Analysing the Relationship Between Public Infrastructures and Economic Growth in Assam

Abstract

This article evaluates the relationship between economic growth and public infrastructures in Assam using the panel dynamic ordinary least square estimation technique and the Dumitrescu–Hurlin panel causality test for the period of 1999–2000 to 2017–18. Empirical findings suggest that there is a long-term relationship between economic growth and public infrastructure indicators, except health infrastructure. One plausible reason for an insignificant relationship between health infrastructure and economic growth may be the insufficient availability of health infrastructure. However, all other public infrastructures have significant influence on achieving high and sustainable economic growth in Assam. In the short run, no public infrastructure has been found to have a significant impact on economic growth except for village electrification. Again, unidirectional causality from economic growth to public infrastructures (except road infrastructure) is acting as a hurdle in the path of the rapid expansion of public infrastructures in Assam. However, evidence of bi-directional causality between economic growth and road infrastructure suggests for extensive government intervention in road infrastructure development to achieve higher sustainable economic growth as well as balanced development across the state of Assam.

Keywords

Public infrastructure economic growth cointegration

Introduction

Assam is among 15 largest states of India in terms of population. According to the 2011 population census, the population of Assam was 31.2 million, which was around 2.57% of total population of India. Assam covers 2.38% of the total geographical area of India. However, its contribution to the national gross domestic product (GDP) is almost 1.67%, reflecting a very low contribution as compared to its share in population and geographical area. Again, per capita income of the state is the lowest in India. In 2017–18, per capita net state domestic product (PCNSDP) of Assam (at 2011–12 prices) was ₹57,835 (Reserve Bank of India 2020), which was around two-thirds of per capita net domestic product (PCNDP) of India. This reflects that Assam has not just failed to match with the national average, but it also stands far from achieving the national average. However, the situation was different at the time of Independence of India. During 1950–51, per capita income of Assam was 4.1% higher than that of aggregate national per capita income (Dutta 2012). But the situation of Assam has started changing after 1960–61 as it started falling behind national per capita income. However, even after falling behind, again in 1990–91, the gap had not widened much as PCNSDP of Assam was 6.32% higher than PCNDP of India (Reserve Bank of India 2020). After 1993–94, PCNSDP of Assam again started falling behind PCNDP of India. Over the years, this gap has widened further. This shows that the Assam economy has not been able to cope with the growth path of the national economy.

One of the prime factors for slower economic growth of Assam has been the low level of investment and poor infrastructure (Planning Commission of India and Government of Assam 2002). Gross fixed capital formation (GFCF), which is an indicator for measuring investment in an economy, was less than 1% of GSDP of the state in 2017–18. Again, share of Assam in GFCF of India was less than 1% in 2017–18. This share has fallen over the years from 0.92% in 2004–05 to 0.61% in 2017–18 (Reserve Bank India 2020). This reflects that investment in Assam is not only low, but it has also failed to cope with other states. In other words, as compared to growth in other states, growth of investment in Assam is very low. However, it is a well-known fact that investment is very much crucial for the development of backward regions. Higher level of investment is also important to sustain economic growth, particularly in developing economies. But private investors may be reluctant to make investment in Assam due to its poor infrastructure. According to the 11th Finance Commission Report (Government of India 2000), among the 15 major states of India, Assam’s rank was 13 in terms of infrastructure development. Therefore, dependency on the government investment increases as private investment is very low. But an economically backward state like Assam is also constrained by limited financial resources available to fulfil its investment requirements. To reduce dependency on government investment, more private investment needs to be attracted towards the state. To do that, it is very much essential to develop infrastructure of the state. Now, the main question is whether the low level of infrastructure development has resulted in slower economic growth in Assam? Or is it slower economic growth that has contributed towards the low level of infrastructure development in Assam? To answer these questions, it is important to identify the relationship between economic growth and different infrastructures in Assam. Also, the direction of that relationship will equally be important. Again, the matter of infrastructure development in Assam should rather be looked at from the perspective of a disaggregate level (i.e., at the district level). The rationale for studying the issue at the district level is that disparities have been witnessed even within the state across its districts in terms of both infrastructure development and economic growth. Therefore, it is imperative to study the relationship between infrastructure development and economic growth in Assam at the district level.

