For verification of this paper, please visit on http://www.socialresearchfoundation.com/remarking.php#8

Institutional credit is one of the important factors influencing economic activity. Increased availability of institutional credit to every sectors of economy is expected to boost investment that facilitates proliferation of economic activities in rural and urban areas as well (NABARD. 2019-20:49). Institutional credit agencies act as backbone for the development of any economy and harbinger of economic progress in developing economy like India. Institutional credit agencies or formal credit institution includes public bank, private bank, post-offices, Regional Rural Banks (RRBs), and co-operative banks, while informal agencies include friends, money lenders, relatives, agricultural traders and commission agents. The topic of access to finance and financial inclusion has been of growing interest throughout the world, particularly in emerging and developing countries. Access to financial services can improve well-being of people. It is widely accepted in academic and policy circles, and thus financial inclusion has become the objective of government especially developing country government (Ellis, Karen; et al. 2010:03). Policymakers are now thinking over how to ensure percolation of benefits of financial intermediation and markets throughout the population and across economic sectors. Furthermore, policymakers may also be concerned with the potential negative consequences for macro stability when financial system assets are concentrated in relatively few individuals, firm, households, or sectors (The World Bank). Credit plays an important role in the development of all sectors of the economy. Availability and access to adequate, timely and low cost credit from institutional sources is of great importance to the small and marginal farmers in case of agriculture and micro, small, and medium enterprises in case of industry and service sector. Credit-deposit ratio is low in the Eastern region. Lack of access to institutional sources is one of the most important concerns in the Eastern region of Uttar Pradesh. Thus, government of Uttar Pradesh must focus on 100 per cent financial inclusion and encourage high credit-deposit ratio in the Eastern region. Uttar Pradesh comes in the Central region of India. Central region of India comprises Chhattisgarh, Madhya Pradesh, Uttar Pradesh, and Uttarakhand. During period 2004-2019, deposits and credit by scheduled commercial banks in Uttar Pradesh as compared to Central region states have always been higher (RBI. 2019-20: 321-322). During the same corresponding period, however, except Uttarakhand, according to place of sanction and utilization credit-deposit ratio of scheduled commercial banks in Uttar Pradesh has always been lower as compared to the states of Central region of India (RBI. 2019-20: 317-318). The same situation happens with the Regional Rural Banks (RRBs). The low credit-deposit ratio indicates that scheduled commercial banks in Uttar Pradesh are not making full use of their resources. With 199.81 million population (IBEF. 2018), Uttar Pradesh (U.P.) is a populous state comprises 75 districts divided into four economic regions called Western, Central, Eastern and Bundelkhand region. This study is a comparative study of Eastern region and Western region of Uttar Pradesh and is confined to the study of Eastern region, which is also known as Purvanchal region comprises 28 districts of Uttar Pradesh, viz., Ambedkar Nagar, Amethi, Azamgarh, Bahraich, Balrampur, Ballia, Basti, Chandauli, Deoria, Faizabad, Ghazipur, Gonda, Gorakhpur, Jaunpur, Kaushambi, Kushinagar, Maharganj, Mau, Mirzapur, Pratapgarh, Prayagraj (Allahabad), Sant Kabir Nagar, Sant Ravidas Nagar, Shrawasti, Siddharth Nagar, Sonbhdra, Sultanpur, and Varanasi. Western region of Uttar Pradesh comprises 30 districts of Uttar Pradesh, viz., Meerut, Bulandshahar, Gautam Buddha Nagar, Ghaziabad, Hapur, Baghpat, Saharanpur, Muzzafarnagar, Shamli, Moradabad, Bijnor, Rampur, Amroha, Sambhal, Bareilly, Badau, Pillibhit, Shahjahanpur, Agra, Firozabad, Mainpuri, Mathura, Aligarh, Etah, Hathras, Kasganj, Etawah, Auraiya, and Farrukhabad. The contribution of agro-based industry to employment in Ambedkar Nagar, Amethi, Azamgarh, Bahraich, Balrampur, Ballia, Basti, Chandauli, Deoria, Faizabad, Kaushambi, Maharganj, Sant Kabir Nagar, Prayagraj, Shrawasti, Siddharth Nagar, and Sultanpur is greater. In Ghazipur, Gonda, Jaunpur, Pratapgarh districts contribution of repairing and servicing industry to employment is greater. In Mau, contribution of cotton textile industry to employment is greater. The contribution of food product industry to employment in Gorakhpur and Deoria is greater. The contribution of woolen, silk and artificial thread based clothes industry to employment in Mirzapur and Sant Ravidas Nagar district is greater. The contribution of chemical and chemical based industries to employment in Sonbhadra is greater, while repairing and servicing industry comes on second position. The contribution of repairing and servicing industry, woolen, silk and artificial thread based clothes industry, and ready-made garments and embroidery industry in Varanasi district are greater respectively (GoI, MSME). In Uttar Pradesh number of bank offices as on 31st March 2019 was 17,031 and number of population per bank office was 13,000. As per the Census of India 2011, with 931 density of population per sq. km, Eastern region is densely populated region in Uttar Pradesh. Based on 2011-12 prices, per capita income of Eastern Uttar Pradesh in 2016-17 is low as compared to Western Uttar Pradesh. Sonbhadra is the only district in Eastern Uttar Pradesh, which per capital income is above Rs. 50,000. However, in Western Uttar Pradesh, there are many districts which per capita income is above Rs. 50, 000. For example, Amroha, Meerut, Baghpat, Ghaziabad, Agra, Etah, Kasganj, Bareilly. There is inter-region variation in average household annual income of Uttar Pradesh. The district Ghaziabad of Western region reported highest average household annual income followed by Gorakhpur district of the Eastern region of U.P. (Tripathi, Amarnath). As compared to Western region, number of farmers having small holding (1.0 – 2.0 hectare) in Eastern region of Uttar Pradesh is less, while that of marginal holding (less than one hectare) is greater. In Eastern region not a single district have cent percent irrigated area to sown area in 2015-16, while in Western region districts like Mainpuri, Mathura, Meerut, Baghpat, Ghaziabad, Hapur, Bulandshahar, Gautam Buddha Nagar, and Shamli have 100 per cent irrigated area to sown area. Increase in net irrigated area to sown area in the Eastern region of Uttar Pradesh may improve well-being and credit-deposit ratio.

