DP2019-04

Roles of Agricultural Transformation in
Achieving Sustainable Development Goals on
Poverty, Hunger, Productivity, and Inequality*
Katsushi S. IMAI

March 21, 2019

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Roles of Agricultural Transformation in Achieving Sustainable
Development Goals on Poverty, Hunger, Productivity, and Inequality
Katsushi S. Imai
Department of Economics, The University of Manchester, UK, & RIEB, Kobe University,
Japan
This Version: 21 March 2019
Abstract
This paper examines the role of the transformation of the rural agricultural sector inachieving Sustainable Development Goals 1, 2 and 10 drawing upon the cross-country panel
data over the past four decades for 105 developing countries. We define agriculturaltransformation by three different indices, namely, (i) the agricultural openness index – theshare of agricultural export in agricultural value added of the country, (ii) thecommercialization index - the share of processed agricultural products, fruits, green
vegetables, and meats in all primary and processed agricultural products, and (iii) the productdiversification index to capture the extent to which the country diversify the agricultural
production. Drawing upon the dynamic panel model, we have found that transformation of
the agricultural sector in terms of agricultural openness has increased the overall agricultural
productivity and its growth and has consequently reduced national, rural and urban poverty
significantly. Agricultural openness alleviates child malnutrition, namely underweight andstunting, and improves food security in terms of energy supply adequacy, protein supply,
lack of food deficit and reduction of the prevalence of anaemia among pregnant women. The
agricultural openness is negatively associated with the Gini coefficient at both national andsubnational levels for both rural and urban areas. Except for Latin America, productdiversification reduces agricultural productivity, implying the efficiency gains from
economies of scale of fewer crops. On the other hand, the commercialisation does not
generally increase the agricultural productivity and this may be related to a positive effect of
the higher share of cereal production on productivity observed in Sub-Saharan Africa andLatin America. Policies improving the efficiency of agricultural production, for example
through better rural infrastructure, or promoting agricultural exports, through regional
economic integrations or reducing transaction costs such as tariff and non-tariff barriers,
would help to achieve SDGs indirectly through the productivity improvement.

JEL Codes: C23, I30, I14, O18, Q19
Key Words: Agricultural Transformation, Rural Transformation, Poverty, Inequality, Nutrition,
Hunger, Sustainable Development Goals (SDGs),
Acknowledgements: This study is funded by Strategy and Knowledge Department, IFAD
(International Fund for Agricultural Development). The author is grateful to Paul Winter, Rui
Benfica, Fabrizio Bresciani, Tisorn Songsermsa, Kashi Kafle, Pierre Marion, Neh Paliwal andparticipants in the research seminar at Strategy and Knowledge Department (SKD), IFAD, on 5th
September 2017, for valuable comments The author acknowledges useful advice from Raghav Gaiha
and Fabrizio Bresciani who contributed to an earlier draft on Asia and the Pacific which this study
builds on. SKD, IFAD, has kindly provided the data on rural and urban poverty. The author
acknowledges valuable comments from two anonymous reviewers. The opinions expressed in thispublication are those of the author and do not represent those of IFAD.
Corresponding Author: Katsushi S. Imai (Dr.): Department of Economics, University of Manchester,
Arthur Lewis Building, Oxford Road, Manchester M13 9PL, UK; Email:Katsushi.Imai@manchester.ac.uk

1Roles of Agricultural Transformation in Achieving Sustainable
Development Goals on Poverty, Hunger, Productivity, and Inequality
I.

Introduction

While many of the middle and low-income countries across the world are still dependent on
the agricultural sector in raising income and producing employment, the nature of agriculture
has dramatically changed over the last four or five decades. In particular, the agricultural
sector has experienced structural transformation, which has been induced by globalisation,industrialisation, and urbanisation. Despite economic growth, a large section of people inrural areas still suffers from abject poverty and malnutrition, implying that overall economic
growth has bypassed many.
The growth-inequality relationship is intricately associated with the relationship between
structural transformation and inequality. If labour productivity in rural areas rises at a slower
rate than in urban areas, the disparity between rural and urban areas will widen. Rural-tourban migration, however, could have an offsetting effect if migration is temporary andbenefits more rural households than before during the urbanisation process. While many of
the rural regions have benefited from more integrated wholesale and retailing networks andsupply chains (e.g. expansion of supermarket chains to rural areas, horticulture or contract
farming with multinational firms, agricultural production and sales more integrated withurban regions and developed world, and diversification of rural non-farm sector), whether it
decreases inequality or not is unclear and depends on geographical distributions of these
networks. If structural transformation increases overall productivity and outputs in rural
areas, the structural transformation could reduce income inequality at national levels.
However, if backward regions (e.g. mountainous areas) are left out of the structuraltransformation, it is likely to increase inequality. It is thus important to understand better
whether inequality has increased as the country experienced structural transformation in rural
areas.
2

Of particular importance in this context are farm and non-farm linkages and whether
higher rural incomes are in part due to more diversified livelihoods and the emergence ofhigh-value chains and the extent to which these have reduced rural-urban disparities anddampened migration. Apart from easier access to credit in order to strengthen farm/non-farmlinkages, and smallholder participation in high-value chains, other major policy concerns
relate to whether remittances could be allocated to more productive uses in rural areas, 1
through higher risk-weighted returns-specifically, and whether returns could be enhanced inagriculture and rural non-farm activities while risks are reduced (Imai, Malaeb, andBresciani, 2016).
While a large body of the literature has confirmed that agricultural growth ordevelopment leads to poverty reduction (e.g. Christiaensen et al., 2011; Thirtle et al. 2003; deJanvry and Sadoulet, 2009), to our knowledge, there have been no work that has
quantitatively analysed the effects of agricultural transformation - or the structuraltransformation of the agricultural sector - on poverty. A main objective of this study is to fillthis gap in the literature and examine in detail the role of transformation of rural agricultural
sector in achieving Sustainable Development Goal (SDG) 1 (poverty eradication), 2(reduction of hunger and malnutrition and doubling agricultural productivity) and 10(reduction of inequality) drawing upon the cross-country panel data over the past four
decades for 105 developing countries. Methodologically, using the dynamic panel model, we
will take account of the dynamic transmission of agricultural transformation to agricultural
productivity – defined as log agricultural value added per worker and its growth as a broadmeasure of agricultural productivity as well as agricultural TFP growth (Fuglie, 2012, 2015).
In this model, the agricultural transformation is treated as an endogenous variable by using
the dynamic panel model to derive a useful insight into the causal relationships among key
variables. Finally, we will apply the FE-IV model to take account of the effects of3

agricultural transformation on SDGs 1, 2 and 10 via improvement in agricultural
productivity.
The rest of this chapter is organised as follows. After reviewing the concepts ofagricultural transformation in Section II, we will provide three different measures ofagricultural transformation we will use in this study in Section III. Section III also discusses
our proxies for SDGs and summarises the data. Section IV then outlines empirical models to
capture the effects of agricultural transformation on SDGs 1, 2 and 10. Section V reports anddiscusses the results of the models specified in Section IV. The final section provides
concluding observations with policy implications.

II.

Concepts of rural or agricultural transformation

While ‘rural transformation’ (RT) is a broader concept than ‘agricultural transformation’ (AT)as the former includes the transformation of non-agricultural sector, we will primarily focus
on the structural transformation of the agricultural sector (AT). Conceptually, we draw andbuild upon Dawe (2015). While Dawe discusses in detail transformation of the agricultural
sector of middle-income Asian countries, he does not provide a clear definition of‘agricultural transformation’. Citing Reardon and Timmer (2014), Dawe first discusses ‘thestructural transformation of economies’ and then argues that AT is one of the five key
transitions as a result of sustained income, that is, (i) urbanization, (ii) growth of the rural
non-farm economy, (iii) dietary diversification, (iv) a revolution in supply chains andretailing; and (v) transformation of the agricultural sector. Consistent with the last transition,
he argues that ‘(t)here are at least three key changes that might be expected to occur during
the agricultural transition: mechanization, increases in farm size, and crop/productdiversification’ (Dawe, p.13, emphasis added). Dawe then reviews some statistical evidence
to show how mechanization took place, farm size increased, and crop diversification took4

place in middle-income Asian countries, China, Indonesia, Malaysia, the Philippines,
Thailand, and Vietnam. However, as Dawe did not define AT clearly, it is not obvious whatsort of transformation is envisaged. For instance, farm size did not increase uniquely in
different areas of these countries (Figures 14-15 on pp.20-21 in Dawe), but it is not evident
whether this heterogeneity implies that AT took place in some parts of the country and did
not in other parts. It is not clear either whether crop/product diversification took placeconsistently across these countries (e.g. Malaysia became more specialised in oil crops).
As the term suggests, ‘transformation’ should imply a fundamental change of the form of
agriculture, rather than a simple change in production or surrounding environment or
economy, which was not fully discussed by Dawe (2015). Barrett et al. (2017) focuses moreon the efficiency of the agricultural sector from a broader perspective. They view thetransformation essentially as the process whereby the agriculture becomes more efficient andproductive. On the other hand, Ba et al. (1999, p.1) defined AT as a process in which (1)agriculture becomes increasingly reliant on input and output markets, (2) it integrates more
fully with other sectors of the economy, and (3) local producers in the food systemincreasingly incorporate modern scientific knowledge into their practices. The first and thesecond aspects imply a greater exposure of the agricultural sector not only to the nonagricultural sector of the country but also to the rest of the world.
In this paper, following the literature, we define AT as fundamental changes inagricultural production leading to more efficient production which involves (i) changing
cropping patterns with declining shares of grains and rising shares of non-grains, inparticular, fruits, vegetables, dairy products, and meat, and (ii) a greater exposure to theagricultural sector and smallholders to the rest of the world, in particular, export markets.
Our definition follows the theoretical literature of structural transformation, such as Reardon
and Timmer (2014) who focus on diversification, commercialization, and scale of operations.
5However, we also incorporate in our definition of AT the integration of factor market which
has a close link with agricultural transformation (see Figure 1, p.109 of Reardon andTimmer).

III.

Data

Following the discussions on Agricultural Transformation (AT) in the last section, thissection will first provide our three measurements of AT. It will then discuss our measurementof SDGs. Finally, we will describe the data and the other variables used in our empiricalanalyses.
(1)

Measurement of Agricultural Transformation

While AT encompasses various aspects and can be defined in different ways, given the data
constraints, we will use the following three measures, namely, the commercialization index
or the non-cereal share, agricultural openness and product diversification to capture salientfeatures of AT following our definition of AT we provided in the previous section.

Commercialization Index or Non-Cereal Share
We use the FAOSTAT data to construct our commercialization index. The index is definedas the share of processed agricultural products, green vegetables, fruits and meat in the total
agricultural products, including processed and primary products. A precise definition is given
in Appendix 1. The main idea behind this index is that the crops or livestock that has beenprocessed are more likely to be commercialised than primary crops or livestock 2 and thatfruits and green vegetables are likely to be commercialised irrespective of the degree ofprocessing. On the former, we assume, for instance, that farmers producing maize oil are
more commercialised than those producing maize. While this will be a reasonableassumption, it is noted that our measure captures only a part of the processed agricultural
6

products. For instance, we do not have the data of processed cereals (e.g. corn flakes). The
index thus reflects the overall structure of agricultural production in terms of whether the
agricultural crops or livestock are sold as raw crops or processed crops. To overcome thelimitation, we include green vegetables, fruits and meat in the numerator given the fact theseproducts can be commercialised easily without undergoing on-site processing.
To check the overall performance of the commercialisation index, we have examined therelationship between the commercialisation index and the index of mechanisation. Given that
only crude measures are available from FAOSTAT, we have used the number of tractors per
Land Area of 100km2. These variables are positively and significantly correlated with an
overall correlation coefficient 0.19 for all developing areas. We have found that a positive
association is the strongest for Asia where the coefficient of correlation is 0.42. A similar
positive relationship between the two variables is found in both Latin America and SubSaharan Africa with the coefficient of correlation 0.30 and 0.18 respectively. As we will see
later, in our cross-country panel estimations for all developing areas, the commercialisation
does not have a statistically significant positive effect on agricultural productivity. To
supplement this, we will construct the variable called ‘Non Cereal share’ (or Cereal Share),that is, the share of non-cereal product production in total agricultural value added. These
two measures reflect the first dimension of our definition of AT, ‘changing cropping patterns
with declining shares of grains and rising shares of non-grains’.

