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					Development and Transition I
         What do we mean by
“By the problem of economic development I mean simply
the problem of accounting for the observed pattern,
across countries and across time, in levels and rates of
growth of per capita income. This 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, so I would suggest
that we withhold judgment on the scope of this definition
until we have a clear idea of where it leads us.”
                                      Robert E. Lucas (1988)
          What do we mean by
“[W]e should never lose sight of the ultimate
  purpose of the exercise, to treat men and
  women as ends, to improve the human
  condition, to enlarge people’s choices… A unity
  of interest would exist if there were rigid links
  between economic production (as measured by
  income per head) and human development
  (reflected by human indictors such as life
  expectancy or literacy or achievements such as
  self-respect, no easily measured). But these two
  sets of indicators are not very closely related.
                                  Paul P. Streeten [1994]
         How do we Measure
1. Level of Income: GNP and

  – What is GNP and how is it measured?

  – Why are GNP and GNP/Capita the most
    commonly used measures?

  – What are the criticisms of these measures?
Some Classifications of Developing
   Countries Based on GNP:
United Nations (within the third world):
• least developed countries (44)
• developing nations (88, non-oil exporting)
• OPEC (13)

World Bank
• low-income              (per cap GNP < $785 in 1997)
• middle-income           (per cap GNP $786-$3,126 in 1997)
• upper-middle-income     (per cap GNP $3,127-$9,655 in 1997)
• high-income             (per cap GNP >$9,656 in 1997)

• LIC’s – Low-income countries (within which LLDC’s)
• MIC’s – Middle-income countries
• NIC’s -- newly industrializing countries
GDP per Capita, PPP

1999 current international U.S. Dollars

                                                                                   C. & E . Eu ro pe & Ce ntra l A s ia

        High -Inc o m e                                                                            Ea st A s ia & the P a cific

                      5,109                                                               So uth A s ia
                                                        Su b- Sa har an A fr ica
                      2,115               Latin A m er ic a & C ar ibbe an          Mid dle E as t & N or th Afr ic a

Source: World Bank, World Development Indicators 2001; and William Davidson
Institute calculations
Growth in Real GDP per Capita
1990 - 2000
 Average annual % change



4                       3.49


2                                      1.72           1.54                    Sub-      C. & E.
                                                                             Saharan   Europe &
1                                                                             Africa    C. Asia

     East Asia & South Asia           High        Middle East    Latin        -0.50
       Pacific                       Income        & North    America &
-2                                                  Africa    Carribbean                -1.78

Source: World Bank, World Development Indicators 2001; World Bank, Global
Economic Prospects 2002; and William Davidson Institute calculations
Real GDP Index

 (1989 Base Year)

Source: William Davidson Institute based on various EBRD Transition Reports,
OECD Economic Outlook Vol. 69 July 2001; and Davidson Institute calculations
    2. Social Indicators as Alternative
        Measures of Development:

Physical Quality of Life Index (PQLI)
  Developed by M. D. Morris. Based on an unweighted
  average of each of the following indices going from 1
  to 100 (with 100 being “best”):
   – Life Expectancy at Age 1
       Index =1 for lowest value (28 in Guinea-Bissau in
         1950) ;
       Index = 100 for highest value (77 in Sweden in
   – Infant mortality
       (Scaled 1 to 100 with upper limit of 9/1000
         achieved in Sweden, etc.)
   – Literacy (Actual percent)
Human Development Index (HDI)
  Undertaken by the UNDP and found in the Human
  Development Report (annual series, begun in 1990).
  Ranks countries on a scale of 0 (worst) to 1 (highest
  level of human development) based on 3 goals:
   – Longevity (life expectancy at birth)
   – Knowledge (weighted average of adult literacy (66%)
     and mean years of schooling (33%)
   – Standard of Living (real per capita income in PPP)
             In the end…
• There is a fairly close correspondence
  between the PQLI, HDI and GDP/capita
Infant Mortality Rate

Per 1,000 live births

                    High Income             8.1

      C. & E. Europe & C. Asia                        27.7

             East Asia & Pacific                           39.9

 Latin America & Carribbean                                  40.9
  Middle East & North Africa                                           60                        1990
                       South Asia                                                  86.8

            Sub-Saharan Africa                                                           101.5

                                       0          20      40          60    80       100

Source: World Bank, Global Economic Prospects 2002; and William Davidson
Institute calculations
Illiteracy Rate