Review of Literature

Numerous studies have been undertaken to link infrastructure with economic growth. However, it has implications for other aspects related to economic growth. Supporting this view, the World Bank (1994) in its Annual Report mentioned that the diversification of production activities can be possible through adequate provisioning of infrastructure facilities. These facilities also help to promote international trade. Also, it is capable of dealing with population growth and reducing poverty in any country. Therefore, adequate availability of infrastructure is the key to success or failure of a country (World Bank 1994). Infrastructure is also considered a pre-condition for achieving sustainable economic growth (Canning and Fay 1993; Rao 1980; Rostow 1960). According to Rao (1980), infrastructure could help in increasing productive capacity and can maximise economic growth generated through a mutually additive effect arising out of coordination among inputs, outputs, space and time. He further added that to achieve and sustain the self-accelerating process of economic growth, it should be preceded, accompanied and followed by infrastructure development. Therefore, it is essential to develop infrastructural facilities before taking any economic activity and should be on top of the list of priorities for the development of any economy (Mellor 1976). According to Mellor (1976), regions which are rich in infrastructure will be able to attract more private capital, greater output and employment. Again, public infrastructure through creation of amenity services is capable of attracting private capital towards the region. Therefore, the regions with more and better public infrastructure are also capable of growing faster and vice versa (Eberts 1990). Conversely, inadequacy of public infrastructure can also retard private investment (Reinikka and Svensson 2002). There may be a possibility of crowding out of private investment by public capital investment financed through distortionary taxes or borrowing from financial market. But, by principle, it should be a short-term phenomenon. In the medium or long term, an increase in the public capital stock can raise the growth of output when government borrowing falls as a result of higher tax revenue. But if the crowding-out effect persists for a longer period, then an increase in public infrastructure financed through government borrowing may hamper economic growth rather than felicitating it (Agénor and Moreno-Dodson 2006). Again, with increase in the productivity of factor inputs used in the production process, public infrastructure can also have an additional indirect effect on labour productivity, which consequently leads to higher economic growth (Agénor and Neanidis 2006). Therefore, the impact of infrastructure development can be visible through increase in the productivity of other factors of production, improved international competitiveness, economic diversification, technological innovation, increased employment and reduced poverty (Kessides 1996).

Empirical evidence shows a very strong output elasticity of public capital or public infrastructure (Aschauer 1989; Munnell 1990). According to a World Bank report, 1% increase in infrastructure leads to 1% growth in output across all countries (World Bank 1994). Again, studies on South Asian countries found a significant long-term positive relationship between output and infrastructure after controlling for other relevant variables, such as gross domestic capital formation, labour force, international trade and human capital (Sahoo and Dash 2012). Moreover, just like physical infrastructure, social infrastructure has also contributed significantly towards economic growth in South Asia (Dash and Sahoo 2010; Sahoo and Dash 2012; Sahoo et al. 2012). Specifically, there is a unidirectional causality from infrastructure development (both economic and social) to economic growth in India. Therefore, they suggested that by investing more on infrastructure development, India will be able to promote and sustain higher economic growth (Dash and Sahoo 2010). But there can be instances where growth can contribute towards infrastructure development (i.e., reverse causality). Again, sector-specific studies also found that infrastructure development contributed positively towards productivity growth in sectors such as agriculture and manufacturing, which ultimately reflected in higher economic growth (Goel 2003; Mitra et al. 2012; Zhang and Fan 2004). Moreover, infrastructure can help in eliminating poverty and income inequality through its growth channel. Intuitively, more proportionate increase in income will be accrued to the poor in a growing society (Calderón and Servén 2004). Therefore, infrastructure development should be on top priorities of governments to address the issue of poverty and inequality while aspiring for higher growth levels.

