the restructuring of economies under imf...

Sonali Mehta-Rao Senior Honors Thesis

Spring 2010

The Restructuring of Economies under IMF Programs: A Sectoral Analysis

The conditions incorporated into the International Monetary Fund (IMF)’s lending programs remain a controversial subject of study because of their strong implications for post-colonial economic development and stability. Most global studies have taken a generalist approach, primarily to determine whether or not the IMF has a positive impact on overall economic growth, but economists have failed to reach a strong consensus. This study will instead look at how IMF programs affect the distribution of labor and capital to different sectors of a nation’s economy. The findings indicate that IMF programs consistently restructure economies, leading to a service sector growth and decline in agricultural output. They also decrease the size of the industrial workforce relative to the other sectors of the economy and increase manufacturing exports as a percentage of total merchandise exports.

Sonali Mehta-Rao

Senior Honors ThesisSpring 2010

2

The graph below shows changes in output by sector in the Dominican Republic from

1966 to 2000. As illustrated by the graph, all three sectors stay about the same size for a long

period of time. Suddenly, shortly after the Dominican Republic enters its first IMF agreement,

we see a significant spike in the size of the service sector, while both agricultural and industrial

production suddenly decline. Is the case of the Dominican Republic an isolated example, or do

IMF programs have these same effects across the board?

In answering this question, this paper offers a concrete assessment of the systematic

structural consequences of IMF programs. While the overall impact of IMF programs on overall

economic growth remains controversial and difficult to assess because it is such a broad topic,

this more focused approach allows us to see definitive significant results regarding the impact of

IMF programs on the three sectors of the economy—Agriculture, Industry, and Services.

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The findings indicate that agricultural output declines significantly and the service sector

expands. While the volume of industrial output does not change significantly as a result of IMF

programs, industrial employment is shown to decrease, and manufactures exports (representative

of the industrial sector) increase. It is also important to note that while these changes in

employment and exports occur in industry, there is no significant comparable effect in the

agriculture or service sectors.

These results have the potential to provide valuable insight into the analysis of the

economic implications of IMF policy. A country’s path to development, especially in an

increasingly global, free-market economy, is heavily influenced by the relative strength and

growth rates of each sector of its economy. The allocation of resources across sectors is likely to

play a large role in determining key exports, income distribution, and the volume of capital

inflows and outflows, among other things.

A restructuring of the economy is also likely to have a social impact by redistributing

income and status across social groups, particularly in countries where certain social or ethnic

groups engage in particular economic activities. This could potentially exacerbate ethno

linguistic tensions. Changes in relative sector size can also drive long-term demographic

changes, such as migration into or out of urban areas depending on the changes in the types of

jobs available.

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Literature Review

The International Monetary Fund (IMF) was established on July 22, 1944 at the Bretton

Woods Conference, with the mandate to monitor and help maintain pegged but adjustable

exchange rates in the industrialized world. It originally began as a fund made up of contributions

from member countries to enable countries facing balance of payments deficits to purchase

foreign exchange. However, it quickly became apparent that market driven exchange rates were

preferred to rates regulated by the IMF, so in the 1970s the IMF moved into its new, more

controversial role of lending to and monitoring the economic activities of the developing world.

In order to better understand the literature, it is important to first get a general idea of the

conditions that are typically attached to IMF loans. Although the IMF structural adjustment

programs vary on a country-to-country basis, there are certain recommendations that are seen in

a majority of cases. Privatization (including the reduction of government subsidies), capital

market liberalization, and deregulation of the private sector are three overarching developmental

goals that the IMF has promoted through its programs in the past. Devaluation, intended to lower

domestic consumption and increase the volume of exports, is another common requirement of

IMF programs. There has been much debate regarding the usefulness of these objectives and

criticism of the implementation strategy.

In the past, the results and impact of IMF programs have been measured using both large

and small-scale studies that focus exclusively on the effects on overall economic growth, income

distribution, and balance of payments. This sectoral approach will provide a more comprehensive

understanding of the economic implications of IMF programs in terms of redistribution of

resources to different segments of society, providing a new perspective on the results of past

studies.

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The original mandate of the IMF was to enhance global economic stability by addressing

Balance of Payments deficits, so the most obvious result to study is the effect of IMF programs

on overall BOP and the current account component of the BOP. There have been many studies

with different approaches that have looked at this problem, and most have come to the

conclusion that IMF programs are helpful in addressing BOP deficits and have a positive impact

on the current account component of the BOP. For example, University of California professor

of Latino Studies at University of California, Manuel Pastor, former IMF economist, Thorvaldur

Gylfason, and former director of the IMF Institute, Mohsin Khan, have all found a statistically

significant positive effect of IMF programs on BOP, particularly the current account of the BOP.

Since the IMF began to shift roles in the 70s, economists and political scientists have

shifted their focus accordingly to study the impact of IMF programs on more complex topics

such as overall economic growth and development. However, according to the literature, there is

less agreement on this topic than BOP, in part because there are many different opinions on how

to define successful development. Some argue that the end goal should be to improve the quality

of life of the country’s inhabitants, while others focus on employment and achieving minimal

living standards. Others define successful development as a shift away from government

regulation to reliance on market principles and export promotion. The ambiguity of the term

“development” in this context has undoubtedly contributed to much of the ambiguity in results.

