Authors’ own creation
Type | Definition |
---|---|
Cities | Towns/settlements with populations greater than 50,000 |
Satellite urban towns | Towns/settlements with populations between 1,500 and 49,999, where 20% or more of the usually resident used population’s workplace address is in “Cities” |
Independent urban towns | Towns/settlements with populations between 1,500 and 49,999, where less than 20% of the usually resident employed population’s workplace address is in “Cities” |
Rural areas with high urban influence | Rural areas (themselves defined as having an area type with a population less than 1,500 persons, as per census 2016) are allocated to one of three sub-categories, based on their dependence on urban areas Again, employment location is the defining variable. The allocation is based on a weighted percentage of resident used adults of a rural small area who work in the three standard categories of urban area (for simplicity the methodology uses main, secondary and minor urban area). The percentages working in each urban area were weighted through the use of multipliers. The multipliers allowed for the increasing urbanisation for different sized urban areas. For example, the percentage of rural people working in a main urban area had double the impact of the same percentage working in a minor urban area. The weighting acknowledges the impact that a large urban centre has on its surrounding area The adopted weights for: Main urban areas is 2 Satellite urban communities is 1.5 Independent urban communities is 1 The weighted percentages is divided into tertials to assign one of the three rural breakdowns |
Rural areas with moderate urban influence | |
Highly rural/remote areas |
Area/Typology | Social enterprises | Population | Ratio (SE/10,000 inhabitants) | |||
---|---|---|---|---|---|---|
% | % | |||||
Highly rural/remote areas | 865 | 20.4 | 412,457 | 8.8 | 21.0 | 10.8 (total rural) |
Rural areas with moderate urban influence | 580 | 13.7 | 587,041 | 12.5 | 9.9 | |
Rural areas with high urban influence | 447 | 10.6 | 754,794 | 16.1 | 5.9 | |
Independent urban towns | 991 | 23.4 | 770,329 | 16.4 | 12.9 | 8.0 (total urban) |
Satellite urban towns | 293 | 6.9 | 597,355 | 12.8 | 4.9 | |
Cities | 1,058 | 25.0 | 1,567,945 | 33.4 | 6.7 | |
Total | 4,234 | 100 | 4,689,921 | 100 | 9.0 |
Authors’ own creation
Type of area | Childcare (%) | Community infrastructure and local development (%) | Health, youth services and social care (%) | Heritage, festivals, arts and creative industry (%) | Sport and leisure (%) | Training and work integration (%) | Information, support and financial services (%) | Housing (%) | Food, agriculture, catering (%) | Environment, circular economy and renewable energy (%) | Retailing (%) | Transport (%) | Manufacturing (%) | Other (%) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Highly rural/remote areas | 28.7 | 23.8 | 8.6 | 15.7 | 5.3 | 3.5 | 3.6 | 2.9 | 3.1 | 2.3 | 1.4 | 0.2 | 0.2 | 0.6 |
Rural areas with moderate urban influence | 32.2 | 21.9 | 9.0 | 10.5 | 9.5 | 3.6 | 2.4 | 2.2 | 3.1 | 2.4 | 1.2 | 0.9 | 0.2 | 0.9 |
Rural areas with high urban influence | 23.7 | 22.4 | 9.4 | 10.1 | 13.4 | 4.7 | 5.6 | 3.4 | 3.6 | 2.2 | 0.4 | 0.0 | 0.2 | 1.1 |
Independent urban towns | 23.8 | 14.6 | 14.5 | 12.7 | 8.9 | 5.8 | 7.1 | 5.2 | 2.2 | 1.3 | 1.8 | 0.7 | 0.4 | 1.1 |
Satellite urban towns | 23.5 | 16.4 | 20.1 | 8.5 | 7.5 | 6.1 | 4.1 | 4.4 | 1.4 | 2.4 | 2.7 | 1.7 | 0.3 | 0.7 |
Cities | 28.9 | 7.9 | 18.9 | 5.6 | 4.3 | 9.5 | 9.8 | 7.0 | 2.2 | 4.0 | 0.6 | 0.2 | 0.4 | 0.7 |
All Ireland | 26.7 | 16.4 | 13.7 | 10.7 | 7.6 | 6.1 | 5.8 | 4.5 | 2.7 | 2.6 | 1.2 | 0.5 | 0.3 | 0.9 |
Authors’ own creation
Hypothesis | Decision |
---|---|
: the presence of social enterprises is significantly associated with the type of rural–urban areas | Supported |
: the presence of social enterprises is positively associated to areas with lower population density and greater distance to services and amenities (remoteness). | Supported |
: the presence of social enterprises within the capital city (Dublin) is significantly higher compared to the national average and to other rural and urban areas of Ireland | Not supported |
: there is a significant relationship between the sectors of activities in which social enterprises operate and the type of rural–urban areas in which they are based | Supported |
: there is a positive association between areas with lower population density and greater distance to services and amenities (remoteness) and the presence of social enterprises in community and local development | Supported |
: there is a negative association between areas with lower population density and greater distance to services and amenities (remoteness) and the presence of social enterprises operating in sectors related to welfare objectives such as childcare, health and social care | Not supported |
Authors’ own creation
df | -value | Decision | ||
---|---|---|---|---|
SEs ratio – rural/urban area | 309.17 | 5 | 2.2e ** | Supported |
Authors’ own creation
Comparison (pairwise) | Z | P. unadj | P. adj (Bonferroni) |
---|---|---|---|
Highly rural/remote areas – Rural areas with moderate urban influence | 6.432 | 1.26E-10 | 1.89E-09** |
Highly rural/remote areas – Rural areas with high urban influence | 9.694 | 3.21E-22 | 4.81E-21** |
Highly rural/remote areas – Independent urban towns | 3.866 | 0.000111 | 0.0017** |
Highly rural/remote areas – Satellite urban towns | 12.304 | 8.65E-35 | 1.308E-33** |
Highly rural/remote areas – Cities | −14.341 | 1.21E-46 | 1.81E-45** |
Rural areas with moderate urban influence – Rural areas with high urban influence | −3.256 | 0.001129 | 0.0169* |
Rural areas with moderate urban influence – Independent urban towns | 3.007 | 0.002637 | 0.0396* |
Rural areas with moderate urban influence – Satellite urban towns | 6.11 | 9.98E-10 | 1.50E-08** |
Rural areas with moderate urban influence – Cities | −6.657 | 2.79E-11 | 4.19E-10** |
Rural areas with high urban influence – Independent urban towns | 6.491 | 8.55E-11 | 1.28E-09** |
Rural areas with high urban influence – Satellite urban towns | 2.979 | 0.002889 | 0.0433* |
Rural areas with high urban influence – Cities | −2.772 | 0.005563 | 0.0834 |
Independent urban towns – Satellite urban towns | 9.378 | 6.74E-21 | 1.01E-19** |
Cities – Independent urban towns | −11.05 | 2.19E-28 | 3.28E-27** |
Cities – Satellite urban towns | 0.879 | 0.379665 | 1 |
Authors’ own creation
Alternative hypothesis | JT | -value | Decision | |
---|---|---|---|---|
Positive association area remoteness and ratio social enterprises | Increasing | 73161607 | 0.001** | Supported |
Authors’ own creation
t-test (Welch Two Sample t-test) | |||||
---|---|---|---|---|---|
Pairs (categories) compared | df | ci (95%) | Decision | ||
Dublin City – satellite urban towns | 1.6129 | 5,163.3 | 0.1068 | (−0.22, 2.24) | Not supported |
Dublin City – rural areas with high urban influence | 1.1337 | 6,491.1 | 0.2569 | (−0.46, 1.75) | Not supported |
Authors’ own creation
df | -value | Decision | ||
---|---|---|---|---|
Association between sector of activity SEs and rural–urban typology | 445.99 | 70 | 2.2e ** | Supported |
Authors’ own creation
and | Alternative hypothesis | JT | -value | Decision |
---|---|---|---|---|
: Positive association rural–urban remoteness and ratio social enterprises in community local development | Increasing | 13 | 0.02778* | Supported |
Negative association rural–urban remoteness and ratio social enterprises in welfare services | Decreasing | 3 | 0.06806 | Not supported |
* p < 0.05
Source: Authors’ own creation
The main source for selecting the papers for the literature review was a search on Scopus (conducted in early 2023), with the search string: TITLE-ABSTRACT-KEYWORDS (“geography” OR “rural” OR “urban” OR “regional”) AND “social enterprises”. From this search only papers where geography was considered an explanatory factor/dimension in the analysis of the features and/or work of social enterprises were selected. The article Douglas et al. (2018) was added by the authors.
Nomenclature of territorial units for statistics (see Eurostat, https://ec.europa.eu/eurostat/web/nuts/background )
The classification of regions into one of the three categories is based on the following criteria:
Population density. A community is defined as rural if its population density is below 150 inhabitants per km 2 (500 inhabitants for Japan to account for the fact that its national population density exceeds 300 inhabitants per km 2 ).
Regions by % population in rural communities. A region is classified as predominantly rural if more than 50% of its population lives in rural communities, predominantly urban if less than 15% of the population lives in rural communities, and intermediate if the share of the population living in rural communities is between 15% and 50%.
Urban centres. A region that would be classified as rural on the basis of the general rule is classified as intermediate if it has an urban centre of more than 200,000 inhabitants (500,000 for Japan) representing no less than 25% of the regional population. A region that would be classified as intermediate on the basis of the general rule is classified as predominantly urban if it has an urban centre of more than 500,000 inhabitants (1,000,000 for Japan) representing no less than 25% of the regional population.
More information about this methodology is available at: “Social Enterprises in Ireland – a Baseline data collection exercise” www.gov.ie/ga/foilsiuchan/b30e5-social-enterprises-in-ireland-a-baseline-data-collection-exercise/#:∼:text=In%202022%2C%20the%20Department%20of%20Rural%20and%20Community,sector%2C%20an%20online%20survey%20was%20developed%20and%20published
QGIS (Quantum Geographical Information System) is a free and open-source software for spatial analysis. See https://qgis.org/en/site/
Now Tailte Éireann, see https://data-osi.opendata.arcgis.com/
The more recent data for population at small area level at the time of this study was from Census 2016.
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This study have been funded by the Department of Rural and Community Development, Government of Ireland – NUI Post-Doctoral Fellowship in Rural Development 2022. The authors would like to thank you the funders for their support and three anonymous reviewers and the editors of the journal for their feedback.
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Objectives This cohort study reported descriptive statistics in athletes engaged in Summer and Winter Olympic sports who sustained a sport-related concussion (SRC) and assessed the impact of access to multidisciplinary care and injury modifiers on recovery.
Methods 133 athletes formed two subgroups treated in a Canadian sport institute medical clinic: earlier (≤7 days) and late (≥8 days) access. Descriptive sample characteristics were reported and unrestricted return to sport (RTS) was evaluated based on access groups as well as injury modifiers. Correlations were assessed between time to RTS, history of concussions, the number of specialist consults and initial symptoms.
Results 160 SRC (median age 19.1 years; female=86 (54%); male=74 (46%)) were observed with a median (IQR) RTS duration of 34.0 (21.0–63.0) days. Median days to care access was different in the early (1; n SRC =77) and late (20; n SRC =83) groups, resulting in median (IQR) RTS duration of 26.0 (17.0–38.5) and 45.0 (27.5–84.5) days, respectively (p<0.001). Initial symptoms displayed a meaningful correlation with prognosis in this study (p<0.05), and female athletes (52 days (95% CI 42 to 101)) had longer recovery trajectories than male athletes (39 days (95% CI 31 to 65)) in the late access group (p<0.05).
Conclusions Olympic athletes in this cohort experienced an RTS time frame of about a month, partly due to limited access to multidisciplinary care and resources. Earlier access to care shortened the RTS delay. Greater initial symptoms and female sex in the late access group were meaningful modifiers of a longer RTS.
Data are available on reasonable request. Due to the confidential nature of the dataset, it will be shared through a controlled access repository and made available on specific and reasonable requests.
https://doi.org/10.1136/bjsports-2024-108211
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Most data regarding the impact of sport-related concussion (SRC) guidelines on return to sport (RTS) are derived from collegiate or recreational athletes. In these groups, time to RTS has steadily increased in the literature since 2005, coinciding with the evolution of RTS guidelines. However, current evidence suggests that earlier access to care may accelerate recovery and RTS time frames.
This study reports epidemiological data on the occurrence of SRC in athletes from several Summer and Winter Olympic sports with either early or late access to multidisciplinary care. We found the median time to RTS for Olympic athletes with an SRC was 34.0 days which is longer than that reported in other athletic groups such as professional or collegiate athletes. Time to RTS was reduced by prompt access to multidisciplinary care following SRC, and sex-influenced recovery in the late access group with female athletes having a longer RTS timeline. Greater initial symptoms, but not prior concussion history, were also associated with a longer time to RTS.
Considerable differences exist in access to care for athletes engaged in Olympic sports, which impact their recovery. In this cohort, several concussions occurred during international competitions where athletes are confronted with poor access to organised healthcare. Pathways for prompt access to multidisciplinary care should be considered by healthcare authorities, especially for athletes who travel internationally and may not have the guidance or financial resources to access recommended care.
After two decades of consensus statements, sport-related concussion (SRC) remains a high focus of research, with incidence ranging from 0.1 to 21.5 SRC per 1000 athlete exposures, varying according to age, sex, sport and level of competition. 1 2 Evidence-based guidelines have been proposed by experts to improve its identification and management, such as those from the Concussion in Sport Group. 3 Notably, they recommend specific strategies to improve SRC detection and monitoring such as immediate removal, 4 prompt access to healthcare providers, 5 evidence-based interventions 6 and multidisciplinary team approaches. 7 It is believed that these guidelines contribute to improving the early identification and management of athletes with an SRC, thereby potentially mitigating its long-term consequences.
