Why do some countries have higher standards of living than others? This is a major question addressed by macroeconomics, one of the two subfields in economics. While microeconomics examines the decision making of individuals and firms and how they interact in markets, macroeconomics focuses on the economy as a whole and how it interacts with the rest of the world.

Measuring GDP involves finding the market value of each of the millions of goods and services that an economy produces, from bread and haircuts to computers and airplanes; GDP is then calculated by aggregating these market values into one total dollar value. In theory, the task appears straightforward – start by taking the quantity of each good or service produced, then multiply the quantity by the price at which it sold to find its dollar value. Then, add up these dollar values for all goods and services the economy produced in a given year to find the total market value or nominal GDP.

In reality, the complexity of modern economies renders the measurement and aggregation across different goods and services a very challenging task. Furthermore, while the output of material goods in an economy is relatively easy to compute, this is not so for services. For example, it is challenging to value the output of an insurance company or a bank.

A change in the nominal GDP reflects both changing quantities and changing prices. In economics, we rely on real variables, variables that adjust for changes in the price level over time, to make intertemporal comparisons. Nominal values are measured in terms of dollars while real values are measured in terms of quantities. In other words, real values account for the effect of inflation, the rate of increase in the average level of prices. Real GDP is a more appropriate measure of welfare than nominal GDP because a change in the real GDP unequivocally indicates a change in the quantities produced or the size of the economy.

While nominal GDP measures output in terms of current market prices, real GDP measures output in terms of constant prices, that is, it measures the value of production in a given year using the prices in a base year. To convert the nominal GDP in real terms, we need to “deflate” it by some measure of the aggregate price level such as the Consumer Price Index, which tracks movements in the average price level in an economy. The relationship between real and nominal GDP is as follows:

(1)

The Consumer Price Index (CPI) compares the cost of a fixed basket of goods and services bought by the “typical” household in a given period relative to the cost of that basket in a base period. The fixed basket used to compute Canada’s CPI contains thousands of goods and services, produced both domestically and imported from abroad, divided into 8 major categories: food; shelter; household operations, furnishings, and equipment; clothing and footwear; transportation; health and personal care; recreation, education and reading; and alcoholic beverages, tobacco products and recreational cannabis.

As the composition of the basket does not change over time, a rise in the cost of the basket reflects a rise in the average price level. The choice of base period is arbitrary – any year in our data can be designated as a base year. The CPI compares the cost of the basket in any other period to the cost of the basket in the base year. Conventionally, the value of the index is set equal to 100 in the base period to allow for easy comparisons between years. However, you must be mindful of that when converting nominal variables into real terms – see Examples 2 and 3.

Table 1: Canada’s nominal GDP and CPI, 2010 – 2020

Year	Nominal GDP (in millions)	CPI (2012 = 100)	Annual inflation rate (%)	Real GDP (in millions)
2010	$1,666,048	95.7
2011	1,774,063	98.5
2012	1,827,201	100.0	1.50%	1,827,201
2013	1,902,247	100.9	0.89%	1,885,279
2014	1,994,898	102.9	1.94%	1,938,676
2015	1,990,441	104.0	1.06%	1,913,886
2016	2,025,535	105.5	1.42%	1,919,938
2017	2,140,641	107.1	1.49%	1,998,731
2018	2,235,675	109.6	2.28%	2,039,849
2019	2,311,294	111.8	1.97%	2,067,347
2020	2,206,764	112.6	0.71%	1,959,826

Data Source: Statistics Canada

Have a look at the second column of Table 1. Notice the dramatic impact of the COVID-19 pandemic on the Canadian economy – in nominal terms, GDP in 2019 was higher compared to year 2020. Column 3 shows the steady rise in the aggregate price level over the period. To compute the annual inflation rate in Column 4, we find the percentage change in CPI between two consecutive years. Notice that the inflation rate varies in a very narrow range, between 0.71% and 2.28% during this 10-year period. This is expected as the Bank of Canada has targeted inflation since 1991. The Bank aims at keeping total CPI inflation at 2% – the midpoint of a target range of 1 to 3%.

