Mobilizing Informal Economic Sector to Uphold Urban Institutional Resilience: A Case Study of Rawalpindi, Pakistan

Abstract

The informal economy is a large part of the urban economy. The informal economy accounts for about half of Pakistan's GDP. This research examined nine different areas of Rawalpindi's Central Business District's business sector (CBD). A survey of 404 respondents from 16 CBD marketplaces enables a comprehensive examination of who works in the informal and formal economic sectors, how much they earn, their goals, perception of their job, and their degree of similarity to the rest of the working population. Furthermore, the statistics illustrate the pro-cyclical connections between the informal economic sector and the formal economy. The Multinomial Logistic Regression (MLR) technique is used for the analysis. The MLR results indicated the informal economic sector holds positive relation with earning members in a family, business expertise, average business sale, and negative relation with education level, satisfaction with government tax policies, household expense, and average investment in the business. From a resilience standpoint, governance is considered an intentional collective action to preserve a stable system condition. Hence, the current study recommends tax reforms and government institution reorganization to mobilize the informal sector and make effective institutional governance.

1. Introduction

Cities are intricate systems that interweave together tens of thousands of economic, social, institutional, and environmental components that significantly impact individual and community well-being. There are many ways to describe the informal economy. It’s typically used to describe work done outside of established regulatory frameworks, whether in law or practice (Trebilcock, 2005). The informal sector is unstructured outside any official regulation and the taxation system. The informal economy may be split into many categories. For example, we may classify workers into four categories: subcontracted workers, self-employed individuals, people who work for other families or domestic helpers, and small business owners (Vanek et al., 2014; Arby et al., 2010). Continuing with the subject of informal sectors. Informal employment comprises employment in the formal and informal sectors. Employment in the informal sector has not decreased over the years. Informal employment has different social and economic dimensions (Khan & Hussain, 2021). Although the divides are fluid, as we shall see in the next sections, this research mainly focuses on informal employment in the informal sector and formal sector (Hudson, 2010).

The term “resilience” has surged in popularity in both academic and policy discourse in recent years, with many reasons for this rapid increase. Resilience theory’s greatest asset may be that it uncovers ways to manage complex socio-economic systems and their sustained operation (Lang, 2012). In cities, social and environmental resiliency is fluid and provides various routes to sustainability, such as persistence, transformation, and transformation. The temporal scale is recognized as being important, and the focus is placed on flexibility rather than specificity (Meerow et al., 2016).

The rapid expansion of cities in the developing world gives a presumptuous way to rural-urban migration and hence urbanization is accelerated (Ritchie & Roser, 2018). Over the last twenty years, many urban areas have experienced dramatic growth as a result of rapid population growth and as the world’s economy has been transformed. As a result, half of the world’s total population now lives in urban settlements. The urban area is comprised of formal and informal sectors (Basiago, 1998). They both work side by side and are hence necessary for the growth of each other. The challenges offered by the informal sector are way more than the formal sector. Informal sectors are exempted from taxes, unreliable economy, housing conditions are not habitable, and they are more prone to any natural hazard (Lv & Xu, 2021).

In developing countries, this phenomenon is more evident such as India, where 86% of its economy is informal, and Ghana, where 98% of the economy is dependent on the informal sector (Arbi, 2010). A country like Pakistan does not have any different state of affairs (Serfraz et al., 2022). According to the World Employment trend, in 2017 around 70% of its economy is informal. In this situation, what could be the best methodology to make the city’s institutions more resilient and make them equipped for any approaching circumstances.

Institutional failure and ineffectiveness of policies are both linked to economic sector inefficiencies and ineffectiveness (Khan et al., 2022). There is the issue of the link established between economic growth and institutional resilience (Gulzar et al., 2010). Unfortunately, literature is scarce on institutional resilience and informal sector changes. The majority of research has focused on a single or a few aspects of this phenomenon. There is no obvious route to such changes, and these consequences must be considered when examining the impact of institutional evolution on economic performance. Furthermore, institutional resilience is a multifaceted phenomenon. As a result, rather than concentrating on a very few sub-dimensions, it should be assessed as a whole.

