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The development and implementation of vibrational spectroscopic methods such as near infrared (NIR) or Raman spectroscopy has increased significantly as the use of computer technology and chemometric methods has become more available. Considering the pharmaceutical domain, these methods have been extensively applied to quantify active pharmaceutical ingredients, excipients, or physical properties either as offline method for intermediate/final product characterization [1] or as real-time-monitoring methods implemented within blending [2], granulation [3], extrusion [4], tableting [5], coating [6], or freeze-drying processes [7].
The high-throughput analysis associated with vibrational spectroscopy favored its application to gain better process understanding, sustaining the pharmaceutical product development from a Quality by Design and Process Analytical Technology point of view [8], thus enhancing the opportunity to develop well-understood, well-controlled, and continuously optimized manufacturing processes and products [5]. The nondestructive nature of vibrational spectroscopic methods is of great importance in the quality evaluation of production batches, as they allow the testing of a high number of samples or the entire process, depending on the type of method. Using classical methods, such as chromatography the quality of a 1–3 million tablet batch is certified on 20–30 tablets, and many functional excipients that directly influence product performance are not quantified. These limitations are exceeded by implementing process analytical instruments, such as NIR or Raman [9].
Near infrared spectra are generated by molecular vibrations that imply a change of the dipole moment (─CH, ─NH, ─OH, ─SH) and are further complicated by overtones and combination bands that reduce the specificity of spectra. In case of Raman spectroscopy, the spectra are generated by inelastic scattering, caused by chemical groups that undergo a change in polarizability when excited with an incident light beam. These differences in molecular contribution to the generation of spectral data make the two methods complementary [10].
NIR and Raman spectra are considered a source of multivariate data, as they contain information related to physical and chemical properties of the analyzed sample. Thus, the application of chemometric methods for extracting predictive spectral variability and reducing orthogonal sources of variation is indispensable [11]. The sensitivity to both physical and chemical properties of the sample can be considered an advantage, if the analyst wants to predict several quality attributes of a drug product, such as content uniformity and crystalline structure. However, if only active content characterization is desired and polymorphism is not considered to be a critical attribute, but it is present, the calibration phase still has to include both aspects to ensure the accuracy of prediction for active content. The main disadvantage of vibrational spectroscopic methods relates to the need of an extensive calibration set that needs to include chemical, physical, instrumental, and environmental variability that is expected in future prediction sets and analysis conditions.
Vibrational spectroscopy is well suited to the means of multivariate calibration, as each observation is characterized by analytical signal/absorbance recorded at multiple wavelengths. Using multiple predictor variables instead of one wavelength overcomes some univariate calibration problems related to selectivity, precision, and diagnosis, resulting in a more robust calibration model [12].
The milestone in the development of a vibrational spectroscopic method is the chemometric model that is able to accurately predict the sample properties considered in calibration phase. Before building a model, there are several key steps that need to be considered, as they directly influence its quality and predictive performance. The first step would be the specification of responses along with variation ranges, followed by the selection of instrumental method and configuration, building a representative calibration set, recording of spectral data, data pre-processing, and developing the multivariate regression model that is further tested using external prediction sets. Each step plays an important role; however, a well-built calibration set is the best starting point to a well-performing model, as it is the source of spectral data that is used for further processing and model development.
In the calibration set development phase, the analyst has to incorporate the expected variability of future prediction sets, to ensure the representativeness of the samples. This expected variability is given first by the quality attributes that are to be predicted, for example, the concentration ranges of important formulation constituents. Frequently, this is not enough for a robust model, and other type of variability has to be included in the calibration process, such as process-induced variability or environmental variability. Production samples contain process-induced variability; however, constructing a calibration set solely on production samples is not appropriate as the factor ranges do not cover the required interval. A first option would be to prepare pilot-plant samples reproducing full-scale conditions. As the number of responses increases, the calibration set becomes larger and quickly becomes unfeasible due to the high costs of production. The second option would be to prepare laboratory samples in which the concentration ranges of desired components are varied simultaneously within appropriate ranges to avoid correlations [13].
The calibration set development strategy applied for the development of quantitative spectroscopic methods depends on the sample complexity (the number of responses and the number of interfering factors included in the calibration) and on the type of method that is developed, here considering off-line or real-time-monitoring methods. In the following section, a description of calibration opportunities will be provided starting from the simplest cases and heading toward more complex situations.
The most simple calibration situations include a low number of responses, one or two, here considering a chemical and a physical property of a sample. In this case, the calibration set development strategy simply resumes to the preparation of a sample with different levels of the investigated property. Mbinze et al. developed quantitative NIR and Raman methods for the assay of antimalarial oral drops and prepared a calibration set by diluting a stock solution of quinine to obtain three concentration levels. For each level, three series with three replicates were prepared resulting in a calibration set with 27 samples [14]. Tomuta et al. used NIR to characterize meloxicam tablets by evaluating content uniformity, tablet hardness, disintegration, and friability. For content uniformity assay, the calibration set included active ingredient concentration range (five levels), days (three), and batches (three) as a source of variation, whereas in the case of physical properties assay the middle formulation was compressed on seven levels of compression force, ranging from 5 to 42 kN. Compressing the powder mixture with different forces yielded tablets with different hardness, disintegration, and friability. Different settings of a one-process factor were enough to induce variability in physical properties of the samples [15]. In a similar study, Tomuta et al. developed NIR method for physico-chemical characterization of low active content indapamide tablets (2%, w/w) [1]. Virtanen et al. evaluated the crushing strength of theophylline tablets through Raman spectroscopy by considering both a process factor and a formulation factor to generate variability in tablet surface roughness. In this case, the tablets were prepared considering two particle sizes of theophylline, as raw material for the granulation phase, followed by mixing with lubricants and by compressing each granulate on five different compression forces [16].
The impact of polymorphism is a well-recognized phenomenon in the pharmaceutical industry, as the differences in crystalline structure of the same active ingredient generate different physical properties that get reflected in the quality of the final medicinal product. Croker et al. developed NIR and Raman methods to quantify FII and FIII of nootropic drug-piracetam from binary mixtures using a calibration set of 15 formulations with FII ranging from 0 to 100% [17].
Gómez et al. calibrated a Raman method for the content uniformity control of low-dosebreak-scored acenocumarol tablets by under and overdosing the powdered commercial medicinal product, by adding either lactose or the active pharmaceutical ingredient to the mixture. Two commercial products with different content uniformity were considered and the two calibration sets included 7 samples in the range of 1–3% (w/w) and 12 samples in the range of 0.35–1.50% [18]. Creating calibration sets by under-overdosing samples can result in correlated concentrations between API and excipients [19]. Collinearity between concentrations leads to spurious predictions by attributing changes to the correlated formulation component instead of the real contributor [20].
Changing the production scale generates samples that incorporate different types of variability from the primary conditions through which the calibration set was prepared. As laboratory-prepared samples lack manufacturing variability, the accuracy of prediction may be affected for production prediction sets. This limitation has been exceeded by extending the calibration set with production samples [13], adjusting the sampling strategy, pre-conditioning the calibration set to future expected environmental conditions [21], or by mathematically adding process variability to laboratory samples [20].
Blanco et al. developed NIR methods to control individual steps of paracetamol tablet manufacturing, resuming to an intermediate granulation step and tableting. Prior to building a calibration model, both laboratory-prepared samples and industrial production samples were taken into account to evaluate the eventual spectral differences. In case of the granule-active content assay, the calibration set was built solely on laboratory-prepared samples, whereas in the case of tablet assay the differences between laboratory and production samples made the calibration set include both, in order to ensure representativeness. For granule particle size characterization, samples collected over a period of 2 years ensured the presence of future expected variability in prediction set [22].
Blanco et al. used NIR to characterize mirtazapine tablets in terms of content uniformity and tablet hardness. For active ingredient content, the calibration set included production tablets from 20 batches and 34 laboratory-prepared samples, whereas for tablet hardness the laboratory samples were compacted in the range of 300–740 MPa. Including production samples for both responses reduced the systematic errors and gave better predictions [13]. By adding spectra from different manufacturing scales to the calibration set, the spectral variability becomes more representative, an important aspect for prediction accuracy. As the number of manufacturing samples is lower compared to the initial calibration set, proper weighting is necessary to avoid the dominating tendency of the larger dataset. To this regard, Farrel et al. applied Tikhonov regularization as a multi-criterion-based weighting selection method to augment the performance of NIR models regarding their ability to predict production scale products [23].
Blanco et al. proposed a method to incorporate physical variability that originates from production into the calibration. The concept relies on calculating a process spectrum, which added to the laboratory sample spectra incorporates process-related physical changes. The process spectrum represents the difference between the laboratory sample spectrum and the intermediate/final product spectra of an identical composition prepared on a different scale. The variability given by the process spectra can be further increased by multiplying the data with different coefficients [20, 24].
In situations where solid-state transformations occur within the manufacturing process, it is frequently desired to construct the calibration set with components obtained through the same method to have more representative formulations. Netchacovitch et al. used Raman spectroscopy to determine crystalline itraconazole in amorphous solid dispersions prepared by hot-melt extrusion. Calibration set included three levels of concentration and was built by using crystalline API powder, six batches of grinded extrudates with amorphous API, and placebo-grinded extrudate [25].
Pan et al. calibrated NIR method for the quantification of low-level Irbesartan Form B from pharmaceutical tablets. Form B is known to have a limited solubility and is formed from Form A via a solution-mediated process. To incorporate physical variability into the calibration set, the sample preparation procedure supposed the use of specifications similar to the manufacturing process. The robustness of the method to process induced physical variability, the effect of tablet hardness, granule size, and atmospheric humidity was evaluated. It was demonstrated that the prediction accuracy was influenced only by relative humidity, generating a positive bias in samples stored at 50%RH. Therefore, the entire calibration and validation was reconsidered by pre-conditioning the samples at 25°C and 50%RH for 20 h, prior to recording the spectra and building the model. This way, the robustness of the method was increased to future expected variations in environmental conditions [21].
As the number of factors increases, the calibration set becomes more complex and different strategies have to be applied to avoid correlated responses. If two formulation components C1 and C2 are correlated, a change in the concentration of C1 can be spuriously predicted as a change in C2. In DoE, factors are varied simultaneously in a systematic manner, providing orthogonality, an essential condition for estimating regression coefficients [26]. There are several design types that can be used for calibration purposes, starting from the classic full factorials down to central composite, mixture, or D-optimal designs. Considering more complex formulations, NIR spectroscopy has been applied to determine the amount of amoxicillin in the presence of seven other excipients. By applying a three-factor (API, saccharose, and other excipients) experimental design, the concentration of factors was varied orthogonally [27]. Ferreira et al. used a calibration set prepared according to a DoE with three factors: hydrochlorothyazide, cellulose, and other excipients to train a NIR method for the quantification of the active ingredient in pharmaceutical samples [28].