Access to institutional credit is one of the important inputs for the development of an economy. It capitalizes entrepreneurs to undertake new investment, and/or adopt new technology. Pal, Rajesh (2013) finds that if access is a problem, then simplified procedures and thresholds may be sufficient to achieve the objective of financial inclusion and this is exactly what is being done by no-frills account. In his study author finds that there is inverse relationship between income and loan taken from money lenders, i.e., as income increases loan taken from money lenders decreases. This shows that increase in income encourage people to take loan and other financial services from institutional sources. Clearly, mere opening of more and more accounts in bank is not the objective of financial inclusion if these accounts are not used as platform to access a range of financial services that meet the various requirements that the household is trying to satisfy. Financial inclusion is the use of financial services by individuals and firms. Financial inclusion allows individuals and firms to take advantage of business opportunities save for retirement, invest in education, and insure against risks (Demirguc-Kunt, Beck, Honohan. 2008). Access to appropriate credit reduces poverty and increases the income of agricultural households (Binswanger and Khandker 1995; Carter 1989; Carter and Wiebe 1990; Feder et al 1990; Pitt and Khandker 1996, 1998; Khandker and Faruqee 2003; Guirkinger 2008; Awotide et al 2015; Narayanan 2016; Luan and Bauer 2016; Kumar et al 2017). Kumar, Anjani; et al. (2010) find that the promotion of loan waiver schemes by political parties motivated farming households to take credit from formal sources. Similarly, awareness of the direct benefit transfer scheme also had a significantly positive effect on taking credit from both formal and informal sources. On the other hand, agricultural households which were dependent on remittances preferred to get credit from informal sources. Households that had opened Jan Dhan Yojana (JDY) bank accounts after the Prime Minister Jan Dhan Yojana (PMJDY) was launched in 2014 were more likely to borrow from informal sources. Households that had sought information from any source had a higher propensity to use both formal and informal sources of credit. Agricultural households that had more livestock preferred to take credit from formal sources. The results show that credit access is strongly associated with the socio-economic and demographic characteristics of agricultural households. Access to credit has increased their household income substantially and has significantly raised yields of major staple crops (Kumar, Anjani; et al. (2010).

