Human Development Index—Revisited: Integration of Human Values

Abstract

In the present article, the concept of Human Development Index (HDI) has been revisited so as to integrate the human values using the Corruption Perception Index (CPI) measured by Transparency International, for different countries, as a simple generalization within the framework of existing Anand and Sen’s (1994) and Haq’s (1990) prescription. In this framework, a geometrical average of three indices corresponding to three dimensions, namely, the life expectancy, years of schooling and standard of living (gross national income per capita), was taken. In order to have HDI in broader perspective, one has to integrate another important dimension known as human values. The main hypotheses of this article are: (a) human values play an important role in human development; (b) CPI has a strong correlation with HDI; and (c) CPI is intimately linked with the outcome of human values. The important finding of this article is the successful establishment of these hypotheses which brought appreciable changes in the HDI when human values are integrated with it. As a result, most of the countries showed a vast decline in HDI (up to 30 per cent), affecting the respective ranking of different countries. Such a value-based HDI (VHDI) is supposed to motivate even the richer countries to improve their HDI ranking in future.

Keywords

Human Development Index (HDI)HDI ranking human values Corruption Perception Index (CPI)value-based HDI (VHDI)

Introduction

Human development, as envisaged by United Nations Development Programme (UNDP), encompasses three basic aspects of human welfare, namely, life expectancy, literacy and years of schooling and a measure of income (gross national income [GNI] per capita), which were referred to as ‘most critical’ in the first report of UNDP (1990) on human development. Jolly, Emmerij and Weiss (2009), under a United Nations (UN) Intellectual History Project, underlined the following points:

Human development was formally defined in the first UNDP report as a process of enlarging people’s choices for their development. The most critical have been, as mentioned earlier, to live a long and healthy life, to be educated and to have access to resources needed for decent standard of living. Additional choices include political freedom, guaranteed human rights and self-respect.

Later, the concept of human development was refined and elaborated and, as a paradigm, emphasized broadening choices and strengthening capabilities based on the conceptual and analytical framework of Nobel Laureate Sen and his group.

The holistic approach of human development combines four fundamental ideas, namely, human rights and self-determination, peace and human security, participation and empowerment and economic and social advancement, which should be pursued together.

Further, human development emphasizes three areas of concerns:

inclusion of broader group of governmental and non-governmental actors in the decision-making process;

broadening the scope of indicators used to monitor human development; and

the values and principles at the heart of human development approach must extend to include human rights, justice and human solidarity.

These concerns are important for strengthening human values and capabilities far beyond the economic/material returns and market efficiency.

They concluded that human development as a paradigm has yet to be fully grasped even within the UN.

Nevertheless, the present Human Development Index (HDI) remains based on the three critical factors, as mentioned earlier.

Critique of HDI

The HDI which focused exclusively on longevity/health, education and GNI per capita, country-wise, was criticized by various experts on different grounds, such as, the method of assessment, groupings at the country level, global perspective and ecological considerations.

A natural, important and serious question arises: why at all is the UN planning to launch development programmes for ‘other countries’? An answer appears to lie, to some extent, in the ‘Oneness’ of ‘One’ with ‘Another’. Can the UN appreciate this multidimensional space of ‘Oneness’ and not three-dimensional choices for HDI?

In the context of HDI, the views of Basu (2005) on human values and Indian traditions, consistent with its philosophy of ‘happiness and peace to all’ irrespective of caste, creed, groups, country and ecological considerations, are very much pertinent. According to Basu, ‘The provisions of material amenities alone would bring about Human Development, is far from reality.’ After all, human being is not an economic/material entity without soul. Ranis, Stewart and Samman (2006) termed this HDI as an incomplete measure due to the consideration of a limited set of three dimensions of human development. They effectively elaborated 11 important dimensions for human development/flourishing, thus going beyond those included in HDI. They did mention the role of spiritual (a positive concept) well-being and the corruption perception (a negative concept) as indicators of the community well-being. However, little was done to integrate either of these dimensions with the HDI, even the more significant ones like corruption or human values.