Pakistan is one of the emerging economies in the world, and it offers hybrid economic activities. In Pakistan, the informal economy is 30–45 % over the course of ten years, i.e., 2008–2018 (Hayat & Rashid, 2020). There are many factors involved in the boost of the informal economy in the country. High denomination currency notes are considered one of the major causes of the existence and expansion of the informal economy in Pakistan. Others are newly designed taxation systems in the country which exempt VAT from the informal sector. On the other hand, the per-capita per month of a household is 4000 rupees which is the maximum purchasing power on any day in a month. The higher denomination currency added to the round as 5000 rupees is not used for general transactions; hence it gives a boost to hoarding and illegal activities in the country (Kemal & Qasim, 2012).

Pakistan is facing a huge problem in the conversion of the informal economy to the formal economy, such as cultural and social division, illiteracy, and lack of institutional capacity (Haider & Badami, 2010). According to gender classification, the informal employment trend that has been recorded over the year 2006–7 is 71.6% and 71.4% in the year 2017–18. In this ratio, the male participation is 71.6% (2006–7) to 71.5% (2017–18) the trend remains the same over the years. In the case of female participation, the trend that has been recorded is 69% (2006–7) to 70.7% (2017–18) (Khan & Khalil, 2017).

The declining growth in the provincial revenue generation is due to a decrease in non-tax revenue generation. In the year 2016–17, the main contributors to tax generation are GSTS (general sale tax on services and goods), property tax such as land automation, and invoice monitoring systems. On the other hand, there was a decline in tax collection from excise and other sources of tax collection in the province. The expenditure grew by 20% from 7.4%. The major contributor of these expenditures came from development expenditure which is 44%. The development expenses are dominated mainly by public administration and the economic affairs of the province (Zaidi et al., 2019).

A recent report of the World Bank named Pakistan at 100: Shaping the Future 2047 states that when compared to nations with comparable economic levels, Pakistan’s services industry is a significant contributor to GDP. The focus of service sector growth is on low-skilled jobs in wholesale and retail trade, as well as administration. The service sector growth has been in lower-skilled positions, including wholesale and retail commerce, as well as administration (Ahmed et al., 2019). Pakistan’s services sector is substantial when we compare it with countries with similar income levels. While services sector growth has been focused on low-skilled jobs in wholesale and retail trade and administration, overall, service sector growth has been in lower-skilled positions, including wholesale and retail commerce, as well as administration (Benjamin et al., 2014).

The institutional framework for planning at the various levels includes the Planning Commission of Pakistan, each provincial-level Planning and Development Departments/ Board, and district and divisional level Planning frameworks (Shaheen & Khan, 2016). The Planning Commission, including the Planning, Development & Reforms Division, occupies the central position in the overall planning institutional framework. The Prime Minister is the Chairman of the Planning Commission. It comprises the functions such as, preparing the national plan and reviewing and evaluating its implementation, formulating the annual plan and ADP (Annual Development Plan), facilitating capacity building of agencies involved in development and policymaking and organizing research and analytical studies for economic decision making (Wing, 2014).

At the provincial level, activities of all nation-building departments and agencies are coordinated by the Provincial and District Boards. Major functions of Provincial and District Boards are the Annual Development Program (ADP) & Medium-Term Development Framework (MTDF), Economic Issues, and Policy formulation with respect to private sector development and promotion of public-private partnership (Javed et al., 2018).

