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Introduction
The traditional view in international economy considers economic growth as synonymous with the standard of living. From the very concept of growth, some flaws emerge in the measurement of the wealth produced by a country in a given period. In addition, the concept does not distinguish the value generated by activities that are desirable and undesirable from the point of view of social well-being. Moreover, studies such as the ones by Nobel Prize in Economics Amartya Sen (1999; 2000) and Navarro (1968; 2000) highlight that economic growth is not a necessary and sufficient condition to improve the living standards and welfare in society. These studies demonstrated that high economic growth indicators measured by GDP or GNP growth rates, or per capita equivalents, coexisted with indicators of poor basic living conditions and precarious survival. Simultaneously, countries showing low economic growth presented good living conditions.
There is some consensus regarding the need of a novel and comprehensive measure of progress and global prosperity, capable of informing whether people are in a better situation (O’Donnell, 2014). The theme relates to a problem that reaches a considerable part of society, especially in developing countries.
Some indices created in this regard are the Human Development Index, the Gross National Happiness Index (Bates, 2009), the Inclusive Wealth Index (Roman and Thiry, 2016; Yamaguchi et al., 2019) and the Calvert-Henderson Quality of Life Index (Malkina Pykh and Pykh, 2008). Specifically on development indices, the Pakistani economist Mahbub ul Haq, in association with Amartya Sen, developed the Human Development Index in 1990 (United Nations Development Program (UNDP) Brazil, 1990). This index is adopted worldwide by the United Nations and calculated from the longevity, education, and income dimensions. In terms of Brazilian initiatives, public institutions, and representative entities many indexes stand out (Ferreira and Norris, 2007; Instituto Brasileiro de Geografia e Estatística (IBGE, 2023); Federação das Indústrias do Estado do Rio de Janeiro (FIRJAN, 2014); Fundação de Economia e Estatística Siegfried Emanuel Heuser (FEE, 2017); Instituto Paranaense de Desenvolvimento Econômico e Social (IPARDES, 2017); Superintendência de Estudos Econômicos e Sociais da Bahia (SEI, 2017).
In addition to the institutional initiatives, there are indices created by scholars (Soares et al., 1999; Shikida, 2009; Arruda, 2010; Silva et al., 2012; Melo, 2007; Stege, 2011; Barbosa, 2013; Barbosa, 2017; Lima and Maia, 2015). Many indexes were built using multivariate statistics which denotes the acceptability and potential contribution of this approach to handle complex phenomena, such as development dimensions. Still, there is limited contribution of these using the Senian approach (Martinetti, 2000; Laderchi et al., 2003; Krishnakumar, 2014; Baujard and Gilardone, 2017; Barbosa, 2017).
Given the interest to build multidimensional development indexes, this study attempts to operationalize Senian approach and its instrumental freedoms through the construction of an index, useful as management parameter, to rank municipalities and put development in geographical perspective.
Fine detail on each stage of factor analysis and the operationalization of Sen's TDF (Sen, 1999) is made available. The index proposed is designed to capture wellbeing trends for the sake of enhancing awareness and encouraging social action based on empirical evidence. This is supported by adopting temporal coordinates, defined according to availability of disaggregated data, which overlapped with a period of considerable economic growth and advancement of Brazilian social policies (Fahel et al., 2014; Alonso, 2017; Maiorano and Manor, 2017; Martins and Palacio, 2020).
The research scope was focused on the municipalities of the State of Mato Grosso do Sul, Brazil, from 2000 to 2010. The region of the case study embraces an international border strip known for contraband and drug trafficking, intensive agricultural production, conflicts for land involving indigenous communities and for being a setting to the Paraguayan war, the deadliest inter-state war in the Latin America’s history fought from 1864 to 1870.
This research is divided into four sections; the theoretical reference covers the variability of municipal development having Sen’s theory of development as freedom as the theoretical framework. The data collection, sampling, selected and analyzed variables are presented in the methodology. The results are presented, followed by the conclusion.
1. Development as freedom
From the perspective of Sen's Theory of Development as Freedom (TDF) (Sen, 1999; 2000), development could be evaluated as a process of expanding the real freedoms that people enjoy. In this theory, economic growth or wealth is a facilitator (instrument) of the process of obtaining freedoms, not an end in itself of the development process. Sen (1999) listed five main types of instrumental freedoms within the development process, which was described with detail in Andrade et al. (2016). The instrumental freedoms considered are: Political freedoms; Economic facilities; Social opportunities; Guarantees of transparency; Protective security.
According to the Senian approach, instrumental freedoms are interrelated and could reinforce each other. Hence, economic growth (economic facilities) not only contributes to the increase of private incomes but also enables the State to take a more active policy, capable of financing the expansion of the social security network, including protective security devices. Similarly, the creation of social opportunities (public education, health services, freedom of the press) contributes to reducing mortality rates, which may help reduce birth rates and strengthen the influence of women's basic education related to fertility.
Regarding the role of income in obtaining freedoms, there is a range of public or semi-public goods and services offered to the population whose access is free (Sen, 1999; 2000). Five groups of conversion factors influence the conversion of income and personal resources into the quality of life and well-being: i) Personal heterogeneity; ii) environmental diversities; iii) variation in the social climate; iv) differences in relative perspectives; v) income distribution within the family.
In TDF, institutions are crucial since they influence and are influenced by people's instrumental freedoms, as well as affecting factors for converting income into well-being. In this sense, the State, market structures, the legal system, democratic systems, political parties, the provision of education and health services, the media and other communication vehicles, public interest groups and public discussion forums, NGOs, cooperative entities, among others, are analyzed according to their respective contributions to the expansion of freedoms.