Model Specification

Public infrastructures like improved road, electrification, irrigation and education are very important factors for improvement in agricultural output and productivity (Binswanger et al. 1993; Zhang and Fan 2004). Irrigation is something with immense importance in agriculture. It helps farmers to revegetate the soil under cultivation and also to diversify it crops to get higher returns. Improved roads directly connect the farmers to the end consumer benefiting both farmer and consumer by eliminating the additional cost that might exist in the absence of these services and by enabling the appropriate price of the product. Further, education makes the farmer aware about the necessary information and to make use of modern equipment that are needed to improve production and productivity. Electrification helps to adopt modern irrigation technique and equipment to increase production and productivity of the land. In the manufacturing sector, public infrastructures related to agriculture, banking, communication, transport, electricity, health and education sector are very much essential for improving productivity (Goel 2003; Mitra et al. 2012). In line with the agriculture sector, the manufacturing sector also benefits from infrastructure development. Infrastructure development reduces the cost of production leading to high return on investment for the manufacturer (Goel 2003). Transportation, electricity (or power) and communication infrastructures are among those infrastructures that have attracted more importance due to higher returns to these infrastructures and particularly transportation infrastructure, as it reduces the transportation cost associated with any economic activity. Not only that, it also reduces the cost associated with the payment of wages. Improvement in road infrastructure reduces time needed for transportation which brings down the transportation cost. Financial infrastructures like banking facilities are also very significant for both agriculture and manufacture as they release the credit that are crucial for carrying out activities in these sectors. However, education and health infrastructures are something different from other infrastructure as they come under the category of social infrastructure and mostly influence both economic and social outcomes through it impact on human capital. Social infrastructure enables the environment to develop better human capital in any society. Better human capital formation is very much crucial to have a better and more productive labour force. Social infrastructure provides the services that are valued for consumption and quality of labour force, which have consequent effects on social indicators of development. However, these effects can be available only when accompanied by economic infrastructure, such as transportation, electricity and telecommunication facilities.

Based on above-mentioned observations, the following equation has been estimated to capture the relationship between economic growth and different public infrastructures in Assam:

\ln Y_{i t} = α + β_{1} \ln {RD}_{i t} + β_{2} {EV}_{i t} + β_{3} {II}_{i t} + β_{4} \ln {BB}_{i t} + β_{5} {CDR}_{i t} + β_{6} l n E I_{i t} + β_{7} \ln {HB}_{i t} + ε_{i t},

(1)

where Y represents per capita net district domestic product (PCNDDP) and ln represents the natural logarithm. RD is road density, EV refers to the percentage of electrified villages,¹ II is the irrigation intensity, BB is the availability of bank branches, CDR refers to the credit–deposit ratio, EI is the availability of educational institutions and HB is the availability of hospital beds (see Table A1). Except for EV, II and CDR, all the other variables are in their natural logarithmic forms.² ‘i’ and ‘t’ represent the cross-section unit and time period. The expected sign of all the explanatory variables, that is, different public infrastructures is positive. That means, improvement in the level of public infrastructures is expected to lead to a higher level of economic growth in the state.

Data Source and Variables

To evaluate the relationship between economic growth and public infrastructures of Assam, a panel regression model was employed using the annual data of the districts of Assam for the period 1999–2000 to 2017–18. A panel regression analysis provides more degrees of freedom (Baltagi 2005). Economic growth was measured by PCNDDP, and it was converted into its natural logarithmic form. Data on PCNDDP were collected from the Directorate of Economics and Statistics, Government of Assam (n.d.). To fulfil the objective of the study, different public infrastructures were considered as explanatory variables. On the basis of the review of literature and availability of data, we identified some public infrastructures that were presented in Annexure A. The required data on public infrastructures were collected from various reports and publications such as Statistical Handbook of Assam and Infrastructure Statistics of Assam 2014–15 (Government of Assam 2015).

Methods and Estimation Results

Before estimating the relationship between economic growth and public infrastructures in Assam, it was important to check certain characteristics of the data. Since panel data incorporated both cross-section and time-series data, therefore, it was important to test whether the underlying time series followed a stationary process in order to avoid a spurious regression (Ulucak & Bilgili 2018). If it follows a stationary process, then at what level, that is, whether it is stationary at level (i.e., trend stationary) or its first difference or at higher order. Again, when the variables are stationary at first difference, then there may be cointegration relationship (i.e., a long-run relationship) among the variables.

Usually, unit root tests are used to check stationarity of a time series. Existence of a unit root is a characteristic of non-stationary variable. In panel data analysis, different unit root tests are available. These tests can be classified into two groups: first, those tests which assume common unit root process for all panels; and second, those tests which assume individual unit root process for each panel. To show the robustness of the results, one test from each group has been used to check stationarity of the variable considered for this study. These two tests are Levin, Lin and Chu (LLC) test (Levin et al. 2002) and Im, Pesaran and Shin (IPS) test (Im et al. 2003). Results of these panel unit root tests are presented in Table 1.

Table 1.

Results of Panel Unit Root Tests.