Agricultural Openness
Agricultural openness is defined as the share of the aggregate agricultural export in the
agricultural value added. Agricultural export is based on the trade file of FAOSTAT(http://www.fao.org/faostat/en/#data/TP) (in the international US$ (PPP) in 2004-2006). The
agricultural exports include food and non-food agricultural product or exports. Here we do
7not include agricultural import because the import of agricultural crops, while influenced by
globalisation and would influence agricultural production systems to some extent, would be
mainly demand-driven and does not reflect the transformation of the agricultural production
systems. 3 On the contrary, the higher share of agricultural export tends to reflect more
integration of agricultural production into the rest of the world and is deemed a more suitableproxy for the agricultural transformation. The agricultural value added is based on World
Development Indicator (WDI) in 2017. As the agricultural sector of the country gets
structurally transformed (e.g. through mechanisation or contract farming), the relativecompetitiveness of the agricultural product improves and the agricultural openness index
tends to be higher. We have examined the relationship between the agricultural openness
index and the commercialisation index for all developing areas and for sub-regions. These
two indices, as expected, are positively correlated with the coefficient of correlation 0.34 forall developing areas. This measure directly captures the second dimension in our definitionof AT.

Product Diversification
We also use the diversity index at the country level drawing upon Remans et al. (2014) who
used an index called ‘Shannon Entropy diversity metric’ to capture the productdiversification at the country level using FAOSTAT. The index can be defined as:
𝐻 ′ = − ∑𝑅𝑖=1 𝑝𝑖 ln 𝑝𝑖
where 𝑅 is the number of agricultural products and 𝑝𝑖 is the share of production for the item
𝑖, available from FAOSTAT. The production share, 𝑝𝑖 , is defined in terms of the monetary
value at a local price for each product, 𝑖. If the country produces more agricultural products,
including processed and unprocessed crops and the monetary values of products are more
evenly divided among different items, the diversity index, 𝐻 ′, takes a larger value. On the8contrary, if the country produces a smaller number of agricultural products and the monetary
value of one or two specific products is large, 𝐻 ′ is smaller. The advantage of using thisindex is to simultaneously capture both how equally the country spreads agricultural
production and how many products it produces. A limitation is that it does not take accountof similarities of products in defining the diversity.
We have checked whether the product diversification index is positively associated withthe commercialisation index. Contrary to our prediction, commercialisation is not
significantly correlated with product diversification (the correlation coefficient: -0.11). A
negative association is strong in Latin America than in Asia or Sub-Saharan Africa. Thisimplies that the commercialisation may take place with specialisation, rather thandiversification in some regions (e.g. Latin America). Given that our three measures ofagricultural transformation are not strongly correlated and capture its different dimensions,they are inserted as explanatory variables at the same time. 4 We conjecture that the productdiversification index partly captures the second dimension in our definition of AT.

(2) Measurement of SDGs
SDG 1
Our interest lies in examining how agricultural transformation helps the country achieve
SDG 1 (ending poverty in all its forms everywhere), SDG2 (ending hunger, achieve foodsecurity and improved nutrition and promote sustainable agriculture), and SDG 10 (reducing
inequality within and among countries).
On SDG 1, the specific target is set as ‘By 2030, eradicate extreme poverty for all people
everywhere, currently measured as people living on less than $1.25 a day’ (emphasis has
been added by the author) among other goals (e.g. reducing at least by half the proportion ofmen, women and children of all ages living in poverty in all its dimensions according to9national definitions). 5 Because it is not possible to use the ‘all its dimensions’ and also
because the World Bank has replaced poverty data at $1.25 a day (2005 PPP) by those at$1.90 a day (2011 PPP) after the launch of SDGs in 2015, we will use the poverty headcountratios at $1.90 and at $3.10 (2011 PPP), while supplementing them with the original
measures for SDG 1, that is, poverty headcount ratios at $1.25 and at $2.00 (2005 PPP). We
further use poverty headcount ratios at $1.25 and at $2.00 for rural and urban areas whichwere provided by Strategy and Knowledge Department (SKD), IFAD. A major limitation
about international poverty measures (as well as hunger, food security and inequality
measures) is that the data are highly unbalanced to reflect the years when household surveys(e.g. Living Standard Measurement Surveys) were conducted in each country. However,taking the five-year average will mitigate the problem of unbalance in the panel data. It isalso noted that the cross-sectional coverage of poverty data (as well as the data on the Gini
coefficient and child malnutrition) is fairly good (with more than 110 countries).

SDG 2
SDG2 stipulates the goals on food security, malnutrition, and agricultural productivity. For
instance, they include the goals: ‘By 2030, ending hunger and ensure access by all people, inparticular the poor and people in vulnerable situations, including infants, to safe, nutritious
and sufficient food all year round’ (emphasis added by the author), ‘By 2030, ending all
forms of malnutrition, including achieving, by 2025, the internationally agreed targets onstunting and wasting in children under 5 years of age, and addressing the nutritional needsof adolescent girls, pregnant and lactating women and older persons’ (emphasis has been
added), and ‘By 2030, double the agricultural productivity and incomes of small-scale foodproducers, in particular women, indigenous peoples, family farmers, pastoralists and fishers,
including through secure and equal access to land, other productive resources and inputs,
10knowledge, financial services, markets and opportunities for value addition and non-farm
employment’ (emphasis has been added), among other sub-goals.6 As it is impossible to deal
with all the goals, we will focus on (i) agricultural TFP growth, (ii) malnutrition prevalencein terms of ‘weight for age’ and ‘height for age’ (% of children under 5), (iii)the prevalence of undernutrition (FAOSTAT), and (iv) food security indices, namely,‘Average dietary energy supply adequacy (%)’ 7 . ‘Average protein supply (g/capita/day)’,‘Depth of the food deficit (kcal/capita/day)’ and ‘Prevalence of anaemia among pregnant
women (%)’. The idea here is that we cover more than one dimension of food security, suchas (i) food availability (dietary energy supply adequacy), (ii) food access (depth of fooddeficit), and (iii) utilization (child malnutrition indicators and prevalence of anaemia).

SDG 10
SDG10 is even more diverse than SDGs 1 and 2 and it is not easy to choose the variables forthe empirical analysis. It stipulates the sub-goals as ‘By 2030, progressively achieve andsustain income growth of the bottom 40 per cent of the population at a rate higher than thenational average’ (emphasis has been added by the author) following the World Bank
arguments in discussion SDGs while the target year, 2015, was approaching (e.g. Dollar etal., 2016 8 ). However, the coverage of the data is not good enough to use it as a main
objective variable. Other sub-goals include: ‘By 2030, empowering and promoting the social,economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity,
origin, religion or economic or other status.’; ‘Ensuring equal opportunity and reduce
inequalities of outcome, including by eliminating discriminatory laws, policies and practices
and promoting appropriate legislation, policies and action in this regard’, ‘Adopting policies,
especially fiscal, wage and social protection policies, and progressively achieve greater
equality’ among other sub-goals. As these broad goals are rather qualitative or not easy to11quantify by using the historical cross-country data, we will use the Gini coefficient at thenational as well as sub-national levels (i.e. rural and urban areas), while supplementing theanalysis by using income growth of the bottom 40 per cent of the population in a highly
parsimonious specification.

(3) Data and Variables
Our empirical analysis is based mostly on FAOSTAT (http://faostat.fao.org/), World
Development

Indicatorsdevelopment-indicators)

(WDI)
and

2017

the

(http://data.worldbank.org/data-catalog/world-

World

Bank

poverty

database(PovCalNet,

http://iresearch.worldbank.org/PovcalNet/). These datasets have been widely used in theliterature and their quality is deemed high, although there are some limitations for specificvariables as we will discuss later. The agricultural total factor productivity (TFP) estimates
are taken from Fuglie (2012 and 2015).9
Table 1 summarizes descriptive statistics of key variables in time-series and crosssectional dimensions and their number of observations at regional levels to provide an idea ofthe data availability. Appendix 2 lists the names of countries, while further details of the data
and definitions are provided in Appendix 3. We have structured the data as five-year averagepanel data to adjust the business cycle of the data following the empirical macro literature.
Most of our variables are based on WDI and FAOSTAT, both of which aggregate microlevel household, labour force or industry survey data. As such, it is difficult to quantify theexact magnitude of measurement errors, it is conjectured that taking the five-year average
will address the errors which occur randomly, while non-random errors (e.g. underrepresentativeness of the very poor in the informal sector, or under-reporting of income by
the rich) cannot be addressed. As such, our results on SDGs will have to be interpreted withcaution.
12

[Table 1 to be inserted around here]
As we will discuss in details, our main hypotheses are (i) whether agricultural transformation
affects agricultural productivity directly and (ii) whether agricultural transformation
indirectly reduces poverty, inequality or malnutrition indirectly through the agricultural
productivity improvement. As we detailed in the previous section, the agriculturaltransformation is proxied by three measures, the commercialization index, the agricultural
openness index and the product diversification index. We will proxy agricultural productivity
by three variables, namely, (i) log of agricultural value added per worker, (ii) the firstdifference in log of agricultural value added per worker (that is, the growth rate of
agricultural value added per worker), and (iii) the agricultural Total Factor Productivity(TFP). These are considered broad proxies for agricultural productivity of the country anddeemed an appropriate proxy for one of the sub-goals SDG 2. Our main dependent variable,
log agricultural value added per capita based on WDI has a good coverage of both timeseries and cross-sectional dimensions, covering 7 five-year periods for 113 countries (Table1). This is considered a broad measure of agricultural productivity in terms of monetary
values of agricultural products to be produced per an agricultural worker. A limitation is that
it is sensitive to sudden demographic changes in rural agricultural sectors, due to migrations.
Our three AT variables and most of our variables have few missing observations in bothtime-series and cross-sectional dimensions, covering at least 7 five-year periods for morethan 100 countries. However, the fragility index covers only 4 five-year periods and thus wehave tried the cases with and without the fragility index. In case the fragility index isincluded (is excluded) in the model, the maximum correlation coefficient in absolute value is0.50 (0.36), which will not cause any concerns for multicollinearity. 10
Furthermore, we have compared the values of selected variables in the initial or early five
years (1980-1984 for agricultural transformation or 1990-1994 for SDG variables) and the13

last five years to see the overall changes of these variables for both unbalanced and balanced
panel data. Here the early periods are selected so that we have a reasonably large sample to
make meaningful comparisons.11 We have found that (i) both agricultural value added per
worker and agricultural TFP significantly increased on average over the years (for bothbalanced and unbalanced panel), (ii) on AT variables agricultural openness and non-cereal
share significantly increased on average, while the commercialization index and the productdiversification remain broadly unchanged, and (iii) all the SDG variables – except poverty at
US$1.9 a day (for the unbalanced panel), weight for age (for the balanced panel) and the Gini
coefficient (for both) - significantly improved over time. This simple comparison is based on
unconditional averages of the variables and ignores their dynamic changes or conditional
factors and thus detailed investigations based on econometric estimations will be carried out
in Section V.

Other Covariates
The degree of fragility is captured by ‘fragility index’ proxied by CPIA rating ofmacroeconomic management and coping with fragility (1=low to 6=high), which reflects avariety of indicators, observations, and judgments based on country knowledge and on
relevant publicly available indicators. The high value implies low fragility and the low-value
high fragility. We have also tried conflict indices, but prefer the ‘fragility index’ as theformer does not cover many countries. We have used the annual growth rates of agriculturalvalue added per worker based on Imai, Cheng, and Gaiha, (2017), drawing upon WDI.
We will use the unbalanced panel data for 105 developing countries (or its subset
depending on the choice of explanatory variables) covering East/South East Asia and thePacific, South Asia, East Europe and Central Asia, Middle East and North Africa, SubSaharan Africa, and Latin America and Caribbean, with a few robustness checks based on
14the balanced panel data.12 We use the data since 1980 till 2015, but some covariate (e.g.
fragility index) is available in 1990-2010 and so the number of years included in the
regression varies depending on the specification. If the fragility index is dropped, the data
cover nearly four decades. Although the data have some limitations arising from the fact thatmany variables from WDI and FAOSTAT were constructed by aggregating the micro-level
data, they are deemed adequate for the objectives of our study.