2. Methodology
The Relative Municipal Development Index (RMDI) was estimated using multivariate factor analysis (FA) (Cooper, 1983; Melo, 2007) similar to Soares et al. (1999), Melo and Parré (2007), Shikida (2009), Arruda (2010), Stege (2011), Silva et al. (2012), Barbosa (2013; 2017) and Lima and Maia (2015). The equations used were:
Where Z represents the vector of standardized variables (Z i s), β is the matrix of loadings or factor loads, F is the vector of common factors, and ε the vector of random errors. The factors F j , j = 1,2,..., m, were represented by a linear combination of the standardized variables Z i , i = 1,2,..., p, as in (2):
Where (Z 1k , Z 2k ... Z pk ) are the observed values of the Z i standardized variables for the k-th sample element and the w ji , i=1,2,...,p coefficients are the weights of each variable Z i in the factor F j , called factor scores (Mingoti, 2005). From (2) we can build the Gross Relative Municipal Development Index (GRMDI). One of the advantages of FA in index construction is that the weights of each variable in the index and the factors are not arbitrary, determined by the linear relationship structure of the variables.
Where f j is the proportion of the total variance explained (in decimal scale) by the factor F j , with j = 1, 2,..., m, w ij are the factor scores of the i-th sample element in the factor F j , with i = 1,2,..., 78, representing the 78 municipalities, GRMDI i is the value of the GRMDI for the i-th city, GRMDI min is the lowest observed GRMDI value, and GRMDI max is the highest observed value.
Finally, from the interpolation of the GRMDI values, considering the highest unit (1) and the lowest (0), the Relative Municipal Development Index (RMDI) for each the i-th city was obtained, to which ordination was attributed (equation 4).
The definition of municipal development levels was based on the value of the average (M) and the standard deviation (σ) of the RMDI of each year considered. From adapting the classifications proposed by Melo (2007), Stege (2011), and Barbosa (2013; 2017), three levels of development were defined based on average (M) and Standard deviation (σ): High: (RMDI ≥(M+1σ)); Medium (M≤ RMDI <(M+1σ)); Low (RMDI <M). The use of the term 'relative' in the designation of RMDI is justified because it is not possible to use RMDI values for comparisons with other indexes and locations, given the differences in index periods and variables used. The level of development and ranking for the municipalities for 2000 and 2010 were established separately (high, medium and low). The data was explored regarding the changes in ranking from both years.
From the standardized variables (Z i s), the sample correlations matrix was obtained (P pxp ). The FA adjustment validation tests were performed based on Kaiser and Rice (1974) and Mingoti (2005), the Measure of Sampling Adequacy (MSA) based on Hair Junior et al. (2005) and the quantity m of factors to be extracted from the FA, was defined based on Kaiser (1958) and Hair Junior et al. (2005). The number of factors m, the loadings matrix and the diagonal matrix of specific variances were estimated via the main component’s method. The communalities were obtained through the main diagonal of the estimated matrix and represents the sum of the square of loadings of each variable; the estimated diagonal matrix provides the unicity (specific variances) of each variable and represents the proportion of the variability associated with the random error (Mingoti, 2005).
The loadings (β ij , i, j = 1,2,..., p) are the coefficients of the factors that measure the correlation between the variables and the extracted m factors. The variables that have the highest loadings become representative of the factor (Mingoti, 2005).
The rotation performed was varimax (Hair Junior et al., 2005). For each sample element k, k = 1,2,..., n, the Least Weighted Square method was used to estimate the factor scores. Statistical procedures were performed in R software, using the packages Rundr, psych and matrix calc (R Core Team, 2018).
2.1. Study area and data source
The study area consisted of 78 municipalities of the State of Mato Grosso do Sul, Midwest Brazil. Currently, agribusiness accounts for majority of gross domestic product in the State. The data used were obtained from the Atlas of Human Development in Brazil, of the United Nations Development Program for the years of 2000 to 2010, built from the Demographic Census of the Brazilian Institute of Geography and Statistics (IBGE).
2.2. Instrumental freedoms and development dimensions: selected variables for the RMDI in Mato Grosso do Sul
The selection of the 24 variables for the composition of the RMDI, which contemplated different dimensions of development and the Senian instrumental freedoms, was based on the literature focused on development indices (Melo, 2007; Arruda, 2010; Stege, 2011; Silva et al., 2012; Barbosa, 2013; Barbosa, 2017; Lima and Maia, 2015) and the TDF of Sen (2000). In table 1, the descriptive statistics of all variables assigned to the development dimensions and instrumental freedoms are shown.
In general, the evolution of indicators was favorable in the established temporal coordinates (table 1), suggesting an improvement in development conditions. Still, in some variables the variation coefficient (CV) increased, suggesting the increase of development disparities among the municipalities.
Compared to Municipal Human Development Index - MHDI1 (UNDP Brazil, 2013) and other indices identified in the literature, the RMDI contemplated a wider range of development dimensions and all Senian instrumental freedoms (the only one that operationalized the five major ones). The RMDI methodology is more flexible, making it possible to cover local and regional particularities and characteristics. Moreover, from the factor analysis, the weights of both variables and factors of the RMDI were statistically determined, not arbitrarily, as occurs in the MHDI and other indexes (Barbosa, 2017).
