Sen and Sources of Unfreedom

In this article, I aim to explore the sources of unfreedom that, according to Amartya Sen, inhibit development. These sources include poverty, tyranny, poor economic opportunities, systematic social deprivation, neglect of public facilities, and intolerant, repressive states. Sen emphasises that for true development to occur, these sources of unfreedom, which hinder individuals' capabilities, must be eradicated.

Poverty stands as one of the most significant barriers to freedom and development. It restricts access to essential goods and services such as food, shelter, education, and healthcare. This lack of resources not only limits economic opportunities but also impedes individuals' ability to participate fully in social activities and make choices that enhance their well-being. Without addressing poverty, any effort towards development remains incomplete.

Tyranny represents another major obstacle to development. Under oppressive and autocratic regimes, people are denied basic political freedoms and civil rights. Censorship, lack of political participation, and absence of legal protections are common in such environments, stifling both personal and collective freedoms. This political repression prevents societies from progressing and individuals from reaching their full potential.

Similarly, poor economic opportunities constrain development. When individuals lack access to markets, credit, employment, and entrepreneurial ventures, they are unable to improve their living standards or pursue their aspirations. Economic stagnation and high unemployment rates are clear indicators of these limited opportunities. This underscores the need for policies that foster economic growth and inclusivity.

Systematic social deprivation also severely limits development. When certain groups are excluded from mainstream social, economic, and political life due to caste, race, gender, ethnicity, or other social factors, they are denied access to education, healthcare, and job opportunities. This exclusion perpetuates cycles of disadvantage and prevents a significant portion of the population from contributing to and benefiting from development efforts.

Public facilities such as healthcare, education, sanitation, and transportation are crucial for enhancing people's capabilities. Neglecting these facilities leads to inadequate service provision, which hampers human development. Effective public policy, therefore, must ensure robust investment in these areas to facilitate overall development and create an environment where individuals can thrive.

Intolerance and repression by the state or society further restrict freedoms. Overactive repressive states often employ surveillance, arbitrary detention, and violence to maintain control, suppressing dissent and curtailing freedoms of expression, association, and movement. Such environments create fear and inhibit the social and political engagement necessary for development.

Addressing these sources of unfreedom requires a holistic approach to development. This means not only fostering economic growth but also ensuring political freedoms, social inclusion, and investment in public goods. By expanding human capabilities and ensuring that all individuals have the opportunity to lead lives they value, societies can move towards true development that benefits everyone.

 In summary, Amartya Sen's framework highlights the interconnectedness of freedoms and development. By eradicating poverty, combating tyranny, enhancing economic opportunities, addressing social deprivation, investing in public facilities, and ensuring political and civil liberties, we can create a world where development is synonymous with the expansion of freedom. This comprehensive approach is essential for fostering sustainable and inclusive development in the contemporary world.

Development as Freedom

Earlier this year,  I embarked on a mission to read Amartya Sen's seminal work, Development as Freedom, from cover to cover. As part of this endeavour, I aimed to share insights from the book on this platform to engage with fellow readers. However, my journey was temporarily halted when I took on a new role with the British Council. 

After nearly five months, I am now ready to resume this intellectual journey. without further ado, let me introduce the book. 

Published in 1999 by Oxford University Press, Development as Freedom has significantly impacted the field of development economics. In this book, Amartya Sen, an economics Nobel laureate, redefines development as the expansion of the set of real freedoms that people enjoy. This perspective challenges and shifts focus away from traditional indicators such as GDP growth, industrialisation and technological advancements. While these indicators are important for bringing about economic development, Sen argues that they should be viewed as means to the ultimate end - enhancing human freedoms and capabilities. 

Sen posits that true development should be measured in terms of expansion of the set of real freedoms that people enjoy. This broader perspective integrates social and economic arrangements with civil and political liberties. Thus, genuine development, according to Sen, is characterised by the broadening of individual's freedom and capabilities, rather than merely growth in income or technological progression. 

In subsequent articles, I will delve deeper into Sen’s conceptualisation of development and explore the framework he proposes for assessing development. Stay tuned as we unpack the profound ideas that have reshaped our understanding of development today. 

The Economic Impacts of Afghani Appreciation

In the past two years, we have seen Afghanistani Afghani appreciate against USD and other currencies. This appreciation will have several economic implications for the country:

Import Costs: A stronger currency reduces the cost of imports, leading to lower inflation rates as imported goods would be cheaper. This can increase the purchasing power of Afghanistani consumers and businesses. In fact, inflation in the country decreased by more than 29 percent between Oct 2022 and August this year, which I think is mainly due to the lack of purchasing power among Afghanistani households and businesses.


Source: Xe.com


   Source: Trading Economics 


Export Competitiveness: A stronger Afghani could make Afghanistan’s exports more expensive and less competitive on the international market. This could potentially lead to a decrease in export volumes, worsening the already negative trade balance of our country.

Foreign Debt: If Afghanistan has foreign debt in USD or other foreign currencies, the stronger Afghani will make it cheaper to service this debt. This means it will reduce the country's debt burden.

Investment Flows: Assuming other things (specially government institutions worked effectively), the rise in the currency could also influence foreign investment. Investors would see the stronger currency as a sign of economic stability, which could attract foreign direct investment. However, we know that Taliban are not recognized by any country – they remain largely distrusted across the world. Let us not forget that no single country has yet formally recognized the Taliban regime in Afghanistan.

Remittances: Many Afghanistani families rely on remittances from
family members working abroad, especially in the neighbouring countries such as Iran and Pakistan. As a result of the stronger Afghani, sadly, the value of remittances in local currency terms will decrease, reducing the income of families that depend on remittances.

