ABSTRACT

This study employs quantitative and qualitative methods to identify the relationship

between agricultural development, poverty reduction, and income inequality. Building upon the

World Bank’s Enabling the Business of Agriculture study (2016) and data from the World

Development Indicators (2015) for the years 2000 to 2014, we test two hypotheses. The first

pertains to agricultural development and poverty reduction to assess to what extent agricultural

development reduces poverty. The second, in a similar fashion, addresses the relationship

between agricultural development and income inequality. To supplement our quantitative

analysis of these questions, we include a case study of agricultural development, agricultural

policy reforms, and their impact in Vietnam and Tanzania. We find evidence that agricultural

development reduces poverty.

Keywords: Agriculture Development, Poverty Reduction, Income Distribution

The Economic Impact of Agricultural Development on Poverty Reduction

and Welfare Distribution

TAYLOR ELWOOD, KATHERINE WIKRENT, DOU ZHANG, AND CHENQI ZHOU

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CONTENTS

1 Introduction ........................................................................................................................................... 3

2 Literature Review .................................................................................................................................. 5

2.1 Agricultural Development and Poverty Reduction ....................................................................... 5

2.2 Alternative Explanations for Poverty Reduction ........................................................................ 10

3 Mechanisms: Linking Agricultural Development to Poverty Reduction and Inequality .................... 12

4 Data and Model ................................................................................................................................... 15

4.1 Data ............................................................................................................................................. 15

4.2 Model .......................................................................................................................................... 17

5 Results ................................................................................................................................................. 19

6 Case Study .......................................................................................................................................... 21

6.1 Vietnam ....................................................................................................................................... 21

6.1.1 Context ................................................................................................................................ 21

6.1.2 The World Bank’s EBA Study-Vietnam............................................................................. 23

6.1.3 Seed ..................................................................................................................................... 24

6.1.4 Fertilizer .............................................................................................................................. 24

6.1.5 Market ................................................................................................................................. 25

6.1.6 Finance ................................................................................................................................ 26

6.1.7 Machinery ........................................................................................................................... 27

6.1.8 Private Sector Participation in Vietnam .............................................................................. 27

6.1.9 Summary ............................................................................................................................. 29

6.2 Tanzania ...................................................................................................................................... 30

6.2.1 Summary ............................................................................................................................. 32

7 The Relationship between EBA Score, Poverty Rate and Agricultural Development ....................... 34

8 Limitations .......................................................................................................................................... 36

9 Concluding Remarks and Policy Implications .................................................................................... 38

References ............................................................................................................................................... 40

10 Appendix A: Data, Models, and Results ......................................................................................... 44

10.1 Table 1: Indicators and Definitions ............................................................................................. 44

10.2 Table 2: Countries and Groups ................................................................................................... 45

10.3 Table 3.1: Indicator Means by Income Level ............................................................................. 46

10.4 Table 3.2: Indicator Means by Region ........................................................................................ 47

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10.5 Table 4.1: Poverty Models .......................................................................................................... 48

10.6 Table 4.2: Income Models .......................................................................................................... 49

10.7 Figure 1: The Relationship Between EBA Score and Poverty Rate ........................................... 49

10.8 Figure 2: The Relationship Between EBA Score and Poverty Rate ........................................... 51

11 Appendix B: Maps of Vietnam and Tanzania ................................................................................. 52

12 Appendix C: Potential Threats to the Validity of the Analysis ....................................................... 57

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1 INTRODUCTION

Poverty reduction remains the most important objective of the international development

community. At the United Nations Sustainable Development Summit last year, world leaders and

development practitioners convened to assess the progress of the Millennium Development

Goals (MDGs). This summit gave birth to a new order for development, the Sustainable

Development Goals (SDGs). Though it comes as no surprise, it is still important to recognize

that eradicating extreme poverty and hunger is the first of these SDGs.

The correlation between extreme poverty and dependence on the agricultural sector has

prompted many scholars to study the effect of agricultural development on poverty reduction

(Prowse and Braunholtz-Speight 2007; World Development Report 2008 [WDR 2008]; Bresciani

and Valdes 2007). Agricultural development not only leads to increased food production and

greater food security, but also increases the wages and employment rate of poor people involved

in farm activities. Agricultural development was incorporated into MDG efforts to reduce the

share of people living in extreme poverty and hunger by half (WDR 2008). With the inception of

the SDGs, it remains to be seen how agricultural development will be incorporated into future

efforts to combat global poverty.

In this analysis, we attempt to build upon the existing literature describing the role of

agriculture in poverty reduction and income equalization through a mixed methods analysis.

Specifically, we apply the methodology of a recent World Bank initiative, Enabling the Business

of Agriculture (EBA) (EBA 2016), to assess how agricultural policies have led to agricultural

development and poverty reduction. We draw conclusions consistent with those in the literature,

namely that agricultural development has the capacity to reduce poverty.

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The remainder of the paper is structured as follows: Section 2 reviews the existing

literature to provide a contextual basis of previous research conducted on agricultural

development. Section 3 underscores the theoretical framework of our quantitative study with

definitions, causal mechanisms, and hypotheses. Section 4 sets forth the methodology for testing

our hypotheses and summarizes our data sources. Section 5 highlights the key findings. Section 6

supplements Section 5 with case studies of Vietnam and Tanzania. In Section 7, we discuss the

EBA composite score included in our analysis. Section 8 documents the limitations of this study.

We close with a discussion of our general conclusions and specific policy recommendations in

Section 9.

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2 LITERATURE REVIEW

2.1 AGRICULTURAL DEVELOPMENT AND POVERTY REDUCTION

According to the UN Millennium Development Goals Report 2015, even though extreme

poverty has declined significantly over the last two decades, about 800 million people today still

live in extreme poverty and suffer from hunger (United Nations 2015). Poverty reduction, which

consists of multidimensional and cross-sectoral strategies and actions, relies heavily on

agricultural development in most developing economies (Prowse and Braunholtz-Speight 2007;

World Bank 2008; Cervantes-Godoy and Dewbre 2010). Agriculture is a vital development tool

for reducing global poverty. Implementing agriculture-for-development agendas and policies will

make a difference in the lives of hundreds of millions of rural poor (EBA 2016).

Scholars have long studied the impact of agricultural development on poverty reduction

(Irz et al 2001; Lin et al 2003; Christiaensen et al 2011). A common finding throughout these

studies is that agriculture is the single most influential sector in reducing poverty (Thorbecke and

Jung 1996; Irz et al. 2001). Datt and Ravallion (1996) measure the sectoral composition of

economic growth as it influences poverty alleviation in India using time series household-level

data. They find that it is rural sector growth, namely agricultural development, which appreciably

reduced poverty in India, whereas urban growth had no discernible impact. Subsequent studies

reinforce this notion that poverty reduction is maximized when addressed through agricultural

development. Datt and Ravallion (1998) demonstrate that increased farm output reduces both

rural and urban poverty. In a separate study (2002), the authors find that the effect of non-

agricultural economic growth on poverty is more inelastic than rural sector growth, indicating

that rural sector development has a greater impact on poverty reduction than urban sector

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development. Furthermore, Anriquez and Stamoulis (2007) provide quantitative evidence in

support of the proposition that agriculture and rural economy are fundamental for yielding

substantive and sustainable anti-poverty returns. Similar sectoral impacts of agriculture on

poverty reduction are also found in Timmer (1997), Mellor (1999), DFID (2004), and Cervantes-

Godoy and Dewbre (2010).

Technological advancement is one common determinant of agricultural development

prevalent in the reviewed literature (Afolami and Falusi 2006; Asfaw et al. 2012; Irz et al. 2001).

Advancements and investments in the agricultural sector, as part of the initiatives contributing to

broader public policy goals, were found to increase absorptive capacity and the ability to adapt

and apply existing technologies. This leads to a gradual increase in productivity and social

welfare (United Nations 2015). The world fell short of achieving the MDGs by 2015 in part

because the technological advances required for long-term poverty reduction were not fully

developed (Sachs 2005). Agricultural technological advancements, among all the technologies,

are particularly effective in reducing poverty (Irt et al. 2001; Lin et al. 2003; Mendola 2007;

Janvry and Sadoulet 2010; Andrew Dorward et al. 2004). Diao and Pratt (2007) study the

relationship between technological enhancements and poverty reduction in Ethiopia, revealing

that, in order to achieve technological development goals such as the generation of staple foods,

certain investments spanning improved irrigation, the adoption of enhanced seed varieties, and

improved fertilizer are necessary. Asfaw et al. (2012) more broadly review Tanzania and

Ethiopia and find that improved chickpea and pigeonpea varieties result in lower legume prices

and higher consumption expenditures gains. These gains eventually reduce poverty. Cross-

country evidence suggests that enhanced seeds can produce higher yields, which will satisfy the

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food demand of the poor. Specifically, several studies advocate for improved seed and crop

varieties after finding that new cotton and groundnut varieties exert positive and significant

impacts on yields, household incomes, and poverty reduction in Pakistan and Uganda (Ali and

Abdulai 2009; Kassie et al 2011). Otsuka (2000) extends this notion to Asia as a whole, but

asserts more specifically that developing yield-increasing technologies should be the core of

agricultural development because these technologies will be the most effective tool in reducing

poverty. Additionally, without technological advances in agriculture, labor productivity and per

capita farm production will fall (Hernandez et al. 2006; Haggblade et al. 2010).