Table 1 Instrumental freedoms, development dimensions, and variables selected for the RMDI
| Instrumental Freedoms | Dimensions | Variables | Average | Median | CV (%) | |||
|---|---|---|---|---|---|---|---|---|
| 2000 | 2010 | 2000 | 2010 | 2000 | 2010 | |||
| Social opportunities | Health and longevity | Z1: Life expectancy at birth | 69.7 | 74.3 | 69.8 | 74.4 | 2.3 | 1.9 |
| Z2: Child mortality (up to 1 year old). per 1000 live births | 26.5 | 19.2 | 26.2 | 18.8 | 17.9 | 13.8 | ||
| Z3: Probability of survival up to 60 years | 78.3 | 82.0 | 78.3 | 82.3 | 2.9 | 2.5 | ||
| Education | Z4: Gross attendance rate to basic education (%) | 94.2 | 96.3 | 93.9 | 96.8 | 6.4 | 5.3 | |
| Z5: Gross attendance rate to elementary education (%) | 110.0 | 110.8 | 110.0 | 110.2 | 5.8 | 4.6 | ||
| Z6: % of the pop. ≥18 years with complete secondary education | 15.1 | 26.7 | 13.8 | 26.7 | 33.2 | 23.8 | ||
| Z7: % of the pop. ≥25 years with complete higher education | 3.5 | 7.8 | 3.0 | 7.3 | 51.5 | 31.6 | ||
| Economic facilities | Income and labor market | Z8: Natural logarithm (LN) of per capita income | 6.0 | 6.4 | 6.0 | 6.4 | 5.0 | 4.0 |
| Z9: LN of per capita income. except null income | 6.0 | 6.4 | 6.0 | 6.4 | 4.8 | 3.8 | ||
| Z10: % of the pop. 25-64 years in relation to total population | 19.4 | 48.8 | 19.5 | 49.6 | 4.2 | 7.3 | ||
| Income distribution | Z11: LN of the maximum per capita income of the 1/5 poorest | 4.1 | 5.1 | 4.2 | 5.2 | 10.8 | 8.9 | |
| Z12: LN of the minimum per capita income of the 1/10 wealthiest | 6.6 | 7.0 | 6.6 | 7.0 | 4.7 | 3.4 | ||
| Z13: Ratio of income per capita of the 10% wealthiest /40% poorest | 20.3 | 15.3 | 18.2 | 13.7 | 42.4 | 48.6 | ||
| Z14: LN per capita income of the 1/5 poorest | 4.1 | 4.6 | 4.2 | 4.8 | 10.8 | 13.3 | ||
| Z15: LN per capita income of the 1/10 wealthiest | 7.5 | 7.8 | 7.5 | 7.8 | 5.2 | 3.8 | ||
| Social opportunities | Housing condition | Z16: % of the pop. in households with bathroom and piped water | 78.8 | 91.8 | 83.4 | 95.6 | 15.6 | 9.9 |
| Z17: % of the pop. in households with electricity | 91.0 | 97.3 | 93.2 | 99.2 | 9.6 | 5.0 | ||
| Political freedoms | Social vulnerability | Z18: Dependency ratio (% of pop. <15 e ≥65 years/pop. of 15-64 years) | 59.4 | 50.3 | 58.1 | 48.1 | 11.9 | 15.8 |
| Z19: % extremely poor (per capita household income ≤R$ 70 monthly) | 10.7 | 5.9 | 9.4 | 3.8 | 61.4 | 112.5 | ||
| Social opportunities | Z20: % of children (≤14 years) extremely poor (per capita household income ≤R$ 70 monthly) | 16.1 | 9.1 | 15.1 | 5.7 | 59.1 | 104.6 | |
| Economic facilities | Z21: % of children (≤14 years) vulnerable to poverty (per capita household income ≤R$ 255 monthly) | 71.2 | 51.1 | 70.8 | 52.2 | 15.6 | 25.6 | |
| Transparency guarantees | Z22: Average per capita income of those vulnerable to poverty (≤R$ 255 monthly) | 136.57 | 156.84 | 137.05 | 163.71 | 10.63 | 13.49 | |
| Z23: % of the pop. in households density >2 people per dormitory | 36.8 | 26.1 | 36.0 | 23.9 | 23.9 | 36.2 | ||
| Protective security | Z24: % of people in households vulnerable to poverty and no one has complete elementary education. | 41.1 | 18.3 | 40.6 | 17.2 | 23.7 | 45.5 | |
Note: CV (variation coefficient).
Source: authors’ own elaboration based on data from AtlasBR (2013).
The variables that represent the dimension of development Health and Longevity (Z1 to Z3), Education (Z4 to Z7), and Housing Condition (Z16 and Z17) correspond to the instrumental freedom social opportunities. These dimensions denote basic capabilities directly related to quality of life and the provision of well-being, the basis for developing and strengthening other types of instrumental freedoms (Sen, 1999). In the Brazilian case, the provision of this freedom is strongly dependent on the provision of the State and some institutions' performance, considering the social rights guaranteed by the Brazilian Federal Constitution (1988) especially in the dimensions of Health, Longevity and Education.
The variables representing the Income and Labor Market dimension (Z8 to Z10) and Income Distribution (Z11 to Z15) correspond to the instrumental freedom economic facilities. This freedom not only increases the generation and distribution of private income, but also allows more budgetary space for social policies, contributing to the improvement of social opportunities and the reduction of social vulnerabilities (Sen, 1999).
The variables representing the dimension of development Social Vulnerability (Z18 to Z24) represent all five main instrumental freedoms proposed by Sen (2000). Likely, vulnerabilities associated with income and its distribution, housing, and educational condition are the result of the country's own economic, productive, social, political, and institutional structure. These are influenced by changes in these development components over time, changes that are also responsive to the agency. As an example, the Bolsa Família Program, an institutional mechanism of conditional income transfer in Brazil, which was able to reduce poverty and inequality in the 2000s (Paes Souza and Santos, 2009), alleviating social vulnerability of beneficiaries (protective security).
3. Results
3.1. Validation of the multivariate tool: adherence of the TDF and its instrumental freedoms
The adjustment tests of the FA (2000 and 2010) showed that variables were not mutually independent (Barlett’s sphericity test: 3797.05; 4690.95 (p-values < 0.001). The models were consistent (KMO: 0.84; 0.88) (Kaiser and Rice, 1974), and Mean Error (0.0012; -0.0019) and Square Root Mean Square Error (0.0255; 0.0255) were close to zero, indicating suitable adjustment of the FA models. In the Measure of Sampling Adequacy (MSA), all variables had acceptable values from the FA source (above 0.5), except for variables Z4 and Z5 in the model for 2010. Variables Z4 and Z5 in 2010 did not show other adjustment problems (communalities) nor compromised the adjustment, therefore, they were maintained in the proposed model. Based on the Kaiser (1958) criterion, six factors (m=6) were retained for the year 2000 and four factors (m=4) for the year 2010, corresponding to eigenvalues higher than the unit (1). Overall, the factors retained, accounted for more than 90% of the total variability of the data (table 2).
