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FINANCING RURAL INFRASTRUCTURE IN DEVELOPING COUNTRIES: THE CASE OF INDIA RAJARAMAN, Indira* Abstract: Motivated by the robust empirical evidence on the positive growth and poverty eradication outcomes of public investment in rural infrastructure, this paper investigates variations in utilization by subnational state governments in India of a recent non-concessional lending facility for financing rural infrastructure projects. Contrary to prior expectations that only states in a robust fiscal situation would voluntarily approach a non-concessional window, a fixed effects panel regression establishes that the scheme was accessed in years of fiscal stress. A second exercise on irrigation funding in a low rainfall state shows allocations to high rainfall rather than low rainfall districts within the state. This is not an efficient allocation, in the light of empirical findings for India and China of higher returns to rural infrastructure in low-potential rainfed areas. Together, the results point to the need for fiscal conditionalities on the borrowing government, going beyond default guarantees. Lending has to be made conditional on upfront evidence of sectoral or general fiscal recovery mechanisms, linked to the sectoral pattern of use, with the time-pattern of disbursement dictated purely by project considerations. Poorer states, and less endowed regions within states, will need technical assistance to identify financially viable projects, since projects readily available off the shelf are typically available for better endowed regions, where the demands in terms of technical complexity and community involvement are lower. JEL Classification: H41, H54, R12 Keywords: rural infrastructure, spatial balance, fiscal recovery, Asia, India
*
Indira Rajaraman, RBI Chair Professor, National Institute of Public Finance and Policy, New Delhi, India. E-mail:
[email protected] 53
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1. Introduction This paper is motivated by the robust empirical evidence on the positive growth and poverty eradication outcomes of investment in rural roads and other infrastructure, and on the higher incremental returns to infrastructure provision in relatively poorly endowed regions. These findings have been repeatedly confirmed not only for India but elsewhere in the developing world as well. What this literature leaves unexamined is the issue of how rural infrastructure is to be funded. Village road connectivity has the classical characteristics of a public good, for which private provision is not possible, unlike inter-city highways for example.1 Funding of rural infrastructure is, in essence, a fiscal issue. In the absence of outright grants, fiscal revenues must be found either upfront or for payback of debt. This is so even if debt funding carries some element of concessionality. Direct recovery through user charges is possible in some sectors like irrigation, but can face constraints on pricing imposed by compliance considerations (Rajaraman, 2003a). India has had a lending facility for rural infrastructure since 1995-6, perhaps the only one of its kind anywhere in the world. The Rural Infrastructure Development Fund (RIDF) is a demand-driven nonconcessional scheme targeted at subnational state governments. The success of schemes like the RIDF in redressing inter-state inequities and imbalances (Ahluwalia, 2000), would depend on the cross-state pattern of utilisation of the fund. It is this issue that constitutes the central focus of study here. The paper reports findings from a fixed effects panel model, where the variable explained is the annual aggregate disbursement under the scheme to each state in each year of the period 1995-2001. A cross-state comparison requires normalization of the absolute receipt of any state by rural area, in order to capture the spatial dimension of rural infrastructure adequacy. Normalization is not done by rural population, since a state with greater population density would thereby get a higher spatial density of infrastructure for an equivalent disbursement per capita. A second exercise regresses district-wise 54