Adult Total (% of people ages 15 and above)

            South Asia                                                                        53.8

         Sub-Saharan                                                           39.4
            Africa                                                                          51.2

        Middle East &                                                       35.8
        North Africa                                                                  47

           East Asia &                           14.9
             Pacific                                     21.7

     Latin America &                          11.9
       Carribbean                                 15.6                                               1999

    C. & E. Europe &              3.5
         C. Asia                   4.6

                            0            10         20           30           40       50

Source: World Development Indicators 2001; and William Davidson Institute
Life Expectancy at Birth

Total (years)

                     High Income                                                          76.2

 Latin America & Carribbean                                                      67.9

              East Asia & Pacific                                                67.4

      C. & E. Europe & C. Asia                                                     69.3

  Middle East & North Africa                                                  64.6

                                                                          62.6                 1999
                        South Asia                                   59
             Sub-Saharan Africa                        49.9

                                        40            50            60            70

Source: World Bank, World Development Indicators 2001; and William Davidson
Institute calculations
Share of World Income and Population
by Region
Constant 1995 U.S. dollars

                                                              1989                        1999
                                                     Regional %     Regional %     Regional %   Regional %
                                                       of World       of World       of World     of World
                                                           GDP             Pop           GDP           Pop

      High Income                                         80.9              16.2       79.7         15.0
      East Asia & Pacific                                   4.2             31.3        6.6         30.7
      Latin America & Caribbean                             5.7              8.3        5.9          8.5
      C. & E. Europe and C. Asia                            5.1              9.0        3.1          7.9
      Middle East & North Africa                            1.6              4.5        1.8          4.9
      South Asia                                            1.4             21.3        1.8         22.2
      Sub-Saharan Africa                                    1.2              9.5        1.1         10.8

Source: World Development Indicators 2001; and William Davidson Institute
  Main stylized facts on growth of per capita
                 income (PCI)
• fact 1
   1) Enormous variation in per capita income
     (PCI) across economies - for example,
     poorest countries in the world have pci with
     less than 5% of pci in richest countries

We see this in table 1 (taken from Jones)
                            Table 1
                        “Rich countries”
Country        GDP per     GDP per      Labor force     Avg. Annual   Years
(GDP data in   cap. 1997   Worker       participation   growth rate   to
1985 U.S. $)   $U.S.       1997 $U.S.   rate 1997       1960-97       double
               (PPP)       (PPP)

U.S.A          20,049      40,834       0.49            1.4           50

Japan          16,003      25,264       0.63            4.4           16

France         14,650      31,986       0.46            2.3           30

U.K.           14,472      29,295       0.49            1.9           37

Spain          10,685      29,396       0.36            3.5           20
                 Table 1, continued
                  “Poor” Countries
Country    GDP per     GDP per      Labor force     Avg. Annual   Years
           cap. 1997   Worker       participation   growth rate   to
           $U.S.       1997 $U.S.   rate 1997       1960-97       double
           (PPP)       (PPP)

China      2,387       3,946        0.60            3.5           20

India      1,624       4,156        0.39            2.3           30

Zimbabwe   1,242       2,561        0.49            0.4           192

Uganda     697         1,437        0.49            0.5           146
                 Table 1, continued
                 “Growth Miracles”
Country     GDP per     GDP per   Labor force     Avg.          Years
            cap. 1997   Worker    participation   Annual        to
            $U.S.       1997      rate 1997       growth rate   double
            (PPP)       $U.S.                     1960-97
Hong        18,811      28,918    0.65            5.2           13

Singapore   17,559      36,541    0.48            5.4           13

Taiwan      11,729      26,779    0.44            5.6           12

South       10,131      24,325    0.42            5.9           12
                    Table 1, continued
                    “Growth Disasters”
Country     GDP per     GDP per   Labor force     Avg. Annual   Years
            cap. 1997   Worker    participation   growth rate   to
            $U.S.       1997      rate 1997       1960-97       double
            (PPP)       $U.S.
Venezuela   6,760       19,455    0.35            -0.1          -517

Madagasc    577         1,334     0.43            -1.5          -46

Mali        535         1,115     0.48            -0.8          -85

Chad        392         1,128     0.35            -1.4          -48
    How to measure income levels in
      international comparisons?