Nevertheless, evidence regarding the impact of SRC guidelines implementation remains remarkably limited, especially within high-performance sport domains. In fact, most reported SRC data focus on adolescent student-athletes, collegiate and sometimes professional athletes in the USA but often neglect Olympians. 1 2 8–11 Athletes engaged in Olympic sports, often referred to as elite amateurs, are typically classified among the highest performers in elite sport, alongside professional athletes. 12 13 They train year-round and uniquely compete regularly on the international stage in sports that often lack professional leagues and rely on highly variable resources and facilities, mostly dependent on winning medals. 14 Unlike professional athletes, Olympians do not have access to large financial rewards. Although some Olympians work or study in addition to their intensive sports practice, they can devote more time to full-time sports practice compared with collegiate athletes. Competition calendars in Olympians differ from collegiate athletes, with periodic international competitions (eg, World Cups, World Championships) throughout the whole year rather than regular domestic competitions within a shorter season (eg, semester). Olympians outclass most collegiate athletes, and only the best collegiate athletes will have the chance to become Olympians and/or professionals. 12 13 15 In Canada, a primary reason for limited SRC data in Olympic sports is that the Canadian Olympic and Paralympic Sports Institute (COPSI) network only adopted official guidelines in 2018 to standardise care for athletes’ SRC nationwide. 16 17 The second reason could be the absence of a centralised medical structure and surveillance systems, identified as key factors contributing to the under-reporting and underdiagnosis of athletes with an SRC. 18
Among the available evidence on the evolution of SRC management, a 2023 systematic review and meta-analysis in athletic populations including children, adolescents and adults indicated that a full return to sport (RTS) could take up to a month but is estimated to require 19.8 days on average (15.4 days in adults), as opposed to the initial expectation of approximately 10.0 days based on studies published prior to 2005. 19 In comparison, studies focusing strictly on American collegiate athletes report median times to RTS of 16 days. 9 20 21 Notably, a recent study of military cadets reported an even longer return to duty times of 29.4 days on average, attributed to poorer access to care and fewer incentives to return to play compared with elite sports. 22 In addition, several modifiers have also been identified as influencing the time to RTS, such as the history of concussions, type of sport, sex, past medical problems (eg, preinjury modifiers), as well as the initial number of symptoms and their severity (eg, postinjury modifiers). 20 22 The evidence regarding the potential influence of sex on the time to RTS has yielded mixed findings in this area. 23–25 In fact, females are typically under-represented in SRC research, highlighting the need for additional studies that incorporate more balanced sample representation across sexes and control for known sources of bias. 26 Interestingly, a recent Concussion Assessment, Research and Education Consortium study, which included a high representation of concussed female athletes (615 out of 1071 patients), revealed no meaningful differences in RTS between females and males (13.5 and 11.8 days, respectively). 27 Importantly, findings in the sporting population suggested that earlier initiation of clinical care is linked to shorter recovery after concussion. 5 28 However, these factors affecting the time to RTS require a more thorough investigation, especially among athletes engaged in Olympic sports who may or may not have equal access to prompt, high-quality care.
Therefore, the primary objective of this study was to provide descriptive statistics among athletes with SRC engaged in both Summer and Winter Olympic sport programmes over a quadrennial, and to assess the influence of recommended guidelines of the COPSI network and the fifth International Consensus Conference on Concussion in Sport on the duration of RTS performance. 16 17 Building on available evidence, the international schedule constraints, variability in resources 14 and high-performance expectation among this elite population, 22 prolonged durations for RTS, compared with what is typically reported (eg, 16.0 or 15.4 days), were hypothesised in Olympians. 3 19 The secondary objective was to more specifically evaluate the impact of access to multidisciplinary care and injury modifiers on the time to RTS. Based on current evidence, 5 7 29 30 the hypothesis was formulated that athletes with earlier multidisciplinary access would experience a faster RTS. Regarding injury modifiers, it was expected that female and male athletes would show similar time to RTS despite presenting sex-specific characteristics of SRC. 31 The history of concussions, the severity of initial symptoms and the number of specialist consults were expected to be positively correlated to the time to RTS. 20 32
A total of 133 athletes (F=72; M=61; mean age±SD: 20.7±4.9 years old) who received medical care at the Institut national du sport du Québec, a COPSI training centre set up with a medical clinic, were included in this cohort study with retrospective analysis. They participated in 23 different Summer and Winter Olympic sports which were classified into six categories: team (soccer, water polo), middle distance/power (rowing, swimming), speed/strength (alpine skiing, para alpine skiing, short and long track speed skating), precision/skill-dependent (artistic swimming, diving, equestrian, figure skating, gymnastics, skateboard, synchronised skating, trampoline) and combat/weight-making (boxing, fencing, judo, para judo, karate, para taekwondo, wrestling) sports. 13 This sample consists of two distinct groups: (1) early access group in which athletes had access to a medical integrated support team of multidisciplinary experts within 7 days following their SRC and (2) late access group composed of athletes who had access to a medical integrated support team of multidisciplinary experts eight or more days following their SRC. 5 30 Inclusion criteria for the study were participation in a national or international-level sports programme 13 and having sustained at least one SRC diagnosed by an authorised healthcare practitioner (eg, physician and/or physiotherapist).
The institute clinic provides multidisciplinary services for care of patients with SRC including a broad range of recommended tests for concussion monitoring ( table 1 ). The typical pathway for the athletes consisted of an initial visit to either a sports medicine physician or their team sports therapist. A clinical diagnosis of SRC was then confirmed by a sports medicine physician, and referral for the required multidisciplinary assessments ensued based on the patient’s signs and symptoms. Rehabilitation progression was based on the evaluation of exercise tolerance, 33 priority to return to cognitive tasks and additional targeted support based on clinical findings of a cervical, visual or vestibular nature. 17 The expert team worked in an integrated manner with the athlete and their coaching staff for the rehabilitation phase, including regular round tables and ongoing communication. 34 For some athletes, access to recommended care was fee based, without a priori agreements with a third party payer (eg, National Sports Federation).
Main evaluations performed to guide the return to sport following sport-related concussion
Data were collected at the medical clinic using a standardised injury surveillance form based on International Olympic Committee guidelines. 35 All injury characteristics were extracted from the central injury database between 1 July 2018 and 31 July 2022. This period corresponds to a Winter Olympic sports quadrennial but also covers 3 years for Summer Olympic sports due to the postponing of the Tokyo 2020 Olympic Games. Therefore, the observation period includes a typical volume of competitions across sports and minimises differences in exposure based on major sports competition schedules. The information extracted from the database included: participant ID, sex, date of birth, sport, date of injury, type of injury, date of their visit at the clinic, clearance date of unrestricted RTS (eg, defined as step 6 of the RTS strategy with a return to normal gameplay including competitions), the number and type of specialist consults, mechanism of injury (eg, fall, hit), environment where the injury took place (eg, training, competition), history of concussions, history of modifiers (eg, previous head injury, migraines, learning disability, attention deficit disorder or attention deficit/hyperactivity disorder, depression, anxiety, psychotic disorder), as well as the number of symptoms and the total severity score from the first Sport Concussion Assessment Tool 5 (SCAT5) assessment following SRC. 17
Following a Shapiro-Wilk test, medians, IQR and non-parametric tests were used for the analyses because of the absence of normal distributions for all the variables in the dataset (all p<0.001). The skewness was introduced by the presence of individuals that required lengthy recovery periods. One participant was removed from the analysis because their time to consult with the multidisciplinary team was extremely delayed (>1 year).
Descriptive statistics were used to describe the participant’s demographics, SRC characteristics and risk factors in the total sample. Estimated incidences of SRC were also reported for seven resident sports at the institute for which it was possible to quantify a detailed estimate of training volume based on the annual number of training and competition hours as well as the number of athletes in each sport.
To assess if access to multidisciplinary care modified the time to RTS, we compared time to RTS between early and late access groups using a method based on median differences described elsewhere. 36 Wilcoxon rank sum tests were also performed to make between-group comparisons on single variables of age, time to first consult, the number of specialists consulted and medical visits. Fisher’s exact tests were used to compare count data between groups on variables of sex, history of concussion, time since the previous concussion, presence of injury modifiers, environment and mechanism of injury. Bonferroni corrections were applied for multiple comparisons in case of meaningful differences.
To assess if injury modifiers modified time to RTS in the total sample, we compared time to RTS between sexes, history of concussions, time since previous concussion or other injury modifiers using a method based on median differences described elsewhere. 36 Kaplan-Meier curves were drawn to illustrate time to RTS differences between sexes (origin and start time: date of injury; end time: clearance date of unrestricted RTS). Trajectories were then assessed for statistical differences using Cox proportional hazards model. Wilcoxon rank sum tests were employed for comparing the total number of symptoms and severity scores on the SCAT5. The association of multilevel variables on return to play duration was evaluated in the total sample with Kruskal-Wallis rank tests for environment, mechanism of injury, history of concussions and time since previous concussion. For all subsequent analyses of correlations between SCAT5 results and secondary variables, only data obtained from SCAT5 assessments within the acute phase of injury (≤72 hours) were considered (n=65 SRC episodes in the early access group). 37 Spearman rank correlations were estimated between RTS duration, history of concussions, number of specialist consults and total number of SCAT5 symptoms or total symptom severity. All statistical tests were performed using RStudio (R V.4.1.0, The R Foundation for Statistical Computing). The significance level was set to p<0.05.
The study population is representative of the Canadian athletic population in terms of age, gender, demographics and includes a balanced representation of female and male athletes. The study team consists of investigators from different disciplines and countries, but with a predominantly white composition and under-representation of other ethnic groups. Our study population encompasses data from the Institut national du sport du Québec, covering individuals of all genders, ethnicities and geographical regions across Canada.
The patients or the public were not involved in the design, conduct, reporting or dissemination plans of our research.
During the 4-year period covered by this retrospective chart review, a total of 160 SRC episodes were recorded in 132 athletes with a median (IQR) age of 19.1 (17.8–22.2) years old ( table 2 ). 13 female and 10 male athletes had multiple SRC episodes during this time. The sample had a relatively balanced number of females (53.8%) and males (46.2%) with SRC included. 60% of the sample reported a history of concussion, with 35.0% reporting having experienced more than two episodes. However, most of these concussions had occurred more than 1 year before the SRC for which they were being treated. Within this sample, 33.1% of participants reported a history of injury modifiers. Importantly, the median (IQR) time to first clinic consult was 10.0 (1.0–20.0) days and the median (IQR) time to RTS was 34.0 (21.0–63.0) days in this sample ( table 3 ). The majority of SRCs occurred during training (56.3%) rather than competition (33.1%) and were mainly due to a fall (63.7%) or a hit (31.3%). The median (IQR) number of follow-up consultations and specialists consulted after the SRC were, respectively, 9 (5.0–14.3) and 3 (2.0–4.0).
Participants demographics
Sport-related concussion characteristics
Among seven sports of the total sample (n=89 SRC), the estimated incidence of athletes with SRC was highest in short-track speed skating (0.47/1000 hours; 95% CI 0.3 to 0.6), and lower in boxing, trampoline, water polo, judo, artistic swimming, and diving (0.24 (95% CI 0.0 to 0.5), 0.16 (95% CI 0.0 to 0.5), 0.13 (95% CI 0.1 to 0.2), 0.11 (95% CI 0.1 to 0.2), 0.09 (95% CI 0.0 to 0.2) and 0.06 (95% CI 0.0 to 0.1)/1000, respectively ( online supplemental material ). Furthermore, most athletes sustained an SRC in training (66.5%; 95% CI 41.0 to 92.0) rather than competition (26.0%; 95% CI 0.0 to 55.0) except for judo athletes (20.0% (95% CI 4.1 to 62.0) and 80.0% (95% CI 38.0 to 96.0), respectively). Falls were the most common injury mechanism in speed skating, trampoline and judo while hits were the most common injury mechanism in boxing, water polo, artistic swimming and diving.
Access to care.
The median difference in time to RTS was 19 days (95% CI 9.3 to 28.7; p<0.001) between the early (26 (IQR 17.0–38.5) days) and late (45 (IQR 27.5–84.5) days) access groups ( table 3 ; figure 1 ). Importantly, the distribution of SRC environments was different between both groups (p=0.008). The post hoc analysis demonstrated a meaningful difference in the distribution of SRC in training and competition environments between groups (p=0.029) but not for the other comparisons. There was a meaningful difference between the groups in time to first consult (p<0.001; 95% CI −23.0 to −15.0), but no meaningful differences between groups in median age (p=0.176; 95% CI −0.3 to 1.6), sex distribution (p=0.341; 95% CI 0.7 to 2.8), concussion history (p=0.210), time since last concussion (p=0.866), mechanisms of SRC (p=0.412), the presence of modifiers (p=0.313; 95% CI 0.3 to 1.4) and the number of consulted specialists (p=0.368; 95% CI −5.4 to 1.0) or medical visits (p=0.162; 95% CI −1.0 to 3.0).
Time to return to sport following sport-related concussion as a function of group’s access to care and sex. Outliers: below=Q1−1.5×IQR; above=Q3+1.5×IQR.