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data on disbursement of RIDF funds on irrigation projects, confined for data availability reasons to a single low rainfall state (Rajasthan). The RIDF is fully funded by Indian commercial banks from the unutilized portion of their sectoral lending requirement to agriculture, stipulated at 18 percent of net bank credit. Faced with tightened prudential norms over the last ten years, banks have seized the RIDF as a way by which to meet their sectoral lending requirements, which account for the bulk of defaulting loans (RBI, 2001).2 The RIDF has been renewed each year since 1995-6 in eight ‘tranches’ from RIDF-I to RIDF-VIII, and is intermediated by the National Bank for Agriculture and Rural Development (NABARD). Subnational state governments are the targeted borrowers, although the potential borrower set has very recently been widened to include NGOs, rural self-help groups, and local governments. The total disbursement of RIDF funds in any year to a state is the sum of disbursements under the various running tranches. Section II surveys the empirical literature on the impact of rural infrastructure on agricultural productivity and rural poverty, and on fiscal recovery in infrastructure. Section III outlines the parameters of the RIDF and the sectoral pattern of RIDF sanctions and disbursements. Rank correlation coefficients are computed between normalised RIDF disbursements and state rankings by per capita SDP and by an infrastructure index. Section IV presents a fixed effects panel specification, relating annual (normalized) disbursements under the RIDF to a concurrent (annually variable) measure of fiscal status. The model is estimated across annual disbursements, aggregating over the first six tranches of RIDF, covering the period 1995-2001. The state-specific fixed intercepts partition the states into those where RIDF disbursements are higher than warranted by the fiscal indicator, and those where it is lower. These intercepts merely have segmentation value, and represent a residual basket of state-specific causes. Section V concludes. 2. The literature: rural growth, poverty, and infrastructure Empirical studies consistently show the positive impact of infrastructure on growth performance (Baffes and Shah, 1992; 55
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Canning and Fay, 1993). Rural infrastructure in particular, coming predominantly through public investment, has been shown to improve agricultural productivity and lower rural poverty (Binswanger et al., 1989; Ahmed and Hussain, 1990; Fan, Hazell and Thorat (FHT), 2000; and Fan and Hazell, 2001). Binswanger, Khandker and Rosenzweig (1989) use panel data for 85 randomly chosen districts in India across 13 states from 1960-1 to 1981-2 and establish that roads contribute significantly to the growth of agricultural output and fertilizer use and also to bank expansion. The study estimates that private investment responds to public investment with elasticities in the range 0.26 to 0.90; it is the only source of such estimates for rural India. The FHT (2000) study also uses Indian state level data from 1970 to 1993. Expenditure on roads and R&D are estimated as having the largest impact on productivity growth and rural poverty reduction, summing across direct and indirect impacts. Fan and Hazell (2001) extends the methodology of the earlier study to district level data from 1970 to 1995, and shows that investments in low-potential rainfed areas yield the highest returns. Similar results are reported in the paper for China, where all public investments had their biggest impact on poverty in the low-potential western region, and the lowest in the high-potential coastal region. 3 Clearly, the implementational efficiency, composition (both spatial and by type), and operating efficiency of capital expenditure matter as much, or more, than the quantum by itself (Devarajan, et al, 96). These findings motivate the investigation that follows of the spatial pattern of disbursement of RIDF funds. There is very little empirical literature directly addressing the issue of fiscal recovery for nonconcessional lending schemes targeted at provision of rural infrastructure. To the extent the infrastructure is of the rural roads type, classically non-excludable and non-rival public goods, fiscal recovery will be a function of the generalized fiscal position of the states, with user charges possible only in sectors with identifiable beneficiaries like irrigation. This then leaves open the issue of 56
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whether drawals under the scheme are related to the fiscal indicators of the state. The panel regression explores this issue. 