Given the concurrent deflation and appreciation of the Afghani, Afghanistan’s economy faces significant challenges. With exports becoming more expensive and less competitive, and the local value of remittances dropping, there is increased financial stress on families and businesses. This stress, in turn, reduces consumer spending, which will more than before worsen the deflationary spiral. Addressing the low liquidity among households and businesses is thus a critical issue for avoiding this crisis to deepen further. 

Did you know that Power BI is an excellent interactive data visualization tool?

 Power BI is a business analytics tool developed by Microsoft. It provides interactive visualizations and business intelligence capabilities with an interface that is easy to use for creating reports and dashboards. Essentially, it turns our unrelated sources of data into coherent, visually immersive, and interactive insights. Whether our data is a simple Excel spreadsheet or a collection of cloud-based and on-premises hybrid data warehouses, Power BI lets us easily connect to our data sources, visualize (or discover) what’s important, and share that with others.

Here's a sample report I have produced using Power BI: 



Let's breakdown what Power BI is used for:

Data Connection: Power BI can connect to a wide range of data sources, including Excel files, databases, and cloud-based data, and it brings all the data together into a single view.

Data Transformation: Once the data is connected, Power BI allows users to transform and clean that data. Users can modify, aggregate, enhance, and clean the data as per the business requirements.

Data Visualization: Power BI enables users to create visualizations (like charts, graphs, and maps) and interactive dashboards. It provides a drag-and-drop functionality which makes creating reports and dashboards quite easy.

Data Analysis: With Power BI, users can explore data, discover patterns, and glean insights. The tool offers various analytics and artificial intelligence (AI) capabilities that can automatically spot patterns, help users understand what the data means, and predict future trends.

Data Sharing: After creating reports or dashboards, Power BI provides facilities to share the visualized data with others in the organization. This helps in making informed decisions by providing the right people with the right information.

Embedded Analytics: Power BI also allows users to embed analytics in an app or website for wider usage, extending its capabilities to more users.

Power BI helps us in our businesses to make data-driven decisions by enabling us to visualize and analyze our data in a more comprehensible and interactive way.


Download Power BI here: Power BI Desktop

Do you want to know why inflation is bad and why the Bank of England is rising interest rates?

Inflation refers to the rise in prices of goods and services over time. When inflation is high, your money doesn't go as far because everything gets more expensive. Imagine you could buy a loaf of bread for £1 last year, but this year it costs £1.20. That's because of inflation. If your wages or savings aren't increasing at the same rate as inflation, you'll find it harder to buy things or maintain your standard of living.


Inflation can be bad for several reasons:

  • It reduces the purchasing power of money, meaning your money buys less.
  • If it's unexpected, businesses might struggle to set the right prices for their products.
  • People might delay spending because they expect prices to keep rising.
  • If wages don't keep up with inflation, people might feel poorer and become less confident about the economy.

The Bank of England (and other central banks) has a tool to try and control inflation: interest rates. Here's how it works:

When the Bank of England raises interest rates, borrowing money becomes more expensive, and saving money becomes more attractive. If borrowing is more expensive, people and businesses might cut back on spending and borrowing. This reduced spending can slow down the economy a bit and, in turn, help reduce inflation.

On the other hand, when saving becomes more attractive because of higher interest rates, people might decide to save more and spend less. This again reduces the demand for goods and services, which can help to control rising prices.

So, the Bank of England raises interest rates to make borrowing less appealing and saving more appealing, which can help cool down an overheated economy and keep inflation in check.

A More Formal Presentation of Factor Analysis

In a previous post, a simplified overview of Factor Analysis was provided. In this one, I would like to present a more formal description of it.  

Note: You can download a pdf version of this article using the link provided below this post. 

In factor analysis, we have a set of observed variables, X_1,X_1,…,X_p, which we believe are linear combinations of underlying unobserved factors, F_1,F_2,…,F_m, plus some error term, ɛ. The error term accounts for all other unobserved variability in the X variables.


Mathematically, for each observed variable, we express it as:

X_i=a_i1 F_1+ a_i2 F_2+a_im F_m+ ɛ_i,i=1,2,...,p

Where: 
X_i = Observed variable
F_j = Unobserved latent factors
a_ij = Factor loadings of factor j on variable i
ɛ_i = Unique variance (error term) associated with variable 
p = Total number of observed variables
m = Number of factors

Observed Variables (X_i): The actual data you have—like scores on a test, economic indicators, etc.

Unobserved Latent Factors (F_j): Variables not directly observed but inferred from the mathematical model. These capture the underlying processes or constructs that might explain the patterns in your data.

Factor Loadings (a_ij): These are the coefficients which indicate the degree to which each X_i is influenced by each F_j. Factor loadings are akin to weights, showing the impact of factors on the observed variables.

Error Term (ɛ_i): This represents the portion of variability in X_i that cannot be explained by the common factors.

Factor Extraction and Retention:
Factor extraction typically begins with calculating the covariance or correlation matrix of the observed variables. Eigenvectors and eigenvalues of this matrix are computed to determine the factors and how much variance each factor explains. The number of factors to retain might be determined using various criteria, such as Kaiser’s criterion (eigenvalue > 1) or the scree plot method.

Example in Econometrics:
Imagine an economist is looking at variables like GDP growth, unemployment rate, inflation rate, and interest rates (X_1,X_1,…,X_p) and hypothesizes that they are influenced by underlying, unobserved factors like economic stability and monetary policy (F_1,F_2).