Thus, there are multiple studies showing that IMF programs promote economic growth but they

have begun to be undermined by recent studies with different models, many of which find a

significant negative effect on economic growth rates.

In the 80s and early 90s, many studies covered this topic, approaching it with different

methodologies. Results were very mixed, many of them statistically insignificant. Khan used a

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more sophisticated approach to address nonrandom selection in his 1990 study, which found a

significant negative effect on growth in the short run, which tapered off as time passed. A later

study by Patrick Conway, economist at University of North Carolina, found that the initial

negative effect became a positive effect after three years. Many economists and political

scientists used this study to argue that countries must endure the negative effects of IMF

programs in the short run in order to benefit in the long run.

Later on, however, another series of studies found a statistically significant negative

effect on growth. Adam Przeworski and Vreeland conducted a study in 2000 that showed a

negative effect on annual output growth of about 1.5 percent for 79 countries between 1971 and

1990. Studies in 2003 by economists Michael Hutchinson and Ivan Noy and 2005 by economists

Robert Barro and Jong-Wha Lee also found that IMF programs have a statistically significant

negative effect in the long run.

The third general area of interest is the effect of IMF programs on income distribution.

The three major studies in this area, conducted by Manuel Pastor, Gopal Garuda, and James

Vreeland, have all found that IMF programs exacerbate income inequality. Vreeland explains the

theoretical reasons for this finding in his book. He points out that devaluation, a common

recommendation of IMF programs, makes it more profitable to produce exports, but makes

imports more expensive for consumers. Depending on the occupations and behavior of the poor,

this could potentially hurt or help them. For example, poor rural farmers who produce products

for export would benefit from devaluation, while poor urban consumers would be hurt by this

change. These studies are also supported by the findings of World Bank economist, William

Easterly, who found that IMF and World Bank programs mute the effects of economic growth

for the poor.

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There is another group of studies focused not on results, but on the procedures driving the

formation of IMF policies. Additional literature seeks to understand the selection process that

determines which countries go under IMF programs and why certain countries receive favorable

treatment. There are also a number of studies that examine patterns in compliance. This literature

is less relevant to my topic from a comparative standpoint, as they deal more with power and

process and less with assessment of end results. However, the studies that examine the power

structures behind IMF decisions are important to consider when drawing conclusions and

considering implications of the results because the interests of the top IMF policy-makers are

likely to be linked to this study’s results.

James R. Vreeland draws on a wide range of studies in order to provide a comprehensive

overview of the IMF, its history, the politics surrounding its actions, and its global impact in his

book, The International Monetary Fund: Politics of Conditional Lending. Among many aspects

of IMF procedure, Vreeland is critical of the inherent structural advantage that the U.S. has

because of its veto-power over the Managing Director, who makes decisions based on a “general

consensus” in the absence of strict voting procedures. This prevents smaller, less powerful

countries from forming a voting block in order to make their voices heard. One particularly

interesting study by Strom Thacker, a political scientist at Boston University, looked at voting

patterns at the United Nations as a determinant of IMF programs and found that countries that

voted with the U.S. were more likely to receive an IMF program than countries that did not.

Another study by Randall Stone finds that countries receiving favorable amounts of U.S. foreign

aid are also likely to receive favorable treatment by the IMF.

In his paper, “IMF conditionality: theory and evidence”, Alex Dreher provides a

background and critical analysis of IMF conditionality. He explores the underlying motives and

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justifications for enforcing conditionality and assesses their success in achieving their stated

goals. He then moves on to attempt to measure overall compliance and to look at the incentives

for governments to comply. He finds that if country authorities are committed to reform,

conditionality is unnecessary and if they are not, conditionality is unhelpful.

Alex Dreher and Nathan Jensen in their paper, “Independent Actor or Agent? An

Empirical Analysis of the Impact of U.S. Interests on IMF Conditions”, put together findings

from multiple studies revealing that the U.S. uses its position of influence in the IMF not only to

push through structural adjustment policies around the world that are in its own economic

interests, but also to use loans and conditionality to create incentives for other countries to

cooperate with U.S. foreign policy goals. Others have asserted that the interests of commercial

U.S. banks also impact the behavior of the IMF disproportionately. Thomas Oatley and Jason

Yackee present evidence that the amount of U.S. bank exposure in a developing country is a

determinant of the size of the IMF loans received and Broz and Hawes find that the bank

exposure of other countries does not have a similar effect.

In the past, the primary focus of these studies has been on overall economic effects.

While there has been a general lack of consensus over the years regarding aggregate

expansionary effects of IMF programs, I will show that the IMF has clear, discernable economic

effects by way of a consistent restructuring of the three sectors of the economy.

The results generated by this sectoral approach will in turn provide a deeper, more

thorough understanding of the economic implications of IMF programs in terms of international

trade and redistribution of resources to different segments of society. This is important

information because a country’s path to development will be different depending on the relative

strength of each sector of its economy. Particularly in an increasingly liberal free-market

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economy, the reallocation of resources from one sector to another will have an effect on

determining key exports and on capital flows into and out of the country.

