DP2014-32

Agricultural Em ployme nt,
Wages and Poverty
in Developing Countrie s*
Katsushi S. IMAI
Raghav GA IHA
Constanza Di NUCCI
August 1, 2014

* 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.

Agricultural Employment, Wages and Poverty in Developing Countries
Katsushi S. Imai*
School of Social Sciences, University of Manchester, UK & RIEB, Kobe University, Japan
Raghav Gaiha
Faculty of Management Studies, University of Delhi, India & Statistics and Studies forDevelopment (SSD) Division, Strategy and Knowledge Management Department, International
Fund for Agricultural Development (IFAD), Italy
Constanza Di Nucci
Statistics and Studies for Development (SSD) Division, Strategy and Knowledge Management
Department, International Fund for Agricultural Development (IFAD), Italy
30th July 2014
Abstract
Drawing upon panel data estimations, we have analysed the relationships among agricultural
productivity, employment, technology, openness of the economy, inequality in land
distribution and poverty. First, we have identified a number of important factors affecting
agricultural productivity, such as agricultural R&D expenditure, irrigation, fertilizer use,
agricultural tractor/machinery use, reduction in inequality of land distributions, or reduction
in gender inequality. Second, while agricultural wage rate is negatively associated with
agricultural productivity and food price in levels, the growth in agricultural wage rate is
positively correlated with the growth in agricultural land or labour productivity as well aswith the growth in food price, particularly after 2000. Contrary to the ILO’s (2012) claimthat the gap has widened recently, this suggests the narrowing gap between wage and labour
productivity once we focus on the conditional relationship between the two. Third,
agricultural employment per hectare tends to increase agricultural productivity after taking
account of the endogeneity of the former, while the growth in agricultural employment per
hectare tends to increase the growth in non-agricultural employment over time withadjustment for endogeneity of the former. In this context, we have reviewed the recent
literature and emphasised the importance of enhancing agricultural productivity andemployment. Fourth, both agricultural growth and non-agricultural growth tend to lead to
reduction in overall inequality. Finally, increase in agricultural productivity which is treatedas endogenous will reduce poverty significantly through the overall economic growth.
Overall, policies to increase agricultural productivity and agricultural employment are likely
to increase non-agricultural growth, overall growth and reduce poverty, where guaranteeing
gender inequality is likely to be one of the key factors.
JEL Codes: C20, I15, I39, O13
Corresponding Author: Katsushi Imai (Dr), Economics, School of Social Sciences, University ofManchester, Arthur Lewis Building, Oxford Road, Manchester M13 9PL, UK; Phone: +44-(0)161-2754827 ; Fax: +44-(0)161-275-4928 ; E-mail: Katsushi.Imai@manchester.ac.uk
Acknowledgements: This study is funded by IFAD (International Fund for Agricultural Development)
We are grateful to Thomas Elhaut, Director, SSD, for his enthusiastic support and guidance throughout
this study and for his insistence on highest standards of analytical rigour. The views expressed are
personal and not necessarily of the organisations to which we are affiliated or of IFAD.

1

Agricultural Employment, Wages and Poverty in Developing Countries
I.

Introduction

The purpose of this paper is to re-examine the relationships among agricultural productivity,
employment, wage rates and poverty, based on econometric analysis of cross-country panel data.
We propose to extend our study (Gaiha and Imai, 2008) by (i) updating the datasets, (ii) adding a
few additional variables, such as, gender indices1, and (iii) estimating poverty by a system of
equations where agricultural productivity is treated as an endogenous variable.
Although agricultural growth is central to economic growth and poverty reduction indeveloping countries, there have been relatively few studies to analyse the determinants ofagricultural growth and to link poverty reduction to growth of employment in agriculture using
cross-country datasets, with Gaiha and Imai (2008) as one of the few notable exceptions. As wediscussed recently in a series of studies (Imai and Gaiha, 2014; Imai, Gaiha, and Garbero, 2014;
Imai, Abekah-Nkrumah, and Purohit, 2014), contrary to the recent study by Collier and Dercon(2013) questioning the role of smallholders in development process, we have shown that
agricultural growth has played a key role in the overall economic growth and poverty reduction.
In this context, the present study uses cross-country panel data and investigates the relationships
among agricultural productivity, employment, wage rates and poverty, using state-of-art
econometric methods.
The rest of the paper is organised as follows. The next section discusses the background issue
to motivate our econometric study. Section III outlines the data sources and variables. Section IV
presents the econometric models we will use, followed by discussions of econometric results in
1

The gender equality index (discrete variable taking values from 1 to 5) assesses the extent to which thecountry has installed institutions and programs to enforce laws and policies that promote equal access formen and women in education, health, the economy, and protection under law. See p.8 for the definitionand discussions of the gender equality index.

2Section V. Section VI reviews the recent literature on agricultural youth employment. Section
VII offers concluding observations.

II.

Background - Employment, Wages, Productivity and Poverty2

In 2013, global economic growth slowed down to 2.9 per cent, its lowest rate since 2009 andmore than 1 percentage point below the average annual growth rate over the pre-crisis decade3.
The only two regions worldwide in which growth did not slow between 2012 and 2013 wereSouth Asia and East Asia, which saw accelerations from 3.6 to 3.9 per cent and from 6.6 to 6.7
per cent, respectively. All other regions lost momentum in growth, with Central and SouthEastern Europe growing at a rate of 2.5 per cent, Latin America and the Caribbean at 2.7 per centand Sub-Saharan Africa at 4.8 per cent in 2013. A more pronounced deceleration took place inSouth-East Asia and the Pacific, where the growth rate dropped from 5.7 per cent in 2012 to 4.9
per cent in 2013. The largest growth decelerations took place in the Middle East and North
Africa, mainly due to political events (ILO, 2014)
Responding to this slowdown of overall economic growth, employment growth also slowed
down in 2013 across most regions, leading to a further upward revision of unemployment rates.
Global employment grew by a mere 1.4 per cent in 2013 - broadly unchanged from 2012, but
lower than in any year of the pre-crisis decade. Employment growth deteriorated in every
geographic region except South Asia and North Africa. The largest slowdowns occurred inCentral and South-Eastern Europe and CIS, Latin America and the Caribbean and South-East
Asia and the Pacific. As a consequence, the crisis-related global jobs gap, measuring the numberof jobs lost in comparison to pre-crisis trends, widened further to 62 million workers in 2013. As
2This section draws upon Section 6 of Gaiha (2014a).
Much of this review is based on ILO (2012, 2014).

