DP2016-30

Dynamics of Rural Transformation andPoverty and Inequality in Asia and thePacific*
Katsushi S. IMAI
Raghav GAIHA
Fabrizio BRESCIANI

Revised February 5, 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.

Dynamics of Rural Transformation and Poverty and Inequality in Asia and the PacificKatsushi S. Imai, The Department of Economics, The University of Manchester and RIEB,
Kobe University
Raghav Gaiha, Global Development Institute, University of Manchester and Population
Studies Centre, University of Pennsylvania
Fabrizio Bresciani, Asia and the Pacific Division of International Fund for Agricultural
Development
Date: This version: 5th February 2019
Abstract
This chapter analyses the dynamics of transformation which has taken place in rural
areas of Asia and the Pacific, with a focus on their effects on poverty and inequality inboth rural and urban areas. It draws upon an up-to-date country panel dataset covering 21
countries in 1960-2014 in the region. We find that transformation of the agricultural
sector in rural areas in terms of commercialisation and product diversity has dynamicallyincreased agricultural value added per capita and its growth rate, and consequently
reduced both rural and urban poverty significantly in our sample countries. The effect ofagricultural transformation in reducing child malnutrition is also corroborated, while
inequality in rural areas is reduced only at the initial stage of development of agriculture
in low income countries. Our analysis also confirms that agricultural transformation, in
terms of commercialisation and product diversification, promotes total factor
productivity (TFP) with lags, which reduces both rural and urban poverty significantly.
Acceleration of agricultural transformation, for instance, through the policies promoting
rural infrastructure or facilitating the synergy between public and private investment inrural areas, is likely to reduce rural and urban poverty with a caveat that the inequality
may increase as the process deepens.
Key Words: Rural Transformation, Agricultural Transformation, Agricultural
Productivity, Poverty, Inequality, Asia
JEL Codes: C23, I32, Q18
Corresponding Author: Katsushi S. Imai (Dr.) 3.066 Arthur Lewis Building, Departmentof Economics, School of Social Sciences, University of Manchester, Oxford Road,
Manchester M13 9PL UK; Telephone: +44-(0)161-275-4827, Fax: +44-(0)161-275-4812
Email: Katsushi.Imai@manchester.ac.uk.

1

Dynamics of Rural Transformation and Poverty and Inequality in Asia and the Pacific 1(forthcoming as Chapter 15 in Handbook of Poverty, edited by Bent Greve, Routledge, 2019)

Authors 2: Katsushi S. Imai, Raghav Gaiha, and Fabrizio Bresciani
15.1. Introduction
While many countries in Asia – in particular, in East Asia and South Asia – have experienced
rapid economic growth during the last decade, its rural areas have experienced structuraltransformation, induced by globalisation, industrialisation, and urbanisation. Despite
economic growth, a large section of people in rural areas still suffer from abject poverty andmalnutrition, implying that economic growth has bypassed many (IFAD, 2016).
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

1

This study is funded by Asia and the Pacific Division (APR), IFAD (International Fund for
Agricultural Development). The authors are grateful for valuable comments from one internal
reviewer at IFAD, Kostas Stamoulis (FAO), Andrea Cattaneo (FAO), and other participants in‘Expert Consultation on Focusing Agricultural and Rural Development Research and Investment on
Achieving SDGs 1 and 2: A joint initiative of FAO, IFAD, CGIAR, and the World Bank: In
partnership with the EU’ in Rome in January 2017. The second author acknowledges valuable advice
from David Bloom. The authors also thank Bilal Malaeb for his advice on data processing andeconometric estimations. The opinions expressed in this publication are those of the authors and do
not necessarily represent those of IFAD.
2
Katsushi Imai is Associate Professor in Economics at the Department of Economics, the Universityof Manchester. Raghav Gaiha is Honorary Professorial Research Fellow at Global Development
Institute, University of Manchester and Visiting Scholar at Population Studies Centre, University ofPennsylvania. Fabrizio Bresciani is Lead Economist at Asia and the Pacific Division of International
Fund for Agricultural Development. Contact Author: Katsushi S. Imai (Dr.) 3.066 Arthur Lewis
Building, Department of Economics, School of Social Sciences, University of Manchester, Oxford
Road, Manchester M13 9PL UK; Telephone: +44-(0)161-275-4827, Fax: +44-(0)161-275-4812
Email: Katsushi.Imai@manchester.ac.uk.

2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 is unclear and depends on geographical distributions of these networks.
If structural transformation increases overall productivity and outputs in rural areas, thestructural transformation would reduce income inequality at national levels. However, if forexample backward regions (e.g. mountainous areas in North-East India or north mountainregions in Vietnam) are left out of structural transformation, it is likely to increase inequality.
One of the main objectives of this chapter is to understand better whether inequality andpoverty have increased as the country experienced structural transformation and theunderlying reasons.
Of particular importance are farm and non-farm linkages and whether higher rural
incomes are in part due to more diversified livelihoods and the emergence of high-value
chains and the extent to which these have reduced rural-urban disparities and dampened
migration. Apart from easier access to credit in order to strengthen farm/non-farm linkages,
and smallholder participation in high-value chains, other major policy concerns relate towhether remittances - sent either from internal migrants from rural to urban areas orinternational migrants - could be allocated to more productive uses in rural areas, throughhigher risk-weighted returns, and whether returns could be enhanced in agriculture and rural
non-farm activities while risks are reduced.
This study examines how the structural transformation in rural Asia and the Pacificdynamically influences poverty and inequality by applying econometric models to the crosscountry panel data to capture the dynamic relationships among rural/agriculturaltransformations, growth/productivity, and poverty/inequality.
The rest of this chapter is organised as follows. After reviewing the concepts ofagricultural transformation in Section 15.2, we will discuss three different measures of3

agricultural transformation in Section 15.3. Section 15.4 first outlines an empirical model anddiscusses the results. Section 15.5 is devoted to concluding observations with policy
implications.

