POPULATION GROWTH AND ITS IMPACT ON ECONOMIC DEVELOPMENT IN NAGALAND: AN EMPIRICAL ANALYSIS

This study empirically tests the impact of population growth on economic development of Nagaland for the period of 1981-2011. Demographic transition helps in creating a policy environment that takes maximum advantage of the demographic potential of the State. The regression technique was incorporated to investigate the relationship between population growth and economic development. The findings indicate that the labour forces were shifting from the low-productivity agriculture sector to the higher-productivity industry and service sectors. The domino effect of the study indicates that population growth has positively and significantly contributed to economic development but is negatively affected by the unemployment rate. Now although on the one hand, if it increases growth but on the other hand, it creates a problem of unemployment and leads to a decline in net state domestic product and per-capita income. The government is advised to utilize this additional workforce efficiently as a policy tool to achieve a high and desired level of growth.


INTRODUCTION
Nagaland's shifting demographics are generating new economic opportunities. Economic growth would accelerate if the working population can be productively employed (Jamir, 2019b;Sharma, 2008). This study has attempted to understand and forecast economic growth in Nagaland, which focuses on theoretical and empirical literature on the impact of demographic changes and economic growth (Solow, 1956;Mankiw et al., 1992;Bloom et al., 2000;Reher, 2011;David, 2009;Jokhi and Pandya, 2016;George, 2008;Higgins and William, 1997). The economy of the State is highly dependent on agriculture and allied activities. More than half of the workforce is employed in the agriculture sector (Jamir, 2021b;Jamir, 2021d;Jamir, 2022a;Jamir, 2022b;Jamir, 2020b). This has ensured a low per capita income, which is below the national average (Jamir, 2020a;Jamir, 2019a). But in terms of some demographic characteristics, Nagaland is more like the developed countries of the west, while its economy resembles that of the developing countries of the east (Anulawathie Menike, 2014; Barro and Sala-i-Martin, 2004;Bloom and Williamson, 1998;Iqbal et al., 2015;Laxminarayan, 1970;Jamir and Ezung, 2017a;Jamir and Ezung, 2017b). For decades, researchers and policymakers have discussed the essence of the relationship between population and economic growth. High and unregulated fertility rates lead to rapid population growth, which pushes per capita consumption below the subsistence level, according to Malthus' claim (Malthus, 1798). Population pessimists claim that population growth is disruptive to economic development because it increases demographic overheads, stifling capital accumulation and technological innovation (Coale and Hoover, 1958). "Population optimists" or "boomsters," on the other hand, stress the value of population growth for growing productivity, encouraging technological advancement, and achieving economies of scale (Boserup, 1981). Some research, on the other hand, found no evidence of a substantial effect of population growth on economic development, resulting in "demographic neutralism." The main research problem this paper address is: Has the population growth promoted economic development in Nagaland from 1981-2011? Or, on the contrary, has the expanding population become an obstacle to the State's economic growth? To answer these questions, standard econometric analyses, such as the regression model was used by using population and economic development determinants, are used in this study to examine the long-run relationship between population growth and economic performance in Nagaland.

METHODOLOGY Study Area
Nagaland is located in the Far East region of East Asia towards Northern part of Indo-Myanmar mountain ranges. It is situated in the northeastern part of India between 25°6' to 27°4' North latitudes and 93°0' to 95°15' East longitudes, with an area of about 16,579 sq. km. Nagaland is bounded by the Indian states of Arunachal Pradesh to the northeast, Manipur to the south, and Assam to the west and northwest, and the country of Myanmar to the east. Nagaland becomes the 16 th State of the Indian Union on the I st December 1963. As per Census 2011, the population of Nagaland is 1,978,502. The State is comprised of 16 administrative districts and 1428 inhabited villages. Each district is inhabited by one or more tribes, thereby imparting to it a distinct linguistic, cultural, traditional, and socio-political characteristic (Census of India, 2011). The Nagas, an Indo-Asiatic people, form more than 20 tribes, as well as numerous subtribes, and each one has a specific geographic distribution. The Konyaks are the largest tribe, followed by the Ao, Angamis, Sumi, and Lotha. Other tribes include the Sangtams, Phoms, Changs, Khiemnungams, Yimkhiung, Puchury, Zeliangs, Chakhesangs, Rengma, Kachari and Kuki. Nagaland is primarily a land of agriculture and has a population of about 2 million people. The State's population is predominantly rural with about 71.14% of its population living in the rural sector (Census of India, 2011). The terrain is predominantly hilly and is covered by a rich and varied floral and faunal assemblage. People are based on agriculture and allied activities for their sustainable livelihood. Compared to other food crops about 80% of the cropped region is under rice production. The farm production and productivity for all food crops are very low, in contrast to other states. Presently, the Jhum/shifting to terraced cultivation ratio is 4:3 (Nayak, 2013). Forestry is also an important source of income generation for the rural sector (Ghosh, 2016). Nevertheless, agriculture and forestry contribute a majority of Nagaland's Gross Domestic Product (GSDP). Nagaland's GSDP grew at 9.9 percent compounded annually for a decade, thus more than doubling the per capita income (Nagaland Economy Report, 2011-12).

