1. Faculty of Humanities and Social Sciences, Tribhuvan University, Kathmandu, Nepal.
2. Faculty of Humanities and Social Sciences, Tribhuvan University, Kathmandu, Nepal.

* Correspondence: Subashchaulagain2011@gmail.com

Keywords: Nepal, PCI, Domestic Product, GDP, South Asia

1. INTRODUCTION

Nepal with a total area of 147,181 square kilometers a little country by geographical standards. It is situated in south Asia between the enormous countries of China and India. It is a landlocked nation, bordered by China on its northern border and India on its three other sides (east, west, and south). Geographically, it is split between the Tarai area (17%), the hilly region (68%) and the mountainous region (15%). From east to west, the Himalayan Mountain range is present. There are eight peaks there that rise to a height of at least 8000 meters, including Mount Everest. The Tarai (plane land) area and hills presumably grew parallel to one another. The nation is said to have abundant water resources. Every economy has historically been based on the agricultural, industrial, and service sectors. In the stage of high and mass consumption, the service sector emerges as a byproduct of industrial sector growth and drive the economy towards the stationary state where all economies would converge [1]. The relationship between these sectors is such that agricultural sector growth stimulates industrial development, which drives the economy towards maturity. Despite attempting to pursue a growth path over the past ten years, Nepal’s outcomes have been on average 4.3% (Ministry of Finance [MoF], 2018), and rent seeking has grown in popularity. By establishing an industrial council, the autocratic Rana Regime’s final ten years of rule were markedly chaotic in terms of economic progress. The first few decades in the 1950s following independence from the Rana Regime saw rapid economic growth. After 1990, there has been encouraging economic growth through liberal economic policy. Additionally, the rise of the Maoists in 1996 prompted a political and economic disaster that ended the chances for socioeconomic development. With three levels of government—the federal government at the top, the provincial government in the middle, and the local government at the bottom—political parties, including the Maoists, formed a new understanding in 2006 that offered hope [2]. The economic sector known as the primary sector is focused on the production of essential components from renewable natural resources. Agriculture, forestry, fishing, and mines are a few primary sector activities. The manufacturing industry is a sector of the economy. The secondary sector converts natural resources raw materials obtained from natural into artificial resources items produced by humans and targeted for human use. The manufacturing and construction industries are included in the secondary sector since they both involve processing raw materials to varied degrees. The tertiary sector of the economy, which is also referred to as the service sector, includes a number of enterprises, including as banking institutions, colleges and universities, hotels, and restaurants. The average income per person in a region for a given year is known as the per capita gross domestic product (PCI). The national income divided by the number of people also determines per capita income. The standard of living of a nation is frequently assessed using per capita income, which is typically stated in terms of a widely accepted international currency, such as the euro or the US dollar. The economic production of a country per person is measured by gross domestic product (GDP) per capita. The economic growth per people in a country is used to measure its level of wealth. Since an increase in Gross domestic product (GDP) is a key factor in determining human welfare, economic growth of the nation is always a big concern on a global scale. Improvements in development elements are directly correlated with higher real production and income. Improved access to basic necessities for a living is one benefit of higher GDP growth, but it also increases government savings and tax revenue. But in order to attain high and sustainable growth, the economy must shift from rural agriculture to sophisticate industrial or services sectors. This is because permanent modifications to their economic structures throughout time have allowed most developed and rising economies to experience quick and stable economic growth. They have seen their economy gradually shift from rural subsistence agriculture to modern industry, and finally to a services-dominant economy [3]. According to the contributions of the main sectors, the structure of the Nepalese economy has changed. According to the data for Nepal, the agricultural sector has been steadily declining, the secondary sector has only slightly increased, and the tertiary sector has grown quickly. According to Nepal Rastra Bank [NRB], 2021, the primary sector’s share decreased from 47.7% in 1991 to 26% in 2021. Although the manufacturing sector’s share is just 13.57% and the service sector’s share is 61.06% (NRB, 2021). Secondary and tertiary sectors have received the primary sector’s share decline’s distribution, with the latter receiving the lion’s share. As a result, the study anticipates the need to examine empirical relationships between significant sector shares and Nepal’s real GDP in addition to traditional growth regresses. It assumes that the sectoral shift, as measured by its contribution to real GDP, will contribute to growth by shifting a limitless amount of labor from the industrial and service sectors of modern agriculture. Data from the World Bank show that in 2021, the worldwide per capita GDP rose by 4.8%. Despite having populations well over a billion, economies like China and India have had per capita GDP growth rates substantially above the global norms in the twenty-first century. Luxembourg, Ireland, Norway, Switzerland, and Qatar are the five nations with the highest per capita GDPs. Nevertheless, Burundi, South Sudan, Madagascar, Sierra Leone, and Central African Republic are the five nations with the lowest GDP per capita. In addition, with a GDP per capita of $1223, Nepal maintains its 216th-place ranking among all nations, while India and China move up one spot each (World Bank [WB], 2021). According to social and economic data, Nepal is still a developing nation. Its GDP per capita is ranked 188th out of 216 nations ($1223, World Bank, 2021), and its human development index is 142nd out of 189 (United Nations Development Program [UNDP], 2019). Economic growth is hampered by a number of factors, including the fact that a significant amount of the population still relies on the farming, lack of industrialization, the burden of a trade deficit, insufficient development spending compared to high regular expenditures, rising underemployment and unemployment rates, political instability, growing inequalities and insecurity, and rising disparity inflation, these kinds of crises are getting worse. Analysis of connected aspects is required in order to pinpoint the crisis’s root reasons for the poor rate of growth in the economy and discover a strategy to escape its vicious cycle. Finding out the solution to those problems may benefit from the accurate identification of the problem’s primary causes. According to the Ministry of Finance [MOF], 2019 the agricultural sector in Nepal contributes roughly 26% of GDP, while the secondary and tertiary sectors account for 13% and 61% of GDP, respectively. Additionally, 66% of the population depends on the agricultural sector. Additionally, Nepal maintains its 188th-place ranking out of 216 nations in the globe with a GDP per capita of $1223. However, the distribution of agricultural share patterns to secondary and tertiary industries is ongoing, and it appears that the economy is moving in the right direction terms of sectoral shares contribution to overall economic growth. This study examines the relationship between Nepal’s primary, secondary, and tertiary sectors’ output and per capita GDP. The study aimed to determine if these sectors contribute positively to the country’s GDP and if they enhance per capita real GDP. The study uses secondary data from 1991 to 2021, including agricultural, forestry, fishery, mining, production, gas, electricity, water, and construction. Tertiary sector data includes food and beverage, hotel, communications, logistics, storage, retail and wholesale industries, medical and social service, schooling, public administration and defense, real estate rent, and businesses. The findings will be used to develop a strategy for balanced sectorial growth and structural reform. The study consists of five chapters: Introduction, study background, problem description, study objectives, hypothesis, and significance of study, study limits, and organization. The study methodology is explained in the third chapter, and the data presentation and analysis are presented in the fourth chapter. The study concludes with major findings, conclusions, and recommendations.