The area selected for the city is Rawalpindi, which is the capital of Rawalpindi and is considered to be the twin city of the capital of the country (see Figure 1). It is the fourth largest city in Pakistan. The city has its historical value as well as one of the diverse and dynamic economies of Pakistan. The total population of the city is 2, 098, 231 (Mian et al., 2010). It is also the third metropolitan of Pakistan. Among eight major cities in Pakistan Rawalpindi has the major unemployment rate, according to the Bureau of Statistics 2015 survey. According to Revenue of Circles, the city is divided into 25 Bazar, collecting Major Revenue for the local Government. These Markets and economic hubs are our main area of study for the size of the informal economic sector. Among these 25 circles, the CBD of the city existed in the center of it, including 16 circles concentrated in one place by DC Valuation Table (2018– 19). The study has been conducted in the central economic zone of the city. It includes the following circles, Purana Qilla, Tyranwaln Bazar, Talwaran Bazar, Bazar Kalan, Nankari Bazar, Naswari Bazar, Urdu Bazaar, Sarafa Bazar, Bahbara Bazar, Kohati Bazaar, Dalgaran Bazar, Raja Bazaar, Bohar Bazar, Moti Bazar, Trunk Bazar, and Kartarpura.

Figure 1: Map of Selected Study on Map of Rawalpindi CBD

2. Methodology

The methodology to deduce results used here is Multi nominal logit model in our estimations as the independent variables are non-binary. The variable of informal sector employment has been regressed over the independent variables by controlling demographic characteristics and country fixed effects. The multi-collinearity problems would arise when we regress the likelihood of informal sector employment to all the discussed variables. As it is reasonable to expect independent variables in the same category to be correlated with each other. The analysis is focused on estimating the variable having a high significance level through Multi-nominal Logistic Regression Modeling (MLM). Multinomial logistic regression is used to predict categorical placement in or the probability of category membership on a dependent variable based on multiple independent variables (Starkweather & Moske, 2011). The relationship with the informal economic sector is developed through a correlation matrix which determines the model equation.

The Multinomial Logistic Regression (MLM) technique is used to develop the model that identifies the primary variables that need government attention to mobilize the informal sector and generate effective institutional governance and policies. The evaluation created a single variable out of a number of listed variables for each category, i.e., we create 7 composite variables from 7 categories of variables. Examining each of seven categories which are Socio-Demographics, General Employment Obser- vations, General Business Observations, General Business Operations, Income and Expense, Work and life Satisfaction, and Drivers and Barriers, the composite variables are deduced from the following four categories Socio-Demographics, Income and Expense, Work and life Satisfaction and General Business Operations using the help of Multi Nominal logit Regression (MLM).

3. Results and Discussion

This section may be divided into subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.

In this section, the findings have been discussed from estimations using Multi-nominal Logistic Regression. The results have been reported for each category of independent variables, and then estimation results from regressions using composite variables.

3.1. Socio-Demographic

The category of Socio-Demography is focused on scrutinizing the various aspects of socio-demographics, both formal and informal, and investigating the characteristic of both the economic sectors. This study figures out the role of social demographic in determining the people’s choice to work in the formal or informal sector. The information of age, sex, educational level, marital status, household size, household expenses, and the number of earning members in the family is used. For this analysis (see Table 1), a logistic model is applied and the results indicate that education level, household expenses, and earning members in the family are playing significant roles in people’s decision to choose the formal or informal sector and these variables are significant at 90% (see Table 1). Whereas age, sex, marital status, and household size are found to be insignificant. It is revealed from the results that the people who have a higher education level, their odds of choosing the informal sector are lower by 32 percent (1–0.780 = 0.32). It is also found that the people who have high household expenses, their odds of working in the informal sector are lower by 0.23 percent (1–0.77 = 0.23). However, people who have more earning members in the family are 47 percent more likely to work in the informal sector.

Table 1: Estimation Results: Socio-Demo Graphs

Note: ^***, ^** and *Indicates significant at 1%, 5% and 10% level of significance.

3.2. General Employment Observations

The general employment category is focused on studying the numerous aspects of general employment observations mainly focused on formal/informal business, how long they have been working and what are their business expertise both formal and informal. The present study takes into account the general employment observations such as family business, working time period, business expertise, training years, the experience of formal employment, and type of business. The results of the logistic model (see Table 2) indicate that for people who are involved in the family business and have a higher number of years of training, their odds of working in the informal sector are lower by 57 (1–0.43 = 0.57) and 42 (1–0.58 = 42) percent respectively. In the type of business, people who don’t have a shop are 3.46 more likely to work in the informal sector (see Table)? It is found that working time period, business expertise, and experience informal employment are insignificant.