Eswaran and Kotwal (1990) argue that access to credit increases the ability of household to bear impact of negative shocks of income, and that availability of credit also allows households to undertake riskier investments as it will enable them to better deal with the consequences of poorly performing investments. Ghosh, Mookherjee & Ray (1999) argue that access and availability of credit encourage risk taking with the adoption of new technology, hence contributing to increase in production and income. Galor & Zeira (1993) find that access to household credit plays a vital role in development of human capital. He finds a positive impact on growth through development of human capital; and that this is affected by the initial distribution of wealth; richer families are better able to invest in human capital accumulation leading to increased growth. De Gregorio (1996) also argues that access to credit promotes human capital accumulation, as credit constraints will force students to work, which will reduce the time available for study. Dehejia & Gatti (2002), Beegle, Dehejia & Gatti (2003), and Jacoby (1994) also find that access to risk-reducing financial services increases investment in schooling (Ellis, Karen; Lemma, Alberto, et al. 2010).

Ajefu, Josheph B (2020) found that financial inclusion has a strong impact on mental health. Further, this study identified the potential channel through which financial inclusion can influence mental health, including food expenditure, remittances, and risk-coping mechanisms. Abraham, Tarfa W (2018) finds in his study that access to financial services by using formal financial institutions and farmer saving clubs benefits vulnerable farmers mostly women (Ellis, Karen; Lemma, Alberto, et al. 2010).

This study is based on secondary data compiled from diverse sources. The data regarding credit- deposit ratio is extracted from Statistical Diary Uttar Pradesh (2019), published by Economic and Statistics Division, State Planning Institute, Planning Department, Uttar Pradesh. httpupdes.up.nic.inesdreportsdbank_may2020Diary_English_2019.pdf. Sources of comparative advantage of districts of Eastern region are extracted from Micro, Small and Medium Enterprises, Government of India, District Industrial Profile of Eastern region districts of Uttar Pradesh. Other secondary data comprise World Bank, research paper and different sites of economic literature available on internet. This study is confined to the study of Eastern region of U.P. The research paper is a comparative study of Eastern and Western region of Uttar Pradesh. Through this research paper the author has tried to find out the impact of access to formal financial institutions on the livelihood of the people of Eastern region.

The data for this research paper is based on secondary data. The Table 1 and 2 shows the deposit-credit ratio of the Eastern and Western regions of Uttar Pradesh.

Table 1: District wise Credit-Deposit of Scheduled Commercial Banks in Eastern Region of Uttar Pradesh as on March 2019