3

3

unemployment continues to persist, by 2018 the global gap is projected to rise to 81 million; thisincludes some 30 million discouraged workers who might never come back to the labour market(ILO, 2014).
The labour market outlook for young people worsened in nearly every region of the world.
The global youth unemployment rate rose to 13.1 per cent in 2013, from 12.9 per cent in 2012
and 11.6 per cent in 2007. The largest increase occurred in the Middle East region. This region
has one of the highest youth unemployment rates in the world, with 27.2 per cent of young
people in the labour force without work in 2013, versus 26.6 per cent in 2012. Central and SouthEastern Europe and CIS, East Asia, South-East Asia and the Pacific and North Africa all saw a
substantial increase in youth unemployment rates (ILO, 2014).
By sector, services accounted for more than half of total global employment growth in 2013,
while agricultural employment accounted for around one quarter. Overall, just below 32 per centof the world’s workforce was employed in the agricultural sector in 2013, a decline of 11.7
percentage points over the previous two decades. The services sector employed 45.1 per cent of
the world’s workers in 2013 and the share of services workers increased by 10.1 percentage
points over the same period. Industrial employment now accounts for around 23 per cent of all
global employment, an increase of only 1.6 percentage points over the past two decades.
Monthly average wages adjusted for inflation - known as real average wages - grew globallyby 1.2 per cent in 2011, down from 2.1 per cent in 2010 and 3 per cent in 2007. Because of its
size and strong economic performance, China weighs heavily in this global calculation. Omitting
China, global real average wages grew at only 0.2 per cent in 2011, down from 1.3 per cent in2010 and 2.3 per cent in 2007 (ILO, 2012).

4

The global trend (productivity rising faster than wages) has resulted in a change in the
distribution of national income, with the workers’ share decreasing while capital income shares
increased in a majority of countries. Even in China, a country where wages roughly tripled overthe last decade, GDP increased at a faster rate than the total wage bill - and hence the labour
share went down. The drop in the labour share is due to technological progress, trade
globalization, the expansion of financial markets, and decreasing union density, which haveeroded the bargaining power of labour. Financial globalization, in particular, may have played abigger role than previously thought. One of the key findings is the growing inequality in income,in terms of functional and personal income distribution.
There is a long run trend towards a falling share of wages and a rising share of profits in many
countries. The personal distribution of wages has also become more unequal, with a growing gap
between the top 10 per cent and the bottom 10 per cent of wage earners. These internal“imbalances” have tended to create or exacerbate external imbalances, even before the GreatRecession, with countries trying to compensate the adverse effects of lower wage shares on
consumption demands through easy credit or export surpluses (ILO, 2012).
ILO (2012, pp 47-53) argued that the gap between labour productivity and wages has been
expanding due to the declining share of labour in comparison with the share of profits in many
countries. However, it is unclear whether the gap between labour productivity and wages has
actually narrowed for developing countries - for which the wage data are limited. Also, even if
the share of labour has declined, it may not necessarily imply a narrowing gap between labour
productivity and wages. The present study will partly address the issue by estimating theconditional relationship between the growth rate of nominal and real wage rates and the growth5rate of labour productivity. The results imply that the gap between the two has been narrowing
after 2000.
In Latin America and the Caribbean, job quality continued to improve. In particular, working
poverty or the share of workers living in households with consumption levels of less than US$2
per day and per person showed a clear and consistent improvement in the past decade, fallingfrom 15 per cent of total employment in 2003 to an expected 6.7 per cent in 2013. The share of
East Asia’s workers living on less than US$1.25 per day fell to 4.5 per cent in 2013 and thecomparable share under the US$2-poverty line declined to 11.2 per cent. Since 1991, the region
has successfully moved 464.5 million workers out of poverty, an astounding and unprecedented
pace of improving household incomes and living standards. In South East Asia and the Pacific,
the share of workers earning less than US$2 a day is estimated to have declined notably, from62.3 per cent in 2000 to 30.5 per cent in 20134. In this study, we propose to build on our earlier
work (Gaiha and Imai, 2008) to re-examine the relationships among agricultural productivity,
employment, wage rates and poverty.

III.

Data

Following Gaiha and Imai (2008), we have construct the panel data from various data sources,
such as, World Development Indicators or WDI 2014 (World Bank, 2014), FAOSTAT (FAO,
2014), and LABORSTA (ILO, 2014). The data on agricultural R&D (government expenditure)are sourced from FAOSTAT. Gender index is based on WDI 2014. We still face a constraint that
agricultural wage series and R&D data are available only for a limited number of developing
countries. Appendix provides the descriptive statistics of variables.

4

Estimates of working poor in South Asia are not given in ILO (2014).

6

IV.

Econometric Models

Econometric specifications follow Gaiha and Imai (2008) and we extend them to examine theeffcts of agricultural productivity based on Imai and Gaiha (2014). The following models are
estimated. Model 1 aims at analysing the relationship among agricultural productivity,
employment, new technologies, and wage rates. In Model 2, the focus shifts to growth rates offarm and non-farm employment, and the relationship between them.

Model 1: Model for the relationships among agricultural productivity, employment,
technology, openness of the economy, inequality in land distribution
Model 1 is formulated to assess the determinants (e.g. technology, R&D, agricultural
employment, inequality in land distribution, openness) of agricultural production per hectare ofarable land (hereafter referred to as agricultural productivity). Agricultural wage is included as
an additional endogenous variable, determined by food price and agricultural productivity.