Related to the study of how agricultural development affects poverty reduction, many

scholars have sought to understand agricultural development’s specific distributional impact.

They build upon the notion that agricultural development reduces poverty by demonstrating its

importance in reducing inequality (Gallup et al. 1997; Hanmer and Naschold 2000; Gollin et al.

2002). Ligon and Sadoulet (2007) conclude that income growth in the agricultural sector has

particular benefits on expenditures for the poorest households and such growth dissipates for

households in higher expenditure deciles. Meanwhile, Christiaensen et al (2011) find that

increases in agricultural GDP per capita reduce measures of extreme poverty more than growth

in other sectors. Gallup et al. (1997) also argue that agricultural development generates higher

incomes. Here, the authors argue that income growth of the rural poor exceeds overall growth.

This implies agricultural development has a more substantial effect on welfare distribution

compared with the other expected effects (DFID 2005).

The UN Millennium Development Goals Report 2015 argues that even though the

proportion of people living in extreme poverty has decreased substantially at the global level,

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this progress has been highly uneven (United Nations 2015). East Asian countries, for example,

outperformed other regions in halving poverty, and Sub-Saharan Africa (SSA) has showed the

least anti-poverty progress (DFID 2004; Prowse and Braunholtz-Speight 2007; Feng 2015;

United Nations 2015). This uneven progress in achieving development goals indicates that

region- and country-specific governance and policies play an important role in shaping

agricultural development. Evidence shows that creating smart and enabling environments and

pro-poor agricultural policies was of fundamental importance in maximizing progress against the

MDGs (IFPRI 2006; Hazell and Braun 2006).1

Pertaining to the prevalent positive relationship between policy and agricultural

development, a common recommendation is for fewer government interventions (Ravallion and

Chen 2005; Klump and Bonschab 2004). Ravallion and Chen (2005) call for fewer market

interventions of the Chinese state in agriculture. Specifically, they lobby for lower taxes and

reduced spending from central governments and more external trade openness to bolster growth.

Klump and Bonschab (2004) draw similar conclusions following their study of the agricultural

development induced by economic reforms in Vietnam. They argue for greater participation

from local units in planning and setting policy. More recently, Cervantes-Godoy and Dewbre

1 Smart agricultural policies, highlighted by the decollectivization of land in some communist countries, demonstrate

the effect policy can have on poverty (Justin Lin 1992; Warr 2001; Barrichello 2004; ADB 2014). China

experienced a drastic agricultural reform beginning in 1978 when the traditional producing team was replaced by the

household production responsibility system as part of fundamental economic reforms led by Deng Xiaoping. This

shift bolstered agricultural development and yielded large gains in poverty reduction (Lin 1992; Ravallion and Chen

2005; Gurel 2014). Lin (1992) finds that the decollectivization and price adjustment reforms in China led to output

and productivity growth within the agricultural sector. However, he also notes that this positive effect was limited to

the initial years following the enacted policies; by 1984, there was little impact from the decollectivization,

suggesting that while policy has the potential to improve agricultural development, continuous efforts are essential

for maximizing the growth potential in this sector. Studies on the impact of decollectivization on rice production in

Vietnam and India arrived at similar conclusions (Pingali and Xuan 1992; Kerkvliet and Selden 1998, Kirk and

Tuan 2009; Rao 1994).

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(2010) indicate that lowering export taxes, overvaluing exchange rates, and decreasing

inefficient state interventions in agriculture would generate a more favorable environment that

would boost agricultural development and reduce poverty.

Smart policies bolster poverty reduction, but the application of such policies has varied.

Take, for example, how regional differences between East Asia and SSA shaped policy

implementation (United Nations 2015). Natural factors such as a lower population density,

inherent and highly concentrated rain fed producing patterns, a relatively low literacy rate and

inferior public health situations compared with Asia (World Bank 2000) account for some of

SSA’s slow progress. SSA’s development problems can also be linked to its policy makers’

inability to solve the continent’s food insecurity problems and political instability that

exacerbates pro-poor agricultural policies (Farrington and Lomax 2000). On the other hand,

region-specific policies also matter, and commonly cited counterexamples include the former

Union of Soviet Socialist Republics (USSR) and Central and Eastern European countries, which

failed to reduce poverty through liberalizing their agricultural economies. (Sachs and Woo 1994;

Roland 2000; Rozelle and Swinnen 2004). The fact that decollectivization policies were effective

for some countries while seemingly not for the others suggests the need for that contextual

analysis be a critical component of the development of agricultural policies.

Recent agricultural development has shifted the policy agenda from direct state

interventions towards state support for an enabling environment for private participation and a

more developed institutional regime (Dorward et al. 2004; EBA 2016). Dorward et al. (2004) in

particular call for broader private involvement, the removal of regulatory controls in agricultural

input and output markets, an elimination of subsidies and tariffs, and reforming, liberalizing, and

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privatizing agricultural parastatals. They also advocate for governments to play a key part in

reducing the transaction risks and costs faced by private agents engaging in agricultural markets.

The EBA Report evaluates the role of policy in shaping the agriculture and agribusiness

sectors. The fundamental premise of this report is that by identifying the consequences of

regulations, policy makers can understand how to unleash agribusiness as a development strategy

(EBA 2016). Specifically, the report identifies six primary indicators where policies have shaped

agricultural development: seed, fertilizer, machinery, finance, transport, and markets. Using

these indicators, the report issues standardized scores to countries in the sample that allow for

cross country comparisons. This conceptualization of policy as it pertains to agriculture informs

our analysis and contributes to our study of agricultural development and its impact on poverty

reduction and income inequality more broadly.

2.2 ALTERNATIVE EXPLANATIONS FOR POVERTY REDUCTION

In addition to achieving poverty reduction through developing the agricultural sector and

establishing pro-poor policies, there is abundant evidence to support the idea that poverty

reduction, especially pertinent to the MDGs, can be achieved from other sectors and

areas.(Lokshin et al. 2007; Dao 2008; Lin et al. 2003) Urbanization is important in poverty

reduction (Ravallion and Chen 2005; World Bank 2000; Datt and Ravallion 2002; Lokshin et al.

2007) in many developing countries, as evidenced by rural to urban migration patterns. This

migration often results in increased remittances, which supplement rural income. Additionally,

female participation in agricultural production and literacy are of particular importance (Dao

2008; Janvry and Sadoulet 2010). Geographic region is also a main determinant on the overall

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performance of agricultural development on poverty reduction (Lin et al 2003; Irz et al. 2001;

Martin and Ivanic 2008; World Bank 2008). Moreover, Cervantes-Godoy and Dewbre (2010)

generalize the shared characteristics of countries that achieved the fastest progress in poverty

reduction. Their study also finds that providing a more favorable macroeconomic environment

will inevitably contribute to creating pro-poor conditions for poverty reduction.

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3 MECHANISMS: LINKING AGRICULTURAL DEVELOPMENT TO POVERTY REDUCTION AND

INEQUALITY

To frame this study, we begin by defining important concepts.

Agricultural Development: The improvement in productivity or efficiency of the agriculture

sector through technological innovations or policy reforms. In the quantitative analysis portion of

this study, we rely on the Agriculture Value Added per Worker metric in the World

Development Indicators to serve as a measure of agricultural development.

Poverty Reduction: A decrease in the number of people living in poverty. For our study, this

concept is operationalized by two measures from the World Development Indicators: Poverty

Headcount Ratio, and Rural Poverty Rate.

Income Inequality: We use the Gini Index and Share of Income Held by the Bottom Quintile,

both quantified in the World Development Indicators, as proxy measures of societal income

inequality.

This paper presents a theoretical framework for understanding the relationship between

agricultural development and poverty reduction. We hypothesize that agricultural development

leads to poverty reduction. Drawing upon the host of authors referenced in the literature review,

we identify two key developments that facilitate poverty reduction: reduced commodity prices

and wage increases. While a variety of factors may also help to explain poverty reduction, we

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focus on these two because of their prevalence in the literature. These mechanisms help

demonstrate the impact of agricultural development on poverty reduction at the household and

individual level.

Agricultural development can lead to a fall in commodity prices, specifically those of

staple crops that are vital to life and livelihood (Berdegue and Escobar 2002; Bresciani and

Valdes 2007). The most fundamental reason for this is that agricultural development often leads

to an increase in output: as the supply of agricultural goods increases, prices fall. In practice,

Minten and Barrett find that doubling rice yields in Madagascar corresponds with a 31-44%

reduction in market rice prices (2008). A reduction in prices facilitates poverty reduction because

those increased quantities and decreased prices increment consumer surplus, making goods more

available to the poor. Thus, poor households are better off thanks to agricultural development.