2. MATERIAL AND METHODS

The methodology of this study work based on the study quality that, aims at fulfilling the objectives set on the chapter one and through the use of the availability of the relevant information. Conceptual and theoretical framework answer these four questions. Primary sector output (PRS), secondary sector output (SES), tertiary sector output (TES), and per capita GDP (PCI) are variables not constants, because column 2 of table verified them as variables. Their unit of measurement is in Rs. Millions. Out of the four variables, three (PRS, SES, TES) became independent variables and PCI is a dependent variable. According to [4] there was a positive association between dependent and independent variables. Using the specified econometric methods, this study employed time series data using an inferential and quantitative study methodology. The study used the secondary sources of data for two objectives. The information was gathered from reports and webpages maintained by the Nepal Rastra Bank, the Central Bureau of Statistics, the National Planning Commission of Nepal, and the Ministry of Finance of Nepal. Additional supporting information was gathered from a variety of periodicals, magazines, papers, Master’s theses, and PhD doctoral theses. Because the study begins since 2020 for the process of thesis writing the study covered time series data consisting 31 years from FY 19990/91 to FY 2020/21. The study symbols FY 1990/91 as equivalent to 1991 and so on for other FY years. It is assumed government of Nepal formally adopt liberalization policy and restore of democracy in Nepal. In terms of model specification, the said study specified equation. The link between the dependent and explanatory variables is described by the equation. The following model is used in the study to examine the relationships in Nepal between the output shares of the primary, secondary, and tertiary sectors, life expectancy, population growth rate, gross capital development, and real per capitate model is based on the [4] study on Nepal’s GDP per capita and sector-by-sector production performance. To demonstrate how dependent and independent variables are related following equation was stated.

PCI_𝑡 = ƒ (PRS_𝑡, SES_t, TES_𝑡) ……………………………….(1)

For the first objective, the log-lin model of simple regression was used to analyze the trend of output of PCI, PRS, SES and TES [5].

lnPCI= ₀+ ₁Y_t………………………………….(i)

lnPRS= ₀+ ₁Y_t……………………………………………..(ii)

lnSES= ₀+ ₁Y_t ……………………………………(iii)

lnTES= ₀+ ₁Y_t……………………………………..(iv)

Here ₀ is an intercept term and ₁ is a slope coefficient. The positive slop coefficient shows an upward trend in dependent variables and vicevarsa. For the second objective of the study the above functional form can be transformed into econometric model as: Population regression function

𝑙𝑛 = + 𝑙𝑛 + 𝑙𝑛 + 𝑙𝑛 + …………………………….(2)

Simple regression line

𝑙𝑛 = + 𝑙𝑛 + 𝑙𝑛 + 𝑙𝑛 (3)

Where,

PCI = per capita GDP.