Caplan (2009) criticized the way HDI scores were produced; in which each of the three components are bounded between zero and one, which does not allow rich countries to effectively improve their ranking, even though there is a lot of scope for economic growth and longevity. Wolff, Chong and Auffhammer (2011) discussed the HDI from the perspective of data error in each of the three components. In 2010, the UNDP revised the method of HDI calculation and classified nations as low, medium and high HDI countries, to take care of some of these suggestions of Caplan (2009) and Wolff et al. (2011).

Such a simplification of a complex situation, reflected in the form of HDI, was appreciated by most of the intellectuals of different countries and was accepted as an absolute index of social welfare. It has been in use to measure the impact of socio-economic policies (Davies & Quinlivan, 2006) on quality of life.

The ‘phenomenon of corruption’ seems to be multifaceted, multidimensional and dynamic over a period of time, and thus ubiquitous at the global level, and as such, it is difficult to have an all-encompassing and universally accepted definition. However, corruption is most commonly defined as the misuse or the abuse of public office for private gains (UNDP, 1999), disguised in various forms. While corruption is commonly attributed to the public sector under different layers, it also exists in other aspects of governance, such as, political parties, private business sector and non-governmental organizations (NGOs) (United States Agency for International Development [USAID], 2005). Corruption creates a cycle that makes sure that benefits are concentrated only on these small sectors of the populace.

UNDP classifies corruption into two types: spontaneous and institutionalized. Spontaneous corruption is usually found in societies observing strong ethics and morals in public services. Institutionalized corruption, on the other hand, is found in societies where corrupt behaviours are perennially extensive or pervasive, where corruption has become a way of life, a goal and an outlook towards public office. According to USAID Anticorruption Strategy, ‘No problem does more damage to alienate citizens, to undermine political stability and economic development, than endemic corruption among the government, party leaders, judges and bureaucrats’ (USAID, 2005).

While suggesting anti-corruption strategies, Klitgaard (1998) opines that ‘leaders must see that it is possible to make systematic improvements without committing political suicide’. According to Klitgaard, the corruption, C, obeys a simple formula C = M + D – A, where M represents monopoly of power; D is the discretion; and absence of accountability is reflected through A. UNDP (2010) modified this equation by adding other dimensions of integrity and transparency, leading to the formula C = (M + D) – (A + I + T), where I is integrity and T is transparency. This formula strengthens the theory that corruption is primarily a failure in governance (Balboa & Medalla, 2006).

Dr Sandeep Pandey (2011), Professor at Indian Institute of Technology (IIT) Varanasi and Magsaysay awardee, gave another simpler definition of corruption according to which the corruption is defined as the violation of natural human values, which include basic values like honesty, integrity, transparency, trust, oneness and living in harmony with nature and environment.

Global Concern about Human Values

World over, people have started thinking in terms of human values in the fields of education, management, leadership, science and technology, environment, sustainable development (Novacek, 2013). The concepts of democracy, liberty and inclusive welfare of all, which are of utmost concern for human beings and for different governments, arose out of the inherent and intense feelings representing human values. The theme ‘human values’ became the central research topic for the UNDP. In this context, recently, Comim and Amaral (2013) brought out the conceptual foundation and evidence for the Human Value Index specific to Brazil. Interestingly, with no regard to human value, any allegation related to corruption on individual political leaders/bureaucrats in the hierarchy or institutions was always invariably diverted to the system of education in the country. In this context, the views of Kalam (1998) are pertinent, emphatic and very elaborate with respect to human values:

The education system should retain the smiles of children which can happen if the system is made creative throughout, under a moral leadership.

Twelve years of value-based education in the school is essential to establish an open and transparent society, with a sense of dignity, self-respect and self-reliance among students.

It should inculcate dignity of labour and indomitable spirit among students.

These views reverberate the ancient Indian wisdom and traditions of human values in education. The new millennium has seen the implementation of this wisdom in most of the schools and higher education institutions in India, and outside India as well. Further, United Nations Educational, Scientific and Cultural Organization (UNESCO, 1997) also proclaimed the year 2000, beginning of twenty-first century, as the International Year of the Culture of Peace. Since then, under the guidance of UNESCO Advisory Committee for Peace, Human Rights, Democracy, International Understanding and Tolerance, all the member states of UNESCO have introduced or are in the process of introducing value-based educational programmes, incorporating both spiritual and secular values to build a culture of peace.