15.2 Concepts and measurement of rural or agricultural transformation 3
While ‘rural transformation’ (RT) is a broader concept than ‘agricultural transformation’ (AT)due to the non-negligible share of non-agricultural sector, we will primarily focus on thetransformation of the agricultural sector, drawing and building upon Dawe (2015). While
Dawe discusses in detail transformation of the agricultural sector of middle-income Asian
countries, 4 he does not provide a clear definition of ‘agricultural transformation’. Citing
Reardon and Timmer (2014), Dawe first discusses ‘the structural 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 and retailing; 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/product diversification’ (Dawe, p.5, emphasis added). Dawethen reviews some statistical evidence to show how mechanization took place, farm size
increased, and crop diversification took 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 clear what sort of transformation is envisaged. For instance, farm
size did not increase uniquely in different areas of these countries (Figures 14-15 on pp. 2122 in Dawe), but it is not clear whether this heterogeneity implies AT took place in some3This sub-section draws upon Imai (2017) where the analysis has been carried out for all developing
countries using a similar model.
4
It is taking place in low income countries too but not quite as visibly.

4parts of the country and did not in other parts 5. It is not clear either whether crop/productdiversification took place consistently across these countries (e.g. Malaysia became more
specialised in oil crops, as illustrated in Figure 17).
In this chapter, we define AT as: “fundamental changes in agricultural production andsmallholders” livelihood in a developing economy as it is globalised, which are characterised
as the three changes: (i) mechanisation and new agricultural technologies, (ii) changing
cropping patterns with declining shares of grains and rising shares of non-grains, in particular,
fruits, vegetables, dairy products, and meat, and (iii) new organisational forms (contract
farming) as well as land and machinery rental markets that would enable smallholders tobenefit from economies of scale (Barrett et al. 2012). While Dawe (2015) focuses mainly on
the first and partly the second aspect, our study captures AT as a broader and more complex
process covering the remaining aspects.

15.2. Measures of agricultural transformation (AT)While AT should be defined from a broader perspective, it is not feasible to use a measure
covering all the above aspects in AT. We will thus construct three measures,commercialization index, agricultural openness and production diversification to capture
salient features of AT.

Commercialization Index
To construct our commercialization index, we use the production file (the value ofagricultural production) (http://faostat.fao.org/site/613/default.aspx#ancor) as well as theprice file (http://faostat.fao.org/site/703/default.aspx#ancor) in FAOSTAT to capture the5

The overall trend in Asia has been that of declining farm size. For example, in Bangladesh farm size
declined drastically from 1.4 ha in 1976/77 to 0.3 ha in 2005. Similarly, India, Pakistan and thePhilippines also experienced significant declines in average farm size over time (Thapa, 2016).

5

extent to which processed agriculture and livestock products are produced in total agriculturaland livestock production.

The values are adjusted based on producer prices in theinternational US$ (PPP) in 2004-2006. More specifically, the index is defined for all theyears in the sample, 1960-2014, as follows.
Commercialisation Index= (C + L)/(C+ L + CP +LP)
where
C= [Monetary value of Production for Aggregate Crops Processed (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)]
L= [Monetary value of Production for Aggregate livestock processed (butter, cheese, milk,lard, yogurt)]
CP = [Monetary value of Production for Aggregate Crop Primary]
LP= [Monetary value of Production for Aggregate Livestock Primary]This measure is based on the assumption that processed agricultural products are more likely
to be commercialized. For instance, we assume that farmers producing maize oil are morecommercialised than those producing maize. While this will be a reasonable assumption, it isnoted that our measure captures only a part of the process where agricultural production of
the country gets commercialised. This index reflects the overall structure of agricultural
production in terms of whether the agricultural crops or livestock are sold as raw crops orprocessed crops. However, the index does not capture the increase in the production of, forinstance, raw vegetables or plantation crops such as rubber, pineapple, bananas, which tend tobe well commercialised. To partly overcome possible limitations of the index, we will use
alternative indices.
We have examined whether our commercialisation index is correlated with the value
added in food, beverage, and tobacco production. As expected, they are positively correlatedwith the correlation coefficient 0.37 (with p-value 0.0000) for the whole of Asia. Figure 15.1
shows the overall association between our commercialisation index and the value added infood, beverage, and tobacco production. At low levels of food production, our6commercialisation index can be high or low with high variance, there is then a positive
relationship at a slightly higher level of food production, and above a certain threshold, thecommercialisation index gets almost flat.

