CC BY
105
new kind of development. This trend is starting to show also in the post-industrial areas of Russia. In the long run the positive relationship between quality of life indicators may occur in the regions
of the Urals and Siberia that have the most advantageous combination of components of scientific and technological development, culture, stability, prosperity and health.
The article has been prepared with the support of RFBR grant No.1306-00008 "Formation and improvement of quality of life as a priority of socio-economic development of Russian regions.»
References
1. Abushenko, V. L., Gritsanov, A. A. (Ed.) (1998). Tsennost [Value]. Noveyshiy filosofskiy slovar [The latest philosophical dictionary]. Minsk, V.M. Skakun Publ., 798.
2. Demograficheskiy ezhegodnik Rossii [Demographic Yearbook of Russia, 2013] (2013). Federalnaya sluzhba gosudarstven-noy statistic [Federal State Statistics Service]. Available at: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/ publications/catalog/doc_1137674209312 (date of access: July, 14, 2014].
3. Demograficheskaya politika: otsenka effektivnosti [Population policy: an assessment of effectiveness]. (2008). Moscow, Ekon-Inform Publ.
4. Kolbanov, V. F. & Rybakovskiy, L. L. (Eds.) (2012). Demograficheskoye nastoyashcheye i budushcheye Rossii [Demographic present and future of Russia]. Moscow, Ekon-inform Publ.
5. Karmanov, A. V. & Kuchmayeva, O. V. (2011). Kontseptualnyye podkhody k issledovaniyu sotsialno-demograficheskikh grip naseleniya [Conceptual approaches to the study of socio-demographic groups of the population]. Voprosy statistic [Questions of statistics], 3, 28-32.
6. Reytingovoye agentstvo «RIA Reyting» (Gruppa RIA Novosti) v ramkakh sovmestnogo proekta s gaze toy «Moskovskie novosti» [Rating Agency RIA Rating» (RIA Novosti) within the framework of a joint project with the newspaper «Moscow news»]. Available at: http://www.riarating.ru/infografika/20121218/610486725.html
7. Tsentralnaya baza statisticheskikh dannykh [The Central database of the statistical data]. Federalnaya sluzhba gosudarstven-noy statistic [Federal State Statistics Service]. Available at: http://www.gks.ru/dbscripts/cbsd/dbinet.cgi.
8. Bongaarts, J. & Feeney, Gr. (June, 1998). On the Quantum and Tempo of Fertility. Population and development review, 24, 2, 271-291.
9. Lesthaeghe, R. (June, 2010). The unfolding story of the second demographic transition. Population and development review, 36, 2, 211-245.
10. Sobotka, T., Skirbekk, V. & Philipov, D. (June, 2011). Economic Recession and Fertility in the Developed World. Population and development review, 37, 2, 267-306.
11. The GoodCountryIndex, 2014. Opublikovan reyting khoroshikh strain 2014 goda [Published rating good countries in 2014]. Tsentr gumanitarnykh technology [Centre for humanitarian technologies]. Available at: http://gtmarket.ru/news/2014/06/25/6834.
Information about the authors
Kuzmin Aleksandr Ivanovich (Yekaterinburg, Russia) — Doctor of Economics, Doctor of Social Sciences, Professor, the Leading Researcher, Institute of Economics of the Ural Branch of the Russian Academy of Sciences (29, Moskovskaya str., Yekaterinburg, 620014, Russia, e-mail: [email protected]).
Tukhtarova Yevgeniya Khasanovna (Yekaterinburg, Russia) — Leading Economist, Institute of Economics of the Ural Branch of the Russian Academy of Sciences (29, Moskovskaya str., Yekaterinburg, 620014, Russia, e-mail: [email protected]).
Ilinbayeva Yekaterina Aleksandrovna (Yekaterinburg, Russia) — Economist, Institute of Economics of the Ural Branch of the Russian Academy of Sciences (29, Moskovskaya str., Yekaterinburg, 620014, Russia, e-mail: [email protected]).
UDC 339.13
G. Betti, F. Gagliardi, V. Salvucci
MULTIDIMENSIONAL AND FUZZY MEASURES OF POVERTY AT REGIONAL LEVEL IN MOZAMBIQUE1
This study provides a step-by-step account of how fuzzy measures of non-monetary deprivation and also monetary poverty may be constructed at the regional level, based on the Mozambican Household Budget Survey 2008-09 (IOF08). To our knowledge, this is the first attempt to apply Fuzzy Set Theory to poverty measurement in Mozambique.
1 © Betti G., Gagliardi F., Salvucci V. Text. 2014.
The dataset we used is the most recent budget survey available for Mozambique and it is representative of the national, regional (North, Centre, South), provincial and urban/rural level. In order to construct a Fuzzy Set index of poverty, monetary as well as non-monetary indicators are considered, and two different measures of deprivation are subsequently constructed: the Fuzzy Monetary (FM) and Fuzzy Supplementary (FS).
Keywords: poverty, regional measures, fuzzy sets, Mozambique
1. Introduction
Mozambique is among the poorest countries in the world, with a per capita income level of approximately $428, ranking 197 out of 210 countries (World Bank, 2010). After the end of the civil war in 1992, Mozambique underwent a process of sustained growth and poverty reduction that led the country to be considered as a success story by the World Bank and international donors (World Bank, 2008).
Nevertheless, poverty levels remain very high and poor living conditions are widespread throughout the country. The process of poverty reduction has been deeply monitored and analysed by three official national assessments (MPF, 1998; MPF, 2004; MPD-DNEAP, 2010) and several other studies by both Mozambican and international analysts (Hanlon, 2007; Castel-Branco, 2010; Ossemane, 2010; Van den Boom, 2011).
What emerges from the three main household surveys conducted in the 1996-2008 period and from other field-specific surveys, is that Mozambican citizens substantially improved their situation with respect to some non-monetary dimensions: access to education and health services, household asset ownership, and quality of housing. On the other hand, monetary poverty remained fairly stable between 2002 and 2008: the Head Count Ratio slightly increased from a value of 54.1 % in 2002-03 to 54.7 % in 2008-09. However, it is important to note that this stabilisation followed a sharp fall from its previous (69 %) levels in 1996-97.
In the Third National Poverty Assessment (MPD-DNEAP, 2010) an analysis of both monetary and non-monetary poverty is outlined. The Mozambican Government and international donors invested considerably in reducing non-monetary poverty. In particular, education and health are considered key intervention areas, and progressively more people have been granted access to schools and health facilities in urban as well as rural areas (Chao and Kostermans, 2002; Government of Mozambique, 2005; Republic of Mozambique, 2006). Nonetheless, monetary poverty did not decrease between 2002 and 2008. At provincial level, the Southern provinces and some of the rural areas in the North experienced a sharp fall in their Head Count Ratio,
while Central regions witnessed an increase. Nationwide, rural poverty increased from 55.3 % in 2002-03 to 56.9 % in 2008-09, whereas urban poverty decreased from 51.5 % to 49.6 % in the same period.