To compute real GDP in Column 5 of Table 1, we first need to divide the CPI index by 100, which will enable us to compare the purchasing power of $1 in 2012, the base year, to any other year. For example, dividing the CPI in 2015 by 100 yields 104/100 = 1.04. Thus, $1 in 2012 has the same purchasing power as $1.04 in 2015. After that, we divide the real GDP by the CPI expressed as a decimal. The real GDP for any year in Column 5 is now deflated and expressed in 2012 dollars enabling us to make meaningful comparisons of the economy’s physical output over time. Notice also that the nominal and real GDP in the base year, 2012, are the same, which is always the case.

Drag and Drop Activity

Click and drag the numbers in the table below to assemble the correct equation for calculating Real GDP.

The table in Example 3 (Table 1) suggests that indeed, between 2000 and 2020 Canada’s real GDP has risen from about $1.7 trillion to about $2 trillion. Therefore, we can conclude that the size of the Canadian economy has increased over these 20 years. But is the average Canadian in year 2020 better off compared to year 2000? This question may appear peculiar but consider, for example, a situation whereby the Canadian population has expanded dramatically, so that even though the size of the economic pie is larger in 2020, it is now shared among a much larger number of consumers. Real GDP per capita, which accounts for population growth, is one of the most important measures of living standard in economics. Real GDP per capita for a country or region can be computed as follows:

(2)

To compute the real GDP per capita, you need to divide the real GDP by the size of the population for each year. Always take care to note the unit in which a variable is measured. The real GDP per capita you will obtain will be measured in millions of 2012 dollars, the unit of measurement of real GDP. We multiply this ratio by 1 million to convert real GDP per capita into 2012 dollars in the last column of Table 2.

Table 2: Canada’s nominal GDP, real GDP, and population, 2010 – 2020

Year	Nominal GDP (in millions)	Real GDP (in millions)	Population	Real GDP per capita
2010	$1,666,048	$1,740,907	34,004,889
2011	1,774,063	1,801,079	34,339,328
2012	1,827,201	1,827,201	34,714,222	52,636
2013	1,902,247	1,885,279	35,082,954	53,738
2014	1,994,898	1,938,676	35,437,435	54,707
2015	1,990,441	1,913,886	35,702,908	53,606
2016	2,025,535	1,919,938	36,109,487	53,170
2017	2,140,641	1,998,731	36,545,236	54,692
2018	2,235,675	2,039,849	37,065,084	55,034
2019	2,311,294	2,067,347	37,601,230	54,981
2020	2,206,764	1,959,826	38,037,204	51,524

Data source: Statistics Canada

Drag and Drop Activity

Click and drag the numbers in the table below to assemble the correct equation for calculating Real GDP per capita.

GDP counts all goods and services produced in the territory of a country by both domestic and foreign nationals. While GDP focuses on the location of production and ignores ownership of factors of production, a closely related concept, Gross National Product (GNP), focuses on the ownership of factors of production and ignores its location. GNP is a measure of the total market value of all final goods and services produced during a specific period by a nation’s factors of production regardless of their location. For example, a student from China working part-time in Canada will contribute both to Canada’s GDP and China’s GNP. Similarly, the value of output produced by a U.S.-owned company operating in Ontario will be counted in the U.S. GNP and Canada’s GDP.

GDP and GNP are typically very similar and widely used for comparisons. However, GNP (or Gross National Income, GNI), is conventionally used for international comparisons by organizations such as the World Bank. International comparisons are further complicated by the fact that currencies differ across countries and for meaningful cross-country comparisons, GDP must be expressed in a common currency.

Even conceptually, real GDP is an imperfect measure of the performance of an economy. First, it measures the value of only market activities, i.e., goods and services exchanged in legal market transactions. But there are plenty of goods and services we produce for ourselves in the household such as dinners and childcare that are not counted in the GDP. If you buy a take-out dinner that costs $20, this will add $20 to Canada’s GDP. However, if you cook an equivalent dinner for yourself, it will not be accounted for in the GDP. More significantly, the time and effort it takes to raise a child at home, time and effort caretakers do not get paid for, are not counted in a country’s GDP. In general, household production, goods, and services that household members produce using their labour and capital for their own consumption, are not counted in the GDP. This is a significant shortcoming when we make international comparisons. A significant share of goods and services produced in developing countries are not exchanged in market transactions and therefore, not counted in the GDP. While adjustments are made to reflect such nonmarket activities, these adjustments are inevitably imprecise.