VI. Empirical Models for Agricultural Transformation, Agricultural Growth andPoverty
The main purpose of our empirical model is to assess the effect of agricultural transformation(AT) on SDG 1 (poverty), SDG 2 (hunger, food security, and agricultural productivity) and
SDG 10 (inequality). To do so, we will first estimate the effect of AT on agricultural
productivity by treating the former as an endogenous variable. This is important because as
the agricultural productivity improves, the degree of agricultural transformation is also
influenced. We will then estimate the effect of the agricultural productivity on SDGs where
agricultural productivity is estimated by AT in the first stage. Details of estimation strategies
are provided below.

Estimation of Agricultural Productivity with Agricultural Transformation Indices
Given that the commercialisation index primarily influences agricultural productivity, we
will first estimate agricultural value added per worker based on (1) pooled OLS, (2) fixedeffects (FE) and random-effects (RE) panel model, and (3) the dynamic panel model (orBlundell-Bond’s (1998) linear dynamic panel model based on a system GMM). Whileagricultural transformation, regardless of its measurement (commercialisation index,
agricultural openness, or agricultural product diversification), affects agricultural output or15

productivity, the process of agricultural transformation is likely to be accelerated byagricultural output or productivity. Given that agricultural transformation (AT) is likely to beendogenous when we estimate agricultural productivity, we apply system GMM by treating
our proxies for AT as endogenous variables by using its own lagged variables in levels anddifferences as instruments.13
Model 1: Pooled OLS, FE or RE model for Agricultural Productivity
𝑨𝑮𝑖𝑡 = 𝛽0 + 𝑨𝑻𝑖𝑡−1 𝛽1 + 𝑿′𝑖𝑡−1 𝛽2 + 𝜇𝑖 + 𝜀𝑖𝑡(1)

For the five-year average panel, t stands for 1 (for 1976-1980), 2 (for 1981-1985), ... , 8 (for2011-2015) covering the last four decades. i denotes countries (1, 2, ... , 105). 𝑨𝑮𝑖𝑡 is adependent variable, an indicator of agricultural productivity. We use either (i) log
agricultural value added per worker, or (ii) the first difference of log agricultural value added
per worker as a proxy for 𝑨𝑮𝑖𝑡 . We will take the lag of agricultural transformation indices
given that the they are likely to be endogenous and using its values will partly mitigate theproblem of endogeneity, while we will use IV or system GMM to address the issue more
rigorously. Here 𝑨𝑻𝑖𝑡−1 indicates the first lag of a vector of agricultural transformation,
namely, the commercialisation index (or in a few specifications, the share of cereal or noncereal products in the total agricultural value added), the agricultural openness index, and the
product diversification index. 𝑿′𝑖𝑡−1 is a vector of explanatory variables: land area,
population density, the fragility index, trade openness, and labour force with secondary
education. 𝜇𝑖 is the country-specific unobservable term, such as, the institutional capacity or
the cultural factor, influencing 𝑨𝑮𝑖𝑡 . 𝜀𝑖𝑡 is an error term, independent and identically
distributed (i.i.d.). This equation is estimated by either pooled OLS, FE or RE model. We
will repeat the same regressions for a subset of the countries, Asia, Latin America andCaribbean and Sub-Saharan separately to see how the effects of 𝑨𝑻𝑖𝑡−1 are different fordifferent regions.
16Model 2: Dynamic Panel Model for Agricultural Productivity′
𝑨𝑮𝑖𝑡 = ∑𝑃𝑗=1 𝛼𝑗 𝑨𝑮𝑖𝑡−𝑗 + ∑𝑄
𝑘=0 𝛽𝑘 𝑨𝑻𝑖𝑡−𝑘 + 𝑿 𝑖𝑡 𝛾 + 𝜂𝑖 +𝜀𝑖𝑡

(2)

We will estimate this equation by system GMM (Arellano and Bover, 1995; Blundell andBond, 1998) because (i) previous values of 𝑨𝑮 are likely to influence the present value of 𝑨𝑮
due to its persistence and (ii) 𝑨𝑻 is likely to be endogenous. Hence 𝑨𝑻𝑖𝑡−𝑘 is treated as
endogenous in the dynamic panel estimation.14 The dynamic panel estimation based on thesystem GMM estimator explicitly models the dynamic realisation of agriculturaltransformation by having lagged dependent variables.15 As an alternative to the standard firstdifferencing approach16, we can use the lagged differences of all explanatory variables as
instruments for the level equation and combine the difference equation and the level equation
in a system. Here the panel estimators use instrumental variables based on previous
realisations of the explanatory variables as the internal instruments, using the ArellanoBover and Blundell-Bond System GMM estimator with additional moment conditions. 17
Such a system gives a more consistent estimator under the assumptions that there is nosecond order serial correlation and the instruments are uncorrelated with the error terms. As arobustness check, in one case, we will add an external instrument, namely, a lagged value oftransport equipment machinery, to capture the overall effectiveness and transaction costs intransporting or exporting goods from rural areas to cities or abroad.
We have repeated all the regressions at the regional levels (namely Asia, Latin America,and Sub-Saharan Africa). Given a small n and a relatively small sample size in this case, afinite sample correction has been made for the variance by using the robust estimator when
two-step estimations are feasible (Windmeijer, 2005).

17Model 3: Fixed-Effects IV Model for SDGs (Poverty, Malnutrition, Food Security, andInequality)
First Stage:
𝑨𝑮𝑖𝑡 = 𝛽0 + 𝑨𝑻𝑖𝑡−1 𝛽1 + 𝑿′𝑖𝑡−1 𝛽2 + 𝜇𝑖 + 𝜀𝑖𝑡(3)Second Stage:
̂ 𝑖𝑡 𝛾1 + 𝑿′𝑖𝑡−1 𝛾2 + 𝜗𝑖 + 𝜀𝑖𝑡
𝑺𝑫𝑮𝑖𝑡 = 𝛾0 + 𝑨𝑮

(4)

Here in order to make the estimation for SDG tractable 18, we will relax the assumption that
𝑨𝑻𝑖𝑡−1 is endogenous in the equation for 𝑨𝑮𝑖𝑡 in Model 2 (system GMM). We assume that,by taking the lag of 𝑨𝑻, namely agricultural commercialisation index, agricultural openness,and production diversity, 𝑨𝑻𝑖𝑡−1 is exogenous in Equation (3) and serve as instruments to
identify the first-stage equation. In other words, following Imai, Gaiha, and Bresciani (2016)
we will estimate the effect of 𝑨𝑻 on 𝑺𝑫𝑮 only through increase in production of the
agricultural sector per worker.
Whether 𝑨𝑻𝑖𝑡−1 serves as a valid instrument should be discussed for each goal in SDGs,i.e., a dependant variable in the second stage. On poverty, while the literature suggests that ahigher agricultural productivity or output leads to poverty reduction, it is unlikely for theincrease in agricultural export, the increase in more processed crops or the increase in kindsof agricultural crops (keeping the agricultural production constant) to influence national,
urban or rural poverty directly. This is because 𝑨𝑻 is the transformation of the agricultural
sector, leading to increase productivity, but poverty is reduced as a result of changes inagricultural income/outputs, not as a result of the structural transformation itself. Of course,
even without any change in the agricultural production or productivity, increasing the shareof processed crops or livestock, shifting the domestic sale to export of crops, or introducing
the new types of crops may lead to increase rural non-farm sector jobs and reduce poverty inthe long run. However, in the short run, it is reasonable to assume that 𝑨𝑻 primarily and18

directly affects agricultural productivity ( 𝑨𝑮) and its effect on poverty is realised only
indirectly through the improvement in 𝑨𝑮. The validity of this assumption can be checked by
specification tests. The same reasoning would apply when we estimate other goals in SDGs,
such as rural or urban poverty, child malnutrition, food security indices and inequality. For
instance, the overall food security could increase as a result of an increase in rural nonagricultural jobs due to the shift of domestic sales of crops to export, but this effect is indirect
and is realised only in the long run.
Alternatively, in a few specifications, we estimate Equations (2) and (4) simultaneously.
̂ 𝑖𝑡 ) and
That is, in estimating Equation (4), we first derive the predicted value of 𝑨𝑮𝑖𝑡 (𝑨𝑮those of the error term 𝜀̂𝑖𝑡 in Equation (2) and then put them in the fixed effects model wherea variable in SDGs is estimated by those prediction terms and control variables where thestandard errors are adjusted by bootstrapping. This is aimed to overcome the data limitations
where some of the variables on SDGs are available for a limited number of years, while 𝑨𝑮𝑖𝑡
has a better coverage. The prevalence of undernutrition (POU) is estimated by the variables
̂ 𝑖𝑡 is not statisticallyon agricultural transformation directly by the fixed effects model as 𝑨𝑮
significant in the first stage of the FE-IV estimation outlined above.
V. Results
In this section, we will summarise and discuss the results derived for Models 1-3 for the fiveyear average panel data.19(1) Results of Models 1-2: All Developing Areas
Table 2 shows the results on the effect of agricultural transformation indices on the level ofagricultural value added per worker using the five-year average panel. Case 1 and Case 3 ofTable 2 (pooled OLS and RE model) indicate that there is an overall positive and significant
association between the commercialisation index and agricultural productivity (broadly
defined as log agricultural value added per worker), while the agricultural productivity is
19negatively associated with product diversity in Case 1. Given that the commercialisationindex is shown as the share (0 to 1) and that the left-hand-side variable is in the log, a 1%
increase in the commercialisation in the previous year/period is associated with the 0.4%1.4% increase in agricultural value added per worker. However, it is noted that the pooled
OLS does not take account of the country unobservable effect and the RE model is notfavoured over the FE model as the Hausman test is statistically significant. So we cannot
make any claim on the causality between the two variables.
[Table 2 to be inserted around here]
The main conclusion that can be safely derived from Table 2 is that agricultural openness
causes higher agricultural production per worker, a broad measure of agricultural
productivity after taking into account the endogeneity of agricultural openness in Cases 4-8of Table 2. This is suggested by statistically highly significant coefficient estimates ofagricultural openness in these cases. In case of System GMM (Cases 4-8), a 1% increase in
the agricultural openness leads to an increase in agricultural value added per capita by 0.040.05%.20 The difference is due to the fact that the effect of lagged agricultural production is
included in the latter. We have dropped the fragility index in Cases 6 and 7 of Table 2 andthus cover nearly four decades, while other cases cover only two decades. However, the signand statistical significance of coefficient estimates for 𝑨𝑻 are similar in these cases. We canconclude that among three proxies for 𝑨𝑻, the agricultural openness is key to promoting
agricultural productivity. On the results of control variables, we observe that, if the country ismore fragile (or vulnerable), the agricultural value added per worker tends to be negatively
affected.
Sensitivity tests are carried out in Cases 6-8 of Table 2. First, to see whether the initialdifferences in the agricultural development matters, we have carried a robustness check in
Case 6 where the initial level of agricultural value added per worker (defined as the value in20

the earliest period during the first three periods in the case where the five-year average panel
is used, or the value in the earliest year during the first 15 years for the annual panel). The
initial level of agricultural development is positive and not statistically significant. Thisimplies that the country which had already developed in agricultural productivity in earlyyears tends to keep the high levels of agricultural productivity in later years. It is noted thatan overall pattern of the results remains same, though the commercialisation index turns
negative. In Case 7 we have used not only internal instruments but also an external
instrument, that is, a lagged value of transport machinery. In Case 8 we have dropped the 26
countries with missing observations (71 observations) in Case 5 to make the panel balanced.
The result does not change substantially. 21
In Cases 5, 6, and 8 of Table 2, the commercialisation index has turned out to be negativeand significant, which is somewhat puzzling given that we have hypothesised thatcommercialisation is one of the key features of agricultural transformation. One of thereasons is that, by the construction of the index with a focus on processed crops, fruits andgreen vegetables, the commercialisation index is negatively correlated with the share of
cereal crops, which is sometimes important for the higher agricultural production. The
coefficient of correlation based on our five-year panel data is 0.06. Barrett et al. (2017) havesuggested that in Zambia maize and cereal output have doubled (with more than 50%
increase in cereal yield) and poverty remained unchanged during the period between 2006
and 2011, while in Rwanda cereal output tripled and poverty reduced by 12% (45% of which
due to an increase in agricultural productivity and marketing) from 2006 to 2011. So in somecountries, the intensifications of cereal production appear to be key to productivity increase.
In Table 3 we have replaced the commercialisation index by the share of cereal
production based on the five-year average panel and a system GMM (without the fragility
index) for all developing areas, Asia, Latin America, and Sub-Saharan Africa. In Case 1 of21

