1.2 Module 61: Consumption and Investment Spending

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Consumer Spending

Should you splurge on a restaurant meal or save money by eating at home? Should you buy a new car and, if so, how expensive a model? Should you redo that bathroom or live with it for another year? In the real world, households are constantly confronted with such choices—not just about the consumption mix, but also about how much to spend in total. These choices, in turn, have a powerful effect on the economy: consumer spending normally accounts for two-thirds of total spending on final goods and services. In particular, the decision about how much of an additional dollar in income to spend—the marginal propensity to consume—determines the size of the multiplier, which determines the ultimate effect on the economy of autonomous changes in spending.

But what determines how much consumers spend?

Current Disposable Income and Consumer Spending

The most important factor affecting a family’s consumer spending is its current disposable income—income after taxes are paid and government transfers are received. It’s obvious from daily life that people with high disposable incomes on average drive more expensive cars, live in more expensive houses, and spend more on meals and clothing than people with lower disposable incomes. And the relationship between current disposable income and spending is clear in the data.

The Bureau of Labor Statistics (BLS) collects annual data on family income and spending. Families are grouped by levels of before-tax income, and after-tax income for each group is also reported. Since the income figures include transfers from the government, what the BLS calls a household’s after-tax income is equivalent to its current disposable income.

Figure 61-1 is a scatter diagram showing the relationship between household current disposable income and household consumer spending for American households by income group in 2012. For example, point A shows that in 2012 the middle fifth of the population had an average current disposable income of $46,777 and average spending of $43,004. The pattern of the dots slopes upward from left to right, making it clear that households with higher current disposable income had higher consumer spending.

For each income group of households, average current disposable income in 2012 is plotted versus average consumer spending in 2012. For example, the middle income group, with an annual income of $36,134 to $59,514, is represented by point A, indicating a household average current disposable income of $46,777 and average household consumer spending of $43,004. The data clearly show a positive relationship between current disposable income and consumer spending: families with higher current disposable income have higher consumer spending.

Source: Bureau of Labor Statistics.

It’s very useful to represent the relationship between an individual household’s current disposable income and its consumer spending with an equation. The consumption function is an equation showing how an individual household’s consumer spending varies with the household’s current disposable income. The simplest version of a consumption function is a linear equation:

where lowercase letters indicate variables measured for an individual household.

People with high disposable incomes tend to spend more than those with lower disposable incomes.

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In this equation, c is individual household consumer spending and yd is individual household current disposable income. Recall that MPC, the marginal propensity to consume, is the amount by which consumer spending rises if current disposable income rises by $1. Finally, a is a constant term—individual household autonomous consumer spending, the amount of spending a household would do if it had zero disposable income. We assume that a is greater than zero because a household with zero disposable income is able to fund some consumption by borrowing or using its savings.

Notice, by the way, that we’re using y for income. That’s standard practice in macroeconomics, even though income isn’t actually spelled “yncome.” The reason is that I is reserved for investment spending.

Recall that we expressed MPC as the ratio of a change in consumer spending to the change in current disposable income. We’ve rewritten it for an individual household as Equation 61-2:

Multiplying both sides of Equation 61-2 by Δyd, we get:

Equation 25-3 tells us that when yd goes up by $1, c goes up by MPC × $1.

Figure 61-2 shows what Equation 61-1 looks like graphically, plotting yd on the horizontal axis and c on the vertical axis. Individual household autonomous consumer spending, a, is the value of c when yd is zero—it is the vertical intercept of the consumption function, cf. MPC is the slope of the line, measured by rise over run. If current disposable income rises by Δyd, household consumer spending, c, rises by Δc. Since MPC is defined as Δc/Δyd, the slope of the consumption function is:

The consumption function relates a household’s current disposable income to its consumer spending. The vertical intercept, a, is individual household autonomous consumer spending: the amount of a household’s consumer spending if its current disposable income is zero. The slope of the consumption function line, cf, is the marginal propensity to consume, or MPC: of every additional $1 of current disposable income, MPC × $1 is spent.

