Obra de Edu Barbero

 

 

 

Introducción:

En este apartado sintetizamos los contenidos metodológicos de las aproximaciones analíticas con las que se intenta evaluar la evolución de los niveles de vida,  e incluimos una breve bibliografía sobre cada una de ellas.

 

 

 

Índice:

Índice de Desarrollo Humano (IDH)

Land-time Budget Analysis (LTBA)

Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM)

 

 


Índice de Desarrollo Humano (IDH)

(Síntesis realizada por Emanuele Felice)

The Human Development Index (HDI) was introduced in 1990 by the United Nations Development Program, in its annual Human Development Reports (UNDP, 1990). Through the years, it gained widespread success, so much so that it is currently the most popular measure of well-being alternative to Gdp.

Its conceptual foundation must be found in the Sen’s capabilities approach (1985). Functional capabilities are substantive freedoms people have reason to value: mainly the ability to live a long and healthy life, «longevity»; the ability to decide about one own future, assured by an adequate «education»; the ability to engage in economic transactions and to satisfy material needs, «resources». These three dimensions, longevity, education and resources, can be recognized as the three basic components of human life, according to an approach to human well-being which incorporates personal (practical) freedom and social development, and looks more inclusive than the traditional utilitarian approach,.

Up to 2009, in the yearly Human Development Reports the three components were computed according to the following formula:

 

(1) HDI = 1/3*LeI + 1/3*EI + 1/3*II,

 

that is, an arithmetic mean of the Life expectancy index (LeI), which measures longevity, of the Education index (EI), which measures education, and of the income index (II), which measures material resources.

More in detail, the three indices were calculated as follows:

 

(2) LeI = (Le – 25) + (85 – 25),

 

where Le stays for Life expectancy at birth, and 85 and 25 are respectively the (theoretical) maximum and minimum thresholds;

 

(3) EI = 2/3*ALI + 1/3*GEI,

 

where ALI is the Adult literacy index, i.e. the percentage of people able to read and write out of the total population, and GEI is the Gross enrolment index, i.e. the number of students enrolled in primary, secondary and tertiary/higher levels of education, regardless of age, as a percentage of the population of official school age (from 5/6 to 24/25) for the three levels;

 

(4) II = (log(Gdp per capita) – log(100)) / (log (40000) – log (100)),

 

where Gdp per capita is expressed in international 1990 United States dollars at purchasing power parity (1990 US$ PPP), 40000 and 100 are respectively maximum and minimum thresholds (again: theoretical), and log stands for the natural logarithm. This component index is expressed in logarithmic form, under the hypothesis of decreasing returns of Gdp to well-being, which is in line with the functional capabilities approach: that is, at a higher level of per capita Gdp, a further increase results into a minor return to well-being.

 

 

II. Improving the HDI

 

Since its introduction in the early 1990s, the HDI received growing acceptance, not least thanks to its underlying simplicity, up to the point that it now looks as the only measure which can seriously defy the long-standing primacy of Gdp. Economic historians also worked with it, and HDI estimates are now available for a period spanning from the second half of the nineteenth century until our days: see for example Nicholas Crafts (1997, 2002) for cross-country comparisons, and Felice (2007a) for the Italian regions. However, the HDI also attracted widespread criticisms, from McGillivray (1991) onwards. Broadly speaking, these criticisms can be catalogued into three categories, not necessarily alternative to each others: a) those who rejected some or all of the components of the HDI (and the related conceptual framework) and, in some cases, proposed new and alternative indices, such as the Genuine progress indicator and similar (Cobb and Cobb, Jr., 1994); b) those who accepted the basic components of the HDI and its conceptual foundations, but added new dimensions, such as political freedom, inequality, pollution; c) those who concentrated on the way the three components were measured and computed. In a handful of years, the bibliography has grown huge.

We will limit our attention to the last category. Most noticeably for economic historians, Leandro Prados de la Escosura (2010), following Kakwani (1993), has recently proposed an «improved» HDI, along with historical estimates for the world and its main regions covering the period spanning the late XIX century until our days. As the author puts it:

 

What defines the new human development index? In the first place, its social, non-income dimensions [longevity and education] are derived using a convex achievement function as an alternative to the linear transformation employed in the UNHDI. Thus, in the new index, as a social indicator reaches higher levels, its increases represent higher achievements than if the same increase would take place at a lower level, whereas in the UNDP linear transformation the same change results regardless of the starting level. Second, in an attempt to reduce substitutability among the index components, its three dimensions (longevity, access to knowledge and average incomes) are combined into the new IHDI using a geometric average, rather than the arithmetic average used in the UNHDI. The final outcome is a new human development index which, by not concealing the gap between rich and poor countries, casts a much less optimistic view than the one provided by conventional UNDP index while satisfying the HDR concern for international differences. (Prados de la Escosura, 2010, pp. 841-842)