(1)
where

is log of value added per hectare of arable land (in constant 2005 US

dollars, taken from WDI2014) i denotes country, and t denotes year.

measures acountry’s agricultural productivity. The explanatory variables include technology comprising log
of agricultural machinery/ tractors per hectare of arable land (5)5, log of share of irrigatedcaptures the total value of powered agricultural machinery and tractors (in use) as an aggregate
category and as such these are labour saving equipment in nature. We realise the importance of7

land in total arable land (();), and log of fertiliser consumption per hectare of arable land

refers to log of total employment in agricultural sector per hectare of arable

land (based on WDI2014);

is log of agricultural R&D expenditure her hectare (inconstant 2005 US dollars, taken from FAOSTAT2014) 6

is log of Gini

coefficient of land distribution to capture the inequality in land distribution. Lower inequality inland distribution is likely to be associated with better incentives as well as higher efficiency ofsmall farmers or labourers in agricultural production or activities. This raises total agricultural
productivity and production.
refers to openness of an economy to the rest of the world or to degree of integration
with global markets. We have used trade share (the share of sum of exports and imports in GDP),
Frankel-Romer measure and Sachs-Warner measure. The Frankel-Romer index is the aggregatedfitted values of trade share, derived from a bilateral trade equation with geographical variables(e.g., area, population) (Frankel and Romer, 1999) On the other hand, the Sachs and Warner
measure is a binary variable based on a series of trade related indicators- tariffs, quotas, black
market premium, social organisation and the existence of export marketing boards (Sachs andWarner, 1995).
is the gender equality index (discrete variable taking values from 1 to 5) whichassesses the extent to which the country has installed institutions and programs to enforce laws
and policies that promote equal access for men and women in education, health, the economy,

distinguish the type of implements by e.g. the requirement for labour use, but FAOATAT does not allow
us to do so for the sample countries.
6
Gaiha and Imai (2008) used Thirtle, Lin and Piesse (2003), but we use FAOSTAT2014 because thelatter covers more countries.

8and protection under law.7

is a time effect constant for all countries for a particular year,

an unobservable individual (country) effect, andisis an i.i.d. error term. The coverage ofcountries and the period considerably varies according to the specifications (Table 1)
Equations (2) and (3) specify the determinants of agricultural employment and wage rates,
respectively.

(2)
where

, log of agricultural employment per hectare, is estimated byagricultural value added, and log of monthly agricultural wage,, log of. Monthly agricultural

wages have been taken from LABORSTAT, the ILO data set. We use both nominal agricultural
wage rate - which has been derived as “average agricultural monthly wage rate in local currency
adjusted only by exchange rates” (Table 2a) - and real agricultural wage rate, “average
agricultural monthly wage rate in local currency adjusted only by both annual average Consumer
Price Index and exchange rates” (Table 2b).

is the share of land used forcrops other than cereal crops in total arable land. This is a proxy for agricultural diversification
towards high value commodities, e.g. fruits and vegetables.
constant term,

is a time effect,is the gender index. is a

is an individual (country) effect, andis an error term.

Log of Agricultural wages is estimated by equation (3):(3)

7

It is noted that employment and wage data disaggregated by gender and aggregated at country levels areunavailable for most of the sample countries. Gender Index has thus been used to address the gender issueof employment and wage.

9

whererefers to either log of nominal agricultural wage rate (the results of which arepresented in Table 3a) or log of real agricultural wage rate (Table 3b).

, our proxy for the

agricultural productivity, has been inserted to examine how agricultural productivity has beentranslated into wage rates.

is log of consumer food price index, which will

negatively affect real wage rates, or negatively or positively affects nominal wage rates
depending on the general-equilibrium effect of food price on wage rates, which could be positiveor negative.

is log of average years of schooling years at the levelof primary education. The expected sign is positive as the quality of labour force tends to be
improved by education.

is log of population growth that captures theincrease in labour supply of younger generations.

is log of the rate of inflation,

which captures the inflationary pressure of the economy (e.g. excessive money supply) or the
degree of uncertainty of the economy. The inflation is supposed to reduce real wage rate, whilenominal wages are slow to adjust to the change in prices. is a constant,an individual (country) effect, andis a time effect,

isis an error term.

As an extension, we will examine the effect of agricultural and non-agricultural employment
on wage rate by adding

andas explanatory variables, as in Equation (3)’.

(3)’
In order to examine the dynamic relationship between agricultural wage rate and agricultural
productivity over time and how the relationship between the two variables has changed over time,
it would be necessary to examine the change in agricultural wage rates and the change in the
agricultural wage rate. As a variant of (3), we will estimate the equation (3)”.

10

(3)”
As an extension,

has been replaced by the first difference of log Agricultural Value

Added per Worker to examine the relationship between the growth rate of labour productivity
and that of wage rate (Table 3c).
We cannot apply simultaneous system equations estimation because of the small overlap
between samples for different variables. For efficient use of these samples, a compromise ismade. First, equations (1)-(3) are estimated separately as fixed-effects or random-effects
specifications. 8 Second, we estimate real agricultural wages using fixed or random effects
specifications of equation (3) Using the predicted values, we use IV (instrumental variable)
specifications for equations (1) and (2) simultaneously.

Model 2: Model for agricultural and non-agricultural employment growth
Following Gaiha and Imai (2008), we estimate an alternative model for agricultural employment
growth and non-agricultural employment growth. While Gaiha and Imai used the Arellano-Bond
GMM estimator (Arellano and Bond, 1991) to estimate the dynamic panel model, the present
study applies Blundell and Bond (1998)’s System GMM (SGMM) estimator9 which builds upon
the Arellano-Bond GMM model and is implemented with the finite sample correction of the twostep standard errors proposed by Windmeijer (2005). First, agricultural employment growth isestimated as a function of lagged growth rates of agricultural employment growth, lagged
8

The issue of endogeneity will be addressed later.
Blundell and Bond’s System GMM uses additional moment conditions as the Arellano and Bondestimator may perform poorly if the autoregressive parameters are too large or the ratio of the variance ofthe panel-level effect to the variance of idiosyncratic error is too large.
911changes in agricultural wage rates – which are treated as endogenous in SGMM - and laggedchanges in share of area devoted to non-cereal crops in the total arable area. Here growth rates
can be expressed as the first differences of logarithmic transformation of a variable. Second, thegrowth in non-agricultural employment is estimated by its own lags and the agricultural
employment growth which is treated as endogenous , and it is instrumented by its own lagged
values in the SGMM model.

(4)
(5)
where

denotes non-agricultural employment (defined as the total number of people

employed in sectors other than agriculture). The growth rate of non-agricultural
employment,, is estimated by its own lags and the growth rates of agricultural

employment (and) where they are treated as endogenous and instrumented

by their own lags in SGMM, in order to examine the effects of growth of agricultural
employment on that of non- agricultural employment over time.

