Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 61 Development Expenditure Allocation and District Wise Social Development: A Case Study of Punjab Pakistan Noman Arshed a, Hafeez ur Rehman b, Amna Gulzar c a Assistant Professor, Department of Economics, Division of Management and Administrative Science, University of Education, Lahore, Pakistan b Professor, Department of Economics and Quantitative Methods, Dr Hasan Murad School of Management, University of Management and Technology, Lahore, Pakistan c Department of Economics and Quantitative Methods, Dr Hasan Murad School of Management, University of Management and Technology, Lahore, Pakistan ARTICLE DETAILS ABSTRACT History: Accepted 25 April 2023 Available Online June 2023 In the wake of achieving Sustainable Development Goals, this study sets to investigate the allocation of development expenditures as government policy intervention and its role in the uplifting the social condition in districts of Punjab. The constriction of a comprehensive social development is indexed using principal factor analysis. This study considered panel data for 36 districts of Punjab ranging from 2008 to 2017. Estimates from the panel Random Effect model advocated that the rise in the development expenditures by the Government of Punjab follows U shape relationship with the social condition of the province. This quadratic model helps to estimate district-wise effects of government development intervention on social development and its components via which resource mobilization optimization can be done. © 2023 The authors. Published by SPCRD Global Publishing. This is an open access article under the Creative Commons Attribution- NonCommercial 4.0 Keywords: Development Expenditure Mobilization, Social Development Index, Panel Random Effect Model JEL Classification: H53, O12 DOI: 10.47067/reads.v9i2.214 Corresponding author’s email address: hafeez.rehman@umt.edu.pk 1. Introduction Social development refers to the change that can put the people at the center of economic activity. It means a firm assurance that development process guarantees benefit to people and it focuses not only the poor but also an acknowledgement to that people and the way they interact with each other shapes development process (International Institute of Social Studies). Similarly, it can be said that social development means investing in people which implies the elimination of obstacles and the creation of capabilities. Hence, it facilitates them to accomplish their goals in life with self- esteem and pride. Social deevelopment creates change in non-economic disciplines like democracy, social freedom, peace, social justice, strong institutions, health & nutrition and education which lead to well-being of every individual (Paiva, 1977; Jones, 1981; Meinert, 1987; Estes, 1990). A recent study by Inabo and Arshed (2019) assessed the effect of mortality rate, access to improved water and access to sanitation Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 62 facilities for the case of Nigeria. The results showed that increase in access to water and sanitation and decrease in infant moratality rate lead to increase in economic progress. Similar outcome evidenced by Rizvi (2019) for the case of education expenditures and its role on growth. Fiscal policy is an effective tool in achieving economic growth which then can translated into sustainable growth by extending expenditures to development sector. While explaining the suitability of fiscal policy for social development, we must explore the opposing theories behind it. According to Keynesian economists, increase in government spendings definitely effects economic growth in short run and may increase the long run too if it increases the capacity or resources (Loizides & Vamvoukas, 2005; Hassan & Kalim, 2012). But according to classical economists, economy is already fully employing resources in longer run, government spending will only distorts it by creating crowding out effect. Hence there is a need to investigate the effect of fiscal policy at a smaller scale where the effects of expenditures are not agreegated out. Pakistan economy has shown a continuously growth but at the same time, macroeconomic factors are imbalanced. Pakistan’s GDP growth rate is increased from 5.4% to 5.8% in 2018 (Economic Survey, 2018). In terms of human development, Pakistan ranks as one of the lowest in South Asia specifically in education and health. Literacy rate (10 years and above) is 58% only. Average annual population growth rate (1998-2017) is souring to 2.40%. Female participation in labour force is the lowest in the region. Infant mortality rate is 63 per 1000 birth and under-five mortality rate is 89 per 1000 birth depict uncertainty socially (Economic Survey of Pakistan, 2017-18). Unsatisfactory economic condition, increased poverty, lack of education, inadequate healthcare facilities, sectarian conflict, extremism, lawlessness further deteriorate the situation. To deal with social, economic and political problems, Pakistan requires a different development approach. Decentralization of power makes the planning process possible at the grass root level. It is one of the outcomes of the 18th amendment in the 1973 constitution which led to the abolition of the concurrent list now, the provinces have the responsibility