3. The parameters of the RIDF scheme The RIDF compels our attention, not because it is the only funding provision for rural infrastructure, but because of what can be learned from the pattern of utilization of a non-concessional scheme voluntarily accessed by subnational governments in a fiscal federation. At around 50 billion rupees annually in recent years, it is of the same order of scale as annual flows to the rural areas from the Central government, on employment schemes that are characterized as current expenditure, but really have a rural infrastructure focus. Started in 1995-6, it has been extended every year in numbered tranches, from RIDF-II in 1996-7 to RIDF-VIII in 2002-03. Each tranche targets a specified corpus. Details on the parameters of each tranche of the scheme are available in Rajaraman, 2003b. A few operating features of the scheme are listed below. These are of relevance for the specification of the model that follows. RIDF is operated as a reimbursement scheme with prior expenditure by the state,4 which then activates the NABARD disbursement mechanism. NABARD’s speed of response makes for very short intervals between expenditure and reimbursement, and each disbursement can be used to fund subsequent expenditures, so that after the initial instalment there are no subsequent claims on the state exchequer. The requirement that state governments fund half the total project cost was dropped after RIDF-I to 10 percent. Actual disbursement under any tranche can take place at any time either during the tranche year, or subsequent years.5 Thus, even if the pattern of sanctions targets cross-state parity, the pattern of disbursement remains determined by state government willingness to draw the funds. Thus, the RIDF is entirely demand-driven in its pattern of disbursements. The interest charged to states on RIDF advances has declined over time in accordance with the general interest rate decline, but even the peak rate of 13 percent in 1995-6 was lower by 150 basis points than 57
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the rate charged on loans from the Government of India.6 Thus, even though the scheme is non-concessional, it has always offered a rate advantage over other sources. Commercial banks have been willing contributors from their unmet sectoral commitment to agricultural lending within the priority sector, subject to a maximum of 1.5 percent of net bank credit. The spread between the lending rate and the deposit rate paid to commercial banks was 50 basis points until 2001, after which the deposit rate payable has been graded to shortfalls in meeting sectoral targets for agriculture. Currently the spread ranges between 50-350 basis points. There are separate accounts for drawals under the RIDF to protect the scheme against fungibility, with formal guarantees against default from the RBI. Thus, NABARD as the intermediary was protected against default. Even with the formal widening of the universe of potential borrowers starting with RIDF-V in 1999-2000, to gram panchayats, NGOs and self-help groups, state governments continue to guarantee loan servicing. The scheme did not intervene in the issue of how state governments were to service RIDF loans. In the first year of the scheme RIDF lending was confined almost exclusively to irrigation projects. The share of irrigation came down sharply to less than half in RIDF-II, and has subsequently fallen to about a quarter. The compensating increase in share has gone to rural roads, which now account for nearly half of RIDF funds. With this goes a shift in the loan servicing requirements of the facility. Overall, aggregating across the six tranches, irrigation accounts for 39 percent of total sanctions, and roads and bridges together for 53 percent, with the remainder going towards watershed management and other miscellaneous categories. Average sanctioned project cost has come down over the years, from 4.5 million in RIDF-I to 1.2 million today in RIDF-VI, suggesting that the scheme has become increasingly configured towards more divisibility, and greater potential evenness in spatial distributions. By end-March 2001, cumulated disbursements summing across all tranches amounted to Rs. 92.51 billion, around half of total sanctions. Table 1 presents the diagonal array of cumulated disbursements under the six tranches against total 58
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sanctions. The table highlights a number of interesting features of RIDF disbursements. Despite the explicit selection of projects with a 2-3 year gestation/phasing profile, the cumulated disbursements at the end of three years for any tranche of the RIDF has been around one half for all but RIDF-I, exhibiting a fair degree of disbursement drag. It is in this context that the inter-state pattern of disbursement becomes a matter of interest and importance. Table 1: Disbursements As a Percent of Sanctions (percent) RIDF Tranche Sanctions Cumulated Disbursements Rs. billion 1996 1997 1998 1999 2000 2001 RIDF-I 18.99 20.40 62.32 75.49 81.24 89.09 91.77 RIDF-II 25.89 11.26 32.21 55.49 72.50 80.83 RIDF-III 26.65 8.13 25.73 52.99 70.73 RIDF-IV 31.20 4.24 21.65 44.30 RIDF-V 36.39 11.49 32.68 RIDF-VI 46.33 20.71 Total 185.45 20.40 32.86 34.72 36.96 43.66 49.89 Source: NABARD, Annual Reports, assorted years.