Table 3 for all developing areas, while agricultural openness remains positive and significant,cereal share is also positive and significant, implying that the higher cereal production will
improve the broad measure of agricultural productivity in terms of log agricultural value
added per worker. This is due to the positive effect of crop share in Sub-Saharan Africa andLatin America (Cases 3 and 4), while it is negative and significant for Asia (Case 2) where
cereal production has become mature and shift to non-cereal production is more important.
In disaggregated cases, agricultural openness is positive and significant only for Latin
America, reflecting the possibility that the role of openness matters more in countries withhigher GDP per capita and at a higher level of structural transformation. In all the regional
regressions where system GMM is applied, standard errors are based on the robust estimator
for the small sample in regional regressions (Windmeijer, 2005).
[Table 3 to be inserted around here]
One of the reasons why the share of cereals is positive for Latin America and Sub-SaharanAfrica, while it is negative for Asia, is that the land abundance determines the extent towhich cereal production contributes to the overall agricultural productivity in the long run,that is, the country with the relatively small arable land area cannot raise the productivity
without relying on large land. To test this, we have inserted an interaction term of the landarea and the share of lagged cereal production (as well as the cereal share and the land area)
in Case 5. The result shows that the interaction is positive and statistically significant at the
1% level, while the coefficient estimate of the cereal share is positive and non-significant.
This implies that the initial endowment of the country explains the role of cereal (or noncereal share) as well as of the commercialisation index in determining the agricultural
productivity.
As an extension we have tried a case where the dependent variable is the growth (rather
than the level) of agricultural value added per worker, that is, the first difference of log of22

agricultural value added per worker for the five-year average panel based on the system
GMM. 22 Consistent with the results in Table 2, if the share of agricultural export inagricultural value added of the country increases, the agricultural productivity increases. We
can conclude that a 1% (or 10%) increase in the agricultural export share leads to the
productivity

growthby

0.04%-0.05%(or

0.4%-0.5%).

On

the

contrary,

thecommercialisation index is negatively and significantly associated with the productivity
growth. It is conjectured that only a shift from primary crops to processed crops or green
vegetables/fruits may not lead to the productivity growth because there is a time lag for the
new crop productions to get more productive (e.g. due to the initial fixed investment) or
because cereal productions are sometimes more productive than non-cereal productions. We
have also tested our earlier hypothesis by inserting the cereal share and the initial level of log
agricultural value added per worker. First, the share of cereal products positively influences
the growth of agricultural productivity. The initial level of log agricultural value added per
worker, on the contrary, is positive and statistically insignificant. Inserting this term will notchange the overall pattern of the coefficient estimates of other variables.23(2) Results of Model 2: Asia, Latin America, and Sub-Saharan Africa
Given the possible heterogeneity of the effect of agricultural transformation on agriculturaldevelopment, we applied the same model for Asia, Latin America and Caribbean, and SubSaharan Africa as presented in Table 4. Given the results in Table 3, we use the non-cereal
share as an alternative to the commercialisation index. We find that in Asia agricultural
openness decreases agricultural TFP growth in Case 3.24 More importantly, the results inCases 1 and 3 confirm that in Asia the non-cereal share increases not only log agriculturalvalue added per capita but also the growth in agricultural TFP growth index defined by
Fuglie (2012, 2015). The reason is unexpected, but it could reflect a negative association

23

between the agricultural TFP growth index and log agricultural value added per worker in thenegative range of the former (see Footnote 22).
The results on Latin America and the Caribbean are shown in the second panel of Table 4.
For instance, if we focus on Case 4 in which the system GMM model is validated by AR
tests and Sargan and Hansen tests, we find that both agricultural openness and productdiversification increases our broad measure of agricultural productivity. That is, in addition
to the role of openness, diversification of the agricultural product has a central role inincreasing agricultural productivity in Latin America. However, the agricultural openness isnegative and significant in Cases 6 and 7, to reflect the differences in the proxies for
agricultural productivity.
The third panel of Table 4 reports the results for Sub-Saharan African countries.
Consistent with previous results, agricultural openness is positively associated with bothlevel and growth of the overall agricultural productivity. In Case 11, however, we find that
agricultural openness promotes the growth of agricultural TFP. The non-cereal share and the
product diversification index are mostly negative and significant for Sub-Saharan Africancountries, implying that specialisations at the aggregate level are important for promoting
agricultural productivity in these countries with comparatively lower GDP per capita.
[Table 4 to be inserted around here]
That is, for Sub-Saharan Africa, it would be crucial for policymakers to specialise certain
crop productions, such as cereals, to improve the efficiency through the economies of scale,and focus on increasing the share of agricultural export in the total agricultural production. 25(3) Results of Model 3: SDG 1
We now apply the FE-IV model for both five-year average panel in Table 5.26 The model
consists of the two stages: the first stage where log agricultural value added per worker is24estimated

by

our

three

measuresofagriculturaltransformation,

namely,thecommercialisation index, the agricultural openness index, and the product diversificationindex (instruments) with control variables and the second stage where the poverty headcountratio is estimated by log of agricultural value added per worker that was estimated in the firststage. The objective variables are poverty headcount ratios at US$1.9 or US$3.1 a day (2011
PPP) at national levels (Cases 1-2 of Table 5) or US$1.25 or US$2 a day (2005 PPP) at thenational level, or at rural or urban areas (Cases 3-8 of Table 4). In the first stage agricultural
openness is positive and significant in all the cases in Table 4, while the commercialisation issignificant in Cases 1-4 and the product diversification index is significant in Cases 5-8.
Sagan test together with Stock-Yogo test implies that the instruments are valid in all thecases except Case 8.
[Table 5 to be inserted around here]
The estimates of the elasticity of poverty with respect to agricultural openness (evaluated at
the mean of the poverty headcount ratio) are shown at the bottom of Table 5. The firstconclusion is that agricultural openness tends to reduce poverty more strongly at the higher
poverty thresholds. In other words, the poverty reducing effects at the lower poverty
thresholds are limited. Second, based on the five-year average panel data, a 1% increase in
the agricultural openness tends to reduce poverty ay by 0.062% to 0.188% at higher poverty
thresholds and by 0.03% to 0.06% at the lower thresholds, though the latter are notstatistically significant. These are not high at first sight, but poverty-reducing effects arelikely to be substantial as the effects will be accumulated over the years. Thirdly, agricultural
openness is likely to reduce both rural and urban poverty, with the absolute magnitude of
reduction larger for rural poverty. We conclude that the agricultural transformation, proxiedby the agricultural openness, has a significant poverty-reducing effect. We have used thepoverty headcount ratios, but similar conclusions have been obtained for poverty gap
25measures. In terms of policy implications, it is important for policymakers to facilitate
agricultural export directly by removing tariff and non-tariff barriers, or indirectly by
investing rural or transport infrastructure to reducing transaction costs, in order to improve
the overall agricultural productivity and eradicate extreme poverty.

(4) Results of Model 3: SDG 2
As we have already examined the effects of agricultural transformation indices on
agricultural productivity in details, we will estimate the effects of agricultural transformation
indices on child malnutrition, undernutrition and food security. The same model, namely, the
FE-IV model is used except the case where the prevalence of undernutrition is estimated as
the predicted value of the log of agricultural value added is not statistically significant in thesecond stage. The results are reported in Table 6.
Stock-Yogo test and Sargan test validate our instruments in terms of the relevance and theexclusion restriction except for Cases 4-6 where the F-statistics are lower than the StockYogo threshold. In Table 6, the agricultural openness is positive and significant in the firststage and log agricultural value added per worker is negative and significant in all the cases.
As before, the elasticity estimates are reported at the bottom of Table 6. It is found that anincrease in agricultural openness significantly reduces the prevalence of underweight. A 1%
increase in the agricultural openness leads to 0.038% reduction in the rate of prevalence ofchild underweight evaluated at its mean, other factors being equal. Likewise, a 1% increasein the commercialisation index leads to 0.013% reduction in the rate of prevalence of childunderweight.
[Table 6 to be inserted around here]
We also found that a 1% increase in the agricultural openness on average leads to 0.098%
reduction in the rate of prevalence of stunting evaluated at its mean, other factors being
equal. We have also confirmed that if agricultural sector is more open to the rest of the26

economy, food security is likely to improve in a number of dimensions, such as energy
supply adequacy, protein supply and (lack of) food deficit and the rate of pregnant women
who suffer from anaemia. In the final column, the result is presented on the prevalence ofundernutrtion (POU). It is found that if agricultural openness increases by 1%, the POU tends
to decrease by 0.006. However, the result should be interpreted with caution as it is notstatistically significant.

(5) Results of Model 3: SDG 10
Next, we have estimated the effects of agricultural transformation on inequality (SDG 10)
using the Gini coefficient at national, rural and urban levels as well as welfare growth rate of
the bottom 40% in Table 7. While the FE-IV model is applied to the Gini coefficient, a
simple single univariate model is used for the growth of the bottom 40% of the population.
Instruments have been validated in all the cases in terms of the instrument relevance test
(Stock-Yogo test) and the test for exclusion restrictions (Sargan Test).
[Table 7 to be inserted around here]
Table 7 shows that one of the instruments, agricultural openness is positive and significant in
the first stage in all the cases where FE-IV model is applied, while log agricultural value
added per worker is negative and significant in the second stage regression for the Gini
coefficient. The combined elasticity estimates are shown at the bottom of Table 7. A 1%
increase in the agricultural openness corresponds to 0.028% decline in the national Gini,
0.03% decline in the rural Gini and 0.027% decline in the urban Gini. This appears not to belarge at first sight, but cumulative effects could be substantial. That is, the agriculturaltransformation is found to have a substantial inequality-reducing effect.
However, we have found a negative and significant relationship between log agriculturalvalue added per worker and the welfare growth rate of bottom 40%. That is, agricultural
27

growth tends to deter the welfare growth of bottom 40%. Given that the main determinant ofagricultural value added per worker is agricultural openness, we can infer that agriculturaltransformation in terms of increased openness of the agricultural sector to the rest of theworld would increase inequality once we focus on the growth rate of bottom 40%. However,
because this is a crude measure of inequality that ignores the distribution among the bottom
40%, we would place more importance on the results which confirm inequality-reducing
effects of agricultural transformation when the Gini coefficient is applied.
We have confirmed that the agricultural openness is significantly associated withimprovement in a number of measures in SDGs 1, 2 and 10. However, a caveat is that not all
the countries have been successful in increasing the agricultural openness. In most of thecountries in Latin America and Sub-Saharan Africa, the index increased only gradually.
Overall, the agricultural openness index increased by 1.7% annually (by 1.0% for Asia, 3.1%
for Latin America and by 1.1% for Sub-Saharan Africa). But this average masks theheterogeneity across different countries as while some countries have managed to increase
the agricultural openness greatly, agricultural openness remained unchanged in somecountries. For instance, Botswana (South Africa) increased the agricultural openness by 5.8%
(2.9%) annually, while it was stagnant in other SSA countries. In Asia, Malaysia or Vietnam
has had a high growth rate in the agricultural openness (3.3% and 2.9% respectively), but theincrease is slow in South Asian countries. Hence how the country changes the export
structure strategically is key to embodying a positive effect of the agricultural openness on
SDGs.

