhibit negative current account balances due to negative net income receipts, \nleading to GDP exceeding gross national income in these economies. Woodgate (2021, 2023) proposes two types of FDI-led growth models, tax \nhavens and export platforms, to understand the impact of FDI on demand, output, \nnational income, and growth in economies with a significant presence of MNCs. Both types are based on the ‘commercialisation of state sovereignty’ (Palan 2002), \nfeaturing low effective corporate tax rates, the establishment of special economic \nzones and investment promotion agencies to attract FDI.14 Tax havens are net \nrecipients of profits shifted within MNCs without receiving much tangible capital \nnor significant employment. Profit shifting takes place via transfer pricing or \nintellectual property royalties leading to growing net exports and output without \nactual value adding activities taking place in the tax haven. 14  Not all countries pursuing such a strategy are necessarily successful in attracting FDI. Those who are \nbenefit from some sort of first mover advantages (Woodgate 2021).\n15  Woodgate (2021, p.9) lists twelve tax havens that “have been found to be a net recipient of shifted profits \nin the literature” (Macao, British Virgin Islands, Luxembourg, Bermuda, Cayman Islands, Singapore, Ireland, \nPuerto Rico, Switzerland, Malta, the Netherlands and Hong Kong) and eight export platforms which have at \nleast 40% of their net exports coming from foreign-owned firms (China, Thailand, Malaysia, Slovakia, Estonia, \nthe Czech Republic, Hungary and Sweden). 14  Not all countries pursuing such a strategy are necessarily successful in attracting FDI. Those who a\nbenefit from some sort of first mover advantages (Woodgate 2021).i ii\n15  Woodgate (2021, p.9) lists twelve tax havens that “have been found to be a net recipient of shifted prof\nin the literature” (Macao, British Virgin Islands, Luxembourg, Bermuda, Cayman Islands, Singapore, Irelan\nPuerto Rico, Switzerland, Malta, the Netherlands and Hong Kong) and eight export platforms which have \nleast 40% of their net exports coming from foreign-owned firms (China, Thailand, Malaysia, Slovakia, Eston\nthe Czech Republic, Hungary and Sweden). 2.7  \u0007Fiscal policy In recent years, a consensus on the positive effects of expansionary fiscal policy on \nmacroeconomic performance emerged (Stockhammer et al. 2019, pp. 58–60). This \neffect is associated with the notion of fiscal multipliers which refer to the increase \nin output induced by the increase in public spending or decrease in taxation. Not \nonly is there a large amount of literature that finds fiscal multipliers to be larger \nthan one, they also seem to be higher in recessions compared to normal times and \nupswings (Gechert and Rannenberg 2018). Additionally, fiscal multipliers of public \nspending are found to be larger than those of taxation, while those of investment \ntend to exceed those of consumption. Furthermore, multipliers are larger in more \nclosed economies due to fewer demand leakages through imports (Gechert and \nRannenberg 2018). For ECEs, studies contend that fiscal multipliers are smaller \nthan for DCEs (Hory 2016; Ilzetzki et al. 2013). Moreover, fiscal consolidation may \ncome with negative hysteresis effects as the reductions in output can have long-term \nnegative effects on the economic performance while fiscal expansion could prevent \nsuch effects or even trigger positive ones (DeLong and Summers 2012; Gechert \net al. 2019). A more long-term perspective is taken in a strand of literature that introduces \nnon-capacity generating autonomous demand components into Kaleckian growth \nmodels. Allain (2015) uses autonomous government expenditure growth as such a \ndemand component in a basic Kaleckian framework with Harrodian instability. In \nthe medium (and under certain conditions, long) run, it is the growth rate of the \nautonomous demand component towards which the rate of accumulation converges. Therefore, higher growth rates of government expenditure imply higher rates of \ngrowth and accumulation. Amending the model by government debt, Hein (2018) \nand Hein and Woodgate (2021) show that constant growth rates of government \nexpenditure are under certain conditions compatible with stable public debt to GDP \nratios. Kohler and Stockhammer (2022) and Hein and Martschin (2021) have simi-\nlarly stressed the pivotal role of fiscal policy as a growth driver for DCEs.i i\nExpansionary fiscal policy will boost demand via government consumption and \ninvestment, as well as, private demand through social transfers and tax reductions. Hence, expansionary fiscal policy as a growth driver will be conducive to a domestic \ndemand-led regime. Furthermore, public expenditure can have positive supply-side \neffects. 2.6  \u0007Vertical foreign direct investment: tax havens and export platforms National income in these \neconomies grows through MNCs FDI only if part of the income is absorbed through \ntaxation of the shifted profits, which may be reinjected via government expenditure. Export platforms, on the other hand, “host foreign affiliates that are engaged in the \ngenuine production of goods and services” (Woodgate 2021, p.8) bringing tangible \ncapital and employment to the host country. Here output grows as foreign affiliates \nestablished via FDI produce to service external demand. This creates national \nincome in the form of wages paid to domestic households and taxes paid on the \nprofits of foreign affiliates. Wages and taxes in turn create demand and output as \nthey are consumed, respectively, reinjected into the economy.15 1 3 364 B. Jungmann Hence, FDI will boost net exports and be conducive to an export-led regime, \nhowever, the large presence of MNCs and net foreign income receipts may lead to \na negative current account. While positive net exports of tax havens are misleading \nas no true value adding activities take place, export platforms do service external \ndemand. The establishment of productive capacities in export platforms results in \ninvestment that may lead to productivity gains and a more diverse and sophisticated \nexport basket, resulting in increased price and non-price competitiveness. 2.7  \u0007Fiscal policy Not only may it prevent negative hysteresis effects but also promote productivity \nthrough public investment, leading to higher potential output, price competitiveness, \nand non-price competitiveness. 1 3 3 Growth drivers in emerging capitalist economies: building… 365 According to post-Keynesian and particularly Modern Monetary Theory, fiscal \nexpenditure in monetary sovereign countries is only constrained by potential \noutput (Wray 2019); government debt-to-GDP ratios can remain stable even with \nprimary deficits (Domar 1944; Hein and Martschin 2021). Hence, we contend \nthat the fiscal policy stance is foremost a policy variable that reflects political \nconflict (Kalecki 1943). However, the conditions of monetary sovereignty, such \nas issuing a domestic currency, having taxes, government expenditures and public \ndebt denominated in that currency and operating a free floating exchange rate \n(Wray 2019) do arguably not pertain to ECEs (Epstein 2019). Another possible \nconstraint on expansionary fiscal policy are balance-of-payments problems. These are especially conceivable for ECEs as the financial liabilities they incur \nassociated with current account deficits – e.g. triggered by expansionary fiscal \npolicy leading to import exceeding export demand – are denominated in foreign \ncurrency (Bonizzi et al. 2019). Table 2 summarizes the presented growth drivers, the main demand components \nthey drive, the growth model they are conducive to, further characteristics and which \ngrowth drivers are unlikely to occur simultaneously. 