Variable	Level		First Difference
Variable	LLC	IPS	LLC	IPS
lnY	0.263 (0.604)	0.720 (0.764)	−4.185*** (0.000)	3.434*** (0.000)
lnRD	0.809 (0.790)	3.110 (0.999)	−9.290*** (0.000)	−9.156*** (0.000)
EV	1.022 (0.846)	0.821 (0.794)	−7.025*** (0.000)	−5.264*** (0.000)
II	−1.033 (0.150)	−0.898 (0.184)	−10.904*** (0.000)	−12.778*** (0.000)
lnBB	3.521 (0.999)	5.094 (1.000)	−2.601*** (0.004)	−1.661** (0.048)
CDR	−1.079 (0.140)	−0.333 (0.369)	−1.228 (0.109)	−4.087*** (0.000)
lnEI	−0.467 (0.320)	1.759 (0.960)	−5.123*** (0.000)	−6.455*** (0.000)
lnHB	0.877 (0.809)	1.825 (0.966)	−8.344*** (0.000)	−7.938*** (0.000)

Notes: Figures in the parenthesis are P values of the test statistics.

***, ** represents significance at 1% and 5% levels.

The results of both the panel unit root tests suggest that we cannot reject the null hypothesis of non-stationarity at level. Therefore, all the variables are considered to be non-stationary at level. However, the null hypothesis of non-stationarity of the variables under consideration can be reject at their first difference. That means, all the variables are stationary at first difference. Since all the variables are stationary at first difference, that is, integrated of order 1, there may exist cointegration relationship among the variables. To check that, the panel cointegration test developed by Kao (1999), Pedroni (2004) and Westerlund (2007) has been performed.

From Table 2, we can observe that all the three panel cointegration tests provide evidence for the existence of cointegration among the variables. Since the existence of the cointegration relationship is proved; therefore, the long-run relationship between economic growth and public infrastructures in Assam can be estimated using a cointegration estimation technique. In this respect, fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) estimators are the two economic tools that can be utilised. Both these estimators are highly effective in dealing with endogeneity issue among the regressors and the problem of serial correlation in the error terms (Kao & Chiang 2001; Narayan &Smyth 2007; Dogan and Seker 2016a; Danish et al. 2019). Again, second generation estimators like the weighted DOLS which allow for long-run variances to be heterogeneous and the weighted FMOLS which have heterogeneously cointegrated panel are also capable of handling the issue of heteroskedasticity in long run variance (Kao & Chiang 2001; Mark &Sul 2003; Dogan and Seker 2016b; Danish et al. 2019). For the present study, we are using the weighted DOLS estimators as it provides highly efficient estimators even in case of small samples (Danish et al. 2019; Dogan and Seker 2016a).

Table 2.

Results of Panel Cointegration Tests.

Kao Panel Cointegration Test
Modified Dickey–Fuller t	−0.8084 (0.2094)
Dickey–Fuller t	−2.6387 (0.0042)***
Augmented Dickey–Fuller t	−3.9922 (0.0000)***
Unadjusted modified Dickey–Fuller t	−2.2510 (0.0122)**
Unadjusted Dickey–Fuller t	−3.4682 (0.0003)***
Pedroni Panel Cointegration Test
Modified Phillips–Perron t	6.5851 (0.0000)***
Phillips–Perron t	−1.6121 (0.0535)*
Augmented Dickey–Fuller t	−1.5211 (0.0641)*
Westerlund Test for Panel Cointegration
Variance ratio	7.5716 (0.0000)***

Notes: Figures in the parenthesis are P values of the test statistics.

***, ** and * represent significance at 1%, 5% and 10% levels, respectively.

The results of weighted DOLS are presented in Table 3. The long-run coefficients of public infrastructure indicators like road density, electrified villages, availability of bank branches, credit-deposit ratio and availability of educational institutions are positive and highly significant at a 1% significance level. Similarly, the coefficient of irrigation intensity is also positive and significant but at a 5% significance level. However, the coefficient of availability of hospital beds is found to be insignificant even at a 10% significance level. These results are in line with the expected ones, except for availability of hospital beds. In other words, public infrastructure indicators like road density, electrified villages, irrigation intensity, availability of bank branches, credit-deposit ratio and availability of educational institutions have significant importance in promoting economic growth in Assam. One plausible reason for having insignificant relationship between availability of hospital beds and economic growth could be insufficient availability of hospital beds in all districts of Assam. Because, none of the districts is having one beds for every one thousand population living in it. It reflects insufficient availability of hospital beds in the state. However, as mentioned by Bhattacharya, Gupta & Sikdar (2020) health infrastructure enhances labour productivity only after attaining a threshold level. Among the public infrastructure indicators having significant relationship with economic growth, availability of bank branches is found to be the most productive followed by availability of educational institutions, electrified villages, road density, credit-deposit ratio and irrigation intensity.