GDP does not account for the underground economy or black market where cash and barter transactions are not reported to the government. In some cases, these transactions serve the purpose of avoiding taxes such as unreported tips while in others, they are related to illegal activities such as trade in illegal drugs or guns. Because of its very nature, the size of the underground economy is impossible to calculate. Its share in a country’s GDP will vary across countries and the larger that share, the more the country’s GDP will underestimate the size of the economy.

In contrast, negative by-products of production such as pollution, climate change, resource depletion, and environmental degradation are not subtracted from GDP. However, when we expend efforts to correct for these harms or “bads” as they are known in economics, our spending is counted in the GDP. Example 5 illustrates the issue. Most economists would agree that negative externalities should be subtracted from GDP and “goods” or benefits such as life expectancy should be added but there is no agreement on how to value them.

GulfCorps monitoring restored oyster reefs.

Finally, real GDP per capita is a measure of living standard for the average or typical person in an economy. However, it does not account for the distribution of income and income inequality. As Angus Deaton, the winner of the 2015 Nobel Prize in Economic Sciences states, “[a]verages are no consolation to those who have been left behind.” Professor William Easterly, a leading development economist, notes that life during growth does get better, on average, but some are left behind and the benefits of growth can accrue only to a select few. It is little consolation for a poor person in a rich country that the average person in the country enjoys a high standard of living.

In summary, increasing human wellbeing maybe the ultimate goal of policy makers. But human wellbeing is difficult to define and measure. In economics, we use the real GDP per capita as a measure of economic development. As Robert Lucas, winner of the 1995 Nobel Memorial Prize in Economic Sciences, states “[t]his may seem too narrow a definition, and perhaps it is, but thinking about income patterns will necessarily involve us in thinking about many other aspects of societies too.”

Flashcard Activity

So far, we know how to measure the standard of living, how to make meaningful comparisons between countries and within a country over time. But how do we measure changes in the living standard over time? Real GDP per capita growth is the best measure of changes in economic well-being. The simplest way to measure economic growth is by taking the percentage change in real per capita GDP between two consecutive periods. If we denote real GDP per capita in year t as Y_t, then the growth in real per capita GDP, g, between any two years, t and t – 1, expressed as a decimal is given by:

(3)

From Equation (3), we can express Y_t in terms of Y_t-1 and the growth rate:

(4)

Take the natural logarithm (the logarithm to the base e), ln, of both sides of equation (4):

(5)

A useful Maclaurin expansion-based approximation is that for any small positive number, a,

(6)

Three useful properties of logarithms are the product rule, Equation (7), quotient rule, Equation (8), and power rule, Equation (9):

(7)

(8)

(9)

where b is a positive number. Applying first Equation (7) and then Equation (6) to Equation (5) yields:

		(10)

Equation (10) is a useful approximation for computing growth rates that is commonly used by economists.

We use Equation (3) to compute the annual growth rate in Canada’s GDP in the third column of Table 3. We leave it as an exercise to you to compute the growth rate using the approximation in Equation (10). Notice the large variation in the growth rate of the Canadian economy from one year to another; these are transient cyclical changes. While business cycle fluctuations are a very important macroeconomic concept, our focus here is not on the short run but the long run. It is trends that drive the standard of living over time and differences in long-run growth rates determine differences in standards of living across countries.

Table 3: The growth rate in Canada’s real per capita GDP, 2010-2020

Year	Real GDP per capita	Real GDP per capita growth
2010	$51,196
2011	52,449	= 0.0245 or 2.45%
2012	52,636	0.35%
2013	53,738	2.09%
2014	54,707	1.80%
2015	53,606	-2.01%
2016	53,170	-0.81%
2017	54,692	2.86%
2018	55,034	0.63%
2019	54,981	-0.10%
2020	51,524	-6.29%

Data source: Statistics Canada

How many years would it take the GDP to double if it grows at a constant rate of g? If we denote with Y₀ the starting level of output, then we are looking for T, the number of years, such that

(11)

As the economy grows at a constant rate of g, the relationship between the initial output at t = 0 and the output at t = T is given by:

(12)

Substituting Equation (12) into Equation (11) yields:

(13)

To solve for T in Equation (13), take the logarithm of both sides of the equation and apply the power rule of logarithms to obtain:

		(14)

Equation (14) is the “Rule of 70,” a useful approximation used by economists to make quick comparisons, where g is expressed in decimals. Table 4 shows the doubling time at several different growth rates.