16  Table  A2 in the supplementary material provides an overview on the periodic averages of GDP \ngrowth and current account balances for the pre- and post-GFC years. 3  \u0007Growth drivers before and after the Global Financial Crisis: \nan empirical exploration for 19 emerging capitalist economies This sections sets out an empirical exploration of growth drivers for a set of 19 \nECEs which size was constrained by data availability. First, descriptive data for \nthe growth drivers is displayed for the years between 2000 and 2019 to detect \nsalient features in their development over time and cross-regional and -country \ncharacteristics. Second, as done by Kohler and Stockhammer (2022), we check \nvia simple linear regressions for the pre- and post-GFC years for cross-country \ngrowth drivers. The trajectory of ECEs in terms of their growth models has been more \ndiverse than that of economies in the core (Akcay et al. 2022; Hein et al. 2021; \nKohler and Stockhammer 2022). To make sense of this heterogeneity but also \nfor visualization purposes, we investigate our sample of ECEs in subgroups. For \nthe time being we apply a regional grouping (Asia, Central and Eastern Europe, \nLatin America and Middle East/Africa) which, as we will see, captures the \nheterogeneity of some growth drivers better than others. As visible in Fig. 1, the \nGFC constituted a sharp decline in growth throughout the sample with varying \nmagnitude. Asian countries exhibited on average milder downturns and show the \nhighest growth levels throughout the years while the growth performance in Latin \nAmerica seems to be the most dismal. The trend of lower growth following the \nGFC across the sample, is the most pronounced in Latin America. Only Israel, \nIndia and Indonesia withstood that trend.16 Hence, although Fig. 1 displays 1 3 B. 3  \u0007Growth drivers before and after the Global Financial Crisis: \nan empirical exploration for 19 emerging capitalist economies Jungmann 368 −10\n−8\n−6\n−4\n−2\n0\n2\n4\n6\n8\n10\n12\n14\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCHN\nIDN\nIND\nKOR\nMYS\nTHA\n−10\n−8\n−6\n−4\n−2\n0\n2\n4\n6\n8\n10\n12\n14\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCZE\nHUN\nPOL\nRUS\n−10\n−8\n−6\n−4\n−2\n0\n2\n4\n6\n8\n10\n12\n14\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nARG\nBRA\nCHL\nCOL\nMEX\n−10\n−8\n−6\n−4\n−2\n0\n2\n4\n6\n8\n10\n12\n14\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nISR\nSAU\nTUR\nZAF\nFig. 1   GDP growth of 19 ECEs between 2000 and 2019, regionally grouped. Sources: World Bank \n(2021), author’s calculations and presentation. Notes: Information on the empirical indicator and country \nabbreviations can be found in Table A1 Fig. 1   GDP growth of 19 ECEs between 2000 and 2019, regionally grouped. Sources: World Bank \n(2021), author’s calculations and presentation. Notes: Information on the empirical indicator and country \nabbreviations can be found in Table A1 serious heterogeneity in terms of growth deceleration and acceleration, we will \nuse the GFC in what follows as a structural breaking point for the 19 ECEs. Moreover, for several DCEs, the GFC constituted a structural breaking point in \ngrowth models: The debt-led private demand boom was followed by a debt-driven \nstagnation that was not in every country countered by sustained public demand. In consequence, GDP growth in the core slowed down in the years after the crisis \n(Kohler and Stockhammer 2022; Hein et al. 2021) constituting a drag on foreign \ndemand from the ECEs perspective (Akcay et  al. 2022). That said, we do not \nrule out the possibility of alternative structural breaking points which may differ \nbetween countries and we will explicitly consider the end of commodity super \ncycle around 2014 as an alternative breaking point when assessing commodity \nprices. 3  \u0007Growth drivers before and after the Global Financial Crisis: \nan empirical exploration for 19 emerging capitalist economies Jungmann 366 Table 2   Overview of growth drivers in ECEs\nGrowth driver\nMain demand component\nGrowth model\nFurther characteristics\nUnlikely to occur with\nIncome distribution\nPrivate consumption\nDomestic demand-led\nNet effect on demand and growth \ndepends on the demand regime\nPrivate debt\nPrice competitiveness\nPrice competitiveness\nExports\nExport-led\nNegative balance sheet effects, \nredistribution towards profits\nIncome distribution\nCommodity prices\nNon-price competitiveness Exports\nExport-led\nNot necessarily as domestic demand \ndepressing as price competitive-\nness\nCommodity prices\nCommodity prices\nExports\nExport-led\nCyclical behaviour, risk of prema-\nture De-industrialization, declin-\ning terms of trade\nPrice competitiveness\nNon-price competitiveness\nPrivate debt\nConsumption and investment financial sector development: \nDomestic demand-led\ndebt-financed household consump-\ntion and residential investment: \nDebt-led private demand\nFinancial sector development is \nconducive for long-term growth; \nfinancial boom-bust cycle, which \nmay be marked by subordinated \nfinancialisation\nHousehold debt: rising wage \nshares and reduced income \ninequality\nAccompanied by capital flows: \nprice and non-price competi-\ntiveness\nVertical FDI\nExports and investment\nExport-led\nTax havens: rising exports due to \nprofit shifting of MNCs\nExport platforms: genuine value \nadding activities which may lead \nto increases in price and non-price \ncompetitiveness\nAdditional demand depends on \nincome absorption which is higher \nin export platforms Table 2   Overview of growth drivers in ECEs\nGrowth driver\nMain demand component\nGrowth model\nFurther characteristics\nUnlikely to occur with\nIncome distribution\nPrivate consumption\nDomestic demand-led\nNet effect on demand and growth \ndepends on the demand regime\nPrivate debt\nPrice competitiveness\nPrice competitiveness\nExports\nExport-led\nNegative balance sheet effects, \nredistribution towards profits\nIncome distribution\nCommodity prices\nNon-price competitiveness Exports\nExport-led\nNot necessarily as domestic demand \ndepressing as price competitive-\nness\nCommodity prices\nCommodity prices\nExports\nExport-led\nCyclical behaviour, risk of prema-\nture De-industrialization, declin-\ning terms of trade\nPrice competitiveness\nNon-price competitiveness\nPrivate debt\nConsumption and investment financial sector development: \nDomestic demand-led\ndebt-financed household consump-\ntion and residential investment: \nDebt-led private demand\nFinancial sector development is \nconducive for long-term growth; \nfinancial boom-bust cycle, which \nmay be marked by subordinated \nfinancialisation\nHousehold debt: rising wage \nshares and reduced income \ninequality\nAccompanied by capital flows: \nprice and non-price competi-\ntiveness\nVertical FDI\nExports and investment\nExport-led\nTax havens: rising exports due to \nprofit shifting of MNCs\nExport platforms: genuine value \nadding activities which may lead \nto increases in price and non-price \ncompetitiveness\nAdditional demand depends on \nincome absorption which is higher \nin export platforms Growth drivers in emerging capitalist economies: building… 367 Table 2   (continued)\nGrowth driver\nMain demand component\nGrowth model\nFurther characteristics\nUnlikely to occur with\nFiscal policy\nPublic consumption and \npublic investment\nDomestic demand-led\nTo a large extent a policy variable; \nmay also boost private demand \nthrough social transfers and tax \nreductions; potentially increasing \npotential output and measures of \ncompetiveness 1 3 B. 3.1  \u0007Income distribution: wage share and Gini coefficient of disposable income Income distribution as a growth driver is assessed through the wage share in \nnational income and the Gini coefficient of disposable income. Figure 2 a) reveals \na striking range, roughly between 20 and 60 percent of GDP, between which the \nwage share varies within the sample. Cross-country differences appear more \npronounced within than between regions. Regarding the development over time, 1 3 Growth drivers in emerging capitalist economies: building… 369 20\n30\n40\n50\n60\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCHN\nIDN\nIND\nKOR\nMYS\nTHA\n20\n30\n40\n50\n60\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCZE\nHUN\nPOL\nRUS\n20\n30\n40\n50\n60\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nARG\nBRA\nCHL\nCOL\nMEX\n20\n30\n40\n50\n60\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nISR\nSAU\nTUR\nZAF\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHATUR\nZAF\n2\n4\n6\n8\n10\n−2.5\n−2.0\n−1.5\n−1.0\n−0.5\n0.0\n0.5\n1.0\n1.5\n2.0\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2000 − 2008\nslope: 0.014; p−val: 0.972; R^2: 0.00\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\nZAF\n2\n4\n6\n8\n10\n−0.50\n−0.25\n0.00\n0.25\n0.50\n0.75\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2009 − 2019\nslope: −0.635; p−val: 0.597, R^2: 0.02\n(b) real GDP growth in % (y−axis) and annual change in wage share in % of GDP (x−axis)\n(a) wage share in % of GDP (y−axis) between 2004 and 2019 (x−axis)\nFig. 2   Wage shares and GDP growth for 19 ECEs between 2004 and 2019. Sources: World Bank (2021) \nand ILO (2023); author’s calculations and presentation. Notes: Information on the empirical indicator \nand country abbreviations can be found in Table A1 Fig. 2   Wage shares and GDP growth for 19 ECEs between 2004 and 2019. Sources: World Bank (2021) \nand ILO (2023); author’s calculations and presentation. Notes: Information on the empirical indicator \nand country abbreviations can be found in Table A1 most ECEs do not exhibit remarkable level changes in their wage share. Notable \nis the pronounced increase of the wage share in Argentina until 2017 and the \nsteady increase witnessed in Malaysia. Looking at the bivariate coefficients in \npanel b) of Fig. 3.1  \u0007Income distribution: wage share and Gini coefficient of disposable income 2, we see no cross-country relation between changes in the wage \nshare and growth, as indicated by the flat line and insignificant coefficient. In the \npost-GFC period, the slope of the line becomes negative implying that countries \nwith falling wage shares tended to grow faster. However, the coefficient is highly \ninsignificant.fi i\nFigure  3 presents the development of the Gini coefficient for disposable \nincome, a measure for the personal distribution of income. The coefficient \ncan take values between zero and 100, with zero implying a perfectly equal \ndistribution and 100 implying that all income accrues to one person. With \nthe exception of the Middle Eastern/African subgroup, there appears to be \nsome homogeneity within the regions. The CEE countries exhibit on average \nthe lowest Gini coefficient, i.e. the most equal distribution. While the Latin \nAmerican countries started at comparatively high levels, there is a trend of \ndeclining inequality visible in all these countries until around 2015. In panel b) \nof Fig. 3, we see a positive relation between the Gini and growth for the pre-GFC \nperiod that is significant at the 5% level, implying that countries have grown \nfaster the more their income inequality increased. In the post-GFC period, the \ncoefficient stays positive without reaching the 10% significance level. As such a \nrelationship is not suggested by economic theory, we view this rather as a result \nof endogeneity or reversed causality that will not further be considered. 1 3 B. 3.1  \u0007Income distribution: wage share and Gini coefficient of disposable income Jungmann 370 30\n40\n50\n60\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCHN\nIDN\nIND\nKOR\nMYS\nTHA\n30\n40\n50\n60\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCZE\nHUN\nPOL\nRUS\n30\n40\n50\n60\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nARG\nBRA\nCHL\nCOL\nMEX\n30\n40\n50\n60\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nISR\nSAU\nTUR\nZAF\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nTHATUR\nZAF\n2\n4\n6\n8\n10\n−0.50\n−0.25\n0.00\n0.25\n0.50\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2000 − 2008\nslope: 3.082; p−val: 0.02; R^2: 0.294\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\nZAF\n2\n4\n6\n8\n10\n−0.50\n−0.25\n0.00\n0.25\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2009 − 2019\nslope: 3.352; p−val: 0.126, R^2: 0.132\n(b) real GDP growth in % (y−axis) and annual change in Gini coeff. of disp. income (x−axis)\n(a) Gini coefficient of disposable income (y−axis) between 2000 and 2019 (x−axis)\nFig. 3   Gini coefficient of disposable income and GDP growth for 19 ECEs between 2000 and \n2019. Sources: World Bank (2021) and Solt (2020); author’s calculations and presentation. Notes: For \nthe pre-GFC simple linear regression Saudi Arabia was excluded due to insufficient data. Information on \nthe empirical indicator and country abbreviations can be found in Table A1 Fig. 3   Gini coefficient of disposable income and GDP growth for 19 ECEs between 2000 and \n2019. Sources: World Bank (2021) and Solt (2020); author’s calculations and presentation. Notes: For \nthe pre-GFC simple linear regression Saudi Arabia was excluded due to insufficient data. Information on \nthe empirical indicator and country abbreviations can be found in Table A1 3.2  \u0007Price and non‑price competitiveness Price competitiveness is proxied via the real effective exchange rate (REER), defined as \nthe price of a domestic consumption basket relative to a weighted consumption basket \nof trading partners. Following the Bank for International Settlements (BIS), an increase \n(decrease) in the REER refers to a real appreciation (depreciation) and is associated with \na decrease (increase) in international price competitiveness. Visible in Fig. 4 panel a), \nall ECEs experienced periods of real appreciations and depreciations, which differ in \ntheir extent. On average, the volatility of the REER seems less pronounced in Asia and \nthe CEE countries. Notably, the volatility in both regions decreased in the years after \nthe GFC. An exception to this is Russia, which experienced comparatively marked \nREER volatility throughout the years. Such volatile REERs can also be witnessed \nin the Latin American subgroup, in South Africa and to a lesser extent in the Middle \nEastern countries. Prior to the GFC, shown in panel b) of Fig. 4, real appreciations were \nmore prevalent and the relationship between the REER and growth was positive, i.e. appreciations and losses in price competitiveness were associated with higher growth. After the GFC, a slight majority experienced depreciations, particularly the Latin \nAmerican and CEE countries. Hence, the CEE countries with the exception of Russia, \nwere not only able to reduce the volatility of their REER but also to achieve on average \nincreases in price competitiveness throughout the post-GFC years. In Asia, all countries \nexcept Malaysia, experienced real appreciations. The positive relationship between \ngrowth and real appreciations prevailed after the GFC, becoming even more pronounced \nand significant. 