The median difference in time to RTS was 6.5 days (95% CI −19.3 to 5.3; p=0.263; figure 1 ) between female (37.5 (IQR 22.0–65.3) days) and male (31.0 (IQR 20.0–48.0) days) athletes. Survival analyses highlighted an increased hazard of longer recovery trajectory in female compared with male athletes (HR 1.4; 95% CI 1.4 to 0.7; p=0.052; figure 2A ), which was mainly driven by the late (HR 1.8; 95% CI 1.8 to 0.6; p=0.019; figure 2C ) rather than the early (HR 1.1; 95% CI 1.1 to 0.9; p=0.700; figure 2B ) access group. Interestingly, a greater number of female athletes (n=15) required longer than 100 days for RTS as opposed to the male athletes (n=6). There were no meaningful differences between sexes for the total number of symptoms recorded on the SCAT5 (p=0.539; 95% CI −1.0 to 2.0) nor the total symptoms total severity score (p=0.989; 95% CI −5.0 to 5.0).
Time analysis of sex differences in the time to return to sport following sport-related concussion in the (A) total sample, as well as (B) early, and (C) late groups using survival curves with 95% confidence bands and tables of time-specific number of patients at risk (censoring proportion: 0%).
SRC modifiers are presented in table 2 , and their influence on RTP is shown in table 4 . The median difference in time to RTS was 1.5 days (95% CI −10.6 to 13.6; p=0.807) between athletes with none and one episode of previous concussion, was 3.5 days (95% CI −13.9 to 19.9; p=0.728) between athletes with none and two or more episodes of previous concussion, and was 2 days (95% CI −12.4 to 15.4; p=0.832) between athletes with one and two or more episodes of previous concussion. The history of concussions (none, one, two or more) had no meaningful impact on the time to RTS (p=0.471). The median difference in time to RTS was 4.5 days (95% CI −21.0 to 30.0; p=0.729) between athletes with none and one episode of concussion in the previous year, was 2 days (95% CI −10.0 to 14.0; p=0.744) between athletes with none and one episode of concussion more than 1 year ago, and was 2.5 days (95% CI −27.7 to 22.7; p=0.846) between athletes with an episode of concussion in the previous year and more than 1 year ago. Time since the most recent concussion did not change the time to RTS (p=0.740). The longest time to RTS was observed in the late access group in which athletes had a concussion in the previous year, with a very large spread of durations (65.0 (IQR 33.0–116.5) days). The median difference in time to RTS was 3 days (95% CI −13.1 to 7.1; p=0.561) between athletes with and without other injury modifiers. The history of other injury modifiers had no meaningful influence on the time to RTS (95% CI −6.0 to 11.0; p=0.579).
Preinjury modifiers of time to return to sport following SRC
Positive associations were observed between the time to RTS and the number of initial symptoms (r=0.3; p=0.010; 95% CI 0.1 to 0.5) or initial severity score (r=0.3; p=0.008; 95% CI 0.1 to 0.5) from the SCAT5. The associations were not meaningful between the number of specialist consultations and the initial number of symptoms (r=−0.1; p=0.633; 95% CI −0.3 to 0.2) or initial severity score (r=−0.1; p=0.432; 95% CI −0.3 to 0.2). Anecdotally, most reported symptoms following SRC were ‘headache’ (86.2%) and ‘pressure in the head’ (80.0%), followed by ‘fatigue’ (72.3%), ‘neck pain’ (70.8%) and ‘not feeling right’ (67.7%; online supplemental material ).
This study is the first to report descriptive data on athletes with SRC collected across several sports during an Olympic quadrennial, including athletes who received the most recent evidence-based care at the time of data collection. Primarily, results indicate that the time to RTS in athletes engaged in Summer and Winter Olympic sports may require a median (IQR) of 34.0 (21.0–63.0) days. Importantly, findings demonstrated that athletes with earlier (≤7 days) access to multidisciplinary concussion care showed faster RTS compared with those with late access. Time to RTS exhibited large variability where sex had a meaningful influence on the recovery pathway in the late access group. Initial symptoms, but not history of concussion, were correlated with prognosis in this sample. The main reported symptoms were consistent with previous studies. 38 39
This study provides descriptive data on the impact of SRC monitoring programmes on recovery in elite athletes engaged in Olympic sports. As hypothesised, the median time to RTS found in this study (eg, 34.0 days) was about three times longer than those found in reports from before 2005, and 2 weeks longer than the typical median values (eg, 19.8 days) recently reported in athletic levels including youth (high heterogeneity, I 2 =99.3%). 19 These durations were also twice as long as the median unrestricted time to RTS observed among American collegiate athletes, which averages around 16 days. 9 20 21 However, they were more closely aligned with findings from collegiate athletes with slow recovery (eg, 34.7 days) and evidence from military cadets with poor access where return to duty duration was 29.4 days. 8 22 Several reasons could explain such extended time to RTS, but the most likely seems to be related to the diversity in access among these sports to multidisciplinary services (eg, 10.0 median days (1–20)), well beyond the delays experienced by collegiate athletes, for example (eg, 0.0 median days (0–2)). 40 In the total sample, the delays to first consult with the multidisciplinary clinic were notably mediated by the group with late access, whose athletes had more SRC during international competition. One of the issues for athletes engaged in Olympic sports is that they travel abroad year-round for competitions, in contrast with collegiate athletes who compete domestically. These circumstances likely make access to quality care very variable and make the follow-up of care less centralised. Also, access to resources among these sports is highly variable (eg, medal-dependant), 14 and at the discretion of the sport’s leadership (eg, sport federation), who may decide to prioritise more or fewer resources to concussion management considering the relatively low incidence of this injury. Another explanation for the longer recovery times in these athletes could be the lack of financial incentives to return to play faster, which are less prevalent among Olympic sports compared with professionals. However, the stakes of performance and return to play are still very high among these athletes.
Additionally, it is plausible that studies vary their outcome with shifting operational definitions such as resolution of symptoms, return to activities, graduated return to play or unrestricted RTS. 19 40 It is understood that resolution of symptoms may occur much earlier than return to preinjury performance levels. Finally, an aspect that has been little studied to date is the influence of the sport’s demands on the RTS. For example, acrobatic sports requiring precision/technical skills such as figure skating, trampoline and diving, which involve high visuospatial and vestibular demands, 41 might require more time to recover or elicit symptoms for longer times. Anecdotally, athletes who experienced a long time to RTS (>100 days) were mostly from precision/skill-dependent sports in this sample. The sports demand should be further considered as an injury modifier. More epidemiological reports that consider the latest guidelines are therefore necessary to gain a better understanding of the true time to RTS and impact following SRC in Olympians.
In this study, athletes who obtained early access to multidisciplinary care after SRC recovered faster than those with late access to multidisciplinary care. This result aligns with findings showing that delayed access to a healthcare practitioner delays recovery, 19 including previous evidence in a sample of patients from a sports medicine clinic (ages 12–22), indicating that the group with a delayed first clinical visit (eg, 8–20 days) was associated with a 5.8 times increased likelihood of a recovery longer than 30 days. 5 Prompt multidisciplinary approach for patients with SRC is suggested to yield greater effectiveness over usual care, 3 6 17 which is currently evaluated under randomised controlled trial. 42 Notably, early physical exercise and prescribed exercise (eg, 48 hours postinjury) are effective in improving recovery compared with strict rest or stretching. 43 44 In fact, preclinical and clinical studies have shown that exercise has the potential to improve neurotransmission, neuroplasticity and cerebral blood flow which supports that the physically trained brain enhanced recovery. 45 46 Prompt access to specialised healthcare professionals can be challenging in some contexts (eg, during international travel), and the cost of accessing medical care privately may prove further prohibitive. This barrier to recovery should be a priority for stakeholders in Olympic sports and given more consideration by health authorities.
The estimated incidences of SRC were in the lower range compared with what is reported in other elite sport populations. 1 2 However, the burden of injury remained high for these sports, and the financial resources as well as expertise required to facilitate athletes’ rehabilitation was considerable (median number of consultations: 9.0). Notably, the current standard of public healthcare in Canada does not subsidise the level of support recommended following SRC as first-line care, and the financial subsidisation of this recommended care within each federation is highly dependent on the available funding, varying significantly between sports. 14 Therefore, the ongoing efforts to improve education, prevention and early recognition, modification of rules to make the environments safer and multidisciplinary care access for athletes remain crucial. 7
This unique study provides multisport characteristics following the evolution of concussion guidelines in Summer and Winter Olympic sports in North America. Notably, it features a balance between the number of female and male athletes, allowing the analysis of sex differences. 23 26 In a previous review of 171 studies informing consensus statements, samples were mostly composed of more than 80% of male participants, and more than 40% of these studies did not include female participants at all. 26 This study also included multiple non-traditional sports typically not encompassed in SRC research, feature previously identified as a key requirement of future epidemiological research. 47
However, it must be acknowledged that potential confounding factors could influence the results. For example, the number of SRC detected during the study period does not account for potentially unreported concussions. Nevertheless, this figure should be minimal because these athletes are supervised both in training and in competition by medical staff. Next, the sport types were heterogeneous, with inconsistent risk for head impacts or inconsistent sport demand which might have an influence on recovery. Furthermore, the number of participants or sex in each sport was not evenly distributed, with short-track speed skaters representing a large portion of the overall sample (32.5%), for example. Additionally, the number of participants with specific modifiers was too small in the current sample to conclude whether the presence of precise characteristics (eg, history of concussion) impacted the time to RTS. Also, the group with late access was more likely to consist of athletes who sought specialised care for persistent symptoms. These complex cases are often expected to require additional time to recover. 48 Furthermore, athletes in the late group may have sought support outside of the institute medical clinic, without a coordinated multidisciplinary approach. Therefore, the estimation of clinical consultations was tentative for this group and may represent a potential confounding factor in this study.
This is the first study to provide evidence of the prevalence of athletes with SRC and modifiers of recovery in both female and male elite-level athletes across a variety of Summer and Winter Olympic sports. There was a high variability in access to care in this group, and the median (IQR) time to RTS following SRC was 34.0 (21.0–63.0) days. Athletes with earlier access to multidisciplinary care took nearly half the time to RTS compared with those with late access. Sex had a meaningful influence on the recovery pathway in the late access group. Initial symptom number and severity score but not history of concussion were meaningful modifiers of recovery. Injury surveillance programmes targeting national sport organisations should be prioritised to help evaluate the efficacy of recommended injury monitoring programmes and to help athletes engaged in Olympic sports who travel a lot internationally have better access to care. 35 49
Patient consent for publication.
Not applicable.
This study involves human participants and was approved by the ethics board of Université de Montréal (certificate #2023-4052). Participants gave informed consent to participate in the study before taking part.
The authors would like to thank the members of the concussion interdisciplinary clinic of the Institut national du sport du Québec for collecting the data and for their unconditional support to the athletes.
Supplementary data.
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
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Correction notice This article has been corrected since it published Online First. The ORCID details have been added for Dr Croteau.
Contributors TR, FC and SL were involved in planning, conducting and reporting the work. François Bieuzen and Magdalena Wojtowicz critically reviewed the manuscript. TR is guarantor.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
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The study aims to examine the long-term cointegration between the democracy index and foreign direct investment (FDI). The sample group chosen for this investigation comprises BRICS-TM (Brazil, Russia, India, China, South Africa, Turkey [Türkiye], and Mexico) countries due to their increasing strategic importance and potential growth in the global economy. Data from 1994 to 2018 were analyzed, with panel data analysis techniques employed to accommodate potential structural breaks. The level of democracy serves as the independent variable in the model, while FDI is the dependent variable. Inflation and income per capita are considered control variables due to their impact on FDI. The analysis revealed a long-term relationship with structural breaks among the model’s variables. Democratic progress and FDI demonstrate a correlated, balanced relationship over time in these countries. Therefore, governments and policymakers in emerging economies aiming to attract FDI should account for structural breaks and the correlation between democracy and FDI. Furthermore, the Kónya causality tests revealed a causality from democracy to FDI at a 1% significance level in Mexico, 5% in China, and 10% in Russia. From FDI to democracy (DEMOC), there is causality at a 5% significance level in Mexico and a 10% significance level in Russia. Thus, the findings suggest that supporting democratic development with macroeconomic indicators in BRICS-TM countries will positively impact foreign direct capital inflows.
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Economies and governments require capital infusion to augment their production and employment levels. Underdeveloped and developing nations, despite having an abundance of land and labor, grapple with capital deficiencies. Consequently, these countries often seek foreign direct investment (FDI) to address this capital shortfall. Even emerging market economies are not immune to this phenomenon, with challenges intensifying globally post-COVID-19 pandemic. Khan et al. ( 2023 ) highlighted the pivotal role of institutional quality and good governance in attracting FDI. The need for FDI has grown exponentially in an increasingly globalized world characterized by interdependence among states. Democracy and the democratic status of states emerge as critical indicators of institutional quality. Kilci and Yilanci ( 2022 ) posit that the prolonged pandemic triggered the third most significant recession since the Great Depression of 1929 and the Global Financial Crisis of 2008–2009. Consequently, the demand for FDI has surged, positioning foreign investment as the foremost resource for fostering sustainable economic development. In light of the provided frame, this study addresses the following research questions:
What factors attract foreign direct investment to a country?
Which factors positively impact FDI?
Reviewing the existing literature reveals that scholars from diverse disciplines address similar questions using political variables like political stability and democracy levels or economic variables such as economic stability and natural resources . However, the impact of democracy on FDI is often overlooked . For example, studies by Baghestani et al. ( 2019 ) and Gür ( 2020 ) investigated variables like oil prices, exchange rates, exports, imports, and the global innovation index but seldom considered democracy’s role in attracting FDI . Similarly, studies examining the relationship between democracy and FDI, like those by Yusuf et al. ( 2020 ) and Ahmed et al. ( 2021 ), generally excluded data from BRICS-TM countries.