• Column 1 shows GDP per capita with purchasing power
  parity (PPP) exchange rate – explains why U.S. is
  richest country in the world. Simple example of PPP
  exchange rate: Economist’s exchange rates based on
  prices of McDonald’s Big Mac around the world. PPP
  exchange rate based on prices of many goods.
     How to measure income levels in
       international comparisons?
• Column 2 shows GDP per worker. We get this by dividing GDP per
  capita by Labor Force participation rate (column 1 / column 3)
  GDP per worker often taken as a productivity measure (i.e. output
  produced per worker), while GDP per capita taken as welfare
  measure (output that can be used for consumption and investment
  per person).
  BUT, case can be made for using GDP per worker as measure to
  compare welfare across countries,
  because home production and shadow economy activities are not
  included in official GDP, while GDP per worker gives measured
  output divided by measured input.
  Income levels in international perspective

• Table 1 shows rich versus poor countries in the first two
  panels – tremendous differences in GDP per capita
  between e.g. U.S. and Uganda: U.S. is roughly 29 times
  richer than Uganda on this measure.
• Since labor force participation is the same in both
  countries, the main reason for this great divergence
  cannot be the effort that a society puts into economic
  activity (we can take labor force participation as a rough
  measure of “effort”). Instead, technology drives these
  differences as we shall see later.
Income levels in international perspective,
• Figure 1 shows that in 1995 more than half the world’s
  population has a GDP per worker that is less or equal to
  10% of the U.S. GDP per worker.
• Most of these people live in China (1/4 of world
  population) and India (1/6 of world population).
• Figure 2 shows how that the distribution of world income
  has become somewhat more equal since 1960. There
  has been a fall in the share of world population living in
  countries with low percentages of US GDP per worker
  and a rise in the share of world population living in
  countries with high percentages of US GDP per worker.
Figure 1
Figure 2
     Main stylized facts on growth (fact 2)

2) Rates of economic growth vary substantially across

Last two columns of table 1 are directly related to growth.
Average growth rates – change in ln (GDP/worker) – vary
  widely among the shown countries. Within the groups
  shown there are also great differences as there are
  between groups (e.g. growth miracles vs. growth
 Main stylized facts on growth (fact 2), continued

• Last column shows how long it takes to double income.
Lucas introduced the rule of thumb that 70/g gives t* ,
   where t* is the time it takes to double income and g is
   the growth rate (in percentage)
{y(t)=y0egt shows income at time t as a function of initial
   income (y0) and an exponential growth rate g (as a
   We have t* when y(t)= 2y0, so 2y0= y0egt*
     t*= log2/g, Since log20.7 and multiplying by 100, we

   get Lucas’ rule of thumb: t*= 70/g}
 Main stylized facts on growth (fact 2), continued

• The main message from the last column of table
  1 is that small differences in growth rates if they
  persist over even moderate time spans can lead
  to very large differences in per capita income.
     Main stylized facts on growth (fact 3)

3) Growth rates are not generally constant over time.
   For the world as a whole growth rates were close to
   zero for most of history, but increased sharply in the
   20th century. Individual countries show changing
   growth rates over time.

Figure 1.3 shows world GDP per capita since 1500. Note
   the very flat slopes between 1500 and 1850, with growth
   accelerating in second half of 19th century and taking off
   in 20th century. World per capita GDP was roughly $500
   in 1500 and was in 2000 about 10 times as large.
Figure 3
 Main stylized facts on growth (fact 3), continued

A simple thought experiment shows that the world could not have
   grown at a rate of 2% since e.g. 1750. Assume that it had
   actually grown at such a rate, then every 35 years its per capita
   income would have doubled.
    over 250 years income would have roughly risen by a factor
   of 27 = 128  if in 2000 per capita GDP of the world were
   $20,000 it would have been $150 in 1750 (at prices of 2000).
   But today even the poorest country has a per capita income of
   $300.  even over 250 years the world’s per capita GDP could
   not have grown at a rate of 2%;  given the actual per capita
   GDP of the world of about $5000 there must have been long
   stretches of ~ zero growth in human history.
{Note: we do the thought experiment because we do not have
   really reliable data going back much beyond 1850.}
 Main stylized facts on growth (fact 3), continued

• Examples of varying average annual growth
  rates of GDP per worker within countries
India: 1960-1997: 2.3%
       1960-1980: 1.3%
       1980-1997: 3.5%
China: 1960-1997: 3.5%
        1960-1979: 1.9%
        1979-1997: 5.0% (!market-oriented
       Main stylized facts on growth (fact 4)