PRS = Primary Sector Output.

SES = Secondary Sector Output.

TES = Tertiary Sector Output.

𝛽₁₌ constant term

  = error term.

t = Time subscript for time series data.

𝛽₁, 𝛽₂, 𝛽₃ and 𝛽₄ are parameters and 𝛽₁ 0, 𝛽_{2 >}0, 𝛽₃  0, 𝛽₄ .

The data analysis tool made use of econometric methods. To make it acceptable to change the non-linear models into log-linear ones and to make it easier to calculate elasticity, all of the variables in each model are translated into natural logarithms. The fundamental characteristics of the converted variables, such as their mean, standard deviation, shape (skewness and kurtosis), variability, and Jarque-Bera normality, are computed and displayed as a summary statistic. To identify the order of integration of each variable utilized in the empirical model, a unit root test (also known as an enhanced Dickey-Fuller test) was performed. The study used the Engle and Granger cointegration test, ECM, Autocorrelation, Multicollinearity, and Normality tests. This time-series data-based empirical analysis makes the assumption that the underlying time series should be stationary. If the mean, variance, and covariance of a time series data set do not change over time, the data is considered to be stationary [6]. However, the majority of macroeconomics time series are now acknowledged to be non-stationary [7]. Regression models provide an erroneous association when applied to non-stationary data, rendering hypothetical test findings invalid. Determining whether a time series is stationary or non-stationary is therefore essential to avoiding a false association. There are numerous ways to test stationary, including the Phillips-Peron test and the modified Dickey-Fuller test. However, this study uses the enhanced Dickey-Fuller test to describe the unit root test. The Dickey Fuller test was created by Dickey and Fuller in 1970 and is named in their honor. The modified Dickey-Fuller exam looks like this:

The equation for no intercept and no trend is,

Δ𝑌_𝑡 = 𝛾_𝑖𝑌_𝑡 u_𝑡 … … … … … … … … … … … … … …… … (4)

The equation for the only intercept and no trend is,

Δ𝑌_𝑡 = 𝛼₁ + 𝛾_𝑖𝑌_𝑡 u_𝑡 … … … … … … … … … . … … … … . (5)

The above equation both intercept and trend is,

Δ𝑌_𝑡 = 𝛼₁ + 𝛾_𝑖𝑌_𝑡 u_𝑡 … … … … … … … … … … … … (6)

Where ΔY_t = First difference

The null hypothesis of ADF is γ_i =0 against the alternative hypothesis of γ_i<0. The series is stationary if we reject the null, however if we do not, the series is non-stationary. The series is referred to as I (0) or integrated with order 0 if it is stationary without any differencing. Similar to this, the series is referred to as I (1) or integrated of order 1 if it becomes stationary following the first difference. The Engle-Granger test is a cointegration test. Based on the results of the static regression, it creates residuals (errors). When the variables are stationary at the first difference but non-stationary at the level, this test is used to co-integrate the variables. The cointegration test was proposed by [8]. It entails estimating the cointegration regression using OLS, obtaining the residual Ut, and then performing the unit root test for Ut. The following hypothesis is tested by this test:  Null Hypothesis (H₀): U_t has a unit root at level means U_t is non-stationary at a level. Alternative Hypothesis (H₁): U_t has no unit root at level says Ut is stationary at level.  The null hypothesis is rejected and the alternative hypothesis is accepted if the augmented Dickey-Fuller test statistic is higher than Engle-Granger’s critical value, indicating that Ut is stationary at level. The variables are co-integrated and have a long-term connection if Ut is stationary at level. The regression model won’t be illogical or spurious when Ut is stationary at a level, either. We utilize Equation (2) to develop the long-run model using the OLS approach and test the Engle-Granger Cointegration. The cointegration between the variables was examined, and the following error correction model was generated as:

 ECM_t = (U_t) = lnPCI − (β₀ + β₁lnPRI_t + β₂lnSES_t + β₃lnTES_t) … … … (7)

After calculating the values of ECM for different periods then this study tested the stationary of ECM. The variables in equation (2) are cointegrated and exist a long-run model is spurious or not, depending on whether the error correction term or residual is stable at level. When R-squared exceeds Durbin-Watson Statistics, this is a sign of false regression. However, the model is false since the ECM’s residual is stable at a given level and its R-squared value exceeds that of Durbin-Watson statistics. We estimate the error correction model to account for the short-run dynamics of the model and estimate the pace of adjustment from short-run disequilibrium to long-run equilibrium when all variables are stationary only after the first difference and cointegrated into one another. Listed below is the ECM model:

ΔlnPCI =  + ΔlnPRS + ΔlnSES + ΔlnTES + ECM (t-1) ………. (8)

Where,

ΔlnPCI = First difference of natural log of per capita GDP.

ΔlnPRS = First difference of natural log of output of primary sector.

