The data show an equitable relationship between men and women, with females representing 50.5% of the sample, with a predominance of those born between 1980 and 1994. Most of the professionals interviewed were also married (43.3%).
Physicians worked mainly in the city of Salvador and its metropolitan region (74.75%) and most had a short time since graduation, with the period of graduation between 2011 and 2021 being the most frequent (35.3%). Most of the interviewees already had a specialization or medical residency, as described in Table 1.
Table 1
Demographic variables of the studied sample. N = 99.
Variables
|
N (%)
|
Sex
|
|
Female
|
50 (50.5)
|
Male
|
49 (49.5)
|
Marital status
|
|
Married
|
43 (43.3)
|
Divorced
|
12 (12.1)
|
Single
|
29 (29.3)
|
Stable union
|
14 (14.1)
|
Widower
|
1 (1.0)
|
Region of professional activity
|
|
Interior of the state of Bahia
|
14 (14.1)
|
Salvador and Metropolitan Region
|
74 (74.7)
|
Both
|
3 (3.0)
|
None of the above
|
8 (8.1)
|
Age group
|
|
Born between 1940 and 1959
|
19 (19.2)
|
Born between 1960 and 1979
|
31 (31.3)
|
Born between 1980 and 1994
|
43 (43.2)
|
Born between 1995 and 2010
|
6 (6.1)
|
Year of graduation from college
|
|
Before 1980
|
13 (13.1)
|
From 1981 to 1990
|
18 (18.1)
|
From 1991 to 2000
|
19 (19.2)
|
From 2001 to 2010
|
14 (14.1)
|
From 2011 to 2021
|
35 (35.3)
|
Level of training
|
|
General practitioner
|
15 (15.2)
|
Resident doctor
|
13 (13.1)
|
Doctor already with specialization/residence
|
70 (70.7)
|
Other
|
1 (1.0)
|
Source: data collected by the author, 2022.
Most of the doctors interviewed provided services through a legal entity (63.6%). Of the physicians who owned companies, 17.8% were business owners whose main activity did not involve the provision of health services.
The most prevalent monthly income of the studied group was above R$ 17, 000.01 (60.6%) as shown in the graph in Fig. 2. About 36% of professionals contributed more than 50%, but not the entire amount of family income. However, many still participated with 100% of the family income (31%) as shown in Fig. 3.
The verification of the collected data points out that the characteristics of the sample, despite being obtained for convenience, coincide with some of the passages of the 2020 medical demography report, which, among other findings, reveals a greater number of young professionals active, in addition to the increase in the number of women working in the area. Although men are the majority of physicians working in Brazil, the numerical discrepancy between genders has been gradually decreasing. In the years 1990, 2015, and 2020, men represented respectively 69.2%, 57.5%, and 53.4% of the population of working physicians. Among practicing physicians in 2020, women were already the majority in the age groups up to 29 years old (58.5%) and between 30–34 years old (55.3%).
The drop in the average age of professionals in the field working in Brazil, currently estimated at around 45 years, can be explained by the growth in the number of courses and vacancies for undergraduate Medicine, with the consequent entry of recent graduates into the labor market. The 2020 demography also shows that 61.3% of working physicians had one or more specialist titles.
The report also reveals the inequality in the geographical distribution of professionals in a similar way to this study, since it concludes that in the group of capitals, there are 5.65 doctors per thousand inhabitants, while the inhabitants of the group of cities in the interior have 1.49 doctors per thousand inhabitants (Scheffer et al., 2020).
Also according to the demography of 2020, medicine in Brazil has immense adhesion of professionals, as 93% of doctors claim to practice only or dedicate themselves exclusively to the profession. This study also draws attention to two important points: the increase in the concentration of employment contracts per individual, since the percentage of professionals with four or more contracts increased from 24.2% in 2014 to 44% in 2019, and the increase in working hours worked, with almost half of the doctors (45.9%) saying, in 2019, they worked more than 60 hours a week, against 32.4% in 2014.