GDP Growth=a_11 F_1+a_12 F_2+ɛ_1  
Unemployment Rate=a_21 F_1+a_12 F_2+ɛ_2
Inflation Rate=a_31 F_1+a_32 F_2+ɛ_3
Interest Rate=a_41 F_1+a_42 F_2+ɛ_4

The task is then to use the observed data to estimate the factor loadings (a_ij) and deduce the nature of the latent factors. This often involves rotation methods to make the solution more interpretable.

Factor analysis provides a robust technique for economists, and others, to explore and understand the dimensional structure of observed variables, providing insight into unseen influences in their data. While the math can be complex, the foundational understanding remains rooted in identifying and understanding these underlying, latent factors.

If you are keen to read more on factor analysis, please check out these books: 
Factor Analysis: Statistical Methods and Practical Issues by Jae-On Kim and Charles W. Mueller. This book provides a comprehensive overview of factor analysis and is widely respected in the field.

Applied Multivariate Statistical Analysis by Richard A. Johnson and Dean W. Wichern. This book covers a variety of multivariate techniques, including factor analysis, and is quite accessible for various skill levels.

A Handbook of Statistical Analyses using SPSS by Sabine Landau and Brian S. Everitt. Though software-specific, this handbook presents the application of numerous statistical methods, including factor analysis, using practical examples.

Sorry for the poor presentation of variables and equations. It is because Blogger does not support math language the way we type them in Microsoft Word or LaTeX. If you would like to see and read the equations and variables in a nice and neat shape, please download a pdf version of this article using the following link: A More Formal Presentation of Factor Analysis

Factor Analysis: A Simplified Overview

Factor Analysis is a statistical technique that we use to identify underlying relationships between different variables. Imagine we have a lot of related variables, and we suspect that these could be influenced by a few underlying factors. Factor analysis helps us to unearth these underlying factors.

                                                        Diagram source: Statistics By Jim

Basic Concept:

Think of factors as underlying (and unobservable) variables that somehow influence the observable variables we measure directly. Factor analysis tries to find out how many of these hidden factors might be influencing the patterns of response we see in our data and what variables are related to which underlying factor(s).

Example:

Imagine we are researching why students get the grades they do. We have data on various variables, such as attendance rate, hours spent studying, sleeping hours, part-time job hours, and so forth. These variables can be many and somewhat overwhelming to analyse individually.

Let’s dive a bit into the example:

Identifying Factors: We hypothesize that these observable variables (e.g., study hours, sleep hours) might be influenced by a few unobservable factors like Work Ethic or Time Management.

The Relationship:

Maybe hours spent studying and attendance rate are both influenced by an underlying factor we might label as Diligence.

While sleeping hours and part-time job hours might be influenced by Time Management.

Why bother?

It helps us reduce our workload: Instead of dealing with 5, 10, or 50 variables, we can group them under a few factors and work with those, making our analysis more straightforward and interpretable.

It provides insight into the patterns or structures (the underlying factors) in our data: We can understand what hidden influences might be driving the observable patterns in our data.

Steps in Factor Analysis:

Extraction: Extract the minimum number of factors that can aptly represent the patterns in the relationship among variables.

Rotation: Rotate the factors to ensure that they make sense (both statistically and theoretically). Rotation can help simplify and interpret the data.

Interpretation: Assign labels to the factors (like 'Diligence' or 'Time Management' in our example) and interpret the data accordingly.

Application:

Once the factors are identified and interpreted, we can:

·       Use them to understand how different variables interact.

·       Develop strategies (like study plans or interventions) that target the underlying factor, affecting all associated variables simultaneously.

To sum-up, Factor Analysis simplifies data by finding the unobservable factors influencing the patterns of observed variables, aiding in data interpretation and strategy formulation, especially when dealing with numerous variables.

The Role of Complementarities, IRS and Multiple Equilibria in the Process of Economic Development

In 1970’s the world of economic thought experienced a re-emergence of neoclassical economics that has traditionally been pre-occupied with laissez-faire. Given such obsession, neoclassicals posited that the application of standard microeconomic principles in the third world context would tackle the issue of underdevelopment as it (i.e. underdevelopment) is born out of lack of markets due to weak or non-existent property rights.  In this essay, with the help of QWERTY example, I want to argue that even if one comes from a neoclassical perspective, still there will exist market failure that may require some form of state intervention; and in the LDCs, underdevelopment will be the result of such massive coordination failures. In particular, under neoclassical assumptions, the issues of complementarities and increasing returns to scale will lead to the existence of multiple equilibria where only one is pareto-efficient; in the presence of multiple equilibria, issues such as path dependency and history will force the economy to get stuck with pareto-suboptimal equilibrium.    

                                                Figure source: Growth Dynamics, Multiple Equilibria, and Local                                                            Indeterminacy in an Endogenous Growth Model of Money, Banking and                                                 Inflation Targeting by Rangan Gupta and Philton Makena

The types of externalities can broadly be categorized into technological and pecuniary ones. While market can capture technological externalities (i.e. due to such externalities market might be in disequilibrium), pecuniary externalities are not captured in the General Equilibrium framework. Pecuniary externality occur when the profit of one producer is affected by the actions of other producers through market prices; in other words, pecuniary externality arises when the price of one good in an economy depends on the prices of other goods in the economy: 

where P is the price at which the first firm sales its products. 

