The economic impact of agricultural development on poverty reduction and welfare distribution

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

16
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 + 𝜀
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 + 𝜀

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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 %
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
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
10APPENDIX A: DATA, MODELS, AND RESULTS
10.1TABLE 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
10.2TABLE 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

46
10.3TABLE 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

47
10.4TABLE 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

48
10.5TABLE 4.1: POVERTY MODELS

49
10.6TABLE 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
11APPENDIX 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

57
12APPENDIX 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
).

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