Furthermore, agricultural development can positively impact wages (Berdegue and

Escobar 2002; Otsuka 2000; Irz et al. 2001). As the agricultural sector becomes more productive,

higher yields and higher productivity make agricultural labor more valuable. As wages increase,

poor households that participate in the agricultural sector enjoy an increase in income that is

associated with agricultural development. Malagasy farmers experienced anywhere from a 65-

89% increase in wages when yields doubled (Minten and Barrett 2008). An increase in wages

contributes to poverty reduction by enabling increased consumption. Similarly, one study also

concludes that households that adopted enhanced seeds, one form of agricultural development,

had statistically higher consumption expenditures (Asfaw et al 2012).

Numerous studies have found that this type of development provides a strong income

equalizing force. That is to say, the poor disproportionately benefit from agricultural

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development and poverty reduction. Insofar as the poor primarily work rurally, growing staple

crops, they become the primary beneficiaries of agricultural development (Diao and Pratt 2007,

Irz et al. 2001). Another study found that agricultural income growth has a statistically

significant impact on the consumption expenditure for those in the lowest earning decile (Ligon

and Sadoulet 2008). Separately, Datt and Ravallion (1996) find that agricultural development

benefits both the rural and urban poor, whereas non-agricultural development does not produce

the same impacts. These studies provide a clear message that not only does agricultural

development directly reduce poverty, but that it also indirectly reduces inequality. We test these

hypotheses empirically in the following section. Education may have an equalizing impact on

incomes in developing countries, but data limitations preclude us from including education levels

in our statistical models.

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4 DATA AND MODEL

4.1 DATA

To test the hypothesis that agricultural development reduces poverty we draw upon

poverty and inequality indicators collected by the World Bank:

Poverty Headcount Ratio at $1.90 a day: The percentage of the population living

on less than $1.90 a day at 2011 international prices

Rural Poverty Rate: Rural population’s mean shortfall from the poverty lines as a

percentage of the poverty lines

Gini Index

Income Share Held by the Lowest 20%

GDP Per Capita

Agriculture Value Added per Worker: A measure of productivity, the output of

the agriculture sector per worker

Export as Percent of GDP

Trade as Percent of GDP

Rural Population Rate: Percentage of population living rurally

Government Expenditure

Inflation Rate

Exchange Rate

Two measures, $1.90 per day poverty headcount ratio (national level) and rural poverty

rate are our primary dependent variables for the poverty models because they are commonly used

in the literature (Cervantes-Godoy and Debrew 2010; Dao 2008). Our income distribution

models use income share held by the lowest 20% and the Gini Index (World Bank estimates) as

the dependent variables. In both our Poverty and Income models, we use the natural logarithm of

agriculture value added per worker (constant 2005 US$) to measure agricultural development

(Dao 2008; Cervantes-Godoy and Dewbre 2010) and incorporate a series of control variables

used in Lin et al. (2003). These control variables include the Gini Index (only in our poverty

model), natural logarithm of GDP per Capita, Exports as a Percent of Total GDP, Trade as a

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Percent of GDP, Rural Population Rate, and Government Expenditure as a Percent of Total

GDP. Additionally, we include two financial variables because the financial sector is an

important factor influencing poverty reduction and these two variables have been used in the

literature (Bresciani and Valdes 2007): Inflation Rate and Exchange Rate. In our Income

Distribution model, we mainly focus on the relationship between agricultural development and

welfare (income) distribution after controlling for many of the same explanatory variables in the

poverty models. All models apply state and time fixed effects. The explanation and description

of each variable can be found in Table 1 of Appendix A.

The panel data comprises a total of 36 EBA developing countries from East Asia &

Pacific, Europe & Central Asia, Latin America and Caribbean, Middle East and North Africa,

South Asia, and Sub-Saharan Africa spanning the years 2000 to 2014. The EBA dataset initially

consisted of 40 countries, but we omitted Myanmar because of missing values and also

developed countries so as to focus solely on developing countries. These data give us a sample

size of 177 and 93 for our Poverty Models A and B, respectively, and 178 for both our Income

models. Appendix A, Tables 2, 3.1, and 3.2 include for our categorization of region and income

level and summary statistics for each indicator by region and income level, respectively.

The descriptive statistics of the data offer meaningful insight for contextualization. We

find that poverty rate is highest among low-income Sub-Saharan African countries and lowest in

Europe, Middle East, and North Africa. Meanwhile, the Latin America and the Caribbean region

has the most inequality, as measured by the income share of the lowest quintile and Gini Index.

It is also important to note that agricultural value added per worker is low in Sub-Saharan Africa,

East and Pacific Asia, and South Asia, despite the fact these are agrarian regions regions.

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4.2 MODEL

Building upon theory and practices in the literature and using data from the World

Development Indicators, we develop four models to determine the relationship between

agricultural development and poverty reduction and with income distribution. We convert all

variables not initially measured in percentage units into natural logarithm forms, which allows us

to test elasticities, as is common practice in many studies (Lin et al. 2003; Anriquez and

Stamoulis 2007; Cervantes-Godoy and Dewbre 2010) In the first two poverty models, we

regressed poverty headcount ratio at $1.90 a day (2011 PPP, % of population) and rural poverty

rate (% of population) on agricultural value added per worker and a series of control variables.

Below is the general form of our two poverty models:

𝑃𝑜𝑣𝑒𝑟𝑡𝑦 𝑅𝑎𝑡𝑒𝑖𝑡 = 𝛼𝑖 + 𝛽1 𝐿𝑛 (𝐴𝑔 𝑉𝑎𝑙𝑢𝑒 𝐴𝑑𝑑𝑒𝑑 𝑝𝑒𝑟 𝑊𝑜𝑟𝑘𝑒𝑟)𝑖𝑡 + 𝛿𝑡 + 𝑋𝑖𝑡 + 휀

In this equation, vector X denotes our control variables, as mentioned in the Data section and

additionally, we generated country (𝛼𝑖) and year (𝛿𝑡) binary indicators to incorporate country

and time fixed effects where 𝑖 denotes country and 𝑡 represents year. We include fixed effects in

our models to account for any unexplained differences across time and space that may influence

poverty. Fixed effects also help us to address concerns surround omitted variables and estimating

accurate coefficients.. Similarly, we employ a multiple regression to test our income models. The

general income model can be written as follows:

𝐼𝑛𝑐𝑜𝑚𝑒 𝐼𝑛𝑒𝑞𝑢𝑎𝑙𝑖𝑡𝑦𝑖𝑡 = 𝛼𝑖 + 𝛽1 𝐿𝑛 (𝐴𝑔 𝑉𝑎𝑙𝑢𝑒 𝐴𝑑𝑑𝑒𝑑 𝑝𝑒𝑟 𝑊𝑜𝑟𝑘𝑒𝑟)𝑖𝑡 + 𝛿𝑡 + 𝑋𝑖𝑡 + 휀

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where vector X still denotes all the controlled variables. The definitions of 𝛼𝑖and 𝛿𝑡remain the

same as in the poverty models. The main purpose of building this model is to determine if

agricultural development can equalize income distribution. Appendix A also contains the actual

equations for our income models as well. Using these equations as the basis for testing our

hypotheses regarding agricultural development, poverty reduction and income inequality; we

expect 𝛽1 to positively correlate with poverty reduction and negatively correlate with income

inequality. Mathematically, we expect to find that 𝛽1 is negative for both poverty models as well

as for income model A, but negative for income model B.

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5 RESULTS

Table 4: Simplified Regression Models Determinants of Poverty Reduction and Income Inequality

Poverty A Poverty B Income A Income B

Ln (Ag Value per Worker) 21.11*** -14.29** 3.3459 0.0097

Control X-Yes/No Yes Yes Yes Yes

Observations (N) 177 93 178 178

Adjusted R-Square 0.68 0.90 0.15 0.12

***P<0.01, **p<0.05 Source: The World Bank Group. 2016. World Development Indicators

Table 4 provides the simplified output from each model and offers some insight into the

relationship between agricultural development, poverty reduction, and income inequality. The

full results of our ordinary least-square estimates for each model are presented in Tables 5.1 and

5.2 in Appendix A. In Poverty Model A, the coefficient for agricultural value added per worker

was positive, contrary to our hypothesis. That is to say, the model predicts that improving

agricultural productivity per worker leads to increases in poverty as measured by the $1.90

headcount ratio. However, from Model B, we see that agricultural development leads to

decreases in rural poverty, and this coefficient is statistically significant.