-2

Commercilisation_Index
-1.5
-1
-.5

0

Figure 15.1 The relation between the Commercialisation index and the per capita value added
in food, beverage, and tobacco production

0

1.00e+13
5.00e+12
Food_beverages_tobacco

1.50e+13

We have also examined the relationship between the commercialisation index and the index
of mechanisation. Given that only crude measures are available from FAOSTAT, we haveused the number of tractors per land of 100 km2. These are positively and significantly
correlated with an overall correlation coefficient of 0.53 for the entire Asia. Figure 15.2
confirms the positive correlation between the two variables.

-2

Commercilisation_Index
-1.5
-1
-.5

0

Figure 15.1 The relation between the Commercialisation index and the per capita value added
in food, beverage, and tobacco production

0

5.00e+12
1.00e+13
Food_beverages_tobacco

1.50e+13

Agricultural Openness

7Agricultural trade openness is simply defined as [aggregate agricultural export]/ [agriculturalvalue

added].

Agricultural

export

is

based

on

the

trade

file

of

FAOSTAT(http://faostat.fao.org/site/535/default.aspx#ancor) (in the international US$ (PPP) in 20042006). The agricultural exports include food and non-food agricultural products. Here we do
not include agricultural imports because the import of agricultural crops, while influenced by
globalisation and influencing agricultural production systems to some extent, would mainly
be demand-driven and does not reflect the transformation of the agricultural production
systems we discussed earlier. 6 On the contrary, higher share of agricultural export tends toreflect more integration of agricultural production into the rest of the world and is deemed amore suitable proxy for the agricultural transformation. The agricultural value added is basedon World Development Indicators (WDI) published and released in 2016. As the agricultural
sector of the country gets structurally transformed (e.g. through mechanisation or contract
farming), the relative competitiveness of the agricultural product improves and theagricultural openness index tends to be higher.

Product Diversification
We propose to 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 production
diversity at the country level using FAOSTAT. The index can be defined as:
𝐻𝐻 ′ = − ∑𝑅𝑅𝑖𝑖=1 𝑝𝑝𝑖𝑖 ln 𝑝𝑝𝑖𝑖

where 𝑅𝑅 is the number of agricultural products and 𝑝𝑝𝑖𝑖 is the share of production for the item 𝑖𝑖,

available from FAOSTAT. The production share, 𝑝𝑝𝑖𝑖 , is defined in terms of the monetary
value at a local price for each product, 𝑖𝑖. If the country produces more agricultural products,
6

We could construct the share of the input import in the input consumption, but the data on thefertilizer and pesticides import are available only after 2002 from FAOSTAT and unsuitable for thepresent purpose.

8

including processed and unprocessed crops and the monetary values of products are more
evenly divided among different items, the diversity index, 𝐻𝐻 ′ , takes a larger value. On thecontrary, if the country produces a smaller number of agricultural products and the monetary
value of one or two specific products is large, 𝐻𝐻 ′ is smaller. Figure 15.2 indicates that our

product diversification index is highly correlated with the share of non-cereal production. The
correlation coefficient for all the sample 21 countries in Asia is 0.41 - except the top end(above 90%) of non-cereal share where these two variables are negatively correlated for
Malaysia. The correlation is stronger for the countries in South Asia with the correlation

coefficient 0.74 (p-value 0.0000) and weaker for East and South East Asia with thecorrelation coefficient 0.22 (p-value 0.0003).

Figure 15.3 The relation between the product diversification index and the share of value of
non-cereal production in the total value of agricultural production

9Remans et al. 's index called ‘Shannon Entropy diversity metric (- sigma(i to R)
1.5
2
2.5
3
3.5

20

40

60
Non_cereal_share

80

100

15.3. Data, Empirical Models and Results
Data
Our empirical analysis is based mostly on FAOSTAT (http://faostat.fao.org/), World
Development

Indicatorsdevelopment-indicators),

(WDI)
the

2016
World

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

database(PovCalNet,

http://iresearch.worldbank.org/PovcalNet/), World Bank World Governance Indicators(WGI)(http://data.worldbank.org/data-catalog/worldwide-governance-indicators),

andQuality of Government Dataset (http://qog.pol.gu.se/data). The agricultural Total Factor
Productivity estimates are taken from Fuglie (2012 and 2015). Table 1 summarizes the main
variables we will use in the econometric estimations.
Table 15.1 Descriptive Statistics of Variables
Variable
Dependent Variables
dlogagrivapc
logagTFP
dlogagTFP
povertyhc200
povertyhc200_raw
povertyhc190
Poverty190_raw
povertyhc310

povertyhc310_raw

Definition (Data source)
Annual growth of agricultural value added per
capita (WDI)
Agricultural TFP Index (log) based on FAOSTAT(Fuglie, 2012 and 2015).
Growth rate of agricultural TFP.
Log of Poverty Headcount Ratio based on
US$2.00 (PPP at 2005). (WDI)
Poverty Headcount Ratio based on US$1.25(PPP in 2005). (WDI)
Log of Poverty Headcount Ratio based on
US$1.90 (PPP at 2011). (WDI)
Poverty Headcount Ratio based on US$1.90(PPP in 2011). (WDI)
Log of Poverty Headcount Ratio based on
US$3.10 (PPP at 2011). (WDI)
Poverty Headcount Ratio based on US$3.10(PPP in 2011). (WDI)

10

Obs

Mean

Std. Dev.