3.2  \u0007Price and non‑price competitiveness 1 1 3 1 3 Growth drivers in emerging capitalist economies: building… 371 −50\n−25\n0\n25\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCHN\nIDN\nIND\nKOR\nMYS\nTHA\n−50\n−25\n0\n25\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCZE\nHUN\nPOL\nRUS\n−50\n−25\n0\n25\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nARG\nBRA\nCHL\nCOL\nMEX\n−50\n−25\n0\n25\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nISR\nSAU\nTUR\nZAF\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\nZAF\n2\n4\n6\n8\n10\n−6\n−4\n−2\n0\n2\n4\n6\n8\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2000 − 2008\nslope: 0.104; p−val: 0.408; R^2: 0.041\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\nZAF\n2\n4\n6\n8\n10\n−6\n−5\n−4\n−3\n−2\n−1\n0\n1\n2\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2009 − 2019\nslope: 0.434; p−val: 0.066, R^2: 0.185\n(b) real GDP growth in % (y−axis) and REER growth rate in % (x−axis)\n(a) REER growth rate in % (y−axis) between 2000 and 2019 (x−axis)\nFig. 4   REER and GDP growth for 19 ECEs between 2000 and 2019. Sources: World Bank (2021) and \nBIS (2021b); author’s calculations and presentation. Notes: Information on the empirical indicator and \ncountry abbreviations can be found in Table A1 Fig. 4   REER and GDP growth for 19 ECEs between 2000 and 2019. Sources: World Bank (2021) and \nBIS (2021b); author’s calculations and presentation. Notes: Information on the empirical indicator and \ncountry abbreviations can be found in Table A1 Non-price competitiveness is assessed using the ECI – a higher ECI implies higher \nnon-price competitiveness. Comparing the ECI levels in panel a) of Fig. 3.2  \u0007Price and non‑price competitiveness 5, we see that \nthe CEE countries, with the exception of Russia, are found in the higher ranks with an 0.00\n0.25\n0.50\n0.75\n1.00\n1.25\n1.50\n1.75\n2.00\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCHN\nIDN\nIND\nKOR\nMYS\nTHA\n0.00\n0.25\n0.50\n0.75\n1.00\n1.25\n1.50\n1.75\n2.00\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCZE\nHUN\nPOL\nRUS\n0.00\n0.25\n0.50\n0.75\n1.00\n1.25\n1.50\n1.75\n2.00\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nARG\nBRA\nCHL\nCOL\nMEX\n0.00\n0.25\n0.50\n0.75\n1.00\n1.25\n1.50\n1.75\n2.00\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nISR\nSAU\nTUR\nZAF\nARGBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\nZAF\n2\n4\n6\n8\n10\n−0.025\n0.000\n0.025\n0.050\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2000 − 2008\nslope: 22.22; p−val: 0.09; R^2: 0.16\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\nZAF\n2\n4\n6\n8\n10\n−0.025\n0.000\n0.025\n0.050\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2009 − 2019\nslope: 46.608; p−val: 0.012; R^2: 0.32\n(b) real GDP growth in % (y−axis) and annual change in ECI (x−axis)\n(a) ECI levels (y−axis) between 2000 and 2019 (x−axis)\nFig. 5   ECI and GDP growth for 19 ECEs between 2000 and 2019. Sources: World Bank (2021) and \nOEC (2021); author’s calculations and presentation. Notes: Information on the empirical indicator and \ncountry abbreviations can be found in Table A1 Fig. 5   ECI and GDP growth for 19 ECEs between 2000 and 2019. Sources: World Bank (2021) and \nOEC (2021); author’s calculations and presentation. Notes: Information on the empirical indicator and \ncountry abbreviations can be found in Table A1 3 3 372 B. Jungmann improving trend. The Asian and Latin America countries exhibit heterogeneity in terms \nof the ECI level but similar trends. Although to varying degrees, all Asian countries were \nable to increase their non-price competitiveness. In Latin America, only Mexico and to a \nlesser extent Colombia were able to do so. Notably, Saudi Arabia exhibited a significant \nincrease in its ECI in the years following the GFC, especially, as other countries marked \nby a high share of commodity exports, Brazil, Chile and Russia, experienced decreasing \nnon-price competitiveness. 3.3  \u0007Commodity export price index Figure 6 illustrates the recent commodity super cycle which started around 1999. Until the GFC, real energy and metal prices increased by over 100% and those of \nfood by 75%. After a short setback due to the GFC, commodity prices continued \ntheir upwards trajectory until the end of 2014. Traded on world markets, countries \nface the same commodity export and import prices. How commodity prices affect \na countries’ demand and growth will depend among other factors on the share of \ncommodity exports in a country’s GDP. Therefore, we will assess the role of \ncommodity prices in driving ECEs growth via a commodity export price index \n(CEPI) provided by the IMF that weighs a country’s export commodities’ prices \naccording to their share in the country’s GDP (Gruss and Kebhaj 2019) – i.e. the \nCEPI growth rate is higher for the country with a higher share of that commodity in \nits GDP. The pronounced variation in CEPI growth rates reflects the diverse degrees \nof commodity exports to GDP. Therefore, we present the CEPI growth rates with \nslight variation: The bottom right quadrant in panel a) of Fig. 7 groups those five \ncountries with significant higher CEPI growth rates, i.e. the most commodity export \nreliant countries in our sample: Chile, Indonesia, Malaysia, Russia and Saudi Arabia. In all countries, the development of the individual CEPI growth rates resembles the \ncommodity super cycle. With the structural break in this growth driver being so \napparent, we deviate from the GFC periodization for assessing the bivariate cross-\ncountry coefficient and instead choose 2000–2014 as our first period in line with \nthe commodity super cycle. Furthermore, we excluded India and China from the \nsample as it is suggested that their growth considerably drives commodity prices \n(Erten and Ocampo 2013). The bivariate coefficients are positive and insignificant in \nboth periods (panel b) Fig. 7). 3.2  \u0007Price and non‑price competitiveness Looking at panel b) which relates the annual changes in \nthe ECI to growth, we see that Asian and CEE countries exhibited more dynamism in \nimproving their ECI. In both periods, we observe a positive and significant bivariate \ncoefficient. The significance in the pre-GFC sample however hinges on China. In the \npost-GFC years the coefficient increases and becomes more significant. 