For non-monetary dimensions, each of the three dimensions considered (housing conditions, ownership of durable goods, and access to public goods and services) is separately compared in 2008-9 with the same dimension six years before but without computing a general composite welfare indicator. The results indicate that on average housing conditions improved between 2002 and 2008, though differences at sub-national level remain high nowadays. Ownership of durable goods also improved: the percentage of households owning a radio, a TV, a fridge, a mobile, a telephone, a car, and a bike or motorbike increased by 5.7 points. Turning to the access to public goods and services, it emerges that access to education peaked such that in 2008-09 more than 76 % of all children aged 6-13 were attending school which reflects a big jump if compared to a figure of 66.8 % in 2002-03. Moreover, geographic inequality in access to education decreased over time and access to health facilities improved. At the same time, other non-monetary dimensions of deprivation did not improve in a substantial way: access to safe water and chronic malnutrition, for example, remained more or less stable1.
The present paper intends to give more information to policy makers about poverty situation in the country; this is done by estimating fuzzy multidimensional measures at both national and regional level. The work proceeds as follows: in Section 2 we illustrate the concept of multidimensional poverty, as well as the Fuzzy Set technique and its application to poverty estimation. In Section 3 we introduce the dataset that is used throughout the study, while in Section 4 we set out the empirical analysis and the resulting poverty estimates at the regional level. Section 5 concludes.
1 Chronic malnutrition (stunting) is still suffered by 46.4 % of under-five children, which is among the highest percentages in the world (WHO, 2011).
2. Multidimensional poverty and fuzzy set theory
In order to understand poverty and social exclusion, it is necessary to consider deprivation simultaneously in different terms (i.e. as low income as well as different non-monetary aspects of deprivation). The need to adopt a multidimensional approach has been noted, among others, by Kolm (1977); Atkinson and Bourguignon (1982); Maasoumi (1986); Tsui (1995); Sen (1999). Moreover, the multidimensional nature of poverty is a widely recognised fact, not only by the international scientific community, but also by many official statistical agencies (e.g. Eurostat, Istat) as well as by international institutions (United Nations, World Bank).
In the present work we go beyond the conventional study of poverty based simply on the poor/ non-poor dichotomy defined in relation to a chosen poverty line. Instead, poverty and multidimensional deprivation are treated as matters of degree based on the individual's position in the distribution of income and other aspects of living condition. State of deprivation is thus seen in the form of 'fuzzy sets' to which all members of the population belong yet to varying degrees. This fact brings with it more complete and realistic view of the phenomenon but also an increased complexity at both the conceptual and the analytical levels.
A number of authors have applied the concepts of fuzzy sets to the analysis of poverty and living conditions (Chiappero Martinetti, 1994; Vero and Werquin, 1997, inter alia). Our application is based on the specific methodology developed by Cerioli and Zani (1990), Cheli (1995), Cheli and Lemmi (1995), Cheli and Betti (1999), Betti et al. (2002), Betti et al. (2004), Betti et al. (2006), Betti and Verma (2008).
Under the so-called traditional approach, poverty is characterized by a simple dichotomization of the population into poor and non poor defined in relation to a chosen poverty line, z. This approach presents two main limitations: firstly, it is unidimensional (i.e. it refers to only one proxy of poverty, namely low income or consumption expenditure), and secondly it reduces the population to a simple dichotomy. However, poverty is a much more complex phenomenon that is not formed solely of its monetary dimension but must also take account non-monetary indicators of living condition. Moreover it is not an attribute that characterises an individual as being either present or absent, but is rather a difficult to define predicate that manifests itself in different shades and degrees.
The fuzzy approach considers poverty as a matter of degree rather than an attribute that is simply present or absent for individuals in the population. In this case, two additional aspects have to be introduced:
1. The choice of membership functions (m.f.), i.e. quantitative specifications of individuals' or households' degrees of poverty and deprivation;
2. The choice of rules for the manipulation of the resulting fuzzy sets.
The traditional approach can be seen as a special case of the fuzzy approach, where the membership function may be seen as = 1 if y < z,
= 0 if y > z, where y is the income of individual i and z is the poverty line.
An early attempt to incorporate the concept of poverty as a matter of degree at methodological level was made by Cerioli and Zani (1990) who drew inspiration from the theory of Fuzzy Sets initiated by Zadeh (1965). Subsequently, Cheli and Lemmi (1995) proposed the so called Totally Fuzzy and Relative (TFR) approach in which the m.f. is defined as the distribution function F(y) of income, normalised (linearly transformed) so as to equal 1 for the poorest and 0 for the richest person in the population.
2.1 Income poverty: the Fuzzy Monetary (FM) measure
In the present study we make use of a fuzzy monetary indicator as found in Betti et al. (2012). The proposed FM indicator is defined as a combination of the (1 - F(M) i) indicator, namely the proportion of individuals richer than individual i (Cheli and Lemmi, 1995), and of the (1 - L(M) i) indicator, namely the share of the total income received by all individuals richer than individual i (Betti and Verma, 2008). Formally:
ц = FMt =(l - F(M). )a-1 (l - L( m ).
Z WY 1 Уу > y,
Y=,+1
n
Z wY 1 Уу > У1
v Y=2
Z Уу 1 Уу > У
y=,+1
n
Z wy Уу 1 Уу > У1
/ v y=2
, (1)
where y is the income, F is the income distri-
-'y ' (M), i
bution function, wy is the sample weight of individual of rank y (y = 1,—, n) in the ascending income distribution, L(M) i represents the value of the Lorenz curve of income for individual i. The parameter a is estimated so that the overall FM indicator (which is calculated simply as the weighted mean of the individual FM), is equal to the Head Count Ratio computed for the official poverty line.
2.2 Non-monetary poverty: the Fuzzy Supplementary (FS) measure
In addition to the level of monetary income, the standard of living of households and individuals can be described by a host of indicators, such as housing conditions, possession of durable goods, health conditions, education, perception of hardship. To quantify and put together diverse indicators of deprivation several steps are necessary. In particular, decisions are required for assigning numerical values to the ordered categories, weighting the score to construct composite indicators, choosing their appropriate distributional form and scaling the resulting measures in a meaningful way.
First, from the large set which may be available, a selection has to be made of indicators which are substantively meaningful and worthwhile for the analysis of interest. Secondly, it is useful to group different indicators into statistical components (or dimensions) in order to reduce dimensionality. Whelan et al. (2001) suggest, as a first step in an analysis of life-style deprivation, to systematically examine the range of deprivation items to see whether the items cluster into distinct groups. Factor analysis can be used to identify such clusters of interrelated variables. To quantify and put together diverse indicators several steps are necessary.