Li et al. calibrated Raman method to quantify active ingredient content considering the presence of different sources of variability: degradation compound, relative humidity, change of scales, and compression force. Laboratory samples were prepared based on a 32 full-factorial design where the active ingredient ranged between 80 and 120%, from which a subset of samples were spiked with the degradation product, added in two molar ratios. Each powder mixture was compacted at 8 and 30 kN in laboratory scale and three design points were compacted at manufacturing scale [29]. Casian et al. developed NIR and Raman methods for the quantification of two APIs found in significantly different concentrations from immediate release tablets. The calibration set was built on a full-factorial design with two factors and five levels with a total of 25 formulations [10]. The use of full-factorial designs is feasible with two factors if five levels of variation are used [52]. Adding one more factor will generate 125 experimental runs that are impractical [26, 53].
Netchacovitch et al. calibrated a Raman method to quantify low-level polymorphic impurities in a pharmaceutical formulation through a 12-run central-composite experimental design [25]. Central composite designs are extensions of the two-level full-factorial designs that are built by adding symmetrically axial points. Dependent on the position of axial points, factors can be varied on three levels (central composite face-centered design) or five levels (central composite circumscribed) [26].
Short et al. used NIR to evaluate relative density and crushing strength of four component tablets. Compared to other studies, where only the compaction pressure was considered as a factor to induce variability in the investigated response, in this case formulation composition was varied also. The calibration set consisted of 29 formulations (mixture design) with each formulation being compressed at different pressures [30]. Lyndgaard et al. developed a Raman method to quantify paracetamol content from tablets through blisters. The calibration set included 18 formulations, selected on the basis of a ternary mixture design (paracetamol, starch, and sucrose) with each factor being varied on six levels [31]. Igne et al. evaluated the effect of API physical form, excipient particle size, different manufacturer, and changes in environmental conditions on the performance of a NIR model. The calibration samples were prepared according to a 29-run quaternary mixture design with every formulation being compressed at two of five different forces. Only changes in the particle size of lactose produced biased predictions in both ambient and chamber conditions. The authors tested variable-selection methods to increase method robustness to raw material variability [32].
Griffen et al. used Raman spectroscopy to quantify all tablet constituents, three active ingredients and two excipients. In this case, the calibration set was built on a first-order (linear) five-level, five-factor mixture design that uniformly covered the concentration ranges of the components. The concentration of individual components ranged from 1 to 85% (w/w) [33]. Mixture designs are well suited for formulation application, where the sum of all ingredients adds up to 100% and where factors cannot be manipulated independently one from another. Porfire et al. used a D-optimal design with three variables and five levels to build a calibration set with 63 formulations with the purpose of quantifying encapsulated simvastatin and two functional excipients
A D-optimal design is frequently applied for a high number of factors as it gives a lower number of runs compared to factorial designs. The D-letter originates from its criterion of selecting the best subset of factor combinations from a pool of theoretically possible combinations, which relies on maximizing the X’X matrix Determinant [26]. In another study, Heinz et al. trained NIR and Raman to quantify ternary mixtures of alpha, gamma, and amorphous forms of indomethacin from ternary mixtures using a 13-sample calibration set built on a cubic model experimental design [35]. Lin et al. developed an at-line blend uniformity NIR method for simultaneous quantification of four active ingredients with structural similarity, found in different concentrations. Calibration was built on six formulations, where five factors (four APIs and one diluent) were varied on six levels while avoiding correlations. The performance of the model was improved by adding a set of spectral data from a different production scale [43].
When DoE is used, correlations are significantly reduced dependent on the type of design, number of factors, and experimental runs. However, an increased number of factors will require a high number of experimental runs to avoid collinearity, which rapidly increases the costs. Several papers have addressed the question of how many samples are needed to ensure a robust calibration [19]. The fact that models with similar performance were developed on a reduced design compared to its full-factorial counterpart suggests the presence of redundant information in full-factorial designs [36].
Saraguca proposed a method that relies on building the model on a limited number of samples and uses the remaining formulations to test the predictive performance in terms of RMSECV and RMSEP. In the following steps, the calibration set was extended by transferring one formulation at a time from the test set until the calculated cross-validation and prediction errors stabilized. The sample selection procedure focused on maximizing the concentration variability of all components [19].
Alam et al. proposed a method for calibration set development in spectral space instead of concentration space. Orthogonality in spectral response will yield a better estimation of coefficients with a minimum number of samples, while orthogonality in concentration space will not necessarily translate into spectral orthogonality, as the contribution of each component to the sample spectrum is different. The method is based on decomposing the pure component spectra of a formulation into orthogonal directions (scores), which will be varied around a model tablet score through DoE. The model tablet score represents the score of the spectra recorded on a target formulation projected onto the orthonormal basis vector of the pure components spectra. After designing the spectral space calibration set, the composition of each spectra is retrieved by mathematical means [37].
The application of vibrational spectroscopy for in-line monitoring implies the use of fiber optic probes mounted at the interface of the process itself to acquire spectral data with a defined rate. The simplest way to calibrate an in-line method is to acquire real-time spectra through the entire process length along with collecting samples at regular intervals. The response values obtained through reference methods are correlated with the spectral data, considering the process time as a link between the two [38, 39, 40]. More extensive calibrations also evaluate the effect of sample presentation, changing process, and formulation parameters, to challenge the robustness of the methods.
For coating application, the calibration strategy relies on the linear variation of spectral response as the contribution of the coating material increases and the tablet core contribution decreases [41]. Moes et al. developed quantitative NIR method using three batches of tablets by varying the tablet core weight (240–200–160 g) and the amount of coating suspension resulting in different coating thicknesses [42]. Möltgen et al. used five full-scale experimental runs to develop a quantitative NIR method (one run) and to evaluate the effect of changing exhaust air temperature and spray rate (two runs) and the effect of tablet density and flow motion in the coater (two runs). For quantitative calibration, samples were collected through the entire process and analyzed using reference methods [6]. For the quantification of coating thickness by means of Raman spectroscopy, Kauffman et al. calibrated the method by considering film thickness and film composition variables. Tablets were coated on five levels ranging 0.5–6% weight gain by varying their residence time in the coater. As for film composition, three different TiO2 levels were evaluated due to the strong Raman signal of this component offering the potential for an indirect measure [41]. In the case of thin coatings, the generation of a calibration set can become a difficult task and can become limited due to the lack of reference methods. In this situation, an alternative to classical regression methods would be the Science-Based Calibration (SBC) approach, which allows the calibration without a reference method by separating spectral variability into orthogonal (covariance matrix) and predictive parts (related to the coating). Möltgen et al. applied SBC to develop quantitative NIR method for in-line evaluation of thin hydroxypropyl methylcellulose (HPMC) coatings through four experimental runs. For calibration, the pure HPMC spectrum was used as the coating response spectrum and the covariance matrix included hardware, core, water, and process-related noise. The method developed without reference samples predicted accurately coating thickness values in the range of 8–28 μm demonstrating the value of SBC [43].
In order to predict granule moisture content in a six-segmented fluid bed dryer through NIR spectroscopy, a calibration set of 20 experiments was applied. Granules were prepared with five moisture levels by varying the drying air temperature and drying time. Each moisture level had four replicates prepared on two different days [3].
Clavaud et al. developed a global regression model for moisture content estimation from freeze-dried medicine. As expected, the calibration set was extensive, including three types of active ingredient with different concentrations, different vial diameters, and excipient amounts. To include intra- and inter-product variability, 5 batches and 100 samples were used for each product [44]. Martinez et al. calibrated NIR method for in-line quantification of two active ingredients in a batch-blending process by investigating the influence of sample presentation. With regard to this, the high-loading API was used either in the form of a cohesive powder or in a granular form prepared by melt-extrusion. The observed spectral differences were resumed to the polymer wavelength absorption band that coincided with the water region. The offline calibration of the method was built on 13 samples which included both forms of the high-loading API [2].
Wahl et al. evaluated in-line the content uniformity of ternary mixtures with an NIR mounted on the feed frame of a tablet press. For calibration, the active ingredient and two excipients concentrations were varied through eight experiments selected by means of a D-optimal design and two extra runs added to ensure equidistant steps in the content of each component. Spectral data were recorded in a dynamic acquisition mode, simulating real conditions [5].
Karande et al. developed NIR method for real-time monitoring of tableting based on a 105-sample calibration set generated through a simplex lattice design with four factors (chlorpheniramine maleate, lactose, microcrystalline cellulose, and magnesium stearate). Prior to building the calibration, the effect of sampling was evaluated by recording NIR spectra in both static and dynamic conditions. The differences between measurements revealed the importance of ensuring similar sampling conditions for calibration as for actual real-time monitoring [9]. For another application, Karande et al. evaluated the effect of different spectral-sampling strategies on the performance of an NIR model, to accurately predict blend components in quaternary mixtures. Calibration samples (24 formulations-D-optimal mixture design) were recorded in three ways: laboratory mixing and static spectral acquisition; IBC (intermediate bulk container) mixing and static spectral acquisition; IBC mixing and dynamic spectral acquisition. Dynamic sampling yielded the best calibration model with highest accuracy, demonstrating the importance of selecting similar sampling conditions to the actual testing [45].
Based on the presented examples found in literature, the most frequently applied methods to design a calibration set were as follows:
One chemical/physical property: formulations with three to five levels of variation for the response that span the desired range of concentration/physical property.
One chemical and one physical property: formulations with three to five levels of variation for the chemical response and for the physical property calibration are considered only for target formulation (five levels).
Two chemical/physical properties: any type of DoE (full-factorial, central composite, mixture design, D-optimal) to avoid collinearity and spurious predictions.
Three chemical/physical properties: simple lattice mixture designs or D-optimal designs.
In-line methods: models built by correlating sampled product properties with in-line collected spectra. Most rigorous studies also investigated the effect of process parameters on the NIR spectra.
The dependence of the NIR spectra on the sample’s chemical and physical properties caused by absorption and scatter effects can be an advantage of this type of spectroscopy, but at the same time, the scatter effects caused by sample variations or even by environmental phenomena can create a series of analytical problems. In such cases, each type of interferences has to be considered in the calibration model development. In the following section, the importance of chemical, physical, and environmental interferences will be described, providing insights on specific spectral variations produced by each category and highlighting how to handle them in order to increase model robustness [1, 2].
Generally, a quality NIR analysis should provide a model that manages a correct interconnection of the spectral variables with the samples properties of interest. At the same time, an ideal calibration model will not react to instrument variation, environmental changes, background interferences, and will be mostly focused on the information of interest. Chemometrics is the science that enables the extraction of relevant information, as well as the reduction of unrelated information as well as interfering parameters.