Model 3: Model for the effect of agricultural value added per capita growth and nonagricultural per capita growth on the change in inequality
To see the overall relation between agricultural and non-agricultural growth on inequalitychanges, in one of our recent papers, we have applied various models (e.g. static and dynamic
panel models) in Imai and Gaiha (2014). As an extension of this study, in equation (6), we haveestimated, the first difference in inequality, by12̂, the predictedvalue of the first difference in agricultural value added per capita, and

̂

, the

predicted value of the first difference in non-agricultural value added per capita.

̂
̂

̂

̂

(6)

Here we have added the first difference in log of agricultural employment per hectare and thefirst difference in log of non-agricultural employment per hectare as explanatory variables which have been predicted by Model 2 - to examine the effects of changes in agricultural or nonagricultural employment on the change in inequality. In equation (6), the dynamic panel model,
based on the Blundell and Bond’s SGMM estimator, has been applied to incorporate the effect of
the lagged inequality changes on the current inequality change.

̂
̂

̂

̂

(6)’

As an alternative specification, the static panel model has been estimated, as in equation (6)’. In
this specification, the growth terms in agricultural and non-agricultural employment have been
lagged.

Model 4: Model for agricultural productivity and Poverty
As an extension of Model 1, we apply 3SLS (three stage least squares) to the following systemequations (1)’, (7) and (8) for (lagged)(log of value added per hectare of arable land),our proxy for agricultural productivity,(log of GDP per capita) and13

(either poverty gap or poverty headcount ratio based on the international poverty line (US$1.25
or US$2.00)) as an extension of Imai and Gaiha (2014).

(1)’

(7)

(8)
The specification for(equation (1)’) is similar to equation (1), but we adopt a simplified

version to make the estimation feasible given the limited sample availability of explanatory
variables for equation (1) Here we take the first period lag in estimating equation (1)’considering the fact that the agricultural productivity,
log of GDP per capita. The lagged value of, is endogenous to,is estimated by the lagged value of(log of agricultural machinery/ tractors per hectare of arable land), the lag of log share ofirrigated land in total arable land (hectare of arable land ((), and the lag of log fertiliser consumption per) as well as the lagged value of trade openness

To capture the effect of time as well as the regional fixed effects, we haveinserted, time effects,, andor a set of regional dummy variables for six regions(namely, South Asia; East or South East Asia and the Pacific; Sub-Saharan Africa; Middle East
& North Africa; Latin America & the Caribbean; Central Asia & East Europe – where Middle
East & North Africa is the reference case)is the error term, supposed to be independent andidentically distributed (i.i.d.).

14

Following Imai and Gaiha (2014),

in equation (7) is estimated by, capturing the intensity of conflict obtained from CSCW and Uppsala
Conflict Data Program (UCDP) at the Department of Peace and Conflict Research, Uppsala
University (available at http://www.pcr.uu.se/research/UCDP/). This covers armed conflicts,
both internal and external, in the period 1946 to the present. To take account of the endogeneityof agricultural productivity, the lagged value of(estimated simultaneously by equation(1)’) is used to capture the effect of agricultural productivity on the overall economic growthover time. Other explanatory variables include

as a measure of the uncertainty inthe export price (excluding oil, food and gold) at the country level estimated by GARCH (1, 1)(Imai and Gaiha, 2014). Lagged value of the inequality has been inserted to capture the effect ofinequality on growth (ibid., 2014). Regional dummies and time effects have also been inserted inequation (7).
To capture the effect of economic growth on poverty after taking account of the effect ofagricultural growth on the former, poverty gap or headcount ratio is estimated in equation (8) by,(uncertainty in export price), andsimultaneously estimated by equation (7). Time effect,, which are, regional dummies and the error term,

have also been used to estimate equation (8).

V.

Econometric Results

This section summarises the econometric results for Models 1, 2, 3 and 4 outlined in the lastsection to derive a few useful insights into the relationships among agricultural productivity,
employment, wage rates and poverty. As the results are voluminous, we will provide
explanations selectively.

15Model 1: Model for the relationships among agricultural productivity, employment,
technology, openness of the economy, and inequality in land distribution
Tables 1a and 1b report the results for equation (1) where log Ya, agricultural value added per
hectare, is estimated. Depending on the inclusion of time effects, the choice of explanatory
variables (varying due to the limited country coverage of a few explanatory variables) and the
choice of the model (whether fixed-effects (FE) model or random-effects (RE) model isselected). We have indicated the coefficient estimates favoured by Hausman specification tests inbold. Below our explanations are mainly based on the models which are selected by Hausman
specification test. Coefficient estimates of year dummies are not included to save the space.
Table 1a: Elasticity estimates of agricultural value added per hectare (Model1, Single
Equation (1)): Dependent Variable: log Ya = Agricultural value added per hectare; Fixed
Effects (FE) or Random Effects (RE) Model
Column No.

(1)
FE

(2)
RE

EXP.
VARIABLES

logAmlogTrade

(3)
FE

(4)
RE

with timeeffects

with timeeffects

(7)
FE

(8)
RE

(9)
FE
with
time
effects

(10)
RE
with
time
effects0.215***

0.067

0.376***-0.024-1.0680.122***

0.0960***0.146***

0.115***(0.0277)(0.0263)(0.0423)(0.0240)(0.731)(0.827)(0.0369)(0.0353)(0.0240)(0.0246)0.104

0.149**-0.133***-0.269***-2.823-0.4450.362***

0.363***

0.124***

0.131***(0.0680)(0.0651)(0.0414)(0.0902)(1.784)(1.512)(0.0413)(0.0410)(0.0330)(0.0357)-0.02

0.0834***0.102***

0.108***

0.0430*0.0458*(0.0421)(0.0319)(0.0363)(0.0349)(0.0246)(0.0253)0.014

0.149**-0.0690.164***

logIrr

-0.613*

logFert(0.374)0.032-0.234***0.241(0.357)(0.109)(0.0365)(0.552)(0.568)(0.111)(0.0714)(0.0796)(0.0469)

1.398***

0.602***

0.772***-0.2370.743***

0.267***

0.213***

0.044

0.120**(0.167)(0.0505)(0.366)(0.107)(0.0971)(0.0771)(0.0725)(0.0538)-4.017***-7.061***(0.951)(0.410)-0.3850.875**(0.368)(0.368)