3.4  \u0007Finance: corporate and household debt To assess the role of private debt as a growth driver, we look at the development \nof private sector debt, i.e. the sum of household and NFC debt, as a percentage 1 3 Growth drivers in emerging capitalist economies: building… 373 0\n1992\n1993\n1994\n1995\n1996\n1997\n1998\n1999\n2000\n2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\n2015\n2016\n2017\n2018\n2019\nAll Commodity Price Index\nAgriculture Price Index\nMetals Price Index\nEnergy Price Index\nFig. 6   Monthly commodity price indices (2016 = 100), 1992–2019. Sources: IMF (2021a); author’s presentation 0\n20\n40\n60\n80\n100\n120\n140\n160\n180\n200\n220\n240\n260\n280\n300\n320\n1992\n1993\n1994\n1995\n1996\n1997\n1998\n1999\n2000\n2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\n2015\n2016\n2017\n2018\n2019\nAll Commodity Price Index\nAgriculture Price Index\nMetals Price Index\nEnergy Price Index\nFig. 6   Monthly commodity price indices (2016 = 100), 1992–2019. Sources: IMF (2021a); author’s presentation 374 B. Jungmann −2.0\n−1.5\n−1.0\n−0.5\n0.0\n0.5\n1.0\n1.5\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCHN\nIND\nKOR\nTHA\n−2.0\n−1.5\n−1.0\n−0.5\n0.0\n0.5\n1.0\n1.5\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCZE\nHUN\nISR\nPOL\nTUR\nZAF\n−2.0\n−1.5\n−1.0\n−0.5\n0.0\n0.5\n1.0\n1.5\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nARG\nBRA\nCOL\nMEX\n−20\n−15\n−10\n−5\n0\n5\n10\n15\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCHL\nIDN\nMYS\nRUS\nSAU\nARG\nBRA\nCHL\nCOL\nCZE\nHUN\nIDN\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\nZAF\n1\n2\n3\n4\n5\n6\n0.0\n0.5\n1.0\n1.5\n2.0\n2.5\n3.0\n3.5\n4.0\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2000 − 2014\nslope: 0.327; p−val: 0.223; R^2: 0.1\nCHL\nCOL\nCZE\nHUN\nIDN\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\nZAF\n1\n2\n3\n4\n5\n6\n−2.0\n−1.5\n−1.0\n−0.5\n0.0\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2015 − 2019\nslope: 0.21; p−val: 0.76; R^2: 0.01\n(b) real GDP growth in % (y−axis) and cepi growth rate (x−axis)\n(a) cepi annual growth rate (y−axis) between 2000 and 2019 (x−axis)\nFig. 7   CEPI and GDP growth for 19 ECEs between 2000 and 2019. 3.4  \u0007Finance: corporate and household debt Sources: World Bank (2021) and \nIMF (2021a); author’s calculations and presentation. Notes: Information on the empirical indicator and \ncountry abbreviations can be found in Table A1 Fig. 7   CEPI and GDP growth for 19 ECEs between 2000 and 2019. Sources: World Bank (2021) and \nIMF (2021a); author’s calculations and presentation. Notes: Information on the empirical indicator and \ncountry abbreviations can be found in Table A1 of GDP. As elaborated in 2.5, this is a very broad measure that may be driven by \nvarious factors with diverse effects on growth, e.g. financialisation processes or \nfinancial development. Furthermore, the evolution of private debt is endogenous to \nthe development of growth. Hence, this examination is only a first step towards a \nmore sophisticated analysis of finance growth drivers in ECEs. Panel a) of Fig. 8 \nshows that the cyclicality varies among the countries in terms of the magnitude and \nbreaking points. In some countries the years around the GFC constitute a breaking \npoint. A case in point is China which saw a significant increase in its private debt \nlevel in the years following the GFC, rendering it the country with the highest \nprivate debt level in the sample. Despite the high degree of variety within the Asian \nregion, it has arguably the highest private debt levels. The CEE countries exhibit \na rather steady increase, with Hungary and its pronounced financial cycle, which \nreversed after the GFC, being the exception. For the Latin American countries, \nChile is that exception, exhibiting similarities to the Chinese trajectory albeit at a \nlower level. Looking at the bivariate coefficients, where we have separated NFC \ndebt from that of households, we see in panel b) that deleveraging was slightly \nmore prevalent in the pre-GFC years. After the GFC, however, a clear majority saw \nincreasing NFC debt. In the post-GFC period, the bivariate coefficient for NFC debt \nturns positive however not reaching the 10% level of significance. Panel c) relates \nthe annual change in household debt to growth. The bivariate coefficient becomes \nmore positive in the post-GFC years. While it also increases its significance, it does \nnot reach the 10% level either. of GDP. As elaborated in 2.5, this is a very broad measure that may be driven by \nvarious factors with diverse effects on growth, e.g. financialisation processes or \nfinancial development. Furthermore, the evolution of private debt is endogenous to \nthe development of growth. 3.4  \u0007Finance: corporate and household debt Hence, this examination is only a first step towards a \nmore sophisticated analysis of finance growth drivers in ECEs. Panel a) of Fig. 8 \nshows that the cyclicality varies among the countries in terms of the magnitude and \nbreaking points. In some countries the years around the GFC constitute a breaking \npoint. A case in point is China which saw a significant increase in its private debt \nlevel in the years following the GFC, rendering it the country with the highest \nprivate debt level in the sample. Despite the high degree of variety within the Asian \nregion, it has arguably the highest private debt levels. The CEE countries exhibit \na rather steady increase, with Hungary and its pronounced financial cycle, which \nreversed after the GFC, being the exception. For the Latin American countries, \nChile is that exception, exhibiting similarities to the Chinese trajectory albeit at a \nlower level. Looking at the bivariate coefficients, where we have separated NFC \ndebt from that of households, we see in panel b) that deleveraging was slightly \nmore prevalent in the pre-GFC years. After the GFC, however, a clear majority saw \nincreasing NFC debt. In the post-GFC period, the bivariate coefficient for NFC debt \nturns positive however not reaching the 10% level of significance. Panel c) relates \nthe annual change in household debt to growth. The bivariate coefficient becomes \nmore positive in the post-GFC years. While it also increases its significance, it does \nnot reach the 10% level either. 3.4  \u0007Finance: corporate and household debt Sources: World Bank (2021) \nand BIS (2021a); author’s calculations and presentation. Notes: In the pre-GFC period simple linear \nregression, South Africa was excluded due to insufficient data. Information on the empirical indicator \nand country abbreviations can be found in Table A1 3.4  \u0007Finance: corporate and household debt 1 3 Growth drivers in emerging capitalist economies: building… 375 50\n100\n150\n200\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCHN\nIDN\nIND\nKOR\nMYS\nTHA\n50\n100\n150\n200\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCZE\nHUN\nPOL\nRUS\n50\n100\n150\n200\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nARG\nBRA\nCHL\nCOL\nMEX\n50\n100\n150\n200\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nISR\nSAU\nTUR\nZAF\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\n2\n4\n6\n8\n10\n−5\n−4\n−3\n−2\n−1\n0\n1\n2\n3\n4\n5\n6\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2000 − 2008\nslope: −0.155; p−val: 0.302; R^2: 0.07\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\nZAF\n2\n4\n6\n8\n10\n−3\n−2\n−1\n0\n1\n2\n3\n4\n5\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2009 − 2019\nslope: 0.386; p−val: 0.183; R^2: 0.1\n(b) real GDP growth in % (y−axis) and annual change in NFC debt to GDP in % (x−axis)\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\n2\n4\n6\n8\n10\n−2\n−1\n0\n1\n2\n3\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2000 − 2008\nslope: 0.362; p−val: 0.219; R^2: 0.093\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\nZAF\n2\n4\n6\n8\n10\n−1\n0\n1\n2\n3\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2009 − 2019\nslope: 0.613; p−val: 0.141; R^2: 0.12\n(c) real GDP growth in % (y−axis) and annual change in household debt to GDP in % (x−axis)\n(a) Private sector debt to GDP in % (y−axis) between 2000 and 2019 (x−axis)\nFig. 8   Private debt and GDP growth for 19 ECEs between 2000 and 2019. Sources: World Bank (2021) \nand BIS (2021a); author’s calculations and presentation. Notes: In the pre-GFC period simple linear \nregression, South Africa was excluded due to insufficient data. Information on the empirical indicator \nand country abbreviations can be found in Table A1 Fig. 8   Private debt and GDP growth for 19 ECEs between 2000 and 2019. 