Table 2: Estimation Results: General Employment Observations

Note: ^***, ^** and *Indicates significant at 1%, 5% and 10% level of significance.

3.3. General Business Observations

This category is focused on studying several aspects of general business observations, mainly focused on a type of business under formal and informal division, the area that has been occupied to operate that business, whether they own that business and workspace. The logistic regression model results indicate that installation type, ownership of workplace, and legal agreement for workplace play an important role in choosing formal and informal sectors (see Table 3). People who have moveable installation and don’t have ownership of the workplace are 2.91 and 1.611 more likely to work in the informal sector, respectively. The People who have a legal agreement, their odds of working in the informal sector are 46 percent (1–0.546 = 0.46) lower.

Table 3: Estimation Results: General Business Observations

Note: ^***, ^** and *Indicates significant at 1%, 5% and 10% level of significance.

3.4. General Business Operations

The general business operation consists of main indicators that are the type of working hours, ownership of a business, average monthly investment, average daily sale, and the average profit of the sale. The category is focused on studying the various aspects of general business operations both formal and informal, and investigating the characteristic based on their business operations.

The results of general business operations and their relationship with the informal sector are given in Table 4. It is found that average investment, average business sale, and average profit sale are significant at 90, 90, and 95 percent, respectively. However, working hours are insignificant. It is revealed that for people with high investment and higher profit, their odds of working in the informal business are 14% (1–0.860 = 14) and 35% (1–0.65 = 0.35) lower. Furthermore, it is indicated from the results that people who have higher business sales are 1.45 percent more likely to work in informal businesses.

Table 4: Estimation Results: General Business Operations

Note: ^***, ^** and *Indicates significant at 1%, 5% and 10% level of significance.

3.5. Income and Expensive

The income and expense category focused on main indicators that are the midterm employment expectations of the interviewee, what is their current economic condition, is there any sufficient improvement in their income as compared to last year, income frequency, and the monthly household expense in both sectors.

The logistic regression is applied by taking informal as a dependent variable and improvement in income, midterm employment, current economic condition, and income frequency as independent variables (see Table 5). The results express that improvement in income, and mid-term employment expectations are significant. The people whose income is improving and whose mid-term employment expectations are high are 6.5 and 1.79 percent more likely to work in the informal sector. The other independent variables appeared to be insignificant.

Table 5: Estimation Results: Income and Expensive

Note: ^***, ^** and *Indicates significant at 1%, 5% and 10% level of significance.

3.6. Drivers and Barriers

The driver and barriers category consists of indicators that are Authorization for Work, Authorizing Body, Government Support, and Plan to move back or to another city. The category is focused on inspecting the driver and barriers of both formal and informal and investigating the characteristic of both the economic sectors. The Logistic model is applied and the results indicate that people who perceived that authorizing body is not integrating are 1.3 percent more likely to work in the informal economy (see Table 6).

Table 6: Estimation Results: Drivers and Barriers

Note: ^***, ^** and *Indicates significant at 1%, 5% and 10% level of significance.

3.7. Employment and life Satisfaction Analysis

The work and life satisfaction category consist of five major indicators business or employment, work environment, Income, Family, Education, Life, and Government policy. There is a number of sub-indicators related to these key indicators on which the satisfaction of formal and informal sectors has been analyzed. The most significant ones are analyzed during estimations (See Table 7). This study analyzes people’s employment satisfaction by using income, government support, tax policies, law and regulations, and life satisfaction as the indicator of employee satisfaction. We found the tax policies as a sign at 95 %. It indicates that people who are satisfied with the government policies are more likely to join the formal business or their odds of working in the informal sector are (1–0.85 = 15%) lower (see Table 7).