 

The idea of a convex function results into the use of the following formula, for both LeI and EI:

 

f(x, M0, M) = (log (M – M0) – log (M – x)) / log (M – M0),

 

where x is an indicator of a country’s standard of living (longevity or education), M and M0 are the maximum and minimum values, respectively, and log stands for the natural logarithm. Prados also introduced some minor changes in the maximum and minimum thresholds, because in his wide historical and country range of observations the UNDP maximum and minimum represented cases above the highest and below the lowest, respectively. Thus, for life expectancy the minimum was lowered to 24 years. The three dimensions are then weighted through a geometric average. A similar formula has been used by Felice (2007b) in order to estimate an improved HDI of the Italian regions, in benchmark years from 1891 to 2001.

However, it is worth mentioning that some authors have challenged the assumption of a convex achievement function for the social components (Tsui, 1996), and indeed have even proposed to extend the principle of diminishing returns to education (Noorbakhsh, 1998).

 

 

III. The new HDI

 

In their latest human development report (UNDP, 2010), the United Nations have accepted some of criticisms and made a considerable effort to improve the HDI. Arguably the new index represents a considerable advance upon the old one.

The new HDI is calculated in benchmark years from 1980 up to 2010, according to the formula:

 

(5) new HDI = (LeI*EI*II)1/3.

 

The three components are measured as follows:

 

(6) LeI = (Le – 20) + (83.2 – 20),

 

where 83.2 is the maximum value observed in the sample (Japan in 2010);

 

(7) EI = ((MYSI*EYSI)1/2 – 0) / (0.951 – 0),

 

where MYSI is the Mean years of schooling index, measured as the mean years of schooling divided by 13.2 (the maximum value observed in the sample, United States in 2000, whereas the minimum equals zero), EYSI is the Expected years of schooling index, measured as the expected years of schooling divided 20.6 (the maximum value observed in the sample, Australia in 2002, whereas the minimum equals zero), and 0.951 is the maximum value of the combined Education index observed in the sample (new Zealand in 2010);

 

(8) II = (log(Gni per capita) – log(163)) / (log (108,211) – log (163)),

 

where Gni is the Gross national income, expressed in 2008 US$ PPP, and 108,211 and 163 are respectively the maximum (United Arab Emirates in 1980) and minimum (Zimbabwe in 2008)  values observed in the sample.

In short, the three main innovations are: a) the use of a geometric mean to weight the three components, which reduces substitutability among the three components (i.e., the index performs better when all the three components perform better, and a decrease in one component is hardly compensated by an increase in another) and was common also to the improved HDI; b) the introduction of empirical (rather than theoretical) thresholds; c) a remarkable refinement of the Education indicator (although its suitability to measure capabilities may be questioned).

In the 2010 UNDR, the new HDI is estimated for benchmark years from 1980 up to 2010. The report also presents an inequality adjusted Human development index (IHDI), which is in short a geometric mean of geometric means – each one computed by discounting each dimension’s average value according to its level of inequality, based on a distribution-sensitive class of composite indices. Although the reliability of the inequality estimates is more problematic, especially for historical comparisons, the reasoning behind this multi-level framework is intriguing. In the 2010 UNDR’s words:

 

The IHDI equals the HDI when there is no inequality across people but is less than the HDI as inequality rises. In this sense, the IHDI is the actual level of human development (accounting for this inequality), while the HDI can be viewed as an index of “potential” human development (or the maximum level of HDI) that could be achieved if there was no inequality. The “loss” in potential human development due to inequality is given by the difference between the HDI and the IHDI and can be expressed as a percentage. (UNDP, 2010, p. 217)

 

Out of the possible innovations, the proposal of using a convex function rather than the linear transformation for the non-income components was not introduced, since it was regarded at odds with the capability approach: for example, at a late age a further increase in life expectancy should not result into a more than proportionally greater capability of living a long and healthy life. Indeed, in the case of income, following Anand and Sen (2000), it is reasserted that the concave form of the transformation function is more in line with the capability approach.