to provide services (Burki, 2010). According to Burki (2010), governments are in a better position to provide basic services to people as these are closer to their beneficiaries. Punjab (shown in figure 1) is the 2nd largest province by area of Pakistan after Balochistan with 205,345 Sq Km of area. However, population wise it is the largest province with 536 population density per Sq Km (Punjab Development Statistics, 2018). An average annual population growth rate (1998- 2017) is 2.13% (Economic survey of Pakistan, 2017-18). As per the MICS 2018 report, infant mortality rate is 60 per 1000 births, youth (male) literacy rate (15-24 year) is 77.7% and youth (female) literacy rate (15-24 year) is 71.7%, whereas employment rate among population (age 10 years and above) is 92.9%. Percentage of MPI-Poor people / headcount ratio is 26.1%. Punjab province comprises 36 districts with great diversifications in term of area, environment, culture, language and population, social and economic conditions. Punjab economy plays a pivotal role in Pakistan’s GDP and the overall growth rate of the country. During 2017-18, Punjab’s per capita income is 2% higher than national average. Over the last five years Punjab GDP growth rate was 4.9%. Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 63 Fig: 1 Punjab Province Map District Wise Fig: 2 Infant Mortality Rate in Punjab’s Districts Source: MICS, 2018 Bureau of Statistics Figure 2 is showing infant mortality rate (2018) across all over Punjab. This is an indicator of measuring social and economic well-being of a country. It is quite obvious from the map as the dark areas are reflecting the intensity of the matter where infant mortality rate is very high. Lodhran, Khanewal, Jhang, Chiniot and Hafizabad districts are showing high infant mortality rates. The poor and insufficient facilities provided by public sector have hostile effects on the quality of life. Lack of proper care, vaccination, maternal care, education of mother and unhealthy and poor facilitation in hospitals (2018) Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 64 are the major cause of high infant mortality rate in this region. Government should pay special attention to reduce health issues to ensure health children in Punjab. Table 1 shows the status of human development across the province. Most of districts fall under the category of high level of human development and high-medium human development whereas only 3 districts Muzaffargarh, Dera Ghazi Khan and Rajanpur are in low medium human development. There is no district falls under low human development category. Table: 1 Human Development Index in Punjab districts National HDI Rank District Human Development Index High level of Human Development 1 Lahore 0.877 3 Rawalpindi 0.834 5 Sialkot 0.834 6 Jhelum 0.829 High-Medium of Human Development 7 Gujrat 0.795 8 Chakwal 0.792 9 Attock 0.786 10 Faisalabad 0.782 11 Gujranwala 0.769 12 Toba Tek Singh 0763 15 Norowal 0.748 16 Nankan Sahib 0.740 17 Sheikhpura 0.738 19 Layyah 0.729 20 Sargodha 0.728 21 Multan 0.718 23 Mandi Bahauddin 0.716 24 Kasur 0.714 25 Sahiwal 0.710 26 Khushab 0.706 27 Okara 0.705 28 Hafizabad 0.705 Medium Human Development 30 Khanewal 0.699 33 Jhang 0.682 39 Pakpattan 0.66 41 Lodhran 0.659 42 Chiniot 0.657 43 Vehari 0.655 46 Bahawalpur 0.645 47 Mianwali 0.645 49 Bahawalnagar 0.63 50 Bhakkar 0.628 51 Rahim Yar Khan 0.625 Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 65 Low Medium Human Development 58 Muzaffargarh 0.584 64 Dera Ghazi Khan 0.535 69 Rajanpur 0.506 Low Human Development No District fall in this category Source: Punjab Growth Strategy 2023 Fig: 3 Number of malnutrition children in Punjab’s Districts Source: MICS, 2018 Bureau of Statistics Figure 3 is showing number of malnourished children in all the districts of Punjab. It is quite visible from the map that Lahore district has the highest number of malnourished children among all the districts in Punjab. Although social development has been seen in Punjab, but at the same time it is facing high number of malnourished children. Low birth weight, lack of breastfeeding, lack of knowledge about nutrition, poverty, poor economic conditions, maternal education, and contagious diseases are the main causes. Figure 4 is showing total percentage of literate men across all over Punjab. The dark areas are reflecting the high percentage. It is quite visible that districts in northern Punjab are performing well. South Punjab district are not performing well due to government negligence, lack of proper attention, insufficient resource distribution, lack of facilities, poor economic conditions, poverty and cultural obstacles. Figure 5 is showing total percentage of literate women across all over Punjab. The dark areas are reflecting the high percentage. It is quite visible that districts in northern Punjab are performing well. (2018) Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 66 The position in Rajanpur, DG Khan and Muzaffargarh is alarming. Although literacy rate among male is also low in these districts but the condition of female literacy rate is very pathetic. Cultural obligation is one of the major reasons along with acute poverty prevailing in these areas. Due to government negligence, lack of proper attention, insufficient resource distribution, lack of