All 25 states7 have accessed the RIDF to varying extents. The allIndia average for the cumulative disbursement by 31 March 2001 works out to approximately Rs. 30,000 per rural square kilometer,8 and to Rs. 125 per head of rural population. 9 These nationwide figures are averages across an enormous range, from nearly Rs. 80,000 per sq.km. in Kerala to Rs. 433 per sq.km. in Manipur. Likewise, the per capita figures range between Rs. 509 in Mizoram to Rs. 2 in Bihar. Table 2 presents rank correlation coefficients between states ranked by area-normalised cumulative disbursement and rankings by per capita SDP (for the full set of 25 states), and by a relative infrastructure index (available only for a limited set of 17 states). The 59
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coefficients are 0.52 and 0.79 respectively, both highly significant statistically. Thus, the pattern of use of the RIDF is not corrective of the spatial inequality of infrastructure endowment, nor of the spatial pattern of per capita domestic product. Table 2: Rank Correlation Coefficients Rankings Per capita SDP Relative Infra. 1994-7 Index 1993 No. of states 25 17 Cumulative disbursement Per sq. km rural area
0.52 (2.88**)
0.79 (4.94**)
Source: Government of India (2000) for rural area and SDP per capita 1994-97; CMIE (1997) for the relative infrastructure index, 1993. Notes: The t-statistic is reported below each coefficient in parentheses.The rank correlation coefficient with the relative infrastructure index uses the small sample formula .
4.The model and the results The panel specification below assigns to the fiscal health of state i in year t a uniform slope coefficient across states in explaining yearly disbursements under the scheme, leaving an unexplained residual captured by fixed state-specific and time (yearly) intercepts. No additional term was provided to capture variations over time in the parameters governing the scheme, since any change would be picked up by the time effects. The specification thus separates states by state-specific intercepts into those with high and low utilisation rates, after controlling for the (concurrent) fiscal indicator. Yit = k + α i + χ t + βXit + ε it Where Yit = normalized disbursement for the i-th state in the t-th year, Xit = fiscal indicator for the i-th state in the t-th year, α i = fixed effect for state i, χ t = fixed effect for year t ε it = error term. 60
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The panel regression exercise used three alternative measures of normalized disbursement, Yit . Normalization was not done per capita, since a sparsely populated state becomes entitled in effect to a much smaller disbursement by virtue of its low population. Disbursement per square kilometer (DPSQKM) accommodates the spatial dimension of equality of access to infrastructure, but with the disadvantage that the population dimension does not enter at all. Two other measures were therefore tried, one (DDENINV) which normalizes disbursement by the inverse of rural population density. Essentially, this measure multiplies DPSQKM by rural population, which accords with the non-excludable, non-rival characteristic of access to public infrastructure provision. This measure imposes a fairly exacting requirement for equalization of rural infrastructure across states. If there are two states A and B with the same rural area, where A has twice the rural population density of B, they will carry the same value by DDENINV only if RIDF expenditure in B is twice that in A. Basically, this measure is a population-weighted measure of expenditure per square kilometre. The final measure (DPSQKPSH) weights DPSQKM by the share of the state in the total rural population of the country, rather than by absolute rural population. Thus for disbursement Dit in the ith state in the tth year, i = 1,…25; t = 1995-6, …2000-01: DPSQKMit = DDENINVit = DSQKMPSH it =
Dit / Rural Areai DPSQKMit * Rural Popi, 2001 DPSQKMit * Rural Popi, 2001 ______________________ Σ Rural Popi, 2001
Table 3 presents the coefficients for five alternative fiscal indicators for each of the three normalized measures of disbursement. Of the five fiscal indicators tried, the first two are measures of fiscal imbalance. The absolute fiscal imbalance on the current account, REVDEF, is obtained from the difference between current expenditure and current revenue. This is termed the revenue deficit in India. Current expenditure includes interest on public debt of the government, and wages and salaries. 61
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Table 3: Coefficients of Fiscal Indicators Independent variables Dependent variables DPSQKM DDENINV DSQKPSH REVDEF
RR/EXP
OR/EXP
Slope
0.942
8.094
0.109
t-ratio R2
3.501** 0.685
6.317** 0.734
6.317** 0.734
Slope
-5431.215
-29009.774
-392.16
t-ratio R2
-1.516* 0.659
-1.546* 0.652
-1.546* 0.652
Slope
-8202.144
-29169.843
-394.328
t-ratio R2
-1.202 0.656
-0.813 0.647
-0.813 0.647
Slope
-4508.723
-52389.491
-708.220
t-ratio R2 Slope
-0.463 0.653 -1923.946
-1.030 0.648 10372.404
-1.030 0.648 140.218
t-ratio R2
-0.224 0.652
0.230 0.645
0.230 0.645
SOTR
SOR
Notes: For definitions of dependent variables, see text. Independent variables are the absolute revenue deficit in crore rupees (REVDEF); ratio of revenue receipts to revenue expenditures (RR/EXP); ratio of own revenue to total revenue expenditure (OR/EXP); share of own tax revenue in total revenue (SOTR); and share of own (tax+non-tax) revenue in total revenue (SOR). Double asterisks mark coefficients significant at P ≤ 0.10, and single asterisks for 0.10 < P < 0.15. All figures for R2 are for R (bar) squared.
