Analysis of development and rural water services by state

state to state. In Bihar state just 3% of the rural population have access to a household connection whilst in the state of Sikkim this rises to over 80%

(Census of India, 2011a). This section therefore presents the results from an analysis of state-wise secondary data. It focuses on two key dependent variables which are the improved water supply coverage rate and the household connection coverage rate, both as reported by the Census of India (2011a).

These are then assessed by how they relate to other development variables which are outlined in Table 3-1 below. The analysis used data that was available at the state level in India either through the academic literature or published by reputable institutions such as the Government of India or international bodies, such as the UN. Table 3-1 below shows the data that was examined and justifies why it was selected whilst Table 3-2 shows the dataset.

The data was initially compiled in MS Excel with the analysis being conducted in SPSS. As discussed in the methods chapter, the measure used here is the Kendall’s tau test whilst statistical significance and effect size are assessed against the proposed ladders from Field (2013). The statistical output from the analysis is presented in Table 3-3 and explained below the tables.

Table 3-1 Macro-development measures as potential determinants for success in rural water supply

Measure Description of measure Justification for use Source

GDP Per Capita (PPP)

GDP is the most widely used measure of economic activity given for a specific area over a designated time period, usually a year. The ‘per capita’ measure divides this by the population, whilst PPP makes this

comparable across countries.

Although the use of GDP as a development indicator has been criticised for being economically

deterministic (Costanza et al., 2014), it remains among the most widely used due to the high correlations between GDP and development outcomes such as health (Schell et al., 2007).

Gross Domestic Product per

HDI is a composite indicator; it measures three composite dimensions of human development related to health, education and standards of living.

Based on the synergistic development process described above, it would be expected that the broad increase in health, education and standards of living would lead to positive “feedback loops” in the provision of public services such as rural water services.

Each year the Government of India assess the extent of devolution to the Panchayat Raj Bodies across India.

As discussed later in this chapter there are varying levels of decentralisation between India states. It may be that there is a link between decentralisation and the provision of rural water services and it is therefore interesting to see whether any association can be identified in this state-level analysis

Ranking of States on

A consumption based poverty line is set for rural and urban areas by the Government of India based on a mixed reference period (MRP) measure of consumption data reported in the National Sample Survey (Government of India, 2009b).

It is anticipated high rural poverty rates are likely to be negatively associated with progress in terms of water supply due to the reduced capacity for cost recovery from users (among other issues).

Handbook of Statistics on Indian Economy (Reserve Bank of India, 2015)

Literacy Rate Through the Census of India (2011b) the literacy rate is collected as the number of people living above the age of 7 that can both read and write in any language.

Literacy rates are an important component of the HDI yet they could be especially potentially important indicators for rural water services where there is a high degree of devolution of power to rural communities. distribution of a country as the basis for assessing inequality among the population.

Economic inequity has been associated with poor development outcomes (Easterly, 2007) so it may be expected that a high Gini coefficient would be negatively associated with high coverage in rural water supply.

Data reported from 2004 dataset at the state-level:

(Rajan et al., 2013)

Growth in Poverty Elasticity (Rural Population)

Growth in Poverty Elasticity is a measure of the extent to which increases in GDP lead to decreases in the proportional of people living below the poverty line.

The measure reflects the extent to which the

economic process in any state is leading to equitable outcomes in terms of its impact on the poor.

Therefore, in a similar fashion to Gini coefficients it may offer insight on whether equity in terms of economic development impacts coverage rates.

A simply binary variable has been created based on whether the state is has a largely mountainous physiographical zone or is classified as a different physiographic zone (plains, plateau and coast).

After an initial analysis run and reading of the case studies, it was recognised that the geographical setting could be a determining factor for rural water supply success. In particular, the difference between those states that are largely mountainous or highland against the rest appeared to be particularly strong with regards to improved access. Pre-analysis runs before the correlation were conducted that identified statistically significant differences between the median outcomes for improved water supply in mountain classified states against the rest of the sample but no statistical difference between the plains, plateau and coast categories. Hence, it was considered appropriate to consolidate those physiographic zones into one category for the purpose of the correlation. The pre-correlation analysis tests used were the Kruskal Wallis with post hoc Dunn’s test for stepwise comparison, as used throughout the analysis in later stages of the thesis.