Spectral interferences resulting from variable physico-chemical sample properties (e.g., particle size variation and moisture content) or instrumental effects (e.g., path-length variation, light scattering, and random noise) can be reduced, eliminated, or standardized by using spectral pretreatments, prior to the multivariate data analysis [3]. Since the correct selection of spectral pretreatment can significantly improve the reliability of the model, this topic will be discussed in the following paragraphs. The most common pre-processing techniques can be divided into two groups: pretreatments for spectral normalization and for smoothing/differentiation. The first group achieves spectral normalization through scatter-correction methods. Scatter effects are common for all spectroscopic techniques and the phenomenon appears mostly because of the physical variabilities between samples or path-length variations. Two of those pre-processing concepts are standard normal variate (SNV) and multiplicative scatter correction (MSC) which also normalize the baseline shifts of different samples [4, 5]. The second set of pre-processing methods has the capacity to reduce or remove the noise by smoothing and differentiating the spectral values. The most common spectral derivatives are based on the Savitzky-Golay (SG) [6] and the Norris-Williams algorithms [7].
In most cases, in order to obtain best results, there is the need to apply both types of pretreatment techniques one after the other. Peeters et al. tested both types of pre-processings not only to reduce light scattering effects but also to minimize peak shifts of Raman and NIR spectra. They applied SNV, MSC, and first and second derivatives obtained by calculating 15-point quadratic Savitzky-Golay filters, in order to develop a method for the off-line prediction of tablet properties [8]. Sylvester et al. developed an in-line NIR-monitoring method for a freeze-drying process using the SNV pre-processing in order to remove multiplicative interferences caused by scatter and particle size variations and the first Savitzky-Golay derivative to reduce baseline shifts and to improve the spectral resolution [9]. The successful development of a real-time method for monitoring continuous powder flow from a tableting machine feeder was described by Alam et al. Savitzky-Golay derivatization was first applied for smoothing, followed by SNV for scatter correction [10]. Environmental interferences can be caused by sample, instrument, or even laboratory variations; this type of interferences causes misalignments or shifts of the spectra and is commonly overcome by applying alignment/warping techniques to the data [3]. Those methods stretch or compress the signal in order to match it in the best way possible with a given reference spectra [11, 12].
All pre-processing methods have the purpose to reduce the undesirable variability and interferences from the data, but there is always a risk of choosing an inappropriate type or applying a severe pre-processing that would also remove valuable information. Because of this, choosing the correct technique is one of the most important steps in data pre-processing and model development.
A last useful solution to deal with problems caused by interferences is wavelength selection method. The model development can be done based on the specific spectral domain that contains the information of interest. In order to select the domain of interest or to eliminate irrelevant wavelength domains, principal component analysis (PCA) can be performed. Prior to the PCA, the collected spectra should be pre-processed and column centered, then the analysis can be performed on the data matrix. Finally, the variables should be selected according to high peak loadings obtained for all relevant principal components (PCs), and the position of the resulting features should be compared with the original spectrum to validate the selection.
During the development of a multivariate calibration model, systematic variation such as baseline shifts and scatter effects, not relevant for the prediction of the response variables (Y), is present in predictor variables (X). Pre-processing methods are used in order to remove the systematic variation not related to the Y-matrix, which might impair the interpretation or predictive ability of the developed model.
The main goals of data-pre-processing are the following:
improvement of the robustness and accuracy of subsequent analyses;
improved interpretability: raw data are transformed into a format that will be better understandable by both humans and machines;
detection and removal of outliers and trends; and
reduction of the dimensionality of the data mining task and removal of irrelevant and redundant information [46].
The methods generally used for data pre-processing are divided into two categories. The first consists of classical pre-processing methods, used for normalization, smoothing, and differentiation. The second is represented by methods for variable selection and dimensionality reduction [47]. Among these methods, the most appropriate has to be chosen, such as to only remove unwanted variation, without excluding or altering chemically relevant information [48].
When used in an inappropriate way, pre-processing may introduce artifacts or cause loss of information. Thus, the purpose of the analysis is important for the selection of the most appropriate pre-processing method, because scattering is disruptive for compound identification and quantitation, but is useful to study the physical properties of the sample. As a consequence, the best pre-processing method, ensuring a correct data analysis and robust results, has to be chosen by testing and comparing the results of different methods [48].
In many analytical methods, the variables measured for a given sample are increased or decreased from their true value by a multiplicative factor, which is called the scaling or gain effect. In spectroscopic methods, the scaling effect arises from path-length effects, scattering effects, source or detector variations, so the relative value of variables should be used during multivariate modeling rather than the absolute measured value. The sample normalization is one of the most important pre-processing methods, which is applied in an attempt to correct for multiplicative scaling effects, the shifts and the trends in baseline and curvilinearity, by identifying some aspect for each sample which should be essentially constant from one sample to the next, and correcting the scaling of all variables based on this characteristic [48].
Normalization methods can be subdivided into two main groups: simple normalization methods (min-max normalization, one-norm, vector normalization, standard normal variate), requiring only the information from the spectrum to be normalized, and normalization methods requiring the presence of collective spectral data matrices or of reference spectra (multiplicative scatter correction and extended multiplicative signal correction (EMSC) [46]. Among these, the most used scattering correction algorithms include the SNV and MSC. The two pretreatments give similar results, being considered exchangeable, but the results obtained through both algorithms are compared usually, since they may be different [49]. SNV was proposed to reduce multiplicative effects of scattering, particle size, and multicollinearity changes over the NIR spectra. This approach starts with mean centering and consists of dividing mean-centered spectra by the standard deviation over the spectral intensities [50]. SNV normalizes each spectrum returning a mean of 0 and a variance of 1 spectra dataset [48]. The disadvantage is the assumption that multiplicative effects are uniform over the whole spectral range, so artifacts may be introduced by this transformation.
The de-trend method is another approach to correct for baseline shift, which removes the baseline curvature by expressing it as a quadratic function of the wavelengths. The modeled baseline is subtracted from the spectrum, so de-trend can be used after SNV to circumvent any curvilinear trend, where the baseline drift is a function of wavelength [50]. The MSC pretreatment performs a linear regression of each spectrum on a reference spectrum, which is usually the mean of all available spectra, for example, the average spectra of the calibration set, or a generic reference spectrum can also be applied [49].
The smoothing algorithms are used in order to correct the spectral noise, while differentiation is used to enhance spectral resolution and to eliminate background absorption. The most common ways to achieve smoothing are the use of noise filters for de-noising and smoothing and Savitzky-Golay smoothing/derivative filters for smoothing/resolution enhancement. Noise filters are specific low-pass filters which can be used to reduce random noise. Their drawback is that the signal-to-noise ratio is increased at the expenses of distorting the signal. The most popular smoothing filters are the zeroth-order SG-smoothing/derivative filter, the binomial filter, and the moving average filter [46].
Derivatives are used for their capability to remove both additive and multiplicative effects in the spectra. The first derivative removes only the baseline; the second derivative removes both baseline and linear trend. The first derivative is estimated as the difference between two subsequent spectral measurement points, while the second-order derivative is estimated as the difference between two successive points of the first-order derivative spectra [51]. The most popular derivation method is SG algorithm, proposed by Golay and Savitzky in 1962 [52]. The method has the advantage that computation of the derivatives and smoothing are carried out in a single step. The algorithm used in this method is based on fitting a polynomial in a symmetric window on the raw data, in order to find the derivative at the center point. The parameters of the polynomial are calculated and the derivative of this function is found, this value being used as the derivative estimate for this center point. The same operation is subsequently applied to all points in the spectra. Two decisions are important to be made in this algorithm, i.e., the window width (width of the subset of the data) and the fitted polynomial order. The highest derivative that can be determined depends on the degree of the polynomial used during the fitting [51].
These methods rely on reducing the dimension of the predictor space spanned by a number of variables or wavelengths, in order to find the subspace mainly containing variations related to the response matrix. The orthogonal projection and the variable-selection methods are in this group. Orthogonal signal correction (OSC) and its modified version direct orthogonal signal correction ((D)OSC) are the most common among this group, developed to remove systematic variation in the descriptor matrix, that is not correlated to the response matrix. In other words, the pre-processing is performed in such a way that the removed parts are orthogonal (not linearly related) to the response matrix [53, 54]. The method has the advantage of correcting at once multiple artifacts.
An alternative OSC algorithm was developed by Trygg and Wold and is called orthogonal projection to latent structures (OPLS). The objective of OPLS is the same as of OSC, but the approach is different, i.e., the OPLS method analyzes the variation explained in each PLS component. The non-correlated systematic variation in descriptor matrix is removed, making interpretation of the resulting PLS model easier, and the non-correlated variation can be analyzed further [55].
Variable-selection techniques consist of selecting particular variables related to the response, instead of removing the interference modeled as a spectrum, the aim being to identify a subset of wavelengths that produces the smallest possible error [56]. Selecting the most correlated wavelengths may lead to better performance in PLS and PCR, but, on the other hand, selection of the most correlated wavelengths may eliminate those that correct for the influence of interfering compounds or factors [56].
In practical applications, combinations of pre-processing methods are usually employed in search for the best algorithm, involving more than one pre-processing step. According to Rinnan et al., several rules may serve as guidelines: scatter correction (except of normalization) should always be performed prior to differentiation; normalization can be used at both ends of the correction, but usually is easier to be assessed if it is done prior to any other strategy; MSC gives a smaller baseline correction than SNV with subsequent de-trending; it is not recommended to perform de-trending followed by SNV [51].
The ideal pre-processing strategy should only remove artifacts present in the data, without introducing any unwanted artifacts or variability in the data. When physical properties, that is, tablets’ crushing strength, are evaluated through vibrational spectroscopy, typical pre-processing methods such as SNV, MSC, and the derivatives cannot be used, because they lose the baseline-shifting information, which is relevant for the physical properties. The data in this case should be modeled as such or after normalization [16]. Three approaches are described in the literature, for the selection of the most appropriate strategy: the trial-and-error approach; visual inspection and the use of data-quality parameters [57].
In the trial-and-error approach, all pre-processing methods are applied to the data and the pre-processed data are used as an input to a calibration model, which is further used to assess the quality of the pre-processing strategy by an internal measure, such as RMSEP or RMSECV [57]. For example, Karande et al. chose among various pre-processing methods through comparing the figures of merit (explained variance, R2, RMSEC, and RMSECV) of the developed partial least-squares (PLS1) regression models, for the quantification of micronized drug and excipients in tablets by NIR spectroscopy. The raw calibration spectra were pretreated with SNV followed by first derivative and SNV followed by second derivative pre-processing. All models were developed using the entire spectral range or narrow spectral ranges. The best performance of the calibration method (highest explained variance, lowest RMSEC and RMSECV) was obtained using the whole spectral range, pretreated with SNV followed by first derivative spectral pre-processing [9] . The same approach has been used by Porfire et al. in the attempt to select the best pretreatment method in the development of calibration models for prediction of chemical composition and crushing strength of sustained-release tablets with indapamide. PLS regression was performed for non-processed spectra as well as for spectra treated by various pre-processing methods (i.e., FD, SD, SNV, MSC, FD + SNV, FD + MSC), and the most suitable pretreatment algorithm was chosen based on the results obtained for PLS model validation through cross-validation, i.e., based on its RMSECV and bias [58].