1. Identification of items;
2. Transformation of the items into the [0, 1] interval;
3. Exploratory and confirmatory factor analysis;
4. Calculation of weights within each dimension (each group);
5. Calculation of scores for each dimension;
6. Calculation of an overall score and the parameter a;
7. Construction of the fuzzy deprivation measure in each dimension (and overall).
Aggregation over a group of items in a particular dimension h (h = 1, 2, ..., m) is given by a
weighted mean taken over j items: shi = h' hj,z
I
■ Sh
I
w,
hj
I s
m
(2)
FS=(1 - f(s), ;)a - 1 a - w. (3)
As for the FM indicator, the estimates of a is determined so as to make the overall non-monetary deprivation rate (which is calculated simply as the weighted mean of the individual FSi) numerically identical to the Head Count Ratio computed for the official poverty line. The parameter a estimated is then used to calculate the FS indicator for each dimension of deprivation separately. The FS indicator for the h-th deprivation dimension and for the z'-th individual is defined as combination of the (1 - F(S) hz) indicator and the (1 - L(S), hz.) indicator.
V-u = FShi =(1 - f(s ),hi )a 1 (1 - l( s ),hi ) = "ia-1 r „
I
w.
=z'+1
hy ' hy
> su
I why 1 shy
Y=2
h = 1,2,..
> s,
I
Y=Z +1
why shy
s,„ > s,
I why shy
shy > sh
,m; i = 1,2,
,n;
Vh,
= 0.
(4)
where whj is the weight of the j-th deprivation variable in the h-th dimension. An overall score for the -th individual is calculated as the unweighed mean:
The (1 - F(S) hi) indicator for the z'-th individual is the proportion of individuals who are less deprived, in the h-th dimension, than the individual concerned. F(S) hi is the value of the score distribution function evaluated for individual in dimension h and why is the sample weight of the z'-th individual of rank y in the ascending score distribution in the h-th dimension.
The (1 - L(S) hz) indicator is the share of the total lack of deprivation score assigned to all individuals less deprived than the person concerned. L(S) hi is the value of the Lorenz curve of score in the h-th dimension for the z'-th individual.
As for the Fuzzy Monetary and the Fuzzy Supplementary indicators, the overall index corresponding to each dimension FSh is calculated simply as the weighted mean of the individual FSh. Here it is interesting to note that the overall ranking of the FS indicator cannot directly be obtained from the rankings in each dimension; however, the ranking obtained with FS is consistent with the ranking obtained from FShz1
3. Data
The dataset used in the study is the Mozambican Household Budget Survey 2008-09
Then, we calculate the FS indicator for the z'-th individual over all dimensions as:
1 A possible alternative definition of the overall Fuzzy Supplementary indicator could be the simple average of the corresponding indicators. An advantage would be that the over-
all indicators would fulfill consistency properties with respect to decomposition (Chakravarty et al., 1998; among others). A
drawback would be that the weighted mean of the individual would not be equal to the Fuzzy Monetary and the Head Count Ratio indicators.
hz
h=1
(IOFO8) (Inquerito aos Agregados Familiares sobre Ornamento Familiar 2008-09), a nationally representative household survey conducted by the National Institute of Statistics (INE). The IOFO8 was conducted from August 2008 to September 2009. The survey has a stratified structure with three steps of selection: i) selection of the primary sampling units (PSUs), ii) selection of the enumeration areas1 within the PSUs, and iii) selection of the households within the enumeration areas. Twenty-one strata were constructed, one for each urban/rural sample of the 11 provinces of Mozambique (the province of Maputo City does not have a rural area). The IOF08 has a sample size of 10,832 households and it is representative at the national, regional (North, Centre, South), provincial and urban/rural level. The survey includes information on general characteristics of the individuals and of the households, on daily, monthly and durable goods final consumption expenditures, own consumption, transfers and gifts. Supplementary information for the IOF08 can be found in (INE, 2010; MPD-DNEAP, 2010).
Concerning socioeconomic status, we use data on (real) per capita daily consumption, available from the I0F08. Such variable is used by the Government for official analyses of poverty which makes our results immediately comparable to existing ones. This measure of income also considers the inflation that occurred during the implementation of the surveys, the different values of the Metical — the Mozambican currency — in different periods of the year, and spatial differences in price levels among different provinces and rural/urban areas.2
In order to compute a measure for non-monetary poverty we use information on ownership of durable goods, housing quality, health status and education level.
3.1 Problems with the data The IOF08 is a very rich and detailed dataset. It has been carefully designed and implemented, to the effect of providing reliable information and statistical results. Nonetheless, a few problems with the data were encountered while conducting analysis on multidimensional poverty. In particular, it was found that sampling weights were not calibrated at the household level following a non-response or other problems occurred in the surveying process. Moreover, such weights ranged
1 An enumeration area (EA) represents the area assigned to each enumerator for distributing questionnaires to households and it is the smallest building block of the geographical frame for the Mozambican Household Budget Surveys.
2 This is the same methodology used in official analyses of poverty.
from 54.6 to 93,452.2, and as a result of this, a few households with very high weights significantly influenced statistical results.
In terms of household real consumption — the variable used to assess socioeconomic status — we conformed to official analyses that divide it by the number of household members, and on the basis of such a variable we estimated poverty rates. However, this overlooks issues of intra-household allocation of resources and economies of scale, which might considerably matter when dealing with poverty estimates in a country whose average household size is approximately made up of six members. Indeed, the Head Count Ratio computed dividing household consumption per adult equivalent produces very different poverty estimates where the percentage of poor was 36.8 % vs. 54.69 %.
4. Empirical analysis and results
In this section we describe the steps involved in the measurement of multidimensional poverty in Mozambique at national, provincial and urban/rural level, as outlined in previous sections. This is followed by an analysis of the results for Mozambique's monetary poverty and the different dimensions of non-monetary deprivation.
As introduced in Section 3, the Fuzzy Monetary measures, FM, are based on a household's real consumption divided by its size. Real consumption is obtained by taking into account regional differences in price levels, inflation and seasonal fluctuations. In order to obtain FM, we need to take into account both the proportion of households richer than each particular household and the cumulative share of consumption such richer households receive. Finally, the resulting distribution is transformed such that its mean is equal to the Head Count Ratio: this ensures comparability between the two measures and the two approaches, namely the traditional and the multidimensional one.
As for the Fuzzy Supplementary measures, we use information about thirty-two basic items, as described in Section 3. The deprivation dimensions are initially determined using an exploratory factor analysis: this procedure permits to describe the variability among observed variables — our basic items — in terms of a lower number of unobserved, uncorrelated variables, which are called factors. In the exploratory factor analysis the observed variables are expressed as a linear combination of the underlying factors, without any a priori assumption about the factor structure.
The results of the exploratory factor analysis are then calibrated according to the literature and
to the experience acquired during the fieldwork in Mozambique.
Finally, a confirmatory factor analysis was performed by imposing a priori assumptions on the underlying factor structure. This allowed us to test whether the proposed calibration of initial items into a lower number of dimensions made statistical sense.
After these preliminary steps, thirty-two basic indicators were grouped into six dimensions, roughly corresponding to: i) housing conditions;
ii) more widespread and affordable durable goods;
iii) less common, more expensive durable goods;
iv) housing quality; v) income-related deprivation; vi) health and education.
