Political Economy of Resource Allocation in Balochistan: Assessing the Role of Government Policies and Bureaucratic Mechanisms

Paper critically evaluates the political economy of resources distribution in Balochistan by using a balanced panel approach from 2008-09 to 2018-19. The study has been conducted to highlight the key causes of irregular distribution of development funds in Balochistan and find out their serious implications for people in general. To get the authentic and validated results, a rich dataset has been obtained from the various sources of Government of Balochistan, non-governmental organizations and federal agencies. The results of the study reveal that political groups and top-level bureaucracy influence the resource allocations during the course of budget making process. Politicians and other top bureaucracies keep their own interests in mind and divert public resources to develop their own districts. The districts which are poor in socio-economic indicators, are supposed to be allocated more funds but they are ignored. The study has tried to answer the question that what factors are involved in proper distribution of provincial funds and how to address the issue. However, the study reveals that there is a serious influential impact of senior ministers of cabinets who have always dominated the provincial development funds which ultimately result in the enhanced poverty ratio, and backwardness in other districts of Balochistan which lack proper and influential representatives.


Introduction
Balochistan is the largest and most resource-rich province of Pakistan (Grare, 2013).It stands as a symbol of both promise and paradox in the country's political and economic landscape.With its abundant natural resources ranging from gas and minerals to fisheries and agriculture (Malkani et al., 2017) Balochistan possesses the potential to drive Pakistan's economic growth and alleviate regional disparities.However, the realization of this potential has been hindered by a multitude of factors, chief among them being the intricate interplay of government policies and bureaucratic mechanisms governing resource allocation.
The political economy of resource allocation in Balochistan is a topic of critical importance, encapsulating the complexities of governance, power dynamics, and socio-economic disparities within the province (Siddiqui, 2023).At its core, it delves into how government policies and bureaucratic structures influence the extraction, distribution, and utilization of Balochistan's valuable resources, ultimately shaping the province's development trajectory and the well-being of its inhabitants (Ahmed & Baloch, 2017).
Over the years, Balochistan has been plagued by a history of underdevelopment and unrest, exacerbated by grievances over the equitable distribution of resources and benefits (Mushtaq & Mirza, 2021).The role of government policies and bureaucratic mechanisms in this context cannot be overstated, as they play a pivotal role in determining who benefits from the exploitation of Balochistan's resources and to what extent (Samad, 2015).
Moreover, Balochistan's strategic significance, both in terms of its geographical location and resource wealth, has often made it a focal point of national and international interests, further complicating the dynamics of resource allocation (Javaid & Jahangir, 2020).This has led to a plethora of challenges ranging from issues of sovereignty and resource ownership to questions of transparency, accountability, and environmental sustainability (Ali et al., 2023).
In the light of these challenges, assessing the role of government policies and bureaucratic mechanisms in resource allocation becomes imperative.It necessitates a critical examination of past and present policies, their implementation mechanisms, and their impact on the socio-economic fabric of Balochistan.Such an assessment is crucial not only for understanding the root causes of the province's developmental challenges but also for informing future policy interventions aimed at promoting equitable growth, sustainable development, and socio-political stability in Balochistan and beyond.
This paper seeks to undertake a comprehensive analysis of the political economy of resource allocation in Balochistan, with a specific focus on evaluating the role of government policies and bureaucratic mechanisms.By critically assessing the existing frameworks, identifying key challenges, and proposing potential solutions, it aims to contribute to a deeper understanding of this complex issue and inform evidence-based policymaking for the socio-economic development of Balochistan and the broader region.
Further, this paper highlights the influence of political empowerment on resources distribution particularly development budget in Balochistan among the Districts for the last ten years i-e 2008-09 to 2018-19.The study gauges the relationship of Total Funds Allocation with the Deprivation Index, Population, Areas, Chief Minister, Members of Coalition Government, and top ranked Bureaucracies of all Districts in the Province.

Statement of the Problem
The political economy of resource allocation in Balochistan is plagued by a lack of transparency, equitable distribution, and efficient bureaucratic mechanisms.This complex issue undermines socioeconomic development, exacerbates grievances among local communities, and poses a threat to the regional stability.Addressing these challenges is crucial for promoting sustainable development and peace in the province.
The aforementioned detail discussion reveals that the province of Balochistan has been fronting enormous difficulties due to poor physical infrastructure, poor public services delivery, weak social and economic policies (Mohammed & Farooq, 2002), extreme poverty, worse gender disparity,