Pecuniary externality leads to market imperfection and complementarities that will in turn, along with the assumption of increasing returns to scale (IRS), will lead to the existence of multiple equilibrium. Let’s consider To-Become-Tea-Lovers country. Suppose that two firms, T and S exist in this economy: while T produces tea, S produces sugar; suppose that due to some changes in the taste of people living in this economy, the demand for tea increases and as a result T expands. This will result in increase in the demand for S’ product; so, S will expand. This in turn will have a positive impact on the demand for T’s products because now with more income available from the expansion of S, demand for tea further increase. These two firms will keep reinforcing each other’s profit. Thus, a monopolistic economy will be shaped: imperfect market. This way, pecuniary externality leads to imperfection in the market. Let’s note that in such a scenario, the First Fundamental Theorem of Welfare Economics stands in the way because it rules out Pareto-ranked equilibria in the absence of technological externalities.

Now, to show that complementarities and IRS lead to the existence of multiple equilibria, let’s consider QWERTY example. Dvorak Simplified Keyboard (DSK), according to experiments, is more efficient arrangement compared to QWERTY, and it is said that typing with DSK is 20 to 40% faster compared to QWERTY. However, we are still stuck with the later due to 3 issues, namely (1) technical interrelatedness, (2) economies of scale, and (3) quasi-irreversibility of investment.

Technical inter-relatedness refers to the interdependence that exists between typewriters and typing skill. In the past, when typewriters were invented, it was purchased by businesses for business use. The purchaser wouldn’t himself/herself use the machine, rather it was used by typists who were trained in typing schools. This gives rise to some form of inter-relatedness; the business will buy those types of machines for which typist (soft skill) can be found; on the other hand, the typist would try to learn typing with those types of machine that are available with businesses. Thus, a form of complementarity is formed.

In this situation, when a firm buys a QWERTY-keyboard typewriter, this signals a positive pecuniary externality to those typists who are skilled in typing with QWERTY-keyboard. Also, the demand for QWERTY-keyboard learning will go up which will result in the reduction of average cost of learning QWERTY-keyboard due to economies of scale (IRS). Thus, complementarity and economies of scale create QWERTY path-dependency, meaning the purchase of a QWERTY-keyboard by a firm and subsequence choice of learning this QWERTY-keyboard skill by potential learners will finally lead to dominance of QWERTY-keyboard. However, if the issue of quasi-irreversibility of investment did not exist, it would have been possible, even now, to go to DSK.

Quasi-irreversibility of investment refers to rise of asymmetry that rose between the cost of hardware and software conversion in mid-1890s. In mid-1890’s, the possibility to move back and forth between QWERTY-keyboards and DSKs without any extra cost was made possible but the cost of learning DSK was going up because the demand for QWERTY-keyboard learning was rising. This is called Quasi-irreversibility of investment.

In this example, we see that pecuniary externalities, complementarities, increasing returns to scale, history and path dependency and quasi-irreversibility of investment have led to multiple equilibria, being caught up in low level equilibrium and market failure. In other words, if someone, say government coordinated the actions of business owners and labor force at the initial stage, pareto-optimal equilibrium would have been achieved. Based on this example, we can argue that even coming from a pure neoclassical perspective, market failure can happen and there is a need for coordinated action to help the economy achieve a pareto-efficient equilibrium. In the LDCs, underdevelopment can be a result of massive coordination failures do to the issues of complementarities and increasing returns to scale.  We can also show the existence of multiple equilibria, coordination failure and the need for some form of intervention to coordinate actions by considering an economy where left to themselves, firms may not invest because they might expect that other firms will not invest.

Informal Sector; Composition and Linkages

When the notion of informal economy was first established, influential economists like Arthur Lewis (1954) believed that informal sector would slowly fade away as development happens and finally, when development riches a certain maturity level, the informal economy will completely disappear. History, however, signals rather the opposite of what Lewis and others wished for: formal and informal economies are highly interlinked, and now the dualist narrative seems somewhat irrelevant. This short article discusses the composition of informal sector. It also looks into the prospects of growth for different components of the sector.

Researches show that the informal sector is very heterogenous. In this sector, you can find businesses that do not employ any machinery and laborers work complete their work manually. Some of the businesses have only one worker who is working for subsistence and employ no employees, while some other informal business may employ several workers and some primitive or even a few sophisticated tech machines.

Ranis and Stewart (1999) made a distinction between the traditional and modern parts of informal sector. Figure 1 shows the distribution of informal business along a modernity continuum based on a number of characteristics.


Traditional informal businesses are at the very bottom of the continuum. They employ no hired worker, no or a very low level of capital, have extremely low or no capital usage and make no use of hired labor. As to their location, they operate within the premises of a household or do not have a fixed location, and the type of activities they engage in are very low value-added activities. when we move upwards along the continuum, at the very top of the distribution, we have informal businesses that use some capital, produce standardized goods and services, hire low- and medium-skilled labor, have a fixed address outside the household and offer competitive wages that are comparable with the wages offered in the formal sector. However, what make them informal is the fact that they do not comply with all the rules and regulations imposed by government and other legal entities on the formal sector.

The above discussion of informal sector composition is based on making a distinction between traditional and modern sectors of informal economy. Though, Nattrass (1987) approaches the composition of informal sector from a very different perspective. He bases his theoretical analysis on a triangle, composed of 3 sub-triangles, where parts of industrial reserve army, marginal pole and formal sector make up the components of informal sector (see Figure 2). The main argument in this perspective is that only some part of the marginal pole fits into the informal sector, that the industrial reserve army may or may not participate in informal activities, and that people who are not marginalized can also form part of the informal sector.

The triangle of industrial reserve army has two sub-parts a and b. The people who are seeking full time jobs in the formal sector and are living, probably on savings or borrowing from relatives and friends, constitute sub-part a. They are not part of the informal sector because they are not doing any productive economic activity, and therefore they are excluded from the inner circle of the diagram. Sub-component b consists of those people who have temporally joined the informal sector but theoretically speaking, they could get a job in the formal sector. These people will immediately leave the informal sector when a job is available for them in the formal sector. 