For what concerns the aggregation of different indicators in each single dimension, a weighting procedure was carried out, as described in Section 2. Depending on the distribution of each indicator in the population and its correlation with other indicators in the same dimension, we constructed item-specific composite weights with equal value for all households in the population. The item-specific weights, W, are composed of two parts: Wa,, which is an inverse function of the percentage of people deprived in item j, and Wb, an inverse function of the correlation between item j and all the other items in the same dimension. For each dimension we have that W. = Wa. x Wb..
Intuitively, the first component of the weights, Waj, takes into account that if a high percentage of people possess j, then the few who do not possess j are very deprived; the second component, Wbj, tries to achieve parsimony assigning a lower weight to items that are highly correlated in the same dimension (e.g. high-quality walls and high-quality roof in the 'housing conditions' dimension).1
The result is the identification of six different fuzzy supplementary measures, one for each dimension: FS1, FS2, FS3, FS4, FS5, FS6. Subsequently, we aggregate the different non-monetary dimensions into a single composite Fuzzy Supplementary poverty indicator, FS. This is done by assigning equal weights to each supplementary dimension, based on the assumption that all dimensions are equally important in determining supplementary deprivation. The resulting FS distribution is also scaled so that its
1 This weighting system has been officially adopted by Eurostat (2002) in the 2nd Social Report on Income, Poverty and Social Exclusion for comparative analysis. It has been widely adopted also in other fields; for instance Aassve et al. (2007) have studied the effects of marital disruption on economic well-being, while Betti et al. (2011) have constructed a fuzzy indicator of educational mismatch for university graduates.
mean is equal to the Head Count Ratio, as we did for the monetary poverty indicator, FM. The resca-ling ensures that the traditional and the fuzzy indicators are comparable.
4.1 Poverty estimates at national level
As previously outlined, the overall Fuzzy Monetary (FM) and Fuzzy Supplementary (FS) dimensions are constructed such that their mean is equal to the official Head Count Ratio so they do not convey additional information to our analysis at national level (Head Count Ratio = FM = FS = 54.69 %). Hence, in this subsection we only focus on the values of the supplementary dimensions FS1-FS6. From Figure 1, it can be seen that the factor with highest level of deprivation is FS3 which corresponds to less common, expensive durable goods. Most Mozambicans do not possess any of the items included in this dimension and a level of deprivation of about 0.75 is thus reasonable. Conversely, the deprivation value for less expensive durable goods (FS2) is lower, showing that some durable goods — especially mobile phones and bikes — are becoming more common in the country.
The level of deprivation for housing conditions (FS1) is also very high (0.53), and reflects the fact that many households lack basic facilities in their dwellings. Even so, the proportion of households lacking decent household quality (FS4) is significantly lower (0.31). Income-related deprivation (FS5) appears to be relatively low: this result is probably influenced by the inclusion of a dummy for whether someone in the household had a job (formal or informal) or not. Since most of the households interviewed (about 98 %) had a member with a formal or informal job, the entire dimension was pushed towards low levels of deprivation (0.12). When this variable is eliminated from the FS5 dimension, the average deprivation raises significantly (0.64). This was then taken into account in the following analyses.
Finally, the result for health and education (FS6) shows that education and health conditions in Mozambique are improving. However, one needs to be warned that the relatively low average value of deprivation for this dimension (0.32) is likely to be affected by the low level of deprivation characteristic of chronic illnesses and ability to read and write. Indeed, the level of child malnutrition in Mozambique is still among the highest in the world (WHO, 2011).
4.2 Poverty estimates at provincial level
When fuzzy set poverty analysis is carried out
at sub-national level then it becomes evident how the inclusion of multiple dimensions substantially increases the amount of available information.
Mozambique
Fig. 1. Deprivation by dimension, national level
IHCR ■ FM FS
Fig. 2. Head Count Ratio (HCR), Fuzzy Monetary (FM) and Fuzzy Supplementary (FS), by province
Mozambique is divided into eleven provinces1. These territories are quite heterogeneous with regard to economic development, culture, ethnic and linguistic composition. Consequently, huge differences in poverty rates exist among different zones and provinces in Mozambique. Even though some
1 The eleven provinces of Mozambique are grouped into three bigger zones: the North, which includes the provinces of Niassa, Cabo Delgado and Nampula; the Centre, with the provinces of Zambezia, Tete, Manica and Sofala; the South, containing the provinces of Gaza, Inhambane, Maputo Province and Maputo City.
insights emerged from the official Head Count reports, the multidimensional analysis of poverty we undertook using Fuzzy Set Theory allows us to highlight important characteristics that would otherwise go unnoticed in a traditional poverty assessment.
In particular, by looking at the Fuzzy Monetary (FM) and Fuzzy Supplementary (FS) statistics presented in Figure 2 it appears striking that some of the provinces with low rates of monetary poverty are also much more deprived in other dimensions, and the converse is also true. The Northern
provinces (Niassa, Cabo Delgado, Nampula) and the Central province of Tete, all have much higher Fuzzy Supplementary (FS) averages with respect to their Fuzzy Monetary (FM) ones. The other Central provinces (Zambezia, Manica and Sofala) have similar statistics in both the FM and FS dimension, while the Southern provinces show FS averages that are lower than their respective FM averages.
The analysis of Fuzzy Supplementary dimensions indicates that the South is generally more developed than the Centre and the North, with Maputo City being much less deprived than all other provinces. These characteristics remained hidden using the standard poverty Head Count analysis. This is probably due to various causes: first, consumption is highly dependent on temporary and/or seasonal fluctuations — e.g. a bad harvest in 2008, — while other dimensions as those included in the computation of the Fuzzy Supplementary statistics are more robust to such changes. Indeed, buying an asset, a durable good or investing in education requires an evaluation of a household's economic status that is only partially related to the level of income/consumption in a given year. Moreover, a large part of the Mozambican population has consumption levels that are close to the poverty line, hence even small fluctuations can alter the poverty Head Count statistics in a substantial way. This is one of the main drawbacks of using a dichotomous index like the Head Count Ratio for the analysis of a complex phenomenon such as poverty. In fact, poverty Head Count analyses based on Mozambican Budget Surveys generally yielded strange or non-robust results, with strong fluctuations in the Head Count Ratio and re-ranking of poor and rich provinces (Van den Boom, 2011; pp. 7-8).
A deeper investigation into supplementary factors yields additional results. As for housing conditions (FS1), we can identify three distinct groups of provinces on the basis of their FS1 averages: the Central provinces (Zambezia, Tete, Manica and Sofala) and the province of Nampula are the most deprived in this dimension with an average of about 0.60 for Nampula, Manica and Sofala but roughly 0.70 for Zambezia and Tete. In the second group, with an average deprivation of about 0.40, we find two Northern provinces (Niassa and Cabo Delgado) and two Southern provinces (Gaza and Inhambane). Finally, the least deprived provinces are again Maputo Province and Maputo City, the latter with an average level of deprivation of 0.03.
In the FS2 dimension we put together some durable goods that are more widespread than others, like mobile phones, bikes and motorbikes, ra-
dios, watches and TVs. Indeed, most provinces show similar average levels of deprivation in this dimension, ranging within the 0.44-0.55 interval where Nampula is the most and Niassa and Manica the least deprived with 0.60, 0.36, and 0.33 scores respectively.