Hypotheses

The IMF’s structural adjustment programs often contain requirements that are likely to

have very different consequences across sectors. The IMF has been known to push deregulation

by the federal government in favor of rapid privatization as one of its conditions. While

privatization may make the service sector more efficient, there are many cases that show that

rapid privatization of agriculture has met with major problems. In his article, “The Malawi

Model”, Joshua Kurlantzick asserts that privatization of agriculture has resulted in food

shortages across the developing world, as private traders do not have the power and resources to

respond to food emergencies and governments have seen their food reserve stocks dwindle,

leaving little staple foods left for emergencies. As a point of contrast, he uses the example of

Malawi, a small country in southern Africa that was able to avert food shortages by ignoring the

donor countries and putting into place a government-controlled agricultural subsidy program.

Capital market liberalization is also likely to immediately benefit industry and services

(particularly financial services) by opening the door to foreign investment in these areas, but is

not likely to increase investment in agricultural development. The IMF also puts pressure on

governments to change spending patterns, which is likely to have strong sectoral implications.

For example, in accordance with the free-market ideology of its top economists, the IMF

agreements often end up cutting agricultural subsidies as well.

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Hypothesis: Countries that enter an IMF program will experience a consistent restructuring of

the economy in which one (or two) particular sector(s) grows in size and resources while the

other sector(s) shrink. For example, IMF programs might decrease the size of the industrial and

agricultural sectors and increase the share of services produced by all economies that enter

programs. It is also possible that it promotes agricultural growth and service sector growth at the

expense of services. There are a number of possible combinations. I compare this against the

null, which states that IMF programs do not cause any statistically significant, consistent

restructuring of economies.

Data Description

I have determined that output (relative to overall output), employment (relative to overall

employment), and exports (relative to overall exports) are the best available indicators of a

particular sector’s relative strength in the economy. Using these indicators we will be able to

monitor changes in workforce, size, and levels of production of each sector.

Independent variables

1. UnderIMF: binary variable where 0 indicates that a given country is not under any IMF

programs during a given year and 1 indicates that a country is under one or more IMF

programs, but may or may not have started the program during that year

2. StartIMF: binary variable where 0 indicates that a country has not started a new IMF

program during a given year or the previous year, but may or may not already be under

an IMF program, and 1 indicates that a country has just started an IMF program during

the given year or the previous year.

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Dependent variables

In order to measure the effects of IMF programs on the agricultural, industrial, and service

Sectors, I have chosen to use variables measuring value added, exports, and employment levels

within each sector. These three measures will be most effective way to examine relative sector

strength because they give us an idea of relative production output, economic value, and size (in

terms of workforce) of each sector.

1. Agriculture, Value Added (% of GDP)(Agrigdp): Used to measure agricultural output

relative to overall output; Value added in agriculture corresponds to ISIC divisions 1-5

and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock

production. Value added is the net output of a sector after adding up all outputs and

subtracting intermediate inputs. It is calculated without making deductions for

depreciation of fabricated assets or depletion and degradation of natural resources. The

origin of value added is determined by the International Standard Industrial Classification

(ISIC), revision 3.

2. Industry, Value Added (% of GDP) (Indgdp): Used to measure industrial output

relative to overall output; Value added in industry corresponds to ISIC divisions 10-45

and includes manufacturing (ISIC divisions 15-37). It comprises value added in mining,

manufacturing (also reported as a separate subgroup), construction, electricity, water, and

gas. Value added is the net output of a sector after adding up all outputs and subtracting

intermediate inputs. It is calculated without making deductions for depreciation of

fabricated assets or depletion and degradation of natural resources. The origin of value

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added is determined by the International Standard Industrial Classification (ISIC),

revision 3.

3. Services, Value Added (% of GDP) (Servgdp): Used to measure output generated by

the service sector relative to overall output; Value added in services correspond to ISIC

divisions 50-99 and they include value added in wholesale and retail trade (including

hotels and restaurants), transport, and government, financial, professional, and personal

services such as education, health care, and real estate services. Also included are

imputed bank service charges, import duties, and any statistical discrepancies noted by

national compilers as well as discrepancies arising from rescaling. Value added is the net

output of a sector after adding up all outputs and subtracting intermediate inputs. It is

calculated without making deductions for depreciation of fabricated assets or depletion

and degradation of natural resources. The industrial origin of value added is determined

by the International Standard Industrial Classification (ISIC), revision 3.

4. Agricultural Raw Material Exports (% of Total Merchandise exports) (Agriexp):

Used to measure the volume of agricultural exports relative to all merchandise exports;

Agricultural raw materials comprise SITC section 2 (crude materials except fuels)

excluding divisions 22, 27 (crude fertilizers and minerals excluding coal, petroleum, and

precious stones), and 28 (metalliferous ores and scrap).