It is a fact that, even though it is a long course, with a high financial cost and with important impacts on the social life and mental health of students, the medical profession attracts a lot of interest from candidates, given the possibility of salary gains above the national average - the average income of Brazilian workers was R$2,489 in the quarter ended in January 2022, according to the Brazilian Institute of Geography and Statistics (IBGE), while almost half of doctors earned more than R$16,000 per month, according to the 2020 demographics - as well as the high employability rate in almost all of our territory. Indeed, such benefits are almost always achieved at the expense of the quality of life of such professionals. However, studies have already shown that proper behavior regarding personal finances results in better physical, emotional and interpersonal health (Dew & Xiao, 2011; Drentea, 2000).
The habit of managing financial resources should be more relevant than ensuring robust monthly income. The concern with obtaining more money and the eventual achievement of this objective leads people to not plan for the future and not correctly manage their expenses (Eker, 2006). The challenges to which the medical profession has been exposed in recent years may be mitigated as workers in the field are introduced to good financial practices.
The analysis of the data collected here shows that the average reached by the physicians was 75.8 (SD 13.7) out of 100 points. The questions of the scale used were, at the time of its construction, purposely subdivided into categories of personal finance skills: 4 questions that evaluate cash management, 3 questions that evaluate credit management, 5 questions that evaluate the habit of saving and investing and 3 questions that assess behavior in maintaining property insurance (Table 2).
In the analysis from the perspective of the subcategories of financial skills, the doctors interviewed showed the worst performance was in the management of savings and investments, with an average of 66.6 (SD 23.71) points. The financial skill best managed by respondents was credit management, with an average of 95.45 (SD 10.55) points.
Table 2
Financial behavior by FMBS and its subdivisions. N = 99.
Variables
|
Average
|
SD
|
Amplitude
|
Financial Management Behavior Scale (FMBS)
|
75.85
|
13.76
|
35–100
|
Cash management
|
78.47
|
14.34
|
37.50–100
|
Compare prices
|
78.79
|
22.69
|
25–100
|
Pay bills on time
|
95.45
|
9.69
|
75–100
|
Register expenses
|
59.09
|
33.96
|
0–100
|
Stays on budget
|
80.56
|
22.46
|
0–100
|
Credit Management
|
95.45
|
10.55
|
58.33–100
|
Full paid balance
|
98.99
|
4.49
|
75–100
|
Limit use
|
85.35
|
25.00
|
0–100
|
Loan payment
|
95.45
|
16.11
|
0–100
|
Savings and investment
|
66.66
|
23.71
|
10–100
|
Emergency savings
|
80.56
|
28.47
|
0–100
|
Save paycheck
|
69.70
|
29.93
|
0–100
|
I save long term
|
75.76
|
27.76
|
0–100
|
Contribute retirement
|
63.64
|
40.76
|
0–100
|
Buy bonds, stocks
|
43.69
|
39.50
|
0–100
|
Insurance
|
67.77
|
29.72
|
0–100
|
Health insurance
|
81.06
|
35.73
|
0–100
|
Property insurance
|
80.30
|
34.86
|
0–100
|
Life insurance
|
49.49
|
44.46
|
0–100
|
Source: data collected by the author, 2022. SD = Standard deviation.
The medical population was purposely selected for the study due to their socioeconomic characteristics that stand out from the rest of the Brazilian population. This group comprises people with a high level of education, with a monthly income above the national average, and n to enjoying a full supply of jobs since the beginning of their careers. If with these favorable conditions for heritage construction, the sample still showed deficits in resourcefulness in dealing with personal finances, we imagine it to be veryIt is likely that other groups of professionals may perform even worse, a fact that can have a relevant impact on the well-being of the population. On the other hand, for Kyiosaki (2000), “what is needed to make money is not money, but financial literacy”.