Li and Resnick ( 2003 ) assert that the two paramount features of modern international political economy are the proliferation of democracy and increased economic globalization . It has become apparent that FDI inflow is a manifestation of high-level globalization and the diffusion of democracy. According to the United Nations Conferences on Trade and Development (UNCTAD), 2002 data between 1990 and 2000, three-quarters of the total international foreign direct capital was directed toward democratic and developed countries (Busse, 2003 ).
The conceptualization of democracy, within both theoretical and historical frameworks, has been marked by inherent challenges (Suny, 2017 ). Aliefendioğlu ( 2005 ) defines democracy as the amalgamation of the ancient Greek terms “Demos” and “Kratos,” centered on the principle of self-governance by the people. In essence, democracy encompasses the utilization of popular sovereignty by and for the citizenry (Keser et al., 2023 ). Haydaroğlu and Gülşah ( 2016 ) contend that the contemporary manifestation of democracies is rooted in representative democracy, wherein individuals exercise their sovereignty by selecting representatives to act on their behalf. The spread of liberal or representative democracy is believed to be a driving force behind this shift in economic structures. The relational intersection between FDI flow and democratic mechanisms needs to be investigated. At this point, Voicu and Peral ( 2014 ) argue that economic development and modernization operate as background factors that affect the development of support for democracy. Therefore, an opinion emerges that there is an inevitable intersection between FDI flow and democratic mechanism.
Despite the sustained attention from academia and the public, the detailed understanding of democracy’s effect on FDI remains limited (Li & Resnick, 2003 ). There is a noticeable gap in the literature concerning studies investigating the impact of democracy on FDI, specifically in BRICS-TM countries , which are emerging markets that attract significant FDI. Moreover, the absence of structural break panel cointegration tests in previous analyses accentuates these gaps, forming the primary motivation for this research . The study aims to fill these voids by empirically examining the relationship between democracy and FDI using data from the emerging markets of BRICS-TM countries. These countries require substantial foreign capital and are crucial for the stable development of the global economy since they are expected to become pivotal centers in the multipolar world system. The study differs from other publications, employing unique methods, such as structural break panel cointegration tests, to address these objectives.
Reducing costs, increasing employment-oriented production, and enhancing export capacity are paramount in global competition. If a country cannot achieve these advancements with its existing potential and dynamics, attracting foreign capital becomes imperative, necessitating the creation of multiple attraction points to entice foreign direct investments. Consequently, attracting foreign capital is significant in today’s globalized world. This study provides insights into this pressing issue in the contemporary global competitive landscape by analyzing the long-term relationship between democracy and foreign direct investment. Considering their prominence in the world economy due to recent economic growth and competitive structures, the selection of BRICS-TM countries as a sample group underscores the study’s importance. The study acknowledges the strategic importance and increasing power of BRICS-TM countries, especially China and India, which have consistently attracted significant foreign capital in recent years. Using panel data analysis techniques that incorporate structural breaks addresses a crucial gap in the literature, offering a more accurate analysis of the democracy-foreign direct investment relationship in the BRICS-TM sample group. However, data constraints related to model variables alongside the limitations of evaluating results within the framework of the chosen sample group are acknowledged later in the “ Discussion ” section.
Lastly, there appears to be a gap in the existing literature concerning studies that investigate the impact of democracy on FDI flow in BRICS-TM countries . The countries that attract more FDI than others raise the question of whether their democracy level empirically influences the amount of FDI. Moreover, upon examining the limited studies exploring the relationship between democracy and FDI, it is evident that none applied the structural break panel cointegration test in their analyses. These gaps collectively serve as the primary motivation for this research. Thus, the study aims to address these gaps in the existing literature and scrutinizes whether there is cointegration between the level of democracy and FDI in a country by utilizing sample group data from emerging markets of BRICS-TM countries. This selection is significant as these countries are among emerging economies with considerable developmental potential. In essence, this study aims to empirically unveil the relationship between democracy and FDI , a crucial requirement for developing economies striving to attract more foreign capital for sustainable development . Additionally, this study employs distinctive methods, such as the structural break panel cointegration test, to investigate the subject, further elaborated in the “ Research Method and Econometric Analysis ” section.
In global competition, the imperative to reduce costs, increase employment-oriented production, and enhance export capacity is paramount. Given a country’s potential and dynamics, if these enhancements prove elusive, the necessity arises to attract foreign capital and establish various attraction points to incentivize foreign direct investments. Therefore, attracting foreign direct investment (FDI) to a country holds tremendous significance in today’s globalized world. Before investing, foreign capital rigorously assesses the potential profit opportunities and scrutinizes various socio-economic indicators, especially democracy. For these reasons, by analyzing the long-term relationship between democracy and foreign direct investment in the BRICS-TM sample, this study incorporates analyses and inferences regarding this crucial challenge in today’s globally competitive environment.
Furthermore, it is anticipated that the strategic importance and influence of BRICS-TM countries will continue to escalate in the upcoming years. Notably, countries in the sample group, particularly China and India, have consistently attracted substantial foreign capital, and their economies exhibit ongoing growth. As evident from the graphical analysis in the study, China stands out as the world leader in attracting foreign direct investment. Considering the economic size of Russia and Brazil, the geo-strategic location of Türkiye, and the natural resource wealth of China, India, and Mexico, it is apparent that these countries are central attractions for foreign direct capital. Events with significant consequences on the global stage, such as economic crises, wars, earthquakes, and elections, can induce substantial fluctuations and structural breaks in national economies. Hence, using panel data analysis techniques that allow for structural breaks in the study fills a critical gap in the literature. This approach provides a more accurate analysis of the democracy-foreign direct investment relationship in the BRICS-TM sample group. The primary limitation in the study’s analysis is the constraint arising from the variables included in the model. Additionally, selecting the BRICS-TM sample group as the focus on developing countries can be considered another limitation, restricting the evaluation of results within this specific sample framework. The study anticipates that the policy recommendations derived from the analysis findings will guide policymakers, market players, and new researchers.
The article is organized into the following sections: (1) “ Introduction ” section: This section initially furnishes broad information concerning the subject matter, elucidating the lacunae in the existing literature and delineating the limitations of the study. (2) “ Theoretical Frame and Literature Review ” section: Subsequently, the second section delves into the examination of the theoretical framework, scrutinizing the prevailing status of the literature. (3) “ Research Method and Econometric Analysis ” section: The third segment comprehensively addresses the research methodology employed and expounds upon the econometric analysis conducted. (4) “ Results ” section: The ensuing fourth chapter presents the study’s findings and results. (5) “ Discussion ” section: These results and findings are then systematically expounded upon in the fifth chapter within the context of the current literature. (6) “ Conclusion ” section: Culminating the study is a concluding section encapsulating the critical insights derived, followed by policy recommendations.
As previously indicated, scarce studies have delved into the correlation between democracy and foreign direct investment (FDI). A comprehensive examination of the existing literature reveals a notable dearth of research focused on BRICS-TM countries, with most of them overlooking “democracy” as a variable and/or the connection between “democracy and FDI.” Conversely, researchers investigating FDI predominantly explore its associations with other variables, such as “exports and imports.”
The following two tables summarize the status of the current literature on the issue and its findings. In Table 1 , the literature on BRICS and/or BRIC + S + T + M countries, as well as its variables, methods, and findings, is given. Then, in Table 2 , the studies researching the relationship between democracy and FDI, their methodology, sample groups, and findings are summarized.
As can be seen in Table 1 , BRICS-TM countries were very rarely studied, and almost all of these studies neglected “democracy” as a variable and/or the relation between “democracy and FDI.” Alternatively, the studies that did examine FDI researched its relation with other variables such as export and import. Unique methods, such as structural break panel cointegration tests, were applied to investigate the issue, and this method comprises the novel part of the study. The details can be seen under the “ Research Method and Econometric Analysis ” section.
In summary, the literature review provided in Table 1 covers the relationship between democracy, foreign direct investment (FDI), and various other economic variables, focusing on BRICS-TM countries. Below is an analysis of the essential findings and gaps identified in the literature:
By applying AI (ChatGPT) to the information provided in Table 1 (studies on BRIC + S + T + M countries), key findings are double-checked and summarized below:
Limited focus on BRICS-TM countries: The literature review notes a scarcity of studies on BRICS-TM countries, with a lack of attention to the “democracy” variable in the context of FDI.
Variable relationships explored: Various studies investigate the relationships between different economic variables and FDI, such as oil prices, exchange rates, gross domestic product (GDP), international tourism, economic output, carbon emissions, exports, imports, and innovation.
Diverse methodologies: Researchers employ diverse methodologies, including directional analysis, panel ARDL cointegration, survey research, and panel cointegration, to analyze the relationships among variables.
Within this frame, a summary of the studies investigating the relationship between democracy and FDI or using similar variables is given in Table 2 .
As presented in Table 2 , none of the above studies analyzed the relationship among democracy, FDI, inflation , and GDP variables for BRICS-TM countries. In addition, none of the studies applied a structural break panel cointegration test in their analysis. All these gaps motivate the authors of this study to conduct such research.
Additionally, applying AI (ChatGPT) to the information provided in Table 2 , key findings from Table 2 are double-checked and summarized below (studies on the relationship between democracy and economics):
Limited studies on democracy and FDI in BRICS-TM: The literature highlights a gap in research, as none of the studies in Table 2 specifically analyze the relationship between democracy, FDI, inflation, and GDP variables in BRICS-TM countries.
Contradictory findings on democracy and economic growth: The studies in Table 2 present contradictory findings on the impact of democracy on economic growth. Some find a positive and significant effect, while others do not establish a significant relationship.
Methodological variety: Various methods, such as dynamic fixed effects, panel data regression analysis, panel cointegration, and causality analysis, are employed to explore the relationships between democracy, FDI, and economic growth.
Upon inspection of the limited studies, contradictory results emerge, even when employing data from diverse sample groups. An illustrative example is found in the work of Busse ( 2003 ), whose research can be summarized as follows:
Results from regression analysis between FDI and democracy reveal that analogous to studies by Rodrik ( 1996 ) and Harms and Ursprung ( 2002 ), multinational corporations (MNCs) exhibit a preference for countries where political rights and freedoms are legally and practically safeguarded.
Countries that enhance their democratic rights and freedoms tend to attract more FDI per capita than predicted (Busse, 2003 ).
Li and Resnick ( 2003 ) posited that investors typically favor regimes with advanced democracy and robust legal systems over states where their properties are at risk in dictatorial regimes. From this standpoint, one can infer that a significantly high level of democracy correlates with a markedly high level of FDI. In other words, property rights violations are diminished in developing countries with robust democracies, leading to increased FDI levels (Li & Resnick, 2003 ).
However, Haggard ( 1990 ) presents a contrary perspective, arguing that authoritarian regimes may appeal more to investors seeking to safeguard their economic assets and properties. An amalgamation of opposing views arises: investors from countries with underdeveloped democracies prefer collaboration with authoritarian regimes, whereas investors from developed nations lean toward familiar democratic regimes.
Despite the contradictory and complex findings from the limited number of studies on the potential relationship between democracy and FDI, it is contended that two influential factors contribute to investment flow toward countries with legally guaranteed and well-developed democratic rights. Firstly , as proposed by Spar ( 1999 ), a transition occurs from critical sectors like agriculture and raw materials to production and tertiary sectors in the flow and stock structure of FDI in developing countries. Secondly , there is a transformation in the interest and motivation of multinational enterprises toward developing countries based on sectoral development (Busse, 2003 ). This underscores the impact of democratic organizations established to secure democratic rights on FDI. In instances where poor democratic governance renders a country less appealing to foreign investors, the country faces a dilemma: choosing between the limited options of “loss of foreign capital” or “democratization” (Li & Resnick, 2003 ). Spar ( 1999 ) emphasizes that as the reliance on governments and their policies decreases, the need for a more democratic environment, a reliable and stable legal system, and appropriate market conditions becomes increasingly crucial for the overall well-being of the country’s economy.
Upon scrutinizing the most recent studies on the subject, a trend of contradictory findings becomes apparent. For instance, Yusuf et al. ( 2020 ) found that the democracy coefficient, as a variable signifying its impact on economic growth, lacks significance for West African countries in the short and long run. In contrast, Putra and Putri ( 2021 ) asserted that “democracy has a positive and significant effect on economic growth in 7 Asia Pacific countries.” Similar to Yusuf et al., in a panel data analysis encompassing the period from 1970 to 2014 and involving 115 developing countries, Lacroix et al. ( 2021 ) concluded that “democratic transitions do not affect foreign direct investment (FDI) inflows.”
A comprehensive review of existing empirical studies reveals a notable scarcity in the number of inquiries into the relationship between democracy and foreign direct investment (FDI) (Li & Resnick, 2003 ). Moreover, the available studies yield contradictory results on this matter. Addressing this issue, it is noteworthy that Oneal ( 1994 ) conducted one of the initial qualitative examinations on the impact of regime characteristics on FDI. Despite not identifying a statistically valid relationship between regime type and FDI flow, Oneal’s research is an early exploration of this intricate relationship.
Explorations into the connection between investor behavior and political regime characteristics, particularly in determining whether democratic or authoritarian features foster more foreign direct investment (FDI), have yielded divergent outcomes. Derbali et al. ( 2015 ) found a statistically significant relationship between FDI and democratic transformation. Through an econometric analysis encompassing a sample of 173 countries, with 44 undergoing democratic transformation between 1980 and 2010, the authors observed a substantial increase in FDI flow associated with democratic transitions.