No

No

No

No

logLandGini

GenderConstant(6)RE

0.232***

logAg R&D

Year Dummies

(5)
FE

No

No





1982
-2006

1982
-2006

0.598





19822008

19822008

4.333

22.71

10.50

35.42

24.05

10.39

6.384

6.77

8.006

7.826

(1.494)(3.888)(0.208)(1.780)(9.577)(3.655)(0.371)(0.35)(0.320)(0.264)16

Observations

152

R-squared
Number of
code1

0.581
7

Hausman Test:
Test:
Ho: difference incoefficients not
systematic

152

64

12

64

0.962
7

2

5

6

2

5

0.751

21

21

21

21

2

Chi (5)=9.21
2
Prob>Chi =0.1012
Favours RE model

Chi (5)=37.30**
2Prob>Chi =0.0000
Favours FE model

327

327

0.313

2

Chi (28)=1257.66**
2Prob>Chi =0.000
Favours FE model

327

327

0.647

6

Chi (4)=5.86
2Prob>Chi =0.2099
Favours RE model

122

Chi (32)=27.56
2
Prob>Chi =0.6910
Favours RE modelStandard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1; The results based on the model chosen based on Hausman test
statistic are shown in bold.

Table 1b: Elasticity estimates of agricultural value added per hectare (Model1, Single
Equation (1)); Dependent Variable: log Ya = Agricultural value added per hectare; Fixed
Effects (FE) or Random Effects (RE) Model
Column No.

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

FE

RE

FE
with timeeffects

RE
with timeeffects

FE

RE

FE
with timeeffects

RE
with timeeffects

EXP.VARIABLES

logAm

logAg R&D

logIrrlogFert

0.0657*0.052

0.141***

0.134***

0.0657*0.049

0.141***

0.123***(0.0398)(0.0386)(0.0240)(0.0244)(0.0398)(0.0385)(0.0240)(0.0250)

0.192***

0.190***

0.0942***0.0837***0.192***

0.189***

0.0942***0.0800***(0.0421)(0.0407)(0.0276)(0.0277)(0.0421)(0.0418)(0.0276)(0.0294)

0.001

0.089

-0.129

0.075

0.001

0.09

-0.1290.125**(0.120)(0.0834)(0.0829)(0.0591)(0.120)(0.0840)(0.0829)(0.0530)

0.455***

0.360***-0.0790.055

0.455***

0.358***-0.0790.076(0.107)(0.0910)(0.0898)(0.0718)(0.107)(0.0929)(0.0898)(0.0685)

Sachs and Warner(SW)-0.321-0.266(0.454)(0.350)Frankel-0.005-0.005(0.00937)(0.0155)
Year Dummies

No

No





1982
-2006

1982
-2006

No

No





1982
-2006

1982
-2006

6.908

7.812

8.971

8.774

6.908

7.683

8.971

8.570

(0.428)(0.475)(0.375)(0.378)(0.428)(0.441)(0.375)(0.295)Observations

276

276

276

276

276

276

276

276

R-squared

0.198

Number of countries

18

Constant

Hausman Test: Test:
Ho: difference incoefficients not
systematic

18

Chi2(4)=9.23*
Prob>Chi2 =0.0556
Favours FE model

0.198

0.747
18

18
Chi2(29)=4.46
Prob>Chi2 =1.0000
Favours RE model

18

0.747
18

Chi2(3)=10.00**
Prob>Chi2 =0.0404
Favours FE model

18

18

Chi2(29)=2.46
Prob>Chi2 =1.0000
Favours RE modelStandard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1; The results based on the model chosen based on Hausman test
statistic are shown in bold. *Sachs & Warner Indicator or Frankel-Romer indicator has been dropped since there is no time variance.

First, on the variables on technology, log of agricultural machinery/ tractors per hectare ofarable land () is mostly positive and significant (13 out of 18 cases). That is, more
17

intensive use of labour-saving technology (i.e. agricultural machinery) tends to be associatedwith improved agricultural land productivity (proxied by agricultural value added per hectare). If
we focus on the cases favoured by Hausman specification tests (i.e. the results shown in bold),the share of irrigated land in total arable land () is also positive (significant in columns(8) and (10)), suggesting that irrigation is associated with higher agricultural productivity.
Fertiliser consumption is positive and significant in most cases as expected. It is difficult toobtain a single conclusion about the relationship between openness and agricultural productivity
because the sign of the coefficient estimate varies depending on the specifications. For instance,
trade openness defined as the share of import and export in GDP is positive and significant incolumn (2), but it is negative and significant in column (3) once year dummies are included.
Consistent with earlier research (e.g. Imai et al, 2010), Sachs & Warner index or Frankel index isstatistically insignificant. Government R&D expenditure in agricultural is positive andsignificant in most cases to underscore the importance of governmental spending in the sectorspecific R&D in raising agricultural productivity. As expected, more unequal land distribution
tends to lead to lower agricultural productivity. Our coefficient estimate of land Gini is moreintuitive than Gaiha and Imai’s (2008).
We have added the gender index to differentiate our study from Gaiha and Imai (2008) andexamine whether gender parity matters in explaining agricultural productivity. While Hausman
test favours the fixed-effects model (column (5)), if we focus on the results of random-effects
model, the gender index is found to be positive and significant in explaining agricultural
productivity. That is, other things being equal, the country with the institutional framework
allowing more equality between men and women tends to have a higher agricultural productivity.

18

Given the limitations in using the gender index as one of the explanatory variables 10, the results
are consistent with the important role of women in promoting agricultural productivity indeveloping countries.
Tables 2a and 2b show the results of elasticity estimates for agricultural employment per
hectare (logL) based on equation (2) - with nominal agricultural wage rates (Table 2a) and withreal agricultural wage rate. In Table 2a, contrary to Gaiha and Imai (2008), the coefficientestimate for logYa - our proxy for agricultural productivity – is negative and significant in thefirst column (Case 1, FE) which may reflect the fact that agricultural productivity improvement
may sometimes involve the introduction of labour-saving technologies or agricultural machinery.
On the contrary, logYa is positive and significant in the fourth column, which is consistent with
Gaiha and Imai (2008). Average nominal monthly wage has the same (negative) sign as in Gaiha
and Imai, but it is statistically significant in the present study (Columns (1) and (4. The share ofland devoted to non-cereal crop is statistically insignificant. Gender index is statisticallyinsignificant (column (6)).
Table 2a: Elasticity estimates of agricultural employment per hectare (Equation (2))
Dependent Variable: log L = Agricultural employment per hectare; Fixed Effects (FE) or
Random Effects (RE) Model, with nominal agricultural wage rate
Column No.