From the poverty models, we draw two conclusions: 1) agricultural development can be a

tool to combat rural poverty, and 2) assessing the national effects of agricultural development

will require further research. Poverty Model B estimates that while holding all else constant, a

one percent increase in agricultural value added per worker reduces the poverty rate by 0.14

percent. This would suggest that for states with high levels of rural poverty, agricultural

development may be a desirable development strategy. Meanwhile, the strong positive

relationship between the poverty headcount ratio and agricultural value added per worker in

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Model A suggests that the broad impact of agricultural development may be quite the opposite of

the intended outcomes. One conjecture for this estimation is that as agricultural productivity

increases, fewer people work in agriculture. Instead, these people relocate in urban settings

where unemployment and cost of living may be higher, leaving them in poverty. This

phenomenon needs further investigation to have meaningful influence on the existing

development dialogue.

Regarding our income models, the findings are even less conclusive. We were unable to

make any inferences about the relationship between agricultural development and income

inequality. There are likely several omitted variables, given the relatively weak predictive power

of the model as denoted by the small adjusted R-squared term. Nevertheless, the absence of

results could suggest that there are more appropriate determinants of income inequality. Thus,

while our model predicts no relationship, we conclude that further research will be necessary to

better comprehend the relationship between agricultural development and income inequality.

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6 CASE STUDY

To strengthen our quantitative analysis of the links between agricultural development and

poverty reduction, we examine how agricultural policies—especially regulatory policies—could

impact agricultural development in Vietnam and Tanzania. While these two countries are from

different regions, one from tropical Asia and another from SSA, Vietnam and Tanzania are

highly representative of the poverty reduction efforts and obstacles in their respective regions.

These two cases exemplify the role policy can play in agricultural development and poverty

reduction. For further context on these two countries, please see Appendix B, which contains

several maps comparing Vietnam and Tanzania to their neighboring countries on selected

indicators.

6.1 VIETNAM

6.1.1 Context

The World Bank Group describes Vietnam as a development success story. In 1986,

Vietnam launched a famous economic and political reform, Doi Moi, which progressively turned

an isolated, state-led country into a market-oriented and open economy. The per capita income of

Vietnam increased sharply from $100 in 1986 to over $2,000 in 2014 (World Bank 2016

[Vietnam Overview 2016]). This development lifted Vietnam’s economic status from one of the

poorest countries in the 1990s to a lower-middle income country today. As a natural

consequence of this sharp economic development, the nation’s poverty has decreased

appreciably: the national poverty rate has decreased from over 50% in early 1990s to 22% in

2006, using the $1.90 2011 PPP line as a poverty indicator, as quantified in the World

Development Indicators. Poverty reduction has progressed continuously in the most recent 10

22

year. Today, only 3% of the Vietnamese population lives under $1.90 poverty line (Swinkels,

Turk 2002, Thoburn 2013, Bautista 2009). The following maps illustrate Vietnam's current state,

relative to its neighboring ASEAN countries, in terms of indicators used in this study.

National-level policy and reforms have been regarded as the main engine for Vietnam’s

development success. In the 1980s, considering that the majority of the country’s poor

population (80%) was found in rural areas, where agriculture is the primary livelihood, Vietnam

adopted an agriculture-based development strategy to decentralize developmental opportunities

to rural people (Bautista 2009). As part of this Doi Moi reform, de-collectivization of agricultural

land policy was enacted in Vietnam starting in the late 1980s and early 1990s. This pro-poor

agricultural land reform dramatically shifted Vietnam’s 25-year collective farming system to a

household-based land policy, resulting in relatively more fair land distribution among the rural

population (Ravallion and Walle 2001). Moreover, Vietnam extended its land policy in 1993,

which unleashed land-use rights and these rights can be inherited, transferred, exchanged, leased

and mortgaged (Swinkels, Turk 2002). With the increased access to agricultural land for almost

all farmers, the Vietnamese gained economic mobility and independence.

The availability of diverse agricultural inputs including land, water, seed, and human

labor, significantly boosted agricultural productivity in Vietnam. Agricultural development relies

on proper biophysical and eco-social environments (Ittersum, and Rabbinge 1997). Vietnam’s

land reform sparked agricultural development with increased labor capacity and land.

Additionally, Vietnam’s policies supported seed innovation technology and a thriving fertilizer

market. Another important condition, which supported Vietnam’s agriculture boom, is sufficient

water, namely, decent amount of irrigated land. Vietnam has sufficient irrigated water from

23

Mekong Delta, which currently raises half of the world’s rice production and 70% of its exported

rice. In the Mekong River Delta, for example, irrigated land is highly suitable for rice growing,

as a natural consequence of climate, soil, and socioeconomic advantages. Of the 3.9 million

hectares of the Vietnam Mekong Delta, 2.9 million (65%) are currently used for agricultural

production (Nguyen, Minh, and Kawaguchi, 2002). With the combination of these primary

conditions (water, land, seed, and labor), agricultural productivity has increased remarkably in

the past two decades. The largely improved yields have not only satisfied domestic demand, but

also made Vietnam the second largest rice exporter worldwide since 2006 (Fulton and Reynolds

2015; Tsukada 2011). Over 3 million tons of rice production is exported from Vietnam per year,

which accounts for 10% of the world’s total rice market (Nguyen, Minh, and Kawaguchi 2002).

6.1.2 The World Bank’s EBA Study-Vietnam

In 2016, EBA evaluated 40 countries’ current agricultural and agribusiness policies in six

categories. Table 6.1 summarizes Vietnam’s scores and corresponding ranks on each EBA topic.

Higher scores represent better regulatory performances in the agricultural sector.

Table 6.1: Vietnam’s EBA Scores and Ranking

Seed Fertilizer Machinery Finance Markets Transport

Scores 62.5 70 24.4 45.3 80.4 54.8

Numeric

Ranking 23 11 36 21(27) 19 35

Percentile

Ranking (n=40)

42.5% 72.5% 10% 47.5%

(32.5%)

52.5% 12.5%

Source: The World Bank Group. 2016. Enabling the Business of Agriculture Report

These scores show that Vietnam has strong policies on fertilizer quality control, with a

score of 70, and efficient market regulations, with a score of 80.4. Vietnam’s policies for seed,

transport and finance are acceptable. However, its agricultural machinery policy is poor, with the

24

corresponding score of 24.4, ranking at the 10th percentile in the country sample (n=40). The

following analysis will supplement EBA’s scores for Vietnam with a literature review.

6.1.3 Seed

Vietnam has a long history of seed innovation under the influence of the Asian Green

Revolution. In 1960, farmers in Tropical Asian were on the frontier of adopting the released

modern variety (MV) of rice from the International Rice Research Institute (IRRI). MV refers to

the short-statured, fertilizer-responsive, multiple disease- and insect-resistant, superior-quality

grain (Estudillo and Otsuka 2012). These MV increased the cropping intensity and raised higher

yields, especially for farmers in South Vietnam, where the Mekong River creates an irrigable and

favorably rain fed environment (Cassman and Pingali 1995). Poor people also benefitted from

the popularity of agricultural technology improvements, which they believe are more profitable

(Paris and Chi, 20005). The Vietnamese government recognized the importance of innovation in

seed varieties and formed partnerships with countries and research institutions to help the

country develop its rice sector. In doing so, Vietnam’s government welcomed innovations on

seed varieties that improved agricultural productivity (Estudillo and Otsuka 2012).

6.1.4 Fertilizer

After the introduction of MV in the 1960s, there has been an increased demand for

fertilizer given the yields of MVs were more responsive to a higher application of fertilizer

(Estudillo and Otsuka 2012). However, prior to the economic reforms of the 1990s, fertilizers

were provided and distributed by the Vietnamese government with very high prices, as a

consequence that there are very few domestic fertilizer producing companies in Vietnam and

25

fertilizer importation was strictly prohibited. In the 1990s, liberalization of the fertilizer market

led to a sharp decrease in the price of fertilizer (Benjamin, Brandt 2002), which tremendously

bolstered the usage of fertilizer in agriculture production.

In order to better support the agriculture production, Vietnam’s fertilizer subsidy policies

should be more pro-poor. The Vietnamese government subsidizes fertilizer because it plays a

significant role in agricultural production (Estudillo and Otsuka 2012). However, delivery

system weaknesses allow private businessmen to capture most of the profit, and poor individual

farmers and small-scale farming producers do not directly benefit from the government’s subsidy

programs (Dien 2015). Moreover, Nguyen Tien Dung, General Director of the Agricultural

Products and Materials JSC (APROMACO), noted that the biggest challenge for fertilizer

producers is price fluctuation. Without the government subsidy, domestic fertilizer produce

companies could still survive competition with foreign companies (Vietnam News 2012).

Therefore, a cost and benefit analysis should be applied to make the fertilizer subsidy programs

more effective, decreasing the cost of individual household’s agricultural inputs.

6.1.5 Market

The relaxation of trade restrictions catalyzed Vietnam’s agricultural development. Before

opening its market, Vietnam used to be a rice importer, even with its geographic advantages for

raising crops. In 1988, restrictions on South-North trade within Vietnam were abandoned, and

quantitative restraints on foreign exchange were substituted by tariffs (Thoburn 2009). Opening

both the domestic and international markets not only decreased the cost of agricultural inputs

such as fertilizer and seeds, but also boosted the income of rice-raising farmers given the

resulting increased rice prices and the traded quantity. Based on the Vietnam Living Standards

26

Survey conducted by Benjamin and Brandt (2002), rural households throughout all Vietnam

benefitted from the changes made to the rice market, but it was southern farmers who gained the

most.