Thus, the question of values remains central for human development and for capability approach. Yet, surprisingly, for unknown reasons, the capability approach propounded by Sen has always been indifferent about human values. After Basu (2005) and Ranis et al. (2006), Dahl (2013) and Deneulin (2011) recently took up the issue of human values for human development and proposed some analytical tools to examine the dynamics of value formation and its influence on the policy for advancing human development (Alkire & Deneulin, 2009a, 2009b). In fact, in conventional economic analysis, issues related to human values, which are very much relevant and important to human race, have always been sidetracked, without which economic analysis loses its (all-inclusive) meaning and thrust with respect to human welfare and the related HDI.

Human values have been defined numerous times by several authors and scholars in the past. According to Rokeach (1973), ‘The value concept is able to unify apparently diverse interests of all the sciences concerned with human behavior.’ Schwartz and Bilsky (1990) proposed that human values represent three universal requirements of human existence: (a) needs of individuals as biological organisms; (b) requisites of coordinated social interactions; and (c) survival and welfare needs of groups. According to the ancient Indian scriptures, values have been conceptualized in the form of sarve bhavantu sukhinah (happiness to all), indicating the scope of values across physical, mental, intellectual and spiritual dimensions, so as to qualify for all-inclusive development of human being. This belief has also been reflected in the writings of Upadhyaya (1965), an Indian philosopher and social worker, in the form of integral humanism. The present study does not attempt to define the human values afresh. Rather, it is an integration of all the values in their natural and pure form, that are important for human beings to happily co-exist and evolve together along with all living beings, in the socio-environmental sphere.

Quantification of Human Values

The issue of quantification of human values is a big question with not many options and it seems to be really a puzzle. Recently, Harder and Burford (2013) posed a question, ‘Can values be measured’, and they worked on it through action-based research, without any satisfactory solution to the problem at hand. Fortunately, in this situation, the issue finds a remote friend in the form of ‘Corruption Perception Index’ (CPI), 2010, of different countries, brought out by Transparency International. Transparency International has a reputation of being an authentic and trustworthy agency. It simply tells that ‘CPI varies between 10 (for the clean/non-corrupt countries) to zero (for most corrupt countries)’, which may be normalized (dividing by 10) on the scale one to zero. At this stage, let us recall that HDI also varies between one and zero, respectively, for fully developed and least developed countries. It is interesting to witness:

The parity behind the phenomena of HDI versus CPI.

There seems to be a strong correlation between the two.

If it is true, then an empirical relation may be visualized and established.

This empirical relation may help in integrating human values with the HDI.

These steps will be discussed in detail in the next section on methodology.

In the context of CPI, ‘corruption’ remains the keyword, which Transparency International defines as ‘the abuse of entrusted power for private gain’, same as that given by UNDP, and encompasses corrupt practices in both public and private sectors. In spite of utmost efforts of various governments to tackle the most pressing socio-economic problems—such as economic inequality, poverty, educational environment, instability of capital markets and environmental pollution—in the country by committing huge sums, corruption has remained a root problem, eroding the progress and efficiency of that country.

Transparency International developed the concept of ‘corruption perceptions’ that helped in ranking different countries according to this perception (Thompson & Shah, 2005). The complete methodology, sources of data and method of calculation are given by Transparency International in CPI Report (2010), where an interesting statistical approach known as Monte Carlo method was used to extract information from 10,000 samples per country. This CPI, in its present form, is also likely to face criticism in the future by different experts on different grounds similar to that of HDI, but the authenticity of Transparency International is not questionable.