3.5  \u0007Foreign direct investment Looking at the development of the FDI stock since 2000 in panel a) of Fig. 9, we see \na rising trend with only few exceptions. The Asian countries are marked by com-\nparatively low to medium/high levels of FDI stock that has developed rather steady. The Middle Eastern and African countries are similar in terms of the level and trend \nhowever in a less stable way. Among the CEE countries, we find with the Czech \nRepublic but especially Hungary two countries with the highest level of FDI stock. Notably, the high level of FDI stock in Hungary was largely build up between 2005 \nand 2009 and plateaued more or less after the GFC. For the Latin American coun-\ntries we see a similar pattern as with private debt as Chile exhibits the largest stock \nof FDI, which growth significantly accelerated after the GFC. In all Latin American \ncountries with the exception of Argentina, FDI shows a continuous increasing trend. The bivariate coefficients in panel b) relating the FDI inflows to growth are negative \nand insignificant in both periods. 3.6  \u0007Fiscal policy: government structural balance The assessment of fiscal policy as a growth driver is associated with some issues. Important parameters of fiscal policy like government expenditures, tax revenue \nand the resulting government balance are highly endogenous with respect to growth 1 3 B. Jungmann 376 25\n50\n75\n100\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCHN\nIDN\nIND\nKOR\nMYS\nTHA\n25\n50\n75\n100\n50\n100\n150\n200\n250\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCZE\nHUN (RHS)\nPOL\nRUS\n25\n50\n75\n100\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nARG\nBRA\nCHL\nCOL\nMEX\n25\n50\n75\n100\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nISR\nSAU\nTUR\nZAF\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\nZAF\n2\n4\n6\n8\n10\n0\n1\n2\n3\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2000 − 2008\nslope: −0.39; p−val: 0.417; R^2: 0.04\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nSAU\nTHA\nTUR\nZAF\n2\n4\n6\n8\n10\n0\n1\n2\n3\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2009 − 2019\nslope: −0.494; p−val: 0.232; R^2: 0.08\n(b) real GDP growth in % (y−axis) and net FDI inflows in % of GDP (x−axis)\n(a) FDI stock per GDP in % (y−axis) between 2000 and 2019 (x−axis)\nFig. 9   FDI and GDP growth for 19 ECEs between 2000 and 2019. Sources: World Bank (2021) and \nLane and Milesi-Ferretti (2018); author’s calculations and presentation. Notes: In the pre-GFC period \nsimple linear regression, Hungary was excluded as an extreme outlier. Information on the empirical indi-\ncator and country abbreviations can be found in Table A1 Fig. 9   FDI and GDP growth for 19 ECEs between 2000 and 2019. Sources: World Bank (2021) and \nLane and Milesi-Ferretti (2018); author’s calculations and presentation. Notes: In the pre-GFC period \nsimple linear regression, Hungary was excluded as an extreme outlier. Information on the empirical indi-\ncator and country abbreviations can be found in Table A1 and economic activity. To avoid this problem, the structural government balance is \nemployed which tries to filter out the cyclical and endogenous elements of the gov-\nernment balance. 17  The determination of the cyclical and structural elements of the government balance involves various \nestimations which have become a centre of discussion. Current estimation methods have been accused to \nunderestimate the potential output and output gap. This would then label fiscal policy more expansionary \nthan it actually is (see e.g. Heimberger and Kapeller 2017). By choosing the annual change in the struc-\ntural government balance we try to avoid at least the estimation issues in terms of the level (Hein and \nMartschin 2021). 3.6  \u0007Fiscal policy: government structural balance The more negative (positive) the structural government balance, \nthe more expansionary (contractionary) the fiscal policy stance.17 The four quad-\nrants in panel a) of Fig. 10 suggest that the GFC constitute some form of break-\ning point in terms of the fiscal policy stance. For the Asian countries this is not as \napparent as the levels and the trends are rather heterogeneous. The CEE countries \nexhibit with some variation a trend of improving structural government balances, \ni.e. a trend towards more contractionary fiscal policy. In the Latin American coun-\ntries, the GFC separates two distinctive periods, the former one exhibiting higher \nstructural balances and improving tendencies and the latter period a lower level and \ndownward trend – this development is most pronounced in Brazil and Argentina. For \nthe Middle East and African countries, the GFC was followed by lower structural \ngovernment balances. Notably, 2015 constituted a breaking point for Israel and Tur-\nkey after which the structural government balances fell considerably. 1 3 Growth drivers in emerging capitalist economies: building… 377 −10\n−8\n−6\n−4\n−2\n0\n2\n4\n6\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCHN\nIDN\nIND\nKOR\nMYS\nTHA\n−10\n−8\n−6\n−4\n−2\n0\n2\n4\n6\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nCZE\nHUN\nPOL\nRUS\n−10\n−8\n−6\n−4\n−2\n0\n2\n4\n6\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nARG\nBRA\nCHL\nCOL\nMEX\n−10\n−8\n−6\n−4\n−2\n0\n2\n4\n6\n2000\n2002\n2004\n2006\n2008\n2010\n2012\n2014\n2016\n2018\nISR\nTUR\nZAF\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nTHA\nTUR\nZAF\n2\n4\n6\n8\n10\n−0.50\n−0.25\n0.00\n0.25\n0.50\n0.75\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2000 − 2008\nslope: 1.1; p−val: 0.362; R^2: 0.05\nARG\nBRA\nCHL\nCHN\nCOL\nCZE\nHUN\nIDN\nIND\nISR\nKOR\nMEX\nMYS\nPOL\nRUS\nTHA\nTUR\nZAF\n2\n4\n6\n8\n10\n−0.5\n−0.4\n−0.3\n−0.2\n−0.1\n0.0\n0.1\n0.2\n0.3\n0.4\nAfrica\nAsia\nCEE\nLatin America\nMiddle East\n2009 − 2019\nslope: −1.141; p−val: 0.59; R^2: 0.02\n(b) real GDP growth in % (y−axis) and government structural balance annual change in % of GDP (x−axis)\n(a) Government structural balance per GDP in % (y−axis) between 2000 and 2019 (x−axis)\nFig. 3.6  \u0007Fiscal policy: government structural balance 10   Government structural balance and GDP growth for 19 ECEs between 2000 and 2019. Sources: \nWorld Bank (2021) and IMF (2021b); author’s calculations and presentation. Notes: Information on the \nempirical indicator and country abbreviations can be found in Table A1 Fig. 10   Government structural balance and GDP growth for 19 ECEs between 2000 and 2019. Sources: \nWorld Bank (2021) and IMF (2021b); author’s calculations and presentation. Notes: Information on the \nempirical indicator and country abbreviations can be found in Table A1 The simple linear regressions in panel b) of Fig. 10 emphasize the descriptive \nfinding that fiscal policy became overall more expansionary after the GFC. Relating \nthe fiscal policy stance to growth, in the pre-GFC period, higher structural balances, \ni.e. more contractionary fiscal policy, were associated with higher growth. This rela-\ntionship reversed after the GFC. However, both cases lack significance. 