Table 7: Estimation Results: Employment and life Satisfaction Analysis

Note: ^***, ^** and *Indicates significant at 1%, 5% and 10% level of significance.

3.8. Composite Variables

The composite category comprises variables that show a high significance level among their respective categories and all those significant variables are studied together to form one composite variable where we can deduce the predictability equation based on the most significant variable, which is education, earning members in a family, business expertise, average business investment, average business sale, household expense, satisfaction with tax policies (see Table 8).

Table 8: Estimation Results: Composite Variables

Note: ^***, ^** and *Indicates significant at 1%, 5% and 10% level of significance.

By following the general to a specific procedure, we derive the logistic regression model by taking education, earning members in the family, Business expertise, average business investment, average business sale, and household expense and satisfaction tax policies. All variables are significant at 90 percent. The results indicate that for people who have higher education their odds of working in the informal economy are 20 percent (1–0.80 = 0.20) less than those who are less educated. People who have more earning members in the family and have business experience, they are 1.42 and 1.39 more likely to work in the informal sector, respectively. For people who, on average, have a high investment in the business, their odds of working in the informal sector are lower by 15 percent (1–0.85 = 0.15). People who have on average high business sales are 1.53 more likely to work in the informal sector. However, for people who have high household expenses and are more satisfied with the tax policies, their odds of working in the informal sector are lower by 34 (1–0.66 = 0.34) and 12 (1–0.88 = 12) percent, respectively. It is revealed from the value of Chi-square and R-square that the overall model is significant.

To present the regression equation as:

Y = (β_C * constant) + (–β_E * X₁) + (β_EM * X₂) + (β_BE *X₃) + (–β_BI * X₄) + (–β_BS* X₅) + (–β_HH * X₆) + (–β_STP * X₇)

Where X₁ is Education, X₂ is Earning Members in a Family, X₃ is Business Expertise, X₄ is Average Business Investment, X₅ is Average Business Sales, X₆ is Household Expense, and X₇ is Satisfaction with Tax policies

Y = – 0.278 – 0.221 X₁+ 0.352 X₂ + 0.330 X₃ – 0.161 X₄ – 0.426 X₅ – 0.415 X₆ – 0.124 X₇

The above-mentioned predictability equations envisage the size of informality in the surveyed bazaar of Rawalpindi based on seven significant variables deduced through regression analysis.

4. Policy Recommendations

Appropriate and focused government policy that takes place at the local level may greatly benefit businesses in the informal sector by providing access to low-cost financing (Taneo et al., 2022). Affordable financial services are very important, particularly in the informal sector. Because there is no fixed location where creditors can locate you, you may have a tough time getting credit (Skinner, 2008). There is a need for enhanced and safe company premises in addition to this. During the rainy season, those who work in exposed areas are interrupted.

Preservation facilities are a challenge for the majority of produce merchants since perishable products are not suitable for storage. Although the last problems of low demand and high operating costs remain, at least we’ve already taken care of the issues of high demand and cheap operating costs (Vanek et al., 2014). The policy should be built on an accurate analysis of poverty risk profiles identified in various aspects of urban activity. This will be necessary to build and plan any significant urban development program that will also benefit the vulnerable and marginalized groups, as well as other groups (Bonnie et al., 2015). Gender issues should be taken into consideration. It is critical to mainstream gender issues in other development sectors, such as the trade industry, since the resource-intensive industrial growth strategy is disadvantaging educated women and men (Alila & Mitullah, 1999). In Pakistan, women are over-represented in agriculture, which is the country’s most important informal sector; but, in other sectors, such as wholesaling, food & restaurant management, services, and so on, they are significantly under-represented (Waqar et al., 2021).

The policy should be guided by a more systematic analytical approach to determining the locations and distribution of various kinds of informal economic activities in cities. Informal settlements, as well as their surrounding areas and patterns of migration, also fall under this category. Better knowledge of informal economy mobility patterns is required, especially the role of accessibility, career advance- ment, and business creation (Torgler & Benno, 2009).