The marginal pole triangle consists of people who have no formal sector skills or experience.  A section of these people (see d in Figure 2) probably works for low wages in the informal sector. Component e consists of the truly marginalized people who are not even employed in informal sector.

The other triangle is formal sector triangle which includes people working in the formal sector full-time (see f in Figure 2); but at the same time, some of them have jobs in the informal sector to supplement their income (see c in Figure 2).

In the framework that Nattrass has drawn, formal and informal sectors are negatively correlated, because most of whose who are working for informal sector are also looking for jobs or are capable of getting jobs in the formal sector. This means expansionary activities in formal sector pull labor force from the informal sector; but a recession will push many to the informal sector. In different terms, when formal sector expands, informal sector will shrink and vice versa. In this sense, informal sector is a necessary appendage to a capitalist economy that cannot full fill its obligations optimally, and thus leaves a part of its duty to the informal sector. In other words, informal sector is born out of a necessity that stem from faulty function of capitalism. Faulty function in the sense that its labor market cannot employ everyone in the formal sector; so, it produces an appendage such as informal sector to tackle the issue of unemployment and underemployment to some degree. However, note that the benefit of formal sector expansion will be extremely small for marginal pole, but significant for industrial reserve army and formal sector workers who complement their income with earnings from informal sector.

Some economists, like Stark, see informal and formal sectors as complements for each other. They argue that the two sectors depend on each other. The formal sector produces some of its goods in informal market, for the informal sector’s labor is much cheaper. Take the RMG sector for example. This conception of relationship between formal and informal sectors, again, sees informal sector as a byproduct of capitalism, but not because it cannot function optimally, rather because the existence of informal sector is profitable to capitalism.

Some other economists, such as Datta Choudhury, propose inverse linkages between formal and informal sectors. In Datta Choudhury’s model, formal sector produces Xu and Mu while the informal sector produces Mz. We see that M is produced competitively in both sectors. That why M is produced in both sectors is because some businesses do not or cannot choose to enter formal sector due to constraints, legal obligations and the extra costs that the formal sector imposes on them. In the formal sector, the cost of labor is very high, while in the informal sector, the cost of credit is extremely high; but, because the two sectors compete on M, they cannot set their prices higher than market prices. This model suggests that in some products, formal and informal sector have inverse relationship. Lower prices, better productivity and improved technology in the formal sector might reduce the sight of informal sector. Conversely, hike in cost of production of M in formal sector may increase the size of informal sector.

Suppose the government steps in and provides credit support to the informal sector, and bring down credit costs substantially. In such a situation, because informal sector becomes more profitable, the formal sector businesses will seek ways to join informal sector to reduce their labor costs and reap the benefits of low wages in the informal sector. However, the credit costs in the informal sector are very high that government intervention cannot produce any remarkable result. Thus, in the developing world, informal sector cannot graduate to formal sector because of persistently high costs of credit.

As to the relation of informal sector with growth, the existing literature indicate forward production links for modern informal firms with formal firms. It can be shown that growth and competitiveness in the formal sector benefits the modern informal sector. Competition within formal sector leads formal businesses establish links with the informal sector and this will expand activities in the informal sector.

In the consumer market, traditional informal firms survive operate in a different market segment than formal firms, and thus they do not pose a direct threat to the formal sector. But, modern informal firms may grow to the level that they can compete with formal firms. This will pose a threat to the size and growth of formal firms. Informal firms are advantaged over formal SMEs because they have lower start-up and operational costs. When it comes to size expansion, informal businesses are at disadvantage because they cannot expand beyond certain size even if their profitability permits it. Thus, from the consumer market perspective, the size and growth of informal firms will also depend on the regulatory framework.

Ranis and Stewart (1999) argue that the demand for products from the informal sector depends, among other things, on the level of per capita income and its distribution. A low per capita income means income inequality is high and thus a larger part of people will demand goods and services provided by traditional informal firms. This means the traditional informal sector will grow when income per capita is low or when income inequality is high. But, when income increases or when income distribution improves, more and more people will demand goods and services produced by the modern informal sector. This implies that modern informal sector will grow if income increases or when income inequality declines.


References:



LEWIS, W. A. (1954). Economic Development with Unlimited Supplies of Labour. The Manchester School, 22(2), 139–191. doi:10.1111/j.1467-9957.1954.tb00021.x 


Nattrass, N. J. (1987). Street trading in Transkei—a struggle against poverty, persecution, and prosecution. World Development, 15(7), 861–875.


Ranis, G., & Stewart, F. (1999). V‐Goods and the Role of the Urban Informal Sector in Development. Economic Development and Cultural Change, 47(2), 259–288. doi:10.1086/452401 

The Link b/w Productivity and Size in the New Economic Geography Angle and in the Standard Agglomeration Literature

When people and firms live side by side in cities and industrial areas, some benefits come to exist out of this coexistence. These benefits are studied under the title of agglomeration economies. In this writing, I will try to figure out the difference between New Economic Geography angle and standard agglomeration literature in suggesting that productivity and size may be directly associated. Also, I will try to interpret rank size rule when applied to city size distribution in a country. 