(5) Results of Model 3: SDG 1 and 2 at regional levels
To see how other indices affect SDGs, we have estimated the Model 3 where in the firststage the dynamic model is used and in the second the fixed effects model is applied by using
28

the predicted values of log agricultural value added and the error term with standard errors
bootstrapped. This is carried out for selective cases of regional regressions for Asia, Latin
America, and Sub-Saharan Africa where insightful results are obtained in the first stage.27
We have found that for Asia the lagged share of non-cereal production significantly
reduces poverty headcounts at US$3.10 and US$1.90 as well as the prevalence of stunting
and underweight. For instance, a 1% increase in the lagged share of non-cereal production
reduces poverty at US$3.10 by 0.12%. In Latin America, on the other hand, the productdiversification index significantly reduces the poverty headcount at US$3.10. A 1% increasein the diversity index leads to, on average, 0.085% decline in the poverty headcount at
US$3.10. While this is larger than the effect of the agricultural openness (0.0074%), we
avoid comparing the estimates across different measures of agricultural transformation as a
1% change has a different meaning according to which index we focus on. In Sub-SaharanAfrica, though the estimates are statistically insignificant, an increase in the cereal share is
likely to decrease poverty, the results which are in sharp contrast with those for Asia.

VI.

Concluding Remarks and Policy Implications

This paper has analysed the role of the transformation of the rural agricultural sector (or‘agricultural transformation’ in short) in achieving Sustainable Development Goals (SDGs)
1, 2 and 10 drawing upon the cross-country panel data over the past four decades for 105
developing countries. Agricultural transformation has been featured in recent years as animportant change in the agricultural sector and in the rural economies of developing
countries as the rural agriculture sector has become closely linked to not only the nonagricultural or industrial sector of the country and the rapidly-growing urban sector of thecountry but also the agricultural and non-agricultural sectors of both developed anddeveloping countries.
29

The process of agricultural transformation is diverse and it is not straightforward toquantify it in a meaningful way. So we define agricultural transformation by three different
indices, namely, (i) the agricultural openness index - the share of agricultural export inagricultural value added of the country, (ii) the commercialization index - the share ofprocessed agricultural products, fruits, green vegetables, and meats in all primary andprocessed agricultural products, and (iii) the product diversification index to capture theextent to which the country diversify the agricultural production.
Agricultural development has been considered to be important for poverty reduction, but
it remains unclear whether agricultural transformation influences agricultural development
and poverty, SDG 1. Also important yet unclear is whether and the extent to which
agricultural transformation would reduce hunger, malnutrition and food security (SDG 2) andinequality (SDG 10). Our study is important because, as far as we know, this is the first study
to quantify the agricultural transformation - even in a limited manner - and evaluate its effecton agricultural productivity and SDGs. Methodologically, not only the dynamic nature of
agricultural production but also the endogeneity of agricultural transformation is taken into
account by the model of system Generalized Method of Moments (system GMM).
First, we have found that transformation of the agricultural sector proxied by the
agricultural openness index has dynamically increased the broad measure of agricultural
productivity (in terms of agricultural value added per worker) and its growth and has
consequently reduced national, rural and urban poverty significantly. However, the povertyreducing effects of the agricultural transformation are relatively larger at the higher poverty
thresholds (US$3.1 per day (2011PPP) and US$2 per day (2005PPP)) than at the lower
poverty thresholds (US$1.9 per day (2011PPP) and US$1.25 per day (2005PPP)) for bothpoverty headcount and poverty gap measures. So there is a possibility that the moderately
poor benefit more than the extremely poor from the process of agricultural transformation.
30

Our results may be driven by a cluster of countries with relatively high growth in both
agricultural openness and agricultural productivity and so the results will have to beinterpreted with caution given that it may not be straightforward to the country increases
agricultural openness in a short period.
Second, agricultural openness is negatively associated with child malnutrition in terms ofunderweight and stunting, improves food security measures, such as the indices of energy
supply adequacy, protein supply, depth of food deficit and the prevalence of anaemia among
pregnant women.
Third, the agricultural openness tends to reduce the Gini coefficient at both national andsubnational levels (for both rural and urban areas), while it is likely to deter the welfare
growth of the bottom 40%. So we have good evidence to support the claim that agriculturaltransformation in terms of agricultural openness helps achieve SDG 1 (poverty reduction),
SDG 2 (reducing hunger and malnutrition, improving food security, and doubling
agricultural productivity) and SDG 10 (reducing inequality).
Fourth, whether more commercialisation in terms of the higher share of processed crops,
fruits and green vegetables improves agricultural production depends on the specifications
and geographical regions. For instance, if we use pooled OLS or random effects model, thehigher commercialisation index is associated with higher agricultural value added per
worker. Also, in Latin America where the agricultural sector is more mature than otherregions, commercialisation tends to improve agricultural TFP. In a few cases thecommercialisation index is negative and significant. This could be due to the fact that factthat the higher commercialisation captures the lower share of cereal production. In one case,the higher share of cereal crops – together with agricultural openness – was found toincreases log agricultural value added per worker (particularly in Sub-Saharan Africa andLatin America).
31Fifth, product diversification, contrary to our expectation, reduces agricultural
productivity in a number of cases. This is likely to be due to the fact that the economies ofscale of agricultural production are often important in raising the country’s agricultural
productivity. Also, if similar crops are produced in a large area of the country, positive effecton productivity due to learning from one region to another, or competition across different
regions is greater. For instance, among Asian countries, Malaysia has achieved a higher
agricultural productivity with the low product diversification (Imai, Gaiha, and Bresciani,2016). However, among Latin American countries, product diversification was found toincrease agricultural productivity.
Our study has a number of policy implications. First, agricultural productivity growth,for instance, through higher agricultural openness will help developing countries achieve
SDG 1 (poverty reduction), SDG 2 (alleviation of malnutrition and hunger; securing foodsecurity), and SDG 10 (reduction of inequality). Because SDG 2 includes doubling
agricultural productivity, our analysis suggests a win-win solution where the facilitation ofagricultural trades, for instance, through promoting regional economic integrations, reducing
transaction costs such as tariff and non-tariff barriers, would improve agricultural
productivity and address other SDG goals of poverty, hunger and inequality. However, if thecountry’s agricultural openness has become already high (e.g. Thailand, Vietnam), there isno scope for increasing the agricultural openness and a policy is crucial to further increase
the agricultural productivity or directly support the poor. If the country’s agricultural
openness is low, it can increase the agricultural productivity by promoting the agricultural
export, which could indirectly improve SDG measures. However, it should be noted that theeffects of agricultural openness on SDGs are indirect. What is crucial is for the country tochoose a set of policies to maximise agricultural productivity (where increasing the openness
is only one of the potential choices) in order to achieve SDGs.
32

Second, our paper shows that agricultural transformation is likely to improve agricultural
productivity, which would reduce both moderate poverty and extreme poverty. However, thepoverty-reducing effect is higher for the former. It is noted here that agriculturaltransformation itself does not have any distributional effect. Agricultural productivity
improvement will bring about benefits for the moderate poor, rather than the extreme poor,
because the latter may be left out of the process of accessing the new technologies,
infrastructure, or other opportunities for agricultural transformations. Under thesecircumstances, a policy is still important to support the poorest while the rural agricultural
sector undergoes a transformation. Also, given that there is a negative correlation between
the agricultural value added per worker and the welfare growth of the bottom 40%, theeventual/indirect distributional impact of policies promoting the process of agriculturaltransformation should be carefully monitored.
Third, our results suggest that product specialisation is important for improving
agricultural productivity, except for Latin America.28 This could be related to other findingsof the current study, such as an important role of agricultural openness - where the neoclassical trade theory implies higher efficiency through specialisations - as well as theimportance of cereal production in improving overall productivity. Depending on the stage of
agricultural development, facilitating the production and exporting of cereal crops will
increase the agricultural productivity, while at the higher level of development (e.g. Latin
America), product diversification would increase agricultural productivity.
Finally, if efficiency though the economies of scale is important - suggested by the rolesof product specification (which are implied by negative coefficient estimates of productdiversifications in several cases e.g. in Tables 3 and 4) - there is a case for promoting synergy
between public and private investment in rural areas and for improving the quality of rural
infrastructure. A priority is to strengthen rural infrastructure. Our results suggest that the33transformation process incorporating poor smallhoders through easier access to credit, landand output markets leads to poverty and inequality reduction. As prospects of absorption of
growing rural labour force in manufacturing and services and other activities are limited, job
expansion in rural areas is imperative. Some of the preceding proposals would help create
more employment in rural areas, raise wage rates and dampen rural-urban migration. Landrental markets would facilitate the redistribution of land in favour of more efficient small
farmers and help consolidation of small farms into more viable units.

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37

Table 1 Descriptive statistics of key variables in time-series and cross-sectional
dimensions and their number of observations at regional levels
Variable
Agriculture value added per
worker (first difference)

SourceWDI(constant 2010 US$)

Agriculture value added per
worker (first difference oflog) (constant 2010 US$)

Agricultural TFP Index
growthCommercialisation Index

Agricultural Openness

Product Diversification

Land Area

Population Density

Fragility Index

openness

Mean

Overallbetween (crosssection)

7.58

within (time)
Available time
periods
WDI

Fuglie, 2012

FAOSTATFAOSTATFAOSTAT

WDI

WDI

WDI

WDI

OverallMin

Max
overall

Observations
Asi
Latin
a
America

SS
A

1.20

4.94

12.43

776

138

171

288

1.19

5.38

11.40

113

20

22

41

0.29

6.56

8.60

7
19802014

7

8

7

0.14

-0.44

0.52

663

118

149

247

Between

0.10

-0.11

0.51

112

19

22

41

WithinAvailable time
periods

0.10

-0.41

0.65

6
198520146

7

6Overall

0.10

Std.
Dev.

0.02

-0.14

0.15

1229

228

242

484

Between

0.01

-0.01

0.03

113

21

22

44

WithinAvailable time
periods

0.02

-0.14

0.15

11
19612014

11

11

11

0.18

0.17

0.99

1265

231

242

462

Between

0.17

0.26

0.99

115

21

22

42

WithinAvailable time
periods

0.04

0.33

0.93

11
19612014

11

11

11

0.82

0.00

13.17

831

156

197

316

Overall

Overall

0.01

0.68

0.36

Between

0.69

0.03

5.39

108

19

21

40

Within

0.55

-3.27

8.15

8
19752014

8

9

8

0.49

0.27

3.40

1265

231

242

462

BetweenOverall

0.48

0.53

3.27

115

21

22

42

Within

0.09

2.08

2.83

11
19612014

11

11

11

Overall

2.54

1923

0.46

16400

1410

246

276

516

Between

1927

0.46

16400

118

21

23

43

Within4.70

702.40

803

11

11

11

105.3
3

0.78

1245.1
1

11
19612014

Overall758.00

75.4
9

1404

246

276

516

816.21

118

21

23

43

40.04

1.77
342.6
2

504.3911
19612014

11

11

11

Between

97.51

WithinOverall

11.2
2

5.88

0.00

23.80

446

78

84

168

Between

5.73

0.25

23.25

113

20

21

42

Within

1.47

6.94

15.45

4
19612014

4

4

4

overall

70.2
6

36.79

4.30

229.64

1109

193

255

438

between

32.18

19.42

147.10

117

21

23

42

within18.97-12.65173.92

9

9

11

10

38

19752014
Labour force

0.42

0.00

1.00

1428

252

276

528

with secondary

between

0.38

0.07

1.00

119

21

23

44

Education

within

0.18

0.37

1.58

12
19802014

12

12

12

povertyhc $3.1

povertyhc $1.9

Weight for age

Gini

WDI

WDI

WDI

WDI

WDI

overalloverall

0.73

38.6
0

30.79

0.00

98.35

483

100

115

137

between

29.71

0.07

94.53

112

21

23

43

within

9.74

-6.56

75.18

4
19802014

5

5

3

overall

23.0
9

24.30

0.00

94.05

483

100

115

137

between

23.95

0.03

85.57

112

21

23

43

within8.96-13.1262.07

419802014

5

5

3

overall

17.5
6

13.00

0.50

67.30

484

94

117

193

between

11.98

0.60

51.98

111

20

23

44

within

4.78

-2.38

36.99

4
19802014

5

5

4

overall

40.8
2

9.18

19.40

74.33

646

125

144

196

between

7.83

25.69

56.31

119

21

23

44

within

4.21

25.24

59.65

5
19802014

6

6

4

39

Table 2 Effects of Agricultural Transformation on the Level of Agricultural Value
Added per capita (Five-Years Average Panel)Dep. Variable

log agricultural value added

Regions

EXP. VARIABLESAll Developing Areas
Case 1PooledOLS

Case 2

Case 3

Case 4

Case 5

FE

RE

SGMM

SGMM

log Agricultural Value Added per
worker, an initial value
Commercialization Index(-1)