4) A country’s relative position in the world’s
  distribution of per capita incomes can
  change over time.
Countries that are “poor” can become “rich”, e.g.
  the NICs in table 1;
Countries that are “rich” can become “poor”, e.g.
  Argentina – one of the richest countries in the
  world at beginning of 20th century, now having
  only 1/3 of U.S. per capita income.
Poverty and Inequality
        Poverty and Inequality
• Change in focus: from countries to individuals
• Distribution of income (or wealth) within
  countries [given level of country income (wealth)]
• Reasons to be interested in inequality of income
  and wealth distribution:
  – Philosophical and ethical
  – Functional (impact on growth)
• Potential caveats:
  – Flows vs. stocks
  – Functional vs. personal income distribution
Measures of Poverty and Inequality
• We use measures of poverty and
  inequality to make judgments about
  changes in social welfare of a society.
• Each of these measures reflect different
  values -- important that we make them
• Article by Blackwood and Lynch
                Poverty line
• Early discussions centered on minimum income
  necessary to sustain physical existence.
• Evolved to reflect changes in living standards:
  minimum income level required to purchase the
  socially determined essentials for living
• Problems in defining poverty lines (income as
  poor proxy, differences across regions and
  nations – e.g. cultural)
• Absolute vs. relative poverty line (Examples: 1$
  per day PPP; half of the mean income in a
           Four Categories:
1.   Absolute Poverty Measures
2.   Relative Poverty Measures
3.   Absolute Income Measures
4.   Relative Income Measures
  1. Measures of Absolute Poverty
• The Headcount (H=q/n)
   Value judgment: Poverty falls when the number of income
     recipients below P* (q) falls relative to the number of people in
     the population (n)
   Problem: fails to gauge extent of poverty and income distribution
     among the poor
• The Poverty Gap (I=P*- )
   Value judgment: the smaller the gap between the average income
     of the poor () and P*, the less poverty.
   Problem: fails to give number of poor and income distribution
     among the poor
• Income Distribution among the poor
    Value judgment: for the same number of poor and the same
      average income level among the more, the more unequal the
      distribution, the more severe poverty is [e.g. Lorenz curve]
    Problem: fails to gauge level of malaise
Composite Poverty Measures
  1) Sen poverty index

  S = H [I + (1 – I) Gp].
  I = ∑ (z – yi/qz);the average income shortfall as a
       i=1       percentageof the poverty line
  yi = income of the ith poor household
  z = poverty line
  qz = number of households with incomes < z
  H = q/n; headcount ratio
  n = total number of households
  Gp = Gini coefficient among the poor
  0 ≤ Gp ≤ 1

  ∆S/∆H > 0
  ∆S/∆I > 0
  ∆S/∆Gp > 0
            Sen poverty index, continued.

• Axioms:
  – Focus (dependent only on income of the poor)
  – Monotonicity (increases when incomes of the
    poor decrease – only poverty gap)
  – Weak transfer (sensitive to changes in income
    distribution of the poor – higher utility for the
• Index somewhat biased towards policies
  reducing number of poor (more responsive
  to improvement in headcount).
2) Pa Measures (Foster, Greer and Thorbecke, 1981)
  Pa = (1/n) [ ∑ (gi /z)a]

  where: a ≥ ø
       n = total number of households
       gi = poverty gap of the ith household
       q = number of households below the poverty line
       z = poverty line

  If a = 0, then Pa is equal to the headcount ratio.
  If a = 1, Pa is equal to the headcount times the average income
  As a > 1, income distribution becomes more important in
  measuring Pa.
           Pa measure of poverty, continued.

• Satisfy Sen’s three axioms and include factors
  sensitive to changes in inequality, changes in
  inequality, in income shortfall and in the number
  of the poor.
• It explicitly incorporates the idea of consistency
  between the values underlying a poverty
  measure and the values of the policy maker. By
  selecting a specific value of a, the policy maker
  can influence both the nature of the bias and the
  degree of bias in the measure.
 2. Relative Poverty Measures
• Segment that is poor in relation to the income of
  the general population. (Not based on a discrete
  line.) Examples:
  – Average income of the poorest 20%
  – Those who earn less than half of the mean income

• Not so useful if concerned with alleviating the
  degree of immiseration and/or reducing the
  number of those suffering, but providing useful
  informatin on changes in the degree of inequality
3. Absolute Income Measures:
• Value Judgment: what is important is the
  overall welfare of a population, not the
  welfare of the poor.
• Asses social welfare as a function of the
  individual incomes within a society.
• Growth of income = welfare improvement.
  – Ex. Growth of GDP
       Growth in GDP or GNP as a
      measure of welfare improvement
                                  Whose welfare?