The Vietnamese government should work to stabilize the prices of crops in this open

market, given that the fluctuation of rice prices will affect both the domestic market and the

larger international rice market and financial system. The 2007-2008 worldwide rice crisis

exemplifies why governments should work to stabilize prices. In 2007, soaring international rice

prices affected the domestic economy in Vietnam, with the protectionist methods carried out by

Vietnam’s government only worsening the situation (Inoue, Okae, Akashi 2015). This market

has profound macroeconomic effects worldwide. In order to maintain a stable rice market not

only within Vietnam, but also on an international scale, Vietnam should clarify and strengthen its

measures on price adjustment, defining the floor and ceiling prices (Inoue, Okae, Akashi 2015).

In tandem, Vietnam’s government should also stabilize the rice production system and make

distribution more efficient.

6.1.6 Finance

Vietnam has a primary finance system established to support agriculture development,

but more financial services and mature financial market rules need to be developed. Credits

unions and microfinance institutions (MFI) have been established to offer developmental

resources and allow agricultural implementers to share risk. However, the development of the

rural credit market in Vietnam is unbalanced; the formal sector specializes in lending for

production purposes, whereas the informal sector's lending is quite diversified (Duong and

Izumida 2002). Though there are laws regulating financial markets and MFI, Vietnam should

27

strive to achieve greater transparency, as the law now requires that MFI should disclose effective

interest rates (EBA-Vietnam Country Profile).

6.1.7 Machinery

Currently, the use of machinery in Vietnam’s agriculture is not widespread, as its

agricultural system relies more heavily on labor power. According to the EBA study, the

regulation for machinery in Vietnam is underdeveloped. With an underdeveloped machinery

manufacturing industry, Vietnam is greatly dependent on the international market to import

agricultural machinery (Liao and Sheng, 2006). Therefore, the price of machinery is very high.

Machinery is regarded as an indirect input in agricultural production, and is a substitute of labor

power that could largely improve agriculture productivity (Saburo and Ruttan).

Vietnam is currently transitioning from a quantity-focused producer to a credible supplier

of high-quality rice (Rutsaert and Demont 2005). With the rapid urbanization and

industrialization of Vietnam, eventually labor prices will increase to surpass machinery prices.

At that time, an insufficient investment in agricultural machinery would hinder the transition of

Vietnam’s labor-intensive agricultural system to a capital-intensive system, due to a smaller

labor input in agricultural production (Rutsaert and Demont 2005).

6.1.8 Private Sector Participation in Vietnam

Private sector participation in agriculture can reap positive benefits extending from the

global level down to the household level. These benefits include regional spillover effects from

country-level research and development (R&D) projects (Janvry and Sadoulet 2010), increased

technology access and use, and a strengthened and more competitive agricultural market (EBA

28

2016). In addition, the return on investment of partnerships between the public, private, and even

nonprofit sectors is high, as it spurs innovation and knowledge across borders and leads to an

increased uptake of transformational tools and techniques. In this way, private sector

participation paves the path to sustainable competitiveness.

Private sector participation could help Vietnam improve the efficiency of the agricultural

industry as a whole. A cross-sectoral analysis conducted by McKinsey shows that the private

sector in Vietnam vastly outperforms state owned enterprises (SOEs) in measures of

productivity. Whereas SOEs on average need approximately $1.60 in capital to produce one

dollar of revenue, the private sector needs only about $0.50 (McKinsey 2012). The ability of the

private sector to generate a capital efficiency ratio three times that of SOEs is clear evidence that

there exists a productivity gap in the public sector. Collaborating with the private sector to

address structural issues could help the public sector identify ways to improve practices. These

reforms could increase this efficiency ratio, leading to in macro- and micro-level benefits and

overall growth of the agricultural sector.

Governments should not see private sector collaboration as a threat, but rather as an

opportunity to achieve mutually beneficial outcomes. Public private partnerships are often the

most efficient and effective way for national governments to achieve the goals they set on the

national agenda, particularly when it comes to seed production and distribution (James 1996).

Further, collaborations can encourage the private sector to invest in national and local public

projects. This infusion of private capital and resources could help resource-constrained lower

middle-income nations such as Vietnam pilot, monitor, evaluate, and scale agricultural

29

development programs. In this way, jointly financed agricultural projects could help Vietnam

improve the agricultural sector as a whole and achieve targets on its national agenda.

6.1.9 Summary

Vietnam has made great progress in agricultural development and poverty reduction.

Thanks to its favorable environmental conditions like sufficient irrigated water accessibility,

supportive land distribution, and hard-working labor force, agricultural productivity has

increased remarkably. This has led to an increase in agricultural incomes for the rural poor.

Among six important elements for agricultural development, seeds act as a primary agriculture

input, while fertilizer improves soil conditions. Vietnam’s agricultural policies support seed

innovation and a thriving fertilizer market, directly improving biophysical conditions for

agricultural production. A more stable and clear price policy is needed to regulate Vietnam’s

open agricultural market, as is greater transparency in regards to the agriculture-supportive

finance system. When urbanization and industrialization lower the machinery-labor price in the

agricultural sector, the role of machinery in agriculture development will necessarily be larger.

Vietnamese government should keep investing in agricultural development. Admittedly,

an export-driven agricultural economy provided capital for the development of non-agricultural

sectors, thus contributing to the nation’s overall economic boom (Sally P and MacAulay 2002).

However, as the income from industrialization now outweighs rural income, rural to urban

population migration leads to fewer people relying on agricultural incomes. As a result,

agriculture value added as a percent of GDP is decreasing. Though Vietnam has seen widespread

poverty reduction in the past two decades, it is still home to 11.5 million people living under the

$1.90 poverty line (PPP). It will prove politically important to reduce the income gap between

30

farmers and employees in other industries as the national transitions from a labor-intensive to a

capital-intensive agricultural system. (Muller and Zeller 2002).

6.2 TANZANIA

Overall, Tanzanian agricultural policies are well designed and well-established.

Compared with Vietnam, Tanzania has higher EBA scores in agricultural operation policy and

trade policy (see Table 6.2). Except in the case of markets, Tanzania outperformed Vietnam in

every category, achieving marks above 50 for each sub-indicator. However, even with better

agriculture regulations, Tanzania’s agricultural system is not as well developed as Vietnam’s.

Extreme poverty and hunger have long been serious issues in Tanzania. The national poverty rate

in Tanzania has fluctuated in the past three decades, but has constantly stayed above the average

poverty rate in Sub-Saharan Africa. The poverty headcount ratio as the percentage of national

population in Tanzania increased from 70.4% in 1991 to 84.7% in 2000. Though the ratio

decreased to 46.6% in 2011, it is still greater than the average ratio of 44.4% among all

developing Sub-Saharan African countries.

Table 6.2: Tanzania’s EBA Scores and Ranking

Seed Fertilizer Machinery Finance Markets Transport

Scores 71.9 75.0 51.4 74.2 54.5 67.9

Numeric Ranking 6 8 12 4(10) 35 16

Percentile

Ranking (N=40)

85% 80% 70 % 88.3%

(75%)

13.5% 60 %

Source: The World Bank Group. 2016. Enabling the Business of Agriculture Report

Table 6.3: EBA Scores Comparison between Vietnam and Tanzania

31

Operations2 Quality Control3 Trade4

Vietnam 55.7 60.6 48.4

Tanzania 63.2 56.9 73.3

Source: The World Bank Group. 2016. Enabling the Business of Agriculture Report

EBA scores are positively correlated with poverty reduction and agricultural productivity.

However, the case of Tanzania seems to be a deviation from this correlation. Why has the

Tanzanian economy been trapped in a poor status for such a long time, even with its solid

agricultural regulations? The general answer is that there are natural, human, and social factors

driving the underdevelopment of agriculture in Tanzania.

Drought is a major problem, which results in the underdevelopment of the agricultural

sector in Tanzania as well as in other Sub-Saharan African countries. Lands in Sub-Saharan

Africa are believed to be suitable for raising crops given sufficient rainfall. However, the

inconsistent rainfall in the Sub-Saharan region leads to frequent droughts, which disrupt

agricultural systems. Sub-Saharan Africa suffered severe rainfall shortages in 1973, 1984, and

1992, and low rainfall in 1963 and 1989. Southern Lake Victoria in Tanzania also experienced a

severe drought in 1974-75, which adversely affected local food production (Gommes and

Petrassi 1996). In contrast, Vietnam’s Mekong Delta area enjoys regulated rainfall. In fact, flood

and salinization problem in the Mekong Delta were more frequent occurrences than drought.

Therefore, Vietnam’s agricultural system could rely on a greater supply of irrigated water than

Tanzania.