In visual inspection method, the effect of pre-processing is assessed before a model is constructed. Thus, because artifacts have been removed during pre-processing, samples should show more spectral overlap after pre-processing in visual inspection, and differences between groups of samples should be more pronounced. However, as visual inspection may be very difficult and not objective, the data are not usually inspected in “spectral mode” but in a lower dimensional space, obtained usually through principal component analysis [57]. PCA reduces the dimensionality of the problem by generating linear combinations of the original variables returning new “latent” variables. Each original variable is weighted by a loading representing the importance of the considered variable on the variance of the data. The variability of the data is expressed by new dimensions called principal components, and the projection of a pixel onto the PCs is called its score. The result of PCA is the decomposition of the pre-processed matrix in a score matrix and a loading matrix [48]. PCA is used for data overview, for example, for detecting outliers, groups, and trends among observations, for evaluating relationships among variables, and between observations and variables. In PCA, data in the matrix X are transferred into a new coordinate system defined by principal components. The direction in variable space occupied by the most varying data points will define the location of the first PC, and the second PC will be given by the largest variation orthogonal to the first component. PCs are extracted until only minor variation is left unexplained by the PC model, each component consisting of a score vector and a loading vector. Observations close to each other in a score plot have similar properties, and variables close to each other in a loading plot are correlated. Thus, the score plot is useful for the detection of strong outliers, clustering, and time trends [59] .
The detection of strong outliers through PCA is done by analyzing the score plot. The strong outliers are removed, as they may have a degrading impact on model quality. A statistic tool called Hotteling’s T2 may be used in conjunction with the score plot for the detection of strong outliers. This tool is a multivariate generalization of Student’s t-test, defining the normal area corresponding to 95 or 99% confidence. Subsequently, for a better understanding of the properties of grouped data, a splitting of data into smaller groups according to the nature of the clustering is done, and separate PCA models may be fitted. For the detection of weakly deviating observations (moderate outliers), which are not strong enough to show up as outliers in score plots, the residuals of each observation are used. The detection tool is called DmodX (a notation for distance to the model in X-space). A value of Dmodx is calculated for each observation, and the values are plotted in a control chart where the maximum tolerable distance (Dcrit) for the dataset is given. The plot of DmodX enables an overview of the unsystematic process variation, as moderate outliers have DmodX values higher than Dcrit [59].
Before PCA, scaling of data is usually performed, because variables have different numerical ranges so they will have different variance and they will weight differently in the data analysis. The most common approach is the unit variance (UV) scaling, consisting in dividing each variable by its standard deviation. The result is that each variable has equal variance, meaning that the “length” of each variable is identical, although the mean values still remain different [59].
Tôrres et al. used Hotelling’s T2 chart to analyze the NIR spectra of a training (calibration) set for the development of a monitoring method for the stability of captopril in tablets. Before being analyzed by PCA, NIR spectra were smoothed as described by Savitzky-Golay with a 21-point window and second-order polynomial and were processed by MSC for the correction of baseline variation due to non-homogeneity of particle’s distribution [60]. The Hotelling’s T2 chart measures the distance from an observation to the center of the samples under normal operating conditions and evaluates whether a particular sample has a systematic deviation from the samples considered to be under statistical control [61]. As all samples from the training set were assumed to be normal, the training chart was not expected to identify systematic deviations in these samples in the training phase, so the number of PC retained in the model was selected to minimize the number of false alarms (false positives and false negatives) during the training phase of the control charts [60].
Regression analysis is a modeling technique used to investigate the relationship between dependent variables (responses or y’s variables) and independent variables (predictor, factors or x’s variables). According to the number of variable, three cases can be distinguished:
Simple linear regression—one y and one x variable.
Univariate linear regression—one y and several x’s variables.
Multivariate linear regression—several y’s and several x’s variables [62].
The objective of a regression method can be achieved by means of a model where the observed result (dependent variables, response, y’s variables) is described as a function of independent variables (x’s variables) and the noise is left as residual.
In a regression analysis, the relationship between two data matrix X (BxK) and Y (NxM) are related to each other. A regression model can be written as in a matrix form as
where
A good estimate of regression coefficient (B matrix) provides a good fit to Y and good prediction of future unknown parameters yT. More, the regression coefficient vector should be of mechanistic understanding and interpretable [59, 63, 64].
A large number of regression methods were developed, all with the goal of finding the best estimation of B. In the calibration of spectroscopic methods, only multivariate regression techniques can be applied, and the most used are (1) multiple linear regression (MLR), (2) principal component regression, (3) partial least-squares regression, and (4) orthogonal partial least-squares regression (O-PLS). In the last years, some advanced regression methods as (5) Bayesian ridge regression (Bayes-RR) (6) support vector regression (SVR) or (7) decision tree regression (DTR) have started to be used.
Multiple linear regression is an extension of simple linear regression model. In the case of MLR determination, the relationship between x’s—variables and y’s—variables is achieved by means of a model where the responses (y’s—variables) are described as a function of analyzed factors (x’s—variables) and the noise is left in the residual (ε) (Eq. (1)) [65]
The function f is approximated by a polynomial equation (Eq. (3)),
where bi (i = 1, 2, 3, …,n) are the regression coefficients and describe the effect of each term on the response y.
The polynomial equation (Eq. (3)) can be written in matrix way as follows:
where X are the matrix of x’s variables and b the vector, and the multiple linear regression is used to determinate vector b.
If there are orthogonalities between x’s variables, Eq. (4) can be written as
In this equation, matrix XTX become a diagonal matrix and b is easily calculated.
If not all the x’s variables can be controlled, the number of x’s variables extends the number of experimental runs or the number of experimental runs is larger than the number of x’s variables, the co-linearity between x’s variables arises and the orthogonality no longer exists, so the inverse of XTX cannot be applied.
Except the cases when the calibration of spectroscopic methods is performed following the design of experiment strategy in the other multivariate calibration, the orthogonalities do not exist and the MLR cannot be applied. That is the reason why other regression methods based on latent variables as partial last squares are preferred and become popular. When the calibrations are performed based on latent variables, inside of using the original variables in the regression, a new set of orthogonal (latent) variables is calculated and leads to reduction of the original dimension of x’s variables matrix and performs the least-square estimation.
Principal component regression is a regression method based on principal component analysis and it is used when datasets are highly collinear. In a PCA regression, the original set of collinear variables is transformed to a new set of correlated variables. So, the principal component analysis is used to decompose the x’s variables into a principal component (orthogonal basis) and a subset of components in order to predict y’s variables. The basic idea of the principal component regression is to calculate the principal components and then use some of these components as predictors in a linear regression model fitted using the typical least-squares procedure [66, 67].
In the case of PCR determination, the relationship between x’s variables and y’s variables is achieved by means of a matrix of lower dimension (TPT), called principal components, and a matrix of residuals (E).
where
The main idea of principal regression is to replace X matrix of row date to a smaller orthogonal score—loading matrix (TPT matrix) that summarized the original X matrix, and then to relate the T-scores to y’s variables.
The core of PCR is that a small number of principal components is enough to explain the variability into the data. In most of the cases, it might be found out that four to six principal components are enough to explain more that 90% of the variance into the data.
The partial least-squares regression is the most popular method for the creation of models used in the development of NIR and Raman spectrometric methods and is used to develop a linear link between two matrices, the NIR/Raman spectral data and the reference values.
The PLS approach was first proposed by Herman Wold around 1975 for the modeling of complicated datasets in terms of chains of matrices, the so-called path models. PLS regression is preferable to develop calibration models because unlike MLR, it can analyze data with strongly collinear, noisy, and numerous X-variables, and also simultaneously model several response variables [68]. PLS was developed for situation in which the data have more independent variables than observations (the “small n, large p”) or/and where collinearity is present among dataset [69].
The PLS finds a multivariate model (linear or polynomial) that describes the relationship between Y matrix (dependent variables) and X matrix (predictor variables) expressed as
PLS may be easily understood geometrically if we imagine the matrices X and Y as N points in two spaces. The X-space with K axes, and the Y-space with M axes, where K is the number of columns in X matrix and M the number of columns in Y matrix. The objectives of PLS is to find a latent variable so that the best approximate X-space, the best approximate Y–space, and the greatest possible correlation between X-space and Y space.
A PLS model can be written as
where
In a PLS algorithm, there are additional loading called weight. P is the matrix of weigh expressing the correlation between X and U and is used to calculate T. C is the matrix of weigh expressing the correlation between Y and T and is used to calculate U [12, 70].
OPLS has been developed in order to separate information in the X matrix that is correlated with Y matrix form Y-uncorrelated information. The idea of O-PLS algorithm was to remove systematic variation uncorrelated with the response with the goal and to reduce the number of components in order to increase interpretability of the model [55, 69, 71].
The main idea of O-PLS is to separate the systematic variation in X into two parts, one which is related to both X and Y (co-varying noise) and one which is orthogonal to Y (structured noise). Two O-PLS algorithms were developed, the first (O1-PLS) is unidirectional
An O2-PLS model can be written as
where
The matrixes TPT and UCT hold the joint X/Y information overlap [12, 63, 64].
In the last years, O2-PLS has become the preferred regression technique for the development of calibration models in NIR and Raman spectroscopy.
Another regression method recently proposed for multivariate calibration of spectroscopic methods is Bayesian ridge regression. The method presents similarities with least squares, and the estimated coefficients tend toward zero in order to avoid collinearity [44].
In a Bayes-RR regression model, higher-level prior Gaussian distributions can be introduced over α2 and α, and the prediction can be performed by integrating over α2, α, and the regression parameters w. Since this prior distribution is conjugate to the likelihood function, the predictive distribution is also Gaussian [72]
The Bayes-RR is a widely used regression technique in machine learning based on the ridge regression [73], and in the last years its performance for the development of excellent models for spectroscopic calibration has been proved [72, 74, 75].
The support vector machines (SVMs) are a set of learning methods mostly used for classification that can be used as a regression technique which is called the support vector regression. In the last years, SVM started to be used in chemometrics for NIR spectra classification and multivariate calibration. The SVR uses the same principles as the SVM and is based on finding the hyperplane maximizing the margin between classes. The hyperplane maximizing the margin is justified by statistical learning theory endowed with a probabilistic test error bound that is minimized when the margin is maximized. The regression is performed using kernel functions that transform the data into a higher dimensional feature space to make a linear separation possible. The models obtained by SVR are more complex and difficult to interpret in comparison with those obtained by other regression techniques [44, 76, 77].