 

 

 

References

 

 

Anand, S. and A. Sen (2000), The Income Component of the Human Development Index. Journal of Human Development and Capabilities, 1 (1), pp. 83-106.

Cobb, C.W. and J.B. Cobb, Jr. (1994), The Green National Product: A Proposed Index of Sustainable Economic Welfare. Lanham, MD: University Press of America.

Crafts, N.F.R. (1997), The human development index and changes in standards of living: some historical comparisons. European Review of Economic History, 1 (3), pp. 299-322.

Crafts, N.F.R. (2002), The human development index, 1870-1999: some revised estimates. European Review of Economic History, 6 (3), pp. 395-405.

Felice, E. (2007a), Divari regionali e intervento pubblico. Per una rilettura dello sviluppo in Italia. Bologna: Il Mulino.

Felice, E. (2007b), I divari regionali in Italia sulla base degli indicatori sociali (1871-2001). Rivista di Politica Economica, 67 (3-4), pp. 359-405.

Kakwani, N. (1993). Performance in living standards. An international comparison. Journal of Development Economics, 41 (2), pp. 307–336.

McGillivray (1991). The human development index: Yet another redundant composite development indicator? World Development, 19(10), pp. 1461-1468.

Noorbakhsh, F. (1998). A modified human development index. World Development, 26(3), pp. 517-528.

Prados de la Escosura, L. (2010). Improving human development: a long-run view. Journal of Economic Surveys, 24 (5), pp. 841-894.

Sen, A. K. (1985). Commodities and Capabilities. Oxford: Oxford University Press.

Tsui, K.Y. (1996). Improvement indices of well-being. Social Choice and Welfare, 13(3), pp. 291-303.

United Nations Development Programme (UNDP) (1990), Human Development Report. New York: Oxford University Press.

United Nations Development Programme (UNDP) (2010), Human Development Report 2010. 20th Anniversary Edition. The Real Wealth of Nations: Pathways to Human Development. New York: Palgrave Macmillan.

 

 

 

External links (database)

 

 

, 67 (3-4), pp. 359-405, http://www.rivistapoliticaeconomica.it/2007/mar-apr/Eman_felice.pdf.

, WP 10-07 (June 2010), http://e-archivo.uc3m.es/bitstream/10016/8987/1/wp_10-07_.pdf

United Nations Development Programme (UNDP), Human Development Reports, http://hdr.undp.org/en/.

 

 

 

Land-time Budget Analysis (LTBA)

Síntesis realizada por Jesús Ramos Martín

Descripción breve: Como su nombre indica, LTBA realice una contabilidad de los usos del tiempo y del suelo que un determinado sistema humano utilice para su mantenimiento y desarrollo. Aplicado a los hogares, permite ver la interacción entre un hogar determinado y los recursos de que dispone para analizar las restricciones económicas y ecológicas así como las oportunidades de desarrollo a las que se enfrentan. Permite ver como elecciones en cuanto al uso del tiempo entre las diferentes actividades no es independiente del uso del suelo, y como ambos están condicionados por las necesidades de mantenimiento y subsistencia de los hogares. Así, permite ver como cubrir las necesidades básicas implica unos determinados usos del tiempo y del suelo que pueden variar según condiciones tecnológicas o normas sociales. De esta manera es posible establecer fácilmente la relación que existe entre los niveles de vida y de consumo material, y sus implicaciones ecológicas mediante cambios en los usos del suelo.

 

Bibliografía:

Giampietro, M. (2003). Multi-Scale Integrated Analysis of Agro-ecosystems. CRC Press, Boca Raton, 472 pp.

Grünbühel, C., Schandl, H. (2005): “Using land-time-budgets to analyse farming systems and poverty alleviation policies in the LAO PDR”, International Journal of Global Environmental Issues, Vol. 5 (3/4): 142-180.

Grünbühel, C.M., Haberl, H., Schandl, H. and Winiwarter, V. (2003) ‘Socio-economic metabolism and colonisation of natural processes in Sang Saeng village: material and energy flows, land use and cultural change in Northeast Thailand’, Human Ecology, Vol. 31, No. 1, pp.53–85.

Pastore, G., Giampietro, M., Ji, L. (1999): “Conventional and Land-Time Budget Analysis of Rural Villages in Hubei province, China”, Critical Reviews in Plant Sciences, Vol. 18(3): 331-357.