5. Manufactures Exports (% of Total Merchandise Exports) (Manexp): Used to

measure the volume of manufactures exports relative to all merchandise exports;

Manufactures comprise the commodities in SITC sections 5 (chemicals), 6 (basic

manufactures), 7 (machinery and transport equipment), and 8 (miscellaneous

manufactured goods), excluding division 68.

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6. Employment in Agriculture (% of Total Employment) (Agriemp): The percentage of

the of a country’s total employed population made up of agricultural workers

7. Employment in Industry (% of Total Employment) (Indemp): The percentage of the

of a country’s total employed population made up of industrial workers

8. Employment in Services (% of Total Employment)(Servemp): The percentage of the

of a country’s total employed population made up of workers employed within the

service sector

Control variables

1. Polity: Measures the level of democracy or autocracy of a country’s government; scored

on a twenty-one point scale where a score of 0 indicates that a country is purely an

autocracy and a score of 1 indicates that it is a robust democracy and the scores in

between indicate various levels of democratization

2. Population: Total population is based on the de facto definition of population, which

counts all residents regardless of legal status or citizenship--except for refugees not

permanently settled in the country of asylum, who are generally considered part of the

population of their country of origin. The values shown are midyear estimates.

3. GDP Per Capita (LGDPPC): Gross domestic product is the sum of gross value added

by all resident producers in the economy plus any product taxes and minus any subsidies

not included in the value of the products. It is calculated without making deductions for

depreciation of fabricated assets or for depletion and degradation of natural resources.

The GDP value is then divided by total population in order to obtain GDP Per Capita.

4. Year: Variable which controls for general time trends

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Method

Using all of the above data, my unit of analysis is ‘country year’; so all data will be

sorted by country year. I have run multivariate regressions according to the formulas listed below

in order to determine the correlation between entering an IMF program and output, employment,

and exports in all three sectors. I am controlling for Equations 2 and 3 in each model allow us to

observe separately to what extent being under an IMF program versus starting an IMF program

causes the end results.

Model 1:

The first equation will allow us to see the extent to which being under an IMF program and/or

starting an IMF program in a given year is a predictor of agricultural output in the next year

(indicated by Agrigdpt+1), while controlling for the previous year’s agricultural output (indicated

by Agrigdpt-1) GDP Per Capita, population size, form of government, and overall time trends.

The second equation will illustrate the extent to which being under an IMF program (regardless

of start date) in a given year is a predictor of agricultural output in the next year, while

controlling for the same variables. The third equation will show us the extent to which starting an

IMF program in a given year is a predictor of agricultural ouput in the next two years, again

controlling for the same variables.

1. Agrigdpt+1= α + β1Agrigdpt-1 + β2UnderIMF + β3StartIMF +

β4Polity + β5Ln(Populationt-1)+ β6Year + E

2. Agrigdpt+1= α + β1Agrigdpt-1 + β2UnderIMF + β3Polity + β4 Ln(Populationt-1)+ β5Year + E

3. Agrigdpt+1= α + β1Agrigdpt-1+ β2StartIMF + β3Polity + β4

Ln(Populationt-1) + β5Year + E

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Model 2:

The first equation will allow us to see the extent to which being under an IMF program or

starting an IMF program in a given year is a predictor of industrial output in the next year

(indicated by Indgdpt+1), while controlling for the previous year’s industrial output (indicated by

Indgdpt-1), GDP Per Capita, population size, form of government, and overall time trends. The

second equation will illustrate the extent to which being under an IMF program (regardless of

start date) in a given year is a predictor of industrial output in the next year, while controlling for

the same variables. The third equation will show us the extent to which starting an IMF program

in a given year is a predictor of industrial output in the next two years, again controlling for the

same variables.

1. Indgdpt+1= α + β1Indgdpt-1 + β2UnderIMF + β3startIMF + β4Polity + β5 Ln(Populationt-1) + β6Year + E

2. Indgdpt+1= α + β1Indgdpt-1 + β2UnderIMF + β3Polity + β4Ln(Populationt-1) + β5Year + E

3. Indgdpt+1= α + β1Indgdpt-1 + β2startIMF + β3Polity +

β4Ln(Populationt-1) + β5Year + E

Model 3:


starting an IMF program in a given year is a predictor of service sector output in the next year

(indicated by Servgdpt+1), while controlling for the previous year’s service sector output

(indicated by Servgdpt-1), GDP Per Capita, population size, form of government, and overall time

trends. The second equation will illustrate the extent to which being under an IMF program

(regardless of start date) in a given year is a predictor of service sector output in the next year,

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while controlling for the same variables. The third equation will show us the extent to which

starting an IMF program in a given year is a predictor of service sector output in the next two

years, again controlling for the same variables.