The space requirements of informal street stalls and mobile ‘hawkers’, as well as their incorporation into the street design, must be addressed in innovative approaches to street design in Pakistani cities. Street trading and marketplaces account for a significant portion of the informal economy in cities, with merchants dealing in a wide variety of products and services. Significant gaps in knowledge exist, as do major policy inadequacies (Saha, 2001).

Market access through motorized and non-motorized modes of transport, as well as facilities for storage and procedures for the disposal of solid waste, must all be considered. Additionally, security, sanitation, lighting, and child care are critical amenities that need additional study to establish acceptable kinds and levels of supply. The current urban economic center in major cities in Pakistan needs contextual direction throughout its urbanization (Kumar, 2012). It should be emphasized that the informal economy’s origin, presence, and growth are largely a result of rules and taxes placed on the official sectors. The informal economy’s size is positively correlated with the business tax burden. Individuals’ dissatisfaction with government tax policies suggests that these policies should be reviewed and made more flexible to get accepted and executed in both the public and private sectors to manage a hybrid economy by regulation (Torgler, 2003).

5. Conclusion

Several significant findings were found in the study of the dynamics, factors, and effects of informal activities in developing nations. Besides empirical regularities, there is also a strong correlation between the quality of a nation’s institutions and the degree of its economic growth. Another key feature of the informal sector is the emphasis on small-scale, unskilled labor-intensive, self financed undertakings. A major component of the economic activity of Pakistani cities is the informal sector. With this institutional knowledge of urban resilience, social science research on urban transformation takes on potential new dimensions. Analyzing cities and regions as complex and multidimensional or composite systems invites us to look at the interplay of various dynamics and hybrid processes; how they create and generate vulnerability, crisis, and change. Such a system approach has the potential to improve governance research, which is frequently overburdened with normative assumptions about how Government should be. From a resilience viewpoint, governance may be viewed as intentional collective action to maintain a stable condition of the system or to affect the Government into a ‘better’ state.

The research demonstrated that informal economies and civil society are neither marginalized by the formal economy nor disconnected from each other. It was discovered that in Rawalpindi’s Informal economic Sector, people in their twenties, who are not married, and who work eight hours a day are, on average, comparable to Rawalpindi’s Formal employment market workers who are likewise not married, and who work eight hours a day. The particular characteristics of being marginalized and vulnerable set them apart from the broad notion of being a marginalized and vulnerable group. While they are less educated, their reported earnings are higher, and they claim greater levels of job satisfaction. While the informal economy workers put in long hours under unfavorable circumstances, they have strong job satisfaction, and the company they work for offers them a very generous degree of job security.

For the enormous majority in the informal sector, their current business activity is their only source of income and the main source of revenue for their household. The informal sector contributes significantly to the broader economy, with substantial sales and earnings. The average monthly profit of Rawalpindi’s surveyed bazaars exceeds the average income of the city’s formal sector workers and is above the minimum salary in official secondary tier jobs. In Rawalpindi, the informal economy does not exist in isolation but is inextricably linked to the official sector. Between the formal and informal economies, significant money flows occur when informal employees buy products and services from the formal sector. The line between formal and informal is blurred rather than established.

In their interactions with a local authority that is restricted in its ability to relocate them, entities from the informal economic sector hold a competitive edge. They exhibit a great degree of spatial liberty. Similarly, the researchers discovered that the people who work in the informal sector get free health benefits by using the welfare system even if they are tax exempt. This is direct support from the State given to the Informal Sector. Additionally, the researchers determined that almost no one in the informal economic sector pays the taxes yet enjoys the same privileges as those in the official sector who are regular taxpayers.

In the context of long-term economic growth, assessing the resilience of institutions involves looking at how long established institutions affect both Government and the business sector. Regions’ ability to recover effectively from economic shocks that either throw them off course or force them to slow down will be characterized as regional economic resilience in the future years.