4  \u0007ECEs growth drivers: heterogeneity, possible country clusters \nand their relation to international growth models Before further assessing our results, some caveats regarding the methodology, in \nparticular, the bivariate coefficients should be recalled. First, we do establish coef-\nficients that, at best, signify correlation. Correlations are not causalities, however, \nfor causalities to exist there have to be correlations. Furthermore, we derive cross-\ncountry coefficients. The absence of a cross-country correlation does not necessarily \nindicate that this indicator was irrelevant for each country. Instead, it may suggest \nheterogeneity among countries – e.g. in terms of financialisation (Karwowski 2020) \nor growth model (Akcay et al. 2022). Moreover, the very nature of some growth \ndrivers prohibits that they drive growth on a cross-country level. Commodity prices \nfor example rise in a secular way, their net effect on growth however does not only \ndepend on the importance of commodity exports for a country’s overall demand but \nalso on the negative demand effects that higher commodity prices might have. Simi-\nlarly, price competitiveness and the wage share can hardly become cross-country \ndrivers as the net effect of increased price competiveness, respectively, higher profit 1 3 3 3 378 B. Jungmann shares varies among countries (Table 1). A further problem concerning the estima-\ntion of growth drivers is that of endogeneity as GDP growth affects itself various \ngrowth drivers. Going forward, this should be addressed by choosing variables that \nare more exogenous to demand and growth or by employing more sophisticated \nmethods, e.g. via lagged or instrument variables. Albeit these problems, some conclusions can be drawn from our empirical explo-\nration. Our results indicate that non-price competitiveness drove growth across \nECEs, particularly in the post-GFC period (Fig. 5). This is in line with the liter-\nature on economic complexity (Hidalgo and Hausmann 2009), Thirlwall’s (1979) \nlaw, and economic structuralism (Ocampo and Parra 2006) and resembles the results \nfor DCEs (Gräbner et al. 2020; Hein and Martschin 2021; Kohler and Stockham-\nmer 2022). Hence, our results underscore the importance, as highlighted by Kohler \nand Stockhammer (2022), of expanding the notion of competitiveness beyond \nprice factors in the Comparative and International Political Economy literature, \neven for ECEs. Especially, as we likewise fail to find a cross-country role of price \ncompetitiveness.18ii Although lacking significance, the increased importance of expansionary fiscal \npolicy and private debt as growth drivers for the post-GFC years is noteworthy. This is accompanied by overall increased private debt levels and a more \nexpansionary fiscal policy stance (Fig. 8 and 10). 18  In contrast, we find a positive relation between real appreciations and growth. This may simply be \ndue to reversed causality, i.e. growth leading to real appreciations. On the other hand, it may signal the \nnegative effects associated with real depreciations, especially, in the light of progressed financialisation \nand foreign currency denominated debt (McCauley et  al. 2015). Moreover, import dependencies may \nnarrow the possible gains from depreciations. 4  \u0007ECEs growth drivers: heterogeneity, possible country clusters \nand their relation to international growth models Notes: OECD member states \nas of June 2021 excluding the member countries that are part of our sample. Information on the empirical \nindicator and country abbreviations can be found in Table A1 least some of the heterogeneity in the descriptive data. The Asian group is marked \nby comparatively high growth rates, more stable REERs, higher and rising \nECI levels and high private debt levels. The Latin American countries exhibit \ncomparatively low growth rates, high levels of income inequality but with a \ndownward trend, rather unstable REERs and a relatively expansionary fiscal policy \nstance, especially after the GFC. The heterogeneity among the Middle Eastern \nand African countries are too pronounced. The CEE countries, often with the \nexception of Russia, exhibit medium to high growth rates and comparatively low \nlevels of income inequality. Moreover, their REERs are rather stable, particularly, \nafter the GFC when they achieved overall depreciations while their fiscal policy \nstance became less expansionary at the same time. Finally, their ECI levels and \nFDI stock is rather high. least some of the heterogeneity in the descriptive data. The Asian group is marked \nby comparatively high growth rates, more stable REERs, higher and rising \nECI levels and high private debt levels. The Latin American countries exhibit \ncomparatively low growth rates, high levels of income inequality but with a \ndownward trend, rather unstable REERs and a relatively expansionary fiscal policy \nstance, especially after the GFC. The heterogeneity among the Middle Eastern \nand African countries are too pronounced. The CEE countries, often with the \nexception of Russia, exhibit medium to high growth rates and comparatively low \nlevels of income inequality. Moreover, their REERs are rather stable, particularly, \nafter the GFC when they achieved overall depreciations while their fiscal policy \nstance became less expansionary at the same time. Finally, their ECI levels and \nFDI stock is rather high. It is noteworthy that, apart from Russia, the CEE countries not only share \nsimilarities in the descriptive statistics of growth drivers, but also resemble each \nother in the development of their growth models, following the path of many other \nEuropean DCEs to become or remain export-led after the GFC (Dodig et al. 2016; \nHein et al. 2021). At the same time, these countries were already subsumed in the \n‘dependent market economies’ category within the second VoC generation (Nölke \nand Vliegenthart 2009). 