Exploring the backward and forward linkages of production network in a developing country

Major economic events like the Great Depression and the 1970s oil shock highlight the importance of understanding how economic shocks propagate through interconnected production units. Viewing an economy as a network of interdependent production units, where each depends on input suppliers and forwards output to downstream units, is crucial. Network theory helps understand this interdependence and inform policy responses to economic disruptions. In Pakistan, events like the 2022 floods and persistent power outages illustrate the significant impact of inter-sectoral connections on the national economy. Disruptions in key sectors cascade through the network, causing widespread economic consequences. Understanding this interconnectedness is essential for effective policymaking, particularly in allocating public expenditure to maximize economy-wide impact. Social Network Analysis (SNA) provides techniques to analyze these complex relationships within production networks, offering measures to quantify network characteristics and identify influential sectors. This paper applies SNA to Pakistan's production network using input-output data to identify influential sectors and understand the transmission mechanisms of economic shocks.

The literature on inter-industry analysis frequently uses input-output (IO) tables to analyze inter-linkages between economic sectors and their implications for shock propagation, environmental spillovers, value addition, and exports. Studies like Hidalgo et al. (2007) utilize network analysis to measure the evolution of international trade, while others such as Blöchl et al. (2011) and Montresor and Marzetti (2009) apply network measures to analyze shock propagation and innovation processes, respectively. The SNA approach, with its various centrality measures (degree, closeness, betweenness, eigenvector), provides insights into the importance and connectivity of individual sectors within the overall network. This study builds upon this existing literature by focusing on Pakistan's production network, an area with limited research using SNA and IO table analysis.

This study uses Pakistan's 2011 input-output table from the GTAP-9 database, encompassing 57 sectors. SNA techniques are applied to analyze the production network, where sectors are represented as nodes and input-output linkages as directed edges. The weight of each edge corresponds to the Leontief Inverse coefficient, indicating the strength of the linkage. Node size represents the sector's share of total output. The analysis employs various centrality measures: * **Degree Centrality:** Measures the number of connections (in-degree: inputs received; out-degree: outputs provided). * **Closeness Centrality:** Measures the average distance of a node to all other nodes. * **Betweenness Centrality:** Measures the number of shortest paths between other nodes that pass through the given node. * **Eigenvector Centrality:** Measures a node's importance based on its connections to other important nodes. These measures are used to visualize the network and identify influential sectors. Ego-networks (two-step networks centered around key sectors) are constructed to analyze the local structure and impact of specific sectors. The study also examines the relationship between value-added and different centrality measures.

Pakistan's production network shows a skewed distribution of weighted in-degree and out-degree centrality, indicating unequal sectoral importance. The network is sparsely connected, with only a few sectors having numerous connections. Transportation and trade services exhibit the highest out-degree, highlighting their strong forward linkages. Manufacturing sectors demonstrate comparatively weaker connectivity than service sectors. Electricity, petroleum, and chemicals sectors are identified as the most influential sectors in terms of overall network impact, despite having moderate direct downstream absorption. These sectors are crucial inputs across various industries, making them significant indirect influencers. Analysis of ego-networks for electricity, petroleum & coal, textiles, transport, business services, and wholesale & retail trade reveals the direct and indirect connections and importance of these sectors within the broader network. The textile sector, despite being a large contributor to GDP, has a relatively small ego-network, suggesting limited domestic integration. The transportation sector has the highest betweenness centrality, emphasizing its role as a connector across the network. Electricity and petroleum products show the highest eigenvector centrality, highlighting their vulnerability to external shocks. There's a positive correlation between weighted in-degree and out-degree, but value-added shows no significant relationship with overall degree centrality. However, weighted in-degree is negatively associated with value-added, while weighted out-degree is positively associated with value-added, with service sectors having high values for both.

The findings indicate a strong dependence on a few key sectors, particularly in energy and transportation, making Pakistan's production network vulnerable to shocks in these areas. The weak forward linkages in the manufacturing sector suggest a need for increased integration into global value chains. The skewed distribution of centrality measures points towards a need for policy interventions to promote greater diversification and resilience. The significant influence of electricity, petroleum, and chemicals highlights the potential benefits of enhancing efficiency and competitiveness in these sectors. The positive association between out-degree and value-added underscores the importance of forward linkages in fostering economic growth. The negative correlation between in-degree and value-added suggests that over-reliance on input purchases may hinder value creation. The study's findings are relevant for understanding economic structure, informing policy decisions, and promoting economic development in Pakistan and similar developing economies.

This study provides a comprehensive analysis of Pakistan's production network using SNA and IO data. The findings highlight the crucial role of electricity, petroleum, and chemical sectors and the need for enhanced efficiency in these industries. The relative weakness of forward linkages in the manufacturing sector and the overall sparse connectivity necessitate policy focus on diversification and integration into global value chains. Future research could explore the dynamic aspects of the network, incorporating international trade and temporal changes, to better understand the resilience and evolution of Pakistan's production system.

The analysis is based on a static input-output table, neglecting temporal dynamics and international trade flows, which could influence the network structure and sector importance. The level of sector aggregation might also influence the results. Furthermore, the study focuses primarily on structural aspects of the network and does not directly model the transmission of economic shocks.