Decision tree regression is a type of decision tree algorithm that can be applied to solve regression problems. Decision trees represent one of the main techniques used for discriminant analysis, classification, and prediction in knowledge discovery. It is widely used because it closely resembles human reasoning and is easy to understand. The principle is to compute a regression in a tree structure from breaking down a dataset into smaller and smaller subsets. Recently, some applications in multivariate calibration of spectroscopic methods have been proposed [44, 77, 78, 79].
This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number PN-III-P2-2.1-BG-2016-0201.
Economic performance and development of market economy institutions depend on many factors. It became evident that economic and cultural factors play an important role. There is a lot of evidence that this role has been under-conceptualized and analytically-experimentally underexploited.
The sub-Saharan African country Namibia is a prime example of a country with diverse and extraordinary nature and culture but one that must overcome challenges. As a result of numerous drastic events in the past and the country’s recent national independence, Namibia’s government is facing several cultural and economic difficulties. Every 4–6 years a new National Development Plan (NPD) appears from the domestic government with different focuses. At this point of time, the fifth NPD has been introduced, describing the main goal in working together toward prosperity [1]. Similar goals have been recognized by the World Bank in 2019, stating that Namibia is facing three major long-term objectives: the combatting of inequality, unemployment, and poverty within the nation. These are formally known as the triple challenge [2].
The concept of economic culture is related to the concept of political culture which was consolidated by Almond and Verba [3] and others. Berger and Peter [4], in their book, focus on the theory of the economic culture of capitalism, exploring the social, political and cultural matrix, or context within which these particular economic processes work. In doing so, it does not presuppose a direct or indirect connection and does not presuppose that culture determines the economy or that economic factors determine culture. The concept of economic culture draws attention to the relationships that such an empirical study has to explore.
On the basis of the relevant economic knowledge, economic culture can be considered as a whole in the economy of the related knowledge, experience, perceptions, evaluations, norms, and styles of behavior of whole nations or certain groups of the population within a particular society [5], which affects the economically relevant decision-making and conduct of actors or groups of actors. Economic culture has constituent and regulatory elements [6]. Thus, the (often unconsciously) structure of economic perception of the world, as well as the values and norms that determine their motives and limitations of their behavior, is more accessible to everyday awareness [7]. According to Weiss’s and Fershtman’s economic culture, “it is by no means a static system of value fields that determine the maneuvering space of behavior and outline the directions of action that they make for meaningful” [8]. “Culture is therefore the one that directs the process of the evolution of a system” [9]. The orientation is carried out partially with the help of internationalization of value performances and, insofar as the direct guidance instruments are more abandoning, the “guiding function of cultural forms and value fields” is all the more important [5]. According to Jones “economic institutions do not exist in a vacuum but rather in a context of social and political structures, cultural patterns, and, indeed, structures of consciousness (values, ideas, belief systems). An economic culture then contains a number of elements linked together in an empirical totality. The question concerns the manner of linkage [10]. Boyd and Richerson perceive culture as “the transfer of knowledge, values and others to the behavior or behavior of relevant factors through teaching and imitation from one generation to the next” [11]. The Berger’s concept of culture argues that “economic institutions do not exist in a vacuum but rather in a context of social and political structures, cultural patterns, and, indeed, structures of consciousness (values, ideas, belief systems). An economic culture then contains a number of elements linked together in an empirical totality. The question concerns the manner of linkage” [5]. Cultural factors have a holistic impact on the process of emergence and on the structure of flows and interconnection networks within a given formal framework and affect the recognition, guidance, and also the effectiveness of formal institutions. The conflicts between formal institutions that have emerged during the transition and between cultural factors that act as informal institutions form an essential part of the societal developmental dynamics [5].
Economic institutions do not exist in a vacuum but in the context (matrix) of social and political structures, cultural patterns, and conscious structures (values, ideas, belief systems). Economic culture (in capitalism, in socialism, in Hinduism, or in any other society) contains a number of elements linked together. The question is in what way are they connected [4].
Transition to a modern market economy requires an integrated and well-rounded study of the specific features and factors of modern economic transformation. Undoubtedly, man is always the most important factor in the production and development of the economy. An important historical role of a democratic society is to free creative human resources from social and economic barriers and to enable people to work for their own benefit and profit. Motivation and interests are of primary importance in the creation of a modern market economy. Subjective social factors are a useful aspect of economic culture. This includes the economic policy, the quality of management, and the productivity of each individual and determines the functioning of socioeconomic laws. Economic culture is constantly changing people in society who work according to their level of economic knowledge, their managerial skills, and their view of the economy [12].
In a democratic society, economic culture represents economic relations and creative resources for the development of the economic life of social bodies, specialists, and entrepreneurs. They are all coordinated as the overall economic activity of people, where work practice and economic behavior play an important role in socioeconomic development. Economic culture is linked to the development of society and is crucial in ensuring a satisfactory life for people and for the development of a new quality of social life. The company is unified with economic culture, which operates not only through economic integration but also through the economic policy of the state. Democracy needs an industrial force of high quality. By people seeking new ways of satisfying their needs, economic culture is an objective need for a democratic society and becomes a coincidence for the country’s economic policy. Economic culture is changing with social development. Today, an economic-cultural person must have economic knowledge, the ability to economize resources and draw attention to the quality and quantity of production, the ability to make decisions, and care for investment in time and resources. The main components of the economic culture are [12]:
Economic knowledge
Belief
Experience
Talent
Depending on these components, economic culture can be described as a way of creatively shaping the economic activity of people, based on deep economic and technological knowledge related to their problem and profession, sufficient scope of objective laws of socioeconomic development, and belief based on economic activity and experience [12].
Economic performance and the development of market economies are dependent on many factors. According to Elster et al. [13], Bulgaria and Slovakia faced major problems in adopting democratic institutions and market economy and understanding and internalizing their will in the 1990s, as their rapid and forced industrialization was in contrast to cultural and political modernization. Because of the traditional cultural implications of the Soviet type, the basic communist concepts and perceptions of the agrarian society (mostly under cover) can survive in the behavioral patterns, values, and worldviews of the communist era. The Soviet Communist regime served as a suitable host, which enabled the sustainability of many forms of traditional dominance of agrarian society also in industrial rather than simultaneously modernized society [14]. It is important that the legacy of the social and cultural capital of the past is able to adapt to the requirements of the present [13] in: [14]. It has become evident that economic-cultural factors also play an important role. There is a lot of evidence that this role has been under-conceptualized and analytically-experimentally underexploited. The project “Researching Transition Economic and Culturally” is based on four fundamental hypotheses [5]:
Success of economic transition in terms of a stable and economically viable change in the system depends on supporting economic and cultural factors.
Supportive economic-cultural factors occur in different transition countries or groups of countries of varying degrees. In some countries, the prevailing economic and cultural environment for transition is more aggressive than accelerating.
Further progress of transition to less successful countries depends on the extent to which the obvious conflicts between the dominant elements of the traditional economic culture and the democratic and market economy mechanisms supporting transition can be overcome.
Economic culture is a concept that should be taken into account both in the analysis of global societies and in the research of individual groups of actors.
In developing the appropriate conceptuality, it is possible to rely on an increasingly detailed conceptual apparatus, which is the result of an international cultural-oriented economic research. The reasons for the breakthrough of economic cognitive research are mutually accelerating factors of real change (Asian tigers, the collapse of global political blocs that hitherto covered cultural differences, different waves of transition) and the development of theory (the growth of the influence of the institutional economy, the evolutionary economy and economic sociology).
Culture is a dynamic category; it changes over time. Changes are positions, values, norms, principles and customs, ideology, beliefs, behavior, etc. All of this is also linked to social structures, for example, in the economic system. Culture with its elements is the basis for the design and functioning of a social order and hence of economic regulation. It is understandable that social changes are changing with the changes in culture. On the other hand, we can expect that the change in economic regulation will affect the change of culture.
Economic activity of people is thus certainly among those that need to be judged from the point of view of good or bad. In the economy, people enter into urgent interrelationships, within which the basic existential issues are addressed, both at the individual and family level and at the level of the broader society as a whole. The socioeconomic order can only be effective if at the same time as the material goods increase, it also ensures the life of a human being to as many people as possible. In ethical issues in the economy, it is primarily because in material matters we are always looking for humanity [15].
The concept of culture is very wide, as it covers every part of personal life and has a direct impact on the patterns of life. Culture is a learned way of life within a given society. In essence, culture is a set of values and patterns of learned behaviors that are shaped as a result of living within a particular society.
This is enabled by the following processes:
Cultural heritage as a way of transferring cultural values and norms from generation to generation (reproduction of cultural patterns)
Upgrading culture based on the internal potential of cultural groups
Borrowing from other cultures as a way of assuming certain elements of other cultures
Culture does not stand still but gradually changes.
In the broader sense, the economic system is defined as a set of mechanisms and institutions, laws, rules, traditions, and values that form a certain pattern of decision-making and the realization of economic decisions in the process of social reproduction and a certain pattern of behavior of economic operators [16].
Throughout history, each country has created an indicative concept of culture that has its own specific characteristics. In connection with these cultural characteristics, each country has also developed a specific economic arrangement. Therefore, we can not claim that the economic system is identical in all countries. Of course, this can not be true, as economic potentials and culture, as an important element of economic regulation, are different in different countries. This intertwining of economy and culture was largely neglected in older economic theories. Recent economic theorists, however, are trying to capture the cultural aspect of the economy in their theories.
An indirect link between culture and economy is possible through the role of the state as an institution and instance. In a dynamic market system with incomplete and diffuse information, culture is always the basis for competitive advantages in the realization of transactions.
North [17] states that the rules of the game in society are—or more formal—the constraints that people have built to create interactions. Consequently, they structure the incentives in political, social, or economic exchanges. Institutional changes shape a way of social development and are crucial to understanding historical change.
Institutions can not be seen, felt, touched, or measured—they are constructs of the human mind. However, neoclassical economists recognize their existence and are usually used (implicitly or explicitly) in their models as parameters [17]. In his work of the institution, institutional changes, and economic success, North [17] laid the foundations for the analysis of institutional changes on economic performance.
Keith Hudson [18], in his article in The Economist, asks what is crucial for the development—geography, institution, or politics. For many years, economists have emphasized the importance of good economic policy, and lately more emphasis is placed on long-stay institutions—political stability, property rights, the legal system, patterns of land ownership, etc. Other economists, on the contrary, emphasize geography, especially climate diseases, usability of certain technologies, agricultural opportunities, and access to the sea (influencing the extent of international integration). These explanatory factors do not necessarily have to be mutually exclusive1.