The FS3 dimension, instead, consists of those durable goods that are less affordable and thus less common among Mozambicans such as cars, fridges or freezers, irons, computers, printers, other tools, and sewing machines. As evidenced in the analysis at national level, this is the factor for which average levels of deprivation are highest. This is particularly true in the North and in the Centre, where five provinces (Niassa, Cabo Delgado, Nampula, Zambezia and Tete) have average values that exceed 0.80, in contrast to other Central provinces (Manica and Sofala) with values around 0.75 which perform a little better. Once again, the Southern provinces of Gaza, Maputo Province and Maputo City have much lower deprivation levels with scores 0.52, 0.43, and 0.28 respectively, confirming the finding that Southern provinces are less deprived than Northern and Central provinces in various dimensions.
As for access to safe water, energy sources for cooking, in-house lighting and the likes — included in the FS4 dimension, — we find that the average level of deprivation is relatively low. For Northern and Central provinces it ranges between 0.28 for Sofala and 0.41 for Niassa, while all Southern provinces perform comparatively better.
As presented in Subsection 4.1, the FS5 dimension (income-related deprivation) is the one for which average levels of deprivation are lowest. In this case, there are no noticeable differences between provinces. However, when the variable "formal or informal job" is taken out, then it emerges that there is a group of provinces including Manica, Sofala, Maputo, and Maputo City, with average deprivation values between 0.40 and 0.50 whilst all other provinces perform comparatively worse with values of around 0.65-0.75.
Finally, the last supplementary dimension (FS6) takes into account education measured as education level and ability to read and write and health measured as child malnutrition and chronic illnesses. In this case, Maputo Province and Maputo City record an average level of 0.120.15, while the estimated values for other provinces range between 0.28 for Sofala and 0.38 for Zambezia, which amounts to more than twice the level of deprivation of the two most Southern provinces.
What this subsection makes clear is that the analysis of dimensions other than consumption
■ ' < 1 Gaza ■ liihambane 13 Mapuîo Pr MapuîoCid
South
Note: FS1 = housing conditions; FS2 = more widespread and affordable durable goods; FS3 = less common, more expensive durable goods; FS4 = housing quality; FS5 = income-related deprivation; FS6 = health and education. Fig. 3. Fuzzy Supplementary dimensions (FS1-FS6), by region and province
North Centre South
Note: HCR = Head Count Ratio; FM = Fuzzy Monetary; FS = Fuzzy Supplementary; FS1 = housing conditions; FS2 = more widespread and affordable durable goods; FS3 = less common, more expensive durable goods; FS4 = housing quality; FS5 = income-related deprivation; FS6 = health and education. Fig. 4. Deprivation by dimension and province
substantially improves the mapping of provincial differences regarding poverty. In particular, the higher level of development of the Southern provinces distinctly surfaced in more than one dimension (FS, and particularly FS1, FS3, FS4, FS6). At the same time, understanding which factors are most influential upon deprivation, yields a deeper insight about which characteristics are more unequally distributed throughout the country.
While Figure 2 presents the Head Count Ratio, Fuzzy Monetary and Fuzzy Supplementary averages for all provinces, which highlights the differences that exist between monetary and overall
non-monetary deprivation, in Figure 3, instead, all the different supplementary dimensions are shown, divided by region and province. In Figure 4 both the monetary and individual non-monetary dimensions are shown for each region and province on a net graph. This kind of graph provides additional information about the overall condition of each province compared to other provinces in the same region.
Finally, Figure 5, which reports maps of deprivation by dimension, is particularly informative since it allows a comparison of all provinces in all dimensions, and reveals the gap between the
HCR FM FS
Note: HCR = Head Count Ratio; FM = Fuzzy Monetary; FS = Fuzzy Supplementary; FS1 = housing conditions; FS2 = more widespread and affordable durable goods; FS3 = less common, more expensive durable goods; FS4 = housing quality; FS5 = income-related deprivation; FS6 = health and education. Fig. 5. Maps of deprivation, by dimension
UJ
0
1
o
> "D
0
1
>
NJ O
Table
Deprivation by dimension, provincial and urban/rural level
Mo: ambique Niuia Cabo Dt ¡gado Nimputa Zambtl u Tttt
urban rural urban rural urban rural urban rural urban rural urban rural
Ht id Count Ratio 0.4«: 0.5691 0.4224 0.2392 0.4429 03552 0.459 0 566- 0.6362 0.718 0.5302 0.4015
SE 0.0164 05170 0j0512 05383 0.0866 0.0382 05543 05343 0.0680 05331 05796 0.0502
mtinFM 0i04; 0.Í6S6 0,4664 0.3393 0.4922 0,4239 0íl05 0.560^ 0J9SÍ 0.6503 0.5462 0.4SOS
SE 0.0114 0.0 IOS 0.0221 053C0 0.0583 0.0226 05400 05204 0.0485 05243 0.04-3 0,0309
mean FS 0.33« 0.63*8 0,383" 0.6165 0.4358 0,623 0.4901 0.7072 0.4557 0.6743 0.479 0,6926
SE 0.0103 0,0067 0.0313 05301 0.0387 0.0191 05312 05155 05457 05119 0,0*09 0,0190
m tu FS1 0.259$ 0.6545 0.3521 0.5357 0.3099 0.4533 0.4646 0.-063 0.4923 0.7756 0.4927 0.75SS
SE 0.0113 O.OOM 0.0269 05359 05232 0.0233 05325 05160 05543 05147 0.0953 0.0274
m tin FS2 0.487 0.5119 0.3531 0.3639 0.5145 0.5183 0Í 847 0.602 0.4285 0.5063 0.5599 0Í 053
SE 0.0117 0.0087 0.0232 052Í7 0.0527 0.0227 05437 05159 0527S 05159 0.0497 0,0023
mean FS3 0Í 24 8 0.8421 0.65S6 0.3822 0.6328 0.8 862 0.7224 09333 0.7072 0.S5S1 0.64S1 0Í191
SE 0.0133 0.0095 00334 05240 05675 0.0192 053S5 0.0113 0.0484 05319 0.077? 0.0161
m tan FS4 0.1317 0.3848 0.1711 0.477? 0.1951 0.4253 0.1637 0 3953 0.184 0.3763 0.1743 0.432
SE 0.0119 0.0136 0.0475 05493 0.0434 0.0418 05454 05357 05451 05230 05500 0.0*10
m tan FS5 0,107 S 0.U95 0.1069 0.1221 0.1251 0.1099 0.1205 0.133 0.1064 o.i m 0.0366 0.1199
SE 0.0033 Q.0G24 0:0103 050*3 05127 0.0064 05092 05046 0512? 05072 05136 0.0075
mean FS6 0.2135 0.3699 0.2355 0.3321 0.2972 03969 02951 03945 0 2769 0.4039 0.2978 03525
SE 0.2135 03699 05221 0517S 05232 0.0204 05161 05164 05349 05262 0.0371 0.0160
n 5222 5609 384 430 240 540 570 1005 336 1187 192 576
Manica Sofala Caía Inhambant Maputa Fr. MapitoCid.
