Hall&Jones: Why Do Some Countries Produce So Much More

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Ifo Institute for Economic Research at the University of Munich

Hall&Jones: Why Do Some Countries ProduceSo Much More Output per Worker than Others?

1. A Simple Model of Human Capital

2. Empirics 1: Can Human Capital Account for the

Observed Cross-Country Differences?

3. Empirics 2: Estimating the Effect of SocialInfrastructure

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Social Infrastructure as Underlying Reason for Economic Growth (1)

• In the first part of their paper, Hall and Jones show that humancapital is not the whole story.

• But what else then determines economic growth?

• There is no doubt that people react to incentives: Does the set of rules, institutions and policies in a country encourage investment and production over (socially unproductive) rent-seeking?

• This set of rules, institutions and policies is called social infrastructure.

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• It is useful to divide social infrastructure into three different

groups:

– The first group consists of features of the government’s

fiscal policy. For example, the tax treatment of investment

and the allocation of government spending between

investment and the allocation of government spending

between investment projects and other spending directly

affect allocation between investment and consumption.

– The second group of institutions and policies that

make up social infrastructure consists of factors that

determine the environment that private decisions are

made. For example, if crime is unchecked or there is civil

war or foreign invasion, private rewards to investment and

to activities which raise overall output are low….

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…Similarly, at more fundamental level, if contracts are not

enforced or the courts’ interpretation of them is

unpredictable, long term investments are less attractive.

– The final group of policies consists of rent-seeking activities

by the government itself. As Hall and Jones stress,

although well designed government policies can be an

important source of beneficial social infrastructure, the

government can be a rent-seeker. Government

expropriation, the solicitation of bribes, and the doling out of

benefits in response to lobbying or to actions that benefit

government officials can be important forms of rent-

seeking.

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• Regarding Institutions and policies, which are the main components

of social infrastructure, one can state that:

– Institutions are the man-made rules that shape human

behaviour.

– Institutions and policies that favour production encourage

people to engage in the creation and the transaction of

goods and services.

– Diversion means an institutional framework with incentives

against production, and in favour of redistribution.

– Diversion may be illegal (theft, corruption, or the payment of

“protection money”) or legal (expropriation, taxation, lobbying).

– Production or diversion is primarily determined by the

government, which is often a chief agent of diversion.


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Measurement of Social Infrastructure (1)

• The ideal measure of social infrastructure would quantify

the wedge between the private return to productive

activities and the social return to such activities.

• In practice, however, there does not exist a useable

quantification of wedges between private and social returns,

either for single countries or for the large group of countries

considered in this study.

• As a result, Hall and Jones must rely on proxies for social

infrastructure and recognize the potential measurement

error.

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• Hall and Jones form their measure of social infrastructure

by combining two indexes:

– The first one is a measure of government

antidiversion policies (GADP) created from the data

assembled by a firm that specializes in providing

assessments of risk to international investors, Political

Risk Services.

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– The second one captures the extent to which a country is

open to international trade using the Sachs-Warner index

(Sachs and Warner, 1995). The index measures the fraction

of years during the period 1950-1994 that the economy has

been open and is measured on a [0,1] scale.

A country is open if it satisfies all of the following criteria:

- nontariff barriers cover less than 40 percent of trade

- average tariff rates are less than 40 percent

- any black market premium was less than 20 percent

during the 1970’s

- the country is not classified as socialist by Kornai (1992)

- the government does not monopolize major exports

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• In most of the results presented, Hall and Jones impose the

restrictions (after testing) that the coefficients of these two

proxies for social infrastructure are the same.

• Hence, Hall and Jones focus primarily on a single index of

social infrastructure formed as the average of the GADP and

openness measure.

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The Model (1)

εβα ++= SL/Ylog

• To examine the quantitative importance of differences in

social infrastructure as determinants of income differences

across countries, Hall and Jones hypothesize the following

structural model:

ηθδγ +++= XL/YlogS

and

where S denotes social infrastructure and X is a collection

of other variables.

,

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The Model (2)

• There are two important aspects:

– First, social infrastructure is quite likely an endogenous variable. Economies are not exogenously endowed with the institutions and

incentives that make up their economic environments, but rather social infrastructure is determined endogenously, perhaps

depending itself on the level of output per worker in an economy.

– Second, we do not observe social infrastructure directly and use a

proxy variable computed as the sum of GADP and the openness variable, normalized to a [0,1] scale. This proxy for social

infrastructure is related to true social infrastructure through random measurement errors.

• To address both issues, the measurement errors as well as the

endogeneity concerns, Hall and Jones apply an instrumental

variable approach.

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Instruments (1)

• The instruments of Hall and Jones are various correlates of the

extent of western European influence.