Exp. VARIABLES
Log YaLog Wage (nominal
agricultural wage rate)
Log NonCereal(1)
Case 1FE(2)Case 1RE(3)Case 2FE(4)Case 2RE(5)
Case 3FE(6)Case 3
RE-0.651***(0.173)-0.445**(0.180)-0.334(0.322)2.053***(0.219)0.0493(0.351)0.807(1.389)-0.0473*(0.0244)-0.116(0.490)-0.0607**(0.0262)
0.0766(0.524)-0.0389(0.0284)-0.485(0.572)-0.142***(0.0486)
6.117***(0.708)-0.0985(0.509)-0.75(2.368)2.313(1.568)-6.585(9.220)1.162(0.907)

No

No





No

No

Gender Index *Year Dummies
10

We do not consider the endogeneity of gender index due to the limited availability of possible
instruments. Because of its limited country coverages, the gender index cannot be instrumented, e.g.,
European’s settlers mortality or population density in 1500 is used in the empirical literature estimating
the relation between institution and growth (Imai et al., 2010).

19

ConstantObservations
R-squared
Number of countries-1.477(1.710)
76
0.293
17

-4.142(1.793)
76
17

1984
-2008

1984
-2008

-4.849(3.197)
76
0.626
17

-25.98(2.454)76
17-6.845(3.477)8
0.364
3-34.63(6.954)8
3Hausman Test: Test:
Chi2(3)=23.85**
Chi2(27)=1122.76**
Chi2(3)=51.84**
Ho: difference in
Prob>Chi2 =0.0000
Prob>Chi2 =0.0000
Prob>Chi2 =0.0040
coefficients not systematic
Favours FE model
Favours FE model
Favours FE modelStandard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1; The results based on the model chosen based on Hausman test
statistic are shown in bold.
*Gender Index has been dropped due to multicollineraity.

In Tables 2b, log of nominal average monthly agricultural wage rate is replaced by log of real
average monthly agricultural wage rate. Because the number of observations has declined due tounavailability of the CPI data for a few countries, gender index cannot be added. The results inTable 2b are mostly consistent with those in Table 2a with a few changes. log Ya is negative andsignificant in column (1). Real agricultural wage rate is statistically insignificant, while nominal
agricultural wage rate is negative and significant in column (1) of Table 2a. Log Non-Cereal isnegative and statistically insignificant.
Table 2b: Elasticity estimates of agricultural employment per hectare (Equation (2))
Dependent Variable: log L = Agricultural employment per hectare; Fixed Effects (FE) or
Random Effects (RE) Model, with real agricultural wage rate
Column No.

Exp. VARIABLES
Log YaLog Wage (real agricultural
wage rate)
Log NonCereal
Year Dummies

Constant
Observations
R-squared
Number of countries
Hausman Test: Test:
Ho: difference incoefficients not systematic(1)
Case 1FE(2)Case 1RE(3)Case 2FE(4)Case 2
RE-0.678***(0.186)-0.549***(0.188)-0.617(0.367)2.066***(0.367)0.00112(0.0123)-0.144(0.514)-0.00103(0.0129)-0.106(0.528)-0.00125(0.0149)-1.19(0.751)-0.0819**(0.0410)5.776***(1.138)NoNo-2.301(1.865).
62
0.249
15

-4.161**(1.882).
62
15

Chi2(3)=9.96*
Prob>Chi2 =0.018
Favours FE model





1984
-2008

1984
-2008

-3.283(3.576).
62
0.708
15

-26.63***(3.763).
62
15

Chi2(27)=
136.05**
Prob>Chi2 =0.0000
Favours FE model

20

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1; The results based on the model chosen based on Hausman test
statistic are shown in bold.

Table 3a reports the estimates of log monthly nominal agricultural wage rate as a function oflog agricultural land productivity (log Ya - log of value added per hectare of arable land), foodprices, log of primary education years, population growth, and log inflation. The first six
columns show the results of the level equations and the last six columns those of the firstdifference equations to examine the relationship between the growth rate of land productivity
and the growth rate of nominal agricultural wage rate before and after 2000. log farm
employment (Log L) and log nonfarm employment (Log LN) have been added only to the level
equations in one of the cases to see the effect of expansion on agricultural wages. In Table 3b we
replace the dependent variable by log monthly real agricultural wage rate while using the exactly
same specifications. Furthermore, in Table 3c, in order to investigate the relationship between
the growth rate of labour productivity and the growth rate of agricultural wage rates - bothnominal and real – before and after 2000, we have estimated the first difference equations byreplacing log Ya by “log Agricultural Value Added per Worker”, our proxy for labour
productivity.

21

Table 3a Estimates of log Nominal Agricultural Wage (Model1, Single Equation for (3)), Level and First Difference Equations;
Dependent Variable: log Wage= log nominal agricultural monthly wage (or First Difference in log Wage); Fixed Effects (FE)or Random Effects (RE) Model
Columns No.

Dep. Variable
Exp. VARIABLES
logFoodPrice
logYa
Log primary ed
years
Population growth
Log inflation

(1)

(2)

Case 1
Total Sample
1992-2008
log Agri WageRE
FE

Level Equation(3)(4)
Case 2
Total Sample
1992-2008
log Agri Wage
FE
RE

(5)

(6)

Case 3
Total Sample
1992-2008
log Agri Wage
FE
RE

-0.450*(0.242)-1.116(1.980)-0.701***(0.218)-0.661(0.571)

0.0993(0.401)
0.632(2.769)-1.085***(0.347)-0.39(0.360)0.0494(0.0579)
0.669(0.612)-0.208(0.414)-0.515(0.883)13.97**(6.955)-0.582(0.830)0.762***(0.216)4.623(3.039)-0.45(0.530)0.732***(0.204)19.25**(7.712)-0.886(0.997)0.679***(0.254)2.136(2.208)-0.837*(0.451)1.203***(0.295)-0.13(0.186)0.0165(0.0736)
0.0164(0.279)-1.873*-1.279***(0.415)1.543***(0.353)-0.644(0.608)-4.254(1.037)(3.992)