The average annual growth rate in per capita real GDP for a developed economy is typically 2 to 3%. A developing economy can grow much faster because it can adopt technology, production practices, and institutions from developed economies. Using the rule of 70, a developed economy will double its output every 25 to 35 years. In contrast, China, a developing economy, has grown at a spectacular average rate of about 7% per year over the last 40 years, thus witnessing a doubling in its standard of living every decade.

Figure 1: Cross-country differences in average real GDP per capita growth rate
over the period 1960 – 2020. Data source: World Bank

Figure 1 shows the average growth rate over the post-World War II period around the globe. The darker the colour, the higher the growth rate. China’s “growth miracle” is an exception, even among developing countries. There are poor countries like Niger and Madagascar with negative average real growth rates over this 60-year period.

Economic growth is important for both rich and poor countries. Over time, small differences in growth rates accumulate to large differences in the standard of living because of the power of compound growth, which works very much the same way as compound interest. Thus, if policy makers can increase growth even by a tiny amount, this would translate into large differences in living standards over the long run.

Microinsurance or inclusive insurance can be defined as the provision of insurance coverage to low-income individuals in developing countries. What is known as “microinsurance” today originates in the practice of microfinance organizations offering credit-life insurance, which covers the outstanding debt in the event of a borrower’s death, to their clients. However, microinsurance has taken on a life of its own and for some time it has existed outside the shadows of its better-known cousin, microfinance.

Microinsurance is another example of a concept where we stumble on a roadblock if we are looking for a generally agreed upon definition. Perhaps the most widely cited definition is coined by Craig Churchill who defines microinsurance as “the protection of low-income people against specific perils in exchange for regular premium payments proportionate to the likelihood and cost of the risk involved.” To complicate matters further, the distinction between traditional, conventional, or mainstream insurance and microinsurance is somewhat blurred. Crucially, the distinction hinges on the market segment served by the insurer with microinsurance targeted at low-income clients who cannot access conventional insurance.

Next, we will look closely at Churchill’s definition to identify the key characteristics of microinsurance, which are summarized in Table 6.

The target group of microinsurance are low-income individuals and households. The typical microinsurance client is a woman who lives in a densely populated urban area or in a remote village of a country with little or no social safety net. She is self-employed or working in the informal sector. With limited or no education, she is either a working poor or faces a high risk of slipping back into poverty. She is severely credit-constrained – with limited or no access to financial products, such as savings account, consumer credit, and traditional insurance. She most likely cannot produce the documents required by the insurer for underwriting or claim payment, such as national identity card, birth, or death certificate. However, she is not destitute – she has some purchasing power that enables her to purchase insurance for at least some of the perils she faces. Those who are destitute and cannot afford to purchase microinsurance are in the purview of the government. This leads us to the second major characteristic of microinsurance: microinsurance is priced based on market principles.

We know from Module 4 that the price of insurance reflects the insurer’s expected loss. We also know from empirical studies that the microinsurance price tends to be higher compared to traditional insurance, other things equal. Putting together our knowledge from Module 4 with our knowledge of the client base for microinsurance, we can reason that the higher price for microinsurance reflects the higher riskiness of the typical microinsurance buyer. However, microinsurance is rarely sold at an actuarially fair premium. The policies carry a premium load, which reflects not only the higher riskiness of the insureds and the limited information the insurance company has on the risk they pose, but also the higher administrative and operating costs such as office building costs, salaries of employees, advertising costs, and commissions to insurance agents and brokers who market the policies to potential clients. Insuring difficult to reach clients, administering small-premium insurance contracts, and making small claim payments is costly for the insurer, which translates into higher premium loads.

Who are the providers of microinsurance products? Both formal and informal insurers offer microinsurance products. Formal insurers such as commercial insurers, cooperatives and mutual funds are regulated and licensed under the laws of the land. They tend to have a dominant market share, but it is informal insurers that dominate the market in number. In Africa, for example, it was commercial insurers who insured about 80% of all lives and properties covered by microinsurance products in 2011. Informal insurers such as community-based organizations and non-governmental organizations (NGOs) are entirely unregulated and unsupervised. While, as we are going to discuss below, these informal insurers serve an important role in the microinsurance market, they tend to have higher insolvency risk than large, regulated insurers, which factors into the premium loading; furthermore, their clients have no consumer protection.