ΔlnSES = First difference of Natural log of output of secondary sector.

ΔlnTES = First difference of natural log of output of tertiary sector.

β₀=Constant β₁, β₂, β₃, β₄ are short-run coefficients.

ECM_t-1 is a one-period lag residual of equation (2). The coefficient of ECM_t-1 provides the speed of adjustment which should be negative and significant. The three explanatory variables Primary Sector Output (PRS), Secondary Sector Output (SES), and Tertiary Sector Output (TES) were used to analyze the associations between the dependent variable Per Capita GDP (PCI) and the independent variable. E-Views 10 and Microsoft Excel were utilized in this investigation. The American Psychological Association (APA) 7th edition was used to write and record the complete thesis in this study. The study applies the following technical terms: It includes obtaining raw resources, the primary sector is also frequently referred to as the extraction sector. These resources may be renewable, like fish, wool, and wind power, or they may be non-renewable, like coal mining and oil extraction. Agriculture, mining, and fishing are among the subsectors of the primary sector. Many people work in agriculture and mining, which makes up a large portion of the primary sector in emerging economies. The primary sector industry has, however, experienced a drastic downturn due to advancing technology and the development of alternative energy sources. Growth in the primary sectors is shown as a percentage. The overall production produced by the various primary sector subsectors is referred to as the primary sector’s output. PRS is an independent variable in this case. The manufactured items are produced and distributed by the secondary sector. As a subsector of the economy, it encompasses manufacturing, construction, and utilities. The secondary sector industry combines raw resources to create finished goods with a greater value added. The manufacturing sectors were initially based on labor-intensive sectors. However, the advancement of technology allows firms to increase their output rates, which lowers the cost of manufacturing. Similar to primary output, secondary output refers to the results of several secondary sector subsectors. SES is an independent variable in this scenario. The tertiary sector of the economy is often known as the service sector. Entertainment, retail, insurance, tourism, and banking are all subsectors of this industry. The intangible component of providing services to customers and businesses is a challenge for the service sector. The service industry has expanded in the 20th century as a result of increased labor productivity, globalization, rising salaries, and technological advancements. The output of tertiary sectors is defined as the sum of its various subsector outputs. TES is an independent variable in this scenario. PCI is the average annual income per person for a given region. The national income divided by population size also determines per capita income. The level of living of a nation is frequently assessed using per capita income, which is typically stated in terms of a widely accepted international currency, such as the euro or the US dollar. The economic production of a country per person is measured by gross domestic product (GDP) per capita. The economic growth per person in a country is used to gauge its level of affluence. The government measures GDP per capita to determine how the economy is expanding together with the population. Global examination of GDP per capita enables comparable understanding of economic success and progress. PCI acts as the dependent variable here.

3. RESULTS AND DISCUSSION

The study provided and analyzed the data gathered from secondary sources in relation to the chapter’s goals and the technique indicated in the previous study. Industries that harvest raw resources for use by other economic sectors are included in the primary sector. Businesses in the primary sector often extract raw materials for other industries, gather and harvest natural goods, process and package raw materials.

Table 01: Performance of Output of Primary Sector (PRS) from 1991 to 2021 (in Rs million)

Years	PRS (in Rs. Million)	Years	PRS (in Rs. Million)
1991	65951	2007	251566
1992	71011	2008	314637
1993	81579	2009	401681
1994	86686	2010	485105
1995	98238	2011	538830
1996	110280	2012	568510
1997	114048	2013	625190
1998	134058	2014	655460
1999	146946	2015	679140
2000	152983	2016	744940
2001	162200	2017	790320
2002	173292	2018	854890
2003	185494	2019	882960
2004	202116	2020	933450
2005	214838
2006	230240	Average	385930

Note. Time period (n=31) years. From Economic survey of Nepal, by Ministry of Finance, 2022, Government of Nepal (https://www.mof.gov.org.np).

Table 01 described the average output of primary sector. From Table 01 it is seen that the average output of primary sector was Rs. 38, 5930 million. It can be seen that the output of primary sector increased gradually during study duration.

Table 02: Performance of output of subsectors of primary sector from 1991 to 2021 in Rs. Millions.

Years	AGF	FIS	MIN	Years	AGF	FIS	MIN
1991	64230	930	795	2007	243323	3868	4296
1992	68976	980	921	2008	305477	4076	5084
1993	79405	1184	990	2009	391519	4236	5926
1994	84367	1202	1117	2010	473270	4879	6956
1995	95646	1250	1342	2011	623303	5466	8751
1996	106484	1301	1495	2012	551421	6519	10570
1997	111056	1440	1553	2013	606444	6646	12100
1998	130863	1510	1685	2014	643051	8659	12750
1999	143448	1645	1815	2015	656222	9328	13580
2000	149332	1844	1817	2016	718189	11081	15670
2001	157652	2164	2148	2017	759493	12377	18450
2002	170634	2168	2310	2018	818263	14717	22000
2003	183621	2503	2506	2019	847030	15490	20440
2004	196686	2682	2748	2020	895645	16275	21530
2005	208591	3181	3134	2021	964307	18923	23960
2006	223536	3287	3417

Note. Time period (n=31) years. From Economic survey of Nepal, by Ministry of Finance ,2022, Government of Nepal (https://www.mof.gov.org.np).