      G = w1g1 + w2g2 + w3g3 + w4g4 + w5g5
      G = index of growth of total social welfare
      w = weight assigned to an income group
      g = the growth of the income of a population group (of whole nation or an
      ethnic group, etc.) Assume that we are referring to quintile groups for a
      nation, then:

Choosing different weights we can obtain different results:
If w = income share of each quintile, get the growth rate of GDP.
     (example of a country like Brazil: highest quintile has 50% of wealth and
    lowest quintile has 10% of wealth; if income of highest quintile also grows
    faster …)
If w = population weights, i.e., w=0.2?
If w = highest for lowest income group?
    4. Relative Income Measures
• Are used to indicate the degree of inequality in
  income distributions and provide no information
  on the degree of absolute poverty
• Fractile Measures:
    – Share of income of the lowest 10%
    – Ratio of share of income of highest 10% to lowest
•   Lorenz Curve
•   Gini Coefficient
•   Generalized Lorenz Curve
•   Atkinson Index
      Four criteria for inequality
1. Anonimity principle
2. Population principle (constant shares)
3. Relative income principle (constant
4. Dalton principle (obtaining a distribution
   using transfers from “not richer” to “not
The Lorenz curve satisfies the these four
   criteria in addition to the three previously
   discussed Sen principles.
      Generalized Lorenz curve
         (Shorrocks, 1983)
• Takes the standard Lorenz curve and
  scales it by the average income of the
• Underlying assumption: preference not
  only for equity but also efficiency (i.e.
  higher real incomes)
• To be able to order more distributions
  gives up the scale independence principle
  Generalized Lorenz Curve, cont.
• GL(y,p)=L(y,p)
• Where: GL(y,p)= Generalized Lorenz
  curve for income distribution y whose
  population size is p
• L = Lorenz curve
•  = mean income of distribution y
                      Atkinson Index
                                1 / 1e 
          n
 I  1    y i / y  f i 

          i 1             
• Where:
yi=avg. Income of the i-th income range
n=number of income ranges
fi=proportion of the population in the i-th income range
y=mean income
e=aversion to inequality as specified by the researcher

• Conforms to the four criteria outlined before.
• It makes explicit value judgments (incorporated in Atkinson’s index
  via the parameter e.

  (0 = income distribution unimportant; 1 = Rawls maxmin principle –
   only the condition of the poorest is considered in determining
   changes in social welfare)
         Atkinson index, cont.
• Interpretation: assume there is a non-egalitarian
  distribution A. We can then distribute the same
  total income so that Gini coefficient becomes
  equal to zero and the total welfare remains
  constant (distribution B). If we have preference
  for equality, a lower total income will be needed
  to achieve the same level of welfare.
 yb<ya for same level of welfare.
• The Atkinson index measures this reduction in
  total income in percentage terms.
• Example: 0.2 means that by redistributing
  income we can attain the same level of welfare
  with 20% less income.
    Methodology is Important
• How you study poverty and income
  distribution matters as it may lead to
  fundamentally different judgments about
  the success or failure of economic growth
  to improve welfare:
• Example of different uses
Ex 1: Relative Income Inequality
                                      Income share of
                   Rate of growth of    poorest 40%
    Country       national income (%) Level % change
Both countries
initially                                      0.363
Country A
later                          9               0.333   -8
Country B
later                         18               0.317   -15
                 Country A Better Off than Country B
Ex 2: Absolute Poverty Approach
                    % of labor force in
                                        Rate of growth of high-
                 High-wage Low-wage wage jobs ("modern
                  jobs (real jobs (real sector labor absorption
    Country      wage = $2) wage = $1)         rate") (%)
Both countries
initially            10            90
Country C
later                20            80                  100
Country D
later                30            70                  200

                 Country D Better Off than Country C
    Ex 3: Relative Poverty Approach

                                    Absolute income of poorest
        Country                       40% of population ($)
Both countries initially                        4
Country E later                                 4
Country F later                                 4