Allocation geography variable versus other binary category for correlation run.

Table 3-2 - State-wise rural water supply and development indicators dataset (sources and definitions as reported in table

Uttarakhand 91% 64% $5,916 0.49 14 12% 80% 29.8 -2.5 Other

West Bengal 98% 11% $3,997 0.492 7 23% 77% 32.4 -4.3 Mountain

Table 3-3 Results of the bivariate analysis of development indicators against rural water supply coverage Household piped water supply Improved Water Supply

# Macro-development Indicator Kendall tau 4 Human Development Index .395 .004 Medium effect (positive),

highly significant

-.277 .049 Small effect (negative), significant

.097 5 Devolution Index (Rank) -.013 .925 Not significant -.095 .523 Not significant .637 6 Below Poverty Line (Rural

Population)

-.498 .000 Medium effect (negative), highly significant

-.031 .821 Not significant .034

7 Literacy Rate .364 .006 Medium effect (positive),

highly significant

-.026 .850 Not significant .806 8 Gini-coefficient .117 .377 Not significant .387 .004 Medium effect (positive),

highly significant

.086 9 Growth in Poverty Elasticity

(2005-2012, Rural below the poverty line, State GDP)

.193 .159 Not significant -.078 .567 Not significant .000

10 Physiographic Zones .130 .401 Not significant -.631 .000 Large effect (negative), highly significant

.000

The analysis showed that there is no significant correlation between states with improved water supply and those with high household piped water supply coverage, suggesting that there are different drivers for each of these factors.

For household piped water supply, there are four states above 75% coverage, eight states between 50-75%, seven states from 25-50% and the rest below 25%. On this measure GDP per capita has a highly significant, strong effect suggesting it is only in states that have sufficient levels of wealth that the transition to household piped water supply is being achieved. The same relationship between wealth and improved water source is not found. There are two likely reasons why: 1) the lower level of capital and recurrent resource commitment and institutional capabilities that are required for basic improved services mean they are within reach of lower wealth levels; 2) due to the inherent human right and constitutional requirement for providing improved access, there is greater emphasis on transfers from the Federal Government, international donors and non-state actors like NGOs to support these types of services meaning domestic-state wealth becomes less important.

In comparison to piped water supply, there is limited variability in the improved water supply coverage rates with 21 out of the 29 states scoring between 90%

and 100% coverage rates. However, out of the 8 states scoring below 90%

seven of them come from the mountainous regions of Northeast India or the Himalayans (with the only exception Rajasthan that includes large areas of desert). This indicates that India is making progress towards universal access to improved water supply across all states, including the very poorest, apart from in areas where the mountainous geography becomes a key factor. This was confirmed by the highly significant association found between the physiographic variable and improved coverage as reported in Table 3 above. Although there could be different reasons for this, it is speculated that there could again be two overarching reasons: 1) mountainous physiographic settings tend to not be conducive to the basic improved water source (handpumps) but rather served by more complex and expensive gravity-fed piped systems making the level of investment for improved-access higher in these regions; 2) inaccessibility of some villages in mountainous physiographic settings further increases the

challenge of delivering basic services as compared, on average, to other physiographic setting.

It is accepted that this descriptive statistical analysis of trends only provides evidence of simple, binary correlations between a selective group of variables.

In this sense it does not provide any evidence of causation, nor does it assess the compounding of one variable onto another, yet it is still considered to provide appropriate insights into the overarching pattern of rural water access across Indian states. It helps illustrate how India has largely achieved its goals around universal improved water supply coverage apart from in the mountainous states. Beyond these states, however, differences in performance in terms of household piped water supply can best be explained by the development status of the states. Whilst no single variable can predict success, GDP per capita (PPP) and the ‘below the poverty’ line measures are most strongly associated with household coverage rates. It is argued in the next section that these factors are reflected in the political economy of states.

3.2 The political economy of development across India