Agricultural Openness(-1)

Product Diversity(-1)
Control Variables
Number of lags ofdependent variables in
the right hand side
Observations
R-squared
Number of countries

Case 6

Case 7

Case 8

SGMM
With aninitial value

SGMM
With anexternal IV

SGMM
Balanced
panel

0.0104[1.523]

1.386***-0.1270.443*-0.155[4.503][-0.423][1.647][-1.101]-0.01210.0430**0.0464**0.0422***[-0.194]
0.281***[-2.600]
Yes[2.185][2.294][6.105]-0.00207-0.0998-0.0181[-0.0141]
Yes[-0.821]
Yes

0

0

370
0.522

370
0.259
102

-0.364***-0.263***0.124-0.364***[-3.225][-9.636][1.239][-3.225]

0.0491***[16.55]0.0432***[5.426]

0.0389***[10.27]0.0491***[16.55][-0.283]
Yes-0.0627[-1.222]Yes

0.0202*[1.732]
Yes-0.00265[-0.0402]
Yes-0.0627[-1.222]
Yes

0

2

2

370

244

417

2
417

2
195

2
346

102

AR(1) in first difference (Pr > z )AR(2) in first difference (Pr>z)Sargan statistic
Over identification tests
Sargan Test (Chi2)
Prob > chi2
Hansen Test (Chi2)
Prob > chi2

92

96

96

64

0.038**
0.148

0.001***
0.08*

0.038
0.148

0.001
0.08

70
0.001
0.08

6.03
0.813
6.48
0.774

6.03
0.052
6.48
0.051

28.7
0.052
28.78
0.051

28.7
0.244
28.78
0.343

21.74
0
19.83
0.078

Breusch and Pagan Lagrangian multiplier test for random effects
chibar2(01) =435.57***
Prob > chibar2 =0
Hausman Test (chi2(7))
29.27
Prob>chi2 =0.0001
Notes: t-statistics in brackets (*** p<0.01, ** p<0.05, * p<0.1). System GMM is based on the two-step robust estimators. Control
variables are Land area, Population Density, Fragility Index, Trade Openness, and Labour Force with Secondary Education.
Same control variables will be used in Tables 3-8.

40

Table 3 Effects of Cereal Share on the Level of Agricultural value added per worker
log agricultural value added

Dep. Variable
Regions

All developing
areas

Data

Lain
AmericaFive-Years Average Panel
Asia

SSA

All developing
areas

Case 1Case 2

Case 3

Case 4

Case 5

EXP. VARIABLES

SGMM

SGMM

SGMM

SGMM

SGMM

Cereal Share (-1)

0.195*-0.225**0.371**

0.702***

0.155[1.684][-2.650][2.161][2.994][1.272]

Cereal Share (-1)*Land
Agricultural Openness (-1)
Production Diversity

2.12e-08***[2.826]
0.0535***0.170

0.0619***0.115

0.0539***[8.506][1.463][6.529][1.348][8.685]-0.1210.0709-0.663***0.138-0.127*[-1.629][1.003][-7.004][1.527][-1.688]

Control Variables
Number of lags of dependent
variables in the right hand side

Yes

Yes

Yes

Yes

Yes

2

2

2

2

2

Observations

325

61

80

124

325

Number of country

95
0

17
0.994

21
0.03

32
0

95

AR(1) in first difference (Pr > z )AR(2) in first difference (Pr>z)

0.118

0.723

0.538

0.087

0.134

10.04

21.55

7.7

20.33

10.09

0

Over identification tests
Sargan Test (Chi2)

Prob > chi2
0.612
0.723
0.808
0.061
0.608
Hansen Test (Chi2)
10.79
10.75
9.05
14.14
10.69
Prob > chi2
0.574
0.55
0.698
0.528
0.556
Notes: t-statistics in brackets (*** p<0.01, ** p<0.05, * p<0.1). Standard errors are based on the robust estimator for thesmall sample in regional regressions (Windmeijer, 2005).

41

Table 4 Regional Disaggregation: Effects of Agricultural Transformation on Agricultural Productivity
Dep. Variable

log
Agricultural
VA percapitaRegions
EXP. VARIABLES
Non Cereal Share(-1)
Agricultural Openness(-1)
Production diversification (-1)

D.log Agricultural
Value Added per
workerAgricultural
TFP
Growthlog
Agricultural
VA percapitaAsia

Dlog
Agricultural
VA percapitaAgricultural TFP growthlog
Agricultural
VA percapitaLatin America

D.log
Agricultural
Value
Added perworkerAgricultural
TFP
GrowthD
Agricultural
TFP
growthSub-Saharan Africa

Case 1Case 2

Case 3

Case 4

Case 5

Case 6

Case 7

SGMM

SGMM

SGMM

SGMM

SGMM

SGMM

SGMM

Case 8
SGMM

Case 9
SGMM

Case 10
SGMM

Case 11SGMM-0.498***-0.0805***-0.0610*0.225**-0.0546

0.0583***-0.1970.0996

0.165***-0.00453-0.494***[2.650][-0.516][2.890][-0.696][0.274][2.937][-0.0981][-2.957][-3.569][-3.484][-1.756]0.170

0.0244-0.0276**0.0500***0.0426***-0.00184**-0.00208***

0.166**

0.177*

0.01880.0494***[1.463][0.345][-2.213][4.561][5.665][-2.665][-7.205][2.378][1.854][1.298][3.002]-0.385***-0.0135-0.00541

0.0709-0.0316-0.00607

0.247*

0.328***

0.02780.0135-0.384***[1.003][-0.808][-0.465][1.916][3.033][0.995][0.876][-3.365][-2.921][-1.232][-0.301]

Control Variables
Number of lags of dependent
variables in the right handside
Observations

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

2

2

2

61

43

51

2
58

2
56

2
60

2
148

2
91

2
90

2
93

2
93

Number of countries

17

20

20

21

31

31

33

33

0.747

18
0.182

20

AR(1) in first difference (Pr > z )

17
0.944

0.199

0.036

0.172

0.01

0.037

0.06

0.052

0.042

AR(2) in first difference (Pr>z)

0.723

0.189

0.636

0.067

0.768

0.239

0.596

0.458

0.83

0.426

0.967

12.57

6.59

30.97

52.2

18.10*

16.45**

50.87***

28.88*Sargan Test (Chi2)
Prob > chi2
Hansen Test (Chi2)
Prob > chi2

21.55**

18.07**

31.95*

0.043

0.012

0.199

0.472

0.097

0.135

0.053

0.021

0

0.068

10.75

6.66

0.078
7.61

12.75

12.49

9.53

13.51

9.2

15.00**

25.6

22.51

0.55

0.465

0.998

0.238

0.085

0.99

1

0.513

0.036

0.269

0.259

Notes: t-statistics in brackets (*** p<0.01, ** p<0.05, * p<0.1). Standard errors are based on the robust estimator for small sample in regional regressions for the dynamic panel estimations (Windmeijer,
2005)

42

Table 5 SDG 1: Effects of Agricultural Transformation on Poverty
Dep. Variable
Regions
Data
EXP. VARIABLESFIRST STAGE
Dep. Variable
Commercialization Index
L1.
Agricultural Openness
L1.
Product diversification
L1.
Control Variables

VARIABLES
log Agricultural Value
Added per worker
Control Variables
Observations
R-squared
Number of country
F test of excluded
instruments
Prob > F=
Sargan statistic(over identification
test of all instruments)
Chi-sq(2) Pvalue
Elasticity Estimates

PovertyHC
USD3.10

PovertyHC
USD1.90

PovertyHC
USD2.00

Case 1IV

Case 2
IV

Case 3
IV

Poverty
Rural
Rural
HC
Poverty HC
Poverty HC
USD1.25
USD2.00
USD1.25
All Developing Areas
Five Year Average Panel
Case 4
Case 5
Case 6IV
IV
IV

log Agricultural value added per worker

Urban
Poverty HC
USD2.00

Urban
Poverty HC
USD1.25

Case 7
IV

Case 8IV

log Agricultural value added per worker-0.682**[-2.00]-0.682**[-2.00]-0.697**[-2.01]-0.697**[-2.01]-0.424[-1.05]-0.424[-1.05]-0.424[-1.05]-0.424[-1.05]0.386***[7.40]

0.386***[7.40]

0.787***[7.29]

0.787***[7.29]

0.460***[8.59]

0.460***[8.59]

0.460***[8.59]

0.460***[8.59]-0.027[-0.19]Yes
PovertyHC
USD310-0.027[-0.19]Yes
PovertyHC
USD190-0.121[-0.82]
Yes
PovertyHC
USD200-0.121[-0.82]
Yes
PovertyHC
USD125-0.452***[-2.68]Yes
Rural
Poverty HC
USD200-0.452***[-2.68]Yes
Rural
Poverty HC
USD200-0.452***[-2.68]Yes
Urban
Poverty HC
USD200-0.452***[-2.68]Yes
Urban
Poverty HC
USD200

PovertyHC
USD3.10

PovertyHC
USD1.90

PovertyHC
USD2.00

PovertyHC
USD1.25

Rural
Poverty HC
USD2.00

Rural
Poverty HC
USD1.25

Urban
Poverty HC
USD2.00

Urban
Poverty HC
USD1.25-0.162***[-2.875]
Yes
371
0.373
85

-0.079[-1.386]
Yes
371
0.265
85

-0.239***[-3.922]
Yes
326
0.286
83

-0.079[-1.247]
Yes
326
0.123
83

-0.2398***[-4.265]
Yes
224
0.351
74

-0.151***[-2.736]
Yes
224
0.333
74

-0.1471***[-3.050]
Yes
224
0.305
74

-0.0832**[-1.996]
Yes
224
0.193
74

F( 3, 279)=F( 3,279) =

F( 3,236) =

F( 3,236) =

19.79***
019.79***
0

19.09***
0

19.09***
0

1.459
0.482

3.743
0.154

0.722
0.697

3.389
0.184

3.879
0.144

4.263
0.119

PovertyHC
USD3.10

PovertyHC
USD1.90

PovertyHC
USD2.00

PovertyHC
USD1.25

Rural
Poverty HC
USD2.00

Rural
Poverty HC
USD1.25

Urban
Poverty HC
USD2.00

Urban
Poverty HC
USD1.25-0.239***-0.079-0.2398***-0.151***-0.147***-0.8323**

0.787***

0.787***

0.460***

0.460***

0.460***

0.460***-0.188***-0.062-0.110***-0.069***-0.068***-0.038***VARIABLES∂Poverty HC/∂ log agrivapw-0.16***-0.0785∂log agrivapw/∂ Ag Openness
0.386***
0.386***∂ Poverty HC/∂Ag openness-0.062***-0.030z-statistics in brackets. *** p<0.01, ** p<0.05, * p<0.1

43F( 3, 143) =F( 3, 143) =F( 3, 143) =F( 3, 143) =26.40***
026.40***
026.40***
026.40***
0

3.864
0.145

6.664**
0.036

Table 6 SDG 2: Effects of Agricultural Transformation on Hunger and Food Security (based on Five Year Average Panel)Dep. Variable
RegionsThe
prevalenceofunderweightThe
prevalenceof stunting

Energy_Supply_Adequacy

Data

Protein_Supply

Depth_Food_Deficit

Anaemia_during_Pregnancy

Prevalence ofunder nutrition

Case 5

Case 6

Case 7

Five Year Average Panel
Case 1Case 2

Case 3

EXP. VARIABLES

Case 4IVFE

FIRST STAGE
Dep. Variable

log Agricultural value added per worker

Commercialization Index
L1.