Data collection and period of study
The empirical study of population and economic development is based totally on a secondary source. The secondary data were collected during 1981-2011.

Research analysis
Model specification: The regression model was used to study the relationship between each of the dependent variables and the set of the explanatory variables. The model is specified as follows: Y=β0+β1X1+β2X2+ …. + βnXn+€ Where y represents the dependent variable, X1, X2…….. Xn are the explanatory variables, β0 is the intercept, β1, β2,……….. βn are the regression coefficients and ε is a random component.

RESULTS AND DISCUSSION Demographic transition in Nagaland: A brief review
In 1981, the proportion of the age 0-14 years was 36.84% of the total population which has increased to 37.29% in 1991, and it fell back to 36.60% and 34.30% in 2001 and 2011 respectively. Aged 60 and above was only 5.93% in 1981 and has fallen to 5.27% and 4.73% in 1991, and again rose to 5.3% in 2011 (Table  1 and Figure 3). The decreasing population of young dependents may contribute more to the economic development of the State. The working age population (age 15-59) increased from 57% in 1981 to 57. 43%, 58.50% and 60.40% in 1991, 2001 and 2011 respectively. Comparing to 57% in 1981, the subsequent decades of ation is more mature and transitioning to a population structure increasing working population showed that the State's popul marked by a bulge in the working age group, even though the level of dependence population with 39.60% is still quite high. The proportion of the working age population in the State has been steadily increasing over the decades (Statistical Handbook of Nagaland, 1981, 2001.     Nagaland 1981Nagaland , 1991Nagaland , 2001Nagaland and 2011 The Table 1 and 2 provides a detailed breakup of the age composition of the population of the State. Both the Table 1 and Figure 3 shows that in the 1991 census, the State was marked by a very wide base showing a high population ratio of children, adolescent and young adult population. Children in the 0-14 year age group consisted of 451044 from a total population of 1209546 or formed a percentage of 37.29%. Aged 60 and above were only 63777 or 5.27% of the total population. This showed that the dependency ratio in the young age population was quite high though the dependent population among the elderly was insignificant. An implication of the population structure showed that a significant amount of investment in human capital in the form of investment in education and health was required for tapping demographic dividends in the subsequent years. Children made up a significant proportion of the population showed that the State was in a high population growth phase where the birth rate was much higher than the death rate. The male-female population ratio of 53:47 in favour of male showed a significant gender imbalance (Nair, 1974;Islam, 1989). Also, since more developed societies usually have either a bulging area in the middle showing the second phase of demographic transition or even a very narrow base widening up along the age cohort, the perfect-pyramid shape of the age structure showed that the State is still in the first stage of demographic transition marked by a low level of economic development.   (Table 4 and Figure 5 and 6). The number of people 60 years and above as a percentage of the total population however fell between 1991 and 2001 from 5.27% to 4.73%. This fall was a result of an increase in other components of the population, namely children and the working age population.    The study found that in the census of population 1991, 2001, and 2011, one can draw certain conclusions to questions raised at the beginning. While the State's population growth was negative between 2001 and 2011 but the number of children added is still significant. Barring some unforeseen exigencies, one can expect the population to grow further. The base of the population pyramid is still very wide, with a very narrow tip at the top (Table 5, Figures 5, 6, and 7). A transition toward a more cylindrical shape pyramid or a narrow base is still a long way to go. The population decline between 2001-2011 could be an aberration rather than a trend to be noticed in future years. The State is still marked by a high dependency ratio, especially among children. This calls for significant investment in human capital to tap demographic dividends. At the same time, there is a steady increase in the working-age population. This calls for ways to create meaningful employment. However, as birth rates fall and medical facilities improve with economic growth, this is expected to increase though the State did witness a fall in old age population proportion between 1991 and 2001. However, it has to be noted that even in that decade, the absolute number of people aged above 60 increased. An encouraging trend from a socioeconomic perspective is the improvement in the gender dimension with female-male ratio improving consistently over the decades in the study. There are, however, questions that cannot be answered by looking at the data presented. The biggest question is the high growth rate of the population of 64% between 1991-2001 and negative population growth between 2001-2011. As pointed out, this could be an aberration rather than the norm going ahead.