facilities, poor economic conditions and cultural differences literacy rate among female is low in districts of south side of Punjab. Fig: 4 Total percentage literate (men) in districts Source: MICS, 2018 Bureau of Statistics Figure 6 is showing average expenditure from 2008 to 2017 in all the districts of Punjab. It is quite visible that a large chunk of development expenditures incurred on Lahore district whereas Bhakkar, Khushab and Hafizabad are the most neglected districts. Based on the evidence provided in terms of disparity of social indicators and the disparity in the development expenditures. Following are the research questions set to be answered • Is a non liner pattern exists between development expenditures and social development? • What is the role of other controlling variables with respect to social development? • Does development expenditures influence indicators of social development in appropriate direction? (2018) Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 67 Fig: 5 Total percentage literates (Women) in districts Source: MICS, 2018 Bureau of Statistics Punjab Government is providing social services to its people as per constitution. The responsibility has shifted from federal Government to provovincial government from 2011. This study is designed to analyze the performance of Punjab Government in terms of the dispersement of total development expenditrues for its districts. For this purpose the objectives of this study are as follow: • To investigate the non-linear impact of development expenditure on social development in Punjab. • To assess how the incidence of development expenditures are influencing the components of social development index. Fig: 6 Expenditure per capita (Rs. Million) in Punjab districts Source: MICS, 2007, 2011, 2014 and 2018 Bureau of Statistics (2018) Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 68 18th amendment of constitution of Pakistan puts great responsibility on the provinces to ensure social, economic and political reforms in order to bring social development. In the previous discussion the position of Punjab Government across the districts is quite obvious where all districts are not equally happy and prosperous. Discrimination has been found among them with regard to social development. There is a dire need to conduct a research in this regard as limited work has been found regarding social development in districts of Punjab Pakistan. 2. Literature Review Social development is about human development and its well-being associated with economic and non-economic disciplines. It is a complex and alarming issue related to human welfare. The term development means the creation of circumstances for the recognition of human personality (Seers, 1972). According to Myrdal (1972), development means to generate employment, reduce poverty and inequity which implies a vertical movement of the social system as whole. Development means an increase in Gross National Income (real income) over a long period of time (Meier, 1976). Does real national income an appropriate indicator to measure development? Macgranahan (1972) emphasized that there are many limitation to take Per Capita Gross National Income (GNP) as a general measure of development. According to Todaro (1977), although many developing countries attained economic growth and development goals during the ear of 1950s and 1960s, but it did not represent to a better standard of living of the people. These findings clearly depict that there were some deficiencies persisted with the definitions of economic growth and economic development. Hicks and Streeten (1979) proposed a new dimension for measuring development. This dimension incorporating social development’s related indicators like health, water supply, education, food, sanitation and hosing into Gross National Product (GNP). Economic growth is no doubt has its importance, but it becomes valuable and workable when it reduces scarcities and improving lives of common people (Sen, 1996). From Adam Smith to Keynes the concept of social development has attracted the economists. It is basically the promotion of a sustainable system where the focus is to empower the marginalized segments, women and men to boost up their condition, to improve their living standard and to achieve improve their economic and social position in the society (Bilance, 1997). Kates (2018) described social development is that every individual should be under the scope of social development and there must be social equality and high priority must be given to the development of human resources. Social development basically implies a change in social institutions. According to Fan et al. (2008), public spending in terms of providing subsides can have positive influence on poor both in the short run and long run. During development stages, developing countries should allocate more government expenditure to those sectors that benefit the majority of poor. Castro et al. (1999) gave the idea of efficiency and equity phenomenon related to public funds spending. Education and health are the basic services which are most of the time are subsidized with public spending to serve the purpose. Funds are efficiently utilized and the outcomes may be generalized. Public spending or investment must ensure benefits to poor. Many research studies found that government spending is helpful in agricultural production growth and poverty reduction (Elias, 1985; Fan & Pardey, 1998; Fan, Hazell, & Thorat, 2000; Fan, Zhang, & Zhang, 2004). Gupta and Verhoeven (2001) analyzed the efficiency of government expenditure on health and education. They took 37 countries from Africa between 1984 and 1995. They found that health and education spending became more efficient in that period. They concluded that for attaining impressive results African countries required more public allocations. Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 69 Decentralization of public funds is the best possible way to ensure good governance and macroeconomic stability. Empirical evidence proved that local administration performed well in comparison of center in term of providing services (Campbell et al., 1991). Fan et al. (2000) conducted a study to investigate the impact of government expenditure (development and non-development) on rural poverty and productivity in case of India. He concluded that government should give more focus on development side. More investment should be done in such sectors: rural roads, agricultural research, education, health, irrigation, sail and water conservation rural and community development. Kumar (2017) suggested that in order to improve the social status of any country government should give prioritization to social sectors. According to Land (1975), “social indicators are statistics which measure social conditions and changes therein over time for various segment of the society.” Bunge (1981) divided the indicators into descriptive and normative form. Normative variables involved judgments. According to him, development is not just about economic up in 1990, UNDP (United Nation Development Programme) introduced Human Development Index which involved 03 indicators for development. Diener and Suh (1997) determined that economic indicators are not sufficient to measure a true picture of social development. Rao (1975) determined 64 social indicators in case of India for measuring Social Development, Chakrevarty (1976) argued that education, health, employment, distribution of income, housing, and consumption are the relevant social indicators. Morris (1979) took life expectancy, adult literacy rate and infant mortality rate and constructed a physical quality of life index and used it in term of social development in his work. Ray (1989, 2008) gave 13 and 10 social indicators for measuring social development. However Ohlan (2014) provided 43 indicators for social development. Social development index (2014) described that “social development as the capacity of a society to meet the basic human needs of its citizens, establish the building blocks that allow citizens and communities to enhance and sustain the quality of their lives, and create the conditions for all individuals to reach their full potential”. Kumar (2017) recommended that the selection of social indicators solely depend on the demography on the country and the availability of the data. A study by Asghar, Hussain and Rehman (2012) assessed the role of government development spending on the poverty of Pakistan between 1972 and 2008. The results from the vector error correction model showed that increase in the health and education expenditures lead to poverty alleviation of Pakistan. Qadir and Majeed (2018) used the data of Pakistan between 1975 and 2016 to assess the role of health expenditres on life expectancy and infant mortality rate. The results show that health expenditures significantly increase life expectancy and decrease infant mortality rate. A study on SAARC economies by (Arshed et al., 2018) indicated that increase in development expenditures reduces income inequality. Similarly, for 73 developing countries, development expenditures were shown to decrease poverty gap (Hassan, Bukhari & Arshed, 2020). A study by Mamoon, Raza and Arshed (2015) used the indicator of road length as a instrument of market access to farmers and infrastructure. The results showed that for the case of Punjab districts, increase in road length does not contribute in infant mortality rate. Further Arshed, Kalim and Anwar (2019) assessed the role of factory employment and population density on the crime rate for districts of Punjab Pakistan. The results showed that increase in employment makes them enganged in legal activities ensuring reduction in crime while high population density puts pressure on the social infrastructure which decreases the standard of living, motivating people to engange in crime. Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 70 Previous studies assessed the role of development expenditures on the social development but lack in accounting for the minimum investment required to make the social infrastructure suitable to improve social development. Secondly, there is dearth of studies at the district level of Punjab Pakistan which assessed the performance of government development expenses on the social development. There are few studies which explored the social development in districts of Punjab. A study by Arshed, Anwar and Sarwar (2016) asseted that increase in education and public order expenditures reduces while increase in health expenditrues and policy expenditures increases the crime rate. A study by Arshed et al. (2019) assessed the role of development expenditure and its types on the incidence of crime in districts of Punjab. The result showed that these development expenses improve the social conditions hence reduce crime rate. 3. Conceptual Framework Kuznets in 1953, 1955 and in 1965 noticed an inverted U shaped relationship between economic development and income inequality. He used data sets of some industrial societies in 19th and 20th centuries and examined that at the initial stage industrial development grew income inequality but later on it stared declining when more development took place. This theory can be applicable in case of Punjab by replacing inequality with social development and growth with development expenditure. Initially social development decreases with the increase in development expenditure as policies and innervations take time to be mature and provide fruitful outcomes. It will hypothesise that development expenditure will follow U shaped pattern with social development index. On the other hand the theory of law of diminishing returns which explains that increase in inputs are initially growth promoting but later on their usefulness falls. This theory can be applied in this model by replacing social development as output target and development expenditure as input. At start development expenditure initially lead to development but over spending may lead to wastage resources or corruption. That is why this model may leads to inverted U shape theory. In following discussion it will try to find out the actual picture or correct situation in Punjab. Either Punjab is successful in achieving social development or not. 3.1 Research Hypothesis This study is designed to analyze the performance of Punjab Government. Either government is successfully achieving its goal nor. For his purpose the research hypothesis of this study is as follow: Ha: Development expenditure improves social status in the districts of Punjab 3.2 Research Model Following figure deicts the theoretical flow of effects from independent variables to dependent variable. The model in figure 7 with hypothesed quadratic effect of government development expenditures, following is the estimation equation. The data is collected for 36 districts between 2008 and 2017. Here population density is used to control for the increase in the pressures on the available social infrastructure. Employment and productivity is incorporated to control for increase in economic progress. Road length in incorporated to control for the effect of connectivity and physical infrastructure. And lastly regime change controls for the decentralization regime. Further the construction of the social development index is made using the indciators provided in table 3. Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 71 Fig: 7 Theoretical Model 4. Date and Methodology 4.1 Data Framework In this study, panel data is be used using districts as cross sections and years as time periods, because of its ability to manage esstiamtion problems like multicollinearity, heteroscedasticity and autocorrelation. Panel Data also provides an efficient estimated results (Hsiao, 2007). For the formulation of social development index, principal factor analysis technique will be employed. (Pakes & Griliches 1984). 4.2 Sample of Data In this study 36 districts of Punjab considered form the period 2008-2017. Different variables would be selected such as development expenditure, population density, productivity, road length /connectivity and employment. Development expenditures data have been collected from Planning and Development Board, Government of Punjab and population density, productivity, road length /connectivity and employment related data have been collected from various issues of Punjab Development Statistics. 𝑆𝐷𝐼𝑖𝑡 = 𝛼1 + 𝛼2𝐿𝐸𝑋𝑃𝑖𝑡 + 𝛼3𝐿𝐸𝑋𝑃𝑖𝑡 2 + 𝛼4𝐿𝐷𝐸𝑁𝑖𝑡 + 𝛼5𝐿𝑅𝑂𝐴𝐷𝑖𝑡 + 𝛼6𝐿𝑃𝑅𝑂𝑖𝑡 + 𝛼7𝐿𝐸𝑀𝑃𝑖𝑡 + 𝛼8𝐷𝑈𝑀𝑖𝑡 + 𝑒𝑖𝑡 Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 72 Table: 2 Variables Representation and Transformation Variable Definition Source of data Social Development (SDI) *Social Development Index Punjab Development Statistics Development Expenditure (LEXP) Development intervention per capita P&D Board Squared Development Expenditure (LEXP2) Incorporating quadratic effect P&D Board Population Density (LPDEN) people per Sq Km Punjab Development Statistics Road length (LROAD) Metaled road length Km Punjab Development Statistics Productivity (LPRO) Wheat productivity (Production/sown area) Punjab Development Statistics Employment (LEMP) No. of employed person Punjab Development Statistics Dummy Variable (DUM) Regime change (pre 2011 = 0 and post 2011 = 1) Self calculated * For the measurement of Social Development Index following social indicators are being used from MICS 2007, 2011, 2014 and 2018. Factor analysis technique will be employed to determine the % age contribution of selected indictors for social development. This study will use variables in natural logarithm form. The reason behind to use this form is that to linearize the model by reducing the intensity of heteroskedasticity. This study will use square form of variable development expenditure the intention behind is that it is used to assume that the effect of development expenditures are following increasing returns is to show the marginal impact (Barro 1990). 