Serrano, T., Giampietro, M. (2009): “A multi-purpose gramar  generating a multi-scale integrated analysis in Laos”, Reports on Environmental Sciences,  num. 3, Instituto de Ciencia y Tecnología Ambientales. Universidad Autónoma de Barcelona. http://www.recercat.net/handle/2072/16100

 

Ejemplo:

Grünbühel and Schandl 2005 utilizan LTBA para observar las restricciones existentes entre elecciones de uso del tiempo entre diferentes actividades en Laos (agricultura, ganadería, empleos remunerados, educación, etc.), y usos del suelo (tipo de plantación y objetivo, autoconsumo, venta etc), y comparan los escenarios posibles con los objetivos de desarrollo oficiales del gobierno de Laos, para mostrar que son totalmente incompatibles, y que determinadas actividades requieren determinados usos del suelo y no es posible modificarlo.

 

Ejemplo 1

 

 

Fuente: Grünbühel and Schandl, 2005

 

La Figura muestra la distribución del uso del tiempo en la comunidad estudiada, mientras que la figura de abajo muestra el uso del suelo. Es la combinación de estos dos usos lo que permite establecer qué actividades son compatibles con el desarrollo rural y con la conservación ambiental.

 

 

Ejemplo 2

 

 

 

 

Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MUSIASEM)

Síntesis realizada por Jesús Ramos Martín

 

Descripción breve: El Análisis Integrado Multiescalar del Metabolismo Social y de los Ecosistemas (Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolsim, MuSIASM), es un marco de análisis introducido por Giampietro y Mayumi (1997, 2000b, 2000a) y finalmente formulado por Giampietro (2003). El enfoque, que supone una aplicación del modelo Fondo-Flujo de Georgescu-Roegen (1971, 1975), ha sido utilizado para analizar las economías del Ecuador (Falconí, 2001), España (Ramos-Martin, 2001), Vietnam (Gomiero y Giampietro, 2001; Ramos-Martin y Giampietro, 2005), China (Ramos-Martin et al. 2007), Chile, Brasil y Venezuela (Eisenmenger et al., 2007), el Reino Unido (Gasparatos et al., 2009), Rumanía, Bulgaria, Polonia y Hungría (Iorgulescu and Polimeni 2009), y Cataluña (Ramos-Martin et al., 2009). Esta metodología permite combinar información monetaria (generación de valor añadido), demográfica (población, y uso del tiempo humano), así como biofísica, en concreto la energía comercializada usada (o energía exosomática), es decir la que aparece en los balances energéticos de la Agencia Internacional de la Energía, o energía endosomática (la que ingerimos en forma de alimentos). Este enfoque nos permite analizar las relaciones sistémicas entre diferentes variables biofísicas, como son la energía y el uso del tiempo, revelando un equilibrio específico que cada país o sistema analizado encuentra en la utilización de recursos limitados. En un segundo paso, combinamos estas variables biofísicas con variables monetarias. El resultado es una “contabilidad” de usos del tiempo y de consumo de energía en las diferentes actividades que conforman una economía que son compatibles con el análisis económico de la generación de valor añadido. De esta manera se ofrece una visión biofísica del proceso económico, mostrando las interrelaciones entre restricciones demográficas, económicas y ambientales.

 

Bibliografía:

 

Eisenmenger, N., Ramos-Martín, J., and Schandl, H. (2007):” Análisis del metabolismo energético y de materiales de Brasil, Chile y Venezuela”, Revista Iberoamericana de Economía Ecológica, Vol 6: 17-39.

Falconí-Benítez, F. (2001): “Integrated assessment of the recent economic history of Ecuador”, Population and Environment, Vol. 22 (3): 257-280.

Gasparatos, A., El-Haram, M., Horner, M. (2009): “Assessing the sustainability of the UK society using thermodynamic concepts: Part 1”. Renewable and Sustainable Energy Reviews, Vol. 13 (5): 1074-1081.

Georgescu-Roegen, N. (1971) The Entropy Law and the Economic Process. Harvard University Press, Cambridge, Massachusetts.

Georgescu-Roegen N. (1975) Energy and economic myths. Southern Economic Journal, 41: 347–81.

Giampietro, M. (2003). Multi-Scale Integrated Analysis of Agro-ecosystems. CRC Press, Boca Raton, 472 pp.

Giampietro, M., Mayumi, K., 1997. A dynamic model of socioeconomic systems based on hierarchy theory and its application to sustainability. Structural Change and Economic Dynamics 8, 453–469.

Giampietro, M., Mayumi, K., 2000a. Multiple-scale integrated assessment of societal metabolism: Introducing the approach. Population and Environment 22 (2), 109–153.

