Economic growth and living standards

Long-Term Growth Trends (US)

Long-Term Growth Trends  Many developing countries in Africa, Central America, and South America stagnated during the 1980s, and have grown slowly since.  They have fallen further behind the United States.

Long-Term Growth Trends  Other formerly low- income nations—Hong Kong, Korea, Singapore, and Taiwan are examples—have grown very rapidly and have caught up or are catching up with the United States

The problem of economic development  Lucas defines it as the problem of accounting for the observed pattern, across countries and across time, in levels and rates of growth of per capita GDP  Motivation: The diversity across countries in per capita income is too great to be believed

The problem of economic development (contd)  Is there some action the govmt of India could take to grow as Japan? If so, what exactly? If not, what is it about the nature of India that makes it so  Consequences for human welfare are staggering. Once one starts to think about them, it is hard to think about anything else!

Alternative measures of development  United Nations Development Programme: Development is about expanding the choices people have to lead lives that they value  The most basic things for human development are:  To lead long and healthy lives  Be knowledgeable  Have resources for a decent standard of living  Participate in the community

Very different income, similar HDI

Our approach  Accounting for differences in GDP is not the only thing that matters  But it certainly is an important one (no extremely poor country has a high HDI)

In the long-run small differences in growth rates matter a lot  Australia was much richer than Japan in 1870  Over Japan grew at an annual rate of 2.5%, while Australia grew at 1.1%  As a result, Japan is now richer than Australia

What happens to GDP under different growth rates?

 Why some countries are rich and some countries are poor (at one point in time)?  What is the engine driving economic growth (what is the force driving the time series behavior of GDP)? Important questions

Methodology  First: accounting decomposition  Identify what makes GDP to be so different across distinct countries  Once we identify the force driving the cross-country disparity we can develop more effective economic policies and better economic models

Growth accounting  GDP is quantity of goods produced in a given period  What determines how many goods can be produced?

Inputs and technical constraints  Inputs  Labor (L)  Machines (capital stock = K)  Technical constraints  Statistical studies suggest gives a good representation of the aggregate technical capabilities of an economy (where Y is maximum output possible given inputs K and L, and  is a positive number smaller than one) gives a good representation of the aggregate technical capabilities of an economy (where Y is maximum output possible given inputs K and L, and  is a positive number smaller than one)

Accounting for the observed differences in output per worker  Growth accounting divides growth in output per worker in two components  Growth in capital per hour of labor (K/L)  Technological change (A)  Implication of the above formula: Any growth not accounted for by growth in capital is allocated to technological change, so this category is a broad catchall concept.

Conclusions  That different countries have different levels (or growth rates) of output per worker can only be due to  Differences in the level (or growth) of capital per worker (K/L)  or differences in TFP  What is more important?  Empirical question that can only be answered by looking at each country’s data

Growth Accounting (intuitive graphical view)  Productivity Curve: relationship between real GDP per hour of labor and the amount of capital per hour of labor, with technology held constant.

Growth Accounting  An increase in capital per hour brings a movement along productivity curve.  Technological change shifts the productivity curve.  Only two things matter: Capital per hour, and technological change

Growth Accounting  The shape of the productivity curve reflects the law of diminishing returns.  The law of diminishing returns states that, as the quantity of one input increases with the quantities of all other inputs remaining the same, output increases but ever smaller increments.

Diminishing returns

One third rule (empirical regularity)  Robert Solow discovered that diminishing returns are well described by the one-third rule: with no change in technology, on the average, a 1 percent increase in capital per hour of work brings a one- third of 1 percent increase in output per hour of labor.  Example: Assume technology remains fixed and the capital stock of the US economy increases by 30%, what would you expect will happen to output per hour as a result of this increase in capital?

Growth Accounting: An application to the US economy  The productivity function and one-third rule can be used to study productivity growth in the United States.

Growth Accounting  From 1963 to 1973 a large increase in productivity (output per hour) resulted from rapid technological change and a modest increase in capital per worker.

Growth Accounting  From 1973 to 1983 productivity growth slowed because the pace of technological change slowed down.  Capital per worker continues to grow at a similar pace to that of the previous decade.

How to achieve faster growth: conclusions from the graphical approach  Growth accounting tell us that to achive faster economic growth we must either increase the growth rate of capital per hour of labor or increase the pace of technological advance.  What policies may achieve faster growth?

Resources are scarce  Objective now: determine which policy will have the largest positive impact on GDP per person (or on its growth rate).  Data + quantitative analysis are required to answer

Growth accounting (quantitative apporach)  One simple tool. Use the properties of logarithms. In particular recall that ln(x)-ln(y) percentage differencebetween x and y ln(x)-ln(y) percentage differencebetween x and y  Example: If ln(GDP1)-ln(GDP2)=0.02 that means GDP1 is approximately 2% larger than GDP2

Accounting for cross-country ALP disparity

Which in words means: Percentage difference between x and y’s ALP = Percentage difference between x and y’s TFP +  *(percentage difference between x and y’s capital)