Theoretical Framework
The political economy of resource allocation in Balochistan can be analyzed through the lens of several theoretical perspectives that help elucidate the underlying dynamics shaping this complex issue.One such framework is the resource curse theory, which posits that resource-rich regions often experience economic stagnation, corruption, and political instability due to factors such as rentseeking behavior, resource mismanagement, and dependency on volatile commodity markets.This theory underscores the importance of understanding that how Balochistan's resource wealth has both positive and negative implications for its socio-economic development.
Additionally, institutional economics offers insights into the role of government policies and bureaucratic mechanisms in resource allocation.Through this lens, the focus shifts to the effectiveness of institutions in facilitating transparent decision-making, enforcing property rights, and ensuring accountability in the resource management.The analysis may also draw upon principalagent theory to examine the relationship between government actors, bureaucratic agencies, and the populace, considering issues of agency capture, information asymmetry, and moral hazard in the allocation process.
The resources of Balochistan are distributed by the mechanism of below formula where entire provinces resources distribution takes place with heavy political consideration (See Graph 1).
If the Chief Minister belongs to those Districts, study assumes that during his tenure those Districts (Home district of CM) get more funds, which reads as, Since the Cabinet Minister for Planning and Development (Senior Minister Or P&D Minister) plays an important role in budget making and funds allocation; therefore, likewise Chief Minister (CM) the P&D Minister is in-lined to allocate more resources to his/her home district.

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Graph. 1
Here, Another key player in resource distribution/allocation is the Additional Chief Secretary (ACS Development).The ACS (Development) is the top ranked bureaucrat who hails from one of the Districts of Balochistan.Thus, it is assumed that during those fiscal years specific district/region gets more allocation to which the ACS belongs.
Minister of the Coalition Government is also a key player in reflecting projects in PSDP for the Districts or constituencies where he hails form.
Sometimes the disproportional share of resources and trial normally schemes are determined on the political and bureaucratic basis rather than on the basis of actual economic conditions.Some key economic indications like deprivation index are not considered while distributing resources.This theoretical underpinning, therefore, leads us to develop the following proposition: The dependent variables for this model are Total Fund Allocation and Total Number of Schemes.

Results and Discussion
10 years data has been used to examine the political economy of resources distribution in Balochistan.
The study finds that the political influences and top ranked bureaucrats have an influential role in the resource distribution (Ahmed, 2023).The study further finds that the Districts where top senior ministers and other influential people belong, the Districts get more funds and projects (Ahmed, 2023).Therefore, the study portrays the fact that the distribution/allocation of funds in Balochistan is based on the politically driven factors among Districts whereas, socio-economic indicators like poverty, backwardness and inverse population density are ignored.The regressions results are presented with the sign and level of significance of the coefficient of all included variables.The reported results are followed by a rigorous analytical discussion.Prior to the empirical results, the descriptive statistics is presented to get prior information of the subject matter.The Column first shows the variables, second column shows number of observations, column third presents Mean, column forth presents standard Deviation, column fifth shows minimum value and last column shows maximum value.In this table first row shows the total funds allocation of last ten years of development budget.The mean and standard deviation are 930.2168 million and 1440.348 million respectively and the minimum amount of total funds allocation is zero and the maximum is 14206.5 million.Second row of the column provides information about the Balochistan's total number of schemes of last ten years of provincial budget.The mean and standard deviation of total number of schemes are 52.5799 and 67.1033 while minimum value is 0 and maximum value is 652.The third row of the above given table provides information about the Districts' share in the provincial budget of last ten years.The minimum share of provincial budget is zero and maximum share is 23.39 with the mean 953103 and standard deviation is 2.3722.
Moreover, the forth row of the table tells that minimum value of deprivation index is 13 and maximum value is 96 with the mean 52.1509 and standard deviation of 12.0604 of last ten years starting from 2008-09 to 2018-19 of development budget being spent on education health and standard of living.The fifth row of the table provides us information about CM; it means that whenever CM is in power, huge resources are shifted to his home district.The minimum share is zero million and maximum share is 1.00 million with the mean and standard deviation are 0 .0287 million and 0.1673 million respectively.In addition, the sixth row gives information about the P&D Minister who also influences in the distribution of budget as per the results of this empirical study.The minimum value of P&D Minister is zero and maximum value is 1 while mean is 0.0350 million and standard deviation is 0.1841 million.The 7th row of the above table shows area of all Districts in Balochistan province.As per the study, resources are not distributed on the basis of area and backwardness (Ahmed & Baloch, 2015).In the 7th row, the minimum value of area is 0.15 and maximum value of area 5.055 with the mean of 1.2803 square km and standard deviation is 1.3480 square km.The 8th row of the aforementioned table provides the information regarding the districts wise population of Balochistan.The minimum value for the population of Balochistan districts wise is 0.03 and maximum value is 2.54 with the mean and standard deviation of 0.3590 million and 0.3268 million population respectively.The 9th row of the above-mentioned table presents coalition government that has also influence in the overall allocation of budget to districts.In this row, the minimum value is zero and maximum value is 1.00 with the mean and standard deviation are 0.6025 million and 0 .4901million.The last row shows the influence of ACS Development, P&D in the allocation of funds to his/her Districts.In this row, the minimum value is zero and maximum value is 1.00.The mean of this row is 0.0328 million and standard deviation is 0.1785 million.The table shows the empirical results by using fixed effect model.The first row shows the total number of schemes, the coefficient of this variable is positive but its value is so less, it means that the variable is statistically insignificant.Because its correction with the total fund allocation is not so strong.