Table 03: Output of Secondary Sector (SES) from 1991 to 2021 (in Rs. Million)

Years	SES (in Rs. Million)	Years	SES (in Rs. Million)
1991	28832	2007	126538
1992	33479	2008	143816
1993	39645	2009	163457
1994	45510	2010	185889
1995	52157	2011	235790
1996	58536	2012	258400
1997	61853	2013	290740
1998	68231	2014	306240
1999	76874	2015	316490
2000	82511	2016	380130
2001	83730	2017	437760
2002	90310	2018	480070
2003	96748	2019	448040
2004	94311	2020	479860
2005	101964	2021	562640
2006	112112	Average	191698.8

Note. Time period (n=31) years. From Economic survey of Nepal, by Ministry of Finance, 2022, Government of Nepal (https://www.mof.gov.org.np). Table02 described the average output of secondary sector. From Table 02 it can be seen that the average output of secondary sector was Rs. 191698.8 million. It can be seen that the output of secondary sector increased gradually during study duration.

Table 03: Performance of output of subsectors of Secondary sector from 1991 to 2021 in Rs. Millions.

Years	MANU	EGW	CON	Years	MANU	EGW	CON
1991	12822	1241	14769	2007	57185	15219	54134
1992	14618	1543	17318	2008	65447	14629	63741
1993	17861	2163	19621	2009	70924	15244	77289
1994	19555	2862	23093	2010	80531	16002	89356
1995	22466	3598	26093	2011	91164	17518	98539
1996	24816	4457	29263	2012	112090	31120	115190
1997	26987	4383	30483	2013	125290	36210	129230
1998	30337	4632	33262	2014	129810	38170	138260
1999	33550	5943	37382	2015	127490	37240	151760
2000	35495	7432	39584	2016	149420	44740	182890
2001	32805	8635	42290	2017	169570	50470	217720
2002	34337	10905	45068	2018	192230	51280	236260
2003	35634	11340	49036	2019	174010	60490	213530
2004	39786	12781	45887	2020	199560	62400	217910
2005	47840	13172	46523	2021	231770	77370	253500
2006	52172	14841	49099

Note. Time period (n=31) years. From Economic survey of Nepal, by Ministry of Finance ,2022, Government of Nepal (https://www.mof.gov.org.np).

Table 04: Output of Service Sectors from 1991 to 2021 (in RS. Million)

Years	TES (in Rs, Million)	Years	TES (in Rs. Million)
1991	50150	2007	401338
1992	60878	2008	480436
1993	70372	2009	553433
1994	77778	2010	619148
1995	88993	2011	843800
1996	100754	2012	950380
1997	113897	2013	1106530
1998	127729	2014	1224900
1999	142431	2015	1345770
2000	158558	2016	1595490
2001	160208	2017	1782940
2002	173944	2018	2007520
2003	192397	2019	2097520
2004	270152	2020	2249170
2005	313528	2021	2535700
2006	355012	Average	717769.5

Note. Time period (n=31) years. From Economic survey of Nepal, by Ministry of Finance ,2022, Government of Nepal (https://www.mof.gov.org.np).

Table 04 described the average output of tertiary sector. From Table 04 it can be seen that the average output of secondary sector was Rs. 717769.5 million. It can be seen that the output of secondary sector increased gradually during study duration.

Table 05: Performance of output of subsectors of tertiary sector (RH, ICTS, RSB) in Rs. Millions.

year	RH	ICTS	RSB	Years	RH	ICTS	RSB
1991	4238	8558	13421	2007	11503	76818	73636
1992	4461	10819	15684	2008	13943	92618	81625
1993	4736	12625	18122	2009	17347	95304	93747
1994	5198	13995	20533	2010	21057	105834	123213
1995	5490	15898	23521	2011	28860	122354	139157
1996	5978	19315	27157	2012	34780	146250	174480
1997	6238	22598	29778	2013	39230	169230	186190
1998	6759	24631	33293	2014	45910	184550	191600
1999	7980	29336	35002	2015	46280	220980	216960
2000	8549	31424	35267	2016	56150	252560	244110
2001	7142	34960	36525	2017	67320	268150	264380
2002	7539	39361	38251	2018	75650	289920	295710
2003	8942	46283	39991	2019	50430	257280	322960
2004	8895	51336	49242	2020	58780	277540	332990
2005	9398	61250	60042	2021	67950	331120	360700
2006	10043	69555	70791

Note. Time period (n=31) years. From Economic survey of Nepal, by Ministry of Finance, 2022, Government of Nepal (https://www.mof.gov.org.np).