The agglomeration literature built on the studies of Henderson (1974) and Sveikauskas (1975) posits that firms in large cities are more productive. This increased productivity comes from the indivisibilities in investment, huge infrastructure base, large market size, lower labour turn-over cost, and easy information-sharing that exist in large cities. Combes et al. (2012) furthers the literature by offering two main explanations for the average increased productivity of firms in larger cities. The first explanation involves firm selection that means competition among firms are tough in larger cities and this allows only the most productive to survive. The second explanation involves agglomeration economies which is possibly reinforced by localized natural primacy. The New Economic Geography (NEG) literature, too, discusses the impact of agglomeration on economic growth. According to this strand of literature, migration and population expansion in cities are motivated by the trade-off between increasing returns and mobility costs. According to Krugman (1991), employees and companies become more productive due to the existence of external-scale economies. What makes the New Economic Geography angle different from standard literature produced by urban economists is the level of their analysis: NEG literature analyses the impact of city size or agglomeration on economic growth at the national level while standard agglomeration looks into the impact of city size on the productivity of urban workers at the city level.

Now, let's see the interpretation of the rank size rule when applied to city size distribution in a country. Zipf’s rank size rule suggests that the largest city is roughly twice the size of the second largest city, about three times the size of the third largest city, and so on. This relationship can also be understood and explained as inverted u-shaped relationship between city size and productivity/growth, meaning that initially as city increases in size, productivity of the firms in that city increase. This continues up to a certain point beyond which growth decreases as city increases. The decrease in productivity beyond the threshold limit in the city size would mean that firms will need to look for fresh urban setting in other places in order to make new investment. In the same way, new migrants may not find it worthwhile to migrate to the cities which have already reached an optimum size. 

To sum up, the New Economic Geography angle analyses the impact of city size or agglomeration on economic growth at the national level while the standard agglomeration looks into the impact of city size on the productivity of urban workers at the city level. And, Zipf’s rank size rule can be understood and explained as inverted u-shaped relationship between city size and productivity/growth, meaning that initially as city increases in size, productivity of the firms in that city increase. This continues up to a certain point beyond which growth decreases as city increases. The decrease in productivity beyond the threshold limit in the city size would mean that firms will need to look for fresh urban setting in other places in order to make new investment.

References: 

Henderson, J. (1974). The Sizes and Types of Cities. The American Economic Review, 64(4), 640-656. Retrieved March 27, 2021, from http://www.jstor.org/stable/1813316 

Leo Sveikauskas, 1975. "The Productivity of Cities," The Quarterly Journal of Economics, Oxford University Press, vol. 89(3), pages 393-413. 

Combes, P., Duranton, G., Gobillon, L., Puga, D., & Roux, S. (2012). The Productivity Advantages of Large Cities: Distinguishing Agglomeration From Firm Selection. Econometrica, 80 (6), 2543-2594. http://dx.doi.org/10.3982/ECTA8442 

Krugman, Paul, 1991. "Increasing Returns and Economic Geography," Journal of Political Economy, University of Chicago Press, vol. 99(3), pages 483-499, June.

The Causes of Rural-to-Urban Migration

Rural to urban migration refers to the movement of people from rural areas into cities and often involves migration of labour and change of carrier. This type of migration is caused by different push and pull factors. In this article, I will discuss the causes in the view of both theoretical as well as empirical literature. Also, I will examine the literature to see whether or not the low income households are better-off in large cities compared to their counterparts in small urban settlements.

Different theories have discussed different determinants and aspects for rural-urban migration. One of the early theories of rural-urban migration belongs to John R. Harris and Michael Todaro. According to Harris-Todaro (1970) model, rural-urban expected income differentials is the most important factor that determines rural to urban migration. While urban labourers/employees receive industrial wages, rural workers earn agricultural wage. In a developing country setting, assuming that the Lewisian turning point has not been reached, there will exist a significant wage differential between the two sectors, i.e. agricultural and industrial sectors. Thus, the model postulates that the rural workers will migrate to urban areas in order to receive a higher wage. The model implies that the rural poor will have a higher propensity to migrate given that their income is almost zero in the rural areas. However, some empirical results have shown just the opposite: propensity to migrate has been lower among the disadvantaged classes of rural areas.

Harris-Todaro model also considers another important determinant of rural-urban migration: distance. As the model suggests, larger the distance, lower will be the migration from rural to urban settings because the cost of migration goes up. This agrees with theories which suggest that, as cities are expanding their boundaries and more and more agricultural lands from rural areas are reallocated towards uses other than agriculture, the rural areas at the outskirts of cities get connected to cities through transport infrastructures; thus, the people of the rural areas near the cities need not migrate to cities. They can participate in economic activities in the cities without migration. Harris-Todaro model also suggests that rural-urban migration also depends on the extent of poverty in the two areas. If the extent of poverty in rural areas is very high, there will be high propensity to migrate to urban areas. Conversely, if the extent of poverty in the urban area is high, then there will be low propensity for rural-urban migration.

While Harris-Todaro model proposes that, due to high wage differentials, the rural poor has a higher propensity to migrate, Banerjee’s theory suggests that the poor cannot afford to migrate. According to him, the rural rich do not need to migrate, the rural poor cannot afford to migrate; so, it is the rural middle income who migrate to urban areas either for earning higher income or for accessing better healthcare, education and other public service facilities. Thus, Banerjee’s theory posits that the level of income is an important determinant of rural-urban migration.

In addition to the above, there are other theories such as Job-search, role of network, altruism versus principle of exchange etc. all of which suggest that migrants, before departing to urban areas, collect a great deal of information regarding their destinations.