Table 3.

Results from the Weighted DOLS.

Variable	Coefficient	Standard Error
lnRD	0.2435***	0.072
EV	0.3398***	0.079
II	0.0094**	0.004
lnBB	0.4478***	0.096
CDR	0.0106***	0.000
lnEI	0.4259***	0.114
lnHB	0.0497	0.092

Notes: Figures in the parenthesis are P values of the test statistics.

*** and ** represent significant at 1% and 5% levels, respectively.

The results of both panel cointegration tests and estimated coefficients of DOLS estimation techniques provide information about existence of long-run relation between public infrastructure indicators and economic growth. Therefore, it is important to check the direction of causality of that long-run relationship. To know that, we are applying the Granger Causality Test proposed by Dumitrescu and Hurlin (2012). The advantage of this test is that it can handle heterogeneity issues (Dumitrescu and Hurlin 2012). The results of Dumitrescu-Hurlin Panel Causality Test show that there is bi-directional causality between road density and economic growth (Table 4). Other public infrastructure indicators like electrified villages, irrigation intensity, availability of bank branches, credit-deposit ratio and availability of educational institutions have unidirectional causality with economic growth where this causal relationship is running from economic growth to public infrastructure indicators (Table 4). These results indicate that public infrastructure growth in Assam occurs due to the demand-side factors. Only, in terms of road infrastructure, both demand- and supply-side factors come into play to promote economic growth. Otherwise, development of all other public infrastructure in Assam occurs only because economic growth is taking place.

Table 4.

Results of the Dumitrescu–Herlin Panel Causality Test.

Null Hypothesis	W-Stat.	Z_bar-Stat.	Prob.	Type of Causality
lnRD → lnY	5.922	4.720	0.00	Bi-directional
lnY → lnRD	7.484	8.295	0.000	Bi-directional
EV → lnY	2.793	0.642	0.520	Unidirectional
lnY → EV	5.958	5.806	0.000	Unidirectional
II → lnY	2.289	−0.180	0.587	Unidirectional
lnY → II	5.967	5.821	0.000	Unidirectional
lnBB → lnY	2.341	−0.094	0.924	Unidirectional
lnY → lnBB	5.588	5.201	0.001	Unidirectional
CDR → lnY	1.943	−0.745	0.456	Unidirectional
l lnY → CDR	3.713	2.143	0.032	Unidirectional
lnEI → lnY	2.064	−0.548	0.583	Unidirectional
lnY → lnEI	4.643	3.661	0.000	Unidirectional
lnHB → lnY	2.190	−0.341	0.732	No causality
lnY → lnHB	3.337	1.529	0.126	No causality

To investigate both the long-run and short-run dynamics of the relationship between public infrastructure and economic growth, a panel error correction model (Panel ECM) is estimated. In line with Banerjee et al. (1993), Shiu and Lam (2004) and Sahoo and Dash (2012), a dynamic regression model is estimated as an error correction model using lagged residual of the long-run estimate as the error correction term.

Δ ln Y_{i t} = α_{1 i} + \sum_{i = 1}^{k} β_{1 i k} Δln Y_{i t - k} + \sum_{i = 1}^{k} β_{2 i k} {ΔlnRD}_{i t - k} + \sum_{i = 1}^{k} β_{2 i k} {ΔEV}_{i t - k} + \sum_{i = 1}^{k} β_{2 i k} {ΔII}_{i t - k} + \sum_{i = 1}^{k} β_{2 i k} {ΔlnBB}_{i t - k} + \sum_{i = 1}^{k} β_{2 i k} {ΔCDR}_{i t - k} + \sum_{i = 1}^{k} β_{2 i k} {ΔlnEI}_{i t - k} + \sum_{i = 1}^{k} β_{2 i k} {ΔlnHB}_{i t - k} + γ_{1 i} ε_{i t - 1} + u_{i t},

(3)

where ∇ is the first difference operator, ‘k’ is the optimal lag length, and $ε_{i t - 1}$ is the error correction term defined as the lagged value of the residual series obtained from the long-run model (Engle and Granger 1987). In Equation (3), statistically significant test statistics of the explanatory variables imply the existence of a short-run relationship. Also, statistically significant ECT confirms the existence of a long-run relationship between X and Y. The estimated short- and long-run relationships are presented in Table (5). Unlike the vector error correction model, the present study has estimated the growth equation and other equations considering public infrastructures as a dependent variable has been omitted. The reason behind estimating an error correction model is to distinguish between short- and long-run effects of public infrastructures on economic growth only.