Table 2 Eigenvalues, individual variance explained, and accumulated variance (2000 and 2010)
| Eigenvalues (λ i ) | % Explained variance | % Accumulated variance | |||
|---|---|---|---|---|---|
| 2000 | 2010 | 2000 | 2010 | 2000 | 2010 |
| 13.349 | 15.266 | 55.621 | 63.609 | 55.62 | 63.61 |
| 2.955 | 3.037 | 12.311 | 12.652 | 67.93 | 76.26 |
| 2.184 | 2.114 | 9.101 | 8.810 | 77.03 | 85.07 |
| 1.237 | 1.339 | 5.155 | 5.578 | 82.19 | 90.65 |
| 1.118 | 0.495 | 4.660 | 2.064 | 86.85 | 92.71 |
| 1.001 | 0.467 | 4.171 | 1.946 | 91.02 | 94.66 |
| 0.532 | 0.301 | 2.216 | 1.256 | 93.24 | 95.92 |
| 0.426 | 0.214 | 1.775 | 0.890 | 95.01 | 96.81 |
| 0.325 | 0.178 | 1.352 | 0.743 | 96.36 | 97.55 |
| 0.207 | 0.162 | 0.862 | 0.675 | 97.23 | 98.22 |
| 0.144 | 0.118 | 0.601 | 0.493 | 97.83 | 98.72 |
| 0.117 | 0.086 | 0.487 | 0.357 | 98.31 | 99.07 |
| 0.106 | 0.070 | 0.441 | 0.290 | 98.75 | 99.36 |
| 0.076 | 0.062 | 0.318 | 0.260 | 99.07 | 99.62 |
| 0.061 | 0.026 | 0.252 | 0.109 | 99.32 | 99.73 |
| 0.056 | 0.021 | 0.232 | 0.088 | 99.56 | 99.82 |
| 0.034 | 0.018 | 0.143 | 0.075 | 99.70 | 99.90 |
| 0.026 | 0.009 | 0.109 | 0.036 | 99.81 | 99.93 |
| 0.021 | 0.007 | 0.088 | 0.028 | 99.90 | 99.96 |
| 0.016 | 0.005 | 0.067 | 0.023 | 99.96 | 99.98 |
| 0.005 | 0.003 | 0.019 | 0,011 | 99.98 | 99.99 |
| 0.003 | 0.001 | 0.010 | 0.005 | 99.99 | 99.998 |
| 0.002 | 0.000 | 0.007 | 0.001 | 99.999 | 99.999 |
| 0.000 | 0.000 | 0.001 | 0.001 | 100 | 100 |
Source: authors’ own elaboration.
3.2. The determinants of municipal development explored
The output of FA for year 2000 and 2010 is presented in table 3 and table 4, respectively.
Table 3 Unrotated loadings, after rotation, communalities, unicities, and total variance (2000)
| Variables | Loadings | Communalities | Unicities | Total Variance | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Unrotated (F) | After rotation (Fr) | ||||||||||||||
| F1 | F2 | F3 | F4 | F5 | F6 | Fr1 | Fr2 | Fr3 | Fr4 | Fr5 | Fr6 | ||||
| Z1 | 0.70 | 0.19 | -0.38 | 0.50 | 0.01 | -0.15 | 0.30 | 0.15 | 0.14 | 0.26 | 0.86 | -0.04 | 0.94 | 0.06 | 1 |
| Z2 | -0.70 | -0.18 | 0.38 | -0.51 | 0.00 | 0.15 | -0.29 | -0.16 | -0.13 | -0.25 | -0.87 | 0.05 | 0.95 | 0.05 | 1 |
| Z3 | 0.65 | 0.02 | -0.24 | 0.42 | 0.02 | -0.38 | 0.25 | 0.20 | 0.25 | 0.00 | 0.79 | 0.09 | 0.80 | 0.20 | 1 |
| Z4 | 0.43 | -0.10 | 0.75 | 0.13 | 0.34 | -0.20 | 0.05 | 0.28 | 0.25 | 0.07 | 0.03 | 0.89 | 0.93 | 0.07 | 1 |
| Z5 | 0.24 | -0.28 | 0.71 | 0.25 | 0.51 | 0.02 | 0.10 | 0.14 | -0.10 | 0.03 | -0.04 | 0.96 | 0.96 | 0.04 | 1 |
| Z6 | 0.69 | 0.44 | 0.11 | -0.28 | -0.12 | -0.39 | 0.13 | 0.25 | 0.86 | 0.27 | 0.21 | 0.02 | 0.93 | 0.07 | 1 |
| Z7 | 0.65 | 0.38 | 0.12 | -0.38 | -0.02 | -0.45 | 0.18 | 0.16 | 0.90 | 0.17 | 0.14 | 0.07 | 0.92 | 0.08 | 1 |
| Z8 | 0.88 | 0.40 | -0.02 | -0.03 | 0.10 | 0.22 | 0.48 | 0.20 | 0.38 | 0.69 | 0.29 | 0.09 | 0.99 | 0.01 | 1 |
| Z9 | 0.85 | 0.44 | -0.01 | -0.03 | 0.12 | 0.22 | 0.46 | 0.16 | 0.39 | 0.71 | 0.28 | 0.09 | 0.98 | 0.02 | 1 |
| Z10 | 0.76 | -0.30 | 0.37 | 0.12 | -0.26 | -0.01 | 0.32 | 0.79 | 0.15 | 0.09 | 0.18 | 0.31 | 0.89 | 0.11 | 1 |
| Z11 | 0.90 | -0.29 | -0.19 | -0.07 | 0.16 | 0.12 | 0.86 | 0.32 | 0.16 | 0.13 | 0.28 | 0.12 | 0.97 | 0.03 | 1 |
| Z12 | 0.86 | 0.23 | -0.15 | -0.20 | 0.21 | -0.04 | 0.62 | 0.09 | 0.54 | 0.37 | 0.29 | 0.07 | 0.90 | 0.10 | 1 |
| Z13 | -0.06 | 0.84 | 0.31 | 0.23 | -0.15 | 0.29 | -0.55 | -0.03 | 0.08 | 0.81 | 0.02 | 0.00 | 0.97 | 0.03 | 1 |
| Z14 | 0.88 | -0.33 | -0.18 | -0.06 | 0.05 | 0.12 | 0.81 | 0.41 | 0.13 | 0.10 | 0.27 | 0.07 | 0.93 | 0.07 | 1 |
| Z15 | 0.72 | 0.58 | 0.08 | 0.03 | 0.03 | 0.33 | 0.26 | 0.17 | 0.32 | 0.85 | 0.22 | 0.06 | 0.98 | 0.02 | 1 |
| Z16 | 0.88 | -0.19 | 0.07 | -0.02 | -0.16 | 0.14 | 0.58 | 0.62 | 0.19 | 0.24 | 0.20 | 0.10 | 0.86 | 0.14 | 1 |
| Z17 | 0.67 | 0.07 | 0.29 | -0.13 | -0.35 | 0.04 | 0.20 | 0.63 | 0.37 | 0.30 | 0.02 | 0.06 | 0.68 | 0.32 | 1 |
| Z18 | -0.83 | 0.25 | -0.34 | -0.14 | 0.21 | -0.06 | -0.38 | -0.76 | -0.15 | -0.18 | -0.21 | -0.32 | 0.93 | 0.07 | 1 |
| Z19 | -0.85 | 0.39 | 0.23 | 0.15 | -0.04 | -0.06 | -0.85 | -0.39 | -0.17 | 0.01 | -0.23 | -0.01 | 0.96 | 0.04 | 1 |
| Z20 | -0.83 | 0.40 | 0.23 | 0.15 | -0.10 | -0.07 | -0.87 | -0.34 | -0.16 | 0.01 | -0.21 | -0.05 | 0.94 | 0.06 | 1 |
| Z21 | -0.82 | -0.03 | 0.16 | 0.20 | -0.35 | 0.03 | -0.73 | -0.03 | -0.42 | -0.23 | -0.25 | -0.17 | 0.86 | 0.14 | 1 |
| Z22 | 0.84 | -0.30 | -0.09 | 0.04 | -0.09 | 0.21 | 0.68 | 0.53 | 0.04 | 0.18 | 0.25 | 0.05 | 0.85 | 0.15 | 1 |
| Z23 | -0.63 | 0.39 | -0.24 | -0.07 | 0.44 | 0.08 | -0.25 | -0.83 | -0.12 | 0.09 | -0.15 | -0.10 | 0.81 | 0.19 | 1 |
| Z24 | -0.90 | -0.26 | -0.13 | 0.10 | 0.09 | 0.12 | -0.36 | -0.42 | -0.60 | -0.40 | -0.29 | -0.13 | 0.93 | 0.07 | 1 |