Decreasing Share of Cultivators and Agriculture Labourers
Nagaland's economy has seen a fall in the contribution of the agricultural sector to the State's gross domestic product (see Figure. 8, 9, 10 and 11). This is in conformity with the theory of structural transformation, which states that an economy moves from the agrarian sector, then to the secondary sector and finally to the service sector.

Stagnant Household Industry Workers
A falling share of workers employed in the agricultural sector assumes an increase in the industrial sector. Since the manufacturing sector is more productive than the agricultural sector, economies which have dominated the world in history had come about as a result of strides made in the manufacturing sector. India realized the importance of the manufacturing sector quite early after independence with the Mahalanobis Model of the Two Sector Model of the 2 nd Five year plan being a prime example. The Make in India program, albeit its failure to take off, given by Narendra Modi as soon as he assumed power in 2014 also shows how India has given importance to the manufacturing sector. In Nagaland too, much emphasis has been given to the manufacturing sector. However, the State has been marked by sick industrial units, low and sporadic investment, lack of accountability and transparency, and a failure of existing units to run at full capacity. The State lacks basic infrastructure in areas such as communication and transport; it also lags behind in other critical areas such as finance and technical know-how (Jamir, 2021a;Jamir, 2021c;Ezung and Jamir, 2018;Jamir and Ezung, 2020). Political unrest, a weak state government, and a lack of proper institutional mechanisms have also contributed to the failure of the manufacturing sector taking-off. The lack of the industrial sector can be seen from the data presented in Table 7, where the total workforce in the household industry sector while increasing, is only 2.29% as of 2011. The highest increase both in percentage and absolute terms of workers in the household industry sector or broadly the manufacturing sector was between 1991 and 2001 when the total workforce in the manufacturing sector increased from 7649 and more than doubled to 18072 for a rise of 136.26% within the ten year period (Figure 16, and 17).

Fluctuations in Other Occupational Categories
Besides the primary and secondary sectors, workers are employed in the tertiary sector, characterized in the Census data as 'Others' consisting of the State's administration, hoteliers, shopkeepers, sports persons, teachers, politicians, lawyers, and so on. The State has seen dramatic changes in this sector. It has been shown from Tables 7 and 8 that the State is  seeing a 1981, 1991, 2001 and 2011.

Increase in Marginal Workers
The State has seen a dramatic increase in the number of marginal workers. The number of marginal workers as of 1981 was 5433, which fell to 4740 in the 1991 Census. However, this jumped to 1.43 lakhs in the 2001 Census and continued to increase to 2.33 lakhs, according to the 2011 Census. The number of main workers has risen in tandem with the total population. As a result of the mismatch between the growth rate of main and marginal workers, the ratio of marginal workers to main workers have risen dramatically in 2001. This can be seen from Table 9 where the ratio of marginal workers to 1000 main workers stood at 14.75 and 9.26 in the 1981 and 1991 censuses, respectively and jumped to 204.29 and 314.28 in 2001 and 2011 censuses respectively (Table 9).  Table 10 shows the number of marginal workers has increased dramatically in the 2001 census and the 2011 census. Accompanied by increasing marginal workers is a steady decline in the proportion of main workers to the total population. This is presented in Table 11.