These are not compressible, so that the excess of these expenditures over revenue receipts is a more accurate measure of fiscal stress than the gross fiscal deficit, which includes compressible 62
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capital expenditures, and is obtained after deduction of privatization receipts. In principle, the revenue and fiscal deficits could move in opposite directions between any two years. The revenue deficit was taken in absolute terms, because the usual normalizing denominator (SDP) was not available for all states and years in the panel. Even if it were, there is a problem with normalizing by state-level SDP figures, which are available only at factor cost, not at market prices. An alternative measure was therefore tried in addition, the ratio of revenue receipts to revenue expenditures (RR/RE), which calls for no defence. The three other fiscal indicators tried focus on the own tax and non-tax revenue of state governments. These indicators were tried in order to test for whether the ability of states to raise their own revenue, or in other words their fiscal independence in terms of transfers from the centre, could have any explanatory value with respect to accessing the RIDF facility. The sign of REVDEF is positive and highly significant. That RIDF disbursements are taken in years of fiscal stress is supported by the second measure of fiscal imbalance (RR/RE), which is the ratio of revenue receipts to revenue expenditures. Here the sign is negative, indicating higher RIDF disbursement in years when revenue receipts fall as a proportion of revenue expenditure, although the level of statistical significance is lower than for the absolute revenue deficit. Thus, the results are consistent across alternative specifications of fiscal stress, and across alternative specifications of the dependent variable. None of the measures of states’ own revenue, in its various forms, carried any statistical significance. Year-specific intercepts are presented in detail in Table 4 for the dependent variable DPSQKM, and for the dependent variable specified as DSQKPSH (the results are identical to specification DDENINV, but the coefficients are lower because of normalization by the total rural population of the country).
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Table 4: Fixed Time Effects DPSQKM DSQKPSH REVDEF RR/EXP REVDEFRR/EXP -3009.52 -3547.05 -93.97 -166.58 (-3.73**) -1241.85 (-1.57*) -1332.86 (-1.69*) -1605.35
(-4.10**) -1529.23 (-1.72*) -1698.33 (-1.97*) -1433.16
(-2.18**) (-3.31**) 18.88 -28.84 -0.45 (-0.56) -19.34 -71.69 (-0.46) (-1.43) -76.86 -45.73
1998-9
(-2.08**) 2176.36
(-1.65*) 2617.05
(-1.87*) (-0.90) 17.33 84.67
1999-00
(2.68**) 5013.22
0.40 153.96
2000-01
(6.24**)
(2.90**) 5590.71 (6.56**)
1996-7 1997-8
(1.61*) 228.18
(3.59**) (4.60**)
Notes: For definitions of variables, see text. Double asterisks mark coefficients significant at P ≤ 0.10, and single asterisks for 0.10 < P < 0.15.