4  \u0007ECEs growth drivers: heterogeneity, possible country clusters \nand their relation to international growth models The indications of private debt \nand expansionary fiscal policy becoming more relevant in driving ECEs’ growth \nfollowing the GFC can be viewed as a consequence of developments in DCEs. ECEs are to a considerable extent subject to DCEs and their growth models, \nespecially, in terms of external demand that determines the feasibility of export-\nled growth models in ECEs. With DCEs reducing their imports and developing \nincreasingly export-led growth models due to austerity policies and private \ndeleveraging (Hein et  al. 2021; Kohler and Stockhammer 2022) the increased \nimportance of private debt and fiscal policy constitutes a form of compensation \nfor this loss in external demand. In consequence, growth models in ECEs became \nrather domestic demand or even private debt-led (Akcay et al. 2022) accompanied \nby a rise in importance of the respective growth drivers (Table  2). This \ndevelopment is evident in the current accounts, with ECEs showing a tendency of \nworsening and DCEs one of improvement, as it is shown in Fig. 11. Not only did \nthe aggregated current account deficit of DCEs shrink after the GFC, it became \nroughly balanced since 2013 and turned into a surplus in 2016. Despite these tendencies, our results for robust cross-country growth drivers \nremain sparse. As mentioned, this can be attributed in part to the heterogeneity \namong ECEs, for example, in terms of their growth models and their respective \ndrivers (Table  2). We have seen that our regional grouping was able to sort at 1 3 Growth drivers in emerging capitalist economies: building… 379 -1000000\n-800000\n-600000\n-400000\n-200000\n0\n200000\n400000\n600000\n800000\n1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018\nARG\nBRA\nCHL\nCOL\nCHN\nCZE\nHUN\nIDN\nISR\nKOR\nMYS\nMEX\nPOL\nRUS\nSAU\nZAF\nTHA\nTUR\nIND\nOECD\nFig. 11   Current account balances in current million US$ of 19 ECEs and OECD member states, 1980–\n2019. Sources: World Bank (2021); author’s calculations and presentation. Notes: OECD member states \nas of June 2021 excluding the member countries that are part of our sample. Information on the empirical \nindicator and country abbreviations can be found in Table A1 Fig. 11   Current account balances in current million US$ of 19 ECEs and OECD member states, 1980–\n2019. Sources: World Bank (2021); author’s calculations and presentation. 5  \u0007Conclusions In this paper, we expanded the concept of growth drivers to ECEs. On a conceptual \nlevel, we made the case for a synthesis of growth drivers with a growth model \noperationalization based on GDP growth contributions and financial sector balances. Building on post-Keynesian and structuralist economics as well as empirical studies \non ECEs, seven growth drivers for ECEs were reviewed: income distribution, \nprice and non-price competitiveness, commodity prices, private debt, FDI and \nfiscal policy. In an empirical exploration we have presented descriptive data for \nseven growth drivers of 19 ECEs between 2000 and 2019. We find that the Asian \nECEs exhibit on average higher growth rates, stable REERs, higher and rising \nnon-price competitiveness and high private debt levels. In Latin America, we find \ncomparatively low growth rates, high levels of income inequality that are abating, \nunstable RERs and relatively expansionary fiscal stances after the GFC. The CEE \ncountries, often with the exception of Russia, exhibit medium to high growth rates, \ncomparatively low levels of income inequality, high non-price competiveness and a \nhigh FDI stock. Moreover, their RERs were rather stable, particularly, after the GFC \nwhen they achieved overall depreciations while their fiscal policy stance became \nless expansionary. Furthermore, we assessed which growth drivers determined growth on a cross-\ncountry level. To that end, we established bivariate coefficients. In that regard robust \nresults remained sparse. The only robust and significant exception concerns non-\nprice competitiveness which we found to be a cross-country growth driver for ECEs. Furthermore, we find indications of private debt and expansionary fiscal policy \nbecoming more important in driving growth in ECEs after the GFC. This increase in \nimportance is in line with the emergence and continuation of domestic demand- and \nprivate debt-led growth models in ECEs following the GFC (Akcay et al. 2022). In \nthis context, the increased importance of private debt and expansionary fiscal policy \nin driving ECEs’ growth can be viewed as a form of compensation for reduced \nexternal demand from DCEs in the course of private deleveraging and austerity \npolicies after the GFC (Hein et al. 2021; Kohler and Stockhammer 2022). Several issues remain regarding the empirical investigation of growth drivers, \nnamely, the problem of endogeneity with respect to growth and the heterogeneity \namong ECEs. 4  \u0007ECEs growth drivers: heterogeneity, possible country clusters \nand their relation to international growth models This suggest that growth drivers will not only be similar \nwithin the same growth models but should also be studied through the critically \nretention of institutionalist aspects from earlier VoC contributions as proposed by \nSchedelik et al. (2021) and implied by Stockhammer and Kohler (2022). 1 3 B. Jungmann 380 Supplementary Information  The online version contains supplementary material available at https://​doi.​\norg/​10.​1007/​s43253-​023-​00101-1. Declarations Conflict of interest  The author reports no conflict of interest. Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, \nwhich permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as \nyou give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-\nmons licence, and indicate if changes were made. The images or other third party material in this article \nare included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the \nmaterial. If material is not included in the article’s Creative Commons licence and your intended use is \nnot permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission \ndirectly from the copyright holder. To view a copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​\nses/​by/4.​0/. 5  \u0007Conclusions Going forward, building on our results, growth drivers should \nbe examined through more sophisticated methods dealing with endogeneity \nissues while the problem of heterogeneity could be addressed through adequate \ngrouping, e.g. along the lines of growth models (Akcay et al. 2022) or institutional \nconfigurations (Schedelik et al. 2021). Finally, while we have proposed that growth \ndriver analysis can inform growth model analysis using common growth model \ncategories, one may extend this synthesis to further categories and methodologies \n– for instance, the investment-led growth model (Mertens et  al. 2022) and the \ngrowth decomposition based on the Sraffian supermultiplier (Campana et al. 2024; \nMorlin et al. 2022; Passos and Morlin 2022). Supplementary Information  The online version contains supplementary material available at https://​doi.​\norg/​10.​1007/​s43253-​023-​00101-1. 3 1 Growth drivers in emerging capitalist economies: building… 381 Acknowledgements  This paper is a substantially revised version of my master thesis submitted to the \nBerlin School of Economics and Law and the University Paris Sorbonne Nord. I am most grateful for the \ncomments, help and advice I received from Eckhard Hein, Ümit Akcay and Jonathan Marie. Furthermore, \nI want to thank three anonymous referees and Engelbert Stockhammer for their comments, which helped \nto improve the paper substantially. All errors are mine. Funding  Open Access funding enabled and organized by Projekt DEAL. Funding  Open Access funding enabled and organized by Projekt DEAL. Data availability  All data used in this study is derived from the public domain resources described in th\ntext and reference list and are also available from the author upon request. 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