Min

Max

748

0.008

0.064

-0.692

0.341

1,272
1,272

4.606
0.0092

0.214
0.035

3.784
-0.309

5.323
0.324

153

3.695

1.064

-2.8134.583

153

53.77326.9810.060

97.810

1,155

0.611

1.634

0.000

5.541

1,155

20.58457.0791.000

255.000

1,155

0.670

1.758

0.000

5.765

1,155

27.39274.4001.000

319.000

Gini
Gini_raw

191
191

3.594
36.811

0.156
5.824

3.248
25.7404.113
61.100

148

3.268

0.677

1.224

4.209

126

31.24816.0133.400

67.300110

24.57921.7940.000

83.500

110

7.067

8.408

0.000

57.600

110
110

2.994
32.669

5.116
5.886

0.000
23.85045.56063.96011047.90030.9660.190

93.04011018.36714.8390.010

69.030

110

9.163

9.031

0.000

57.020109

14.58615.3940.000

62.010

108

4.218

6.623

0.000

51.970

107

1.878

4.906

0.000

47.080107

35.9636.102

24.60071.700107

31.94925.7820.030

86.640106

11.22911.2660.000

57.410

106

5.478

7.114

0.000

51.880110

24.57921.7940.000

83.500861-1.1650.411-2.202-0.245639
861-8.682
0.943

2.526
0.128

-13.535
0.403

5.107
1.152

395
388

-0.524-0.545

0.464
0.857

-1.660-2.810

0.940
1.330

Land area is a country's total area (WDI).
Population density (people per sq. km of landarea).
CPIA rating of macroeconomic management
and coping with fragility (1=low to 6=high)

1,155

3.815

1.166

0.000

5.118

1,155

6.302

1.641

0.000

7.636

1,176

7.618

1.193

1.000

8.000

Imports and exports (value added)/GDP (WDI).

1,155

4.543

2.846

0.000

7.146

Ethnic fractionalization Index *4697-1.0590.706-3.091-0.308

Labour force with secondary education
The degree whether country is landlocked

1,155
455

0.150
-1.856

0.706
1.722

0.000
-4.605

5.209
0.000

primary_yrs
second_years

Average years of schooling at primary school
Average years of schooling at secondary school

1,155
1,155

3.700
3.740

1.522
1.502

1.000
1.000

7.000
6.000

populati~_14
populati~65_
SA
EAP

Population below 14 years old
Population above 14 years old
Whether in South Asia
Whether in East Asia and Pacific

1,155
1,155
1,155
1,155

1495.045
1225.122
0.286
0.524

405.122366.004
0.452
0.500

205.000
2.000
0.000
0.000

2116.000
2080.000
1.000
1.000

logweight_age
weight_age

Gini coefficient (in log)
Gini coefficient (in raw value)
Log of malnutrition prevalence, weight for age,(% of children under 5)
malnutrition prevalence, weight for age, (% ofchildren under 5)

epov_h_rur
epov_gap_rur
epov_gap2_rur
gini_rurmpov_h_rur

mpov_gap_rur
mpov_gap2_rur
epov_h_urb
epov_gap_urb

epov_gap2_urb

extreme rural poverty gap (% of rural population
under $1.25 a day) (SKD, IFAD)
extreme rural squared poverty gap (% of rural
population under $1.25 a day) (SKD, IFAD)
Gini coefficient in rural areas (SKD, IFAD)
moderate rural poverty headcount (% of rural
population under $2 a day) (SKD, IFAD)
moderate rural poverty gap (% of rural
population under $2 a day) (SKD, IFAD)
moderate rural squared poverty gap (% of rural
population under $2 a day) (SKD, IFAD)
extreme urban poverty headcount (% of urban
population under $1.25 a day) (SKD, IFAD)
extreme urban poverty gap (% of urban
population under $1.25 a day) (SKD, IFAD)
extreme urban squared poverty gap (% ofurban population under $1.25 a day) (SKD,
IFAD)

gini_urb

Gini coefficient in urbn areas
moderate urban poverty headcount (% of urban
mpov_h_urb
population under $2 a day)moderate urban poverty gap (% of urban
mpov_gap_urb
population under $2 a day)moderate urban squared poverty gap (% ofmpov_gap2_urb
urban population under $2 a day)extreme rural poverty headcount (% of rural
epov_h_rur
population under $1.25 a day)
Explanatory Variables
Commercialization
Index
Commercialization Index*1 (FAOSTAT)

agopenness
productdiversity

Institution
Political Stability
Land
Population_density
Fragility Index
Openness
Ethnicfractionalization
lab_with_secondary
Riskinland[aggregate agricultural export]/ [agriculturalvalue added] (FAOSTAT)
Production Diversity Index *2 (FAOSTAT)
Aggregate institutional quality (average of voice
and accountability, government effectiveness,regulatory quality, rule of law and control ofcorruption
Political stability and absence of violence (WGI).