The results of national surveys published in June 2017, showed that people in debt have serious psychological and social problems, ranging from reactions of insecurity and irritability to anxiety, anguish, loss of appetite, depression, and unhappiness (CNC, 2017; SPC & CNDL, 2017). Another study presented at the 18th Annual Congress of the South African Heart Association in 2017, conducted with groups of people with and without heart disease in South Africa, revealed that significant financial stress is associated with a 13 times greater chance of having a heart attack. In the current crisis scenario in Brazil, designed, among other factors, by the reduction of the foreign investment index and the increase in inflation, what is perceived is that the absence of FE is a factor that has a significant negative impact on society as a whole, causing immeasurable damage and the distressing feeling of impossibility of recovery, generating a vicious circle that culminates in the worsening of the economic crisis and damage to the health of people.
A survey carried out in 2020 with all Brazilian capitals by the National Confederation of Shopkeepers and the Credit Protection Service reveals that almost half (48%) of Brazilian consumers do not control their budget in any way. Another worrying finding of the study is that even among those who do some control of their finances, they do not do so with adequate frequency of notes and analysis. Another survey carried out by the same institutions in 2016 revealed that eight out of ten consumers have a mistaken concept of the term indebtedness. The most cited confusion has to do with unfulfilled commitments: 46.7% of respondents, especially among women (52.3%) and those belonging to classes A/B (59.6%), believe that being in debt is having overdue or unpaid bills. Only a fifth of the interviewed consumers understood the real meaning of the term and answered that an indebted person has installments of debts for purchases and/or loans due.
The BFM analyzed from the perspective of gender points to better performance among men, with a statistically significant difference between the groups (p = 0.003) (Fig. 4).
The subcategories of competencies in financial management showed no statistically significant difference in skills in cash management (p = 0.985), credit management (p = 0.571), and insurance (p = 0.51) when analyzed from the perspective of gender. However, the ability to manage savings and investments was significantly higher (p < 0.001) in the male group than in the female group.
The OECD also shows that men and women operate their financial lives in different ways. Historically, women entered the labor market late, and therefore it is not uncommon to see that financial issues impact differently between the sexes. According to the medical demographics of 2020, we begin to observe greater participation of women in the medical labor market only in 2009. In Brazil, the chance of a male doctor being among the best-paid in the profession is 17.1%. For female doctors, however, this probability is 4.1% (Salles, 2019).
In the study by Bapat (2020), for audiences from emerging countries and using the FMBS, gender does not show a statistically significant difference between the different clusters. One possible reason for this is that men and women exhibit similar behaviors in their youth.
The comparison of BFM scores between different generations of physicians does not indicate a statistically significant difference (p = 0.570) by the One-Way ANOVA test. There was also no difference between age groups in the BMF subscales for cash management skills (p = 0.681), credit management (p = 0.887), savings and investments management (p = 0.417), and purchase of insurance (p = 0.081). The measures of central tendency and dispersion of each one can be seen in Table 3.
Table 3
BFM score by generation. N = 99.
Variables
|
Average
|
SD
|
Amplitude
|
Financial Management Behavior Scale (FMBS)
|
|
|
|
Born 1940 to 1959
|
76.14
|
17.11
|
35–96
|
Born from 1960 to 1979
|
76.82
|
12.60
|
36–95
|
Born from 1980 to 1994
|
74.72
|
13.85
|
48–100
|
Born after 1995
|
78.05
|
8.39
|
66–88
|
Cash management
|
|
|
|
Born 1940 to 1959
|
80.26
|
20.86
|
37–100
|
Born from 1960 to 1979
|
79.23
|
11.22
|
50–100
|
Born from 1980 to 1994
|
77.03
|
13.48
|
58–100
|
Born after 1995
|
79.16
|
12.28
|
56–87
|
Credit Management
|
|
|
|
Born 1940 to 1959
|
94.73
|
10.47
|
58–100
|
Born from 1960 to 1979
|
95.69
|
5.64
|
83–100
|
Born from 1980 to 1994
|
90.69
|
13.01
|
58–100
|
Born after 1995
|
94.44
|
8.60
|
83–100
|
Savings and investment
|
|
|
|
Born 1940 to 1959
|
62.36
|
28.98
|
10–95
|
Born from 1960 to 1979
|
61.45
|
25.07
|
15–100
|
Born from 1980 to 1994
|
71.39
|
20.36
|
35–100
|
Born after 1995
|
73.33
|
16.02
|
55–100
|
Insurance
|
|
|
|
Born 1940 to 1959
|
75.00
|
16.43
|
33–100
|
Born from 1960 to 1979
|
80.37
|
24.58
|
0–100
|
Born from 1980 to 1994
|
61.24
|
34.30
|
0–100
|
Born after 1995
|
68.05
|
17.01
|
50–100
|
Source: data collected by the author, 2022. SD = Standard deviation.