1. Servgdpt+1= α + β1Servgdpt-1 + β2UnderIMF + β3startIMF + β4Polity + β5Ln(Populationt-1) + β6Year + E

2. Servgdpt+1= α + β1Servgdpt-1 + β2UnderIMF + β3Polity + β4Ln(Populationt-1) + β5Year + E

3. Servgdpt+1= α + β1 Servgdpt-1 + β2startIMF + β3Polity +

β4Ln(Populationt-1) + β5Year + E Model 4:


starting an IMF program in a given year is a predictor of the relative size of the agricultural

workforce in the next year (indicated by Agriempt+1), while controlling for the previous year’s

agricultural workforce size (indicated by Agriempt-1), GDP Per Capita, population size, form of

government, and overall time trends. The second equation will illustrate the extent to which

being under an IMF program (regardless of start date) in a given year is a predictor of

Agricultural employment in the next year, while controlling for the same variables. The third

equation will show us the extent to which starting an IMF program in a given year is a predictor

of Agricultural employment in the next two years, again controlling for the same variables.

1. Agriempt+1= α + β1Agriempt-1 + β2UnderIMF + β3startIMF + β4Polity + β5Ln(Populationt-1) + β6Year + E

2. Agriempt+1= α + β1Agriempt-1 + β2UnderIMF + β3Polity + β4Ln(Populationt-1) + β5Year + E

3. Agriempt+1= α + β1Agriempt-1 + β2StartIMF + β3Polity +


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Model 5:


starting an IMF program in a given year is a predictor of the relative size of the industrial

workforce in the next year (indicated by Indempt+1), while controlling for the previous year’s

industrial workforce size (indicated by Indempt-1), GDP Per Capita, population size, form of


being under an IMF program (regardless of start date) in a given year is a predictor of industrial

employment in the next year, while controlling for the same variables. The third equation will

show us the extent to which starting an IMF program in a given year is a predictor of Industrial

employment in the next two years, again controlling for the same variables.

1. Indempt+1= α + β1Indempt-1 + β2UnderIMF + β3startIMF + β4Polity + β5Ln(Populationt-1) + β6Year + E

2. Indempt+1= α + β1Indempt-1 + β2UnderIMF + β3Polity + β4Ln(Populationt-1) + β5Year + E

3. Indempt+1= α + β1Indempt-1 + β2StartIMF + β3Polity +

β4Ln(Populationt-1) + β5Year + E Model 6:


starting an IMF program in a given year is a predictor of the relative size of the service sector

workforce in the next year (indicated by Servempt+1), while controlling for the previous year’s

service sector workforce size (indicated by Servempt-1), GDP Per Capita, population size, form

of government, and overall time trends. The second equation will illustrate the extent to which

being under an IMF program (regardless of start date) in a given year is a predictor of service

sector employment in the next year, while controlling for the same variables. The third equation

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will show us the extent to which starting an IMF program in a given year is a predictor of

Service sector employment in the next year, again controlling for the same variables.

1. Servempt+1= α + β1Servempt-1 + β2UnderIMF + β3StartIMF + β4Polity + β5Ln(Populationt-1) + β6Year + E

2. Servempt+1= α + β1Servempt-1 + β2UnderIMF + β3Polity + β4Ln(Populationt-1) + β5Year + E

3. Servempt+1= α + β1Servempt-1+ β2StartIMF + β3Polity +


Model 7:


starting an IMF program in a given year is a predictor of the relative volume of agricultural raw

material exports in the next year (indicated by Agriexpt+1), while controlling for the previous

year’s agricultural raw material exports (indicated by Agriexpt-1), GDP Per Capita, population

size, form of government, and overall time trends. The second equation will illustrate the extent

to which being under an IMF program (regardless of start date) in a given year is a predictor of

Agricultural raw material exports in the next year, while controlling for the same variables. The

third equation will show us the extent to which starting an IMF program in a given year is a

predictor of Agricultural raw material exports in the next year, again controlling for the same

variables.

1. Agriexpt+1= α + β1Agriexpt-1 + β2UnderIMF + β3StartIMF + β4Polity + β5Ln(Populationt-1) + β6Year + E

2. Agriexpt+1= α + β1Agriexpt-1 + β2UnderIMF + β3Polity + β4Ln(Populationt-1) + β5Year + E

3. Agriexpt+1= α + β1Agriexpt-1 + β2StartIMF + β3Polity + β4Ln(Populationt-1) + β5Year + E

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Model 8:


starting an IMF program in a given year is a predictor of the volume of manufactured goods

exported in the next year (indicated by Manexpt+1), while controlling for the previous year’s

manufactures exports (indicated by Manexpt-1), GDP Per Capita, population size, form of


being under an IMF program (regardless of start date) in a given year is a predictor of

manufactures exports in the next year, while controlling for the same variables. The third

equation will show us the extent to which starting an IMF program in a given year is a predictor

of manufactures exports in the next two years, again controlling for the same variables.

1. Manexpt+1= α + β1Manexpt-1+ β2UnderIMF + β3StartIMF + β4Polity + β5Ln(Populationt-1) + β6Year + E

2. Manexpt+1= α + β1Manexpt-1 + β2UnderIMF + β3Polity + β4Ln(Populationt-1) + β5Year + E

3. Manexpt+1= α + β1Manexpt-1 + β2StartIMF + β3Polity +


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Results Model 1: The regression results from Model 1, which looks at value added as a percentage of

GDP, are illustrated below in Table 1.

