Global Findex or Global Financial Inclusion indicator is the database of 800 country- level indicator which tries to capture the financial data of all adults and tries to disaggregate it according to the demographic aspects such as geographic location (Urban or Rural), Class, Sex, etc. The indicator also tries to analyse the aspects involving banking by measuring the savings, borrowing (Credit system), payments and risk management through receiving data from demand side (from banks and other stake holders) and supply side (from people). With the emergence of findex, the financial inclusion has broadened its scope of definition from the need for access to credit to need for an access to various financial services and has shifted focus towards necessity for financial education etc.
Why does the measure of financial inclusion indicator matter for any economy? A study by the World Bank shows that when people participate in financial systems, it creates a positive impact on other economies as they perform well in business; invest more in education, thereby increasing investment and consumption. Further this aspect also has a positive effect on employment ratio and income as the credit is able to reach the people from the data collected.
“Among bank regulators in 143 jurisdictions, a recent survey found, 67 percent have a mandate to promote financial inclusion. 4 International organizations, including the G-20 and the World Bank, are also beginning to formulate strategies to promote financial inclusion. In recent years more than 50 countries have set formal targets and ambitious goals for financial inclusion” (kunt et al, 2017). About 2 billion people in the world are not part of formal financial system due to various reasons such as cost, efficiency etc. The necessity for a formal financial system lies in the fact that with its existence, there is always a security for payments and reduces the incidences of the crime and bribery when it comes to government funds and subsidies. Further, with the raise in formal financial system, the informal saving which is not accountable will get slowly transferred into formal savings.
But the problem starts when the indicator stops at the point of analysing the financial gap as a whole and not the gender gap which exists within the financial inclusion for which the data disaggregation is important. Sex disaggregated data is the data divided according to the sex of the person whether they are men or women. The data is segregated according to the sex of the person and not the gender of the person which is socially constructed and varies according to cultures, but analysing the sex disaggregated data has the potential to discover the gap existing between man and women according to their gender roles and expectations. “From a policymaker’s perspective, collecting, aggregating and analyzing sex-disaggregated information at a national level is beneficial because it enables effective monitoring of progress made against targets and in turn encourages smarter policies. Getting ever more granular sex-disaggregated data, such as how many men and women are reached by channel and product, having this broken down by age and location, and tracking this over time, would lead to more nuanced policies that promote market development, encouraging private sector actors to tap into this market” (GBA, 2015).
Thus, sex disaggregated data is something which is very important for the policymakers to consider while making policies as it gives a clear cut image of the gender gap which exists within the country when it comes to financial inclusion. But unfortunately sex disaggregated data is something which is not present in many countries currently due to various feasibility issues, especially in India, where there is a constant gender gap in findex since 2011.
Financial inclusion is something to do with use of the formal financial systems, but when it comes to issues related to gender gap, it is more related to financial accessibility and issues of lack of access due to various social, political and economical factors.
COMPLICATIONS IN SEX DISAGGREGATED DATA
Financial inclusion data is collected from two different sources- demand side data and supply side data. Demand side data is provided by the people who are the users and consumers of financial services and supply side data is provided by financial services providers who are widely banks and other financial institutions. The data is collected on different parameters starting from global to national level through conducting survey and focus group discussions. The demand and supply side data complement each other for providing a holistic perspective of financial inclusions.
Demand side data: The demand side data is collected through random sampling by interviewing people randomly selected from a household, who are not necessarily the head of the household (which is always the men of the family). Thus, findex is a near to accurate measure of individuals’ financial behaviour with less regard to their position in the family.
Though global findex provides data on basis of sex disaggregated data, this is one of the later developments of the indicator and the data doesn’t do much in the case of country specific interventions. “Most of these national-level surveys, such as FinScope, include basic demographic data, including sex reporting. They do not allow for cross-country comparison, but they do provide regulators a way to measure national progress in a more nuanced ways” (Kunt et al, 2009). As the demand side data is collected using random sampling, an increasing importance for sex disaggregated data of demand side is seen among countries.
Supply side data: Though basic account information is recorded by the banks and financial institutions, utilisation of the same for regulatory purposes by analysing the financial inclusion is a recent phenomenon. The IMF’s Financial Access Survey (FAS) was launched in 2009 and is the most comprehensive annual source of global supply-side data on financial inclusion. The data has evolved over the years according to the need for disaggregate data in relation to the financial sector. With increasing digitalisation, there is a need for expansion within the supply side data, where credit unions, financial cooperatives, small and medium sized enterprises, life insurance, MFIs and non life insurance companies including money bank are being included within the Financial Access Survey.
Though the FAS data is extensive and unique data collected and collated by government, still most of the information collected under the survey consists of volunteer based responses, which brings a difference within the reflection of the data as it is incomplete most of the time. Another drawback is that FAS does not disaggregate the data according to the sex. Still incorporating sex disaggregation within the supply side data would be a crucial and critical and there are various complications in doing the same. “At the 2014 AFI Global Policy Forum, an audience poll found that just 17 percent of regulators are collecting financial inclusion data that is disaggregated by sex, but only 10 percent believed that there is no role for regulators in collecting sex-disaggregated data” (Kunt et al, 2015). There is a gap in understanding this entire phenomenon as there is a disconnect between the policy makers’ goals and the data being generated, which is mostly by the central banks, thereby leaving policy makers puzzled as to how to use this data to make policies.
A major complication faced in collecting data is awareness, considering the concept of sex disaggregate data to be new, there is a tendency among regulators to undermine the need for this data as they are engaged in achieving overall financial stability and inclusion, leading to the thought that sex disaggregate data is something which can wait to be acknowledged; without understanding the fact that the economy will do much better if the steps are taken to improve financial inclusion of women with the help of the data.
A cause for concern is that according to IMF’s survey, many regulators see the importance of disaggregating the data according to geography, but not the sex. Lack of infrastructure is also a reason for the lack of sex disaggregated data, where only 55 percent of the bank interviewed for McKinsey & Company research reported that they had infrastructure to sex disaggregate the supply side customers’ data. Certainly, some banks do not collect or store data by tagging the sex of the customers, whereas certain banks may be able to get data related to outreach but not deeper details about the usage of the account.
There are various reasons for the inadequate data when it comes to sex disaggregating, where cultural and social barriers can impact the quality of supply side data collected; on account of the fact that though the account is in the name of women, in many countries, women are not the controllers of the account which flaws the entire hypothesis. In addition to this, joint account system also tricks the data; some countries do not consider joint accounts to measure women’s market performance. A further problem is created as some banks don’t recognise the women owned businesses due to lag of definition, which makes the women move towards consumer banking portfolios, as it is difficult for the banks to understand the consistency of these businesses. Though creating women owned business seems to be a simple solution, it is not that easy as the process of bringing about change within the management is a very expensive process, which leads to the bank to dropping it as it is a cost based measure.
A further complication is caused within certain countries’ jurisdictions, on account of regulatory restrictions when it comes to using the data related to sex disaggregation, in order to reduce the gender discrimination within the formal financial sector. “For example, in the United States, the 1974 Federal Fair Lending Regulations and Statutes Equal Credit Opportunity Act (ECOA), or “Reg-B,” requires all financial institutions and others providing credit to ‘‘make credit equally available to all creditworthy customers without regard to sex or marital status”” (Kunt et al, 2009). Though the law intends to favour women and other minorities, still the interpretation of the law through the lens of data analysis is blocking the vision.