Table 5.

Results of Panel Error Correction Model.

Variable	Test Statistics	P Value
ECT	−0.131***	0.000
∇lnRD	0.014	0.496
∇EV	0.087**	0.019
∇II	0.001	0.399
∇lnBB	0.054	0.480
∇CDR	0.000	0.105
∇lnEI	0.083	0.120
∇lnHB	0.008	0.840

Notes: *** and ** represent significant at 1% and 5% levels, respectively. Optimal lag length is chosen by Schwarz information criteria.

Table 5 shows the results of regression model estimated as an error correction model. It shows that only electrified villages are found to have a short-run relationship with economic growth. All the other infrastructures do not share a significant short-run relationship with economic growth in Assam. Moreover, the error correction term is also found to be statistically significant. It confirms the existence of a long-run relationship between public infrastructures and economic growth, which is in line with findings of cointegration tests mentioned above. Also, the negative coefficient of the ECT indicates that any disruption in the long-run equilibrium path of economic growth will be restored automatically at a speed of nearly 13% per annum.

Conclusion

The article evaluates the relationship between economic growth and public infrastructures in Assam. Using a panel cointegration technique and an error correction model for the period from 1999–2001 to 2017–18, we have found a long-run relationship between economic growth and public infrastructure indicators. Public infrastructures such as road density, electrified villages, irrigation intensity, availability of bank branches, credit-deposit ratio and the availability of educational institutions have a significant positive impact on the economic growth of Assam in the long run. But the availability of hospital beds has failed to have a significant impact on economic growth. It could be due to the insufficient availability of hospital beds all over the state. Among other public infrastructures, the availability of bank branches and educational institutions have also a stronger impact on economic growth than irrigation intensity and credit-deposit ratio that still have a positive influence on the economic growth of the state in the long run. However, in the short run, only electrification of villages contributes significantly and positively towards the economic growth of the state. Given these findings, it is supported that the development of public infrastructure is very much crucial to achieve high and sustainable economic growth in Assam as private investments in this sector are significantly low. Again, the Dumitrescu–Hurlin Panel Causality Test supports a unidirectional causality running from economic growth to public infrastructure indicators, except for road density. In other words, it is the demand-side factors that are contributing towards the development of public infrastructure in Assam. As the economic growth of Assam is relatively slower than that of the major states of India, the unidirectional causal relationship from economic growth to public infrastructures also restricts a rapid expansion in public infrastructure in Assam. It can also be said that the government develops infrastructure (other than road) only when economic growth is high. It allows the government to spend more on public infrastructures. Moreover, the unidirectional causal relationship from economic growth to public infrastructure has also contributed towards widening inter-district disparity in public infrastructures due to higher growth disparity across the districts of Assam. Only, road density is found to have a bi-directional causal relationship with economic growth. In this case, both demand- and supply-side factors are working together. Therefore, the present study suggests that there should be extensive government intervention for the development of road infrastructure (or improving road density), which will contribute towards achieving higher sustainable economic growth across the districts of Assam. The feedback effect between road infrastructure and economic growth will ultimately fuel up development in other infrastructure sectors such as power, irrigation, financial development, education and health. This policy will also be beneficial to achieve balanced regional development in terms of both economic growth and infrastructure by distributing road infrastructure in such a manner that previously underdeveloped districts get relatively more attention than the previously endowed districts.

Appendix A1.

List of Public Infrastructure Indicators and Their Measurements.

Infrastructure Indicator	Measurement
Road density	Roads per hundred square kilometres
Percentage of electrified villages	Share of electrified villages out of total villages
Irrigation intensity	Gross irrigated area as a percentage of gross cropped area
Availability of bank branches	No. of bank branches per lakh populations
Credit–deposit ratio	Share of lending out of the total deposits
Availability of educational institutions	No. of schools per thousand populations in the age group of 6–15 years
Availability of hospital beds	No. of hospital beds per thousand populations

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Informed Consent

There are no human or animal participants in this article and informed consent is not applicable.

Notes

ORCID iD

Nihaljyoti Das

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