Table 1 Value Added (% GDP) by Sector

StartandUnderIMF(Equation1) UnderIMF(Equation2) StartIMF(Equation3)

Variable Agrigdpt+1 Indgdpt+1 Servgdpt+1 Agrigdpt+1 Indgdpt+1 Servgdpt+1 Agrigdpt+1 Indgdpt+1 Servgdpt+1

Agrigdpt‐1

.883**(.006)

‐‐‐

‐‐‐

.883**(.006)

‐‐‐

‐‐‐

.884**(.006)

‐‐‐

‐‐‐

Indgdpt‐1

‐‐‐

.919**(.006)

‐‐‐

‐‐‐

.919**(.006)

‐‐‐

‐‐‐

.919**(.006)

‐‐‐

Servgdpt‐1

‐‐‐

‐‐‐

.890**(.007)

‐‐‐

‐‐‐

.890**(.007)

‐‐‐

‐‐‐

.891**(.007)

UnderIMF

‐.105(.136)

‐.174(.160)

.259

(.161)

‐.209*(.121)

‐.174(.143)

.353**(.144)

‐‐‐

‐‐‐

‐‐‐

StartIMF

‐.325*(.193)

.000

(.228)

.293

(.229)

‐‐‐

‐‐‐

‐‐‐

‐.393**(.172)

‐.111(.204)

.461**(.204)

Polity

.093(.171)

‐1.259**

(.213)

1.488**(.210)

.096

(.171)

‐1.259**

(.213)

1.489**(.210)

.080

(.170)

‐1.280**

(.213)

1.513**(.210)

LnPopt‐1

‐.045(.035)

‐.027(.041)

.055

(.041)

‐.046(.035)

‐.027(.041)

.056

(.041)

‐.045(.035)

‐.026(.041)

.055(.041

GDPPCt‐1

‐.899**(.068)

.208**(.060)

.478**(.055)

‐.903**(.068)

.208**.060

.481**(.054)

‐.886**(.066)

.225**(.057)

.450**(.052)

Year

‐.015**(.005)

‐.006(.006)

.017**(.006)

‐.015**(.005)

‐.006(.006)

.017**(.006)

‐.016**(.005)

‐.007(.006)

.019**(.006)

Constant

39.394**(10.458)

13.799

(11.997)

‐33.172**(12.089)

38.583**(10.450)

13.780

(11.976)

‐32.472**(12.078)

40.437**(10.371)

15.810

(11.852)

‐35.962**(11.967)

NumberofObser‐vations

3931

3925

3918

3931

3925

3918

3931

3925

3918

R2

.954

.893

.899

.954

.893

.899

.954

.893

.899

Sonali Mehta-Rao


21

Model 2: The regression results from Model 2, which looks at employment by sector, as a

percentage of overall employment, are illustrated below in Table 2.

Table 2 Employment by Sector


Variable Agriempt+1 Indempt+1 Servempt+1 Agriempt+1 Indempt+1 Servempt+1 Agriempt+1 Indempt+1 Servempt+1

Agrigdpt‐1

.832**(.014)

‐‐‐

‐‐‐

.833**(.014)

‐‐‐

‐‐‐

.832**(.014)

‐‐‐

‐‐‐

Indgdpt‐1

‐‐‐

.861**(.013)

‐‐‐

‐‐‐

.861**(.013)

‐‐‐

‐‐‐

.861**(.013)

‐‐‐

Servgdpt‐1

‐‐‐

‐‐‐

.867**(.012)

‐‐‐

‐‐‐

.867**(.012)

‐‐‐

‐‐‐

.868**(.012)

UnderIMF

.033

(.394)

.082(.232)

.154(.323)

.248(.363)

‐.171(.214)

.108(.296)

‐‐‐

‐‐‐

‐‐‐

StartIMF

.763(.558)

‐.914**(.327)

‐.159(.453)

‐‐‐

‐‐‐

‐‐‐

.781

(.512)

‐.867**(.301)

‐.073(.415)

Polity

‐.691(.609)

.057

(.358)

1.044**(.494)

‐.741(.608)

.115

(.358)

1.05**(.493)

‐.680(.602)

.075

(.354)

1.076**(.488)

LnPopt‐1

.203**(.100)

.024

(.058)

‐.247**(.082)

.206**(.101)

.018

(.058)

‐.247**(.082)

.200**(.100)

.023

(.057)

‐.246**(.082)

GDPPCt‐1

‐1.469**(.205)

.134

(.092)

1.000**(.162)

‐1.454**

(.205)

.131

(.093)

.997**(.162)

‐1.476**

(.186)

.119

(.082)

.965**(.144)

Year

.006(.022)

‐.027**(.013)

.018(.019)

.005(.022)

‐.025*(.013)

.018(.019)

.006(.022)

‐.027**(.013)

.018(.018)

Constant

.841(44.606)

56.023**(26.234)

‐34.878(37.074)

2.769

(44.599)

52.737**(26.287)

‐35.172(37.051)

1.641

(44.413)

55.503**(26.116)

‐35.251(36.947)


1193

1193

1193

1193

1193

1193

1195

1195

1195

R2

.917

.842

.930

.917

.841

.930

.917

.842

.930

Sonali Mehta-Rao


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Model 3: The regression results from Model 3, which looks at exports by sector, as a percentage

of merchandise exports, are illustrated below in Table 2.