The state-specific intercepts are available in Rajaraman, 2003b. The year-specific intercepts across all specifications show a steady progression from negative, and highly statistically significant intercepts for the first year, 1995-6, to positive, and highly statistically significant intercepts for the final year in the panel, 2000-01. Thus, the time-specific intercepts clearly point to an increasing willingness to access the RIDF facility over time. District-wise data on disbursement of RIDF funds for irrigation are available for Raja sthan, spanning RIDF-I to VIII upto October 2002. A simple OLS cross-sectional regression of absolute disbursement on irrigation projects was performed on average annual rainfall across 27 districts (Table 5). The positive and statistically significant coefficient for rainfall in these regressions is disturbing. A scheme such as the RIDF offers the only possible scope for small-scale ground-water sourced projects, in low-rainfall states like Rajasthan. The most probable reason is that schemes for better endowed areas 64
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are less complex in terms of project design and community involvement. Table 5: District-wise Distribution of RIDF Irrigation Funds Dep. Var Intercept Rainfall No. of (1951-91) districts RIDF -1898.38 6.37 27 Funds (-0.96) (1.81) (Rs. Lakh) Source: Government of Rajasthan, Statistical Abstract for rainfall data; Irrigation Department for irrigation projects by district. Notes: Rainfall data were for pre-1991 districts; disbursements to new districts formed after 1991 were aggregated with those of parent districts. Dausa was merged with Jaipur, where four of its five tehsils originated. Numbers in parameters are t-values.
5. Conclusions Two formal exercises are reported in the paper. The first estimates a fixed effects panel regression across subnational state governments in India, relating area-normalized annual disbursements drawn by state i in year t, across 25 states,10 in each year of the period 19952001, aggregated across all tranches of the RIDF facility, to alternative measures of concurrent fiscal status. The second regresses district-wise data on disbursement of RIDF funds for irrigation which were available (only) for Rajasthan, upto October 2002, on average annual rainfall. The single most startling finding of the panel regression is that a non-concessional lending facility like the RIDF is accessed in years of fiscal stress. The results are robust across alternative specifications of fiscal stress, and of the dependent variable. Although the results say nothing about the quality of projects selected, they show that the timing of execution is subordinated to fiscal considerations. The institutional features of the scheme which support and explain this finding are the interest rate advantage of 150 basis points relative to loans from the Government of India, and the procedural ease of accessing sanctioned funds through the RIDF channel. Further, the requirement that state governments fund half the total project cost was dropped after the 65
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first year to 10 percent. Actual disbursements are timed by the borrower rather than the lender at any time in the year of sanction or subsequent years. Finally, a Central Bank guarantee protected NABARD against default, and enabled willingness to disburse funds even to borrowers in poor fiscal health. In any non-concessional scheme there will necessarily be uneven utilization, but as the rank correlations show, the pattern is not corrective of the spatial inequality of infrastructure endowment, nor of the spatial pattern of per capita domestic product. The statewise figures for cumulated disbursement by 31 March 2001 vary across an enormous range, from nearly Rs. 80,000 per rural sq.km. in Kerala to Rs. 433 per rural sq.km. in Manipur, around an all-India average of approximately Rs. 30,000. Finally, the results of the cross-district allocation of RIDF irrigation funds for Rajasthan show that these are allocated to high rainfall rather than low rainfall districts. This is not an efficient allocation, in the light of empirical findings for India and China of higher returns to rural infrastructure in low-potential rainfed areas. Together, the results point to the need for fiscal conditionalities on the borrowing government, going beyond default guarantees. Lending has to be made conditional on upfront evidence of sectoral or general fiscal recovery mechanisms, linked to the sectoral pattern of use, with the time-pattern of disbursement dictated purely by project considerations. Poorer states, and less endowed regions within states, will need technical assistance to identify financially viable projects, since projects readily available off the shelf are typically available for better endowed regions, where the demands in terms of technical complexity and community involvement are lower. Without fiscal conditionalities and technical assistance the procedural ease of schemes like the RIDF, which is what has driven its utilization despite its non-concessional character, will only deepen the fiscal problem at state-level in the medium term, and will do nothing to correct spatial inequalities in infrastructure endowment.