As such, according to Transparency International, the corruption perception is often, to a great extent, a hidden activity, and therefore is difficult to measure. However, over time, such perceptions have proved to be reliable estimates of corruption. Transparency International has also included freedom of the press and the efficiency of the judicial system, and thus considered it of critical importance to measure both corruption and integrity (all inclusive) in the public and private sectors at all the levels (macro to micro). The CPI, 2010, drew on 13 different assessments/opinion surveys carried out by independent and reputed institutions between January 2009 and September 2010, capturing information about the corruption prevalent in administrative and political circles. Broadly speaking, the surveys and assessments used to compile the index included questions relating to bribery of public officials, kickbacks in public procurement, embezzlement of public funds, scandals and questions that probe the weakness/strength and inefficiency/effectiveness of public sector vis-à-vis anti-corruption efforts. The assessment of ‘corruption perception’ for a country was made by considering a minimum of three of the sources that Transparency International had been focusing on. The CPI, 2010 measures the degree to which public sector corruption is perceived, on the scale 10 (clean) to zero (corrupt), to exist in 178 countries around the world. It shows that nearly three-quarters of the 178 countries score (on the scale of 10) below five, which indicates a serious corruption problem. Somalia is found to be at the bottom, with a score of 1.1, slightly trailing Myanmar and Afghanistan at 1.4 and Iraq at 1.5. The problem may be attributed, on the one hand, to extreme poverty, hunger, malnutrition and corrupt practices existing in some countries.. On the other hand, a few from affluent classes, a greedy lot, have also exploited the masses defining their own values, leading to all kind of malpractices and crime. However, on the higher side of the HDI, Denmark, New Zealand and Singapore are tied at the top of the list with a score of 9.3, followed closely by Finland and Sweden at 9.2.

In view of the given observations, the present work is based on three main hypotheses as follows:

The human values play an important role in human development.

The CPI has a strong correlation with HDI.

The CPI is intimately related to the human values.

Along with these hypotheses, there are two fundamental assumptions of this article:

CPI may be identified as the lack of corruption; and

CPI is linearly related to human values.

These two assumptions are essential in simplifying integration of human values with the HDI.

The main objective of the article is to integrate ‘human values’ with the HDI and carry out its impact analysis.

Methodology

The methodology consists of the following four parts:

Collection of Data

The data are collected from secondary sources: HDI from UNDP (2010); and CPI from Transparency International report (2010).

Correlation between the HDI and the CPI

The coefficient of correlation between CPI and HDI for different countries is calculated to provide a prima facie case to proceed with the problem of integrating human values with the HDI.

Calculation of HDI

In its Human Development Report, UNDP (2010) started using a method of calculating the HDI, suggested by Haq (1990), who devised a simple methodology modifying the earlier approach (with arithmetic average), and by taking a geometrical mean of the three essential indices for material progress of human beings. In the earlier approach, education index (EI) included adult literacy and gross enrolment index that was respectively replaced by mean years of schooling (MYSI) and expected years of schooling (EYSI); and gross domestic product (GDP) factor was replaced by income index (II) related to GNI, along with some minor numeric corrections depending on the prevailing conditions.

The basis for calculating these essential indices remains, in general, to represent a desired variable, say, x, into a dimensionless index between zero and one using the formula:

x - index \frac{x \min (x)}{\max (x) \min (x)}

where min(x) and max(x) are respectively the lowest and highest values the variable x can attain.

The three indices related to life expectancy, education and GNI were obtained using the respective formulae (Anand & Sen, 1994; Haq, 1990) given next:

Life Expectancy Index (LEI) = (LE – 20)/63.2

Education Index (EI) = (MYSI. EYSI)^½/0.951, with

Mean Years of Schooling Index (MYSI) = MYS/13.2

Expected Years of Schooling Index (EYSI) = EYS/20.6

Income Index (II) = [ln(GNI pc) − ln(163)]/[ln(108, 211) − ln(163)]

HDI was found from the geometric mean of these three normalized indices:

HDI \sqrt[3]{LEI EI I I .}

where, ‘LE’ is life expectancy at birth; ‘MYS’ represents mean years of schooling (years that a 25-year-old person or older has spent in schools); and ‘EYS’ implies expected years of schooling (years that a 5-year-old child will spend with his education in his whole life). Finally, ‘GNIpc’ tells about the gross national income at purchasing power parity per capita.

UNDP (2010) prepared an HDI by considering the three important dimensions (Anand & Sen, 1994; Haq, 1990) of human welfare, namely:

A long and healthy life: Life expectancy at birth (LE).

Access to knowledge: Mean Years of Schooling and Expected Years of Schooling (YS).

A decent standard of living: GNI per capita (GNIpc) in US$.