11

LOWI

Whether in low income countries

588

0.413

0.493

0.000

1.000

588
588

0.412
0.094

0.493
0.291

0.000
0.000

1.000
1.000

540

0.652

0.657

0.000

2.000

Gnipcadj

Whether in lower middle income countries
Whether in upper middle income countries
Income class (0 for low income countries; 1 forlower middle income countries; 2 for upper
middle income countries)
Adjusted GNI per capita based on World Atlas
Method (adjusting for local and world price andexchange rate fluctuations).

827

1189.746

1596.007

50.00011850.000

Logagriemp
gA
Logmanuemp
gN
Logseremp

Log of agricultural employment
Annual growth rate of agricultural output
Log of manufacturing employment
Annual growth rate of non-agricultural output
Annual growth rate of service output

192
119
209
155
209

3.057
-0.086
2.795
0.018
3.443

0.732
0.721
0.365
0.116
0.325

0.182
-3.3670.993-0.817
2.573

4.388
3.217
3.384
0.615
4.009

LOWERMIUPPERMIICLASSgS
Annual growth rate of service output
155
0.016
0.064
-0.274
0.322
Notes: *1. This is defined as {[Aggregate Crops Processed (beer, cotton lint, cottonseed, margarine, molasses, oil (such as
coconut oil, cottonseed oil, ground nut oil, linseed oil), palm kernels, sugar raw centrifugal, wine)] + [Aggregate livestock
processed (butter, cheese, milk, lard, yogurt)]} /{[Aggregate Crops Processed (same as above)] +[Aggregate livestock
processed (same as above)]] + [(Raw) Crops ]+ [Aggregate Livestock Primary] + [Aggregate Live Stock, Primary (eggs, skins,wool)]}*2. The index can be defined as 𝐻𝐻 ′ = − ∑𝑅𝑅𝑖𝑖=1 𝑝𝑝𝑖𝑖 ln 𝑝𝑝𝑖𝑖 where 𝑅𝑅 is the number of items of agricultural products and 𝑝𝑝𝑖𝑖 is the shareof production for item 𝑖𝑖, available from FAOSTAT.
*3. Presents agricultural TFP indexes (based year 1992=100) over 1961-2012 using primarily FAO data, supplemented insome 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, 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).
*4. Ethnic fractionalization Index reflects the probability that two randomly selected people from a given country will not belong
to the same ethnolinguistic group. The higher the number, the more fractionalized society. The definition of ethnicity involves a
combination of racial and linguistic characteristics. The result is a higher degree of fractionalization than the commonly used
ELF-index (see el_elf60) in, for example, Latin America, where people of many races speak the same language.

A few points are noted in Table 15.1. First, we will use both old and new World Bank
poverty estimates. The first set of poverty estimates is based on the international poverty lines,
US$1.25 and US$2.00 adjusted by 2005 PPP (purchasing power parity) (Ravallion et al.,2009). The second set of poverty estimates are the revised World Bank estimates which werereleased in 2016 and based on US$1.90 and US$3.10 adjusted by 2011 PPP (World Bank,
2016). While the latter covers more countries and more years, our study primarily focuses on
the former because these have been more widely used in the literature and have served as thebasis for specification of the first goal of Millennium Development Goals (MDGs) as well as
Sustainable Development Goals (SDGs). We will also use rural poverty estimates in ourearlier study (Imai et al., 2014) by using the rural poverty estimates which Strategy andKnowledge Department of International Fund for Agricultural Development obtained fromthe World Bank. These were computed by using World Bank Living Standard Measurement
12

Survey (LSMS) data. Second, we have used variables capturing institutional qualities,
political stability, and state fragility. Institutional qualities and political stability are based on
World Governance Indicators (WGI) which have been widely used in the literature. The
degree of fragility is captured by ‘fragility index’ proxied by the rating of macroeconomic
management and coping with fragility (1=low to 6=high). High value implies low fragility.
We have also tried conflict indices, but prefer the ‘fragility index’ as the former does notcover many countries. Third, we have used the annual growth rates of agricultural and nonagricultural real value added per capita drawing upon WDI. It is noted here that nonagricultural value added is defined as the difference between national GDP and agriculturalvalue added and this is admittedly a rough approximation.
Due to data limitations, the panel dataset covers only 21 Asian and Pacific countries 7,
namely, Bangladesh, Bhutan, Cambodia, China, Fiji, India, Indonesia, Kazakhstan, Lao PDR,
Malaysia, Micronesia, Fed. Sts., Nepal, Pakistan, Papua New Guinea, the Philippines, Sri
Lanka, Tajikistan, Thailand, Timor-Leste, Turkmenistan, and Vietnam for 1960-2014.
However, the data availability varies considerably depending on the choice of variables. As aresult, only a subset of the data is used for the main econometric analyses (e.g. 15-19
countries). 8

An Empirical Model
The main purpose of our empirical model is to assess the effect of agricultural transformation(AT) on poverty and inequality. To do so, we take a two-stage approach. In the first stage, weestimate the effect of AT on a measure of agricultural production or agricultural productivity

7

These 21 countries cover only 43% of the total 48 countries in terms of country numbers, but 84% of
the population of all the countries in the region.
8
We have used the data based on the maximum number of sample countries for each specification.
However, when we restrict our sample to 15 countries in all the cases, the coefficient estimates of key
explanatory variables are more or less same.