Easterley and Levine [19] tested the importance of these groups of factors on a sample of 72 countries. The results of the survey are very impressive, as they have shown that institutional factors have a key effect on economic performance, while other factors are almost non-negligible.
Conceptual insights from contemporary social sciences show that the modern world is increasingly aware of the cultural condition of economic decisions. The decision-makers are not only economic but also cultural-national. The awareness of these complex relationships has not yet been sufficiently defined and researched. The Hofstede’s six dimension model, where people’s behavior conditions their values, is the cornerstone for the empirical model. In any case, we are aware that such an operational model has its own shortcomings, as are all mechanical methods [20].
The sub-Saharan African country Namibia is a prime example of a country with diverse and extraordinary nature and culture but one that must overcome challenges. As a result of numerous drastic events in the past and the country’s recent national independence, Namibia’s government is facing several cultural and economic difficulties. Every 4–6 years a new National Development Plan (NPD) appears from the domestic government with different focuses. At this point of time, the fifth NPD has been introduced, describing the main goal in working together toward prosperity [1]. Similar goals are pursued also by the UNO: The Sustainable Development Goals are the blueprint to achieve a better and more sustainable future for all. They address the global challenges, including those related to poverty, inequality, climate, environmental degradation, prosperity, and peace and justice. The goals interconnect, and in order to leave no one behind, it is important that each goal and target by 2030 are achieved. Similar goals have been recognized also by the World Bank in 2019, stating that Namibia is facing three major long-term objectives: the combatting of inequality, unemployment, and poverty within the nation. These are formally known as the triple challenge [2].
Before the in-depth explanation of the Namibian culture and economy, the reader finds a brief summary of the main indicators, in order to understand the significance of such, in the following sections.
As a project of the United Nations Education, Scientific and Cultural Organization (UNESCO), CDIS has been established in 2009 with the help of the Spanish government. With the contribution of more than 300 partners, the aim is to promote and to protect the diversity of cultural expressions globally. It is a multimethodological tool that measures and visualizes data in seven interrelated policy dimensions, including 22 indicators. Especially, the following dimensions that will be used in this chapter, in order to achieve fact-based assessments, are economy, education, gender equality, social participation, and heritage. This multidimensional instrument is only one project of the UNESCO, accessing international comparisons among nations and their facts of development. Culture for Development Indicator Suite gives an insight into the enrolment of culture in a nation’s development. Additional results are indicating the potential of domestic sectors and exhibit obstacles hindering full potential. Namibia’s government recognized the key factor of culture in its development process and introduced CDIS in November 2011 [21].
Between 1967 and 1973, the Dutch psychologist Geert Hofstede developed the origins of this model while conducting an international survey about national values for International Business Machines (IBM). He defined six dimensions that society needs in order to organize itself. A further purpose of Hofstede’s six dimension model is to understand the contribution of national culture in habits and values in a professional environment. Global data manifest into six cultural dimensions, which enable comparisons among 76 countries, while Namibia is one of them. Especially the following three dimensions will be discussed within this chapter: power distance, masculinity vs. femininity, and long-term orientation. Those dimensions can be used most effectively for the clarification of cultural and economic correlations [22].
Namibia is known worldwide as having tremendous inequality conditions, which can be traced back to their past. The nation’s history still has an effect on their individual degree of trade, governance, and cultural factors [23].
These unequal conditions are affecting the 13 regions of the country in gender, education, health, wages, infrastructure, and a plethora of other aspects. When analyzing Hofstede’s six dimension model, Namibia’s ranking within the power distance dimension highlights these issues. It monitors to which extent power is distributed and how citizens of the analyzed country are satisfied with the distribution. Namibia currently reaches 65 out of 100, which indicates a hierarchical society with high inequalities [22].
The Republic of Namibia has witnessed turbulent historic times, including colonization by several foreign powers, genocide committed by the German’s, and foreign administration by South Africa.
During the nineteenth century, Victorian Britain and Germany were the major players involved in the nation’s colonialization [24]. In the early twentieth century, Namibia experienced the nation’s first genocide. It was committed by the German general Lothar von Trotha, which killed more than 75,000 African primary residents and destroyed their tribal structures. Particularly the ethnic groups of Herero and Nama suffered the most from this event, which led to losses of approximately 80% (Herero) and 50% (Nama) in their tribal size [25, 26].
The previous influence of foreign policies in the twentieth century, e.g., the racial segregation formally known as apartheid, continues to impact the nation to this very day. Such events influenced the population in terms of behavior, structures, and their growth. However, these historic incidents have evoked societal inequalities [24, 27].
Although there is a lack of historical content from generation to generation, former leader of the ethnic group Nama has written controversially in his dagboek (diary, English translation): “… no person, nor his money comes short in our way of living” [24]. According to additional diary entries, it is liable that there have not been major inequalities in at least one of the ethnic groups before 1905 [24].
The national independence obtained in 1990 was an important first step out of several dependencies toward an emerging national and societal self-perception.
Nevertheless, Namibia suffered from a fragmented society in post-independent times: a gap between ethnic groups of the poor and rich arose. This created political and economic instability [28].
A new constitution was written with several different focuses, but the most intriguing was the empowerment of women and gender equality. It states that there should not be any discrimination because of sex [21].
The nation’s second president, Hifikepunye Pohamba, also made these issues to one of his major duties in his period from 2005 to 2015, as well as several National Development Plans that followed [27].
According to data from the World Population Review, Namibia exhibits nine different ethnic groups. The largest ones are the Ovambo with 49.8%, followed by Kavango (9.3%), Herero (7.5%), Damara (7.5%), Whites (6.4%), Nama (4.8%), Caprivian (3.7%), San (2.9%), and Basters (2.5%). Whites are mainly consisting out of Portuguese, British, and German origin. The remaining 5.6% are assigned to a group of mixed races [29].
Currently, 65% of Namibia’s population are living in communal areas owned by the government, which are covering only 40% of the nation. Such is attributable to the event of caging ethnic groups into rural areas in the mid-twentieth century, by the white population [27, 30].
Since their national independence, there has been a lot of resettlement within the nation. Unfortunately, inequalities are still present. An example is the distribution and access to information differentiate among inhabitants. Namibian society records high inequalities in information, information in technology such as TV and radio, and in financial facilities and services.
Furthermore, these issues also occur in terms of natural resources, e.g., water and fish stock [31]. According to the World Population Review, only 91% of Namibia’s population has access to clean drinking water, while 65% is struggling to gain access to appropriate sanitation facilities [29].
One reason for these inequalities is the ongoing exclusive access to private farmlands by the wealthier population. In the long run, this will lead to even higher inequalities as well as challenges among demographics, environment, land distribution, and enjoyment of cultural rights [21, 28].
With a Gini coefficient of 61 out of 100—an indicator of a nation’s distribution of income, economic inequality, and wealth distribution—it again gets determined Namibia is facing high inequalities [32]. The authors Humavindu and Stage strengthen this statement, by speaking distinctly about one of the highest unequal distributions of income on the continent of Africa. Typically, the rural areas are suffering from lower income, in comparison to cities [32].
A controversial impression of Namibia’s society is indicated by Hofstede’s masculinity vs. femininity dimension. Such provides information about the internal societal interaction. Namibia indicates with its result—40 out of 100—to be a rather feminine society. This is reflected in habits such as caring about each other, solidarity, and resolution of conflicts by negotiation. According to Hofstede, social status and inequalities should be of secondary importance which contradicts the situation in Namibia [22].
Another determining index is the Gender Inequality Index (GII), which measures gender-based inequalities out of the following three dimensions: reproductive health, empowerment, and economic development. The nation achieved position 115 out of 160, which confirms gender inequality as a major problem [33, 34].
CDIS is also observing gender inequality in one of their dimensions. According to their expendable result in the Gender Equality Objective Outputs indicator—0.84 out of 1—the governmental efforts are being reflected [21].
According to Ferrant’s article “How do gender inequalities hinder development?,” gender roles and gender inequalities emerge out of culture, religion, and agricultural practices [23].
Confirmable to the issue of gender inequality is the description of gender roles within the nation. There are minor differences in describing their functions among the nine different ethnic groups, but this is summarized as men enjoy higher status in Namibian society than women. When separating into core tasks and functions, men are known as the pillar for family and house. Their characterization is strong and tough and includes tasks, e.g., the protection of their families, responsibility for providing food and income, and livestock farming.
On the other hand, women are seen as physically weak and a dispensable tool for men. Women would not be able to survive without men. Interestingly, some of the ethnic groups are connecting women as the mother of the nation but still giving them a secondary legal status by describing them as a second-class citizen or as the property of men [31].
Women’s suppression leads to governmental recognition. In Namibia’s third National Development Plan, women are empowered to play a full cultural, social, and economic role. According to Ferrant, gender equality is leading to an increasing capita per income which results in long-term advantages for national economies [1, 23].
These monitored gender roles are leading to the disqualification of women in society. This is visible in their limited access to assets, resources, technology, education, and employment.
Especially female-headed households are worse off. The usual case describes the eldest son as a decision-maker. However, these outcomes are also affecting the careers of female habitants. For instance, when applying for a profession, women have fewer chances to succeed when competing against man [31].
Alternatively, a household including male and female often leads to domestic abuse victims. As reported in the case study of Angula, the violence against women and children is still increasing in Namibia [31]. Additional evidence is given by the Perception of Gender Indicator of CDIS, stating that 38% of Namibian population thinks beating wives is fair enough when she, for instance, argues, burns the food, or denies sexual activity [21]. Outreaching, such behavior is leading to absenteeism of women, lower productivity, and collateral reduction in women’s wages. Furthermore, it creates an inferiority cycle for women in Namibian society and economy [23].
As mentioned above, gender inequalities do influence social and economic development. Ferrant confirms this statement by announcing that gender inequalities are internationally leading to a decrease of 3.4% in income per capita [23]. The empowerment and increasing wages of women are leading to a lower fertility rate and more consumption in nutrition, instead of alcohol and cigarettes [28]. That is also confirmed by growth literature, explaining that human and physical capital are the main drivers for economic growth [23].
Another example monitored in Ferrant’s article describes the impact of an increased enrolment of women. If sub-Saharan African females have the same status than Western Europe’s female, the mortality rate for children—less than 5 years—would decrease by 25% [23]. Namibia’s current mortality rate for children under 5 years is 44.2 deaths per 1000 live births [35].
Additionally, according to studies from Ainsworth, Fransen, and Over in 1998, there is a positive correlation between gender inequality and health, especially AIDS/HIV. The HIV infection rate in cities with higher gender gaps increases stronger compared to low gender gap cities [36]. Latest results from UNAIDS are displaying Namibia’s situation. There has been an estimated number of 200,000 people living with HIV and alarming 7400 new infections in the year of 2017. While the number of people living with HIV is slightly increasing, the infection rate in previous years has decreased [37].