Castro ( 2014 ) conducted a test examining the relationship between foreign direct investment (FDI) flow (the ratio of FDI flow to GDP) and indicators of “democracy” and “dictatorship” using a dynamic panel data model. Despite the analysis results failing to furnish evidence supporting a direct connection between FDI and democracy, the author emphasizes that this outcome does not negate the impact of political institutions on the flow of FDI. According to Mathur and Singh ( 2013 ), their study stands out as the inaugural examination focusing on the “importance given to economic freedom rather than political freedom” in the decision-making process of foreign investors. The authors concluded that contrary to conventional expectations, even democratic countries may attract less foreign direct investment (FDI) if they do not ensure guaranteed economic freedom. Malikane and Chitambara ( 2017 ) conducted a study exploring the relationship between democracy and foreign direct investment (FDI), employing data from eight South African countries from 1980–2014. The research findings indicate a direct and positive impact of FDI on economic growth due to the robust democratic institutions emerging as crucial catalysts in the respective sample countries.
Consequently, Malikane and Chitambara’s ( 2017 :92) study suggests that the influence of FDI on economic growth is contingent upon the level of democracy in the host country. Upon scrutinizing the studies above, a pattern of conflicting findings emerges concerning the relationship between the level of democracy and the influx of foreign direct investment (FDI) to a country . Studies commonly emphasize that the impact of democracy on FDI depends upon each country’s developmental stage. The prevalence of confusion, varying findings, and conflicting results underscores the significance of empirical analyses on this matter. A comprehensive examination of the overview identified gaps, and the need for new research is detailed under the subsequent subheading.
After a detailed overview of the existing literature, the main features and gaps can be identified as follows:
Limited studies on democracy and FDI: The literature notes a scarcity of studies examining the relationship between democracy and FDI, and existing studies present conflicting results.
Context-dependent impact of democracy: Contradictory findings suggest that democracy’s impact on FDI may vary depending on a country’s development level.
Gap in BRICS-TM studies: The identified gap in the literature is the lack of research specifically addressing the relationship between democracy and FDI in BRICS-TM countries. The need for a structural break panel cointegration test is also emphasized.
Influence of political institutions: Some studies argue that solid democratic institutions positively influence FDI, while others suggest that economic freedom, rather than political freedom, may be more crucial for attracting FDI.
Requirements for new research: To fill the gap in the literature, new research should be conducted specifically targeting BRICS-TM countries.
Thus, when c onsidering the contradictory findings, future studies should explore the contextual factors influencing the relationship between democracy and FDI in different country settings. Conducting longitudinal analyses could provide insights into the dynamic relationship between democracy and FDI over time. Comparative studies between countries with different levels of democratic development can help in understanding the nuanced impact of democracy on FDI. Last but not least, given the emphasis on structural break panel cointegration tests, future research could incorporate these analytical tools for a more comprehensive understanding of the relationships under consideration.
Last but not least, Olorogun ( 2023 ) conducted research using data from sub-Saharan countries from 1978 to 2019 and found a “long-run covariance between sustainable economic development and foreign direct investment (FDI)” and a “significant level of causality between economic growth and financial development in the private sector, FDI, and export.” So, if a significant relationship can be found between democracy and foreign direct investment, the results may also provide a useful assessment for sustainable development.
In summary, while the literature review reveals valuable insights into the complex relationship between democracy, FDI, and economic variables, there is a clear need for more targeted research in the context of BRICS-TM countries by further exploration of the contextual factors influencing these relationships.
This section of the study delves into the analysis methods and interpretations of the relationship between democracy and foreign direct investment (FDI). The presentation encompasses the dataset and model specifications concerning the variables under scrutiny. Specifically, analyses were conducted utilizing econometric analysis programs, namely, EViews 12 , Gauss 23 , and StataMP 64 . The study culminated with interpreting findings and formulating policy recommendations based on the results obtained.
The study scrutinized the hypothesis to address the initial research inquiry, asserting a correlation between democracy and foreign direct investment (FDI). The research targeted BRICS-TM countries (Brazil, Russia, India, China, South Africa, Türkiye, Mexico) recognized for their increasing prominence in the global economy and anticipated growth in strategic significance. These seven emerging markets were chosen due to their demonstrated potential to attract FDI. The research covered annual data spanning 1994–2018 by employing panel data analysis techniques capable of accommodating structural breaks. Both democracy and foreign direct investments are susceptible to the influence of local and global dynamics, which can induce significant disruptions in the variables.
Consequently, the study utilized tests allowing for structural breaks to enhance the robustness of the analyses. The investigation aimed to uncover the long-term relationship between foreign direct investment and democracy , a critical indicator of economic development for emerging markets in recent years. The model developed for examining the relationship between democracy and foreign direct investment within the specified sample and data range is represented by Eq. 1 :
In the model, cross-section data is represented by i = 1, 2, 3,…. N , while the time dimension is represented by t = 1, 2, 3,….. T , and the error term is by ɛ.
The study’s model setup and variables were adapted from Yusuf et al. ( 2020 ), Putra and Putri ( 2021 ), and Lacroix et al. ( 2021 ) in the literature. Figure 1 shows the research design.
Research design
Table 3 shows the variables and data sources used in the model.
The study designated foreign direct investment (FDI), denoted as LNFDI, as the dependent variable. The independent variable was conceptualized as the democracy variable (DEMOC). To account for potential influencing factors, inflation (INF) and per capita income (PGDP) variables, known to impact FDI, were introduced into the model as control variables to draw upon insights from the existing literature. In the context of panel data analyses, selecting control variables involves consulting the literature to identify factors with substantial influence on the dependent variable. When examining factors impacting foreign direct investment (FDI), a frequently encountered category comprises various macroeconomic variables, among which inflation and per capita income are recurrently employed. Given the study’s sample composition—comprising the BRICS-TM countries—these two variables were incorporated into the model as control variables. This decision was motivated by their recurrent utilization in the literature and their direct relevance to foreign direct investments and production costs. Furthermore, the inclusion of these variables addressed a shared data constraint.
During the data collection phase, the study utilized indices reflecting “political rights” and “civil liberties,” which were acknowledged indicators of “democracy” in the literature. These indices, sourced from the Freedom House Database ( 2020 ), were incorporated into the analysis by calculating their means, which were then used as values for the democracy variable. This approach aligns with the practices of several researchers in the existing literature, such as Kebede and Takyi ( 2017 ), Doucoligaos and Ulubasoglu ( 2008 ), and Tavares and Wacziarg ( 2001 ), who have employed this index. The index operates on a scale from 1 to 7, where 1 represents the highest state of democracy and 7 corresponds to the lowest state. To facilitate analyses, calculations, and interpretation, the index values were scaled to ensure a range between 0 and 100.
Freedom House assesses the degree of democratic governance in 29 countries from Central Europe to Central Asia through its annual “Nations in Transit” report. The democracy score encompasses distinct ratings on various facets, including national and local governance, electoral processes, independent media, civil society, judicial framework and independence, and corruption. Most researchers (Dolunay et al., 2017 ; Martin et al., 2016 ; Osiewicz & Skrzypek, 2020 ; Steiner, 2016 ) frequently utilize the data provided by Freedom House in their studies. In addition to the independent variable of democracy (DEMOC), the model integrates control variables influencing FDI. Capitation (LNPGDP) and inflation (INF) variables were incorporated within this framework. A review of the existing literature reveals that factors affecting FDI, including inflation and per capita income, have been employed in models by researchers (Botric & Skuflic, 2005 ; Chakrabarti, 2001 ; Jadhav, 2012 ; Ranjan & Agraval, 2011 ; Vijayakumar et al., 2010 ).
In the literature, various variables such as “trade openness, level of human capital, unemployment rates, government supports, tax costs,” which are believed to influence foreign capital, are employed as control variables in models. On the other hand, in some research, the impact of institutional quality, such as democracy and governance, on environmental quality is studied. Within this frame, Shahbaz et al. ( 2023 ) found that “institutional quality variables impacted environmental quality differently. In this sense, it is detrimental for policymakers to consider concerted measures to decrease institutional vulnerabilities and reduce the level of the informal economy.” However, in this study, inflation and per capita income variables were chosen due to their prominence as the most frequently used variables in the literature (detailed in the “ Theoretical Frame and Literature Review ” section) and their comprehensive impact on foreign direct capital in terms of macroeconomics.
Furthermore, a shared data problem is evident in all variables from 1994 to 2018 for the BRICS-TM country sample group, particularly in variables other than the control variables in the model. Nevertheless, these issues have yet to be encountered as inflation and per capita income variables are comprehensive and fall within general macroeconomic data. Additionally, including many control variables in the model might obscure the significance of the effect on the dependent variable in hypothesis tests examining the relationship between democracy and foreign direct investment. Consequently, real GDP data, rather than nominal, were utilized in the analysis, and the logarithm of the data was represented as LNGDP.
As explored earlier, foreign investors prioritize economic freedom over political freedom when making investment decisions (Mathur & Singh, 2013 ). In this context, the assurance of economic liberty and the legal protection of property rights may be linked to the level of democracy, particularly in developed countries. This condition explains why the relevant variables should be incorporated into the model and tested. The logarithm of FDI (LNFDI) and per capita income (LNPGDP) variables were employed in the analyses. The rationale behind the logarithmic transformation lies in its capacity to facilitate the interpretation of analysis results and standardize variables on a specific scale. Additionally, taking logarithms of series does not result in information loss in data; it also aids in mitigating autocorrelation issues and allows the series to exhibit a normal distribution.
The primary motivation behind the conducted study is to investigate the impact of the variable “democracy” on foreign direct investments through newly developed panel data analysis tests that allow for structural breaks, which are not commonly used in political science. In this regard, the study aims to be one of the pioneering works testing the relationship between variables related to political science and economics with an interdisciplinary perspective through innovative empirical studies. The methodological framework of this study, which analyzes the relationship between democracy and FDI through annual data from the 1994–2018 periods using panel data analysis and causality test, is outlined below:
Graphical representation of variables and analysis of descriptive statistics,
CD lm1 (Breusch & Pagan, 1980 ), CD lm1 , and LM adj tests (Pesaran et al., 2008 ) were used in the analysis to find the presence of cross-section dependence of variables.
Panel LM test (Im, Lee, & Tieslau, 2010 ) determined whether variables in the model have a unit root.
Delta test (Pesaran & Yamagata, 2008 ) was used to determine the homogeneity or heterogeneity of variables.
Cointegration test with multiple structural breaks (Westerlund & Edgerton, 2008 ) was conducted to determine the presence of cointegration between variables.
Kónya’s causality test (Kónya, 2006 ) was conducted to investigate the existence of causal relationships between variables.
In terms of methodology, the study aims to address a significant gap in the literature on democracy. Given the chosen sample group and the specified period, it becomes evident that structural changes must be considered in the analysis because the variables of democracy and foreign direct investment are particularly susceptible to global developments, leading to substantial shifts in the markets. A literature review indicates a preference for general country-based time series analyses over new-generation tests, with classical panel data analyses commonly employed for the selected country group. In summary, an examination of the literature reveals that studies on this issue predominantly rely on first- and second-generation linear panel data analysis techniques. Therefore, incorporating unit root and cointegration tests is crucial in significantly contributing to the literature, particularly by acknowledging and addressing structural breaks in the study. Additionally, it aligns with the theoretical framework that variables such as democracy and foreign direct capital investments, susceptible to the influence of global developments, are prone to structural changes. Consequently, employing panel data analysis techniques with structural breaks gains significance and enhances the motivation and scientific robustness of the study, mainly when a substantial data range is available.
The study focuses on the BRICS-TM countries: Brazil, Russia, India, China, South Africa, Türkiye Footnote 1 (Turkey), and Mexico . These nations have gained prominence in the global economy, and their strategic significance is anticipated to grow. The selection of this sample group is based on their demonstrated high performance and potential to attract substantial foreign direct investment globally. The study’s unique contribution lies in its examination of the impact of the democracy variable on foreign direct investments within this specific country group, employing innovative techniques not commonly found in the existing literature. Furthermore, the potential increase in foreign direct investment within these countries is expected to influence national and per capita incomes positively. The continuous enhancement of economic well-being and the rising accumulation of foreign direct investments could position these countries as new focal points of attraction in the medium and long term, fortifying their appealing characteristics.
Graphical analyses provide valuable insights into the changes and fluctuations of variables over the years in econometric studies. The visual representation and interpretations of the study variables are presented in Fig. 2 .
Graphical representation of variables
The graphical analysis reveals the trend and volatility of FDI over the study period (1994–2018). Peaks and troughs may indicate significant events or economic shifts influencing FDI.
Democracy index: The graphical representation illustrates the changes in the democracy index across the selected countries. Distinct patterns or shifts may be observed, indicating periods of democratic development or regression.
Inflation (INF): The inflation variable is depicted graphically, highlighting its trajectory over the analyzed years. Fluctuations in inflation rates may correlate with economic events impacting FDI.
Per capita income (PGDP): The per capita income variable is visually presented, demonstrating its variations and trends. Per capita income changes can influence countries’ attractiveness for foreign investments.
These graphical analyses serve as a foundation for understanding the dynamics of the variables under investigation and provide a visual context for further econometric interpretations.
So Fig. 2 provides a comprehensive overview of the variables examined in the study. The following key observations can be made:
Foreign direct investment (FDI): China stands out as the leader in attracting the highest FDI among the BRICS-TM countries. South Africa exhibits the lowest FDI levels in the sample group.