Fixed Effect Model (Total Fund Allocation)
Same is the case with the index like MPI that has less correlation with the total fund allocation.The coefficient of deprivation index is negative = -1.3700.It means that it is statistically insignificant.Negative coefficient means that deprivation of that District is not getting/reflecting fund/allocations, no matter how deprived this District is.It does not get any priority in the overall budgetary allocation.It should have been logically and theoretically allocated more budget/projects to minimize deprivation level.Similarly, the 4th row shows CM(Chief Minister) of the Province, CM coefficient is positive, that is 789.2422 and value of t is very high 4.05 and P is zero it means that this variable is 100% significant.The 100% level of significance shows that this variable is a relevant variable included in the model, as it is positively correlated with the total fund allocation, which means that again like P&D minister, the district CM belongs to would be allocated more projects.The fifth row PDM (P&D minister), where the value of t=2.64 higher than the value of P=0.002.It means that this variable is quite significant.This significance shows that the variable entering in the equation is a relevant variable.The coefficient of variable is positive i-e 129.1641 with certain level of significance.It means that P&D Minister allocates disproportionally more projects to the district he belongs to.The next row shows CG (Part of Coalition Gov't), where the value of t= 2.44 higher than the value of P= 0.016.It shows that this variable is also quite significant included in the model.The coefficient of variable is positive (178.052) with certain level of significance.It means that the Minister/MPA being the part of coalition government also influences the budgetary allocation and allocates more projects to the district he lives in.The next row shows Pop (population of all districts, the coefficient of this variable is also positive, and value of t is greater than p.It means that this variable is significant and correlation with the total funds allocation is strong, it is due to few districts that falls in urban area like Quetta and Kech.The next row shows ACS (Additional Chief Secretary), in this row value of t= 2.77 is higher than the value of P= 0.006.It shows that this variable is also very significant entering in the equation/model.Further, the coefficient of this variable is positive (675.5363) with the certain level of significance.It means that ACS disproportionally reflects more projects to the district he lives in.

Random Effect Model (by using xtreg and option re.)
Table 3: The determinant TFA is the dependent variable The above given table shows the empirical results by using random effect model.The first row of the table presents total number of schemes and the coefficient of this variable is positive, but its value is so small.It shows that the variable is statistically insignificant in the model.Because its correction with the total fund allocation is not so strong, while the value of z is greater than p. Same is the case with the variable like area that has less correlation with the total fund allocation.The third row of the above-mentioned table shows deprivation index, the coefficient of this variable is negative i-e -2.105622.It means that it is statistically insignificant.Negative value of coefficient we mean that deprivation of that district is not getting/reflecting fund/allocations, no matter how much this district is deprived of the facilities of education, health and standard living it doesn't get any priority in the overall budgetary allocation in the province.It should have been logically getting more projects/funds in order to reduce the intensity of deprivation.The next, row shows CM (Chief Minister) of the Province, CM coefficient is positive 633.4927 with the value of z is very high 3.68 and P is zero.It reveals that this variable is 100% significant included in the model.The 100% level significance shows that this variable is a relevant variable included in the model as it positively correlated with the total fund allocation, which means that again CM allocates more funds/projects to the district he belongs to.
The row PDM (P&D Minister), shows that the value of Z= 3.05 is higher than the value of P=0.003 with the coefficient of 165.9427.It means that this variable is quite significant in the model.This significance shows that the variable entering in the equation is a relevant variable.The coefficient of variable is positive with certain level of significance.It means that P&D Minister allocates disproportionally more projects to the district to in which he lives.
The next row shows CG (part of Coalition Gov't), where the value of Z = 3.41 higher than the value of P=0.009.It shows that this variable is also quite significant included in the model.The coefficient of variable is positive (93.5656) with certain level of significance.It means that the Minister/MPA being the part of coalition government also influences the budgetary allocation and allocates more projects to the district where he lives.
The next row of the above given table shows Pop (population of all districts), the coefficient of this variable is also positive and value of Z is greater than P. It means that this variable is highly significant and its correlation with the total funds allocation is strong.But it is due to few districts that fall in urban areas like Quetta and Kech where numbers of Ministers/MPA is in large but other districts have not been allocated funds on the basis of the large population.
The next row shows ACS (Additional Chief Secretary), in this row value of Z= 0.17 is higher than the value of P= 0.863.It shows that this variable is also very significant entering in the equation.Further, the coefficient of this variable is positive (34.4572) with the certain level of significance.It means that the ACS disproportionally reflects more projects to his district than other districts.This table shows the empirical results by using random effect model.The share of districts/total number of schemes in total budget has been assumed as dependent variable in the model while others variables are assumed as independent variables such as population, deprivation index, CM and CG etc.The first row of the table shows number of schemes, the coefficient of this variable is positive but its coefficient value is 0.0073134 smaller.It shows that the variable is statistically insignificant in the model.Because its correction with the variable total share in budget not so strong, while the value of z is greater than p which is 4.97.