Often the foundation for economic and cultural inequalities emerges out of a weakened education sector. In the case of Namibia, a controversial but still similar situation is present.
In previous times, the Namibian population suffered from a low level of education and unskilled workers. The government recognized this issue and destined one-fourth of its annual budget for the national education system [28]. Likewise, education became part of their new constitution in post-independent times. It reports the access and right to education should be given to all citizens [21]. These various governmental efforts have been—partly—prosperous.
The second dimension of CDIS, Education, displays Namibia’s adolescents—aged from 17 to 22—contributing in schooling by 8.4 years on average, while only 9% of them are showing less than 4 years of education [21]. The government succeeded partly because still the access and the contribution between male and female differ. Namibian women are still worse off in education than of their male counterparts [23].
Ferrant emphasizes that the level of corruption and nepotism is decreasing if the contribution of women in education increases. Women are simply less affected by such behavior compared to men [23]. Less corruption and nepotism will also influence the well-being of the next generations, the level and quality of human capital, and the nation’s economic growth in a positive way, as already stated under Section 4 [38].
Even Namibia’s high adolescence birth rate—75% out of 1000 women aged from 15 to 19 in 2016—is indicating a minor education status within the nation [32]. Such behavior influences participation in the labor market, as well as the health situation of adolescent Namibians [21].
Another example of Klaasen, Gatti, and Dollar is strengthening these statements. If a society has the same number of males and females participating in education but distributes scarce knowledge unequal, the outcome will represent more educated men that are less able than women. Furthermore, a gap in education leads to an unequal distribution of income and results in an even higher gender gap [38].
There has already been recognition among several inequality issues by the Namibian government [31]. Working against them is their objective. But how is it possible to escape the vicious cycle of inequalities or at least to raise the equality standard?
Namibian governmental efforts are reflected in the contribution of several programs and by introducing new policies. Policies, for instance, to strengthen the perception of gender equalities, education, and culture and arts, have been designed [21]. The government of Namibia is also revising their National Development Plans every 4–6 years. A focus on crucial equality and cultural aspects has been made by the governmental third National Development Plan [1]. Namibia, with these actions, is trying to bridge the gap between postapartheid and post-independent times. It hopes to get the nation on a reliable track.
As indicated in CDIS education and social participation dimensions, further efforts should be made toward the thinking of Namibian citizens [1]. Due to the horrific events in former times, e.g., decades of occupation and genocide, the Namibian majority is not able to trust each other or to respect other cultures. Mostly there is low recognition of significant benefits emerging out of their multicultural society [1]. If changing these societal perspectives to preferable ones, the equality conditions will be favored in Namibia. Such will lead to greater achievement of sustainable cultural and economic development, as described in Krugmann’s article [28].
The second out of the Triple Three governmental objectives deals with the diminishment of Namibia’s unemployment rate [2]. Due to the fact that women and the young population are affected, it is interlinked to the previous topic of inequality.
According to data from Trading Economics, the unemployment rate in Namibia has increased from 27.9–34% in the years of 2014–2016. Current numbers monitor a minor decrease to 33.4% in 2018, which is equivalent to 364,411 unemployed persons. The youth unemployment rate of 46.1% in 2018 has experienced an all-time high [32]. Adolescents between the ages of 20 and 24 are majorly affected [39].
Compared to the previous numbers, the female unemployment rate is slightly lower. Latest data from 2018 monitor the female unemployment rate achieving 24.7% [2]. This is due to a high informal market within the nation [21].
Yet, women are not the only ones suffering in the Namibian labor market. Also, certain ethnic groups experience disadvantages in the job market due to historic events, cultural habits, and different values. For instance, members of the ethnic group San, which are mainly working in the service sector, are the first ones to be fired, because of their nomadic behavior. Sylvain describes them as decentralized workers in western clothing and justifies their manners with the statement: “You can take the Bushmen out of the bush, but you can’t take the bush out of the Bushman” [30].
In addition, there is the presence of dealing with unemployment: Once Namibians are unemployed, their majority is seeking for assistance of families or friends, in order to get a new job.
An additional observation made by the Namibian Labor Survey 2016 states that the majority of unemployed citizens stays without employment for at least 1 year. This indicates whether there is a low amount of available jobs, labor institutions need to improve, or inhabitants are indolent when it comes to finding new employment [39].
There are different reasons for the current situation of Namibia. First, it is explained by the historic labor movements. At the beginning of the twentieth century, Namibia was a cattle-based society, and wealth was equivalent to a high stock in cattle. However, all of these growing herds needed to be managed. This evoked a labor migration which resulted in a higher population and later in less labor due to changing professions [40].
Second, the Culture for Development Indicator Suite describes Namibia’s tiny industry and the resulting low level of production as another reason for the present situation. The World Bank announces Namibians’ major source of income as a third reason. The minority of Namibia’s population is relying on income from employment, while the majority banks on subsidence farming, pensions, and grants [21].
One aim of the Namibian government is to promote the domestic labor market, especially for women. Their goal is to achieve a rate of 50% of female in decision-making positions, while, e.g., currently only 8% of them are working as regional councils. This rate can be traced back to the minor degree of access to the labor market, as well as the disadvantageous situation for women when competing against men [21].
The Namibia Labour Force Survey 2016 states that the unemployment rate of a nation is directly linked to its economic growth. It is explained as follows: a decreasing unemployment rate, as it is hardly visible in Namibia, is a sign for economic growth. An increasing rate, as there are in the unemployment rates of women and youth, indicates an economy that is not able to absorb the people in working age [39]. Though to the slow but almost steady economic growth (described under Section 12) which results in job creation then, there is an improvement in sight [2].
Another correlation is being made between unemployment and poverty, which arose in post-independent times and will be discussed in the following section.
The third aspect of Namibian governmental triple challenge is the fight against poverty. Poverty goes hand in hand with inequality and especially with unemployment and thereby arises the difficulty of monitoring the aspects of poverty isolated.
Namibia is reflecting poverty particularly in female-headed households, extended families, inhabitants with a low level of education, and farmworkers. When separating the occurrence of poverty into regions and economic sectors, it holds true for the rural communal land and the informal urban sector [2]. Nevertheless, which leverage yields out for Namibian inhabitants?
According to authors Humavindu and Stage and data from the World Bank, most of Namibians had to live with less than 2 USD per day in 2018 [2]. Even worse is the fact that the majority has to deal with an annual income of less than 100 USD [33, 34]. Plenty of inhabitants call upon urbanization, in the hope of better access to resources and professions, but mostly end up in worse conditions or the black market. Namibia’s cities are currently recording an annual population growth of around 5–6% [28].
Wealth and income are distributed by far unequal, as already indicated with the Gini coefficient under Section 3. This also clarifies why the World Bank classified the nation in 2019 as an upper-middle-income nation while having tremendous poverty issues [2].
In terms of the Human Development Index (HDI), which focuses on people and their capabilities by analyzing data from three different dimensions—long and healthy life, being knowledgeable, and decent standard of living—Namibia ranked itself in position 129 out of 189 countries. This refers to a medium human development, which is surprisingly higher than the average in sub-Saharan Africa [33, 34].
Another indication strengthening the country’s poverty is the small degree of domestic industry, as previously described. It causes minor economic development which results in negative outputs for domestic education, employment, and especially the nation’s poverty.
Again, the contribution of history influences the current situation. Former drastic events, as already mentioned under Section 4, are responsible for the dominant degree of poverty in Namibia. Decades of suppression, exploitation, and slaughtering native population has long-lasting effects on their self-perception. Those effects are staying within the Namibian population and do not erase with their national independence or any other progressive event. It takes time to change the social and economic thinking toward favoring the nation and a self-decision-making concept. Namibian population is currently still suffering from former times. CDIS describes that such is especially visible in the gap of tolerating, trusting, and accepting other cultures [21].
Health and nutrition are also influencing the degree of poverty within Namibia. A constant nationwide supply leads to food security, agricultural production, and higher wages, which in return has a positive impact on the degree of poverty [28].
Furthermore, the “western economic” long-term thinking is missing. Namibia scored low in Hofstede’s long-term orientation dimension—35 out of 100—which reflects a normative culture that thinks suspiciously among societal changes and that focuses on accomplishing quick results [22]. Namibian governmental efforts would be wasted if their inhabitants refuse to change. An example of the majorly nonexisting long-term thinking is described by Sylvain. In her article, she characterizes the San—besides their unfavorable economic situation—as spending all their wages on payday [30].
Efforts against poverty have been made by the Namibian government by doing it carefully and always in regard to the possible side effects, e.g., exploitation of natural resources. In compliance with Krugmann’s article, the domestic government is working against poverty in direct and indirect ways. The direct way is tackling health, education, housing, pension, and resources, while the indirect form is dealing with topics as investment promotion and taxes [28]. Hence, Namibia was able to succeed by reducing the national poverty line from 69.3% to 17.4% in the years of 1993–2016, altogether in accordance with an upward trend and stability in their economy and politics [2].
Besides the Triple Three challenge, sustainable economic growth is an additional objective that wants to be achieved by the Namibian government [28].
Humavindu and Stage are describing the current domestic economy as the combination of a modern market sector industry with farming while mainly focusing on sectors that have been successful in the past, e.g., the mining sector [33, 34]. Furthermore, the level of industrial activities remains low which represent an obstacle for sustainable economic growth. As one possible solution, the economic diversification of Namibia has been announced by Krugmann [28].
Additional assistance to achieve sustainable growth is generally given by the implementation of economic favoring policies. However, Namibia, as well as other emerging markets, is often confronted with institutional voids, which result in a lack of the governmental implementation process. One example is monitored by the government dimension of CDIS, describing issues in the implementation of Namibia’s tax policies [21]. Multinational enterprises (MNEs) are able to recognize the favorable situation as comparative advantages while the local content is remaining low. The Institute for Public Policy Research (IPPR) describes the situation as an exploitation of Namibia. To a great extent, these are fundamental obstacles in order to achieve sustainable development [41].
In accordance with the low level of industrial activities, Namibia has a relatively small economy that gets reflected in the nation’s Gross Domestic Product (GDP). The World Bank indicates an increasing GDP of 14,522 billion USD in 2018, which is projected as a minor steady growing one [2].
Namibians have a high level of consumption of foreign goods, services, and activities. This has developed a domestic economy that is highly dependent on imports and exports [2]. Hence, Namibia’s economy has reached a high degree of openness, which is visualized in the domestic GDP. More than 90% of it stems from imports and exports [33, 34]. The main imported goods are to the greatest extent represented in consumer goods, e.g., petroleum products, pharmaceuticals, plastic products, rubber, spare parts, textiles, and timber [42].