On the other hand, studies that used the FMBS for BFM analysis showed a difference between generations, such as the one carried out by Bapat in 2020, for populations from emerging countries, which showed that scores in all three dimensions (cash management, credit, and savings /investment) are higher for older respondents than scores for younger respondents. This result is probably explained by the fact that older individuals carry more financial responsibilities, which would translate into greater competence in managing personal finances.
Even studies that did not use the FMBS, but that measured financial literacy, also obtained variation between individuals of different ages, as evidenced in the following studies: Lusardi and Mitchell, in 2008, noticed the existence of a decline in financial knowledge after the age of 50. Their findings point to an “inverted U” curve in age x financial proficiency parameters analysis. Luigi Guiso, in his study published in 2006, finds that, when choosing stocks, consumers reach their best Sharpe ratios (a tool for evaluating investment funds) around the age of 43. Lucia Dunn (2006) shows a U-shaped credit card interest rates pattern by age.
In line with the results of the present study, the study by Jodi (1996) revealed that age does not predict the BFM and the study by Rajna et al. (2011) indicated that the age of Malaysian medical professionals is unrelated to their financial attitude and practice. However, these were works that did not use the FMBS as a tool. We can infer that the findings of the present study may be associated with changes in today's society, such as those related to new acts that are driven by subjective age and its effects and no longer by what is expected when analyzing an individual's BFM only by their chronological age, which has possibly been causing young doctors, shaped by subjective age, to have an apparent improvement in their financial habits and to be on par with individuals of previous generations, but still with deficits. According to Barak (2009), the self-assessment of age is reflected in changes in lifestyle, social functioning, and behavior related to consumption and productivity (Barak, 2009).
Other authors also consider that the best BFM is reached with advancing age. According to Lusardi et al. (2019), the standard model of the financial life cycle shows that adults approaching the period of retirement must be at the height of their wealth accumulation, as they have had years of opportunity for active participation in the economy. This is the period in which the individual must prepare for a drop in earnings, as they will no longer have employment contracts. However, subjective age, that is, the age perceived by the individual himself, ends up being an important variable in the analysis of the financial life cycle since it can be quite different from the chronological age (actual age of the individual) when analyzing the BFM of the interviewee.
The dissociation between the perception of subjective age and chronological age associated with the expansion of the perspective of life in modern society, whose explanation is multifactorial, ends up pushing to future times the need to maintain monthly income, and consequently the postponement of retirement, an event that among other arguments, it becomes the basis for the recently approved social security reform presented to the nation.
There is also the concept of prospective age proposed by Sanderson and Scherbov (2008), which points to the more years that individuals can expect to live, contextualized by the living conditions of the place where they live and by historical time. The population of French people who were 40 years old in 2005 can be compared to the population of the same country that lived in 1952 at 30 years of age since they are equal in terms of life expectancy - the average of approximately 44.7 years (Batistoni & Namba, 2010).
Our study had as its main proposal the evaluation of the behavior in personal finances according to the chronological age of the sample since it was segmented into generations. It is possible that the BFM is modified by the perception of subjective age and also by the prospective age and that this was the reason for not having a significant difference between the age groups studied here.
It is a fact that youth are more exposed to all kinds of quality information, facilitated by technological advances and the use of the internet. It is expected, therefore, an increase in interest and demand for knowledge about financial issues.