-0.066-0.359-0.369-0.3890.239-0.262[-0.18][-0.95][-1.14][-1.15][0.67][-0.83]-0.525***

0.504***

0.075***

0.046***

0.022***

0.047***-[6.95][6.56][4.52][2.23][1.34][2.25]-

Agricultural Openness
L1.
Product Diversification
L1.

-0.092-0.129-0.224-0.103-0.323-0.007-[-0.67][-0.91][-1.65][-0.73][-2.45][-0.05]-The
prevalenceofunderweightThe
prevalenceof stunting

Energy_Supply_Adequacy

Protein_Supply

Depth_Food_Deficit

Anaemia_during_Pregnancy

Prevalence ofunder nutrition

SECOND STAGE
Dep. Variable

Commercialization Index-9.314L1.

[-0.990]

Agricultural Openness-0.0061L1.

[-1.527]

Product Diversification

4.503[1.379]log Agricultural
Value Added per
workerControl Variables
Observations-0.073***-0.195***0.171***

0.2671***-1.307**-0.205**[-3.057][-4.695][4.249][3.068][-2.191][-2.422]

Yes
373

Yes
367

Yes
515

Yes
414

Yes
447

Yes
439

-

44

Yes
389

R-squared

0.51

0.306

0.363

0.029

0.332

-0.2250.330

Number of country

87

87

98

91

82

96

80

F test of excluded
instrumentsF( 3, 279) =F( 3, 273)=F( 3, 410) =F( 3, 316) =F( 3, 358) =F( 3, 336) =16.24***

15.25***

8.18***

2.26*

2.73

1.93

0

0

0

0.08140.04380.1252

Prob > F=-

Sargan statistic(over identification
test of all instruments)

1.618

2.201

1.216

1.38

0.587

0.669

Chi-sq(2) Pvalue

0.44540.33280.54450.50160.74560.7156Weight-forAge-0.073***Heightfor-Age-0.195***

Energy_Supply_Adequacy

Protein_Supply

Depth_Food_Deficit

Anaemia_during_Pregnancy

Prevalence ofunder nutrition

0.171***

0.267***-1.307***-0.205***-0.525***

0.504***

0.075***

0.046***

0.022***

0.047***

0.013***

0.012***-0.029***-0.010***

Elasticity estimates∂SDG2/∂log agrivapw∂log agrivapw/Ag Openness∂SDG2-0.038***-0.098***/∂ Ag openness
z-statistics in brackets. *** p<0.01, ** p<0.05, * p<0.1.

45--0.006

46

Table 7 SDG 10: Effects of Agricultural Transformation on Inequality
Dep. Variable
Regions
Data

Gini

Case 1
EXP. VARIABLESFIRST STAGE
Dep. Variable
Commercialization
Index
L1.
Agricultural
Openness
L1.
Product Diversification
L1.
Control Variables
SECOND STAGE

Dep. Variable
log Agricultural Value
Added per worker
Control Variables
Observations
R-squared
Number of countries
F test of excluded
instruments
Prob > F=
Sargan statistic(over identification
test of all
instruments)
Chi-sq(2) Pvalue

Elasticity Estimates∂Gini/∂log agrivapw∂log agrivapw/∂Ag Openness∂Gini/∂Ag openness

Growth Rate
Rural
Urban
of Bottom
Gini
Gini
40%
All developing areas
Five-year average panel
Case 2
Case 3
Case 4
IV
OLS-0.243[-0.79]-0.272[-0.57]-0.270[-0.56]-

0.419***[9.51]

0.438***[5.25]

0.439***[5.21]-0.051[0.36]
Yes

0.152[0.69]
Yes

0.152[0.68]
Yes

GrowthRate of
Bottom
40%

Gini

Rural
Gini

Urban Gini

-0.066***[-2.835]
Yes
303
0.056
91
F( 3,
204) =
30.52***
0-0.069*[-1.680]
Yes
72
0.166
22
F( 3,
43) =
9.67***
1E-04-0.061[-1.507]
Yes
71
0.144
22
F( 3, 42)=
9.51***
1E-04

2.841
0.242

3.275
0.194

1.652
0.438

Gini
-0.066***

Rural
Gini
-0.069***Urban Gini
-0.061***0.419***

0.438***

0.439***-0.028***-0.030***-0.027***47-0.784**[-2.327]--

GrowthRate of
Bottom40%

Appendix 1: Details of Commercialisation Index
The commercialisation index is defined as:[{Monetary value of production for aggregate crops processed (i.e. beer, cotton lint, cotton
seed, margarine, molasses, oil (such as coconut oil, cottonseed oil, ground nut oil, linseedoil), palm kernels, sugar raw centrifugal, wine)} + {Monetary value of Production for
Aggregate livestock processed (butter, cheese, milk, lard, yogurt)} + {Monetary value of
production of green vegetables, fruits and derived products, and meat}]/ [Monetary value of all the primary and processed crops and livestock]
The monetary value of production for aggregate crops processed is calculated by combining
the
data
on
production
quantityon‘Cropsprocessed’(http://www.fao.org/faostat/en/#data/QD)
and‘Producer
prices’(http://www.fao.org/faostat/en/#data/PP and (http://www.fao.org/faostat/en/#data/PA).xxix We
also
use
the
data
on
the
value
of
agricultural
production(http://www.fao.org/faostat/en/#data/QV) where the monetary values have been alreadyderived from the price and quantity data files. The values are adjusted based on producer
prices in the international US$ (PPP - Purchasing Power Parity) in 2004-2006.
This measure is based on the assumption that processed agricultural products are more likely
to be commercialized. Below we provide the lists of processed/unprocessed crops andlivestock.
Processed crops:
Margarine, short
Sugar Raw Centrifugal
MolassesOil, soybean Oil,groundnut
Oil, coconut (copra) Palm kernels Oil, palm
Oil, palm kernel
Oil,olive, virgin Oil, sunflower Oil, rapeseed Oil, safflower Oil, sesame Green vegetabletallow Oil, stillingia Cottonseed Oil, cottonseed
Oil, linseed Beer of barley
Wine Oil, maize
Cotton lint
Primary crops:
Fruits and Green vegetables: Total Green vegetables Primary, dry
Green vegetables& Melons, Total
Fruit excl Melons, Cabbages and other brassicas
Tomatoes
Cauliflowers and broccoli
Cucumbers and gherkins
Onions, dry Garlic
Peas, green Carrots and turnips Barley Green vegetables, fresh nes Apples
Pears Quinces
Apricots
Cherries, sour Cherries
Peachesandnectarines
Plums and sloes
Fruit, stone nes
Berries nes, Fruit, tropical freshnesFruit, fresh nes
Non Fruits or Green vegetables: Cereals, nes
Potatoes
Wheat Sugar
beet
Cereals,Total Roots and Tubers,Total
Pulses,Total Treenuts,
Beans,
Total Coarse Grain, Total Cereals (Rice Milled Eqv) Peas, dry
Lentils Pulses, nes
Walnuts, with shell Hazelnuts, with shell
Maize Grapes Watermelons
Figs
Forage and silage, maize
Forage products
Green vegetables
and roots fodder
Rye Oats

Livestock processed:
Cheese of goat mlk Lard Silk raw
Milk&Buttermilk,Dry Butter and Ghee

Tallow Cheese (All Kinds) Skim
Evaporat&Condensed Milk Cream
48

fresh

Butter, cow milkGhee, butteroil of cow milk Milk, skimmed cow Milk,
whole condensed
Whey, condensed
YoghurtMilk,
wholeevaporated
Milk, skimmed condensed Milk, whole dried
Milk, skimmed dried Milk, drybuttermilk
Whey, dryCheese, whole cow milk
Cheese, skimmed cow milk
Whey, cheese Butter, buffalo milk Ghee, of buffalo milk Cheese, buffalo milk
Butter and ghee, sheep milk Cheese, sheep milk
Livestock primary:Meat indigenous, sheep
Meat, goat
Milk, whole fresh goat
Skins, goat, fresh
Meat indigenous, goat Meat, pig
Meat indigenous, pig Meat, chicken Eggs, hen,in shell Eggs, hen, in shell (number) Meat, duck Meat indigenous, duckMeat,
goose and guinea fowl
Meat indigenous, geese
Meat, turkey Meat indigenous,
bird nes
Meat indigenous, turkey
Meat, bird nes Eggs, other bird, in shellEggs, other bird, in shell (number) Meat indigenous, chicken
Meat,
horse
Meat, ass
Meat, mule Meat indigenous, horse
Meat indigenous, ass
Meat indigenous, muleMeat, camel Milk, whole fresh camelMeatindigenous, camel
Meat, rabbit Meat indigenous, rabbit
Meat, game Meat, nes
Offals, nes
Snails, not sea Honey, natural Beeswax
Silk-worm
cocoons,reelable
Meat indigenous, cattle and buffalo Meat indigenous, sheep and goat
Meat, Total Meat indigenous, total Meat indigenous, poultry
Milk,Total
Eggs Primary Beef and Buffalo Meat
Sheep and Goat Meat Meat,
Poultry
Meat, cattle
Appendix 2: A list of countries
Albania ;Algeria ;Angola ;Argentina ; Armenia ;Azerbaijan ;Bangladesh ; Belarus ;Belize ;Benin ; Bhutan ;Bolivia ;Botswana;
Brazil ;Bulgaria ;Burkina Faso ;Burundi ;Cambodia ; Cameroon;
Cape Verde ; Central African Republic ; Chad ; Chile ; China ; Colombia ;Comoros ;Congo, Dem. Rep. ; Congo, Rep. ; Costa Rica ; Cote d'Ivoire ;Croatia ;Czech Republic ;Djibouti ;Ecuador ;Egypt, Arab Rep. ;El Salvador ; Estonia ;Ethiopia ;Fiji; Gabon ;Gambia, The ;Georgia ;Ghana ;
Guatemala ; Guinea ;Guinea-Bissau ;Guyana ;Haiti ; Honduras ; Hungary ;India ; Indonesia ; Iran,
Islamic Rep. ; Iraq ; Jamaica ;
Jordan ;Kazakhstan ; Kenya ;KyrgyzRepublic ;Lao PDR ;Latvia ;Lesotho ;Liberia ;Lithuania ;Madagascar ; Malawi ;Malaysia ;Mali ; Mauritania ; Mexico ;Micronesia, Fed. Sts. ;Moldova ;Montenegro ; Morocco ;Mozambique ;Namibia ;Nepal ; Nicaragua; Niger ; Nigeria ;Pakistan ;Panama ;Papua New Guinea ; Paraguay ;Peru ;
Philippines ; Poland ;Romania ;Russian Federation ; Rwanda ;SaoTome and Principe ; Senegal ;Serbia ;Seychelles ; Sierra Leone ; Slovak
Republic ;Slovenia ;South Africa ; Sri Lanka ;St. Lucia ;Sudan ;
Suriname ; Swaziland ; Syrian Arab Republic ;Tajikistan ; Tanzania ;Thailand ;Timor-Leste ; Togo ; Trinidad and Tobago ;Tunisia ;Turkmenistan ;Uganda ;Ukraine ;Uruguay ;Venezuela, RB ;Vietnam ;Yemen, Rep. ; Zambia

Appendix 3. Definitions and Descriptive Statistics of Variables
Five-year average data49

Variable
log Agricultural Value Added
per worker
Agricultural Value Added per
workerCommercialisation Index
Agricultural Openness
Production diversity
Non-Cereal_Share
Agricultural TFP GrowthLand Area
Population Density
Fragility Index

Trade Openness

Definition

Agriculture value added per worker(constant 2010 US$)log of Agricultural Value Added per worker
Commercialization Index*1 (FAOSTAT)[aggregate agricultural export]/ [agricultural value added] (FAOSTAT)
Product diversification Index *2 (FAOSTAT)
The share of non-cereal product production in
total agricultural value added
Agricultural TFP Index (log) based on FAOSTAT(Fuglie, 2012 and 2015).
Land area (sq. km)
Population density (people per sq. km of Land Area
area)
State fragility index (= cspf_sfi)
CPIA rating of macroeconomic management
and coping with fragility (1=low to 6=high)The share of exports and imports of goods in GDP.
The percentage of the working age population withan intermediate level of education