Serial Number	District	Credit (Rs. Crore)	Deposit (Rs. Crore)	Credit-Deposit Ratio
1.	Ambedkar Nagar	2032	6229	32.62
2.	Amethi	2094	6311	33.18
3.	Azamgarh	3386	17130	19.77
4.	Bahraich	3435	6291	54.60
5.	Balrampur	1710	4498	38.02
6.	Ballia	2138	11088	19.28
7.	Basti	2162	7370	29.34
8	Chandauli	1981	6198	31.96
9.	Deoria	2551	10682	23.88
10.	Faizabad	--	--	--
11.	Ghazipur	3290	14858	22.14
12.	Gonda	3522	8533	41.28
13	Gorakhpur	9275	28957	32.03
14.	Jaunpur	3981	15671	25.40
15.	Kaushambi	1024	3330	30.75
16.	Kushinagar	2883	7908	36.46
17.	Maharganj	1988	4722	42.10
18.	Mau	1794	8751	20.50
19.	Mirzapur	2693	8044	33.48
20.	Pratapgarh	2277	9904	22.99
21.	Prayagraj (Allahabad)	10884	44022	24.72
22.	Sant Kabir Nagar	1242	4050	30.67
23.	Sant Ravidas Nagar	1994	5183	38.47
24.	Shrawasti	900	1636	55.01
25.	Siddharth Nagar	1396	4917	28.39
26.	Sonbhadra	2532	8091	31.29
27.	Sultanpur	2206	8063	27.36
28.	Varanasi	14931	41331	36.13

Source: Statistical Diary Uttar Pradesh. 2019. Economic and Statistics Division, State Planning Institute, Planning Department, Uttar Pradesh.

httpupdes.up.nic.inesdreportsdbank_may2020Diary_English_2019.pdf

Table 2: District wise Credit-Deposit of Scheduled Commercial Banks in Western Region of Uttar Pradesh as on March 2019

Serial Number	District	Credit (Rs. Crore)	Deposit (Rs. Crore)	Credit-Deposit Ratio
1.	Agra	23457	36434	64.38
2.	Aligarh	9235	18702	49.38
3.	Amroha	3475	4909	70.79
4.	Auraiya	1121	3878	28.91
5.	Baghpat	2206	4794	46.02
6.	Bareilly	10297	20401	50.47
7.	Bijnor	8189	11865	69.02
8.	Budaun	3789	5304	71.44
9.	Bulandshahar	6673	12516	53.32
10.	Etah	2781	4437	62.68
11.	Etawah	1993	6677	29.85
12.	Farrukhabad	2399	5487	43.72
13.	Firozabad	3963	7478	53.00
14	Gautam Buddha Nagar	45047	88064	51.15
15.	Ghaziabad	24242	54444	44.53
16.	Hapur	3093	5892	52.49
17.	Hathras	3146	4755	66.16
18.	Kannauj	2270	4193	54.14
19.	Kasgan	1734	2684	64.61
20.	Mainpuri	2452	5144	47.67
21.	Mathura	7392	13969	52.92
22.	Meerut	15659	31129	50.30
23.	Moradabad	9636	15241	63.22
24.	Muzaffarnagar	8223	11810	69.63
25.	Pilibhit	3220	4424	72.78
26.	Rampur	3644	5353	68.07
27.	Saharanpur	8745	13099	66.76
28.	Sambhal	3565	4372	81.54
29.	Shahjahanpur	4975	7673	64.84
30	Shamli	2360	3469	68.03

Source: Statistical Diary Uttar Pradesh. 2019. Economic and Statistics Division, State Planning Institute, Planning Department, Uttar Pradesh.

httpupdes.up.nic.inesdreportsdbank_may2020Diary_English_2019.pdf

Uttar Pradesh credit-deposit ratio is 42.25, which is lower than that of India, which accounted for 78.18, while credit-deposit ratio of Andhra Pradesh, Maharashtra, Tamil Nadu, Delhi, and Telanga is 121.84, 106.47, 109.72, 108.15, and 104.95 respectively, which is higher than the credit-deposit ratio of India. The population per scheduled commercial banks in Andhra Pradesh, Maharashtra, Tamil Nadu and Uttar Pradesh is 7,144; 8,989; 6635; and 11732 respectively. As compared to other states low deposit-ratio and high population per scheduled commercial bank office in Uttar Pradesh reflects that lending by commercial banks in Uttar Pradesh is very low, however, population per scheduled commercial bank office is high. This may be due to lack of access to formal financial institution, lack of education, loan recovery problems, poverty, and unemployment.