In reality, actual data never fit Equation 61-1 perfectly, but the fit can be pretty good. Figure 61-3 shows the data from Figure 61-1 again, together with a line drawn to fit the data as closely as possible. According to the data on households’ consumer spending and current disposable income, the best estimate of a is $18,478 and of MPC is 0.520. So the consumption function fitted to the data is:

The data from Figure 61-1 are reproduced here, along with a line drawn to fit the data as closely as possible. For American households in 2012, the best estimate of the average household’s autonomous consumer spending, a, is $18,478 and the best estimate of MPC is approximately 0.520.

Source: Bureau of Labor Statistics.

That is, the data suggest a marginal propensity to consume of approximately 0.52. This implies that the marginal propensity to save (MPS)—the amount of an additional $1 of disposable income that is saved—is approximately 0.48, and the multiplier is approximately 1/0.48 = 2.08.

It’s important to realize that Figure 61-3 shows a microeconomic relationship between the current disposable income of individual households and their spending on goods and services. However, macroeconomists assume that a similar relationship holds for the economy as a whole: that there is a relationship, called the aggregate consumption function, between aggregate current disposable income and aggregate consumer spending. We’ll assume that it has the same form as the household-level consumption function:

Here, C is aggregate consumer spending (called just “consumer spending”); YD is aggregate current disposable income (called, for simplicity, just “disposable income”); and A is aggregate autonomous consumer spending, the amount of consumer spending when YD equals zero. This is the relationship represented in Figure 61-4 by CF, analogous to cf in Figure 61-3.

Panel (a) illustrates the effect of an increase in expected aggregate future disposable income. Consumers will spend more at every given level of aggregate current disposable income, YD. As a result, the initial aggregate consumption function CF₁, with aggregate autonomous consumer spending A₁, shifts up to a new position at CF₂ and aggregate autonomous consumer spending A₂. An increase in aggregate wealth will also shift the aggregate consumption function up. Panel (b), in contrast, illustrates the effect of a reduction in expected aggregate future disposable income. Consumers will spend less at every given level of aggregate current disposable income, YD. Consequently, the initial aggregate consumption function CF₁, with aggregate autonomous consumer spending A₁, shifts down to a new position at CF₂ and aggregate autonomous consumer spending A₂. A reduction in aggregate wealth will have the same effect.

Shifts of the Aggregate Consumption Function

The aggregate consumption function shows the relationship between disposable income and consumer spending for the economy as a whole, other things equal. When things other than disposable income change, the aggregate consumption function shifts. There are two principal causes of shifts of the aggregate consumption function: changes in expected future disposable income and changes in aggregate wealth.

Changes in Expected Future Disposable IncomeSuppose you land a really good, well-paying job on graduating from college in May—but the job, and the paychecks, won’t start until September. So your disposable income hasn’t risen yet. Even so, it’s likely that you will start spending more on final goods and services right away—maybe buying nicer work clothes than you originally planned—because you know that higher income is coming.

Conversely, suppose you have a good job but learn that the company is planning to downsize your division, raising the possibility that you may lose your job and have to take a lower-paying one somewhere else. Even though your disposable income hasn’t gone down yet, you might well cut back on spending even while still employed, to save for a rainy day.

Both of these examples show how expectations about future disposable income can affect consumer spending. The two panels of Figure 61-4, which plot disposable income against consumer spending, show how changes in expected future disposable income affect the aggregate consumption function. In both panels, CF₁ is the initial aggregate consumption function. Panel (a) shows the effect of good news: information that leads consumers to expect higher disposable income in the future than they did before. Consumers will now spend more at any given level of current disposable income, YD, corresponding to an increase in A, aggregate autonomous consumer spending, from A₁ to A₂. The effect is to shift the aggregate consumption function up, from CF₁ to CF₂.