Fixed Effect Model (Total Number of Schemes)
The next row of the table depicting deprivation index, the coefficient of this variable is once again negative i-e -.0211672 with the negative value of z = -3.65 that is smaller than the value of p= 0.000.It means that it is statistically insignificant.The deprivation index of that district has not been priority while budget distribution.But it should have been theoretically and logically allocated more projects to combat poverty, poor standard of living etc.
The next row shows CM (Chief Minister) of the Province, CM coefficient is positive ie .5730902with the value of Z=1.50 is very high than the value of P is 0.133.It reveals that this variable is 100% significant included in the model.The 100% level significance shows that this variable is a relevant variable included in the model as it positively correlated with the share of total budget/funds, which means that again like P&D Minister, the district CM belongs to would be allocated more projects.
The next row shows PDM (P&D Minister), where the value of Z= 2.51 higher than the value of P=0.011 with the coefficient of .1912457.It means that this variable is quite significant in the model.This significance shows that the variable entering in the equation is a relevant variable.The coefficient of variable is positive with certain level of significance.It means that P&D Minister allocates and influence in the budget making process and reflects more funds to the districts he hails from.
Next variable is Area of Districts that has weak correlation with the share of total fund allocation.The coefficient of this variable is negative i-e -.0071 with the negative value of Z= -0.09 that is smaller than the value of P=0.931.It means that it is statistically insignificant.
Row No. 6 shows Population of Districts, the coefficient of this variable is also positive and value of Z is greater than P. It means that this variable is highly significant and its correlation with share of fund is strong.
The second last row of the table shows CG (part of Coalition Gov't), where the value of z =4.58 higher than the value of P=0.000.It shows that this variable is also quite significant included in the model.The coefficient of variable is positive i-e .6544with certain level of significance.It means that the MPA being the part of Coalition Government also influences the budgetary allocation and allocates more projects to the district he belongs to.

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The last row of the above given table presents ACS (Additional Chief Secretary), in this row value of Z=4.53 is higher than the value of P= 0.006.It shows that this variable is also very significant entering in the equation.Further, the coefficient of this variable is positive and higher than the variables included in the equation i-e .2397201with the high level of significance.

Conclusion
The political economy of resource allocation in Balochistan represents a complex nexus of power dynamics, institutional challenges, and socio-economic disparities.Through our analysis, it becomes evident that the province's vast resources of wealth have not been translated into equitable development or improved living standards for its populace.Instead, opaque decision-making processes, inefficient bureaucratic mechanisms, and historical grievances have perpetuated a cycle of underdevelopment, marginalization, and socio-political unrest.
Addressing these challenges requires concerted efforts to promote transparency, accountability, and inclusive governance in the resource management.Reforms aimed at strengthening institutional capacities, enforcing property rights, and fostering meaningful participation of local communities are crucial for ensuring that benefits derived from resource exploitation are shared equitably and contribute to the sustainable development in Balochistan.Moreover, it is imperative to recognize the interconnectedness of political, economic, and social factors shaping resource allocation dynamics (Sandano, 2014).By adopting a holistic approach that integrates theoretical insights from resource curse theory, institutional economics, distributive justice, and political economy analysis, policymakers can develop more nuanced and effective strategies for addressing the root causes of resource governance challenges in Balochistan.

Authors
¹ Assistant Chief P&D Department Civil Secretariat, Quetta, Balochistan, Pakistan Email: naseerbalochpnd2015@gmail.com ²* Assistant Professor, Department of Political Studies, Lasbela University of Agriculture, Water and Marine Sciences, Uthal, Balochistan, Pakistan.Email: amirluawms@gmail.com time (year) that ranges from 2008-09 to 2018-19.I = District identity, which captures districts of Balochistan.I range from 1 to 29 Districts.29 numbers of Districts have been selected for this investigation.TFAs= Total Fund Allocations DP= Deprivation Index that shows ranking of districts CM = Chief Minister as Dummy I SM = Senior Minister as Dummy II ACS = Additional Chief Secretary as Dummy III CG = Coalition Government as Dummy IV TS= Total Number of Scheme µ: It indicates the error terms in the model.

Table 1 : Descriptive Statistics -First Set of Variables
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Table 2 : The determinant TFA is the dependent variable
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