• Hypothesis:

– Western European countries expanded around the world between

16th and 19th century.

– They brought with them the ideas of Adam Smith, the importance of property rights, and the system of checks and balances.

– This is taken exogenous to the estimation problem (X variables).

• Instruments used:

– language

– distance from the equator

– predicted trade share (does not fit so well here but is often used)

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Instruments (2)

• Language:

– Direct measure of European influence

– The fraction of a country’s population speaking one of the five

primary Western European languages (including English) as mothertongue

– The fraction of a country’s population speaking English as mother

tongue

– They therefore allow English and the other languages to have

separate impacts.

• Distance from the equator:

– Europeans did not like to settle where it is hot ☺

– absolute value of latitude in degrees divided by 90 to place it on a 0

to 1 scale.

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Instruments (3)

• Predicted trade share

– This is an often-used instrument constructed by Frankel and Romer (1996)

– It is the log predicted trade share of an economy, based on a

gravity model of international trade that only uses a country’s population and geographical features.

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Results (1)

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Results (2)

• The main specification in the preceding table reports the

results from the instrumental variable estimation of the

effect of a change in social infrastructure on the log of

output per worker.

• The point estimate indicates that a difference of 0.01 in the

social infrastructure measure is associated with a

difference in output per worker of 5.14 percent. With a

standard error of 0.508, this coefficient is estimated with

considerable precision.

• Calculations of Hall and Jones yield that differences in

social infrastructure can account for a 25.2-fold difference

in output per worker across countries.

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Results (3)

• For comparison, observe that output per worker in the

richest country (the United States) and the poorest country

(Niger) in the data set differ by a factor of 35.1.

• Hall and Jones conclude that the results indicate that

differences in social infrastructure account for much of the

difference in long-run economic performance throughout

the world, as measured by output per worker.

• Countries most influenced by Europeans in past centuries

have social infrastructures conducive to high levels of

output per worker, as measured by the variables, and, in

fact, have high levels of output per worker.

• This evidence means that infrastructure is a powerful

causal factor promoting higher output per worker.

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Policy Implications

• Good institutions are necessary for higher level of income

• Hence, poor countries need policy reform

– rule of law

– anti-diversion policies

• Development aid without reforms will probably have to

effect

• Big question:

– How to make such a reform???


3.4 Environment/Geography

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Why Environment/Geography?

• Social infrastructure seems to be sufficient to explain cross-country differences

• But: it is striking that some regions of the world, especially Sub-Saharan Africa, are particularly poor These countries

face challenges that have to do with their geographic

situation: it is hot and humid which is a good environment for certain diseases.

• Diseases may reduce the productivity and deter

investment (both local and foreign).

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The Importance of Geography/Environment:

Kai Carstensen and Erich Gundlach (2006). The Primacy of Institutions Reconsidered: Direct Income Effects of

Malaria Prevalence, World Bank Economic Review 20(3), 309–339.

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Diseases and Growth (1)

• Diseases have obvious detrimental effect on health and

productivity

• They may also scare off foreign investors and traders

• Example: malaria (especially in Sub-Saharan Africa)

• The WHO believes that “malaria is one of the major public health

challenges that may undermine the development in the poorest

countries in the world”.

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Diseases and Growth (2)

• Number of estimated deaths, world-wide per year (most

recent estimates)

• Malaria: 1 million

• Tuberculosis: 1.8 million

• HIV/AIDS: 3 million

• Smoking 4 million

• Traffic injuries 1.2 million

• (Sources: WHO, UNAIDS, The Economist)

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A First Look: Economic Performance and Malaria

Note: GDP per working-age person in logs, ca 1990. — Risk of malaria transmission measured as the

percentage of the population living in areas of high malaria risk, involving three largely non-fatal species of the malaria pathogen, in 1994.— 130 countries. Source: Hall and Jones (1999), Sachs

(2003), own calculations.

6

7

8

9

10

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0,0 0,2 0,4 0,6 0,8 1,0

GDP per working-age person

Risk of malaria transmission

United StatesGermany

Ethiopia

Zaire

R² = 0.62

0.0 0.2 0.4 0.6 0.8 1.0

Singapore

Hong Kong

Niger

Burkina Faso

Burundi

Mali

Iceland

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Economic Performance and Social Infrastructure

Note: GDP per working-age person in logs. — Index of social infrastructure includes measures of government

anti-diversion policies (law and order, bureaucratic quality, corruption, risk of expropriation, government

repudiation of contracts) and a measure of trade openness (from Sachs and Warner(1995)). — 130 countries, entries refer to about 1990. Source: Hall and Jones (1999), own calculations.