| Explained var. (%) | 55.62 | 12.31 | 9.10 | 5.15 | 4.66 | 4.17 | 25.77 | 16.91 | 13.68 | 13.37 | 12.81 | 8.47 | |||
| Accumulated var. (%) | 91.01 | 91.01 | |||||||||||||
Source: authors’ own elaboration.
Each rotated factor (Fr) was named (labeled) in accordance to the variables that showed the most distinct loading values using data from year 2000 (table 3) and data from 2010 (table 4). The detailed rationale and interpretation of the first two rotated factors (Fr1 and Fr2 in year 2000; Fr1b and Fr2b in year 2010) are described further. Description of additional rotated factors and unrotated factors is not stressed in the present study, although they referred to basic education, income and its distribution. Thus, they werestrongly related to Senian instrumental freedoms social opportunities and economic facilities.
In the rotated factors (Fr) of year 2000 (table 3), the variables Z11, Z12, Z14, Z19, Z20, Z21 and Z22 were strongly correlated with the Fr1 factor. The Fr1 explained 25.77% of the model's variability, the main determinant of development in the municipalities in year 2000. Given the characteristics of the variables with distinct loadings, factor Fr1 was named Income and its Distribution. Positive loadings were observed in variables Z11, Z12, Z14 and Z22, all of them related to Income and its distribution. This suggests contribution of these variables to the municipal development (the increase in these variables increases the level of development). The variable Z12 also contributed positively to the development despite representing an aspect of income concentration.
The Neokaleckian literature, specifically the economic growth models with income distribution, support these results. Considering an investment function dependent on the share of profit in income (or the rate of profit), literature shows that, in profit-led growth regimes, a transfer of income towards profits (capitalists) pulls economic growth (Dutt, 1984; Bhaduri and Marglin, 1990; Hein, 2014). Although these models operate with functional distribution of income (Z12 refers to the personal distribution of income), we considered that the argument remained valid, and we assumed that mainly capitalists formed the upper strata of the distribution. In a profit-led regime, greater concentration of income leads to greater growth by increasing the share of profits in aggregate demand.2 Although not consensual, there is empirical evidence that the economic growth in Brazil during the 2000s (considering the participation of the external sector), occurred mostly under a profit-led regime (Araujo and Gala, 2012; Azevedo et al., 2022). This likely corroborates our arguments. In the Senian approach, economic growth increases personal opportunities to enjoy economic resources for consumption, production, or exchange purposes. In turn, this contributes to the promotion of development via economic facilities.
The variables that presented loadings with negative coefficients and were more strongly correlated with the Fr1 factor in 2000 were Z19, Z20 and Z21, which all related to the social vulnerabilities associated with the destitution of income of the total population and children. The negative coefficient of loadings shows that the increase in the value of these variables contributed negatively to the promotion of development in the municipalities, a fact expected by the TDF. Sen (1999) argued that despite the problems of converting income into economic entitlements, the availability of financial resources for consumption purposes, production or exchange is a constituent part of the instrumental freedom economic facilities, which contributes to increasing economic entitlements and promoting development.
The Fr2 factor explained 16.91% of the total variability and was the second main determinant of municipal development in 2000. The variables whose loadings were more strongly correlated with Fr2 were Z10, Z16, Z17, Z18 and Z23. The loadings with positive coefficient were Z10, Z16 and Z17, therefore, factor Fr2 was named Demographic-Housing. The growth of the population in active age (25 to 64 years old) represents a potential for significant economic growth and increased labor productivity, a phenomenon known as the demographic bonus (IPEA, 2010). This circumstance increases economic entitlements and expands economic facilities. The improvement of housing conditions, on the other hand, increases well-being and reflects the influence of social arrangements and institutions focused on services and infrastructure of a social nature (Andrade et al., 2016). This promotes the expansion of freedoms and development through the expansion of social opportunities.