Labour Force with Secondary
Education
Urban Agglomerations
Transport Equipment

Tariff for primary goods
Poverty headcount ($3.1 aday)
Poverty headcount ($1.9 aday)
Poverty headcount ($2 a day)Poverty headcount ($1.25 aday)
Rural Poverty headcount ($2a day)
Rural Poverty headcount($1.25 a day)
Urban Poverty headcount ($2a day)
Urban Poverty headcount($1.25 a day)Child Malnutrition

Depth_Food_Deficit

Max

776

7.58

1.2

4.94

12.43

1265
831

4389.08
0.68
0.36

11875.94
0.18
0.82

140.170.17
02.50E+05
0.99
13.17

1265

2.54

0.49

0.27

3.4

776

1440

0.42

0.44

1229
1410

0.01
7.58E+05

0.02
1.92E+06

-0.14
460

0.15
1.64E+07

1404

75.49

105.330.78

1245.11

446

11.22

5.88

0

23.8

1109

70.26

36.79

4.3

229.64

1428

0.73

0.42

0

1

0.73

0.42

0

1

0.00

1.00

894

17.1

11.08

1.19

49.97

575

7.59

7.71

0

41.88

556

8.75

8.01

0

55.48

31

0

98.35

483

23.09

24

0

94.05

Poverty headcount ratio at $2.00 a day (2005 PPP)

433

38.94

31.34

0.01

98.1

Poverty headcount ratio at $1.25 a day (2005 PPP)
Rural poverty headcount ratio at $2.00 a day (2005
PPP)
Rural poverty headcount ratio at $1.25 a day (2005
PPP)
Urban poverty headcount ratio at $2.00 a day (2005
PPP)
urban poverty headcount ratio at $1.25 a day (2005
PPP)

433

22.05

24.44

0.01

90.3

255

42.25

32.76

0

99.29

255

25.33

26.01

0

97.53

255

26.83

24.88

0

89.49

255

13.44

16.28

0

74.83

484

17.56

13

0.5

67.3

470

32.45

15.55

1.3

75.1

624

113.01

14.05

70.75

151

519

67.4

16.8

31

124

534

144.49117.382

678.75

570

40.17

12.12

19.7

68.78

40.82
32.64
35.78

9.18
5
5.46

19.4
23.85
24.6

74.33
48.47
51.38

Poverty headcount ratio at $3.10 a day (2011 PPP)

Poverty headcount ratio at $1.90 a day (2011 PPP)

Gini coefficient for the national population (in raw
value)
Gini coefficient for rural areas (in raw value)
Gini coefficient for urban areas (in raw value)

Protein_Supply

Min

38.6

The Gini coefficient
Gini in rural areas
Gini in uran areas

Energy_Supply_Adequacy

SD

483

Anaemia_during_Pregnancy

Height-for-Age

Mean

1428
The percentage of the working age population withan intermediate level of education
Population in urban agglomerations of more than
1 million (% of total population
Machinery and Transport Equipment(% of value added in manufacturing)
Tariff rate, applied, weighted mean, primary
products (%)

malnutrition prevalence, weight for age, (% ofchildren under 5)
Prevalence of stunting, height for age (% ofchildren under 5)
Average dietary energy supply adequacy (%) (3year average)
Average protein supply (g/capita/day) (3-year
average)
Depth of the food deficit (kcal/capita/day) (3-year
average)
Prevalence of anaemia among pregnant women(%)

Weight-for-Age

N

50

646
91
88

Asia
LA
SSA

The growth rate in the welfare aggregate of thebottom 40%

-5.4

9.58

1 if in Asia 0 otherwise
480
0.53
1
0
1428
0.19
0.4
0
1 if in Latin America/ Caribbean or 0 otherwise
1428
0.37
0.48
0
1 if in Sub-Saharan Africa 0 otherwise
Notes: *1. This is defined as: [{monetary value of production for aggregate crops processed (i.e. beer, cotton lint, cotton seed,margarine, molasses, oil (such as coconut oil, cottonseed oil, ground nut oil, linseed oil), palm kernels, sugar raw centrifugal,wine)} + {Monetary value of Production for Aggregate livestock processed (butter, cheese, milk, lard, yogurt)} + {Monetary valueof production of green vegetables, fruits and derived products}] / [Monetary value of all the primary and processed crops andlivestock]1
1
1

Growth Rate of Bottom 40%

58

2.83

3

*2. The index can be defined as 𝐻′ = − ∑𝑅𝑖=1 𝑝𝑖 ln 𝑝𝑖 where 𝑅 is the number of items of agricultural products and 𝑝𝑖 is the shareof production for item 𝑖, available from FAOSTAT.
*3. This is the growth in agricultural TFP indexes (based year 1992=100) over 1961-2012 using primarily FAO data,
supplemented in some cases by national statistics. The output is FAO gross agricultural output (GAO) smoothed using the
Hodrick-Prescott Filter (Lambda = 6.25). Input growth is the weighted-average growth in the quality-adjusted Land Area, labour,machinery power, livestock capital, synthetic NPK fertilizers, and animal feed, where weights are input (factor) cost shares.
Agricultural TFP indexes are estimates by country and for groups of countries aggregated by geographic region and income
class (Fuglie, 2012 and 2015).

51

Endnotes
1

The literature suggests that access to remittances will improve productivity and reducepoverty only if they reach the poor and it is used for productive purposes (acquisition of landor machinery) (e.g. Chiodi et al. 2012; Baldé, 2011).
2

The definition of processed agricultural products follows FAO (2011, 2014). Lists ofprocessed and primary crops or livestock products are given in Appendix 1.
3

We could construct the share of the input import in the input consumption, but the data on

the fertilizer and pesticides import are available only after 2002 from FAOSTAT andunsuitable for the present purpose.
4

We have plotted product diversification index and the share of non-cereal production inagricultural value added. As the country movers from the cereal production to non-cereal
production, it sees an improvement in the agricultural productivity. Hence a positive
correlation is expected. As expected, these variables are positively correlated with thecoefficient of correlation 0.32, while the relationship between the two indices is notnecessarily clear-cut. We can nevertheless confirm that as the country produces more noncereal products the product diversification index tends to be higher. If we disaggregate it, thecorrelation for Asia is much higher (0.41) than for Latin America (0.07) or Sub-SaharanAfrica (0.13).
5

See http://www.fao.org/sustainable-development-goals/goals/goal-1/en/ for more details.

6

See http://www.fao.org/sustainable-development-goals/goals/goal-2/en/ for more details.

7

It is noted that FAO measures rely mainly on calorie availability, rather than calorie

intakes. The measures are based on the means and variances for a limited number ofcountries with detailed micro-data available and a skew normal distribution to determine theproportion of undernourished. However, for the cross-country analyses, this is the best52

dataset for food security in terms of coverage of the countries and thus we have decided touse the FAO measures. See IFAD, WFP, and FAO (2012) for details of FAO measures onfood security.
8

Its discussion paper version was released in 2013.

9

There is a limitation in the data of agricultural TFP (Alston and Pardey, 2014) to be

discussed later.
10This is supported by the maximum variance inflation factor of 1.76 (less than 10, thethreshold commonly used in the literature) in the static case.
11

The results will be provided on request.

12

We used the estimations based on unbalanced data as our preferred cases because the useof balanced data will eliminate 26 countries from the sample, which has a substantial share in
our sample.
13

We have also applied the Fixed-Effects IV model by instrumenting AT by external

instruments, namely, urban agglomerations (population in urban agglomerations of more than
1 million (% of total population)), transport equipment machinery (% of value added in
manufacturing), and tariff for primary goods. The results are broadly consistent with those
based on a system GMM.
14

Ideally, both directions of causality from 𝑨𝑻𝑖𝑡 to 𝑨𝑮𝒊𝒕 , and from 𝑨𝑮𝒊𝒕 to ∆𝑨𝑻𝑖𝑡 should be

explicitly considered in the model. However, we consider mainly the former (by treating 𝑨𝑻𝑖𝑡and its lags endogenous in a system GMM).
15

The inclusion the lags up to the second period is guided by statistical significance of thethird lag as well as specification tests, such as serial correlation tests, AR(1) and AR(2). An
inclusion of the third lag, however, does not change the results significantly, although thenumber of observations reduces.

53

16

Two issues have to be resolved in estimating the dynamic panel model. One is endogeneityof the regressors and the second is the correlation between(𝐴𝐺𝑖𝑡−1 − 𝐴𝐺𝑖𝑡−2 ) and(𝜀𝑖𝑡−𝜀𝑖𝑡−1 )(e.g. see Baltagi, 2005, Chapter 8). Assuming that 𝜀𝑖𝑡 is not serially correlated and that the
regressors in 𝑿𝑖𝑡 are weakly exogenous, the generalized method-of-moments (GMM) firstdifference estimator (e.g. Arellano and Bond, 1991) can be used.
17

See Roodman (2009) for more details of System GMM and practical guides.

18

It is noted that poverty or inequality panel data have more missing variables than the dataon agricultural productivity.
19

We have estimated the same models using the annual panel data and have obtained broadly

similar results.
20

On the results of System GMM in Cases 5-8 in Table 2 are broadly justified by the

specification tests as AR(1) is significant and AR(2) is not at the 5% level, while both Sarganand Hansen tests are not statistically significant at the 5% level. The case with the initial levelof log agricultural value added per worker (Case 6) is weak as the test statistics are
significant at the 10% level for both Sargan and Hansen tests. As the Hansen test is deemed
more robust than the Sargan test, we will mainly adopt the former in evaluating theperformance of the dynamic model.
21

It is not straightforward to choose whether unbalanced or balanced data are used and thisshould be judged with respect to the study objective and the pattern for missing observations(Baltagi and Song, 2006) as making the data balanced may worsen the performance of theestimators when compared to those from the entire unbalanced data (Baltagi and Chang,1994). We have decided to use unbalanced data as covering more countries is crucial for themain objective of our study.
22

The results will be provided on request.

54

23

We have also examined the effects of agricultural transformation on agricultural TFP

growth based on Fuglie (2012, 2015). Contrary to the expectation, agricultural transformation
indices do not have any positive effect on agricultural TFP growth. Given that Fuglie
compared the changes in output and unit costs in constructing the agricultural TFP growth,
they are supposed to be more accurate proxies of agricultural productivity. However, thequestion is how Fuglie’s TFP measure reflects the actual productivity. When we plot log
agricultural value added per worker and the agricultural TFP growth, there is a positive
association in the positive range of TFP growth, while there is a negative relation in its
negative range. A similar relationship is found in the relationship between log agriculturalvalue added per worker and the agricultural TFP index. It is implied that there might havebeen an overvaluation in some (imputed) input values (e.g. machinery) in the agricultural
TFP index. Alston and Pardey (2014) have questioned the validity of the Fuglie’s (2012)
measure of agricultural TFP. If we drop negative values in the agricultural TFP growth, forinstance, the results are similar to those for log agricultural value added per worker and its
growth where the agricultural openness has a positive and significant coefficient estimate.
Details will be provided on request. Given the potential problem in agricultural TFP growthmeasures, this paper focuses on agricultural value added per worker as a broad measure of
productivity.
24

While AR(2) is not significant in Cases 1-3, AR(1) is not significant either and so theresults should be interpreted with caution.
25

We have carried out the same regressions for the balanced panel for the cases where adependent variable is log agricultural value added per capita. The results are broadly similar
to those in Table 4. However, our results will of course never underestimate the role ofhorticultural exports, the growth in urban demand for animal products and other high value
foods in Africa, which may not be fully captured by our model.
55

26

The results based on annual panel data are very similar and consistent. Regressions basedon the balanced panel will produce similar results for some indicators, such as poverty, childmalnutrition and the Gini coefficient. These will be provided on request.
27

The results will be provided on request.

28

It is noted that regional regressions in Table 4 show negative coefficient estimates of

product diversification for Asia and Sub-Saharan Africa.
xxix

However, a limitation of the index is that prices have missing observations and thus

cannot be fully matched with the crop quantity data.

56