4.3 Methodology Zagorski and McDonnell (1995) employed factor analysis method for the formulation of social development index. Majumder et al. (1995) used several methods i-e principal component analysis (PCA), multiple factor analysis, aggregation method, monetary index, ratio index and ranking method. Awan (2012) used factor analysis technique, the taxonomic distance technique and the Z-sum techniques are used for the development of social development index. Panda et al. (2016) applied composite dimension index for this purpose. 5. Result and Discussion 5.1 Index of Social Development Table 3 reports all the indicators which this study have proposed for the constriction of social development index.These indicators are collected from Punjab Development Reports and Multi Indicator Cluster Survey (MICS). Further, table 4 reports the KMO and Bartlett’s test which indicate that the proposed indicators are sufficient in preparation of social development index. Lastly, table 5 reports the mean and standard deviation of the constituted index of social development for each district of Punjab Pakistan. Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 73 Table: 3 Components of Social development index Sr# Indicators Reference Education 1 Youth Literacy Rate 15-24 year (male) (%) Awan (2012), Ohlan (2013) 2 Youth Literacy Rate 15-24 year (female) (%) Awan (2012), Ohlan (2013) Health 1 Percent of Household with Iodized Salt Consumption (%) Awan (2012), Panda et al. (2016) 2 Infant Mortality Rate (Probability of dying between birth and the first birthday) Nutrition 1 Malnutrition (In Numbers) Ohlan (2013) Infrastructure/ Housing 1 Household Possessions (Electricity %) Afzal (2011) 2 Percent of Household by House Ownership (%) Sanitation 1 Water on Premises (%) Afzal (2011) 2 Percentage of Household Members with hand Washing Facilities where Water and Soap are present (%) 3 Percentage of using Improved Sanitation (%) Afzal (2011), Awan (2012) Other 1 Child Labor (%) Afzal (2011), Awan (2012), Panda et al. (2016) Table 4: KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.779 Bartlett's Test of Sphericity Approx. Chi-Square 2164.982 Df 45 Sig. 0.000 Table:5 Social development index (2008 to 2018) District Name Average Standard Deviation Attock 0.92277 0.172475 Bahawalnagar -0.6925 0.204373 Bahawalpur -0.91832 0.32232 Bhakkar -0.7192 0.283501 Chakwal 1.207565 0.298299 Chiniot -0.8041 0.21912 DG Khan -1.44376 0.393867 Faisalabad 1.004215 0.254213 Gujranwala 1.155822 0.163472 Gujrat 1.360551 0.246878 Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 74 Hafizabad 0.146118 0.248129 Jhang -0.7299 0.261463 Jhelum 1.196655 0.119618 Kasur -0.39056 0.326466 Khanewal -0.46949 0.248982 Khushab -0.08503 0.199804 Lahore 1.529616 0.23987 Layyah -0.79057 0.404949 Lodhran -0.861 0.164564 Mandi Bahauddin 0.538601 0.202859 Mianwali 0.111936 0.337741 Multan -0.16381 0.296879 Muzaffargarh -1.46018 0.270024 Nankana Sahib 0.37935 0.360332 Narowal 0.809982 0.244564 Okara -0.39137 0.39455 Pakpattan -0.80586 0.399839 Rahim Yar Khan -0.86599 0.250266 Rajanpur -2.46064 0.33441 Rawalpindi 1.709035 0.134198 Sahiwal -0.00663 0.265917 Sargodha 0.099767 0.311538 Sheikhupura 0.446639 0.230734 Sialkot 1.183032 0.173256 TT Sing 0.384315 0.154173 Vehari -0.40023 0.310706 Figure 8 is showing the status of social development index in all the districts of Punjab. It can be noticed that the position of social development is better in northern districts of Punjab. Lahore, Rawalpindi, Chakwal, Jehlum, Gujrat, Sialkot, Gujranwala and Faisalabad are performing well whereas situation in Rajanpur, DG Khan and Muzaffargarh is disappointed. This figure 9 is showing a U shaped association between social development and expenditure incurred by the Punjab government in all the districts. Here, at the earlier stage government expenditure did not fulfil its purpose to ensure improvement in social status of districts but gradually every unit of government expenditure improve the situation. Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 75 Fig: 8 Social Development Index All across Punjab between 2008 to 2018 Source: MICS, 2007, 2011, 2014 and 2018 Bureau of Statistics Fig: 9 Scatter Plot diagram of Social Development and Development expenditure 5.2 Descriptive Statistics Table 6 reports the descriptive statistics of the indicators which are used in the construction of the social development index. (2008-2018) Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 76 Table 6 Descriptive Statistics Items Minimum Maximum Mean Std. Dev. Infant Mortality Rate 35.00 130.00 75.55 17.31 Percent of Household with Iodized Salt Consumption(%) 0.80 81.00 38.15 19.67 Water on Premises (%) 37.50 99.70 83.92 11.42 Malnutrition (Numbers) 378.00 4906.00 1572.80 945.00 House Ownership (%) 64.90 97.80 86.86 5.77 Household Possessions (%) 63.50 100.00 93.81 7.19 Literacy Rate 15-24 year(male) (%) 52.30 95.70 78.43 9.00 Literacy Rate 15-24 year(female) (%) 31.40 92.90 67.75 14.93 Percentage using improved sanitation (%) 16.87 97.90 65.13 16.66 Child Labor (%) 0.90 32.70 12.51 6.92 The table 7 describes the correlation between the variables. The correlation between social development and government expenditure is 0.2901 which shows a positive relationship. While the correlation of independent variables are lower than 0.93 indicating the absence of