urban rural urban rural urban rural urban rural urban rural urbau
Ht ad Count Ratio 0.4Í37 0.5828 0,5005 0.6293 0.4049 06267 0.4S36 06734 0.6366 0,7633 03615
SE 0.0505 05543 0.0721 05643 0.0429 0.0592 05612 05376 05276 05502 0.0239
mtanFM 0.4818 0.5864 0.4865 0.6197 0.44 59 0.5786 0Í 059 06533 0Í914 0.6649 04054
SE 0.CS22 05314 0.0474 05535 0.0268 0.0412 05427 05277 05195 05303 0.0182
mtanFS 02888 0.6111 0.2849 0.6751 0.2969 0.5045 03566 0.469 02401 0.43 0.1672
SE 0.0255 0.0264 OJ031S 05362 0.0269 50276 05545 05275 05134 05427 0.0066
m tan FS1 02387 0.7322 0.2507 0.8436 0.185 0.4609 0.1875 03525 05584 0.2743 05323
SE 0.0914 0.0363 05404 05279 05276 0.0361 05534 05357 05058 055 H 0.0029
m tan FS2 03Í91 0.3263 0.3906 0.4639 0.4695 0Í 317 0Í061 0Í 738 0Í 049 0.5902 0Í031
SE 0.0620 05230 05334 05636 0.0325 0.0287 05271 0.0288 0.0244 05520 0.0137
m tan FS3 0.4919 0.823 0.5364 0.8331 0.4025 0Í 494 0Í6 0/S752 0 3523 0.5953 02763
SE 0.0350 C.03Q5 0.0488 05235 0.0365 0.0357 05466 05355 05219 05616 0.0159
m tan FS4 0.0615 0.4509 0.0414 0.4196 0.1017 02327 0.1953 02272 0.198* 0.3213 05327
SE 0.0234 0.0519 05109 05533 0.0220 0.0348 05903 05496 0.0268 0.1053 0.0042
m tan FSÍ 05775 0.0955 0.0989 0.093* 0.1164 0.1357 0.1285 0.1381 0.1096 0.0976 059*9
SE 0.0069 0.00*72 0.0147 05030 05076 50050 05039 05050 05082 05151 0.0069
mtan FSi 02123 0.3311 0.1703 0.3435 0.218Í 03567 02499 03099 0.124S 0.2145 0.1234
SE 0.Q22S 0.0195 05216 05357 0.0265 0.0382 05260 05200 05151 05330 0.0CG7
n 336 468 527 324 336 467 382 432 720 ISO 1199
Note: standard errors (SE) are computed
using Jackknife Repeated Replication (JRR) in the form proposed by Verma and Betti (2011).
Centre-North and the South for supplementary dimensions of deprivation as clearly evident. 4.3 Multidimensional poverty estimates by province and area of residence (urban/rural)
In what follows, we present multidimensional deprivation as estimated by province and by area of residence (urban/rural). The huge differences in poverty estimates that exist between urban and rural areas at both national and sub-national level in Mozambique were already introduced in Section 1. Nonetheless, unexpected results emerge from the analysis of supplementary dimensions of deprivation (Table 1): when these are introduced, the urban/rural deprivation gap widens substantially, contrasting with the official analyses based on consumption that estimate a differential of about seven percentage points. Indeed, at national level the Head Count Ratio of rural and urban areas for 2008-09 is about 56.9 % and 49.6 %, respectively (MPD-DNEAP, 2010).
However, when supplementary dimensions of deprivation are considered, a different picture also emerges for Mozambique. The aggregated Fuzzy Supplementary (FS) deprivation level for urban areas is 0.34, whereas the one for rural areas exceeds 0.64. Such difference is due to the urban/rural gap found in the underlying supplementary dimensions. In particular, housing conditions (FS1), possession of less common, more expensive durable goods (FS3), housing quality (FS4) and — to a lesser extent — health and education (FS6) all show very different deprivation levels for urban and rural areas.
For housing conditions (FS1) the urban deprivation level is 0.26, the rural one as high as 0.65. For more expensive durable goods (FS3) they are equal to 0.52 and 0.84 respectively. The values for the housing quality dimension (FS4) are 0.13 for urban areas and 0.38 for rural areas while those for the health and education dimension (FS6) are 0.21 for urban and 0.37 for rural. Much smaller differences exist in the more widespread durable goods (FS2) and income-related (FS5) deprivation dimensions.1 The wide deprivation gap between urban and rural areas typical of most supplementary dimensions at national level is also reflected at the provincial one.
The central regions of Manica and Sofala exhibit the greatest difference between supplementary deprivation values in urban and rural areas. In the supplementary dimensions FS1 (housing conditions), FS3 (more expensive, less affordable du-
1 Again, the urban/rural difference increases for dimension FS5 when the variable "formal or informal job" is not considered: in this case the average urban deprivation becomes 0.51, while the rural becomes one 0.70.
rable goods) and FS4 (housing quality) such difference is conspicuous, ranging 30 to 60 percentage points. While the urban areas of these two provinces are among the less deprived areas of Mozambique in all dimensions, the opposite is true for their rural counterparts. The urban/rural deprivation gap for the dimensions FS1, FS3 and FS4 is substantial also for other provinces such as Niassa, Cabo Delgado, and especially Nampula, Zambezia, and Tete. Moreover, the Southern provinces of Gaza and Maputo Province also show significant differences between rural and urban areas.
Urban and rural deprivation levels are instead comparable for more widespread durable goods (FS2) and income-related (FS5) supplementary dimensions. Some of the rural areas score even better than their relative urban areas in FS2 (Tete, Manica). As pointed out in previous paragraphs, excluding the variable "formal or informal job" from the FS5 dimension changes the results for this dimension substantially. When this variable is excluded the difference between urban and rural areas increases largely for Niassa, Nampula, Tete, Sofala, Gaza and Inhambane.
For what concerns FS6 (health- and education-related indicators), rural areas are systematically more deprived than urban areas. This is plausible, as healthcare facilities and schools are more widespread in urban areas. The average gap between areas of residence amounts to more than ten percentage points, notwithstanding the commitment of the Mozambican government to increase the availability of health and education facilities in rural areas (Chao and Kostermans, 2002; Government of Mozambique, 2005; Republic of Mozambique, 2006).
As previously shown, both monetary deprivation dimensions — Head Count and Fuzzy Monetary — analysed at national level determine radically different results to non-monetary dimensions. This holds true also for the analysis at provincial and urban/rural level. From Head Count Ratio and Fuzzy Monetary estimates it turns out that the poorest region in Mozambique is the rural area of Maputo Province, while the rural areas of Niassa, Cabo Delgado and Tete are richer than their urban counterparts and present the same low deprivation levels of Maputo City, the capital. In these monetary dimensions the urban/rural deprivation gap of Manica and Sofala is not as wide as found in the supplementary dimensions, while the urban/rural Head Count Ratio gap of Gaza and Inhambane is found significantly wider.