Table 06: Performance of output of subsectors of tertiary sector (WRT, Ed, PADe, HAS) in Rs. Millions.

Years	WRT	Ed	PADe	HAS
2000	69928	17375	5288	4178
2001	64778	20823	7237	4626
2002	68695	24582	8070	5408
2003	79218	26313	8018	5824
2004	79839	31671	9548	7017
2005	90214	34996	10967	7842
2006	92648	40939	12227	8568
2007	105306	48722	14352	10963
2008	124121	62642	18556	13744
2009	161067	67739	21695	15382
2010	179306	81797	64040	17087
2011	198164	91566	78610	20431
2012	274270	102200	82630	21280
2013	313360	126550	113470	26740
2014	340850	143090	135050	32000
2015	350780	161260	137830	33190
2016	401490	197830	184940	41450
2017	475650	219540	193660	44060
2018	543040	251590	218570	49780
2019	514980	288460	276660	60330
2020	584390	296660	287660	65280
2021	673140	332440	317290	73660

Note. Time period (n=22) years. From Economic survey of Nepal, by Ministry of Finance ,2022, Government of Nepal (https://www.mof.gov.org.np).

Table 07:  Performance of Per Capita GDP from 1991 to 2021 (in Rs. Million)

Years	PCI (in Rs. Million)	Years	PCI (in Rs. Million)
1991	0.0064	2007	0.0319
1992	0.0077	2008	0.0382
1993	0.0086	2009	0.0454
1994	0.0095	2010	0.059
1995	0.0102	2011	0.0655
1996	0.0113	2012	0.0716
1997	0.0124	2013	0.0809
1998	0.013	2014	0.0867
1999	0.0145	2015	0.0938
2000	0.0159	2016	0.1096
2001	0.0181	2017	0.1219
2002	0.0191	2018	0.1348
2003	0.0194	2019	0.1345
2004	0.0233	2020	0.1465
2005	0.0253	2021	0.1646
2006	0.0289	Average	0.053

Note. Time period (n=31) years. From Economic survey of Nepal, by Ministry of Finance ,2022, Government of Nepal (https://www.mof.gov.org.np).

Table described the average of per capita GDP during study period. From Table it can be seen that the average output of per capita GDP was Rs. 0.053 million.

Table 08: Descriptive statistics of the variables for the study period 1991 to 2021.

	PCI	PRS	SES	TES
Mean	0.052532	385930.0	191698.8	717769.5
Median	0.028900	230240.0	112112.0	355012.0
Maximum	0.164600	1007190.	562640.0	2535700.
Minimum	0.006400	65951.00	28832.00	50150.00
Std. Dev.	0.048581	303583.6	159489.8	761678.8
Skewness	0.913870	0.652296	0.915228	1.041751
Kurtosis	2.485253	1.961159	2.497293	2.741740
Jarque-Bera	4.657227	3.592317	4.654238	5.693248
Probability	0.097431	0.165935	0.097576	0.058040
Sum	1.628500	11963829	5942663.	22250856
Sum Sq. Dev.	0.070805	2.76E+12	7.63E+11	1.74E+13
Observations	31	31	31	31

According to Table the mean per capita GDP in Nepal is Rs.0.0525 million, or Rs. 52500, with a standard deviation of 0.049. Additionally, the highest and minimum PCI values were Rs. 0.164 million ($164000) and Rs. 0.0064 million ($6400). Similar to that, the primary sector’s average production was Rs. 385930 million, with a standard deviation of 303583.6. Additionally, PRS has values as high as Rs. 1007190 million and as low as Rs. 65951 million.  Similar to the primary sector, secondary sector production has a mean value of Rs. 191698.80 million and a standard deviation of Rs. 159489.8. Its values range from Rs. 562640 to Rs. 28832 million, respectively. Furthermore, the mean value of output of tertiary sector is Rs.717789.5 million with a standard deviation of 761678.8. Its maximum and the minimum value is Rs.2535700 and Rs.50150 million. The values of standard deviation indicate that these variables are highly volatile during the study period of 31 years.

Table 09: Long-run model derived by using OLS methods.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
lnPRS	0.398004	0.114177	3.485853	0.0017
lnSES	0.154937	0.095775	1.617721	0.0013
lnTES	0.432781	0.081284	5.324276	0.0000
C	15.77752	0.384752	41.00701	0.0000
R-squared	0.998340	Mean dependent var		-3.412258
Adjusted R-squared	0.998155	S.D. dependent var		1.019728
S.E. of regression	0.043800	Akaike info criterion		-3.298433
Sum squared resid	0.051799	Schwarz criterion		-3.113403
Log likelihood	55.12572	Hannan-Quinn criter.		-3.238118
F-statistic	5411.163	Durbin-Watson stat		1.189081
Prob(F-statistic)	0.000000

Note. Authors Computation using EViews 10.