2 The operations score is average of seed, fertilizer, machinery, finance, markets and transport indicator scores. 3 The quality control score is an average of seed, fertilizer, machinery and markets indicator scores. 4 The trade score is an average of fertilizer, machinery and transport indicator scores.

32

Moreover, poor irrigation systems in Sub-Saharan Africa have been unable to mitigate

the local drought problem. Irrigation has long been seen as an important factor for developing

local agriculture and improving rural livelihoods. However, even with massive investments

throughout the 1970s and 1980s in Sub-Saharan Africa, many technical and management

problems still exist in its irrigation system (Kay, 2001).

Tanzania also has poor human capital resources compared with Vietnam. The national

literacy rate in Vietnam was 96% in 2009, whereas Tanzania’s literacy rate was only 68% in

2010 (WDI). With this low literacy rate, even well intended government-funded programs and

policies could not be implemented given the lack of skills and knowledge of the public. For

example, in the context of a smallholder irrigation investment program, the main problems have

been the poor technical expertise of both the farmers and the management staff (Mrema 1984).

The social factors limiting Tanzania’s agricultural development and poverty reduction are

numerous and often grave. Public health problems in particular are severe, with disease like

AIDS and poor access to health services lowering the life expectancy of the Tanzanian

population. Further, Tanzania suffers from institutional capacity and enforcement issues. Policy

implementation is often sidelined because of the limited enforcement capacity of tax authorities

to ensure tariff compliance and clamp-down on smuggling. These same officials exhibit

unsatisfactory executive ability in ensuring smooth operations and the maintenance of irrigation

schemes (Ole 2011).

6.2.1 Summary

Strong agricultural-supportive policy is not the only factor that determines the

performance of anti-poverty agriculture development initiatives. Agricultural development and

33

poverty reduction are multi-dimensional topics. The impact of related policies is influenced by

many other factors including the specific natural, human and social conditions of the target

country. Moreover, EBA scores could neither explain every aspect of the agricultural policies

nor the progress of agricultural development or poverty reduction in the target country.

Considering EBA methodology currently only covers six categories, more categories and

questions should be added into EBA surveys, such as measuring irrigated water accessibility.

34

7 THE RELATIONSHIP BETWEEN EBA SCORE, POVERTY RATE AND AGRICULTURAL

DEVELOPMENT

Our literature review and case studies of Vietnam and Tanzania reinforce the theory there

is a positive relationship between agricultural policy and poverty reduction (Klump and

Bonschab 2004; Cervantes-Godoy and Dewbre 2010). The correlation coefficient between

poverty rate and agricultural policies should have a negative sign, implying that as policy

improves, poverty decreases. Based on Vietnam and Tanzania’s history of agricultural

development and policy evolution, it is predictable that a more enabling environment should

boost poverty reduction. We tested this hypothesis by running a simple linear regression between

the EBA composite score and poverty rate in all 36 countries after controlling for GDP per capita

in the regression. Averaging all sub-scores generates the EBA composite score. The poverty rate

data is from 2013 and 2014 in WDR. Additionally, we also ran a simple linear regression

between EBA composite score and agricultural value added per worker. Unlike the regression

analysis in the previous section, we didn’t control for time and country fixed effects given that

the EBA score is based on current performance. This is also why we included poverty and

productivity measures from only 2013 and 2014.

The coefficients on $1.90 per day headcount ratio in 2013 and 2014 are -0.26 and -0.18,

respectively. There is no surprise that the magnitude of these correlation coefficients is not very

high, which can be attributed in part to the fact that EBA composite score is a cross-sectional

data generated in 2015. The coefficients might have been biased when using 2015 EBA data to

correlate with poverty rates in 2013 and 2014. However, these coefficients are statistically

significant at the 95% level, meaning we can say with reasonable certainty that a 1 unit increase

in EBA composite score, namely one unit increase of a better regulatory performance, leads to a

35

0.26 and 0.18 unit reduction in the $1.90 per day poverty ratio. When we regressed agricultural

value added per worker on EBA composite score after controlling for GDP per capita, the

coefficients are both 0.53, implying that increasing the EBA score by 2 units could increase the

agricultural value added per worker by 0.53 units. However, this coefficient is not statistically

different from zero at 95% confidence level. Overall, improving agricultural regulations will

increase poverty reduction. Such a regulatory and legal revolution could yield greater

productivity in the agricultural sector. The relationships between EBA composite score and

poverty rate and agricultural value added per worker are summarized in Figure 1 and 2.

36

8 LIMITATIONS

Though we have strived to accurately model the relationship between agricultural

development and poverty by applying principles and practices from the literature to our model,

our study does have internal and external imitations. Inappropriate operationalization, or the use

of imperfect proxy measures, is a key concern. The literature shows that the concepts of

“poverty” and “inequality” are extremely difficult to precisely metricize. For this reason,

definitions and measures of these terms vary between development experts. This could be part of

why poverty model A yielded a positive sign to our primary explanatory variable’s coefficient.

Furthermore, these nebulous concepts are intrinsically bound to geographic, temporal, and

contextual considerations; that is, the definition of “poverty” of a development expert located in

Washington, D.C., may differ considerably from what that of a frontline staff worker in

Tanzania. Many development experts even suggest that, given the complex nature of poverty, the

on accurate measures of poverty are necessarily multidimensional in nature our analysis relies on

the one-dimensional operationalization of a poverty headcount ratio at $1.90/day and a ratio of

rural poverty at national poverty lines. Because these measures are based only on an income

measurement, they may be only proxies, or an imperfect representation of an actual

phenomenon, of true poverty. Relying on these proxies’ measures may threaten both the internal

(or methodological) and the external (or generalizable) validity of our analysis thereby distorting

our understanding of the causal linkages between agriculture development, poverty reduction,

and income inequality.

A second main limitation to our analysis that pertains mainly to our policy analysis

involves our inability to account for policy implementation differentiation. Though we have used

the Vietnam and Tanzania case studies to suggest links between agricultural policies and positive

37

developmental effects, we cannot be sure that the policies approved were enforced exactly as the

policy was written. That is, it is possible that though certain mandates were approved, these

mandates were not enacted according to the letter of the law, uniformly across the entire country,

or uniformly across time. Given the resource and capacity constraints of these countries, it is

expected that any number of implementation problems could have hindered the uniform and

unconditional implementation. Complicating the matters further, given the complex web of

relationships between policy, poverty, and inequality, development policy often has a lag effect;

that is, the true effects of a policy may only be seen an indefinite amount of time after the actual

passing of a policy. The fact that the effect of a policy is always reliant upon contextual and

temporal factors and tends to have lag effects means that attribution is incredibly difficult. In this

way, problems of implementation differentiation and ambiguous attribution are central threats to

the validity of our analysis. For a full discussion of our considerations, please see Appendix C:

Potential Threats to the Validity of the Analysis.

38

9 CONCLUDING REMARKS AND POLICY IMPLICATIONS

Even though quantitative evidence indicates that agriculture may not be the panacea on

overall poverty reduction, it is still the one of the most powerful weapons in combating rural

poverty. Though it is found in many literatures that agricultural development boosts income

distribution, this notion no longer holds in our study especially considering the development

trend in the most recent decade that agriculture has deviated from being the most influential anti-

poverty tool in many countries. While the actual mechanisms through which agricultural

development influences poverty reduction and income inequality may be more nuanced than put

forth in our study, our results have important policy implications for future efforts to fight

poverty as well as national development strategies.

Given the propensity of agricultural development to reduce rural poverty, there are

several questions surrounding current development strategies. A common development model

implemented by countries around the world has been to transition from agrarian to industrial or

service driven economies. As a result, attention to the agricultural sector is waning as countries

pursue alternative development methods. This shift away from agriculture may also be

influenced by improved productivity and new technologies, which has allowed countries to

produce agriculture outputs at the same levels with fewer inputs. However, it could also be that

countries feel that focusing on agricultural development will exacerbate poverty concerns, as our

findings suggest. Notwithstanding this concern, we find little evidence in the literature to

reinforce this notion. Based upon our findings regarding rural poverty rates, = countries with

substantial rural poverty rates might consider forming a development strategy centered upon

39

agricultural development, as we find reason to believe such strategies are most effective in this

regard.

The policy component of agricultural development has and will continue to play an

integral role in reducing poverty and inequality. That being said, the shape and manner in policy

influences these outcomes will largely depend on a state’s capacity to balance government

regulation and intervention while cultivating a business friendly environment. As indicated in the

EBA 2016 report, establishing non-discriminatory regulations and providing more transparent

and accessible information to the public are essential for cultivating this environment. These

actions can facilitate greater poverty alleviation in a world rededicated to development and

accomplishing the Sustainable Development Goals.