From a historical and empirical point of view, we can list some more determinants for rural-urban migration. Net expected return, social norms, skills and education, literacy, gender-based demand, marriage, institutions and the concentration of pools of migrants are some of these factor. Families may send their young adults to urban areas because they are more likely to have a positive net expected return on migration since they are expected to have longer remaining life expectancy. In some societies, social norms determine who should migrate in search of job and higher earning. For example, in Afghanistan, it is usually not welcomed by the society to send a woman to urban areas alone for job purposes. In case of institutions, if strong property rights exist that can ensure safety of once property after migration, then such property rights might encourage rural to urban migration; or, when access to credit market is not possible for migrants in urban settings, this institutional issue might deter rural-urban migration. As an example of concentration of pools of migrants let me point out to the migration of Hazaras of Jaghori district of Afghanistan to Quetta, Pakistan and Sydney, Australia.

In addition to the above factors, one can list a set of push and pull factors (determinants). Push factors are generally tied to the poor economic conditions of household in the rural areas who migrate to urban settings in order to alleviate their poverty. For example, individuals who are unemployment, underemployment in rural areas, earn very low wages and lack assets and land, migrate to urban areas to improve their conditions. Other push factors can be lack of rural infrastructure such as housing, education and healthcare. However, push factors can include forced displacement such as conflicts and climate change. Pull factors refer to relatively better opportunities in the urban areas compared to rural areas. In other words, pull factors are those which attract migrants from rural settings to urban areas.

As to whether or not the low income households are better-off in large cities compared to their counterparts in small urban settlements, literature suggests that low income households are better off in large urban centers as they provide better job opportunities in relative sense. According to the literature, the living conditions in the informal sector is better than what the migrants accessed at the place of origin.


Reference: 

Harris, J., & Todaro, M. (1970). Migration, Unemployment and Development: A Two-Sector Analysis. The American Economic Review, 60(1), 126-142. Retrieved March 27, 2021, from http://www.jstor.org/stable/1807860

The Critical Role of Middle Class in Corruption in South Asia - A Reflection on Mushtaq Khan's Paper

Introduction

In the paper of Patron-Client Networks and the Economic Effects of Corruption in Asia, Mushtaq Khan traces the political influence of the Indian subcontinent’s middle class in their leading role in the anti-colonial struggles and argues that in the post-colonial era, the class has played its part in corruption through a transaction channel that I would like to term it “bribe-for-support”. This view is not far from realities and in the present days, if not all, a part of the body of middle class is playing an active role in state corruption by justifying unethical, illegal or extra-legal and unjust policies of the politicians and state.

                        
          Source: Illegal Wildlife Trade at the Philippine-Southeast Asian Nexus: An Assessment of                    Projects combating Illegal Wildlife Trade in Southeast Asia informing the Philippines and 
          guiding Donor Coordination by Cecilia Fischer


Who are the middle class?

Khan defines the middle class as “the educated sections of the population” who are non-capitalists, non-politicians, non-peasants and surely not landlords[1]. Hence, their main asset using which they can make a living is their intellect. This class can employ the asset either to serve high humane values such as expansion of justice, promoting equality, peace and freedom, or, conversely, succumb to the lures of money, power, or position and engage in justifying/turning a blind eye on unjust and illegal policies and corrupt practices. Khan’s argument implies that the middle class has chosen to succumb instead of resisting. 

The class played a critical role in the subcontinent’s political corruption 

As Khan observes, the politicians understand the importance of the middle class’ support who can mobilize the masses against the ruling class. In the aftereffect of demise of colonialism, the class was much stronger and a real threat to the ruling class. Hence, to have its support, the government would bribe the class in different ways – distribution of cheap land and heavy investment in higher education that was then entirely serving the middle class are two examples of such bribes[2].

Following Khan, I am of the opinion that the middle class has continuously betrayed high ethical and humane values and at high times, it has surrendered to the temptation triangle: money, power and position. The funds saved from spending on running election campaigns paves the ground for bribing the middle class. However, the members of this class have never pushed for electoral financing reforms that shall target banning capitalists from funding candidates’ campaigns which opens the doors for their intervention in state agendas. It is mostly the members of this class that pickup their pens and write to justify the corrupt practices of the politicians during election campaign in order to get a position in the post-election government.

Its role in corruption might have increased. With the increase of power of states and capitalists in recent decades, the influence and importance of middle class has relatively shrunk; this, I believe, might have increased the role of the class in corrupt practices. When relatively powerful, the class can better resist the pressure of different forms from the state and politicians to side with their unjust and illegal policies and practices. A weaker middle class, though, is much more prone to the influence of state and politicians. This means, the body of intellectuals are now more prone to accept corrupt practices of the state, support and justify them out of inability to resist. This is a very obvious fact in the context of Afghanistan. In the four presidential elections that have taken place in Afghanistan in the post-Taliban epoch, it has been the members of the middle class at the forefront of justification of the dark and tyrant past of the politicians. Afghanistan has been repeatedly ranked as one of the most corrupt states in the past twenty years. The interesting point is that most politicians and bureaucrats are coming from the middle class.


Summary

To sum up, concurring with Khan, I believe that the middle class has given in to the fascination of power, money and official government posts. This unethical behavior has helped state and administrative corruption to take roots and grow overtime. It is very famous among the political scientists that in the absence of constant criticism and oversight of intellectuals, power corrupts those who own it. The middle class – defined as the educated section of a society – bears an ethical obligation to oversight power, critique it for the purpose of reform and take active part in the produce, reproduction and distribution of knowledge. I am of the view that in the developing countries, the said section of the society, by siding with the politicians and the ruling class, has failed to fulfill its obligations. One can even argue that a part of, if not the whole, middle class, i.e. the intellectuals, has remained as corruption-justifying instrument in the hands of politicians and rulers in the Indian subcontinent and in the Central Asian and Middle East countries.