Democracy index: China also holds the highest score in the democracy index, indicating its position as the most democratic among the selected countries. South Africa, on the other hand, has the lowest democracy index score.
Per capita income (PGDP): Russia demonstrates the highest per capita income among the countries, suggesting a relatively higher economic well-being. India, conversely, has the lowest per capita income in the sample group.
Inflation (INF): Russia and Türkiye experience the highest inflation rates, while other countries exhibit fluctuating patterns at lower and similar levels.
Table 4 provides a detailed overview of the descriptive statistics for the variables under consideration. The following key statistics offer insights into the central tendencies and variations within the sample group.
The analysis of the basic descriptive statistics in Table 4 yields several noteworthy findings:
Kurtosis values: The INF variable stands out with a kurtosis value exceeding 3, indicating a sharp peak and heavy tails in its distribution. All other variables exhibit kurtosis values below 3, suggesting relatively normal distributions without excessively heavy tails.
Skewness values: LNFDI and LNPGDP variables display negative skewness values, suggesting a longer left tail in their distributions. DEMOC and INF variables exhibit positive skewness values, indicating longer right tails in their distributions.
Jarque–Bera test: The Jarque–Bera test results indicate that the variables are statistically significant and deviate from a normal distribution. This departure from normality suggests that certain factors or events influence the distributions of the variables.
These findings provide insights into the shapes and characteristics of the variable distributions. As indicated by skewness and kurtosis values, the deviations from normality suggest that the variables may be subject to specific influences or events, contributing to their non-normal distributions. Researchers should consider these distributional characteristics when interpreting the results and drawing conclusions from the dataset.
The escalating interdependence among countries in global economies has rendered them susceptible to the impact of positive or negative developments in one nation affecting others. This phenomenon directly results from the deepening global integration associated with globalization. Consequently, econometric studies must incorporate cross-section dependence tests to gauge the extent of interaction between nations. Such tests aim to quantify how a shock in one country reverberates across borders, influencing other countries of the global economic landscape.
Studies addressing cross-section dependency (Andrews, 2005 ; Pesaran, 2006 ; Phillips & Sul, 2003 ) emphasize that failing to account for cross-section analysis may lead to biased and inconsistent results. Thus, all analyses should consider cross-sectional dependence in relevant studies (Breusch & Pagan, 1980 ; Pesaran, 2004 ).
The tests used to determine cross-section dependence were as follows:
When the time dimension is greater than the cross-section dimension ( T > N ), analyses were conducted using Breusch and Pagan’s ( 1980 ) CD lm1 test.
In cases when the time dimension is equal to the cross-section dimension ( T = N ), the CD lm2 test (Pesaran, 2004 ) was used to conduct analyses.
In cases when the time dimension was smaller than the cross-section dimension ( T < N ), analyses were conducted by CD lm test (Pesaran, 2004 ).
In cases when the time dimension is both smaller and greater than the cross-section dimension, analyses were conducted (LM adj ) test (Pesaran et al., 2008 ).
This study’s analysis focuses on the relationship between democracy and FDI across BRICS-TM countries, involving seven countries. With annual data spanning 1994–2018, the cross-section dimension is denoted by N = 7 and the time dimension by T = 25. Given that T > N , the study utilized the CD lm1 test (Breusch & Pagan, 1980 ) and CD lm1 and LM adj tests (Pesaran et al., 2008 ).
Given that T > N for the countries and time dimension, the decision-making is informed by the results of the CD lm1 and LM adj tests. Notably, LM adj test results were prioritized, considering the potential bias in cross-section dependency tests associated with the CD lm1 test. The findings of the cross-section dependence tests are presented in Table 5 .
Upon reviewing Table 5 , it is evident that the probability values for all variables are less than 0.01. Consequently, based on the LM adj test results, the null hypothesis stating “there is no dependence between sections” is rejected, while the alternative hypothesis suggesting “cross-section dependence between sections” is accepted.
The outcomes of the tests align with the characteristics of the contemporary global landscape, where any impactful event or development in one of the BRICS-TM countries has reverberations across others. Whether positive or negative, changes in one BRICS-TM nation can influence others, particularly in areas related to foreign direct investment (FDI) and democracy. As a result, policymakers in these countries should craft their future strategies with a keen awareness of this interconnectedness and the potential spillover effects on FDI and democracy. Indeed, the obtained result is consistent with theoretical expectations. The observed interdependence and influential power of the BRICS-TM country group align with the current dynamics of the globalized world. Their growing significance in the world economy and their strategic importance reinforces the decision that developments within these countries have substantial implications beyond their borders. This outcome urges the need for a nuanced approach to respond to the interconnected nature of these nations in the contemporary global landscape.
In the initial phase of the econometric analysis, the stationarity of the variables in the models was determined through unit root analyses to address the spurious regression problem. Accurate results cannot be obtained when a unit root is present in a series of variables (Granger & Newbold, 1974 ). In panel data analysis, the primary consideration in stationarity tests is whether the countries are independent of each other or not. Unit root tests in panel data analysis comprise first- and second-generation tests, each with distinct characteristics. The first generation of unit root tests is further divided based on the homogeneity and heterogeneity assumptions of the countries. Some authors conducted tests under the homogeneity assumption (Breitung, 2005 ; Hadri, 2000 ; Levin et al., 2002 ), while some others pursued their analysis under the heterogeneity assumption (Choi, 2001 ; Im et al., 2003 ; Maddala & Wu, 1999 ).
Additionally, second-generation tests incorporate cross-section dependency into their analyses, whereas first-generation tests do not account for it. Given the dynamics of the global world, the use of second-generation tests in the literature is deemed more beneficial, as it is more realistic to assume that other countries will be affected by a shock experienced by one of the countries in the panel. Panel unit root tests have gained broader acceptance in time series analysis due to their ability to provide more meaningful results than standard stationarity tests. In recent years, there has been a preference for tests that allow for structural breaks, especially in series sensitive to economic variations such as foreign trade, exchange rates, and foreign capital. Hence, this study utilized panel unit root tests that consider structural breaks to assess the stationarity of variables susceptible to cyclical fluctuations, including democracy, inflation, per capita income, and FDI. Conducting stationarity tests without accounting for structural breaks can yield misleading results, making panel LM unit root tests with structural breaks the method of choice for this study.
The panel LM test (Im, Lee, & Tieslau, 2010 ) examines series in models with a level and trend, considering single and two breaks. In this study, analyses with a single break were preferred due to the shortness of the specified time interval and the events expected to cause breaks in the given period. The LM test statistics were employed to assess the hypothesis of “there is a unit root” (ϕ i = 0). Compared to others, a distinctive feature of this test is its allowance for different breaking times for different countries. Moreover, it permits a structural break under both zero and alternative hypotheses, providing an additional advantage. The asymptotic distribution of the test follows the standard normal distribution, and it remains unaffected by the presence of a structural break. Table 6 presents the stationarity analysis results of the series for seven countries based on the model allowing breaks in level.
The analysis of Table 6 yields the following observations:
In unit root models allowing for a constant break, it is evident that all variables in the panel become stationary when their differences are calculated. In other words, since the series are stationary for the entire panel at the I(1) level, the necessary conditions for cointegration tests are met. The cointegration test indicates that global and local developments in countries cause structural breaks when considering these break dates.
On a country basis, the following conclusions can be drawn from Table 6 :
For the series whose differences are calculated, the FDI variable is stationary at the level value in Russia and India, while the same variable is stationary in India and Türkiye.
The per capita income variable is stationary at a level value only in Türkiye. However, the same variable is stationary in Brazil, India, and Türkiye for the series whose differences are computed.
The inflation variable is stationary at the level value in South Africa and Mexico. However, the same variable is stationary for the series whose differences are computed in Brazil, Russia, and China.
The democracy variable is stationary at the level value in Brazil, South Africa, and Türkiye. However, the variable is stationary in Brazil, Türkiye, and Mexico for the series whose differences are computed.
Table 7 shows the stationarity analysis results of seven countries based on the model that allows breaks in level and trend.
The results in Table 7 can be analyzed based on the following points:
General panel evaluation: Foreign direct investment (FDI) and per capita income variables are stationary at the level values when the panel is considered whole. Taking the difference of these variables increases the degree of stationarity. Inflation and democracy variables, among the other variables in the model, are stationary in the series when the difference is taken. However, they exhibit unit root characteristics at the level values. Overall, all series are stationary at the I(1) level with structural breaks for the entire panel. This suggests that the necessary conditions for the cointegration test are met. The dates of structural breaks indicate that social, political, and economic developments may have caused these breaks in the BRICS-TM countries included in the sample . These findings imply that significant events and changes in the socio-political and economic landscape of the BRICS-TM countries likely influence the structural breaks in the series.
Results from Table 7 can be interpreted on a country-specific basis as follows:
Brazil: FDI and per capita income are stationary at the level value. Inflation is stationary at the level, while democracy is stationary at the difference.
Russia: FDI and per capita income are stationary at the level value. Inflation is stationary at the level, while democracy is stationary at the difference.
India: FDI is stationary at the level value. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.
China: FDI is stationary at the difference. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.
South Africa: FDI is stationary at the level value. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.
Türkiye: FDI is stationary at the level value, per capita income is stationary at the level, and inflation and democracy are stationary at the difference.
Mexico: FDI is stationary at the difference. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.
These country-specific findings indicate variations in the stationarity characteristics of the variables, highlighting the importance of considering individual country dynamics in the analysis. The results of the panel unit root tests, both with and without structural breaks, provide insights into the stationarity of the variables. The interpretation suggests that a shock to one of the countries included in the model can lead to permanent effects that do not dissipate immediately. As confirmed by the tests, the non-stationarity of the series establishes the necessary condition for cointegration tests.
Moreover, when the same tests are conducted by taking the first-order differences of all series to achieve stationarity, it is observed that the variables become stationary at the I(1) level. This indicates that the variables are integrated in the first order, aligning with theoretical expectations. The I(1) characteristic implies that the variables exhibit a tendency to return to equilibrium after a shock, supporting the notion of long-run relationships among the variables.
The homogeneity of coefficients plays a crucial role in determining the relationship between variables in panel data studies. It helps organize subsequent tests used in the analysis. The homogeneity test examines whether the change in one country is affected at the same level by other countries. Coefficients are expected to be homogeneous in models for countries with similar economic structures, while they may be heterogeneous for countries with different economic structures. Pesaran and Yamagata ( 2008 ) developed the delta test based on Swamy ( 1970 ) to determine whether the slope parameters of cross-sections are homogeneous. The null hypothesis for this test is “slope coefficients are homogeneous.” Homogeneity, in the context of panel data analysis, implies that the coefficients of the slopes are the same for all units or entities within the panel. On the other hand, heterogeneity indicates that, at least in one of the entities, the slope coefficients differ from those in the rest of the panel. Testing for homogeneity helps assess whether the relationship between variables is consistent across all units or if there are significant variations.
As seen in Table 8 , the delta homogeneity test was performed to determine whether the slope coefficients of the model differ between units.
The delta test results indicate that the slope coefficients vary between units in the long term, given that the probability values for both test statistics are smaller than 0.05, as presented in Table 8 . This result suggests that the variables exhibit heterogeneity, implying that the relationships between variables are inconsistent across all units over the long term. The obtained result aligns with expectations and is consistent with the theory, indicating that the countries within the BRICS-TM sample exhibit different structures, and the coefficients are heterogeneous. This result suggests that the relationship between variables varies across these countries, emphasizing the sample group’s diverse economic characteristics and behaviors.
Different methods are employed to determine the existence of long-term cointegration among the model’s variables. One set of methods is first-generation tests, which do not require cross-section dependence. The second set includes second-generation tests that consider cross-section dependence but do not incorporate structural breaks (Koç & Sarica, 2016 ). To obtain realistic and unbiased results, it is crucial to conduct tests that take structural breaks into account in cointegration analyses. Therefore, the panel cointegration test-PCWE (Westerlund & Edgerton, 2008 ) was employed, given that the series is stationary at the I(1) level.
PCWE was developed based on unit root tests that utilize Lagrange multiplier (LM) statistics, obtained from multiple repetitions (bootstrap). The merits of this test can be succinctly summarized as follows (Koç & Sarica, 2016 ; Göçer, 2013 ):
It takes into account cross-section dependency and structural breaks.
It accommodates heteroscedasticity and autocorrelation.
It identifies breaks at different dates for each country in terms of both constants and slopes.
Potential inherent problems in the model can be addressed with fully adjusted least squares estimators.
This test is effective in yielding reliable results even with small sample sizes.
This study opted for PCWE tests, given their robust characteristics. Additionally, considering the limited number of countries in the sample and the anticipation of few structural breaks in the specified period, the PCWE test was the preferred choice. As depicted in Table 9 , the determination of statistically significant cointegration between variables is made based on the significance levels of the probability values.
As indicated in Table 9 , cointegration is observed at a 5% significance level in the regime change model and a 1% significance level in the model without a break. The presence of cointegration suggests a long-term relationship between the variables of democracy and FDI in BRICS-TM. In simpler terms, democratic developments and FDI are correlated over the long run, indicating a balanced relationship between them. Future researchers may explore the direction of these variables across different samples. This study specifically tested the existence of a long-term relationship between FDI and democracy, and the inclusion of structural breaks was found to be significant. Governments and decision-makers, particularly in developing countries like BRICS-TM, should consider the relationship between democracy and FDI by taking structural breaks into account to attract foreign investment effectively. Therefore, it is emphasized that “any development related to democracy has the potential to influence FDI, and considering this factor is beneficial in the formulation and implementation of socio-economic policies.” No cointegration is observed in the “change at level” model. Indeed, the obtained results align with the study’s hypothesis. Considering the periods of structural breaks in the countries within the sample, it becomes evident that a long-term relationship exists between the variables incorporated into the model. This issue underscores the importance of considering not only the overall relationship between democracy and FDI but also the specific historical contexts and transitions in individual countries that might contribute to this relationship.