multicollinearity. Table 7: Table of correlation SDI LEXP LPROD LPDEN LROAD LEMP DUM SDI 1.000 LEXP 0.246 1.000 LPROD -0.232 0.064 1.000 LPDEN 0.525 0.1266 0.094 1.000 LROAD -0.008 0.004 -0.004 -0.016 1.000 LEMP 0.338 0.146 0.070 0.525 0.214 1.000 DUM 0.095 0.727 0.216 0.056 0.0073 0.028 1.000 5.3 Estimation Results Table 7 reports the estimates of panel random effect model. This model is selected as the hausman test has been found insignificant difference between the fixed effect and random effect model. Using 350 observations, the wald test is significant showing that overall model is fit. The overall R squared is indicating the the proposed variables are explaining 26.4% of the variation in the social development index. In this selected model the variables like factory employment and population density have a significant postivie effect on social development index. while the dummy variable of decentralization found to show development promoting effects. Development expenditure per capita is found negative and significant and square form of development expenditure per capita has been found positive and significant which means that marginal impact is shifting from negative to positive with increasing expenditures following the Kuznets hypothesis (Kuznets, 1953, 1955, 1965). Some of the variabels are insignificant Agriculture productivity has found out to be insignificantly impacting on social development because of disparities among districts (area, weather, soil, resources etc). Same is the case with like road length; connectivity among districts is not improving social conditions. Factory employment has positive impact on social development. Population density has positive impact on social development, as according to Optimistic Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 77 Theory which tells that higher population density fuels economic growth. Lastly the dummy variable of regime change indicates that when the decentralization regime started it led to a positive effect on the social development index. Table 7 Random Effect Model R-Sq: within = 0.4282 between = 0.2634 overall = 0.2645 Number of obs = 350 Number of groups = 36 Obs per group: min = 4.0 avg = 9.7 max = 10.0 Wald chi2(7) = 235.14 Prob > chi2 = 0.000 SDI Coef. Std. Err. z P.|z| [95% Conf. Interval] LEXP -0.1151 0.0411 -2.80 0.005 -0.195 -0.034 LEXP2 0.0277 0.0059 4.65 0.000 0.016 0.039 LPRO 0.0244 0.0348 0.70 0.482 -0.044 0.093 LPDEN 0.3265 0.0897 3.64 0.000 0.151 0.502 LROAD 0.1142 0.0883 1.29 0.196 -0.059 0.288 LEMP 0.1913 0.0691 2.77 0.006 0.044 0.027 DUM 0.0127 0.0358 0.35 0.724 -0.056 0.090 Intercept -4.9189 0.9177 -5.39 0.000 -6.717 -3.120 Fig: 10 Post regression fit plot of Social Development Index and Development Expenditures Figure 10 of quadratif fit plot using methodology of (Dawson, 2014) shows confirmed the association plot shown in figure 8, which means that based on the actual estimation coefficients, increase in the development expenditrues have positive effect on social development expenditrues. -5.05 -5 -4.95 -4.9 -4.85 -4.8 -4.75 -4.7 -4.65 -4.6 Low Dev Exp. High Dev Exp. S o c ia l D e v e lo p m e n t Dev. Expenses per capita Effect of Dev. Expenses on Development Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 78 Table 8, firstly provides the linearized effect of development expenditures on social development index based on the average incidence of development expendtirues per capita in the district. Secondly, while multiplying the linearized effect with the component matrix weights from the principal factor analysis (shown in appendix table 1), it can be seen that a 1% increase in the development expenditures per capita is moving the components of social development index in right direction except for the case of water on premises, malnutrition and house ownership. Figure 11 shows that for 1% increase in the development expenditures per capita in Punjab, there is 0.07% decrease in infant mortality rate, 0.037% increase in consumption of oidized salt, 0.08% increase in the access to electricity, 0.082% increase in male literacy rate, 0.091% increase in female litracy rate, 0.085% increase in access to improved sanitation and 0.05% decrease in child labor. These effects are supporting the theory but for the case of access to water, malnutrition and house ownership the effects are opposite, hence this study hints that the government expenditures per capita were not integrated / optimized with these targets in the past. There is a need to consider them in future. 6. Conclusion and Policy Recommendation The main purpose of this study is to investigate the impact of development expenditure per capita on social development of the districts of Punjab while controlling for population density, productivity, road length/connectivity and employment. This study considered panel data for 36 districts of Punjab ranging from 2008 to 2017. Development expenditures data have been collected from Planning and Development Board (P&D), Government of Punjab and population density, productivity, road length /connectivity and employment related data have been collected from various issues of Punjab Development Statistics (PDS). This study adapted from the idea of Kuznets curve to test U shaped relationship between government development intervention and social development index. In order to measure social development index for each districts principal factor analysis technique was utilized. The indicators used in the factor analysis are reported in table 3. Model selection tests indicated that panel random effect model technique is appropriate. The estimation confirmed the U shaped / Kuznets curve hypothesis while all independent varibales had positive effect on social development. While creating the channel of effects from the development expenditures to the indicators of social development, it can be seen that out of all indicators only 3 indicators were not properly targeted that is why increase in the development expenditires did not have an appropriate effect on them. Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 79 Table 8 – Marginal Effects Districtwise District SDI Infant Mortalit y Rate House with iodized salt consumptio n Water on premisi s Under 1 year malnutritio n House ownershi p House with electricit y Male literacy Rate 15-24 year Female literacy Rate 15-24 year Improve d sanitatio n Child labor Attock 0.114 -0.083 0.043 -0.047 0.014 -0.029 0.094 0.096 0.106 0.100 -0.059 Bahakkar 0.104 -0.076 0.039 -0.043 0.013 -0.026 0.086 0.087 0.097 0.091 -0.053 Bahawalnagar 0.101 -0.074 0.038 -0.042 0.012 -0.025 0.084 0.085 0.095 0.089 -0.052 Bahawalpur 0.132 -0.097 0.050 -0.055 0.016 -0.033 0.110 0.111 0.124 0.116 - 0.068 Chakwal 0.154 -0.113 0.059 -0.064 0.019 -0.039 0.128 0.130 0.144 0.136 -0.079 Chiniot 0.133 -0.097 0.050 -0.055 0.016 -0.033 0.110 0.112 0.124 0.117 - 0.068 D.G. Khan 0.137 -0.100 0.052 -0.057 0.017 -0.035 0.114 0.116 0.128 0.121 -0.071 Faisalabad 0.109 -0.080 0.041 -0.045 0.013 -0.027 0.090 0.091 0.102 0.096 -0.056 Gujranwala 0.134 -0.098 0.051 -0.056 0.016 -0.034 0.111 0.113 0.126 0.118 - 0.069 Gujrat 0.08 4 -0.061 0.032 -0.035 0.010 -0.021 0.069 0.070 0.078 0.074 - 0.043 Hafizabad 0.105 -0.077 0.040 -0.044 0.013 -0.026 0.087 0.088 0.098 0.092 -0.054 Jhang 0.100 -0.073 0.038 -0.042 0.012 -0.025 0.083 0.084 0.094 0.088 -0.052 Jhelum 0.114 -0.083 0.043 -0.047 0.014 -0.029 0.094 0.096 0.106 0.100 -0.059 Kasur 0.103 -0.075 0.039 -0.043 0.012 -0.026 0.085 0.087 0.096 0.091 -0.053 Khanewal 0.07 9 -0.058 0.030 -0.033 0.010 -0.020 0.066 0.067 0.074 0.070 -0.041 Khushab 0.113 -0.083 0.043 -0.047 0.014 -0.028 0.094 0.095 0.106 0.099 - 0.058 Lahore 0.20 8 -0.152 0.079 -0.087 0.025 -0.052 0.173 0.175 0.195 0.183 -0.107 Layyah 0.111 -0.081 0.042 -0.046 0.013 -0.028 0.092 0.093 0.104 0.097 -0.057 Lodhran 0.105 -0.077 0.040 -0.044 0.013 -0.026 0.087 0.088 0.098 0.092 -0.054 Mainwali 0.149 -0.109 0.057 -0.062 0.018 -0.038 0.124 0.126 0.140 0.131 -0.077 Mandi Baha UD 0.116 -0.085 0.044 -0.049 0.014 -0.029 0.096 0.098 0.109 0.102 - Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 80 Din 0.060 Multan 0.138 -0.101 0.052 -0.058 0.017 -0.035 0.114 0.116 0.129 0.122 -0.071 Muzaffargarh 0.110 -0.081 0.042 -0.046 0.013 -0.028 0.091 0.093 0.103 0.097 -0.057 Nankana Sahib 0.105 -0.077 0.040 -0.044 0.013 -0.027 0.087 0.089 0.099 0.093 -0.054 Narowal 0.122 -0.089 0.046 -0.051 0.015 -0.031 0.101 0.103 0.114 0.107 - 0.063 Okara 0.09 0 -0.066 0.034 -0.038 0.011 -0.023 0.075 0.076 0.084 0.079 - 0.046 Pakpattan 0.111 -0.082 0.042 -0.046 0.013 -0.028 0.092 0.094 0.104 0.098 -0.057 R.Y. Khan 0.105 -0.077 0.040 -0.044 0.013 -0.027 0.087 0.089 0.098 0.093 -0.054 Rajanpur 0.09 8 -0.072 0.037 -0.041 0.012 -0.025 0.081 0.082 0.092 0.086 - 0.050 Rawalpindi 0.155 -0.114 0.059 -0.065 0.019 -0.039 0.129 0.131 0.145 0.136 - 0.080 Sahiwal 0.09 9 -0.072 0.037 -0.041 0.012 -0.025 0.082 0.083 0.092 0.087 -0.051 Sargodha 0.107 -0.078 0.041 -0.045 0.013 -0.027 0.089 0.090 0.100 0.094 -0.055 Sheikhupura 0.128 -0.094 0.049 -0.053 0.016 -0.032 0.106 0.108 0.120 0.113 - 0.066 Sialkot 0.102 -0.075 0.039 -0.043 0.012 -0.026 0.085 0.086 0.096 0.090 -0.053 Toba tek Singh 0.09 9 -0.073 0.038 -0.041 0.012 -0.025 0.082 0.083 0.093 0.087 -0.051 Vehari 0.08 5 -0.062 0.032 -0.035 0.010 -0.021 0.071 0.072 0.080 0.075 - 0.044 Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 81 Fig: 11 Overall net effect of development expenditures SDI Infant Mortality Rate House with iodized salt consumption Water on premisis Under 1 year malnutrition House ownership House with electricity Male literacy Rate 15-24 year Female literacy Rate 15-24 year House using improved sanitation Child labor Net effects 0.097 -0.071 0.037 -0.040 0.012 -0.024 0.080 0.082 0.091 0.085 -0.050 -0.080 -0.060 -0.040 -0.020 0.000 0.020 0.040 0.060 0.080 0.100 0.120 % Net effects of Development Expenditures Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 82 This study proposes the policy makers that, empirically the development expendtirues did nto target the access to clean water, reduction in malnutrition in under 1 year old children and increase in the house ownership of the citizens. 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Review of Economics and Development Studies, Vol. 9 (2) 2023, 61-85 85 Appendix Table Appendix 1: Table of Component Matrix (Correlation) Component Matrix Infant Mortality Rate -0.732 Percent of household with iodized salt consumption 0.380 Water on Premises -0.417 Malnutrition 0.121 House Ownership -0.252 Household Possessions 0.829 Literacy Rate 15-24 year(male) 0.842 Literacy Rate 15-24 year(female) 0.936 Percentage using improved sanitation 0.880 Child Labor (%) -0.515