13

by treating the former as endogenous. In the second stage, we estimate the effect ofagricultural production on poverty and inequality. We have used the dynamic panel model, or
the linear dynamic panel model based on System GMM (Blundell-Bond, 1998). Given asmall n, a number of sample in the cross-sectional component, and a relatively small sample
size, a finite sample correction has been made for the variance by using the robust estimatorwhen two-step estimations are feasible (Windmeijer, 2005). In the second stage, a measure ofpoverty or inequality, disaggregated into rural and urban areas, is estimated by the
agricultural production or productivity predicted in the first stage. 9

Results
We will first discuss whether AT influenced agricultural output. 10 First, thecommercialisation index positively increases agricultural value added per capita with a year’s
lag after taking account of the reverse causality from the latter to the former. If the share ofprocessed agricultural and livestock products in the total agricultural and livestock products
increases by 1%, agricultural value added per capita tends to increase by 0.47% in the nextyear, other things being equal. Given the limitation of the index as a proxy forcommercialisation, we can confirm that the commercialisation - in terms of the higher shareof processed crops - significantly increases agricultural production. The commercialisation interms of the higher share of processed crops also accelerates the growth of agricultural value
added per capita with a time lag after taking account of the reverse causality. Second,
agricultural openness does not significantly increase agricultural production or its growth rate
after taking account of the reverse causality. Third, product diversity tends to increase thelevel of agricultural production as well as its growth rate after taking account of the reverse

9

See Imai (2017) for technical details of the model.
Tables detailing the results will be provided by the first author on request.

10

14

causality. Overall, the agricultural transformation would increase agricultural value added per
capita with some lag.
As an extension, we have examined the effects of agricultural transformation on TFP. The
results are broadly consistent with those for agricultural value added per capita. First, acommercialisation index positively influences TFP with the first lag. Second, the effects ofagricultural openness on TFP are ambiguous. Third, increased product diversity tends toimprove TFP with the first lag. Turning to other variables, a higher population density tends
to dampen both TFP and its growth rate, a fragility index is negatively and significantly
associated with TFP, and education in terms of labour force with secondary education being
positively correlated with TFP growth.
Based on the estimates of agricultural value added and agricultural productivity as afunction of AT, we have examined the effect of agricultural transformation indices (namely,
the commercialisation index and the product diversity index) on poverty or inequality. In
Table 15.2 we report the results on rural and urban poverty for the international poverty
thresholds, US$1.25 and US$2.00 (based on PPP in 2005). Here the predicted value of log
agricultural value added is based on the case in which the commercialisation index is used asa main explanatory variable. Panel A shows the results for rural poverty, while Panel B for
urban poverty. Panel A of Table 15.2 indicates that as the agricultural sector grows, not only
will the share of poor people in rural areas be reduced, but also the severity of poverty andinequality among the rural poor will be reduced. This study is the first, to our knowledge, toshow that agricultural growth has reduced poverty in rural areas, after taking into account theendogeneity of agricultural value added per capita, and using a cross-country panel data witha dynamic model. For instance, if the agricultural value added per capita increases by 1%,
extreme poverty headcount ratio is reduced by 2.59% points. We can infer the relative
magnitude of the effect of agricultural transformation on rural poverty by combining the first15

and the second stage results. If the ratio of commercialised output to the total agricultural
output increases by 1%, agricultural value added per capita will increase by 0.47% next year,
and this will correspond to 1.22% reduction in poverty (=2.59*0.47) based on the mean valueof rural poverty headcount (e.g. 20% to 19.76%= 20%- 20*1.22%) next year, other things
being equal (Panel A of Table 2 and Case 1 of Panel A of Table 15.2). Poverty gap, as well as
its square at the poverty threshold US$1.25 are significantly reduced as agricultural value
added per capita increases (Cases 2 and 3) 11. The relative magnitudes are small, but thepoverty headcount, the depth of poverty and the severity of poverty at the US$2.00 poverty
line have also been significantly reduced.
Table 15.2 Effect of Agricultural Predicted Agricultural TFP on Poverty
Panel A: Rural Poverty
VARIABLES

Case 1Case 2

Case 3

Case 4

Case 5

Case 6extreme rural
povertyextreme rural
povertyextreme rural
povertymoderate rural
povertymoderate rural
povertymoderate rural
poverty

Poverty threshold

US$1.25

US$1.25

US$1.25

US$2.00

US$2.00

US$2.00

Definition

P0 (headcount)

P1

P2

P0

P1

P2

(Gap)

Gap Squared(headcount)(Gap)

Gap Squared

FE

FE

FE

FE

FE

FE

plogagrivapc-4.787***-5.544***-5.370***-2.929***-3.877***-4.527***

predicted log
agricultural value
added per capita(0.926)(1.014)(1.150)(0.619)(0.756)(0.863)Model

VARIABLES

Observations
R-squared

80

80

78

80

80

80

0.5

0.56

0.565

0.412

0.497

0.53

Number of code1

17

17

17

17

17

17

Hausman Test r

FE

FE

FE

FE

FE

FE

In favour ofStandard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.