Often, the buyers of microinsurance are unfamiliar with the purpose of insurance and lack the education and financial literacy needed to comprehend its role in risk management as well as the legal jargon and fine print exclusion restrictions in a standard insurance contract. To cater to this specific market niche, microinsurance products should be purposefully designed to be simple; however, this is rarely the case. Microinsurance policies tend to include complex legal language and require premium payments on a regular basis while potential clients do not receive a steady stream of income. Academicians and practitioners find that health insurance and agricultural insurance are the most highly valued covers by low-income individuals for risk management. In contrast, life and credit life insurance have dominated the market. As we noted before, we can trace the origin of what is known today as microinsurance to the practice of microfinance institutions of offering credit-life insurance to their clients. However, credit-life insurance serves predominantly the needs of lenders rather than the underwriting needs of low-income individuals.

Why is there a disconnect between what people want and the products microinsurers offer? Perhaps the key reason is the inability of insurers to estimate the riskiness of low-income people. Underwriting and business decisions are based on the ability of the insurer to estimate the riskiness of an individual and compute the likelihood that a certain event will occur. Insurers rely on historical data to produce mortality, longevity, and morbidity tables. However, such historical data are rarely, if ever, available for the low-income market in developing countries and existing tables must be adapted to reflect the possibly higher riskiness of the potential buyers for microinsurers

While in relative terms, the perils covered by a microinsurance product could be major or even catastrophic from the perspective of the insured, in absolute terms they are minuscule from the perspective of commercial insurers. Nevertheless, microinsurance should not be thought of as just a scaled-down version of conventional insurance; the products offered by microinsurers should be specifically designed to meet the needs of low-income individuals. In theory, microinsurance can cover any insurable risk covered by traditional insurance such as life, credit life, accident, property, health, and agriculture insurance. Furthermore, environmental risks are growing in prominence with climate change and low-income individuals are disproportionately affected by natural disasters while lacking effective risk management solutions to cope. In practice, however, life and credit life insurance dominate the market.

Conventional insurance products are sold by licensed agents and brokers. Because of imperfections on both the demand and supply side of the market, microinsurance products are sold by both licensed and unlicensed intermediaries. On the supply side, licensed insurance agents do not have an incentive to serve the low-income market. Agents work on a commission commensurate with the insurance premium and it is more lucrative to serve the more affluent segment of the market. On the demand side, the widespread lack of trust in institutions in developing countries hampers the development of the microinsurance market. Governments and large corporations are viewed with distrust by those shunned by the formal financial sector. To sell their products, microinsurers often rely on organizations with grassroots knowledge and access to the buyers such as community-based organizations, informal savings and credit groups, and small business associations. Microinsurers have been particularly innovative in identifying non-traditional delivery channels, such as selling insurance through cell phone providers, professional associations, and retailers.

While the term “microinsurance” has been coined only recently, the practice of microinsurance has deep historical roots in developing countries. For example, burial societies have long served as a risk-management mechanism in South-West Africa. Burial societies are a type of co-operative with voluntary membership intended to help cover funeral expenses upon the death of a member or a member’s dependant. Originating in ancient Rome, burial societies have existed throughout history in many parts of the world. A burial society is a mechanism for risk transfer from an individual to the pool of members, which aims at mitigating the financial burdens of death and its associated funeral costs. They are the precursor of funeral insurance, which is widely offered by for-profit insurers nowadays.

It is hoped that microinsurance will follow in the steps of its closely related cousin, microfinance, and become the next “revolution” in developing countries by enabling low-income individuals to break the poverty trap. In the example given at the beginning of this module, life microinsurance could have offered a safety net to the family of the young girl living in Freetown and prevented it from slipping into poverty after the death of the breadwinner.

Emerging from the shadows of microfinance, microinsurance has witnessed an explosive growth over the last decades. In 2006, microinsurance covered 75 million low-income individuals in developing countries all over the world while this number increased to 135 million in 2009 and up to 377 million in 2020.