40

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44

10 APPENDIX A: DATA, MODELS, AND RESULTS

10.1 TABLE 1: INDICATORS AND DEFINITIONS

Indicator Definition Source

Poverty headcount ratio at

$1.90 a day Poverty headcount ratio at $1.90 a day is the percentage of

the population living on less than $1.90 a day at 2011

international prices

World

Development

Indicator

Rural poverty headcount ratio Rural poverty headcount ratio is the percentage of the rural

population living below the national poverty lines. World

Development

Indicator

GINI index (World Bank

estimate) Gini index measures the extent to which the distribution of

income (or, in some cases, consumption expenditure) among

individuals or households within an economy deviates from a

perfectly equal distribution, a Gini index of 0 represents

perfect equality, while an index of 100 implies perfect

inequality.

World

Development

Indicator

Income share held by lowest

20% Percentage share of income or consumption is the share that

accrues to subgroups of population indicated by deciles or

quintiles. Percentage shares by quintile may not sum to 100

because of rounding.

World

Development

Indicator

Agriculture value added per

worker Agriculture value added per worker is a measure of

agricultural productivity. Value added in agriculture

measures the output of the agricultural sector (ISIC divisions

1-5) less the value of intermediate inputs.

World

Development

Indicator

GDP per capita GDP per capita based on purchasing power parity (PPP).

PPP GDP is gross domestic product converted to

international dollars using purchasing power parity rates.

World

Development

Indicator

Export Exports of goods and services represent the value of all

goods and other market services provided to the rest of the

world. They include the value of merchandise, freight,

insurance, transport, travel, royalties, license fees, and other

services, such as communication, construction, financial,

information, business, personal, and government services.

World

Development

Indicator

Trade Trade is the sum of exports and imports of goods and

services measured as a share of gross domestic product. World

Development

Indicator

Rural Population Rural population refers to people living in rural areas as

defined by national statistical offices. It is calculated as the

difference between total population and urban population.

World

Development

Indicator

45

Inflation Inflation as measured by the consumer price index reflects

the annual percentage change in the cost to the average

consumer of acquiring a basket of goods and services that

may be fixed or changed at specified intervals, such as

yearly. The Laspeyres formula is generally used.

World

Development

Indicator

Exchange Rate Official exchange rate refers to the exchange rate determined

by national authorities or to the rate determined in the legally

sanctioned exchange market. It is calculated as an annual

average based on monthly averages (local currency units

relative to the U.S. dollar).

World

Development

Indicator

Government Expenditure General government final consumption expenditure (% of

GDP) World

Development

Indicator

Source: The World Bank Group. 2016. World Development Indicators

10.2 TABLE 2: COUNTRIES AND GROUPS

High Income Upper-Middle

Income Lower-Middle Income Low Income

East Asia & Pacific Lao PDR, Philippines, Vietnam

Cambodia

Europe & Central

Asia Poland, Russian

Federation Bosnia and

Herzegovina,

Turkey

Georgia, Kyrgyz Republic, Tajikistan, Ukraine

OECD (Chile, Poland)

Latin America &

Caribbean Chile Colombia Bolivia, Guatemala,

Nicaragua

Middle East &

North Africa Jordan Morocco

South Asia Bangladesh, Sri Lanka, Nepal

Sub-Saharan Africa Cote d'Ivoire, Ghana,

Kenya Sudan, Zambia

Burkina Faso,

Burundi, Ethiopia, Mali,

Mozambique,

Niger, Rwanda,

Tanzania, Uganda

Source: The World Bank Group. 2016. Enabling the Business of Agriculture Report

46

10.3 TABLE 3.1: INDICATOR MEANS BY INCOME LEVEL

Indicator Average High Income Upper-Middle Lower-Middle Low Income

Rural Poverty Rate 42.47 40.25 33.95 45.71 43.70

$1.9 Poverty Headcount

Ratio 17.69 0.65 5.94 17.32 47.6

Income Share Held by the

Lowest 20% 6.18 6.32 4.96 6.27 6.9

Gini Index 40.88 39.99 45.71 40.11 39.29

Ag Value Added GDP 22.24 4.12 7.44 20.47 35.41

Ag Value Added/Worker 1606.61 4501.3 4304.99 1356.17 321.64

Trade share in GDP 71.36 66.72 76.62 79.68 56.34

Rural Population Rate 59.36 25.79 34.00 58.38 79.32

Consumption Expenditure 69,499,853,857 330,577,109,638 188,290,076,182 29,631,442,576 7,096,574,509

GDP Per Capita 5294.96 18890.19 11035.94 4182.93 1319.24

Inflation Rate 7.60 3.41 7.34 8.32 7.67

Exchange Rate 1142.00 197.85 545.93 1526.16 990.43

Source: The World Bank Group. 2016. World Development Indicators

47

10.4 TABLE 3.2: INDICATOR MEANS BY REGION

Indicator Name Mean East Asia

& Pacific Europe &

Central Asia LAC MENA South Asia Sub-

Saharan

Africa

Rural Poverty

Rate 42.47 31.29 27.25 60.84 16.80 27.01 46.92

$1.9 Poverty

Headcount Ratio 17.69 19.789 6.433 12.26 0.4 25.95 50.28

Income Share

Held by the

Lowest 20%

6.18 7.1 7.17 3.198 7.9675 7.7933333 6.1697059

Gini Index 40.88 38.252 35.36 53.82 34.3 36.6 42.3

Ag Value Added

GDP 22.24 25.52 12.94 11.46 2.96 22.57 31.06

Ag Value

Added/Worker 1606.61 591.58 3135.38 2888.6 3535.16 524.86 644.76

Trade Share in

GDP 71.36 105.97 85.85 63.48 124.3 49.86 56.76

Rural Population

Rate 59.36 69.55 46.78 33.67 18.3 79.14 70.77

Consumption

Expenditure 69,499,85

3,857 38,239,459,

707 200,434,532,83

4 56,145,386,84

3 15,129,321

,148 36,515,129,6

44 13,678,886,83

5

GDP Per Capita 5294.96 3697.32 10012.28 8787.68 9950.82 3847.68 2019.92

Inflation Rate 7.60 6.47 8.13 3.30 9.94 8.85 8.68

Exchange Rate 1142.00 7598.12 11.60 554.83 0.71 83.78 495.19

Source: The World Bank Group. 2016. World Development Indicators

48

10.5 TABLE 4.1: POVERTY MODELS

49

10.6 TABLE 4.2: INCOME MODELS

10.7 FIGURE 1: THE RELATIONSHIP BETWEEN EBA SCORE AND POVERTY RATE

50

Source: The World Bank Group. 2016. World Development Indicators; EBA 2016

51

10.8 FIGURE 2: THE RELATIONSHIP BETWEEN EBA SCORE AND POVERTY RATE

Source: The World Bank Group. 2016. World Development Indicators; EBA 2016

52

11 APPENDIX B: MAPS OF VIETNAM AND TANZANIA

The following maps offer some regional context. For a variety of indicators used in this study,

we’ve illustrated how Vietnam and Tanzania compare to their neighbors. Source: World

Development Indicators

53

54

55

56

57

12 APPENDIX C: POTENTIAL THREATS TO THE VALIDITY OF THE ANALYSIS *Please note that the following table is adapted from “Strategies to Help Strengthen Validity and Reliability of

Data” by Kathryn E. Newcomer, January 2011

Measurement Validity

Are we accurately measuring what we really intend to measure?

Threat Definition Relevance

Inappropriate

Operationalization

Evaluators have insufficient

knowledge about the concept

of, or the concept is

impossible/too expensive to

measure directly so

approximate or “proxy

measures,” are used.

Poverty, inequality, and

development are complex

concepts that cannot be

cleanly captured by a single

metric, or even any standard

set of metrics. Development

professionals have long

debated how to faithfully

operationalize these

concepts. Though we have

referenced the works of

agricultural development

experts to inform our

selection of proxy measures,

the chosen proxies do not

necessarily measure

underlying phenomena.

Accidental or Purposeful

Misrepresentation

Faulty memory, or records

are not updated in a timely

manner. Accidental

misrepresentation is

especially a problem when

significant calendar time has

elapsed.

Even though the methods

through which World Bank

collects and verifies the data

presented in the World

DataBank are certainly

robust, measurement

accuracy can be a problem

anytime national level values

are generated by aggregating

sub-national data. Along this

aggregation chain are many

opportunities for accidental

or purposeful

misrepresentation: under

resourced or underqualified

local or national census staff

may not have the tools or

58

statistical/survey skills

needed to reliably sample

populations and generate

estimates. These staff may

also face financial or political

pressure to inflate or deflate

their estimates. Even the

World Bank recognizes that

updates and revisions over

time may introduce

discrepancies from one

edition to the next.

Sleeper Effects Effects lag beyond the time

of measurement. In other

words, what’s being

measured may be right, but

the measurement is being

taken at the wrong time.

The effects of anti-poverty

policies may be long term

instead of intermediate; we

may be unable to capture this

lag effect in our analysis of

the interplay of

development/agricultural

policy and poverty reduction.

Change in Definitions Redefining the data

describing or monitoring an

entity makes data from two

or more time periods not

comparable.

The definitions of difficult to

capture concepts like

“poverty line” have shifted

over time, as have the

methodologies with which

statistics like “literacy rate”

are captured and estimated.