For those who depend on agriculture for their livelihood, agricultural credit at the most reasonable rates plays a vital role in agricultural development and facilitates adoption of new technologies, yet it cannot guarantee higher productivity or adequate income for them, as well-being also depends upon many other supporting factors including the availability of agricultural inputs, services and remunerative markets for their products (RBI Report. 2019). 

The relationship between access to financial services and well-being of people are not directly related to each other. There are several mechanisms through which they may be related and the impact of access to financial services on the well-being of people also varies depending on which financial services we are talking about (Ellis, Karen; et al. 2010: 04-05). For individuals, access to financial services encourages them to adopt new technologies for economic activities; help them to increase income. The increase in income may be invested. It may be invested in the form of saving in financial instruments (eg. equity investment), or saving in the form of physical capital (eg. a new tool or piece of equipment that improves productivity such as agricultural machinery), saving in the form of human capital (i.e., investment in education, skill and training development).

According to growth theory (e.g. Solow (1956), and Romer (1990)), “growth depends on the stock of human and physical capital in the economy, as well as technological progress. Investment at the level of the firm or the individual can contribute to all of these things, and thus plays an important role in facilitating long-run economic growth.” This economic growth ultimately leads to increase in well-being of people.

Access to credit, saving, investment, and insurance can also minimize the negative impact of income shocks that can affect the longer term income prospects, if income generating assets are sold at very low prices out of necessity during a household crisis. Access to financial services helps individuals/firms/ households to diversify their resources in different financial assets, and hence ensure regular income, which increases the ability of household to bear the impact of negative shocks during difficult times.