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[1] Khan, M. Patron—Client Networks and the Economic Effects of Corruption in Asia. Eur J Dev Res 10, 15–39 (1998). https://doi.org/10.1080/09578819808426700

[2] Khan, M. Patron—Client Networks and the Economic Effects of Corruption in Asia. Eur J Dev Res 10, 15–39 (1998). https://doi.org/10.1080/09578819808426700

Exploring the Benefits of Formalization Drive for Informal Firms in a Developing Country Setting

Introduction

Informal sector constitutes a large part of a developing economy. It seems that the informal firms take a rational decision to exit from the formal domain by comparing the costs and benefits of formalization. (Suresh De Mel, David McKenzie, and Christopher Woodruff, 2013). De Mel and others found that formalization has a very limited impact on the profitability of the firms who exit the informal sector and join the formal one. Given this, we want to find out if the push for formalization of informal firms in a developing country is of any benefit. Here, I want to argue that any push for formalization in the developing countries will act as a double-edged sword. In other words, it is an act that has both favorable and unfavorable consequences. Still, I believe that the favorable consequences of formalization outweigh its unfordable outcomes.

The unfavorable consequences of formalization drive

Government might waste its budget on firms that cannot stand in the competing world of formal sector. Firms which are in the informal sector can easily compete with formal sector firms and even have an edge over them due to lower wages, non-existence of taxes and other types of costs that are related to activity in the formal sector. In the informal sector, they can survive even if they do not make a profit. This is what makes activity in the informal sector favorable to informal firms.

The firms that can survive in the informal sector might not survive once they enter the formal sector unless there is enough financial support from the government. If we assume that the government will support them with subsidies, then there will not be much room to argue that formalization is good because it adds to the income of the government. If subsidies or financial amounts are paid to induce informal firms to become formal, then one would argue that letting informal firms in the informal sector is better because they will not be a burden on the budget of the government. If we argue that incentives should be paid only for a short time, then one would counter us that the firms might return to the informal sector as soon as the government cut its financial support. Therefore, we can say that one unfavorable consequence of push for formalization through financial incentives is that government might waste its budget on firms that cannot stand in the competing world of formal sector.

A large section of people might not be able to survive. Another somewhat obvious consequence of push for formalization is that a lot of firms might completely die out and disappear after a while when they enter the formal sector without any financial support from the government. Of course, here we are assuming that the government is using force to turn the informal sector formal. This means that a large part of people who could previously survive working in the informal sector now even might not be able to survive.


The favorable consequences of formalization drive

Tax and efficient allocation of resources

Research has shown that informality and lower tax collection are related – a phenomenon that reduces the ability of the government to finance public services (Levy, 2008). Hence, one favorable consequence of formalization is higher tax collection that will enable the government to better finance public services and even invest in the development projects. However, taking our discussions in the previous paragraph into consideration, we should cautiously optimistic about this consequence of formalization.

The coexistence of formal and informal firms leads to an inefficient allocation of resources in the economy through the different marginal production costs (Chang-Tai Hsieh and Peter J. Klenow, 2009). Therefore, formalization will lead to efficient allocation of resources which is another favorable consequence.

Profitability for the firms and contribution to economic growth and development

In addition to the above, another favorable consequence of formalization is access to formal business infrastructure including credit, technological support and access to more markets which ultimately lead to higher productivity and output of the firms. This favorable consequence can be viewed from the perspective of firms, because firms which are active in the informal sector have no or very limited access to what we just pointed out. Similarly, the firms will no longer be subject to harassments and bullying of government authorities asking for bribes.

We said that formalization opens access to formal business infrastructure including credit, technological support and access to more markets which ultimately lead to higher productivity and output of the firms. Higher productivity means, for the firm, higher profits and for the entire economy higher growth. Through this channel, formalization helps an economy with its growth and development.

Impacts on the quality of life of laborers

Apart from the above, I believe formalization will have positive impacts on the quality of life of labor force. When informal firms are formalized, they are required to follow labor laws set by relevant authorities. Wages of laborers will increase and some form of protection will be provided to employees/laborers. This is while in the informal sector, firm owner is restricted by no law and he/she can set any wages and can hire/fire on the spot. Of course the problems that informal sector laborers are facing are not limited to wage and instant firing. Inhumane working conditions are another big issue.

Conclusion and my take 

As discussed above, in the case of financially incentivizing informal firms to join the formal sector, the government might waste its budget on firms that cannot stand in the competing world of formal sector. However, there are chances that the firms stand on their own feet and become competitive profit-maximizing firms. If governments, backed by the use of force, coerce informal firms to go formal, there is a risk that a large section of people might not be able to survive when the informal sector disappears. This is firstly impossible. Even if we assume its possibility, due to inefficient and ineffective outcomes, it is not recommended.

Apart from the above undesired possible outcomes under certain conditions, I believe that formalization drive for informal firms in a developing country setting has various benefits. It can improve the quality of life of laborers employed by the informal sector, it can make survivalist firms profit-maximizing which contribute to the economic growth and development, it can improve the allocation of resources throughout the economy, and finally it adds to the income of the government. Hence, as stated at the opening paragraph of this writing, I believe the favorable consequences of formalization outweigh its unfordable outcomes.


References

Chang-Tai Hsieh and Peter J. Klenow. (2009). Misallocation and Manufacturing TFP In China and India . Quarterly Jouranl of Economics.

Levy, S. (2008). Good Intentions, Bad Outcomes: Social Policy, Informality and Economic Growth in Mexico. Brookings Institution Press.

Suresh De Mel, David McKenzie, and Christopher Woodruff. (2013). The Demand for, and Consequences of, Formalization among Informal Firms in Sri Lanka. American Economic Journal: Applied Economics.