Panel B: Urban Poverty
VARIABLES

Poverty threshold

Case 1Case 2

Case 3

Case 4

Case 5

Case 6extreme urban
povertyextreme urban
povertyextreme urban
povertymoderate urban
povertymoderate urban
povertymoderate urban
poverty

US$1.25

US$1.25

US$1.25

US$2.00

US$2.00

US$2.00

11

It is noted that the Foster-Greer-Thorbecke (1984) (FGT) class of poverty measures are used in ourstudy. The headcount index (P0) measures the proportion of the population that is poor (Haughton andKhandker, 2009, p.67). The poverty gap index (P1) measures the extent to which individuals fall
below the poverty line as a proportion of the poverty line, while the squared poverty gap index (P2)averages the squares of the poverty gaps relative to the poverty line (ibid., 2009, p.67).

16

P1

P2

P0

P1

P2

(Gap)

Gap Squared(headcount)(Gap)

Gap Squared

FE

FE

FE

FE

FE

-4.019***-4.979***-4.032***-3.856***-4.532***-5.012***(10.44)(1.184)(1.211)(0.730)(0.878)(0.995)

Definition

P0 (headcount)

Model

FE

VARIABLES

plogagrivapc

Observations

79

77

74

77

76

76

R-squared

0.608

0.561

0.575

0.621

0.62

0.6

Number of code1

17
FE

17
FE

17
FE

17
FE

17
FE

17
FE

Hausman Test r
In favour of

In Panel B of Table 15.2, we have examined the effect of agricultural value added per
capita on urban poverty based on the commercialisation index. Panel B shows that, as the
agricultural value added per capita increases, the headcount (only at the poverty line of
US$2.00, not US$1.25), the poverty depth, and the severity of poverty (at both US$1.25 andUS$2.00) have been significantly reduced. It is particularly noted that the magnitude of
reduction is larger for urban poverty than for rural poverty, implying a substantially larger
indirect effect of agricultural transformation on urban poverty, for example, as a positiveeffect of rural-to-urban migration (e.g. through finding a job in the urban non-farm sector
with real wages much higher than in rural areas) exceeds its negative effect (e.g. expanding
urban slums with low-paying jobs as a result of migration).
That is, agricultural growth also reduces urban poverty in terms of the share of poor
people based on the headcount index (P0). It is also found that agricultural growth decreases
depth of urban poverty which is based on the poverty gap index (P1) and inequality among
the urban poor based on the squared poverty gap index (p2), a weighted sum of poverty gaps
where the weights are the proportionate poverty gaps themselves. For instance, a 1% increasein agricultural value added is associated with a 2.73% decrease in the moderate poverty
headcount ratio (based on Case 5 of Panel B). A positive externality of agricultural growth is17

an expansion of processing firms in urban areas and thereby employment which then reduces
poverty.
In the regression based on the product diversity index H’, we find similar results with thecoefficient estimate statistically significant in all the cases for both rural and urban poverty.
That is, if the agricultural production is more diverse, poverty in both rural and urban areas issignificantly reduced. 12
We have estimated the effects of (predicted) agricultural TFP on rural and urban poverty
and the overall results remain broadly unchanged. We have found that improvement inagricultural TFP significantly reduces rural poverty. For instance, a 1% improvement in TFP
tends to decrease rural poverty headcount ratio based on US$1.25 a day by 4.78%, keeping
other factors constant. If we combine the first stage and the second stage results, we can inferthat a 1% increase in commercialisation index TFP tends to reduce rural poverty by 0.54%.
This appears to be quite large in terms of the absolute magnitude of the effect of increase in
TFP on poverty. We have found that a rise in agricultural TFP significantly reduces urban
poverty as well. A 1% increase in TFP tends to decrease the urban headcount ratio based on
US$1.25 a day by 4.02%, keeping other factors constant. If we combine the first stage andsecond stage results, we can infer that a 1% improvement in commercialisation index tends toreduce rural poverty by 0.45%. 13
We have also examined the effect of (predicted) agricultural value added per capita on
poverty and child malnutrition in Table 15.3 where in the first stage the commercialisationindex is used as a main explanatory variable. 14 The poverty-reducing effect of agriculturalvalue added per capita is statistically significant for moderate poverty but not for extreme
poverty. Here, if agriculture is transformed and this transformation leads to agricultural

12

The results will be provided on request.
The results will be provided on request.
14
The results are broadly similar if they are based on the product diversity index as a main covariate.
13

18

growth, then the status of child nutritional conditions will improve, for instance, because thehousehold has more agricultural products and food availability will improve, or additional
income due to the agricultural growth can be used for food consumption for children.
Table 15.3 Effect of Predicted Agricultural Value added per capita on Poverty and Child
Malnutrition

VARIABLES
Plogagrivapc
Observations
R-squared
Number of code1

Case 1
Povertyheadcount
US$1.25
Based onCase 2of Table 2
Trans
Index

Case 2
Povertyheadcount
US$1.25
Based onCase 5of Table 2
Product
Diversity

Case 3
Povertyheadcount
US$2.00
Based onCase 2of Table 2
Trans
Index

Case 4
Povertyheadcount
US$2.00
Based onCase 5of Table 2
Product
Diversity

Case 5

Case 6log Child
Underweight
Based onCase 2of Table 2
Trans Index

log Child
Underweight
Based onCase 5of Table 2
Product
Diversity-0.497(0.825)
142
0.248
18

-0.347(0.885)
136
0.241
15

-0.897**(0.402)
142
0.324
18

-0.803*(0.435)
136
0.305
15

-0.888***(0.155)
143
0.743
18

-0.653***(0.171)
126
0.77
15

FE

FE

FE

Hausman Testin favour ofFE
FE
FE
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.