                 Country E as well off as Country F
               Bottom Line
• Countries A, C and E are the same country
• Countries B, D and F are the same country
• 10 people where some working in low wage
  economy and others in high wage economy
          Initial Distribution: (1111111112)
           Countries A,C,E: (1111111122)
           Countries B,D,F: (1111111222)
    Inequality and development
• The presence of inequality affects the way an economy
  works and, through this, our capability to achieve our
• We may expect this to be even more important for
  developing countries, where we have limited resources
  distributed unequally.
• We start from a situation in which assets are already
  distributed (more or less equally) among individuals, as a
  result of processes that have taken place in the past.
  These assets can be exchanged in the marketplace and
  used in the productive process.
• What is interesting to see is:
   – whether inequalities worsen or narrow over time;
   – How are income, employment, wealth and growth rates affected
     by inequality;
   – How do these variables affect the evolution of inequality.
            Inverted-U hypothesis
• Kuznets (e.g. 1955, 1963) studied the ratio of the income share of
  the top 20% of the distribution and of the bottom 60% of the
  distribution for several countries.
• On the basis of his empirical findings he suggested a broad
  hypothesis of development: economic progress, expressed in per
  capita income is initially associated with increasing inequality that
  decreases as benefits of development reach all groups.


                                           Income per capita
Inverted-U hypothesis: evidence
• The ideal test for this hypothesis would be to follow the
  evolution of per capita income and inequality tracking
  several countries over time. Unfortunately, available data
  do not allow such an analysis.
• In practice: analysis of cross section of countries at
  different stages of development (useful but with a limit –
  we have to assume that the countries “behave in the
  same way” – also quantitatively – and differ only in their
  level of development)
• Results: crude evidence in favor of the hypothesis BUT
  with serious doubts.
   – wide variation WITHIN income categories  inverted-U NOT
   – Existence of notable exceptions:
   – Possibility of “statistical effect” (Lorenz curves crossing all the
     time, Gini coefficient going up and down)
Digital Divide…

Per 1,000 people

1000           960


 700                                                                                 1999



 300                          260
 200                    167
                                        100                                   100
 100                                                    46             59
                                                                                     12     25   12   19
          High          C. & E.          Latin       East Asia &    Middle East     South Asia    Sub-
         Income       Europe and      America &        Pacific       & North                     Saharan
                        C. Asia       Carribbean                      Africa                      Africa

Source: World Bank, World Development Indicators 2001; and William Davidson
Institute calculations
Personal Computers

Per 1,000 people



200                                                                                   1995



 50                           39              38
                        18              20                   17               25
                                                         7              13             8       2   3
         High           C. & E.          Latin       East Asia & Middle East        Sub-     South Asia
        Income        Europe and      America &        Pacific    & North          Saharan
                        C. Asia       Carribbean                   Africa           Africa

Source: World Bank, World Development Indicators 2001; and William Davidson
Institute calculations
Internet Access

Hosts per 10,000 people

450                                                                                1999





                         2.3 15.5       1.2 14.8        0.3 2.4        0.1 0.4   0.8 2.3    0.0 0.2
          High          C. & E.     Latin   East Asia & Middle East               Sub-     South Asia
         Income       Europe and America &    Pacific    & North                 Saharan
                        C. Asia  Carribbean               Africa                  Africa

Source: World Bank, World Development Indicators 2001; and William Davidson
Institute calculations

 • The rich/poor income gap has increased

 • Poverty remains a major problem, especially in declining and
   stagnating economies

 • The poor countries
        -have less human and physical capital
        -face a technology gap
        -suffer from poor governance, a lack of institutional
         development, and limited access to world markets
                   In Sum:
“The central economic problems of all societies
include traditional questions such as what, where,
how, how much and for whom goods and serves
should be produced.
     But they should also include the fundamental
question at the national level about who actually
makes or influences economic decisions and for
whose principal benefit these decisions are made.
     Finally, at the international level, it is necessary
to consider the question of which nations and which
powerful groups within nations exert the most
influence with regard to the control, transmission, and
use of technology, information and finance.

Moreover, for whom do they exercise this power?”
                                        Todaro (2000, p. 19)
RECITATION: Referring to David Landes’
article, “Why are we so rich and they so
• Why have the sanguine predictions that high rates of
  growth would enable the less developed economies to
  catch up in a short time with their richer predecessors
  been disappointed?

• Is lateness an advantage for the follower countries?

• If the gap between the rich and poor keeps growing,
  does that mean we have to change the paradigm?

• Where does this leave us?

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