Table is the long-run model, and coefficients are called a long-run coefficient. The stationary of the residual is examined first, then the long-run cointegration among the variables. If the long-run model’s residual is stationary at a given level, the variables are cointegrated and have a continuous connection, then the model is valid. The augmented Dickey Fuller test is used to determine if the variables utilized in the study have stationary properties.

Table 10: ADF test for Unit Root

Variables		Level		First Difference		Order of Integration
		Intercept	Intercept  & Trend	Intercept	Intercept  & Trend
lnPRS	t-statistic	-0.89	-2.067	-3.317	-3.338	I (1)
	p-value	0.776	0.542	0.002	0.002
lnSES	t-statistic	-0.801	-1.864	-3.947	-3.828	I (1)
	p-value	0.80	0.64	0.005	0.02
lnTES	t-statistic	-0.567	-1.572	-5.017	-4.916	I (1)
	p-value	0.86	0.77	0.0003	0.002
lnPCI	t-statistic	-0.290	-1.486	-4.738	-4.640	I (1)
	p-value	0.91	0.82	0.0007	0.004

Note.H₀: lnPCI, lnPRS, lnSES, lnTES have a unit roots. Authors’ computation using EViews-10.

The Table shows that the result of the ADF test statistics of concerned variables used in this study. The variables are referred to as I (0) if they are stationary at level, and I (1) if they become stationary only after the first difference. All variables are non-stationary at level, but only after the first difference, according to the findings of the ADF test. They are all together referred to as I (1). In consideration of the rejection of our hypothesis H0, it follows that variables are stationary at the first difference. All variables lnPCI, lnPRS, lnSES, and lnTES in Table above are stationary at the first difference. The Engel Granger technique is used in this study to determine the long-run cointegration of the variables since all variables are stationary at the first difference. The stationary test of residual is presented in Table.

Table 11: ADF Test for Residual

Null Hypothesis: ECT has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic – based on SIC, maxlag=7)
			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic			-3.489303	0.0154
Test critical values:	1% level		-3.670170
	5% level		-2.963972
	10% level		-2.621007
*MacKinnon (1996) one-sided p-values.
Note. Authors computation using EViews 10.

Table a stationary test of the residual indicates that the absolute value of the augmented Dickey-Fuller test statistic 3.4893 is greater than the absolute value of Engle-Granger critical value 2.9639 at a 5% level of significance. So, the null hypothesis that the ECM has unit root is rejected, that is, ECM is stationary at level. Thus, being the residual term is stationary at level form so this can be concluded that there exists cointegration among the variables and the long-run model is not spurious.  According to the Engle-Granger cointegration test, the stationary of the residual term or error correction term in the long-run model is examined in order to determine if the variables are long-run cointegrated. Both variables are significant at the 1% level of significance because the dependent and independent variables became stationary at the first difference and the residual became stationary at level (the residual stationary at level because the coefficient of Engle Granger tau-statistics is 0.008 and z-statistics is 0.009. This demonstrates that the Engle-Granger test revealed that the dependent and independent variables were co-integrated. As a result, the error correction model (ECM) is made possible. As a result of the dependent and independent variables being integrated at first difference and the residual at level, respectively. The study employed an error correction model to examine the short-term connection between the dependent and independent variables.

The ECM is presented as

∆lnPCI=β₀+β₁∆lnPRS+β₂∆lnSES+β₃∆lnTES+β4ECT (-1) +V_t……………………. (i)

Table 12: ECM, dependent variable is dlnPCI

Dependent Variable: d(lnPCI)
Method: Least Squares
Sample (adjusted): 1992 2021
Included observations: 30 after adjustments
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.020630	0.019571	1.054133	0.3019
d(lnPRS)	0.324082	0.151883	2.133764	0.0429
d(lnSES)	0.258699	0.141878	1.823393	0.0802
d(lnTES)	0.243121	0.121501	2.000977	0.0564
ECT (-1)	-0.610566	0.189812	-3.216695	0.0036
R-squared	0.536711	Mean dependent var		0.108333
Adjusted R-squared	0.462584	S.D. dependent var		0.053310
S.E. of regression	0.039081	Akaike info criterion		-3.495358
Sum squared resid	0.038183	Schwarz criterion		-3.261825
Log likelihood	57.43037	Hannan-Quinn criter.		-3.420649
F-statistic	7.240488	Durbin-Watson stat		2.031551
Prob(F-statistic)	0.000513

Note. Authors computation using EViews 10.