Giampietro, M., Mayumi, K., 2000b. Multiple-scale integrated assessment of societal metabolism: Integrating biophysical and economic representations across scales. Population and Environment 22 (2), 155–210.

Gomiero, T. and Giampietro, M., (2001): Multiple-scale integrated analysis of farming systems: The Thuong Lo Commune (Vietnamese Uplands) case study, Population and Environment, 22 (3): 315-352.

Iorgulescu, R.I., Polimeni, J.M. (2009): “A multi-scale integrated analysis of the energy use in Romania, Bulgaria, Poland and Hungary”, Energy, Vol. 34 (3): 341-347.

Ramos-Martín, J. (2001): “Historical analysis of energy intensity of Spain: from a “conventional view” to an “integrated assessment”, Population and Environment, Vol. 22 (3): 281-313.

Ramos-Martín, J., Giampietro, M. (2005): “Multi-scale integrated analysis of societal metabolism: learning from trajectories of development and building robust scenarios”, International Journal of Global Environmental Issues, Vol. 5 (3/4): 225-263.

Ramos-Martín, J., Giampietro, M., Mayumi, K. (2007): “On China’s exosomatic Energy metabolism: an application of multi-scale integrated analysis of societal metabolism (MSIASM)”, Ecological Economics, Vol. 63 (1): 174-191.

Ramos-Martín, J., Cañellas-Boltà, S., Giampietro, M., Gamboa, G. (2009): “Catalonia’s energy metabolism: using the MuSIASEM approach at different scales”, Energy Policy, Vol. 37 (11): 4658-4671

 

Ejemplo:

 

En Falconí 2001 y Ramos-Martín 2001 se muestra como la situación contemporánea de empeoramiento en los niveles de vida material en Ecuador, junto con su estructura demográfica, así como la estructura demográfica en España, derivan en la emigración masiva desde Ecuador a España que se produjo desde el año 2000. Mediante la utilización de variables como la “tasa de metabolismo exosomático” (energía utilizada por hora de trabajo en una determinada actividad) se pueden observar posibles cuellos de botella que lleven a una expulsión de la población, a un aumento del consumo de energía, o a ambos. El primer cambio tendría efectos de carácter económico y social, mientras que el segundo tendría claros impactos ambientales asociados a la actividad extractiva de las fuentes energéticas, así como a la emisión de gases de efecto invernadero. Otro resultado obtenido fue la clara relación observada entre la productividad del trabajo en las diversas actividades y el consumo de energía por hora de trabajo, lo que condiciona escenarios de desarrollo futuros y estructuras económicas.

 

Nivel 2 de explicación

La utilización de variables que nos definen el tamaño del sistema analizado (como el uso del tiempo en las diferentes actividades), junto con variables que nos miden la interrelación entre el sistema económico y el medio ambiente (el consumo de un flujo clave para la economía, como puede ser la energía), y la generación de valor añadido que se produce por la transformación de esa energía y ese trabajo, permiten ver restricciones y trade-offs en los modelos de desarrollo que un análisis estrictamente económico no vería. Así, por ejemplo, el empeoramiento observado en la intensidad energética en Cataluña en el periodo 1990-2005 (se necesita más energía en 2005 para generar un euro de valor añadido que en 1990) se puede atribuir claramente a perpetuar un modelo de crecimiento económico con muy poco cambio estructural en el período.

 

 

Ejemplo:

Como muestran Ramos-Martin et al. 2009, la productividad del trabajo en Cataluña en el periodo se mantuvo constante. A pesar del fuerte aumento en el consumo de energía (de más de un 3% anual), la cantidad utilizada por cada hora de trabajo se mantuvo constante, y solo aumentó el total por el hecho de absorber la nueva mano de obra. A esto se le debe añadir que el consumo de energía durante los periodos en los que no trabajamos (EMRhh), que se puede utilizar como proxy para el nivel de vida material, aumentó en todo el período, lo que resultó en el aumento del consumo total.

 

Ejemplo 3

Fuente: Ramos-Martin et al., 2009

 

En resumen, si Cataluña creció durante el periodo de análisis, fue gracias a la incorporación de la mano de obra extranjera, que se dirigió a sectores de productividad baja y que reprodujeron el modelo de desarrollo. Por eso, ante el eventual agotamiento del modelo, si Cataluña quiere avanzar en la producción de mayor valor añadido, se puede anticipar que deberá aumentar dramáticamente el consumo de energía.

empresa colaboradora: www.ikebana.es