Introducing supplementary dimensions to the analysis of poverty in Mozambique substantially increases the amount and quality of available in-
formation, providing figures that often contrast with the ones derived solely from monetary poverty estimates.
5. Conclusions
In this study we have shown how it is possible to construct poverty measures relative to monetary and non-monetary dimensions using Fuzzy Set Theory. We applied this technique to the Mozambican Household Budget Survey 2008-09 (IOF08) dataset, the most recent budget survey available for Mozambique.
Our main contribution to the analysis and measurement of poverty in Mozambique is twofold. On the one hand, we estimate a concept of poverty wider than monetary poverty, therefore involving supplementary dimensions. At the same time, we have obtained reliable estimates of poverty rates at sub-national and urban/rural level, by using the Jackknife Repeated Replications method to compute standard errors.
To our knowledge, this is the first study that applies Fuzzy Set Theory to the measurement of poverty in Mozambique. As a result, the figures provided in the study substantially increase the amount and quality of available information about Mozambican households' deprivation. Our estimates — especially those obtained for non-monetary dimensions — complement the ones derived solely from the Head Count Ratio. They also provide new evidence with respect to provincial and urban/rural deprivation levels.
With regards to monetary poverty, the Fuzzy Monetary estimates essentially confirm the official results obtained using the Head Count Ratio. In particular, the ranking of poorer and richer provinces remains unchanged, also when the analysis is carried out at the urban/rural level. This is due to both measures, the Head Count and Fuzzy Monetary, being based on consumption data.
Instead, innovative results come from the inclusion of six supplementary dimensions of deprivation in the analysis of poverty: housing conditions; more widespread and affordable durable goods; less common, more expensive durable goods; housing quality; income-related deprivation; health and education. When these dimensions are considered, some of the provinces showing relatively low Head Count Ratios are found to be among the most deprived with respect to supplementary dimensions of deprivation, and conversely. In particular, the Northern provinces and
the Central province of Tete, all show much higher Fuzzy Supplementary (FS) averages with respect to their Fuzzy Monetary (FM) averages. The remaining Central provinces have similar statistics in both the FM and FS dimension, while the Southern provinces show lower FS averages than their respective FM averages.
The higher level of development of the Southern provinces distinctly becomes relevant to more than one supplementary dimension: housing conditions (FS1); less common, more expensive durable goods (FS3); housing quality (FS4); and, to a lesser extent, health and education (FS6).
Furthermore, in our analysis we point out that deprivation values found in urban and rural areas are very different. When we consider non-monetary dimensions of deprivation it emerges that the urban/rural gap is much wider than it appears from Head Count Ratio or Fuzzy Monetary statistics. The aggregated Fuzzy Supplementary deprivation level for urban areas is estimated to be 0.34, whereas the one for rural areas exceeds 0.64. Moreover, while the ranking of some rural area such as Cabo Delgado, Niassa, and Tete sensibly worsens, it improves for the provinces of Zambezia and Maputo Province).
One partial explanation of this large difference between the results for monetary and non-monetary poverty is that some of the items included in the supplementary analysis are non-essential items, like fridge, car or PC. For example, the highest average level of deprivation is found for FS3 (less common, expensive durable goods), as most Mozambicans do not possess any of the items included in this dimension, especially in the North and in the Centre. In fact it might be objected that the inclusion of these items in the analysis of poverty is not entirely justified. However, the said difference between monetary and non-monetary poverty is large also for those supplementary dimensions like housing conditions, housing quality, or health and education, which certainly denote a situation of deprivation.
Our results are particularly relevant since Mozambique is among the poorest countries in the world, given its per capita income level of approximately $428 (World Bank, 2010), and several donor countries and international agencies involved in poverty reduction plans. Accurate information and measurement of poverty at the local level is thus required and may be used to redirect funds.
References
1. Aassve, A., Betti, G., Mazzuco, S. & Mencarini, L. (2007). Marital Disruption and Economic Well-being; A Comparative Analysis. Journal of the Royal Statistical Society, Series A, 170(3), 781-799.
2. Anand, S. & Sen, A. (1997). Concepts of human development and poverty: a multidimensional perspective. UNDP New York.
3. Atkinson, A. (2002). Social indicators: The EU and social inclusion. Oxford University Press, USA.
4. Atkinson, A. (2003). Multidimensional deprivation: contrasting social welfare and counting approaches. Journal of Economic Inequality, 1(1), 51-65.
5. Atkinson, A. & Bourguignon, F. (1982). The comparison of multi-dimensioned distributions of economic status. The Review of Economic Studies, 49(2), 183-201.
6. Betti, G., Cheli, B. & Cambini, R. (2004). A statistical model for the dynamics between two fuzzy states: theory and application to poverty analysis. Metron, 62(3), 391-411.
7. Betti, G., Cheli, B., Lemmi, A. & Verma, V. (2006). Multidimensional and longitudinal poverty: an integrated fuzzy approach. In: Lemmi A. and Betti G. (Eds.). Fuzzy set approach to multidimensional poverty measurement, Springer, 115-137.
8. Betti, G., D'Agostino, A. & Neri, L. (2002). Panel Regression Models for Measuring Multidimensional Poverty Dynamics. Statistical Methods and Applications, 11(3), 359-369.
9. Betti, G., D'Agostino, A. & Neri, L. (2011). Educational Mismatch of Graduates: a Multidimensional and Fuzzy Indicator. Social Indicators Research, 103(3), 465-480.
10. Betti, G., Gagliardi, F., Lemmi, A. & Verma, V. (2012). Sub-national indicators of poverty and deprivation in Europe: methodology and applications. Cambridge Journal of Regions, Economy and Society, 5(1), 149-162.
11. Bourguignon, F. & Chakravarty, S. (2003), The measurement of multidimensional poverty. Journal of Economic inequality, 1(1), 25-49.
12. Castel-Branco, M. (2010). Intervention at the launch of the book "Economia extractiva e desafios de industrializacao em Mocambique". Instituto de Estudos Sociais e Economicos, 21/10/10, Maputo.
13. Cerioli A. and Zani S. (1990), A fuzzy approach to the measurement of poverty. In: Dagum C. and Zenga M. (eds.). Income and wealth Distribution, Inequality and Poverty, Springer Verlag Berlin, pp. 272-284.
14. Chakravarty S., Mukherjee D. and Ranade R. (1998), On the family of subgroup and factor decomposable measures of multidimensional poverty. Research on Economic Inequality, 8, pp. 175-194.
15. Chao, S. & Kostermans, K. (2002). Improving health for the poor in Mozambique. The fight continues. Health. Nutrition and Population Discussion Paper. World Bank, Washington DC.
16. Cheli, B. (1995). Totally fuzzy and relative measures of poverty in dynamic context. Metron, 53, 183-205.
17. Cheli, B. & Betti, G. (1999). Fuzzy analysis ofpoverty dynamics on an Italian pseudo panel, 1985-1994. Metron, 57, 83-104.
18. Cheli, B. & Lemmi, A. (1995). A"Totally" fuzzy and relative approach to the multidimensional analysis of poverty. Economic Notes, 24, 115-134.