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References Ahluwalia, M. S, 2000. “Economic Performance of States in Post Reforms Period” Economic and Political Weekly, 35, 1637-1648. Ahmed, Raisuddin and Mahabub Hossain, 1990. “Development Impact of Rural Infrastructure in Bangladesh” Research Report 83, International Food Policy Research Institute, Washington, D.C. Baffes, John and Anwar Shah, 1992. “Productivity of Public Spending, Sectoral Allocation Choices and Economic Growth” Paper presented at 1993 Annual Meetings of American Economic Association, Anaheim, California. Binswanger, Hans P., S. R. Khandker and M. R. Rosenzweig, 1989. “How Infrastructure and Financial Institutions Affect Agriculture Output and Investment in India” Policy Planning and Research Working Paper No.163, World Bank, Washington D.C. Canning, David and Marianne Fay, 1993. “The Effect of Infrastructure Network on Economic Growth” Discussion Paper Series, Columbia University, New York. Centre for Monitoring the Indian Economy (CMIE), 1997.Profiles of States, Mumbai. Devarajan, Shantayanan, V. Swaroop and H. Zou, 1996. “The Composition of Public Expenditure and Economic Growth” Journal of Monetary Economics 37, 313--344. Fan, Shenggen and Peter Hazell, 2001. “Returns to Public Investment in Less Favoured Areas of India and China” American Journal of Agricultural Economics, 83(5), 1217--1222. Fan, Shenggen., Peter Hazell and S. K. Thorat, 2000. “Impact of Public Expenditure on Poverty in Rural India” Economic and Political Weekly, 30 September, 35, 3581--3588. Government of India, 1998. Report of the (Narasimham) Committee on Banking Sector Reforms, April. Government of India, (2000). Report of the Eleventh Finance Commission for 2000-2005, June. National Bank for Agricultural and Rural Development, NABARD, Annual Report, assorted years. Rajaraman, Indira, 2003a. “Financing Rural Infrastructure In Irrigation: Fiscal Recovery Issues” IAMR Working Paper No. WP16/CPPG/2003. 67
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Rajaraman, Indira, 2003b. “Inter-State Variations in Utilisation of the Rural Infrastructure Development Fund” Institute of Economic Growth Working Paper E/235/03. Rajaraman, Indira, 2004. “Fiscal Developments and Outlook in India” NIPFP Working Paper No. 15, New Delhi. Reddy, Y. V., 1999. “Infrastructure Financing: Status and Issues” Reserve Bank of India Bulletin, 21--32. Reserve Bank of India, 2001. Report on Trend and Progress of Banking in India 2000-2001. Endnotes 1
For an examination of private financing possibilities for infrastructure, see Reddy (1999). 2 To quote the Government of India (1998) report, “Bank managements tend to have an understandable concern, sometimes bordering on the obsessive, with the need to meet the prescribed quantitative targets…This has led to erosion and sometimes serious deterioration of the loan portfolio.” 3 In terms of productivity, however the western region did not, with the exception of agricultural R & D, rank the highest, thus suggesting some trade-off between growth and equity goals in China. 4 Loans to states under the scheme are capped by the total borrowing entitlement of the state, as stipulated under Article 293 of the Constitution. 5 The actual contribution by a commercial bank in any year is the sum of serial demands arising from pledges in that, or previous, tranches of the RIDF. 6 On-lent from small savings collections. For details, see Rajaraman, 2004. 7 The three new states are incorporated into their erstwhile states. 8 Total RIDF disbursement of 9.2 thousand crore over approximately 3 million square kilometres of rural area (Government of India, 2000). 9 The total rural population was estimated by Census 2001 at 0.74 billion out of a total population of 1.03 billion. 10 The period studied in this paper predates the formation of three newstates. Acknowledgements: The author thanks Arindam Datta for able research assistance. _________________________ Journal published by the Euro-American Association of Economic Development. http://www.usc.es/economet/eaa.htm 68