Figure 6: Percentage of population with microinsurance coverage across income groups in Africa Data source: MicroInsurance Centre at Milliman

In Africa, for example, data collected by the MicroInsurance Centre at Milliman suggest that microinsurance was offered in 20 countries in 2005. This number grew to 32 in 2008 and 36 in 2014. Figure 6 shows the percentage of population with microinsurance coverage or the microinsurance coverage ratio across income groups in Africa for 2005, 2008, 2011, and 2014. Microinsurance is more prevalent in upper-middle income countries (South Africa, which has high microinsurance coverage ratio, was not included in the 2005 sample). While, on average, less than 0.5% of the African population was covered by microinsurance in 2005, this number grew to more than 3.5% in 2011 and reached about 6% in 2014. Microinsurance is still not offered in many countries in the continent. However, in Namibia and South Africa more than 50% of the population purchased microinsurance coverage in 2011. In Namibia, the microinsurance take-up rate grew from 0 in 2005 to 6.7% in 2008 and to a staggering 57.5% of the population in 2011.

Figure 7: Percentage of population with microinsurance coverage across regions in Africa Data source: MicroInsurance Centre at Milliman

Figure 7 shows the microinsurance coverage ratio across African regions. The microinsurance coverage ratio varies substantially across geographic regions. Microinsurance is less prevalent in North and Central Africa compared to the rest of the continent.

There is a large potential market for microinsurance in developing countries. However, the current outreach of microinsurance is far below its global market potential estimated by Swiss Re at 4 billion people that could generate premium income of $40 billion. This modest reach of microinsurance relative to its potential has left academicians, practitioners, and policymakers alike with what we call in economics a “puzzle,” observed economic data or empirical findings which are inconsistent with theoretical predictions.

Can microinsurance foster economic growth? It is not one of the factors driving the long-term development of the economy that we discussed in Section 3. Yet, as an aspect of financial inclusion microinsurance features prominently in the global development agenda. While financial inclusion is not a Sustainable Development Goal (SDG) by itself, according to the UN, it is “positioned prominently as an enabler of other developmental goals” such as eradicating poverty (SDG 1), ending hunger (SDG 2), ensuring healthy lives (SDG 3), achieving gender equality (SDG 5), promoting economic growth (SDG 8), supporting industry, innovation and infrastructure (SDG 9), reducing inequality (SDG 10), and strengthening the global partnership for achieving the SDGs (SDG 17). In this section, we will examine the channels through which microinsurance can foster economic growth.

United Nations poster of 17 Sustainable Development Goals (SDGs)

We start by noting that microinsurance is a financial service and therefore, the broader question is whether there is a link between financial markets and institutions and economic growth. Even though microinsurance and finance more broadly are not among the factors affecting growth directly, microinsurance can impact economic growth indirectly through its effects on the accumulation of technological knowledge, physical and human capital.

The link between financial markets and institutions and economic growth has been extensively studied both theoretically and empirically. Theorists disagree whether it is financial development that fosters economic growth (supply-led growth) or the other way around, economic growth spurs the development of financial markets and institutions (demand-following growth). Further, theorists postulate that the finance-growth nexus depends on the economy’s level of development. We would expect to observe supply-led growth in low-income economies and demand-following growth in high-income economies. That is, the expansion of financial markets drives real economic growth at early states of economic development while economic growth drives the expansion of financial markets in mature economies. Thus, as a financial service, microinsurance can play an important role in the economic development of low-income economies. Here, we will focus on the demand side of the market and examine the risk-coping strategies available to the poor in developing countries in the absence of microinsurance to understand the channels through which the purchase of microinsurance coverage can have a lasting impact on the aggregate economy.

Drought, famine, disease, and crop failures – there are myriads of risks faced by the poor in developing countries. Their income is not only low but also very volatile. If they are to consume what they earn, their consumption will be very volatile as well. However, according to the life-cycle theory of consumption developed by Prof. Franco Modigliani, laureate of the 1985 Nobel Memorial Prize in Economics, we prefer a smooth consumption pattern over our lives rather than volatile consumption, which closely tracks our income. Consumption timing is one of the most important roles financial markets play in the economy. By borrowing, saving, and dissaving at different times in our lives, we can smooth our consumption path and improve our overall satisfaction. However, conventional mechanisms for consumption smoothing, such as borrowing and lending in formal financial markets, are not available to the poor to buffer income swings. More often than not, low-income individuals in developing countries either lack access to existing financial markets or such markets simply do not exist.