The medical demography in Brazil in 2020 also reinforces that the sharp generational renewal is accompanied by new aspirations, with possible repercussions on choices and motivations related to bonds, journeys, specialties, remuneration, use of technologies, and a better balance between personal and professional life.
The assessment of the subtopic of skills in managing/acquiring property insurance showed a statistically significant difference between individuals who participated with more than 50% but not with all the income of the family nucleus, both concerning individuals who were responsible for guaranteeing less than 50% of household income as with those who were 100% providers (Fig. 5).
This subtopic, often neglected by families, guarantees patrimonial protection, as the term itself says. According to Saraiva (2017), financial protection is the act of “taking precautions regarding your financial situation, highlighting the importance of having a reserve for emergencies and taking out insurance”.
It is likely that the feeling of responsibility for their dependents and, therefore, for the standard of living guaranteed to them, increases the perception of the need for patrimonial protection in the face of adversity, for those who have a greater share of the family income (Table 4).
Table 4
Score in terms of life insurance for contributions to family income. N = 99.
Variables
|
Average
|
SD
|
Amplitude
|
Share in total family income
|
|
|
|
100%
|
45.16
|
42.53
|
0 -100
|
More than 50% and less than 100%
|
72.14
|
40.57
|
0 -100
|
About 50%
|
41.07
|
42.29
|
0–100
|
Less than 50%
|
21.05
|
37.51
|
0–100
|
Source: data collected by the author, 2022. SD = Standard deviation.
The demographic variables analyzed in this study may not have presented statistical significance due to the limitations of the research, such as the size and convenience of the sample, the numerical inequality of representatives of the different generations, and the fact that it is a study with self-completion of answers to very sensitive issues, which generates the possibility of socially desirable choices.
Table 5
Results for the BFM in eight studied demographic variables. N = 99.
Variables
|
P-value
|
Marital status
|
0.368
|
Cash management
|
0.679
|
Credit management
|
0.794
|
Savings and investment
|
0.221
|
Safe
|
0.255
|
Workplace
|
0.273
|
Cash management
|
0.277
|
Credit management
|
0.222
|
Savings and investment
|
0.848
|
Safe
|
0.601
|
Year of Formation
|
0.577
|
Cash management
|
0.271
|
Credit management
|
0.271
|
Savings and investment
|
0.213
|
Safe
|
0.190
|
Level of training
|
0.054
|
Cash management
|
0.799
|
Credit management
|
0.203
|
Savings and investment
|
0.718
|
Safe
|
0.052
|
Area of expertise: CLT
|
0.203
|
Cash management
|
0.240
|
Credit management
|
0.433
|
Savings and investment
|
0.994
|
Safe
|
0.065
|
Area of activity: legal entity
|
0.669
|
Cash management
|
0.380
|
Credit management
|
0.053
|
Savings and investment
|
0.260
|
Safe
|
0.415
|
Area of expertise: statutory
|
0.655
|
Cash management
|
0.573
|
Credit management
|
0.946
|
Savings and investment
|
0.080
|
Safe
|
0.334
|
Area of expertise: self-employed
|
0.858
|
Cash management
|
0.364
|
Credit management
|
0.967
|
Savings and investment
|
0.498
|
Safe
|
0.315
|
Area of expertise: business owners
|
0.554
|
Cash management
|
0.593
|
Credit management
|
0.131
|
Savings and investment
|
0.500
|
Safe
|
0.199
|
Revenue
|
0.030
|
Cash management
|
0.218
|
Credit management
|
0.051
|
Savings and investment
|
0.273
|
Safe
|
0.024
|
Family income share
|
0.679
|
Cash management
|
0.426
|
Credit management
|
0.236
|
Savings and investment
|
0.910
|
Safe
|
0.001
|
Age group
|
0.884
|
Cash management
|
0.559
|
Credit management
|
0.177
|
Savings and investment
|
0.162
|
Safe
|
0.149
|
Source: data collected by the author, 2022.