19. Chiappero Martinetti, E. (1994),. A new approach to evaluation of well-being and poverty by fuzzy set theory. Giornale degli Economisti e Annali di Economia, 53, 367-388.
20. Chiappero Martinetti, E. (2006). Capability approach and fuzzy set theory: description, aggregation and inference issues. In: Lemmi A. and Betti G. (Eds.). Fuzzy Set Approach to Multidimensional Poverty Measurement. Springer, 93-113.
21. Duclos, J., Sahn, D. & Younger, S. (2006). Robust Multidimensional Poverty Comparisons. The Economic Journal, 116, 943-968.
22. Eurostat (2002). Income, Poverty and Social Exclusion: 2nd Report. Luxembourg: Office for Official Publications of the European Communities.
23. Government of Mozambique (2005). Education sector strategy plan II 2005-2009.
24. Hanlon, J. (2007). Is poverty decreasing in Mozambique? IESE Conference Paper n. 14.
25. INE (2004). Inquérito Nacional aos Agregados Familiares Sobre Ornamento Familiar 2002/3, Available at: http://www.ine. gov.mz/in queritos_dir/iaf/
26. INE (2010). Inquérito Sobre Ornamento Familiar, 2008/09 — Quadros Básicos. Available at: http://www.ine.gov.mz/inque-ritos_dir/iaf/
27. Klasen, S. (2000). Measuring poverty and deprivation in South Africa. Review of Income and Wealth, 46(1), 33-58.
28. Kolm, S. (1977). Multidimensional egalitarianisms. The Quarterly Journal of Economics, 91, 1-13.
29. Maasoumi, E. (1986). The measurement and decomposition of multi-dimensional inequality. Econometrica, 54, 771-779.
30. MPF (2004). Poverty and Well-Being in Mozambique: The Second National Assessment (2002-2003). National Directorate of Planning and Budget, Ministry of Planning and Finance, Economic Research Bureau, Ministry of Planning and Finance, International Food Policy Research Institute, Purdue University.
31. MPF (1998). Poverty and Well-Being in Mozambique: The First National Assessment (1996-1997). Ministry of Planning and Finance, Universidade Eduardo Mondlane, International Food Policy Research Institute.
32. MPD-DNEAP (2010). Poverty and wellbeing in Mozambique: third national poverty assessment. Maputo: Ministério da Planificado e Desenvolvimento — Direc^ao Nacional de Estudos e Análise de Políticas.
33. Ossemane, R. (2010). O dilema do crescimento empobrecedor. 26/10/10, Tete, Mozambique.
34. Republic of Mozambique (2006), Action plan for the reduction of absolute poverty 2006-2009.
35. Sen, A. (1999). Development as freedom. Oxford University Press, Oxford.
36. Smeeding, T., Saunders, P., Coder, J., Jenkins, S., Fritzell, J., Hagenaars, A., Hauser, R. & Wolf>on, M. (1993). Poverty, inequality and family living standards impacts across seven nations: The effect of non-cash subsidies for health, education and housing. Review of Income and Wealth, 39, 229-256.
37. Townsend, P. (1979). Poverty in the United Kingdom. Allen Lane, Harmondsworth.
38. Tsui, K. (1995). Multidimensional generalizations of the relative and absolute inequality indices: the Atkinson-Kolm-Sen approach. Journal of Economic Theory, 67(1), 251-265.
39. Van den Boom, B. (2011). Analysis of poverty in Mozambique. Available at: http://undp.org.mz/en/Publications/Other-Publications/Analysis-of-poverty-in-Mozambique.
40. Verma, V. & Betti, G. (2011). Taylor linearization sampling errors and design effects for poverty measures and other complex statistics. Journal of Applied Statistics, 38(8), 1549-1576.
41. Vero, J. & Werquin, P. (1997). Reexamining the Measurement of Poverty: How Do Young People in the Stage of Being Integrated in the Labor Force Manage. Economie et Statistique, 8(10), 143-156.
42. Whelan, C., Layte, R., Maitre, B. & Nolan, B. (2001). Income, deprivation, and economic strain. An analysis of the European Community Household Panel. European Sociological Review, 17(4), 357-372.
43. WHO (2011). Nutritional status. Available at: http://www.childinfo.org/undernutrition_nutritional_status.php.
44. World Bank (2008). Beating the odds: sustaining inclusion in Mozambique's growing economy. Washington D.C.: The World Bank.
45. World Bank (2010). World Development Report 2010: Development and Climate Change. Washington D.C.: The World Bank.
46. World Bank (2011). World Development Indicators 2010. Washington D.C.: The World Bank.
47. Zadeh L. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
Information about the authors
Betti Gianni (Siena, Italy) — PhD in Applied Statistics, Associate Professor in Statistics and Economics, Department of Economics and Statistics, University of Siena (7, Piazza S. Francesco, Siena, 53100, Italy, e-mail: [email protected]).
Gagliardi Francesca (Siena, Italy) — PhD in Applied Statistics, Research Fellow Department of Economics and Statistics, University of Siena (7, Piazza S. Francesco, Siena, 53100, Italy, e-mail: [email protected]).
Salvucci Vincenzo (Maputo, Mozambique) — PhD in Economics, Postdoc, Development Economics Research Group, University of Copenhagen; Resident Advisor, National Directorate of Studies and Policy Analysis, Ministry of Planning and Development, Maputo, Mozambique (21, Av. Ahmed Sekou Toure, Maputo, 325087, Mozambique, e-mail: vincenzo.salvucci@ gmail.com).
УДК 338: 657.6
Р. М. Качалов, Н. В. Яковлева
КОМПЛЕКСНЫЙ ПОДХОД К АНАЛИЗУ КАЧЕСТВА УПРАВЛЕНИЯ СОЦИАЛЬНО-ЭКОНОМИЧЕСКИМ РАЗВИТИЕМ РЕГИОНА1
Рассмотрены перспективы применения методов анализа и совершенствования качества управления социально-экономическими системами, базирующихся на концептуальных положениях системной теории экономики, многоуровневой стратификации экономического пространства и операциональной теории экономического риска. Для мезоэкономического уровня выявлены виды системных ресурсов. Определено, как диспаритет различных видов системных ресурсов может повлиять на качество управления социально-экономическим развитием региона. Выявлены потенциальные возможности повышения качества управления за счет более тонкой стратификации экономического пространства и улучшения параметров взаимодействия основных акторов регионального экономического пространства. На основе основных положений операциональной теории управления уровнем экономического риска, предложены способы обоснования управленческих стратегических решений путем выявления и идентификации релевантных факторов экономического риска и разработки на базе полученной информации программы антирисковых мер. Предложены методические решения, способствующие успешному внедрению рассмотренных инструментальных средств в практику регионального управления.
Ключевые слова: мезоэкономика, управление социально-экономическими системами, качество управления, системные ресурсы экономики, стратификация экономического пространства, операциональная теория управления риском
1 © Качалов Р. М., Яковлева Н. В. Текст. 2014.