That is, HDI may be expressed as a function of these three dimensions, (1) HDI = f (LE, YS, GNIpc)

HDI has been worked out by UNDP, calculating all the three dimensions (as indices) on a scale from zero to one and then taking the geometric average of all the three indices, for different countries, which were placed according to their respective ranking. HDI below 0.5 is considered to represent ‘low development’ countries. All the 22 countries in this category are located in Africa. HDI of 0.8 or more is considered to represent ‘high development’ countries like those in North America, Western Europe, Oceania and Eastern Asia, as well as some developing countries in Eastern Europe, Central and South America, Southeast Asia, the Caribbean and the oil-rich Arabian Peninsula. The remaining countries with HDI in the range from 0.5 to 0.8 fall in the category of ‘medium development’ countries.

Integration of Human Values

The blend of human values and HDI should represent the system in its broad perspective. The main assumptions of the article may be expressed in the form of an equation: (2) CPI/10 (≡≡ 1 – C) ∝ Human Values (V) = m .V

where C denotes ‘corruption’ varying from zero to one; and human values (V) varies between one and zero. The proportionality constant, ‘m’, may be assumed, for simplification, to be one. The given equation may also be re-expressed as (3) V + C = 1

This equation seems quite interesting in the sense that a complex system has been simplified to such an extent. If the system is devoid of human values (corruption), only corruption (human values) remains. Further, if corruption, C, is a ‘negative concept’ and negatively affects human development, the CPI (that is, 1 − C) implicitly and effectively represents a ‘positive concept’ in the form of human values which support the human development. Once one is convinced about such a relation, it is straightforward to generalize the arguments presented by Haq (1990) and calculate value-based HDI (VHDI) of different countries. Similar to the three dimensions mentioned earlier in Equation (1), let the human values/CPI be the fourth dimension, which is already available through Transparency International. We try to combine all these four dimensions, using geometric mean, and get the desired VHDI. This is what we have tried to accomplish to understand the HDI in a broader perspective. We restricted ourselves to the study of VHDI only for the year 2010.

Human values, V, Equation (2), is integrated in the form of fourth dimension with the HDI. For this, a geometric mean of all the four above-mentioned indices was taken to get the modified HDI, that is, VHDI, which may be expressed as: (4) VHDI = ƒ (LE, YS, GNIpc, V)

The different HDI, CPI and VHDI, along with the respective ranking of different countries, are presented in Table 1.

Table 1.

Human Development Index (HDI), Corruption Perception Index (CPI) and Value-based HDI (VHDI) of Different Countries, along with their Respective Ranking