Regarding structural breaks in countries in the sample within the scope of cointegration in the regime change model, local and global developments, in general, cause breaks. The reasons for structural break dates in the sample countries are given in Table 10 .
The following items can be aligned with the breaking dates provided in Table 10 :
A recovery in macroeconomics and positive expectations toward agreements with the IMF became prominent after Russia’s transition economies in 1996.
2000 in Brazil is known as the period when the rapid growth trend started after passing the targeted inflation after the 1999 Russian Crisis.
Membership of China in the International Trade Union was evaluated as an essential development in the global economy in 2001.
Experiencing the biggest crisis in history in Türkiye in 2002 and starting a dominant single-party regime were remarkable developments.
The 2005 Election results in Mexico and the hurricane disasters, including an 8.7-magnitude earthquake, created significant socio-economic problems that year.
The ANC party’s coming to power alone in South Africa in 2009 was commented on as a consistent process for the national and regional economy; this situation also removed a series of uncertainties.
The devaluation experienced in India in 2016 has created a significant break.
Of course, the impact of such structural breaks should be considered. Toguç et al. ( 2023 ) argued that “differentiating these short-term and long-term effects has implications for risk management and policymaking.” Since structural break increases risks and uncertainty, foreign capital prefers to invest in other destinations.
This test (Kónya, 2006 ) investigates the existence of causality between variables using the seemingly unrelated regression (SUR) estimator (Zellner, 1962 ). One advantage of this test is that the causality test can be applied separately to the countries that make up the heterogeneous panel. Another important advantage is that it is unnecessary to apply unit root and cointegration tests, as country-specific critical values are produced. According to the test results, if the Wald statistics calculated for each country are greater than the critical values at the chosen significance level, the null hypothesis of “no causality between the variables” is rejected. In other words, a Wald statistic greater than the critical value indicates that there is causality between the variables.
The Kónya causality test results provided in Table 11 revealed a causality from democracy (DEMOC) to FDI at a 1% significance level in Mexico, 5% in China, and 10% in Russia. In addition, from FDI to democracy (DEMOC), there is causality at a 5% significance level in Mexico and a 10% significance level in Russia.
According to the results in Table 12 for the causality between foreign direct investment (FDI) and PGDP, the Kónya causality tests revealed a one-way causality from PGDP to FDI at a 10% significance level in Mexico.
According to the results provided in Table 13 for the causality between foreign direct investment (FDI) and inflation (INF), the results of the Kónya causality tests revealed a one-way causality from inflation to FDI at a 10% significance level in Türkiye and, conversely, a one-way causality from FDI to inflation at a 10% significance level in South Africa.
The study investigated the nexus between democracy and foreign direct investment (FDI) using annual data from a sample of seven countries within emerging markets from 1994–2019. According to cross-section dependence test results, all variables’ probability values were less than 0.01, indicating significant cross-section dependence. The rejection of the null hypothesis, stating “there is no dependence between sections” in favor of the alternative hypothesis suggesting “there is cross-section dependence between sections,” aligns with the contemporary global landscape. In today’s interconnected world, any impactful event or development in one of the BRICS-TM countries has reverberations across others, particularly in areas related to FDI and democracy. These findings underscore the imperative for governments and policymakers in these countries to craft future strategies with a keen awareness of this interconnectedness and the potential spillover effects on FDI and democracy.
Furthermore, the outcomes of the panel unit root test indicate that all variables in the panel become stationary at the I(1) level when their differences are calculated, meeting the necessary conditions for cointegration tests. This result suggests that global and local developments in countries cause structural breaks when considering these break dates. Variations in stationarity characteristics of variables were observed on a country basis, highlighting the importance of considering individual country dynamics in the analysis.
The delta homogeneity test results suggest that the variables exhibit heterogeneity, implying that the relationships between variables are inconsistent across all units over the long term. This aligns with expectations and emphasizes the diverse economic characteristics and behaviors within the sample group of BRICS-TM countries.
The Westerlund-Edgerton cointegration test results reveal significant cointegration between variables, observed at a 1% significance level in the model without a break and a 5% level in the regime change model. This result signifies a sustained relationship between FDI and democracy in BRICS-TM countries over the long term. Future researchers may explore the direction of these variables across different samples, while governments and decision-makers should consider this relationship, particularly in developing countries, to attract foreign investment effectively.
Kónya’s causality test results also provided significant causality between some of the variables in some countries within the sample group. Firstly, there is a causality from democracy (DEMOC) to FDI in Mexico (1% significance level), in China (5% significance level), and in Russia (10% significance level). Secondly, there is also a significant causality from FDI to democracy (DEMOC) in Mexico (5% significance level) and in Russia (10% significance level). Thirdly, a one-way causality could only be found from PGDP to FDI in Mexico (10% significance level). Fourthly, there is also a one-way causality from inflation to FDI in Türkiye (10% significance level) and a one-way causality from FDI to inflation in South Africa (10% significance level). Thus, Kónya’s causality test results supported the hypothesis of the research with significant results.
In conclusion, the empirical findings establish a statistically significant and robust relationship between the level of democracy and the flow of FDI in BRICS-TM countries. These findings underscore the intertwined nature of political and economic dynamics within these nations and highlight the importance of considering both aspects in policy formulation and decision-making processes.
The relationship between the democracy level and foreign direct investment (FDI) of BRICS-TM countries is an area that requires further exploration. Subsequently, comparing the findings of this study with those of previous research reveals its significance. While earlier studies predominantly concentrated on the preferences of host countries in attracting foreign investment, few delved into the factors influencing foreign investors’ choices. A notable exception is by Li and Resnick ( 2003 ), who highlighted the pivotal question of “Why do companies invest in foreign countries?” and proposed a theory positing that “democratic institutions impact FDI flow in both positive and negative ways” (Li & Resnick, 2003 :176). Their conclusions from data analysis of 53 developing countries spanning 1982–1995 align with the current study’s outcomes. Specifically, they found that (1) advancements in democracy lead to heightened property rights protection, fostering increased FDI inflows, and, (2) conversely, democratic improvements in underdeveloped nations result in diminished FDI flows. These findings correspond with our study, given that the sampled countries are a mix of developing and developed nations, mirroring the first scenario described by Li and Resnick.
Derbali et al. ( 2015 ) concluded in a similar vein in their study, examining a massive dataset spanning from 1980 to 2010 with 173 countries, 44 of which underwent democratic transformation. Their observation that “variables related to human development and individual freedom initiate the democratic transformation process, contrary to the social heterogeneity variable” aligns with the results of the present study when interpreted in reverse. This scenario prompts a chicken-and-egg question: Does the level of democracy positively influence the flow of FDI, or does FDI flow positively impact the level of democracy? The authors tackled this issue in the second stage of their analysis and determined that democratic transformation leads to a substantial increase in FDI inflows. Our findings corroborate this perspective with evidence from a different sample group of countries.
Malikane and Chitambara ( 2017 ) concluded in their study analyzing the relationship between FDI, democracy, and economic growth in eight South African countries from 1980 to 2014 that the FDI variable exhibits a direct and positive impact on economic development, explicitly implicating that strong democratic institutions serve as notable drivers of economic growth. Their findings suggest that the effect of FDI on economic growth is contingent on the level of democracy in the host country. In another study on developing countries, Khan et al. ( 2023 ) found that specific determinants of good governance, such as control of corruption, political stability, and voice and accountability, significantly attract FDI inflows. However, other determinants, including government effectiveness, regulatory quality, political system, and institutional quality, significantly reduce FDI inflows. On the contrary, they found that in Asian countries, all institutional quality indicators except control of corruption have a significant and positive effect on FDI inflows (Khan et al., 2023 ). The significant relationships identified between these phenomena across various indicators for developing and Asian countries align with the findings of our study.
Developed and developing nations actively engage in concerted efforts to attract foreign capital investments in the contemporary global economic landscape. Foreign direct investments (FDIs) stand out as a pivotal form of investment that significantly influences a country’s growth and development trajectory. The inflow of direct foreign capital brings multifaceted contributions to a nation’s economy, encompassing vital aspects such as capital infusion, technological advancement, elevated management standards, expanded foreign trade opportunities, employment generation, sectoral discipline, access to skilled labor, and risk mitigation.
In addition to all these, foreign direct investment (FDI) holds significant importance not only in the general context of sustainability but also specifically in sustainable development. To better understand this close relationship between sustainable development and FDI, first briefly examine the concept of sustainability. Simply put, sustainability entails maintaining a favorable condition through methods that cause no harm yet are supportable, legally and scientifically verifiable, defendable, and implementable (Ratiu, 2013 ). From a developmental perspective, it signifies maintaining continuity without losing control. According to Menger ( 2010 ), sustainability can be defined as the ability to grow and survive independently. The author emphasizes that the concept of sustainability is closely related to “creativity” and “cultural vitality,” as well as being an “internally growing” and “self-sustaining” trend with innovative effects that also attract different social strata.
Within the context of all these existing barriers and dilemmas, managing the process of reducing the negative aspects while increasing and offering the positives to people must be handled with care. This intricate process, termed sustainable development, is like the search for the cosmos in chaos as it aims to balance the economic, environmental, and social dimensions of both local urban areas and regional and national areas, and even the global sphere, especially with climate change becoming one of the main negative impacts on the environmental dimension. Gazibey et al. ( 2014 ) also noted that, while some problem areas, such as “poverty reduction” are mainly related to the economic and somewhat to the social dimensions of sustainability, other issues like “climate change” and “reduction of carbon footprint” are more related to the environmental dimension. An in-depth examination reveals that many problems, which may initially seem related to a single dimension, are intertwined with multiple dimensions. Thus, while attracting foreign direct investment to a country may seem primarily related to the economic dimension at first glance, it is closely linked to environmental and social dimensions.
In its most straightforward approach, meeting and satisfying the basic needs of individuals will subsequently prioritize higher-level needs. This, in turn, will support sustainable development in all three dimensions. Thus, while foreign capital invested in a country may initially support economic sustainability, its contribution to the socio-economic levels of individuals will lay the groundwork primarily for social and educational improvement in the medium and long term, secondarily for environmental enhancement to result in a more livable environment. For example, Xu et al. ( 2024 ) argued that “China is currently exploring a sustainable development mode of collaborative governance.” In a good level of governance, all social partners expected to be affected by the possible policies are included in the decision-making process. This process is related to and supports the participation dimension of democracy. So, as the pieces of a chain, a good level of democracy supports the level of governance, and governance supports the accumulation of FDI and economic performance. Consequently, these favorable conditions might pave the way for sustainable development. Another study (Olorogun, 2023 ) found a long-run relationship between financial development in the private sector and economic growth in sub-Saharan Africa, with the data spanning from 1978 to 2019. According to the results of the author’s research, there is a long-run covariance between sustainable economic development and foreign direct investment (FDI) and a significant level of causality between economic growth and financial development in the private sector, FDI, and export.
Indeed, sustainability resembles a ball resting on a three-legged stool: Any absence or imbalance in one of this tripod’s economic, social, or environmental legs will cause the ball to fall. In other words, sustainable development requires addressing all three dimensions in a balanced manner.
This idea brings us to the focus of this research: The level of democracy and the FDI variable and the relationship between these variables essentially concerns all three dimensions. In countries with a higher level of democracy, the possibility of developing policies that consider citizens’ demands and preferences is higher than in countries with lower levels of democracy. Conversely, in countries with lower levels of democracy , the likelihood of prioritizing the preferences and gains of specific individuals or groups over issues such as sustainability, environmental protection, and social welfare is higher. Consequently, this situation will negatively affect both the potential level of FDI attracted to the less developed country and, ultimately, the sustainable development momentum.
To sum up, numerous factors play a crucial role in shaping decisions related to foreign direct investments. Particularly in underdeveloped and developing countries, where domestic capital accumulation might be insufficient, the preference for attracting direct foreign capital investments emerges as a strategic choice over external borrowing. This strategic approach is driven by fostering economic development and sustainable growth while leveraging the benefits associated with foreign capital inflows.
The empirical evidence on the relationship between democracy and the level of foreign direct investment (FDI) often presents conflicting results, influenced by variations in study periods and sample compositions. Notably, these disparities can be traced back to the differing development levels of countries under scrutiny.
Reviewing previous studies reveals a recurring pattern wherein developed countries exhibit a positive and significant correlation between democracy and FDI. Conversely, in underdeveloped or developing nations, a negative relationship tends to prevail between these two variables. This disparity hinges on the distinct behavior of capital owners seeking to invest in already developed countries, where business transactions are grounded in established legal frameworks, property rights, and the rule of law. In contrast, underdeveloped and developing countries often witness capital owners engaging in potentially illicit and unethical business dealings with high risks and potential returns.
These arrangements are frequently based on different interests and assurances with individuals and groups in positions of power. In essence, the ease of resource acquisition, processing, and exportation in underdeveloped countries becomes contingent upon the presence of authoritarian regimes. Such relationships of interest with authoritarian regimes provide investment security for global investors. However, these regimes—keen on preserving these relationships—are disinclined to have their dealings exposed, which in turn leads to increased pressure on their citizens. The resulting mutualistic relationship transforms into a lucrative exploitation process.
