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Irrespective of which dimensions and formula employed in constructing the index, it has to ensure the validity and reliability of the index. Both issues have been considered in the present study in constructing CIFI. The following sub sections discuss these elements in detail.

5.4.2.1 Index validity

In brief, validity refers to ‗a test of how well an instrument measures whatever concept it is measuring‘ (Sekaran, 2003). Specifically, two types of validity aspects are considered in the index construction, namely content and construct validity.

32 _{Authored by Demirguc-kunt and Klapper (2012), the Global Findex indicators are drawn from}

survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 individuals in 148 economies and representing more than 97 percent of the world's adult population. The questionnaire was translated into 142 languages, and interviews were conducted face-to-face or via telephone. The complete set of Global Findex indicators will be collected again in 2014 and 2017. For more detail, refer http://microdata.worldbank.org and Demirguc-Kunt & Klapper (2012).

33 _{The database is based on survey conducted by Demirguc-Kunt, Thorsten Beck and Patric Honohan}

under World Bank. They introduced east and cost barrier (i.e., dimension) for composite measure of access to financial services.

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Content validity specifies whether the instrument ‗adequately measures the concept of interest‘ (Sekaran, 2003), i.e., in this case, the formula and indicators used in the CIFI computation. As far as index formula is concern, for the purpose of the present study, the index is constructed using formula initiated by Sarma (2008, 2010). Apart from what has been stated earlier, this formula has some other advantage over the other formula used in the previous studies in a few aspects as highlighted by Sarma (2008, 2010) as following:

 The formula follows a multidimensional approach of index construction similar to the UNDP approach for computation of some well-known development indexes such as the HDI, the HPI, the GDI and so on34.

 Instead of using average calculation approach (i.e., as used in the UNDP‘s indexes computation), this formula is based on a measure of the distance from the ideal approach which satisfies several interesting and intuitive properties of a development index, i.e., normalization, symmetry (or anonymity), monotonicity, proximity, uniformity and signalling (collectively termed NAMPUS).

 This measure can be used to compare the levels of financial inclusion across economies and across states/provinces within countries at a particular time point. It also can be employed to monitor the progress of policy initiatives for financial inclusion in a country over a period of time.

 Using the formula, information on many aspects (dimensions) of financial inclusion could be incorporated; thus CIFI is easy and simple to compute and it could be comparable across countries.

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In terms of indicators, CIFI is computed using the indicators constructed by Beck, Demirguc-Kunt, et al. (2007) as listed in section 5.3 above. The validity of the indicators has been verified by some robustness tests. The indicators even have been used in Gimet & Lagoarde-Segot (2012) study to examine the barriers to financial inclusion.

The CIFI computation is further verified using the construct validity. Construct validity examined ‗how well the results obtained from the use of the measure fit the theories around which the test is designed‘ (Sekaran, 2003). In this case, following the approach done by Sarma & Pais (2011), CIFI is compared with human development index (HDI). Table 5.2 presents the CIFI mean value for 80 countries and corresponding HDI35 mean value along with their rank.

Table 5.2 Cumulative index of financial inclusion (CIFI) and human

development index (HDI), mean value

No. Country Cumulative index of

financial inclusion (CIFI)

Human development index (HDI)

Mean Country rank Mean Country rank

1 Albania 0.271 28 0.715 48 2 Algeria 0.171 50 0.714 49 3 Angola 0.152 55 0.493 72 4 Argentina 0.045 75 0.801 30 5 Armenia 0.091 66 0.713 50 6 Australia 0.313 20 0.923 2 7 Austria 0.166 52 0.871 17 8 Azerbaijan 0.054 72 0.724 47 9 Bangladesh 0.271 29 0.537 66 10 Belarus 0.170 51 0.767 33 11 Belgium 0.377 13 0.878 14

12 Bosnia and Herzegovina 0.253 36 0.710 51

13 Botswana 0.123 61 0.660 58 14 Bulgaria 0.343 17 0.766 34 15 Burundi 0.033 79 0.370 80 16 Cambodia 0.122 62 0.523 68 17 Canada 0.365 14 0.901 8 18 Chile 0.255 35 0.808 28 19 Costa Rica 0.229 41 0.743 38 20 Croatia 0.338 18 0.801 31

35 _{Before 2010, HDI data is reported once in five years‘ time. Therefore, the mean value for HDI is}

calculated by the author using data in year 2005 (i.e., to represent data in 2007 to 2009), 2010 and 2011.