Our results suggest that agricultural growth tends to reduce child underweight prevalence.
That is, a 1% of growth in agricultural value added per capita tends to reduce childunderweight prevalence by 0.65% to 0.89%. If the first and the second stage results are
combined, we can infer that agricultural growth reduces child underweight prevalence by
0.07% to 0.10%. It is noted that this is an annual estimate and the commercialisation will
have a substantial impact on child nutrition over time. 15
Finally, we have examined the relationship between agricultural value added per capita
and inequality by replacing poverty in the second stage by inequality. We have used log ofGini coefficients for rural areas (called ‘rural Gini) and for urban areas (called ‘urban Gini).
However, in none of the cases (predicted) log of agricultural value added per capita is
statistically significant in explaining variation in inequality. This could be due to either thefact that agricultural transformation or agricultural growth does not reduce inequality or a15This is of course subject to the caveat that intra-household distribution of income and food is often
not equitable (Dreze and Sen, 1995).

19

non-linear relationship between rural Gini and agricultural value added per capita. So we
examined whether there is any nonlinear relationship between agricultural value added per
capita and its square and found that statistically significant coefficients are found only forrural Gini when we restrict the sample to low-income countries only. We have found that as
the agricultural sector grows, inequality of low-income countries tends to fall, but if it further
grows beyond a certain point, inequality tends to increase. So the inequality-reducing effectof the agricultural growth (induced by commercialisation) is observed only at the initial stageof development. This may be due to the strong poverty- reducing effect at the lower poverty
threshold than at the higher poverty threshold for rural areas.

15.5. Concluding Remarks
This chapter has analysed the dynamics of transformation of agriculture in rural Asia and thePacific with a focus on its effect on poverty, based on the up-to-date country panel dataset for
the region. We have examined the effects of agricultural transformation, on various measuresof poverty and inequality where the dynamic effect of agricultural transformation on
agricultural growth is modelled by a dynamic panel model and then poverty is estimated by
the predicted agricultural value added per capita using the static panel model. Agriculturaltransformation is defined by three different indices, namely, agricultural openness index,
commercialization index to capture the share of processed agricultural and livestock
production in the total production, and the agricultural production diversity index based on
how agricultural production is diversified into different products. Our study is importantbecause, as far as we know, this is the first study to quantify the agricultural transformation albeit in a limited manner - and evaluate its effect on agricultural output, growth, andproductivity in the Asian and Pacific context. We have also assessed the effect of these terms
on rural and urban poverty and on inequality in Asia using cross-country panel data.

20

Transformation of the agricultural sector in terms of commercialisation and productdiversity has dynamically increased agricultural value added per capita and consequently
reduced both rural and urban poverty significantly. Our results show that even a small change
in the process of agricultural transformation, such as a 1 % increase in commercialisationindex or in product diversity index, reduces both rural and urban poverty substantially.
However, the effect of agricultural openness has not significantly impacted on agricultural
growth. 16 Presumably, this is linked to slow adaptation to growing competitiveness of
integrated markets. The effect of agricultural transformation in reducing child malnutrition
has also been observed, while inequality in rural areas is reduced only at the initial stage ofdevelopment of agriculture for low-income countries. As an extension, we have examined theeffect of agricultural transformation on TFP. Our analysis confirmed that agriculturaltransformation, in terms of commercialisation and product diversification, has promoted TFP
with lags. This reinforces the positive relationship between agricultural value added andagricultural transformation. We have also found that the predicted TFP significantly reduced
rural and urban poverty. 17
Some policy implications are delineated below. First, there is a case for promoting
synergy between public and private investment in rural areas. A priority is to strengthen rural
infrastructure. Although our analysis does not focus on smallholders, their inclusion in thetransformation process through easier access to credit, land and output markets andupgradation of product quality could lead to significantly larger poverty and inequality
reduction. As prospects of absorption of growing rural labour force in manufacturing andservices and other activities are limited, it is important for policymakers to create enough jobs
in rural areas (e.g. through expanding Rural National Employment Guarantee Schemes in
16

One possibility is that that agricultural openness effect is manifested through agriculturalcommercialisation and product diversity.
17
Barrett et al. (2017) argues that ending extreme poverty will require structural change in agriculture
sectors in Sub-Saharan Africa. Future research should extend our research to all developing countries.

21

remote areas). Some of the preceding proposals would help create more employment in rural
areas, raise wage rates and dampen rural-urban migration. Land rental markets would
facilitate the redistribution of land in favour of more efficient small farmers and help
consolidation of small farms into more viable units. Finally, in view of the demographictransition, expansion of education and training facilities in rural areas would help reap thedividend flowing from a larger workforce.

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