Table 3 Exports by Sector


Variable Agriexpt+1 Manexpt+1 Agriexpt+1 Manexpt+1 Agriexpt+1 Manexpt+1

Agriexpt‐1

.895**(.006)

‐‐‐

.895**(.006)

‐‐‐

.895**(.006)

‐‐‐

Manexpt‐1

‐‐‐

.970**(.005)

‐‐‐

.970**(.005)

‐‐‐

.969**(.005)

UnderIMF

‐.179(.168)

.577*(.312)

‐.210(.150)

.909**(.278)

‐‐‐

‐‐‐

StartIMF

‐.101(.244)

1.069**(.454)

‐‐‐

‐‐‐

‐.216(.218)

1.438**(.405)

Polity

.144(.208)

.231(.397)

.145(.209)

.222(.398)

.116(.207)

.361(.396)

LnPopt‐1

‐.180**(.044)

.417**(.087)

‐.181**(.044)

.423**(.087)

‐.176(.044)

.397**(.087)

GDPPCt‐1

‐.226**(.056)

.235**(.109)

‐.227**(.056)

.238**(.109)

‐.205**(.053)

.172(.105)

Year

‐.008(.006)

‐.007(.011)

‐.007(.006)

‐.008(.010)

‐.008(.006)

0.005(.010)

Constant

19.464*(11.597)

10.135(21.567)

19.184*(.575)

13.170(21.543)

20.379*(11.507)

7.471(21.394)


3304

3301

3304

3301

3312

3309

R2

.897

.949

.897

.949

.897

.949

Sonali Mehta-Rao


23

Analysis

The key finding of this study is that IMF programs consistently promote service sector

growth and hurt agricultural growth in terms of output. The figures to support this finding can be

found in Table 1, which shows that within two years of starting an IMF program, a country’s

agricultural output (as a % of overall GDP) is predicted to decline by approximately four

percentage points and when a country is under an IMF program, regardless of start date, the

predicted decline in agricultural output, though less statistically significant, is approximately two

percentage points. This table also predicts service sector relative growth of four percentage

points in the next year if a country is under an IMF program, and by five percentage points if a

country has just signed onto an IMF program within the last two years.

The regressions from Model 2 and Model 3 also show some statistically significant

results regarding employment and exports. Table 2 shows that employment in the industrial

sector, as a percentage of overall employment, drops by approximately nine percentage points

when a country joins an IMF program. This result is only statistically significant when within

two years of a country starting an IMF program, which shows that this large drop in the

industrial workforce is an immediate effect of starting an IMF program. Table 3 shows that

manufacturing exports as a percentage of merchandise exports increase by approximately nine

percentage points, and within two years of starting an IMF program the number is even larger,

showing an over fourteen percentage point increase in manufacturing exports immediately after a

country joins an IMF program.

There are also several trends in the results among the control variables. The ‘lag’

variables generated to control for previous year’s output (For example, Agrigdpt-1), are highly

Sonali Mehta-Rao


24

significant in predicting future results across the board, and therefore the best overall predictors

of sectoral growth or decline in the study. In Model 1, GDPPCt-1 is a highly significant predictor

of changes in value added as a percentage of GDP across all sectors. Another interesting

observation is that polity is a highly statistically significant predictor of changes in value added

by the industrial and service sectors, but not for agriculture. General time trends (Year) are

significant in predicting changes in value added by the agriculture and service sectors, but not for

industry.

The results from Model 2 show that both GDPPCt-1 and LnPopt-1 are both significant in

predicting changes in employment levels for the agricultural and service sectors, but not for

industry. Polity is a highly statistically significant predictor of changes in service sector

employment, and general time trends (Year) are statistically significant in predicting changes in

industrial employment. Looking at the results from Model 3, GDPPCt-1 and LnPopt-1 are highly

statistically significant predictors of both growth and decline in both agricultural raw exports and

manufacturing exports.

Sonali Mehta-Rao


25

Graphs:

The following graphs illustrate my key finding, that IMF programs decrease the share of

the economy made up of agricultural output and increase the size of the service sector’s share.

The results are most visible in Ghana and the Dominican Republic, where we can very clearly

see significant agricultural decline and service sector growth when the country goes under an

IMF program. The Pakistan graph shows a steady but gradual increase in service sector size and

decrease in agriculture, which is more evenly spread out over time, possibly because Pakistan

has consistently been under IMF programs for the majority of the time from 1966 to 2000.

Sonali Mehta-Rao


26

Sonali Mehta-Rao


27

Future Research

These findings open up a lot of doors for more in-depth future research on IMF impacts. A

study designed to explore more long-term effects of IMF programs is would be useful in

determining whether or not IMF programs have consistently affected the structural development

of economies in the long run or if their impact is only short-term. The results of such a study

would be helpful in clarifying the implications of the economic restructuring for the developing

world in particular. It would also provide useful in assessing the demographic implications of

economic restructuring, such as movement into or away from urban areas, which would only be

seen in the long run because it takes time for large groups of people to make these drastic

lifestyle changes.