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7

8

9

10

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0,0 0,2 0,4 0,6 0,8 1,0

GDP per working-age person

Social infrastructure

United States

Germany

Ethiopia

Zaire

R² = 0.57

0.0 0.2 0.4 0.6 0.8 1.0

Iceland

Singapore

Hong Kong

Niger

Burkina Faso

BurundiMali

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Policy Question

• Is higher spending on the fight against malaria a good idea?

or

• Is it ineffective as long as the right institutions are missing

in poor countries?

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Empirical Approach (1)

• Think of diseases as another shift variable for per capita income

• Use a model where log GDP per capita depend on institutions and malaria:

• LNGDPC: real GDP per capita, 1995 (World Bank)

• INSTITUTIONS: average governance indicator based on 6 categories (Kaufmann et al. 2003)

• MALARIA: proportion of a country’s population that lives with risk of malaria transmission, 1994 (Sachs 2003)

iii MALARIANSINSTITUTIOLNGDPC ⋅+⋅+= 321 βββ

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Income Level

Institutions Disease ecology

Instrumental variables

(2) (1)

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• We need instruments

• Good instruments must be

• uncorrelated with the error term (exogeneity)

• well correlated with the explanatory variable (relevance)

• Think of it in terms of structural equations:

1 2 3

1 1

2 2

1

2

i i i

i i i

i i i

LNGDPC INSTITUTIONS MALARIA

INSTITUTIONS LNGDPC INSTRUMENT

MALARIA LNGDPC INSTRUMENT

β β β

α γ

α γ

= + ⋅ + ⋅

= ⋅ + ⋅

= ⋅ + ⋅

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• Instrument for malaria risk: malaria stability index

• Sachs (2002) suggest a Malaria Stability Index (MSI) to derive a measure of the potential for malaria transmission.

• The MSI combines– climatic factors with

– the robustness and the biting behavior of the locally dominant Anopheles mosquito, and

– the human population density.

• Hence the MSI is only based on the biology and the potential for malaria transmission, not on actual transmission, and thus appears to be a plausible IV for actual malaria prevalence (which may be endogenous).

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Malaria Stability Index

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• Instrument for institutions: should be an exogenousvariation in institutions that is not affected by current

income levels

• Idea (Acemoglu, Johnson, Robinson, 2001)

1. institutions are quite persistent, so look back in history

2. confine to former European colonies

3. analyze how early 19th century settlers shaped institutions

4. exploit this exogenous variation in institutions

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• Implementation

– Regions with low mortality were favored for settlement, and colonies of settlers may have implemented for themselves a set of institutions that resembled the institutions of their home countries by establishing property rights, the rule of law, etc

– In regions where large-scale settlement was not feasible for Europeans because of an unfavorable disease ecology and high rates of mortality, the colonial powers may have imposed a different set of institutions that did not protect private property and did not provide protection against expropriation but instead focused mainly on the extraction of natural resources.

– Hence, use settler mortality in the early 19th century as the instrument for institutions.

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Results

– output per capita differs by a factor of about 60 across our sample (USA vs. Tanzania)

– institutions:→ USA = 1.73→ Tanzania = -1.35→ institutions explain difference by a factor of

exp(0.9*(1.73-(-1.35))) = 21.8

– malaria prevalence:→ USA = 0.0→ Tanzania = 0.95→ malaria explains difference by a factor of exp(-1*(0-0.95)) = 2.6

– a residual factor of 1.06 cannot be explained

( ) ( )

1

1

0.9 INSTITUTIONS 1.0 MALARIA

0.9 INST INST 1.0 MAL MAL

LNGDPC 0.9 INSTITUTIONS 1.0 MALARIA

GDPC

GDPC

GDPC

i i i

USA Tan USA Tan USA Tan

i i i i

u

i

u uUSA

Tan

u

e e e e

e e e

β

β⋅ − ⋅

⋅ − − ⋅ − −

= + ⋅ − ⋅ +

⇒ = ⋅ ⋅ ⋅

= ⋅ ⋅

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Policy Implications (1)

– Let us assume output per capita in the US is 100 and our model is

correct

– Then output per capita in Tanzania is 100/60 = 100/(21.8*2.6*1.06)

= 1.7% of that in the US

– Assume Tanzania would have good institutions: output per capita

would be 100/(2.6*1.06) = 36% of that in the US

– Assume Tanzania would have no malaria: output per capita would

be 100/(21.8*1.06) = 4% of that in the US

– Assume Tanzania would have good institutions and no malaria:

output per capita would be 100/1.06 = 94% of that in the US

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Policy Implications (2)

• The previous message still holds: Good institutions areimportant for higher level of income

• But diseases are detrimental!

• Hence: development aid aimed at health policy and at

reducing the disease ecology may have a positive effect.

Documents

Hall&Jones: Why Do Some Countries Produce So Much More