Table 4 Unrotated loadings, after rotation, communalities, unicities, and total variance (2010)
| Variables | Loadings | Communalities | Unicities | Total Variance | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Unrotated (Fb) | After rotation | ||||||||||
| F1b | F2b | F3b | F4b | Fr1b | Fr2b | Fr3b | Fr4b | ||||
| Z1 | 0.58 | 0.57 | -0.47 | 0.34 | 0.18 | 0.27 | 0.94 | -0.05 | 0.99 | 0.01 | 1 |
| Z2 | -0.58 | -0.57 | 0.47 | -0.34 | -0.18 | -0.27 | -0.94 | 0.05 | 0.99 | 0.01 | 1 |
| Z3 | 0.58 | 0.57 | -0.47 | 0.34 | 0.18 | 0.26 | 0.94 | -0.05 | 1.00 | 0.00 | 1 |
| Z4 | 0.03 | 0.27 | 0.81 | 0.42 | -0.08 | 0.19 | -0.05 | 0.92 | 0.90 | 0.10 | 1 |
| Z5 | 0.00 | 0.12 | 0.76 | 0.56 | 0.01 | 0.00 | -0.05 | 0.95 | 0.91 | 0.09 | 1 |
| Z6 | 0.71 | 0.43 | 0.25 | -0.31 | 0.25 | 0.87 | 0.17 | 0.08 | 0.86 | 0.14 | 1 |
| Z7 | 0.64 | 0.43 | 0.24 | -0.41 | 0.17 | 0.88 | 0.10 | 0.01 | 0.82 | 0.18 | 1 |
| Z8 | 0.92 | 0.27 | 0.14 | -0.15 | 0.54 | 0.77 | 0.29 | 0.07 | 0.97 | 0.03 | 1 |
| Z9 | 0.90 | 0.31 | 0.14 | -0.17 | 0.50 | 0.79 | 0.30 | 0.06 | 0.96 | 0.04 | 1 |
| Z10 | 0.89 | -0.23 | -0.04 | 0.12 | 0.86 | 0.27 | 0.23 | 0.01 | 0.87 | 0.13 | 1 |
| Z11 | 0.97 | -0.19 | 0.02 | -0.02 | 0.88 | 0.43 | 0.17 | -0.02 | 0.98 | 0.02 | 1 |
| Z12 | 0.87 | 0.31 | 0.12 | -0.19 | 0.47 | 0.77 | 0.29 | 0.04 | 0.90 | 0.10 | 1 |
| Z13 | -0.69 | 0.58 | 0.06 | -0.12 | -0.91 | 0.03 | 0.03 | 0.06 | 0.83 | 0.17 | 1 |
| Z14 | 0.97 | -0.21 | 0.01 | 0.05 | 0.89 | 0.37 | 0.20 | 0.02 | 0.98 | 0.02 | 1 |
| Z15 | 0.72 | 0.49 | 0.19 | -0.15 | 0.25 | 0.79 | 0.33 | 0.15 | 0.82 | 0.18 | 1 |
| Z16 | 0.88 | -0.33 | 0.00 | 0.12 | 0.91 | 0.22 | 0.15 | 0.03 | 0.90 | 0.10 | 1 |
| Z17 | 0.84 | -0.32 | 0.04 | 0.03 | 0.85 | 0.27 | 0.07 | 0.00 | 0.81 | 0.19 | 1 |
| Z18 | -0.94 | 0.23 | 0.01 | -0.04 | -0.88 | -0.35 | -0.18 | 0.01 | 0.93 | 0.07 | 1 |
| Z19 | -0.94 | 0.28 | 0.00 | -0.11 | -0.92 | -0.28 | -0.19 | -0.03 | 0.97 | 0.03 | 1 |
| Z20 | -0.93 | 0.28 | 0.00 | -0.14 | -0.92 | -0.26 | -0.21 | -0.05 | 0.97 | 0.03 | 1 |
| Z21 | -0.90 | -0.06 | -0.03 | 0.16 | -0.65 | -0.61 | -0.21 | 0.06 | 0.84 | 0.16 | 1 |
| Z22 | 0.92 | -0.22 | -0.03 | 0.08 | 0.87 | 0.31 | 0.21 | 0.00 | 0.90 | 0.10 | 1 |
| Z23 | -0.75 | 0.38 | 0.08 | 0.08 | -0.81 | -0.21 | -0.02 | 0.17 | 0.72 | 0.28 | 1 |
| Z24 | -0.96 | 0.02 | -0.05 | 0.04 | -0.77 | -0.54 | -0.24 | -0.03 | 0.94 | 0.06 | 1 |
| Explained var. (%) | 63.61 | 12.65 | 8.81 | 5.58 | 44.27 | 24.18 | 14.50 | 7.71 | |||
| Accumulated var. (%) | 90.65 | 90.65 | |||||||||
Source: authors’ own elaboration.
The variables strongly correlated with the Fr2 factor, with negative loading coefficients (Z18 and Z23), show that the increase in their values contributed adversely to municipal development. The increase in the dependency ratio represents an increase in the population that is not in the active age in relation to those in the other age group, a phenomenon that acts in the opposite direction of the demographic bonus and reduces the potential for economic growth and labor productivity. The variable Z23, on the other hand, is associated with a vulnerability factor of home infrastructure, which compromises the well-being.
For 2010, the main determinant of development of municipalities (Fr1b), explained 44.27% of the total variability (table 4). Factor Fr1b was named Income and its Distribution-Housing-Vulnerability. This is empirical support for what Sen (1999) called the empirical interrelationships between instrumental freedoms, which supplement each other and could reinforce each other, contributing to the expansion of personal freedoms and the promotion of development. Factor Fr1b represented the instrumental freedoms economic facilities (Z10, Z11, Z13 and Z14) and social opportunities (Z16 and Z17). It also represented all five main Senian instrumental freedoms in the variables of the social vulnerability dimension (Z18 to Z24), as well as the two already mentioned, the political freedoms, transparency guarantees and protective security, and the institutional influences that these freedoms reflect.