1. Abraham, Tarfa W (2018). “Estimating the Effects of Financial Access on Poor Farmers in Rural Northern Nigeria.” Financial Innovation 4, 25, 2018.https://doi.org/10.1186/s40854-018-0112-2. 2. Ajefu, Joseph B; Demir, Ayse; Haghpanahan, Houra (2020). “The Impact of Financial Inclusion on Mental Health.” SSM Population Health, ELSEVIER. Vol. 11. August 2020. 100630. 3. Awotide, B A, T Abdoulaye, A Alene and V M Manyong (2015). “Impact of Access to Credit on Agricultural Productivity: Evidence from Smallholder Cassava Farmers in Nigeria.” Conference paper presented at the International Association of Agricultural Economists, Milan, Italy, 9–14 August. 4. Beegle, Dehejia & Gatti. 2003 “Child Labor, Income Shocks and Access to Credit.” World Bank mimeo. 5. Binswanger, H P and S Khandker (1995). “The Impact of Formal Finance on the Rural Economy of India.” Journal of Development Studies. Vol 32, No 2:234–262. 6. Carter, M R (1989). “The Impact of Credit on Peasant Productivity and Differentiation in Nicaragua.” Journal of Development Economics. Vol 31, No 1:13–36. 7. Carter, M R and K D Wiebe (1990). “Access to Capital and Its Impact on Agrarian Structure and Productivity in Kenya.” American Journal of Agricultural Economics. Vol 72, No 5:146–50. 8. De Gregorio J. 1996. “Borrowing Constraints, Human Capital Accumulation, and Growth.” Journal of Monetary Economics Vol. 37: 49-71. 9. Dehejia & Gatti. 2002. “Child Labor: The Role of Income Variability and Access to Credit in a Cross Section of Countries.” World Bank mimeo 10. Demirgüç-Kunt, Asli, Thorsten Beck, and Patrick Honohan. 2008. “Finance for All? Policies and Pitfalls in Expanding Access.” Washington, DC: World Bank. 11. Ellis, Karen; Lemma, Alberto; Rud, Juan-Pablo. 2010. “Investing the Impact of Access to Financial Services on Household Investment,” Overseas Development Institute (ODI). London. 12. Eswaran & Kotwal. 1990. “Implications of Credit Constraints for Risk Behaviour in Less Developed Economies.”, Oxford Economic Papers, 42: 473-482. 13. Feder, G, L J Lau, J Lin and X P Luo (1990). “The Relationship between Credit and Productivity in Chinese Agriculture: A Microeconomic Model of Disequilibrium” American Journal of Agricultural Economics. Vol 72, No 5: 1151–57. 14. Galor O and Zeira J. 1993. “Income Distribution and Macroeconomics.” The Review of Economic Studies. Vol. 60, No. 1: 35-52. 15. Ghosh P, Mookherjee D, and Ray D. 1999. “Credit Rationing in Developing Countries: An Overview of the Theory.” In “A Reader in Development Economics.” Ed. By Mookherjee & Ray, London: Blackwell. 16. Government of India (GoI), Micro, Small and Medium Enterprises, Government of India, District Industrial Profile of Eastern region districts of Uttar Pradesh. 17. Guirkinger, C (2008). “Understanding the Co-existence of Formal and Informal Credit Markets in Piura, Peru.” World Development. Vol 36, No 8:1436–52. 18. India Brand Equity Foundation (IBEF) (2018). Uttar Pradesh: A Rainbow Land, May 2018. www.ibef.org. 19. Jacoby, H; 1994. “Borrowing Constraints and Progress Through School: Evidence From Peru.” Review of Economics and Statistics. 76: 151-60 20. Khandker, S R and R R Faruqee (2003). “The Impact of Farm Credit in Pakistan” Agricultural Economics. Vol 28, No 3:197–213. 21. Kumar, A, A Mishra, S Saroj and P K Joshi (2017). “Institutional versus Non-institutional Credit to Agricultural Households in India: Evidence on Impact from a National Farmers’ Survey.” Economic Systems. Vol 41, No 3, pp 420–32. 22. Kumar, Anjani; Sonkar, Vinay K; Saroj, Sunil (2020). “Access to Credit in Eastern India: Implications for the Economic Well-being of Agricultural Household.” Economic and Political Weekly, Vol.55. Issue No.21. 23 May 2020. 23. Luan, D X and S Bauer (2016). “Does Credit Access Affect Household Income Homogeneously across Different Groups of Credit Recipients? Evidence from Rural Vietnam.” Journal of Rural Studies, Vol 47, Part A: 186–203. 24. NABARD. 2019-20. Annual Report 2019-20. National Bank for Agriculture and Rural Development (NABARD). 25. Narayanan, S (2016). “The Productivity of Agricultural Credit in India” Agricultural Economics. No 47, No 4:399–409. 26. Pal, Rajesh (2020). “Financial Inclusion: The Road Map to Balanced Regional Development,” Indian Journal of Regional Science. Vol.XXXXV, No.2, 2013: 122. 27. Pitt, M M and S R Khandker (1996). “Household and Intrahousehold Impact of the Grameen Bank and Similar Targeted Credit Programs in Bangladesh” World Bank Discussion Paper. No 320, World Bank, Washington, DC. 28. — (1998). “The Impact of Group-Based Credit Programs on Poor Households in Bangladesh: Does the Gender of Participants Matter?” Journal of Political Economy. Vol 106, No 5, pp 958–96. 29. Reserve Bank of India (RBI). 2019-20. Handbook of Statistics on Indian States. RBI. 30. Romer P.M. 1990. “Endogenous Technological Change.” Journal of Political Economy, 98: S71-S102. 31. RBI Report. 2019. Report of the Internal Working Group to Review Agricultural Credit. RBI Report, 13th September. 2019 https://www.rbi.org.in/Scripts/PublicationReportDetails.aspx?UrlPage=&ID=942#CP1 32. Solow R.M. 1956. “A Contribution to the Theory of Economic Growth.” Quarterly Journal of Economics. 70: 65-94. 33. The World Bank https://www.worldbank.org/en/publication/gfdr/gfdr-2016/background/financial-access. 34. Tripathi, Amarnath. “Doubling The Farmers’s Income in Uttar Pradesh by 2022 – Opportunities and Constraints.”