Panel (b) shows the effect of bad news: information that leads consumers to expect lower disposable income in the future than they did before. Consumers will now spend less at any given level of current disposable income, YD, corresponding to a fall in A from A₁ to A₂. The effect is to shift the aggregate consumption function down, from CF₁ to CF₂.

Changes in Aggregate WealthImagine two individuals, Maria and Mark, both of whom expect to earn $30,000 this year. Suppose, however, that they have different histories. Maria has been working steadily for the past 10 years, owns her own home, and has $200,000 in the bank. Mark is the same age as Maria, but he has been in and out of work, hasn’t managed to buy a house, and has very little in savings. In this case, Maria has something that Mark doesn’t have: wealth. Even though they have the same disposable income, other things equal, you’d expect Maria to spend more on consumption than Mark. That is, wealth has an effect on consumer spending.

The effect of wealth on spending is emphasized by an influential economic model of how consumers make choices about spending versus saving called the life-cycle hypothesis. According to this hypothesis, consumers plan their spending over a lifetime, not just in response to their current disposable income. As a result, people try to smooth their consumption over their lifetimes—they save some of their current disposable income during their years of peak earnings (typically occurring during a worker’s 40s and 50s) and during their retirement live off the wealth they accumulated while working.

Consumers tend to avoid high-priced brand-name goods and seek out less expensive alternatives when aggregate wealth declines.

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We won’t go into the details of the life-cycle hypothesis but will simply point out that it implies an important role for wealth in determining consumer spending. For example, a middle-aged couple who have accumulated a lot of wealth—who have paid off the mortgage on their house and already own plenty of stocks and bonds—will, other things equal, spend more on goods and services than a couple who have the same current disposable income but still need to save for their retirement.

Because wealth affects household consumer spending, changes in wealth across the economy can shift the aggregate consumption function. A rise in aggregate wealth—say, because of a booming stock market—increases the vertical intercept A, aggregate autonomous consumer spending. This, in turn, shifts the aggregate consumption function up in the same way as does an expected increase in future disposable income. A decline in aggregate wealth—say, because of a fall in housing prices—reduces A and shifts the aggregate consumption function down.

Investment Spending

Although consumer spending is much larger than investment spending, booms and busts in investment spending tend to drive the business cycle. In fact, most recessions originate as a fall in investment spending. Figure 61-5 illustrates this point; it shows the annual percent change of investment spending and consumer spending in the United States, measured in real terms, during six recessions from 1973 to 2009. As you can see, swings in investment spending are much more dramatic than those in consumer spending. In addition, due to the multiplier process, economists believe that declines in consumer spending are usually the result of a process that begins with a slump in investment spending. Soon we’ll examine in more detail how a slump in investment spending generates a fall in consumer spending through the multiplier process.

The bars illustrate the annual percent change in investment spending and consumer spending during six recent recessions. As the lengths of the bars show, swings in investment spending were much larger in percentage terms than those in consumer spending. This pattern has led economists to believe that recessions typically originate as a slump in investment spending.

Source: Bureau of Economic Analysis.

Before we do that, however, let’s analyze the factors that determine investment spending, which are somewhat different from those that determine consumer spending. The most important ones are the interest rate and expected future real GDP.

It’s also important to note that the level of investment spending businesses actually carry out is sometimes not the same level as planned investment spending, the investment spending that firms intend to undertake during a given period. Planned investment spending depends on three principal factors: the interest rate, the expected future level of real GDP, and the current level of production capacity. First, we’ll analyze the effect of the interest rate.

The Interest Rate and Investment Spending

Interest rates have their clearest effect on one particular form of investment spending: spending on residential construction—that is, on the construction of homes. The reason is straightforward: home builders only build houses they think they can sell, and houses are more affordable—and so more likely to sell—when the interest rate is low.

Consider a potential home-buying family that needs to borrow $150,000 to buy a house. At an interest rate of 7.5%, a 30-year home mortgage will mean payments of $1,048 per month. At an interest rate of 5.5%, those payments would be only $851 per month, making houses significantly more affordable. As described in the upcoming Economics in Action, lower interest rates helped set off the great housing boom described in the section-opening story.