Log L(farm employment)
Log LN(nonfarm
employment)
D.logFoodPrice
D.logYa
D.log primaryedyears
D.population
growthD.log inflation
Year Dummies

Observations

No

110

No

110





1992
-2008

1992
-2008

110

110

No

29

No

29

22

(7)

(8)

Case 4
Total Sample
1992-2008
D.log Agri Wage
RE
FE

2.888***(0.687)4.285***(1.502)

1.551***(0.552)
3.892***(1.500)

8.234(6.257)9.019(5.733)-0.0164(0.668)-0.398*(0.215)0.028(0.658)-0.102(0.168)Difference Equation(9)(10)
Case 5
Before 2000
(1992-1999)
D.log Agri Wage
RE
FE

0.257
(1.340)-2.331(4.222)

5.062*(2.699)-0.0985(0.276)-0.229(0.850)-0.859(3.277)4.039*(2.098)-0.0118(0.169)(11)(12)
Case 6After 2000
(2000-2008)
D.log Agri Wage
RE
FE

4.048***(1.012)5.324***(1.699)3.137***(1.010)5.011***(1.764)

7.86(6.123)8.663(5.826)-0.29(0.717)-0.803(0.519)-0.261(0.714)-0.456(0.502)













1993
-2008

1993
-2008

1993
-1999

1993
-1999

2001
-2008

2001
-2008

84

84

34

34

50

50

0.18220.401
R-squared
0.243
0.502
0.592
0.545
Number of
16
16
12
9
8
countries
16
16
6
6
12
9
8
Hausman Test:
Test:
Ho: difference in
Chi2(5)=8.51
Chi2(22)=38.70*
Chi2(6)=51.66**
Chi2(21)=10.86
Chi2(11)=1.86
Chi2(12)=1.36
2
2
2
2
2
coefficients not
Prob>Chi =0.1305
Prob>Chi =0.0153
Prob>Chi =0.0000
Prob>Chi =0.9654
Prob>Chi =0.9989
Prob>Chi2 =0.9999
systematic
Favours RE model
Favours FE model
Favours FE model
Favours RE model
Favours RE model
Favours RE modelStandard errors in parentheses. p<0.01, p<0.05, p<0.1; The results based on the model chosen based on Hausman test statistic are shown in bold.
Primary education has been dropped in columns (5) and (6) due to multicollinearity.

Table 3b Estimates of log Real Agricultural Wage (Model1, Single Equation for (3)), Level and First Difference Equations;
Dependent Variable: log RWage= log real agricultural monthly wage (or First Difference in log RWage); Fixed Effects (FE) or
Random Effects (RE) Model
Columns No.

Dep. Variable
Exp. VARIABLES
logFoodPrice
logYa
Log primary ed
years
Population growth
Log inflation
Log L(farm employment)
Log LN(nonfarm
employment)
D.logFoodPrice

(1)

(2)

Case 1
Total Sample
1992-2008
log Agri WageRE
FE

Level Equation(3)(4)
Case 2
Total Sample
1992-2008
log Agri Wage
RE
FE

(5)

(6)

Case 3
Total Sample
1992-2008
log Agri Wage
FE
RE

-1.512***(0.249)-1.518(2.036)-1.735***(0.225)-0.672(0.610)-0.861**(0.407)0.492(2.822)-2.044***(0.351)-0.336(0.364)-0.977***(0.0862)1.133(0.911)-1.214***(0.394)-0.378(0.841)13.08*(7.151)-0.524(0.853)0.838***(0.223)4.524(3.265)-0.425(0.557)0.818***(0.210)19.41**(7.846)-1.003(1.012)0.745***(0.258)2.072(2.241)-0.905**(0.456)1.305***(0.300)-0.368(0.277)0.169(0.110)0.314(0.416)-3.424**-1.404***(0.395)1.616***(0.336)-0.654(0.579)-4.912(1.543)(3.801)(7)Case 3
Total Sample
1992-2008
log Agri Wage
RE
FE

2.420***(0.692)
4.037**(1.515)8.283

D.logYa
D.log primary

23

(8)

0.876
(0.573)3.699**(1.568)9.205

Difference Equation(9)(10)
Case 4
Before 2000
(1992-1999)
D.log Agri Wage
RE
FE

-0.362(1.339)-2.575(4.218)-1.057(0.866)-0.802(3.339)(11)(12)
Case 5After 2000
(2000-2008)
D.log Agri Wage
RE
FE

3.929***(1.014)5.311***(1.702)7.94

2.991***(1.039)5.077***(1.821)8.777

edyears
D.population
growthD.log inflation
Year Dummies

No

No





1992
-2008

1992
-2008

No

No

(6.311)(5.956)0.00984(0.673)-0.370*(0.217)0.0207(0.683)-0.0478(0.176)4.988*(2.697)-0.0718(0.275)3.807*(2.138)0.0535(0.172)(6.133)(5.987)-0.223(0.718)-0.783(0.520)-0.216(0.734)-0.472(0.533)













1993
-2008

1993
-2008

1993
-1999

1993
-1999

2001
-2008

2001
-2008

29
29
108
108
83
34
49
Observations
108
108
83
34
49
0.1822R-squared
0.495
0.612
0.486
0.594
0.538
Number of
15
15
11
9
7
countries
15
15
6
6
11
9
7
Hausman Test:
Test:
Ho: difference in
Chi2(5)=6.43
Chi2(22)=22.78
Chi2(6)=244.55**
Chi2(21)=15.20
Chi2(11)=.2.62
Chi2(12)=2.97
coefficients not
Prob>Chi2 =0.2668
Prob>Chi2 =0.4140
Prob>Chi2 =0.0000
Prob>Chi2 =0.8127
Prob>Chi2 =0.9949
Prob>Chi2 =0.9958
systematic
Favours RE model
Favours RE model
Favours FE model
Favours RE model
Favours RE model
Favours RE modelStandard errors in parentheses. p<0.01, p<0.05, p<0.1; The results based on the model chosen based on Hausman test statistic are shown in bold.
Primary education has been dropped in columns (5) and (6) due to multicollinearity.