The loadings of variables Z10, Z11, Z14 and Z22 (related to income and the employment market), as well as Z16 and Z17 (housing condition), suggests a direct contribution to the development of the cities in 2010. The increase in the values of the variables related to income represents the increase in economic facilities, mainly the expansion of the coverage of water supply services, electricity, and the presence of bathrooms in the residences, increasing social opportunities, contributing to people's well-being and development. The negative loadings suggest an adverse contribution to municipal development. The variable Z13 is an indicator of income concentration, which economic theory predicts to be negative for the country's development, which would reduce economic facilities, despite the New-Kaleckian theory indicating that such concentration could promote economic growth if it originated from increased profits in a profit-led regime. The variable Z18 represents a reduction in the population in active age, which reduces the potential for economic growth. The variables Z19 to Z21 represent vulnerabilities associated with low family income, which decreases economic facilities. The variables Z23 and Z24 reflect the deterioration of several Senian instrumental freedoms.
The Fr2b factor was named Educational-Income and its Distribution (positive loadings of Z6, Z7, Z8, Z9, Z12 and Z15). The education variables have been mentioned as the associated positive externalities, which increase social opportunities. The other variables reflect the increase in economic facilities.
Notably, several dimensions of development and other instrumental freedoms appeared simultaneously, in year 2000 and 2010. In year 2000 (table 3) and year 2010 (table 4), rotated factors related to the income and its distribution contributed most to explaining municipal development, with the emergence of the importance of the instrumental freedom economic facilities in expanding personal freedoms.
Still, income contributed together with distribution of income, education, health, longevity, housing and vulnerability. In turn, another crucial instrumental freedom for development was social opportunities. The influence of institutions on the freedoms discussed is strategic in the TDF and appealing for the promotion of development.
Overall, the association of the loadings with the rotated factors was straightforward, reinforcing the empirical interrelationships between instrumental freedoms and supporting the adherence of the TDF and its instrumental freedoms in explaining the determinants of municipal development.
3.3. Geographic structure of (under) development in the Midwest of Brazil
In 2000, 17 cities (21.79%) were considered to have a high level of development, 28 (35.89%) a medium level of development, and 33 municipalities (42.33%, the largest percentage) had a low level of development. The levels of development defined for year 2000 were: high: RMDI≥72.22; medium: 60≤RMDI<72.22; low: RMDI<60. The threshold established for the lowest level was RMDI below 60, the average of RMDI for 2000.
In 2010, only six municipalities (7.69%) were classified with high level development. The levels of development defined for 2010 were: high: RMDI≥92.36; medium: 76.15≤RMDI<92.36; low: RMDI<76.15. The municipalities classified as having medium level of development were 44 (56.41%), 10 of which declined from high to medium level in 2010, 24 remained at a medium level, and 10 rose from low to medium level in 2010. The number of municipalities classified as having low level of development in 2010 were 28 (35.89%). There was a reduction in the proportion of municipalities with low and high development levels towards a medium level of development from 2000 to 2010.
The ranking of municipalities, putting the Relative Municipal Development Index (RMDI) in geographic perspective, is presented in figure 1. This showed a geographic structure (contiguity or corridor) of depressed municipalities in both years considered, configuring poorly developed subregions in Midwest Brazil, facing economic stagnation and the perpetuation of precarious services related to individual well-being.
A total of 20 cities were associated to this corridor, out of 33 with a low development level (60.60%) and 25.64% of the total municipalities in the state. Notably, there was an involution of three municipalities, from 31st in 2000 to 60th in 2010, 3rd in 2000 to 58th in 2010, and 8th in 2000 to 65th in 2010 (labels 36, 69 and 56 in figure 1, respectively). The corridor is located within the Brazilian Border Strip with Paraguay3, extending from municipalities in the south of the state, close to the state of Paraná, to municipalities located in the West, close to the region bathed by the Paraguay River (figure 1).
The geographical structure of depressed municipalities persisted from 2000 to 2010, differentiated from 2000 by incorporating the four cities and cutting off one (36, 38, 56, 61 and 46 in figure 1). Twenty-two municipalities (28.20% of the state's total) were located in this corridor.
In general, the cities within the corridor have low population density, low contingent and low population growth rates, low real GDP and per capita GDP, low dynamism of income and employment generation and formalization of the labor market, as well as precarious human development indicators compared to other municipalities in the state. Weaknesses in human development and human capital retention, characteristic of border regions, hinder innovation generation, industrialization, the formation of a developmental mindset, political engagement, collective learning, leadership and entrepreneurship. Together, these contribute to explaining the maintenance of poverty and low levels of development over time. It likely mirrors a rather perverse and vicious circle of poverty and underdevelopment that impairs better living conditions, expansion of freedoms and opportunities (Nurkse, 1957; Myrdal, 1968; Sen, 2000; Pelinski Raiher and Ferrera de Lima, 2018; Ferrera de Lima, 2020), despite advances in economic growth, the labor market, and social policies in the 2000s.
Source: authors’ own elaboration in the ArcGIS program - Environmental Systems Research Institute (ESRI) (2011). ArcGIS Desktop: Release 10. Redlands, CA: Instituto de Pesquisa de Sistemas Ambientais.
Figure 1 Contiguity of depressed municipalities revealed by ranking municipalities with the proposed Relative Municipal Development Index (RMDI)
Notably, the threshold (cutting value) of the RMDI for a high level of development increased from 72.22 in 2000 to 92.36 in 2010, due to the increase in the average of the RMDI. Furthermore, fixing a global value of mean and standard deviation, instead of handling two different sets of parameters (year 2000 and 2010, respectively) did not change the disclosure of a prevailing corridor of depressed municipalities. In average, it portraited an expansion of human freedoms.
The observed increase in the average level of development of the municipalities in the period considered is likely due to several conditions in Brazil. The pro-poor economic growth experienced by Brazil was fundamental for the improvement of development indicators in the decade of 2000. Among other reasons, this is due to the increase in government revenues for financing social spending (health, education, social assistance, and employment programs) (Maiorano and Manor, 2017) and the generation and formalization of jobs (Amuka et al., 2019). The increase of development points to the determining role of state and municipal social spending on human capital and the growth of the service sector (essentially by the Public Administration) to reduce poverty and prompt development (Martins and Palacio, 2020).