HDI Rank	Country	HDI (2010)	CPI	CPI Rank	VHDI	VHDI Rank	Variation in HDI (%)
1	Norway	0.938	8.6	10	0.918	2	–2.14
2	Australia	0.937	8.7	8	0.920	1	–1.85
3	New Zealand	0.907	9.3	1	0.913	3	0.63
4	United States	0.902	7.1	22	0.849	17	–5.80
5	Ireland	0.895	8	14	0.870	10	–2.76
7	Netherlands	0.890	8.8	7	0.888	6	–0.29
8	Canada	0.888	8.9	6	0.889	5	0.05
9	Sweden	0.885	9.2	4	0.894	4	0.98
10	Germany	0.885	7.9	15	0.860	13	–2.80
11	Japan	0.884	7.8	17	0.857	14	–3.08
13	Switzerland	0.874	8.7	8	0.873	9	–0.13
14	France	0.872	6.8	25	0.820	21	–6.04
15	Israel	0.872	6.1	30	0.798	22	–8.56
16	Finland	0.871	9.2	4	0.883	7	1.38
17	Iceland	0.869	8.5	11	0.864	12	–0.54
18	Belgium	0.867	7.1	22	0.825	20	–4.87
19	Denmark	0.866	9.3	1	0.881	8	1.81
20	Spain	0.863	6.1	30	0.792	24	–8.32
21	Hong Kong	0.862	8.4	13	0.856	15	–0.64
22	Greece	0.855	3.5	78	0.684	42	–20.01
23	Italy	0.854	3.9	67	0.702	39	–17.80
24	Luxembourg	0.852	8.5	11	0.852	16	–0.06
25	Austria	0.851	7.9	15	0.835	18	–1.84
26	United Kingdom	0.849	7.6	20	0.826	19	–2.73
27	Singapore	0.846	9.3	1	0.866	11	2.39
28	Czech Republic	0.841	4.6	53	0.724	35	–14.01
29	Slovenia	0.828	6.4	27	0.776	26	–6.23
31	Slovakia	0.818	4.3	59	0.697	41	–14.86
32	United Arab Emirates	0.815	6.3	28	0.764	29	–6.24
33	Malta	0.815	5.6	37	0.742	32	–8.94
34	Estonia	0.812	6.5	26	0.768	27	–5.41
35	Cyprus	0.810	6.3	28	0.761	30	–6.10
36	Hungary	0.805	4.7	50	0.704	38	–12.59
37	Brunei	0.805	5.5	38	0.732	34	–9.07
38	Qatar	0.803	7.7	19	0.794	23	–1.04
39	Bahrain	0.801	4.9	48	0.708	37	–11.56
40	Portugal	0.795	6	32	0.741	33	–6.79
41	Poland	0.795	5.3	41	0.718	36	–9.63
42	Barbados	0.788	7.8	17	0.786	25	–0.26
44	Lithuania	0.783	5	46	0.700	40	–10.61
45	Chile	0.783	7.2	21	0.767	28	–2.07
46	Argentina	0.775	2.9	105	0.606	61	–21.80
47	Kuwait	0.771	4.5	54	0.674	43	–12.59
48	Latvia	0.769	4.3	59	0.665	46	–13.53
49	Montenegro	0.769	3.7	69	0.640	50	–16.71
50	Romania	0.767	3.7	69	0.639	51	–16.67
51	Croatia	0.767	4.1	62	0.656	48	–14.50
52	Uruguay	0.765	6.9	24	0.746	31	–2.56
53	Libya	0.755	2.2	146	0.555	77	–26.53
54	Panama	0.755	3.6	73	0.627	52	–16.89
55	Saudi Arabia	0.752	4.7	50	0.669	45	–11.08
56	Mexico	0.750	3.1	98	0.602	63	–19.83
57	Malaysia	0.744	4.4	56	0.652	49	–12.30
58	Bulgaria	0.743	3.6	73	0.620	54	–16.57
59	Trinidad and Tobago	0.736	3.6	73	0.615	56	–16.36
60	Serbia	0.735	3.5	78	0.611	58	–16.94
61	Belarus	0.732	2.5	127	0.560	73	–23.55
62	Costa Rica	0.725	5.3	41	0.670	44	–7.53
63	Peru	0.723	3.5	78	0.603	62	–16.58
64	Albania	0.719	3.3	87	0.592	66	–17.68
65	Russia	0.719	2.1	154	0.528	86	–26.48
66	Kazakhstan	0.714	2.9	105	0.570	70	–20.17
67	Azerbaijan	0.713	2.4	134	0.543	83	–23.83
68	Bosnia and Herzegovina	0.710	3.2	91	0.582	67	–18.08
69	Ukraine	0.710	2.4	134	0.541	84	–23.75
70	Iran	0.702	2.2	146	0.525	89	–25.18
71	Macedonia	0.701	4.1	62	0.613	57	–12.55
72	Mauritius	0.701	5.4	39	0.657	47	–6.30
73	Brazil	0.699	3.7	69	0.596	65	–14.69
74	Georgia	0.698	3.8	68	0.599	64	–14.09
75	Venezuela	0.696	2	164	0.510	93	–26.80
76	Armenia	0.695	2.6	123	0.544	82	–21.79
77	Ecuador	0.695	2.5	127	0.538	85	–22.56
79	Colombia	0.689	3.5	78	0.582	68	–15.57
80	Jamaica	0.688	3.3	87	0.573	69	–16.79
81	Tunisia	0.683	4.3	59	0.608	60	–10.91
82	Jordan	0.681	4.7	50	0.621	53	–8.85
83	Turkey	0.679	4.4	56	0.609	59	–10.27
84	Algeria	0.677	2.9	105	0.548	80	–19.10
85	Tonga	0.677	3	101	0.552	78	–18.40
87	Turkmenistan	0.669	1.6	172	0.468	105	–30.08
88	Dominican Republic	0.663	3	101	0.544	81	–18.00