These shifting definitions

occur not only across time,

but also across geographies.

Comparing “poverty rate”

across time within and

among countries may

therefore introduce bias into

the final model.

Lack of Dosage

Differentiation

Measuring a treatment as

received or not received

when in fact program

participants receive widely

varying amounts of

“treatment”

Not every anti-poverty policy

will be defined or

implemented identically or

for the same length of time.

Variety and inconsistency is

common in policy enaction

depending on the temporal

and geographic contexts, the

complexity of the policy, and

59

the interpretation of policies

by policy implementers.

Mono-Operation and Mono-

Method Bias

Any one operationalization

of a construct may

underrepresent the construct

of interest or measure

irrelevant constructs,

complicating inference.

Our reliance on potentially

unfaithful proxy

measurements may distort

our understanding of cause

and effect. Opting for a one

dimensional measurement of

poverty based on income, for

example, could result in

analysis which misses the

complexity of the underlying

phenomenon of destituteness.

On the other hand,

multidimensional measures

could enable us to appreciate

poverty as a complex

“experience of deprivation –

such as poor health, lack of

education, inadequate living

standard, lack of income (as

one of several factors

considered),

disempowerment, poor

quality of work and threat

from violence.”

Measurement Reliability

The extent to which a measurement can be expected to produce similar results if repeated.

Threats Definition Relevance

Capacity Dependent

Collection/Coding

Inputting data from multiple

locations may be overly

dependent upon the capacity

of those responsible for

collecting and/or coding the

data to carefully apply the

same criteria in their

decisions on how to collect or

code, and high turnover,

heavy workloads and/or lack

of technical capacity may

render the collection/coding

inconsistent across locations

The success of sub-national

census collection relies on

the technical and statistical

capabilities of resource

constrained, inadequately

trained, and overworked

local staff. These data

collectors and aggregators

may lack the support,

resources, and time needed to

ensure quality data collection

and reporting.

60

Inappropriate Calibration National statistics may be

error prone if information is

gathered at the state and

national levels. Further,

continuously rounding

numbers to generate a high

level estimate can introduce

error into the statistic.

Without full knowledge of

the aggregation methods for

these national statistics, we

cannot be sure of the

consistency of the

measurements, nor can we be

sure that the collection and

aggregation methods

employed are appropriate to

capture the underlying

phenomenon.

Threats to Internal Validity and External Validity

Internal validity Are we able to definitely establish that there is a causal relationship

between a specified cause and potential effect? External validity Are we able to generalize from the results?

Note that virtually any threat to internal validity also affects external validity.

Threats Definition Relevance

History or Intervening

Events

The observed effect is due not to

the program or treatment but to

some other event that has taken

place. For example, while a

program is operating, many

events may intervene that could

distort pre- and post-

measurements as they relate to

the outcome being studied.

Many other influences

outside of the realm of

development policy and

population statistics affect

poverty rates, macro level

inequality, and income

distribution. Because so

much about what drives

poverty and inequality is

unknown, it is difficult to

isolate the impact of

development policy on

reducing poverty and

inequality.

Selection or Selection

Bias

The observed effect is due to

preexisting differences between

the types of individuals in the

study and comparison groups

rather than to the treatment or

program experience.

Mobile, vulnerable, socially

isolated, or geographically

remote populations may not

be captured through

traditional $1.90/day or

national poverty line

headcount methods. Poverty

as defined in this way, then,

61

When the assignment of subjects

to comparison and treatment

groups is not random

(Voluntary), the groups may

differ in the variable being

measured.

may miss out on some of the

poorest individuals in a

country.

Program Not Fully

Implemented

If inadequate resources or other

factors have led to

implementation problems, it is

premature to test for effects.

Even when programs or

interventions have been

implemented as prescribed by

law, it is still wise for evaluators

to measure the extent to which

program participants or service

recipients actually received the

benefit.

Though we can often

pinpoint when antipoverty or

other developmental policies

were officially enacted,

knowing how completely and

uniformly those policies and

programs were rolled out is

impossible without

interviewing frontline

government staff in the

affected countries. Even an

identical agricultural

development policy could

have been rolled out in

markedly different fashions

across disparate geographic

and temporal contexts.

Regression to the Mean

or Regression Artifacts

The observed effect is due to the

selection of a sample on the basis

of extremely high or extremely

low scores of some variable of

interest. Change in the scores or

values on the criterion of interest

may be due to a natural tendency

for extremely high or extremely

low performers to fall back

toward the average value. It

would be misleading to attribute

this change to the intervention.

These threats arise when a

program or other intervention

occurs at or near a crisis point.

To the degree that the fluctuation

is random or occurrence

idiosyncratic due to some cause

of short duration, it is easy to

Macroeconomic trends are

prone to semi-predictable

fluctuations over time. These

natural fluctuations are likely

to continue regardless of

policy shifts. Therefore,

attributing reductions in

poverty or inequality to

policy changes may be

erroneously claiming

causation in the face of

simple correlation.

62

incorrectly estimate to effects of

whatever action or response is

made.

Ambiguous Temporal

Precedence

Lack of clarity about which

variable occurred first may yield

confusion about which variable

is the cause and which is the

effect.

The interwoven nature of

poverty, inequality,

development policy,

macroeconomic indicators,

and population demographics

obstructs us from creating

linear cause and effect logic

models. Furthermore, any

development policy will have

some length of effect lag, but

the precise length of this lag

is impossible to quantify.

Time Effects The data may be so outdated that

they are no longer relevant to the

problem. Thus, although we may

have a sound evaluation of some

past regulation, policy, or

program, there is no reason to

believe that it bears any

relationship to what is going on

currently.

Given the lag effect of

development policy and the

complicated nature of

macroeconomic trends,

policies that are theorized to

have had positive

developmental effects at one

time may not have similar

effects in other temporal

contexts.

Geographic Effects The evaluation may have been

conducted in a specific area of

the country or type of

environment and its results are

not generalizable to other

settings.

A development policy that

had theorized positive effects

in one geographic area may

not have similar effects, and

may even have negative

effects, in another geographic

context. What works in a free

market nation like Chile may

not work in a highly

regulated economy like Cuba.

Multiple Treatment

Interference Effect

A number of treatments or

programs are jointly applied and

the effects are confounded and

not representative of the effects

of a separate application of any

Development policies are

often multidimensional in

nature; they consist of

multiple components, and

attempt to generate positive

63

one treatment or program.

Treatments are complex, and

replications of them may fail to

include those components

actually responsible for the

effects. An effect found with one

treatment variation might not

hold with other variations of that

treatment, or when that treatment

is combined with other

treatments, or when only part of

that treatment is used.

results across a range of

dimensions such as income

generation, educational gains,

or health improvement. This

makes it difficult to separate

out the effects of the different

components of the policy.

Statistical Conclusion Validity

Do the numbers we generate accurately detect the presence of a factor, relationship, or effect

of a specific or reasonable magnitude?

Threats Potential Causes/Defined Examples

Too Small a Sample Size An effect or relationship of

a specific size, regardless

of the analytic approach

used, is not statistically

detected; there is low

statistical power due to

small sample size.

The sample size for our analysis is

limited to a select set of the 36

countries scored by the World

Bank’s EBA analysis. The

number of observations for each

indicator is also quite small due to

data missingness in the WDI

dataset.

Applying Statistical

Analyses to Data

Inappropriate for the

Technique

Appropriateness of the

technique given the data

and the underlying

dynamics in measured

relationships. Application

of inappropriate statistical

techniques for the data at

hand may produce numbers

that are misleading or

incorrect. Each statistical

technique is designed for

application to certain types

of data ( i.e., nominal,

ordinal and interval/ratio),

and for certain types of

relationships between

variables, e.g., linear.

Our OLS regression is likely not

the best fit for the underlying

data, in spite of our best attempts

to improve the fit of the model by

specifying variables and our

methodology according to the

information gathered through our

literature review.

Measurement Problems If a measure has a high

degree of error, it threatens

Our analysis depends on proxy

measures that may not faithfully

64

our ability to statistically

identify relationships or

differences and effects that

are actually present; or

other measurement

problems such as

unreliable proxy variables,

or limited range in

variables of interest.

represent the underlying

phenomena. Further, these proxies

can be arbitrarily affected by

environmental factors. These

measurement issues make it

difficult to generate and justify

statistically significant results.

Fishing and the Error

Rate Problem

Repeated tests for

significant relationships, if

uncorrected for the number

of tests, can artificially

inflate statistical

significance.

Throughout our analysis, we

repeatedly tested regression

coefficients with a 95%

confidence rule. Repeated testing

means that at least 5% of the tests

could be false positives.

Specification Error

Specification effects may

include either omission of

other factors that may

affect the outcomes of

interest (similar to the

history threat under

internal validity) or

inclusion of factors that are

not relevant in an analytical

model devised to predict

specific outcomes.

It is possible that our final model

erroneously contains irrelevant

variables. The inclusion of these

variables (model

overspecification) can artificially

inflate the coefficient of

determination (R2).