When the outcomes of the panel data analysis incorporating structural breaks were examined, it was found that all variables demonstrated significance at the 1% level. The cross-sectional dependency analysis results indicated a significant cross-sectional relationship between the variables. In the panel unit root test, it was observed that the variables in the model exhibited unit roots at the level, but their differences rendered all variables stationary. The delta homogeneity test findings suggested that the variables lacked homogeneity. Furthermore, the results of the panel cointegration test with structural breaks affirmed a long-term relationship, with significance levels of 1% in the model without breaks and 5% in the regime change model. Lastly, the reached bidirectional and one-directional causality between FDI and democracy and other economic variables like inflation and PGDP in the sample group countries require policymakers to focus on each variable carefully especially on the level of democracy if they aim to reach a high level of FDI.
In conclusion, the findings of this study suggest the presence of a long-term relationship between democracy and FDI also supported by causality in some countries within the sample, as revealed through the analysis of data from BRICS-TM countries within emerging markets spanning the period 1994–2018. The significance of this relationship is particularly evident when considering the impact of structural breaks. It is emphasized that governments and policymakers in emerging markets (including those in BRICS-TM), which aim to bolster their economy’s resilience against various shocks, should not only consider structural breaks but also recognize the intricate connection between democracy and FDI. The study underscores that developments in democracy have the potential to influence FDI, emphasizing the importance of factoring this relationship into the formulation and execution of socio-economic policies. Lastly, using panel tests with a structural break, a method uncommonly employed in the empirical analysis of the democracy variable, may contribute as an additional dimension to the existing literature in this field.
In analyzing the relationship between democracy and foreign direct investment, the findings suggest a long-term relationship in all models except for the level change model. These results highlight the significance of democratic developments in the BRICS-TM countries influencing the inflow of foreign direct capital. Therefore, policymakers in emerging markets, particularly within BRICS-TM countries, are encouraged to prioritize democracy and foster democratic developments to attract foreign direct investments. Additionally, given the impact of global and local developments leading to structural breaks, it becomes crucial for these policymakers to closely monitor and interpret international and global events that may affect the resilience of their national economies, both negatively and positively. By doing so, emerging markets can enhance their resilience against various shocks, enabling policymakers to adeptly prepare their economies, private sectors, and stock markets for potential global risks.
Opting for direct foreign capital investments over external debt or short-term investments is a more rational approach for developing countries to accumulate capital for their overall development. As many countries seek to address the scarcity of capital, the understanding of the contributions of foreign capital to development improves, while global competition intensifies to attract foreign capital. Therefore, policymakers should focus on enhancing macroeconomic indicators such as inflation and national income and fostering democratic development, a fundamental trust factor for foreign capital. Demographic and institutional factors also affect the global or social fiscal pressure (Nuță & Nuță, 2020 ). Thus, as an institutional factor, positive developments at the level of democracy are fundamental in attracting foreign capital.
It is crucial for developing countries to prioritize and keep pace with indicators that foreign capital considers significant. Global companies prioritize countries they can trust, where investments can swiftly yield profits due to potential risks. The foundation of democracy in developing nations starts in the family and education realms. Proper education on the importance and necessity of democracy in the curriculum contributes to long-term awareness of democracy. Developing effective education policies within families can address intra-family democracy, fostering a culture of democracy throughout the country.
The reasons listed up to this point reiterate that attracting foreign direct investments to a country is of utmost critical importance for supporting sustainable development in all aspects of the nation. As discussed in the discussion section, while sustainability may appear to be solely related to the economic dimension at first glance, an increase in foreign direct investment toward a country has the potential to indirectly and positively impact the social and environmental dimensions of sustainability as well. When considering that the level of democracy also has a similar effect on the level of FDI, it should be expected that the level of democracy in a country is strongly correlated with the issue of sustainable development.
In conclusion, new researchers interested in this subject are recommended to conduct analyses on different country groups. Updating established models and testing hypotheses using various socio-economic indicators and analysis methods can further contribute to the literature.
The data set is uploaded to the system as a supplementary file and also uploaded to Figshare with the https://doi.org/10.6084/m9.figshare.21701966 .
Turkey’s name changed to Türkiye: According to the United Nations (UN)-Türkiye, the country’s name has been officially changed to Türkiye at the UN upon a letter received on June 1 from the Turkish Foreign Ministry (UN-Türkiye. (2022)). Turkey’s name changed to Türkiye, URL: https://turkiye.un.org/en/184798-turkeys-name-changed-turkiye , Accessed on: 02.07.2022.
Brazil, Russia, India, China, South Africa, Türkiye, Mexico
The Democracy Index variable
Ecological footprint
Gross domestic product
Logarithm of foreign direct investment
Logarithm of per capita income
Multinational corporations
Per capita income
Political institutions
Regression coefficient value
World Development Indicators
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1. From 1994 to 2018, there was significant cointegration between democracy and foreign direct investment (FDI) in BRICS-TM countries among the emerging markets.
2. Democratic developments and FDI move together in the long run and have a balanced relationship between them in Emerging Market Economies.
3. Policymakers in BRICS-TM countries need to develop democracy awareness and ensure democratic developments to attract foreign direct investment to secure a resilient economy in these emerging economies
4. Governments and decision-makers in emerging economies, such as BRICS-TM, who want to attract FDI need to consider the structural breaks and the relationship between democracy and FDI .
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Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.
To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.
After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.
This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.
Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.
To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.
The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.
A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.
While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.
A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.
First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.
Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.
First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.
In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.
When planning a research design, you should operationalize your variables and decide exactly how you will measure them.
For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:
Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.
Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.
In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.
Variable | Type of data |
---|---|
Age | Quantitative (ratio) |
Gender | Categorical (nominal) |
Race or ethnicity | Categorical (nominal) |
Baseline test scores | Quantitative (interval) |
Final test scores | Quantitative (interval) |
Parental income | Quantitative (ratio) |
---|---|
GPA | Quantitative (interval) |
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In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.
Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.
There are two main approaches to selecting a sample.
In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.
But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.
If you want to use parametric tests for non-probability samples, you have to make the case that:
Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.
If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .
Based on the resources available for your research, decide on how you’ll recruit participants.
Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.
Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.
There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.
To use these calculators, you have to understand and input these key components:
Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.
There are various ways to inspect your data, including the following:
By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.
A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.
In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.
Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.
Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:
However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.
Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:
Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.
Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.
Pretest scores | Posttest scores | |
---|---|---|
Mean | 68.44 | 75.25 |
Standard deviation | 9.43 | 9.88 |
Variance | 88.96 | 97.96 |
Range | 36.25 | 45.12 |
30 |
From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.
It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.
Parental income (USD) | GPA | |
---|---|---|
Mean | 62,100 | 3.12 |
Standard deviation | 15,000 | 0.45 |
Variance | 225,000,000 | 0.16 |
Range | 8,000–378,000 | 2.64–4.00 |
653 |
A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.
Researchers often use two main methods (simultaneously) to make inferences in statistics.
You can make two types of estimates of population parameters from sample statistics:
If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.
You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).
There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.
A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.
Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.
Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:
Statistical tests come in three main varieties:
Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.
Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.
A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).
Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.
The z and t tests have subtypes based on the number and types of samples and the hypotheses:
The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.
However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.
You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:
Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.
A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:
The final step of statistical analysis is interpreting your results.
In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.
Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.
This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.
Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.
A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.
In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .
With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.
Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.
You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.
Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.
However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.
Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Methodology
Research bias
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Usually there is no good way to write a statistic. It rarely sounds good, and often interrupts the structure or flow of your writing. Oftentimes the best way to write descriptive statistics is to be direct. If you are citing several statistics about the same topic, it may be best to include them all in the same paragraph or section.
Present descriptive statistics for each primary, secondary, and subgroup analysis. Don't provide formulas or citations for commonly used statistics (e.g., standard deviation) - but do provide them for new or rare equations. ... In the methods section of an APA research paper, you report in detail the participants, measures, and procedure of ...
Descriptive Statistics. The mean, the mode, the median, the range, and the standard deviation are all examples of descriptive statistics. Descriptive statistics are used because in most cases, it isn't possible to present all of your data in any form that your reader will be able to quickly interpret.
Using the data from these three rows, we can draw the following descriptive picture. Mentabil scores spanned a range of 50 (from a minimum score of 85 to a maximum score of 135). Speed scores had a range of 16.05 s (from 1.05 s - the fastest quality decision to 17.10 - the slowest quality decision).
In reporting the results of statistical tests, report the descriptive statistics, such as means and standard deviations, as well as the test statistic, degrees of freedom, obtained value of the test, and the probability of the result occurring by chance (p value). Test statistics and p values should be rounded to two decimal places.
There are 3 main types of descriptive statistics: The distribution concerns the frequency of each value. The central tendency concerns the averages of the values. The variability or dispersion concerns how spread out the values are. You can apply these to assess only one variable at a time, in univariate analysis, or to compare two or more, in ...
Report descriptive statistics to summarize your data. Quantitative data is often reported using means and standard deviations, while categorical data (e.g., demographic variables) is reported using proportions. ... In the methods section of an APA research paper, you report in detail the participants, measures, and procedure of your study. 266.
Descriptive statistics, although relatively simple, are a critically important part of any quantitative data analysis. Measures of central tendency include the mean (average), median and mode. Skewness indicates whether a dataset leans to one side or another. Measures of dispersion include the range, variance and standard deviation.
Descriptive Statistics | Definitions, Types, Examples. Published on 4 November 2022 by Pritha Bhandari.Revised on 9 January 2023. Descriptive statistics summarise and organise characteristics of a data set. A data set is a collection of responses or observations from a sample or entire population.. In quantitative research, after collecting data, the first step of statistical analysis is to ...
In this section, we focus on presenting descriptive statistical results in writing, in graphs, and in tables—following American Psychological Association (APA) guidelines for written research reports. These principles can be adapted easily to other presentation formats such as posters and slide show presentations.
Descriptive statistics are used to examine methods of collecting, tidying up, and presenting research data (Alabi & Bukola, 2023). In addition to descriptive, there is also an evaluative analysis ...
Descriptive statistics are specific methods basically used to calculate, describe, and summarize collected research data in a logical, meaningful, and efficient way. Descriptive statistics are reported numerically in the manuscript text and/or in its tables, or graphically in its figures. This basic …
This paper reviews the use of descriptive statistics to describe the participants included in a study. It discusses the practicalities of incorporating statistics in papers for publication in Age and Aging, concisely and in ways that are easy for readers to understand and interpret. older people, descriptive statistics, study participants ...
In this paper, we present essential methods of Descriptive Statistics for biomedical science students and professionals. We explore data summary techniques such as the mean, median, and mode ...
Research Results. Research results refer to the findings and conclusions derived from a systematic investigation or study conducted to answer a specific question or hypothesis. These results are typically presented in a written report or paper and can include various forms of data such as numerical data, qualitative data, statistics, charts, graphs, and visual aids.
The APA results section summarizes data and includes reporting statistics in a quantitative research study. The APA results section is an essential part of your research paper and typically begins with a brief overview of the data followed by a systematic and detailed reporting of each hypothesis tested. The interpreted results will then be presented in the discussion sections.
5.3 Writing about descriptive statistics The amount of a research report that is devoted to descriptive statistics varies depending on the research project and the type of publication. Some research projects present only descriptive statistics. This may be the case in mixed-methods studies or exploratory studies. However,
Checklist: Research results 0 / 7. I have completed my data collection and analyzed the results. I have included all results that are relevant to my research questions. I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics. I have stated whether each hypothesis was supported ...
Abstract. Descriptive statistics provide an essential foundation for understanding and summarizing large datasets by offering valuable insights into the central tendencies, dispersion, and shape of the distribution. By leveraging measures such as mean, median, mode, range, variance, and standard deviation, researchers can succinctly present the ...
Descriptive statistics is a powerful beast of burden: (1) It co llects and summarize s vast amounts of data and. information in a manageable and organized manner, (2) A fairly straightforward ...
Section 3 outlines the research framework and the hypotheses of this study. Section 4 explains the methodology used in the research. Section 5 presents the findings of this study, with a subsection presenting descriptive statistics and another presenting the analysis of the hypotheses' tested. Section 6 discusses the findings and Section 7 ...
Research papers in the social and natural sciences often follow APA style. This article focuses on reporting quantitative research methods. In your APA methods section, you should report enough information to understand and replicate your study, including detailed information on the sample, measures, and procedures used.
Objectives This cohort study reported descriptive statistics in athletes engaged in Summer and Winter Olympic sports who sustained a sport-related concussion (SRC) and assessed the impact of access to multidisciplinary care and injury modifiers on recovery. Methods 133 athletes formed two subgroups treated in a Canadian sport institute medical clinic: earlier (≤7 days) and late (≥8 days ...
Graphical representation of variables and analysis of descriptive statistics, (2) CD lm1 (Breusch & Pagan, 1980), CD lm1, and LM adj tests (Pesaran et al., 2008) were used in the analysis to find the presence of cross-section dependence of variables. (3) Panel LM test (Im, Lee, & Tieslau, 2010) determined whether variables in the model have a ...
Example: Inferential statistics. You randomly select a sample of 11th graders in your state and collect data on their SAT scores and other characteristics. You can use inferential statistics to make estimates and test hypotheses about the whole population of 11th graders in the state based on your sample data.
Table of contents. Step 1: Write your hypotheses and plan your research design. Step 2: Collect data from a sample. Step 3: Summarize your data with descriptive statistics. Step 4: Test hypotheses or make estimates with inferential statistics.