Structural Transformation by Sectoral Contribution to State's GSDP
The above section has mentioned how the participation of the workforce in the State shows that the State is not following the same path of economic development and how the industrial sector or the secondary sector has been skipped all along. Data on sectoral contribution to State's GSDP reveals a similar pattern of a falling share of the primary sector, a fluctuating (with a decreasing and then increasing) trend of the secondary sector, and an increasing share of the tertiary sector (Table 12, 13 and 14). As explained by Rostow, the traditional society implies a phase of economic stagnation where the level of output per capita is low or more or less remains constant over a period of time. Further, agriculture happens to be the main source of income for the State, and more than 75% of the population is engaged in the stage of traditional society. When Nagaland attained its statehood in 1963, the economy was in the stage of almost 'traditional society'. Agriculture was the backbone of the economy, where more than 85% of the population was engaged in agriculture for livelihood. Further, the technique of production was more or less pre-Newtonian type having a very low per capita output. Further, there was not only a lack of inventiveness and innovations but also lack of effectiveness in the physical world of the post-Newtonian era. "The precondition for take-off" in Rostowian term is a stage of economic transition during which the institutional organization of the economy gradually begin to change and respond to economic growth. The rate of net investment rises and gradually tends to outstrip population growth. The buildup of social overhead capital grows, and an infrastructural base for growth is created. Some technical changes are also introduced in agriculture, which raises its productivity. As explained by Rostow, the process of creating pre-conditions for take-off from traditional society is the result of the following. "New type of enterprising men comes forward in the private economy and government enterprise, or both, willing to mobilize savings and take risks in pursuit of profit to modernization. Investments increase, notably in transport, communications, and raw materials in which other states may have an economic interest (Rostow, 1953(Rostow, , 1959(Rostow, , 1971.

Impact of age structure and NSDP, PCI and agricultural productivity
The correlation between 15-59 of age group populations and NSDP comes out to be 0.97. The R 2 indicates that 94.80% of the observed variability in NSDP is explained by the independent variable, i.e., 15-59 years of age group. The regression coefficient shows that an additional increase in the population between 15-59 years of age increases the NSDP by 4.1 times. Therefore, the value of b is significant at 5%. The estimated correlation between 15-59 of age group populations and PCI comes out to be 0.99. The R 2 indicates that 99% of the observed variability in PCI. The coefficient shows that an additional increase in the population between 15-59 years of age increases the PCI by 2.37 times. Therefore, the value of b is significant at 5%. The correlation between 15-59 of age group populations and agriculture productivity comes out to be 0.94. The R 2 indicates that 88% of the observed variability in agriculture productivity. The regression coefficient shows that an additional increase in population between 15-59 years of age increases agriculture productivity by 1.25 times. Therefore, the value of b is significant at 5%. Hence, demographic transition has a positive impact on economic development. Therefore, the hypothesis is accepted.
The correlation between agri-workers and the primary sector comes out to be 0.98. The R 2 indicates that 97% of the observed variability in the primary sector is explained by the independent variable, i.e., agri-workers. The regression coefficient shows that an additional increase in the population of agri-workers increases the primary sector by 4.83 times. The t-test analysis has shown that the impact of agri-workers on the primary sector is statistically significant at 1%. The estimated correlation between household industry workers and the secondary sector comes out to be 0.93. The R 2 indicates that 86% of the observed variability in the secondary sector is explained by the independent variable, i.e., household industry workers. The coefficient shows that an additional increase in the population of household industry workers increases the secondary sector by 1.55 times. The t-test analysis has shown that the impact of household industry workers on the secondary sector is not statistically significant.
The correlation between other workers and the tertiary sector comes out to be 0.98. The R 2 indicates that 96% of the observed variability in the tertiary sector is explained by the independent variable, i.e., other workers. The coefficient shows that an additional increase in population of other workers increases the tertiary sector by 3.44 times. The t-test analysis has shown that the impact of household industry workers on secondary sector is statistically significant at 1%.

CONCLUSIONS
Population growth could be described as a "destiny" that determines the course of economic development. Demographic transition in different districts, however, has been different from each other. This study attempted to provide additional empirical evidence to the ongoing debate about the intricate relationship between population growth and economic development and chose Nagaland as a case  (Mamuneas et al., 2006;Simon, 1989;Pathy, 1976). The study uses the regression analysis model to investigate the relationship between population and economic growth. Here, the empirical result shows there is a positive relationship between the total labour force and per-capita income, NSDP, and agriculture productivity. The neoclassical growth model also reveals that population growth positively contributes to per-capita income growth. It was also discovered that there is a statistically significant relationship between sectors and the working population. Nagaland's changing demographics are creating a strong impulse for state economic development; the policymakers have a vast opportunity to make this potential of demographic dividend a reality for the development of the state economy. Similar studies were done by (Furuoka, 2009;Thornton, 2001;Kuznets, 1967;Thirlwall, 1994). Thus, it was found that with the increase in the working population, there will be a positive impact on economic growth in State.

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The author declares that they do not have any competing financial, professional, or personal interests from other parties.

Conflict of interest:
The author declares no competing interests.

Research funding:
The author received no financial support for the research, authorship and/or publication of this research article.