The study revealed a positive and significant relationship between primary sector output and per capita GDP at a 5% level of significance. A 1% change in PRS results in a 0.324% short-term change in per capita GDP. A 1% change in SES results in a 0.25 change in per capita GDP and a 0.24% change in per capita GDP. At a 10% level, there is a positive and significant relationship between SES and TES to PCI. The error correction term, -0.6105, is significant at 1% significance level, indicating convergence to long-run equilibrium. The JB test’s chi-square p-value was 0.64, higher than the acceptable value at the 5% level of significance. The residuals were determined to be regularly distributed, as seen by this. (a)The Cumulative Sums Control Chart CUSUM test for the Stability: Since, the plot of CUSUM statistics for d(lnPCI) was found with in the critical line at 5% level of significance Granger-causality test was performed to determine causality among variables from level data

Table 14: Pairwise Granger Causality Tests

Sample: 1991 2021
Lags: 2
Null Hypothesis:	Obs	F-Statistic	Prob.
LNPRS does not Granger Cause LNPCI	29	3.35392	0.0519
LNPCI does not Granger Cause LNPRS		1.26617	0.3001
LNSES does not Granger Cause LNPCI	29	0.75341	0.0481
LNPCI does not Granger Cause LNSES		9.59608	0.4209
LNTES does not Granger Cause LNPCI	29	2.80131	0.0806
LNPCI does not Granger Cause LNTES		1.07243	0.3580

Note. Authors computing using EViews 10. Where PCI=Per capita GDP, PRS= Primary sector output, SES= Secondary sector output and TES= Tertiary sector output

There seems a unidirectional causality from lnPRS to lnPCI and lnSES to ln PCI at 5% level of significance and from lnTES to lnPCI   at 10% level of significance. This suggests that the major independent variables, lnPRS and lnSES, have a significant impact on how per capita GDP is determined in the economy.

4. CONCLUSION

The study findings, expanded scope, conclusions, and suggestions are included in this chapter. In this chapter, the data analysis’s facts and conclusions are given. Recommendations are provided to interested individuals and organizing in addition to summarizing and ending study effort. The researcher initially performed descriptive analysis that involved the stated output. The results shows  mean, median, and standard deviation are Rs. 0.05253 million, Rs.0.0289 million, and Rs. 0.04851. Its greatest and minimum values are respectively Rs. 0.1646 million and Rs. 0.0064 million. Additionally, the mean, median, and standard deviation values for the primary sector production are Rs. 385930 million, Rs. 230240 million, and Rs. 303583.6. According to Table, the secondary sector had a mean value of Rs. 191698.8 million, a median value of Rs. 112112 million, and a standard deviation of Rs. 159489.8. Additionally, the tertiary sector’s mean, median, and standard deviation values are Rs. 717769.5 million, Rs. 355012 million, and Rs. 761678.8 million, respectively. For the first objective the researcher studied the trend line of the dependent and independent variables. The trend line displays an upward tendency for each variable over time. The trend lines for the production of the primary, secondary, and tertiary sectors as well as the per capita GDP, however, showed a positive tendency. Additionally, throughout the last 31 years of the study, it was discovered that the production of the primary, secondary, and tertiary sectors had been increasing at rates of 9.5%, 9.5%, and 13%, respectively, every year. For the second objective, unit root tests were carried for time series data to check the stationarity of the variables. Both the variables and the error term had stationary levels at the first difference. The long-run model is free of spurious regression, according to the Engle-Granger cointegration test, which shows that all the variables included in this study are cointegrated among one another. In the short-term, according to the Error Correction Model’s outcome. According to the study, there is a positive and substantial (at the 5% level) correlation between the production of the primary sector and per capita GDP. It demonstrates that a change of 1% in the PRS causes a change of 0.32% in the per capita GDP. Also, there is a positive and substantial relationship between SES and TES (at the 10% level). They demonstrate that 1% changes in SES and TES respectively lead to 0.25% and 0.24% changes in per capita GDP.At a 1% level of significance, the Error correction term ECM (-1) is negative and statistically significant. Due to the fact that the error correction term was negative, it may be assumed that the value was consistent with convergence to the long-run equilibrium. Additionally, it is possible to claim that roughly 61.05% of the fault was fixed within a year. Additionally, the dependent variable disequilibrium must be adjusted for 1.7 years. The test’s chi-square probability value (P-value), which above the threshold at the 5% level of significance, was 0.42. This demonstrated that the analysis’s serial correlation was absent. Additionally, the Breusch Pagan Godfrey Heteroscedasticity Test Chi-Square P-Value was 0.56, which was greater than five percentage points and indicated the variance of error terms are constant. The value of centered VIF is less than 10 indicates the model is free from the problem of multicollinearity. The study explored the causal relationship between primary, secondary, and tertiary sector output and real per capita GDP in Nepal. It finds a significant positive correlation between these sectors, with a gradual increase in outputs. The study also reveals a unidirectional causal relationship between PCI, PRS, SES, and TES. The tertiary sector’s contribution to per capita GDP is limited, despite being the most productive. Factors affecting per capita income include education, population growth, labor force, liberalization, and technological advancement. The study suggests that investment from both private and government sectors, as well as investment in service sectors and advertising, could improve per capita GDP.

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