89	China	0.663	3.5	78	0.565	72	–14.77
90	El Salvador	0.659	3.6	73	0.567	71	–14.04
91	Sri Lanka	0.658	3.2	91	0.550	79	–16.50
92	Thailand	0.654	3.5	78	0.559	75	–14.47
93	Gabon	0.648	2.8	110	0.525	88	–18.92
95	Bolivia	0.643	2.8	110	0.522	90	–18.75
96	Paraguay	0.640	2.2	146	0.490	99	–23.42
97	Philippines	0.638	2.4	134	0.500	95	–21.69
98	Botswana	0.633	5.8	33	0.620	55	–2.18
99	Moldova	0.623	2.9	105	0.515	92	–17.41
100	Mongolia	0.622	2.7	116	0.505	94	–18.83
101	Egypt	0.620	3.1	98	0.521	91	–15.90
102	Uzbekistan	0.617	1.6	172	0.441	112	–28.65
104	Guyana	0.611	2.7	116	0.498	97	–18.47
105	Namibia	0.606	4.4	56	0.560	74	–7.70
106	Honduras	0.604	2.4	134	0.480	101	–20.61
107	Maldives	0.602	2.3	143	0.473	104	–21.38
108	Indonesia	0.600	2.8	110	0.496	98	–17.35
109	Kyrgyzstan	0.598	2	164	0.455	108	–23.97
110	South Africa	0.597	4.5	54	0.557	76	–6.84
111	Syria	0.589	2.5	127	0.475	102	–19.28
112	Tajikistan	0.580	2.1	154	0.450	110	–22.42
113	Vietnam	0.572	2.7	116	0.474	103	–17.11
114	Morocco	0.567	3.4	85	0.499	96	–11.99
115	Nicaragua	0.565	2.5	127	0.461	107	–18.45
116	Guatemala	0.560	3.2	91	0.487	100	–13.06
117	Equatorial Guinea	0.538	1.9	168	0.415	115	–22.91
118	Cape Verde	0.534	5.1	45	0.528	87	–1.13
119	India	0.519	3.3	87	0.464	106	–10.71
120	Timor–Leste	0.502	2.5	127	0.422	114	–15.98
121	Swaziland	0.498	3.2	91	0.446	111	–10.46
122	Laos	0.497	2.1	154	0.401	118	–19.37
123	Solomon Islands	0.494	2.8	110	0.429	113	–13.24
124	Cambodia	0.494	2.1	154	0.399	119	–19.24
125	Pakistan	0.490	2.3	143	0.406	117	–17.24
126	Republic of the Congo	0.489	2.1	154	0.396	121	–19.04
128	Kenya	0.470	2.1	154	0.384	124	–18.25
129	Bangladesh	0.469	2.4	134	0.397	120	–15.43
130	Ghana	0.467	4.1	62	0.452	109	–3.21
131	Cameroon	0.460	2.2	146	0.382	125	–16.83
132	Myanmar	0.451	1.4	176	0.337	143	–25.36
133	Yemen	0.439	2.2	146	0.370	134	–15.88
134	Benin	0.435	2.8	110	0.390	122	–10.44
135	Madagascar	0.435	2.6	123	0.382	126	–12.06
136	Mauritania	0.433	2.3	143	0.370	132	–14.65
137	Papua New Guinea	0.431	2.1	154	0.360	139	–16.47
138	Nepal	0.428	2.2	146	0.363	137	–15.35
139	Togo	0.428	2.4	134	0.370	131	–13.47
140	Comoros	0.428	2.1	154	0.358	140	–16.29
141	Lesotho	0.427	3.5	78	0.406	116	–4.86
142	Nigeria	0.423	2.4	134	0.367	136	–13.19
143	Uganda	0.422	2.5	127	0.370	133	–12.25
144	Senegal	0.411	2.9	105	0.377	128	–8.35
145	Haiti	0.404	2.2	146	0.347	141	–14.10
146	Angola	0.403	1.9	168	0.334	144	–17.14
147	Djibouti	0.402	3.2	91	0.380	127	–5.54
148	Tanzania	0.398	2.7	116	0.361	138	–9.26
149	Côte d’Ivoire	0.397	2.2	146	0.342	142	–13.71
150	Zambia	0.395	3	101	0.369	135	–6.64
151	Gambia	0.390	3.2	91	0.371	130	–4.83
152	Rwanda	0.385	4	66	0.389	123	0.93
153	Malawi	0.385	3.4	85	0.373	129	–3.04
154	Sudan	0.379	1.6	172	0.305	148	–19.38
155	Afghanistan	0.349	1.4	176	0.278	154	–20.43
156	Guinea	0.340	2	164	0.298	150	–12.43
157	Ethiopia	0.328	2.7	116	0.313	145	–4.76
158	Sierra Leone	0.317	2.4	134	0.296	151	–6.76
159	Central African Republic	0.315	2.1	154	0.285	152	–9.65
160	Mali	0.309	2.7	116	0.299	149	–3.35
161	Burkina Faso	0.305	3.1	98	0.306	147	0.40
162	Liberia	0.300	3.3	87	0.307	146	2.42
163	Chad	0.295	1.7	171	0.257	157	–12.86
164	Guinea–Bissau	0.289	2.1	154	0.267	155	–7.65
165	Mozambique	0.284	2.7	116	0.281	153	–1.28
166	Burundi	0.282	1.8	170	0.252	158	–10.58
167	Niger	0.261	2.6	123	0.261	156	–0.13
168	Democratic Republic of the Congo	0.239	2	164	0.229	159	–4.37
169	Zimbabwe	0.140	2.4	134	0.160	160	14.41