When households face risks with large potential losses and no access to borrowing, they have a strong precautionary demand for liquid savings to prepare for emergency expenditures. However, the accumulation of liquid assets is a difficult undertaking for low-income individuals and most of the world’s poor have very low or non-existent savings to mitigate negative income shocks. As a result, their consumption more closely tracks their income pattern. A large negative income shock, such as the death of the breadwinner in the introductory example of this module, is typically followed by a large reduction in consumption. This may entail withdrawing children from school, ignoring health needs, reducing calorie intake and dietary quality to a level detrimental to human health and well-being. It may also entail the sale of productive assets, the postponement of required repairs or investment in new assets thus eroding both human and physical capital accumulation in the long run.

How do low-income individuals smooth their consumption without access to formal financial markets? We distinguish between two types of risk-mitigation strategies available for consumption-smoothing: ex ante risk management and ex post risk coping strategies. Ex post mechanisms aim directly at consumption smoothing during a crisis while ex ante risk coping strategies aim at the advanced deployment of strategies to smooth income downturns and therefore, indirectly consumption before a crisis strikes.

In the absence of formal insurance and limited or non-existent public safety net, informal risk-sharing arrangements become vital in ex post risk mitigation. Borrowing, intergenerational or interfamily transfers in the form of loans, cash or in kind can help a household weather a downturn in income. Community-based insurance mechanisms, such as the burial societies discussed in Section 4.1, can help a family smooth consumption in the presence of an idiosyncratic shock, i.e., a shock which affects the particular family but not the community in general such as an accident that renders a specific breadwinner unable to work. However, when income shocks are correlated so that the incomes of households in a community move in a tandem over time, community-based risk pooling arrangements are less effective in mitigating risks.

Informal risk-sharing arrangements cannot be considered insurance because they are typically reciprocal in nature. Unwritten, but well understood customs and traditions dictate that the beneficiary of community support today reciprocates this aid in the future. This reciprocal nature of informal insurance mechanisms creates the possibility of corruption and nepotism if the aid recipient today is in a power position in the future. Corruption stifles investment and therefore, the accumulation of capital, which can have a negative effect on economic growth.

To a large extent, ex ante risk management strategies depend on the ex-post risk coping mechanisms available to a household. For example, a large and better socially integrated household with significant asset holdings and access to formal financial markets may feel well insulated against an income downturn. However, even a small income shock can be detrimental for a poor and socially excluded household. Decision-makers in such a household devise strategies ex ante to smooth their income so as to smooth their consumption. This can affect both the choice of occupation as well as savings and investment decisions, ceteris paribus. For example, an empirical study based on western Tanzania in 1990 finds that poorer households derive a larger share of their income from off-farm activities and raising crops compared to more affluent families. However, these activities tend to have a lower expected return compared to the riskier cattle raising. The author rules out risk aversion and comparative advantage as potential explanations for this behavior but attributes it instead to income risk and the inability of low-income households to borrow when needed to smooth consumption.

Therefore, the purchase of microinsurance coverage, which reduces income uncertainty, has the potential to encourage low-income individuals to take up occupations which are more in line with their comparative advantage and risk tolerance thus accelerating human capital accumulation and possibly, productivity growth. Further, microinsurance can enable credit-constrained entrepreneurs to invest in profitable but risky ventures, such as modernizing their farm production or introducing new production technologies, which require a long learning period, thus channeling assets to their most productive uses, accelerating the rate of physical capital accumulation and, therefore, economic growth.

Inequality and economic growth are intricately linked. “[T]he conventional textbook approach is that inequality is good for incentives and therefore good for growth.” However, there is mounting empirical evidence to the contrary. Ex ante risk management has a perverse effect on the income distribution in a country over time. By investing in low-risk, low-return activities, poorer households perpetuate poverty and widen the gap between the rich and the poor. In contrast, by enabling the poor to enter higher-risk, higher-return occupations, enlarge their existing businesses or start up new businesses, microinsurance may help close the gap between the rich and the poorer, and therefore, accelerate economic growth.

In summary, there are many channels through which microinsurance can impact economic growth. However, capturing the effect of microinsurance on growth in the data is challenging for three major reasons. First, the impact of microinsurance, particularly through human capital accumulation, may take years to be reflected in official growth statistics. Second, many if not most of the poor are not employed in the formal sector and carry out a limited amount of market transactions. Therefore, their contribution to the aggregate output may not be tracked by official government statistics. Finally, data on microinsurance take-up rates are difficult to find.

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