Interest rates directly impact whether or not construction companies decide to invest in the building of new homes.

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Interest rates also affect other forms of investment spending. Firms with investment spending projects will only go ahead with a project if they expect a rate of return higher than the cost of the funds they would have to borrow to finance that project. As the interest rate rises, fewer projects will pass that test, and as a result investment spending will be lower.

You might think that the trade-off a firm faces is different if it can fund its investment project with its past profits rather than through borrowing. Past profits used to finance investment spending are called retained earnings. But even if a firm pays for investment spending out of retained earnings, the trade-off it must make in deciding whether or not to fund a project remains the same because it must take into account the opportunity cost of its funds. For example, instead of purchasing new equipment, the firm could lend out the funds and earn interest. The forgone interest earned is the opportunity cost of using retained earnings to fund an investment project.

So the trade-off the firm faces when comparing a project’s rate of return to the market interest rate has not changed when it uses retained earnings rather than borrowed funds, which means that regardless of whether a firm funds investment spending through borrowing or retained earnings, a rise in the market interest rate makes any given investment project less profitable. Conversely, a fall in the interest rate makes some investment projects that were unprofitable before profitable at the now lower interest rate. So some projects that had been unfunded before will be funded now.

So planned investment spending—spending on investment projects that firms voluntarily decide whether or not to undertake—is negatively related to the interest rate. Other things equal, a higher interest rate leads to a lower level of planned investment spending.

Expected Future Real GDP, Production Capacity, and Investment Spending

Suppose a firm has enough capacity to continue to produce the amount it is currently selling but doesn’t expect its sales to grow in the future. Then it will engage in investment spending only to replace existing equipment and structures that wear out or are rendered obsolete by new technologies. But if, instead, the firm expects its sales to grow rapidly in the future, it will find its existing production capacity insufficient for its future production needs. So the firm will undertake investment spending to meet those needs. This implies that, other things equal, firms will undertake more investment spending when they expect their sales to grow.

Now suppose that the firm currently has considerably more capacity than necessary to meet current production needs. Even if it expects sales to grow, it won’t have to undertake investment spending for a while—not until the growth in sales catches up with its excess capacity. This illustrates the fact that, other things equal, the current level of productive capacity has a negative effect on investment spending: other things equal, the higher the current capacity, the lower the investment spending.

If we put together the effects on investment spending of growth in expected future sales and the size of current production capacity, we can see one situation in which we can be reasonably sure that firms will undertake high levels of investment spending: when they expect sales to grow rapidly. In that case, even excess production capacity will soon be used up, leading firms to resume investment spending.

What is an indicator of high expected growth of future sales? It’s a high expected future growth rate of real GDP. A higher expected future growth rate of real GDP results in a higher level of planned investment spending, but a lower expected future growth rate of real GDP leads to lower planned investment spending. This relationship is summarized in a proposition known as the accelerator principle.

As we explain in the upcoming Economics in Action, when expectations of future real GDP growth turned negative, planned investment spending—and, in particular, residential investment spending—plunged, accelerating the economy’s slide into recession. Generally, the effects of the accelerator principle play an important role in investment spending slumps, periods of low investment spending.

Inventories and Unplanned Investment Spending

Most firms maintain inventories, stocks of goods held to satisfy future sales. Firms hold inventories so they can quickly satisfy buyers—a consumer can purchase an item off the shelf rather than waiting for it to be manufactured. In addition, businesses often hold inventories of their inputs to be sure they have a steady supply of necessary materials and spare parts. At the end of 2012, the overall value of inventories in the U.S. economy was estimated at $1.5 trillion, just over 10% of GDP.

A firm that increases its inventories is engaging in a form of investment spending. Suppose, for example, that the U.S. auto industry produces 800,000 cars per month but sells only 700,000. The remaining 100,000 cars are added to the inventory at auto company warehouses or car dealerships, ready to be sold in the future.