Results, Analysis and Discussion

The results are presented in Table 1, only for the year 2010, from the secondary data available with respect to HDI from UNDP (2011) and that of CPI from Transparency International, of different countries, along with their respective ranking. Names of some countries could not be included due to non-availability of the data. The results are obtained in different steps as follows:

At the outset, the Pearson’s correlation coefficient (CC) was found between the HDI and the CPI of 169 countries, to be 0.724, a significant value indicating strong correlation. This proves hypotheses one and two, which gave us the confidence to move further so as to integrate human values with the HDI.

Further, a geometric mean of all the four indices—the three from the main components of HDI and the fourth one for the human value (from CPI)—was obtained to get VHDI, Equation (4). The new ranking of different countries, along with percentage variation in HDI, is also given in the Table 1.

As expected, a vast change (decline up to 30 per cent) in the value of HDI was found for most of the countries, which changed their respective ranking.

It may be interesting, once again, to browse over the concepts (quantified) of human values/CPI (divided by 10) and HDI which vary between the limits one (most clean and developed) and zero (for most corrupt and least developed country). Here, we have introduced ‘C’ representing ‘Corruption’, which varies between zero and one. It apparently reveals straight visualization of CPI similar to Human Value Index, ‘V’. In general, a realistic situation may be depicted to be somewhere in between. It proves the third hypothesis with certainty.

Notwithstanding these arguments in respect of the four important dimensions/indices (Equation 4), the practical aspect remains as a policy matter, that is, to address the challenges of corruption. In this connection, Transparency International advocates stricter implementation of the UN Convention against Corruption (UNCAC) (2005).

The main findings of this communication are as follows:

It could be possible to quantify the corruption, ‘C’, and the human values, ‘V’, making their respective contribution to human development.

A strong correlation between HDI and CPI was found, which justifies the present study.

The current status of human values (through CPI) is supposed to bring down the HDI values of all the countries as CPI is always less than one. However, any relative increase in the value of HDI after integrating the human values is only due to the geometric mean of the four indices involved in the approach of getting the HDI.

All the three hypotheses of this article have been established. Now it is for the respective countries as to how they would like to improve their HDI by focusing on all the four dimensions involved.

With the recognition of human values in educational institutions at the global level, the time is ripe when UN can appreciate, grasp and work in the multidimensional space of ‘Oneness’ for human development as a paradigm.

UNDP (2012) also reported inequality-adjusted HDI for 132 countries where the difference was found to be about 23 per cent on the average. However, based on the present study, it is advisable to allow such adjustments for different inequalities or choices only after VHDI is obtained.

Using VHDI, even rich countries would have an opportunity to improve their ranking in future, resolving the apprehensions felt by Caplan (2009).

Finally, the study fully acknowledges that CPI is a result of independent and collective effort of more than a dozen agencies coordinated by Transparency International and includes Transparency International’s own research as well. Notwithstanding the CPI, the study is only to emphasize the need of incorporation of ‘values’ in HDI (or any similar index), keeping the theme of the article intact.

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