On the other hand, there are exported goods largely consisting of raw materials and semifinished goods, for instance, copper, cut diamonds, gemstones, granite, lead products, marble, uranium, and zinc. A large proportion of exported goods are represented in beef, which is mostly transferred to South Africa and the European Union [42].
The steady increasing economic growth of African emerging markets is favoring the economic situation of Namibia. Primarily responsible are their neighboring countries, such as Angola, Botswana, and South Africa [2].
Defining economic key sectors is essential for any domestic economic growth. Key sectors represent the largest amount of independence among the rest of the economy. If investing in them, the probability of economic growth will be higher than investments into several non-key sectors [33, 34].
One possibility to highlight key sectors of any economy is the use of a social accounting matrix (SAM). The matrix merges data from all international and regional transactions, as well as transfers within the target economy. The most common sectors are resulting out of these data sets, defined as key sectors. In order to process a reliable output, a vast number of national data are needed [43]. Thus, the number of SAMs of emerging markets is slightly poor. Even if an emerging market succeeded in applying a social accounting matrix, their government often will not be able to encourage or invest in the defined key sectors.
Namibia achieved its first and last SAM in 2012, determining mining, mineral processing, and manufacturing as key sectors. According to the output, the nation is highly dependent on trade as already described in its domestic GDP. Such information is important for the improvement of Namibia’s economy, in order to generate the highest possible output [33, 34].
Also relying on such information is the governmental policy-making process. Hence, the government is able to tailor policies to the needs of the domestic economy. Again, institutional voids may hinder such process, as described under Section 12.
Apart from mining, other sectors of Namibia’s economy are represented in mineral processing, governmental services, tourism, transport, logistics, agriculture, fishery, and manufacturing [33, 34].
The High Commission of the Republic of Namibia describes Namibia’s mining sector as the fourth biggest nonfuel mining sector in Africa [42]. Accessing new technologies and including seabed operations enabled the sector to achieve an annual growth rate of 11% in 2018 [2]. Namibia’s mineral resources include offshore oil, diamonds, lead, zinc, tin, silver, and tungsten. Additionally, the nation is globally known as the fifth biggest uranium producer [42].
The second-largest sector with the highest shares in Namibia’s gross domestic product is tourism [42]. In 2015, it experienced a peak of 16.5% in domestic GDP, which decreased to a contribution of 13.8% in 2017 [35]. According to Trading Economics, the number of arrivals has reached almost 1.5 million in 2016 [32].
The agricultural sector of Namibia accomplished 6.85% of GDP in 2016, while more than 70% of their population is depending on farming [32, 33, 34]. Agriculture represents the main income of Namibia’s population and includes, e.g., farming of cattle or crops. Nevertheless, there is still potential in the domestic agricultural sector which can be seen in the example of 2005 where Namibia still imported 50% of its cereals [42].
Due to Namibia’s coastline of more than 1500 km, their fishery sector is also of high importance and represents one of the tenth largest globally. Main catches are hake and horse mackerel which are mostly exported to the neighboring countries, as well as the European Union [44].
Namibia’s economy is a rather small one compared to its dimensions. According to data from the World Bank, Namibia’s population has increased to 2,448,255 million in 2018 [2]. It represents the second least populated country in the world—directly after Mongolia—with a population density of 3.13 people/km2 [29].
The number of inhabitants is still increasing and indicates a relatively young population with high unemployment rates, as already mentioned under Section 14. The young population correlates with the national median age of 21.2 years, published by the World Population Review [29]. Complementarily to the described issues of unemployment and inequalities above is the fact that only 58.5% of women and 65.3% of man participated in the labor market in 2018 [33, 34].
In former times, the Namibian economy was not dependent on singular sectors. Missionaries that arrived in southern Africa during the nineteenth century, mostly due to the geographical and political importance of the trade network Cape Colony, described Namibian communities as multi-resource shepherds. Namibians in all its ethnics have been characterized as flexible and determined, with a wide range of interrelated economic activities [40].
Thus, at the end of the nineteenth century, most of Namibia’s ethnic groups became part in a cattle-based society in which the number of cattle was a crucial sign of wealth. Consequentially, not everyone was able to grow stock organically, and raiding cattle among residents occurred. Some become wealthier, while others changed back to hunting and gathering society or suffered from starvation [40].
Additionally, trade also took place between Namibians and foreign powers. They have been trading cattle for horses, guns, ammunition, and several consumer goods, e.g., tobacco. As a result, a gun society emerged among those inhabitants who had the possibility to take part in trade. Due to repetitive years of raiding, gun owners were better off. Raiding was simply the most effective and common way to prosperity in former times [40].
In the late nineteenth century, Namibia became known as a nation rich in natural resources, e.g., uranium, vanadium, lithium, and tungsten but especially diamonds. Initially, ethnic tribes were able to gain advantages out of the economic situation by renting ox wagons to foreign missionaries [45].
The authors Bollig, Schnegg, and Wotzka are describing the phenomenon of ethnic groups establishing an intra- and inter-regional network of trade, where locals have been able to determine prices. At the beginning of the twentieth century, these golden economic ages were destroyed by colonial powers that occupied Namibia and forced its inhabitants to unfree labor and slavery [40].
The League of Nations in 1920 advocated for Namibia—at that time known as the protectorate of South-West Africa—by announcing South Africa as foreign administrator. Unfortunately, instead of administrating Namibia, South Africa made use of its power and exploited the nation. South Africa suppressed the Namibian economy, as well as their society until the national independence in the late twentieth century [27].
After all of its tumultuous history, Namibia continues facing economic and social disadvantages, for instance, the minor industry and inequalities among ethnicities.
Besides the Triple Three challenge and the aim of sustainable development, the Namibian government is trying to transform the country into an economic gateway of sub-Saharan Africa with several actions [46].
One of them is dealing with stimulating economic growth and employment. Thus, the government is trying to reach a broader scope internationally. The processes of redesigning and promotion of policies are also included, for instance, the Green Scheme program introduced in 2005. Such program is supporting the agricultural sector, in order to create more employment [42].
Additional economic weaknesses of Namibia are also pointed out by CDIS. According to Culture for Development Indicator Suite, the Namibian government should further work on the domestic level of education. Such results in higher gender equality and will lead to economic growth [21].
In order to achieve further development, CDIS also suggest investments to infrastructure, transportation, and information and communication technology (ICT). These suggestions have already been recognized and were part of Namibia’s fourth National Development Plan during 2012/2013 and 2016/2017 [1, 21].
Foreign direct investments (FDIs) are another crucial component for the development of any economy [47, 48]. The Global Economy indicates significant economic success with a continuously annual FDI of approximately 5–6% of GDP [49].
Between the years of 1986 and 2017, Namibia achieved an average FDI rate of 4.21% in GDP [49]. According to latest data from the World Bank, the nation accomplished an amount of almost 220 million USD in FDI in 2018 [2].
The Namibian government tries to increase these numbers by several incentives which favor MNEs to invest. For instance, a no-tax policy for certain machinery and special amortization plans have been introduced [46].
Furthermore, Namibia joined several programs, institutions, and trade zones: World Trade Organization (WTO), World Bank, International Monetary Fund (IMF), Foreign Investment Act, Doha Development Agenda, Export Processing Zone, and South African Customs Union (SACU).
This should lead to higher profits from trade, an increasing number of FDIs, and a greater awareness of the country [42].
Namibia’s Ministry of Industrialization, Trade and SME Development introduces further incentives. These incentives give Namibia access into the manufacturing markets of the USA, the EU, and other nations. They also promote foreign investments by allowing manufacturers to locate their operations wherever they want [46].
On the one hand, the Namibian government tries to raise its reputation on the global trade market. MNEs should invest in the nation and stimulate the domestic industry toward sustainable development. This needs to be done carefully because the current degree of industrial action is low. MNEs have high influence and could also harm their economy or even worse exploit them [28].
On the other hand, the government of Namibia needs to pay attention to its already limited natural resources. According to Krugmann, those are land, water, and fish stock. Higher rates in FDI are often attached to an increasing consumption of resources. This especially holds true for investments in already high resource-consuming sectors, e.g., fishery [28].
Therefore, the Namibian government has to diversify its economy, in order to achieve sustainable development. Krugmann mentions that it is necessary to find the balance between economic, environmental, and social objectives [28].
Hence, plenty of challenges are arising. For instance, almost all of these factors are interlinked with each other. Isolating and influencing single ones are almost impossible.
The increasing number of Namibian citizens, which is equivalent to higher participation in their economy, is resulting in an upward trend in air and water pollution. This is leading to either exploitation of scarce resources or land contamination [28].
Krugmann emphasizes that access to limited resources should be efficient, as well as restricted. In terms of water, it would result in higher import rates of water-intensive goods, e.g., tomatoes. Regarding efficient usage, recycling, reusing, and waste reduction are viable solutions to tackle this issue. Namibia is in need of a governmental strategy toward reinvesting into natural resources [28].
Unemployment and inequalities, as indicated above under Section 8, are also obstacles for sustainable development. Sylvain describes that white settlers owned 65% of Namibia’s land during colonial rule, while they have been only 8% of the nation’s total population [30].
Even though governmental resettlement took place in post-independent times, the UNESCO describes that currently the unequal distribution of land still holds true to a certain degree [50]. The difficulty arising from such is the necessity of owning land in order to have access to resources and participate in agriculture.
As Krugmann states, achieving economic diversity and sustainable development in Namibia is possible if directly supporting the poor population. This includes the promotion of education, the entrepreneurial drive, the nation’s employment options, and the agricultural sector. Additional governmental efforts in renewable resources and the creation of a dynamic industry and service sectors would boost this process [28].
Historic events are large-scale contributors in shaping the fragmented society and the slightly growing economy that Namibian is currently facing. Poor conditions majorly emerged out of former occurrences, as, e.g., colonization, genocide, apartheid, and foreign administration. Domestic residents have to deal with tremendous trust issues among different cultures and societies as well as with one of the highest inequalities worldwide.
The Namibian government evolved effective ways in order to tackle the appearing social and economic objectives. One of them is the repetitive process of designing a National Development Plan that gets replaced every 4–6 years. Thus, it is able to ensure performing flexible in their combat of occurring challenges.
According to the economic situation of Namibia, the country exhibits a minor degree of industrialization that leads to huge opportunities for multinational enterprises to stimulate the domestic economy. Simultaneously it causes threats in terms of exploitation through external interests.
Special attention needs to be paid toward the nation’s already scarce natural resources, which are mainly represented in land, water, and fish stock. Finding the balance between the sustainable economic growth and the right degree of using natural resources will remain as a national objective.
Furthermore, Namibia is supposed to develop its own socioeconomic actions on a regional and international basis, in order to strengthen their self-esteem and the Namibian identity.
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\n\nNot sure if this is the right option for you? Please refer back to the main Publish with IntechOpen page or feel free to contact us directly at book.department@intechopen.com.
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