Inventory investment is the value of the change in total inventories held in the economy during a given period. Unlike other forms of investment spending, inventory investment can actually be negative. If, for example, the auto industry reduces its inventory over the course of a month, we say that it has engaged in negative inventory investment.

To understand inventory investment, think about a manager stocking the canned goods section of a supermarket. The manager tries to keep the store fully stocked so that shoppers can almost always find what they’re looking for. But the manager does not want the shelves too heavily stocked because shelf space is limited and products can spoil.

Firms undertake high levels of investment spending when they expect the economy to grow rapidly. If an economic slump is expected, they do the opposite.

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Similar considerations apply to many firms and typically lead them to manage their inventories carefully. However, sales fluctuate. And because firms cannot always accurately predict sales, they often find themselves holding more or less inventories than they had intended. These unintended swings inventories due to unforeseen changes in sales are called unplanned inventory investment. They represent investment spending, positive or negative, that occurred but was unplanned.

So in any given period, actual investment spending is equal to planned investment spending plus unplanned inventory investment. If we let I_Unplanned represent unplanned inventory investment, I_Planned represent planned investment spending, and I represent actual investment spending, then the relationship among all three can be represented as:

To see how unplanned inventory investment can occur, let’s continue to focus on the auto industry and make the following assumptions. First, let’s assume that the industry must determine each month’s production volume in advance, before it knows the volume of actual sales. Second, let’s assume that it anticipates selling 800,000 cars next month and that it plans neither to add to nor subtract from existing inventories. In that case, it will produce 800,000 cars to match anticipated sales.

Now imagine that next month’s actual sales are less than expected, only 700,000 cars. As a result, the value of 100,000 cars will be added to investment spending as unplanned inventory investment.

The auto industry will, of course, eventually adjust to this slowdown in sales and the resulting unplanned inventory investment. It is likely that it will cut next month’s production volume in order to reduce inventories. In fact, economists who study macroeconomic variables in an attempt to determine the future path of the economy pay careful attention to changes in inventory levels. Rising inventories typically indicate positive unplanned inventory investment and a slowing economy, as sales are less than had been forecast. Falling inventories typically indicate negative unplanned inventory investment and a growing economy, as sales are greater than forecast.

INTEREST RATES AND THE U.S. HOUSING BOOM

The housing boom in the Ft. Myers metropolitan area, described at the beginning of this section, was part of a broader housing boom in the country as a whole. There is little question that this housing boom was caused, in the first instance, by low interest rates.

Figure 61-6 shows the interest rate on 30-year home mortgages—the traditional way to borrow money for a home purchase—and the number of housing starts, the number of homes for which construction is started per month, from 1997 to early 2013, in the United States. Panel (a), which shows the mortgage rate, gives you an idea of how much interest rates fell. In the second half of the 1990s, mortgage rates generally fluctuated between 7% and 8%; by 2003, they were down to between 5% and 6%. These lower rates were largely the result of Federal Reserve policy: the Fed cut rates in response to the 2001 recession and continued cutting them into 2003 out of concern that the economy’s recovery was too weak to generate sustained job growth.

Source: Freddie Mac; Federal Reserve Bank of St. Louis.

The low interest rates led to a large increase in residential investment spending, reflected in a surge of housing starts, shown in panel (b). This rise in investment spending drove an overall economic expansion, both through its direct effects and through the multiplier process.

Unfortunately, the housing boom eventually turned into too much of a good thing. By 2006, it was clear that the U.S. housing market was experiencing a bubble: people were buying housing based on unrealistic expectations about future price increases. When the bubble burst, housing—and the U.S. economy—took a fall. The fall was so severe that even when the Fed cut rates to near zero, and mortgage rates consequently dropped to below 5% beginning in 2009, housing starts merely stabilized. It wasn’t until 2012 that housing starts began to increase again.

Module 61 Review

Check Your Understanding

List the three most important factors affecting planned investment spending. Explain how each is related to actual investment spending.