Furthermore, in much of the 2000s, Brazilian economic growth was stimulated by the “commodities cycle”, marked by the higher prices of agricultural commodities, oil, animal protein and other Brazilian export products, generating a commercial surplus and exchange rate valuation (Pelinski Raiher and Ferrera de Lima, 2018; Pelinski Raiher et al., 2017).
Several Brazilian social programs have likely contributed to the expansion of freedoms and promotion of development, among them: i) Bolsa Familia Program, conditional on direct income transfer under the requirement of vaccination and school attendance of children, designed to break the vicious circle of poverty (Paes Souza and Santos, 2009; Tiburcio, 2018); ii) expansion of primary health care services through the Brazilian Unified Health System (SUS) (Maiorano and Manor, 2017); iii) Fome Zero Program, successful in reducing poverty and food insecurity, especially in rural areas (De Mattos and Bagolin, 2017); iv) Mais Medicos Program, the Fund for the Maintenance and Development of Basic Education (Fundeb), Minha Casa Minha Vida Program and the National Policy for Regional Development (Fahel et al., 2014; Alonso, 2017). In addition to these, there are the Social Security Continuous Cash Benefit, Employment, Income and Insurance policies, and the National Program for Strengthening Family Farming (Pronaf) (BNDES, 2024); Ministério do Desenvolvimento e Assistência Social, Família e Combate à Fome (MDS, 2024); Ministério do Trabalho e Emprego (MTE, 2024).
The decline in poverty and social inequality in Brazil is likely a reflection of income growth, education, the formalization of labor, and the effects programs such as Bolsa Familia (Góes and Karpowicz, 2017; Leite, 2020). It reflects the reduction of wage differentials between groups of workers, the real increase in wages and the minimum wage (reference for the formal and informal job market, for social security and social assistance benefits). Likewise, it reflects the demographic transition (reduction of children up to 14 years, an age group significantly affected by poverty, the increase in the proportion of older people and groups that are more protected against poverty) and reflects the reduction of dependency ratio (Departamento Inter-sindical de Estadística y Estudios Socio-Económicos (DIEESE, 2010; Paiva, 2016; Sotomayor, 2019).
In the region considered, data from the Brazilian Institute of Geography and Statistics (IBGE, 2017) shows that the Gini coefficient of the area of agricultural establishments was 0.856 in 1996, which increased to 0.867 in 2017, besides showing a reduction in the period for only 24 municipalities. Likely, this relates to high land concentration. One of the historical conditions of income inequality in Brazil is the unequal ownership of physical goods. Typically, this refers to real estate, such as land, with expected effects on intergenerational social mobility (Ondetti, 2016).
Currently, much of the economic growth in the Midwest of Brazil is marked by commodity driven structures linked to large rural properties that adopt intensive agricultural production. These structures operate close to smallholders, mainly family-based agriculture, rural settlements and indigenous communities. Typically, these regions have lower levels of urbanization and configure subsistence agriculture, thus, presenting little expressiveness of their industrial or agroindustrial activities (Barbosa et al., 2020; Secretaria de Estado de Meio Ambiente e Desenvolvimento Econômico do Mato Grosso do Sul SEMADE/MS, 2023).
Conclusions
The technique, alongside to the proposition of a development index, allowed operationalizing Amartya Sen's theory of Development as Freedom and its instrumental freedoms. This was demonstrated through the extraction of factors, the main determinants of development. The creation of the RMDI index was built from variables that captured all five main Senian instrumental freedoms, in addition to the institutional influences that these freedoms reflect. Thus, one of the main criticisms of the literature to the referred approach was overcome: the difficulty of conceptual operationalization.
The main determinant of development in 2000 covered essentially variables related to income and its distribution. This suggests that income and the productive structure, which influences the distribution of income, were fundamental to the understanding of the process of municipal development that year. However, in 2010 the main determinant of development covered not only the variables related to income and its distribution but also housing conditions and social vulnerabilities. This reinforces the function attributed to income in Amartya Sen's theory, placing it as an important instrument to expand human freedoms without disregarding the function of other dimensions of development in the expansion of these freedoms.
Our study was able to bridge some concepts related to individual´s freedoms and well-being to the appreciation of municipal heterogeneity, revealing geographical structure of municipal development. We encourage the usage of this alternative approach and theory to reveal and indicate spatial specificities that may facilitate decision making for private and state intervention. Alone, Brazil has over 15,000 km of terrestrial frontiers and shares land boundaries with nine countries. Perhaps future studies should work to unmask continental-sized corridors of underdevelopment.
The outstanding questions are: are the corridors a consequence of the modus operandi of productive structures that prevail? Do corridors prevail and persist from income leaking or because of asymmetric benefits coming from governmental agendas? In the figurative sense, one could picture corridors as the materialization of inequality, corruption, lack of dignity and the ability of people to choose.
The indicators that composed the main determining factors of development pose as valuable guidelines for research and politic agendas interested on mapping, capturing and operating tensions, poverty and levels of violence in territorial boundaries. We defend that the private initiative and public policymakers should break the circular and cumulative causes that reproduce underdevelopment by addressing solutions to deal with problems that reach a considerable part of society. No doubt, there is a long road to travel given the current Brazilian situation of economic and political crisis, tax restriction and limited budgetary resources, aggravated by the covid-19 Pandemic.
Finally, our study also instigates research about the inherent culture and values that still echo from the historic Paraguayan war, and its effect on regional development. The study reinforces practical and theoretical justification to encourage innovative cross-border cooperation, leadership and governance agendas for peripheral regions. The formation and retention of human capital in these regions are key elements to enable the reduction of regional development disparities, to discuss socio-environmentally sustainable and culturally diverse development models, to foster industrialization, creativity, technological innovation, entrepreneurship, a sense of belonging, and social capital, especially in border regions that have structural elements that feed poverty and social inequalities. Freedoms and well-being of people could potentially increase, and citizens could benefit from this.