Another interesting idea for future research would be to look at whether or not the

restructuring effects are the same for countries under democratic and autocratic regimes. This

will simply add another dimension to this study, allowing us to see whether or not regime-type is

a factor implementing economic structural change under IMF programs.

In order to determine the impact of this economic restructuring on the people of the

countries, it would be interesting to look at the correlation between poverty levels and the size of

each sector. This will allow us to see if agricultural decline, for example, is a significant

predictor of increases or decreases in poverty level.

Since there is already so much literature that has found that U.S. interests are important in

driving IMF policy, it would be interesting to examine the connection between U.S. interests and

the restructuring that occurs under IMF programs. This would be more difficult to conduct

because it is a more subjective and complex topic.

Sonali Mehta-Rao


28

Implications

Based on the economic restructuring that is shown in this study and the existing

literature, it is possible to make logical predictions about the socio-economic implications of

these results. First, I will look at the differences in results across the three models and the

possible causes and implications of these variations. For example, my findings show that

agricultural value added decreases under IMF programs, but there is no significant change in the

relative size of the agricultural workforce. This can be explained by the fact that, because their

skill set and way of life tends to be very different from industrial and service sector employees, it

is not easy for agricultural workers to quickly transition into careers in the industrial and service

sectors, even if the money in the economy is shifting into those sectors. Thus, based on the

immobility of agricultural workers in terms of changing jobs, we can draw the conclusion that a

decrease in the agriculture’s share of GDP leaves those employed in agriculture with less

income, as the same number of people must split a smaller slice of the pie.

Another interesting inconsistency deserving of examination is the large jump in

manufactures exports, while agriculture raw materials exports do not change significantly.

Devaluation, commonly prescribed by the IMF, has been shown to increase overall exports, but

this study finds that when a country is under an IMF program, this devaluation-induced increase

in exports is not spread evenly between the sectors, but overwhelmingly benefits only exporters

of manufactured goods. It is unclear why this is so, but one possible theory is that the West’s

tendency to heavily subsidize its agricultural sector destroys the ability of smaller, developing

countries to compete, even with devaluation.

These findings are also particularly interesting in light of the free-trade debate, in which

those who promote free trade argue that the law of comparative advantage will allow everyone to

Sonali Mehta-Rao


29

benefit in the long run. The IMF has consistently promoted free trade over the years, pressuring

countries through loan programs to reduce trade barriers and government subsidies for local

products. However, my results show that IMF programs have resulted in consistent promotion of

growth in the service sector and reduction of agricultural growth. These results are not consistent

with the free-market ideology that different countries should specialize in the sector in which

they have a comparative advantage. Countries that went under IMF programs may have had a

comparative advantage in agriculture if they had continued to grow that sector, but my results

show that IMF programs reduce the size of the agricultural sectors across the board.

Social implications may be observed on a country-by-country basis based on which

ethnicities or social classes are employed within each sector, but I cannot draw any specific

conclusions at this time because this study is not designed to analyze social impact in depth.

However, I hypothesize that the structural reform, particularly if it is not reversed quickly, could

lead to tensions between classes or ethnic groups as one group benefits and another becomes

disadvantaged.

Conclusion

The results of this study clarify the economic impact of IMF programs, particularly in

developing countries. This more-focused approach allows us to observe economic structural

consequences that have not become apparent in past studies that have taken a more generalist

approach to assessing impacts. However, this approach is limited because overall economic

growth is rarely distributed evenly. While scholars have been caught up in debating whether or

not IMF programs promote overall growth, or alleviate poverty in general, they have neglected to

Sonali Mehta-Rao


30

look more closely at the distribution of changes in economic growth—which segments of society

end up benefiting and who ends up getting hurt. I have proven that the service sector consistently

benefits and the agricultural sector consistently declines when a country joins an IMF program.

The specific implications for growth and for socioeconomic development cannot be accurately

predicted from these findings alone, but a number of new hypotheses may be formed based on

my results, providing a wide range of opportunities for future study that will deepen our

understanding of how IMF programs have shaped the global economy.

Sonali Mehta-Rao


31

Works Cited "Data and Statistics: Definitions." World Bank. Web. <http://go.worldbank.org>

Dreher, Alex, and Nathan Jensen. Independent Actor or Agent? An Empirical Analysis of the

Impact of U.S. Interests on IMF Conditions. Working paper. Vol. 118. Swiss Institute for

Business Cycle Research, 2005. Print.

Dreher, Alex. IMF conditionality: theory and evidence. Working paper. Public Choice, 2009.

Web. 14 Dec. 2009.

E., Stiglitz, Joseph. Globalization and its Discontents. New York: W. W. Norton, 2002. Print.

Kurlantzick, Joshua. "The Malawi Model." Democracy: A Journal of Ideas 13 (2009): 60-68.

Print.

Vreeland, James Raymond. The International Monetary Fund Politics of Conditional

Lending. New York: Routledge, 2006. Print. World Bank Development Indicators. Web. <http://web.worldbank.org>.

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