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No. Country Cumulative index of

financial inclusion (CIFI)

Human development index (HDI)

Mean Country rank Mean Country rank

21 Czech Republic 0.405 10 0.859 22 22 Dominican Republic 0.078 68 0.694 55 23 Egypt 0.300 23 0.670 56 24 France 0.191 48 0.877 16 25 Georgia 0.117 63 0.729 41 26 Germany 0.129 59 0.901 7 27 Greece 0.402 11 0.861 20 28 Honduras 0.237 38 0.602 63 29 Hungary 0.245 37 0.815 25 30 India 0.276 27 0.574 65 31 Indonesia 0.154 54 0.657 59 32 Iran 0.206 45 0.728 42 33 Ireland 0.205 46 0.904 5 34 Israel 0.463 3 0.880 12 35 Italy 0.424 5 0.866 19 36 Jamaica 0.134 57 0.728 43 37 Japan 0.508 2 0.881 11 38 Jordan 0.424 6 0.740 39 39 Kenya 0.200 47 0.515 69 40 Korea 0.463 4 0.878 15 41 Kuwait 0.288 24 0.805 29 42 Kyrgyz Republic 0.038 76 0.629 61 43 Latvia 0.266 30 0.810 27 44 Lebanon 0.222 44 0.749 36 45 Lesotho 0.078 69 0.463 75 46 Macedonia 0.237 39 0.496 71 47 Madagascar 0.021 80 0.401 78 48 Malawi 0.072 70 0.758 35 49 Malaysia 0.408 8 0.739 40 50 Mexico 0.064 71 0.667 57 51 Moldova 0.190 49 0.602 64 52 Morocco 0.388 12 0.388 79 53 Mozambique 0.124 60 0.906 4 54 Netherlands 0.405 9 0.902 6 55 New Zealand 0.266 31 0.612 62 56 Nicaragua 0.145 56 0.937 1 57 Norway 0.349 16 0.515 70 58 Pakistan 0.132 58 0.710 52 59 Peru 0.107 64 0.822 23 60 Poland 0.223 43 0.812 26 61 Portugal 0.309 21 0.774 32 62 Russian Federation 0.162 53 0.436 77 63 Rwanda 0.034 77 0.880 13 64 Singapore 0.851 1 0.817 24 65 Slovak Republic 0.226 42 0.870 18 66 Slovenia 0.334 19 0.636 60 67 South Africa 0.288 25 0.861 21 68 Spain 0.262 32 0.899 10 69 Sweden 0.282 26 0.918 3 70 Switzerland 0.258 33 0.485 74 71 Tanzania 0.082 67 0.707 53 72 Thailand 0.349 15 0.706 54 73 Tunisia 0.300 22 0.725 46 74 Turkey 0.230 40 0.459 76 75 Uganda 0.047 74 0.728 44 76 Ukraine 0.256 34 0.899 9 77 United Kingdom 0.412 7 0.745 37 78 Venezuela 0.100 65 0.489 73 79 Yemen 0.034 78 0.537 67 80 Zambia 0.054 73 0.726 45

Note: The HDI mean value is calculated based on data in year 2005, 2010 and 2011. The HDI ranks are re-ranks based on the HDI mean value for the set of 80 countries.

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A comparison of CIFI with HDI shows that most of the countries with high and medium CIFI values belong to the group that is categorized by the UNDP as countries with high human development (i.e., HDI > 0.7). Germany, a high HDI country is reported to have a low CIFI value. Other countries having a high or medium HDI value but a low CIFI are Lebanon, Argentina and Mexico, the same as reported in Sarma & Pais (2011). Apart of these exceptions, CIFI and HDI seem to move in the same direction. As seen in Table 5.2, the CIFI and HDI for the set of 80 countries move closely with each other. This is again, consistent with Sarma & Pais (2011) findings.

Additionally, the correlation coefficient between CIFI and HDI mean values is found to be about 0.30 and is statistically significant36. Hence, this can be generally concluded that countries belong to high level of human development are also countries that relatively have medium to high level of financial inclusion, which in this case, the CIFI is consistent with the index of financial inclusion (IFI) constructed by Sarma (2008, 2010).

5.4.2.2 Reliability of the index

Very briefly, the reliability of a measure indicates ‗the extent to which it is without bias and thus warrants consistent measurement which indicate the stability and consistency of the measurement‘ (Sekaran, 2003).

To ensure the reliability of the index, firstly, we compare the outcome of index constructed in the present study with other studies. The detail of the comparison is

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made in section 5.5.1 below under the general description of CIFI results. In sum, if compared the composite index with previous studies, the results are tend to show the consistency. Secondly, we also consider the findings of most of financial inclusion studies especially Beck, et al., (2007) and Sarma & Pais (2011) regarding its relationship with one of the main determinants of financial inclusion, i.e., the GDP per capita. It is establishes that this variable, which represent the income levels, is one of the important factor in explaining financial inclusion (Sarma & Pais, 2011).

Therefore, the CIFI variable (i.e. the transformed CIFI37) is regressed over GDP per capita. As observed in Table 5.3 overleaf, the result of regression shows that the coefficient for GDP per capita is positive and highly significant with financial inclusion. Thus, this can be generally concluded that CIFI computed in the present study could be used as measurement for level of financial inclusion in a particular country.

Table 5.3 Realibility test for CIFI

Variable Coef. Std. Err. t p > | |

ln(GDP) 0.17*** 0.01 12.53 0.00

The full sample consists 400 country-year observations (i.e., 80 countries with year observations from 2007 to 2011). The dependent variable is the country‘s cumulative index of financial inclusion (CIFI), calculated based on formula initiated by Sarma (2008, 2010). GDP is the natural logarithm of the country‘s value of GDP per capita (i.e., GDP in US dollars at market exchange rates divided by total population). ***, ** and * indicate statistical significance at 1%, 5% and 10% levels, respectively (2-tail test).