Table 3c Estimates of log Agricultural Real Wage (Model1, Single Equation for (3)), First Difference Equations; Dependent
Variable: First Difference in Nominal or Real Average Agricultural Monthly Wage; Fixed Effects (FE) or Random Effects(RE) Model (Land Productivity is replaced by Labour productivity)
Columns No.

Dep. Variable
Exp. VARIABLES
D.logFoodPrice
D.log Agricultural
Value Added per
Worker
D.log primaryedyears

Difference Equation(First Difference in log Nominal Agricultural Wage)
(1)
(2)
(3)
(4)
(5)
(6)
Case 1Case 2
Case 3
Total Sample
Before 2000
After 2000
1992-2008(1992-1999)(2000-2008)
log Agri Wage
D.log Agri Wage
D.log Agri Wage
RE
RE
RE
FE
FE
FE

Difference Equation(First Difference in log Real Agricultural Wage)
(7)
(8)
(9)
(10)
(11)
(12)
Case 4
Case 5
Case 6
Total Sample
Before 2000
After 2000
1992-2008(1992-1999)(2000-2008)
log Agri Wage
D.log Agri Wage
D.log Agri Wage
RE
RE
FE
FE
FE
RE

2.908***(0.689)1.318**(0.550)0.19(1.348)-0.256(0.781)

3.957***(1.007)2.698***(1.020)2.442***(0.694)0.649(0.571)-0.407(1.350)-1.095(0.795)3.835***(1.010)2.520**(1.053)4.678***(1.649)3.218**(1.610)-2.929(4.499)-2.057(3.529)5.839***(1.879)4.281**(1.951)4.449***(1.660)2.854*(1.679)-2.945(4.506)-2.126(3.594)5.803***(1.884)4.155**(2.012)

7.218

7.583

6.717

7.099

7.318

7.907

6.805

7.309

24

D.population
growthD.log inflation
Year Dummies(6.271)(5.869)0.0545(0.666)-0.393*(0.215)0.101(0.670)-0.0856(0.172)

5.208*(2.679)-0.092(0.275)4.369**(2.079)-0.00823(0.168)(6.146)(6.063)(6.315)(6.098)-0.191(0.720)-0.709(0.521)-0.171(0.745)-0.303(0.527)0.0758(0.671)-0.366*(0.217)

0.0948(0.696)-0.0304(0.179)5.046*(2.682)-0.0652(0.275)4.176**(2.117)0.0576(0.171)(6.164)(6.261)-0.124(0.722)-0.69(0.523)-0.121(0.769)-0.294(0.562)

























1993
-2008

1993
-2008

1993
-1999

1993
-1999

2001
-2008

2001
-2008

1993
-2008

1993
-2008

1993
-1999

1993
-1999

2001
-2008

2001
-2008

Observations
84
84
34
34
50
50
83
83
34
34
49
49
R-squared
0.502
0.595
0.543
0.486
0.596
0.535
Number ofcountries
12
12
9
9
8
8
11
11
9
9
7
7
Hausman Test:
Test:
Ho: difference in
Chi2(21)=16.23
Chi2(11)=.1.81
Chi2(11)=.11.61
Chi2(21)=27.56
Chi2(11)=.2.47
Chi2(12)=85.42
coefficients not
Prob>Chi2 =0.7665
Prob>Chi2 =0.9991
Prob>Chi2 =0.4775
Prob>Chi2 =0.1530
Prob>Chi2 =0.9960
Prob>Chi2 =0.0000
systematic
Favours RE model
Favours RE model
Favours RE model
Favours RE model
Favours RE model
Favours FE modelStandard errors in parentheses. p<0.01, p<0.05, p<0.1; The results based on the model chosen based on Hausman test statistic are shown in bold.

25

In Table 3a (with nominal agricultural wage rates), we have found that food price is negative andsignificant (columns (1), (2) and (4)), that is, if food price is high, it is implied that real averagemonthly agricultural wage rate would be lower. This is consistent with Gaiha and Imai (2008).
However, once log L and log LN are added and the number of observations reduces from 110(for 16 countries) to 29 (only for 6 countries) in columns (5) and (6), food price becomes
statistically insignificant reflecting the small number of observations. The coefficient estimate oflog Ya is statistically insignificant in all the cases. As expected, primary education is positiveand significant in columns (1) and (3) (based on fixed effects models). Population growth isnegative and mostly statistically insignificant (except column (4) where it is significant at the
10% level). As expected, log inflation – capturing the annual price changes at aggregate levels is positive and significant (except column (5)), reflecting the positive association between
inflation and nominal monthly agricultural wage rate.
log L and log LN have been inserted in columns (5) and (6) to examine the effect of non-farm
employment on agricultural wage rate where primary education is excluded due tomulticollinearity. This is motivated by the idea that the expansion of non-farm employment may
result in reduced farm labour supply, which will have an upward pressure for the agricultural
wage. If we focus on the fixed effects model result in column (5) (which is favoured by the
Hausman test), we find that the non-farm employment is negatively and significantly associatedwith nominal agricultural wage rate, while farm employment is not significant. This implies thatthe expansion of non-farm employment tends to have an upward pressure. The pattern of theresults is unchanged if we estimate real agricultural wage rate (column (5) of Table 3b).
Columns (7)-(12) of Table 3a report the results for the equation (3)” (wage equation) wherethe variables are first differenced. Both the growth in agricultural land productivity (DlogYa) and26

the growth in food price are positive and significant in explaining the growth in agricultural
wage rate for the entire sample period, 1992-2008. For instance, 1% increase in the growth rate
in agricultural productivity (value added per hectare of arable land) tends to lead to 3.9 %increase in the agricultural wage rate growth in column (8). 1% increase in the food priceinflation tends to increase 1.6 % in the agricultural wage rate growth in the same case. These
results are mainly driven by those after the year 2000. For instance, after 2000, 1% increase in
the agricultural productivity growth tends to increase agricultural wage growth by 5.01%, while
1% increase in the growth rate in the food price tends to raise agricultural wage growth by
3.14%.11 These results imply that in more recent years agricultural wage rates have become moresensitive to the changes in agricultural productivity or food inflation.
In Table 3b, the dependent variable has been replaced by log real monthly average
agricultural wage rate. The pattern of the results is broadly similar with a few minor changes.
The coefficient estimate of food price is negative and significant with the larger values inabsolute terms. Contrary to normal expectations and somewhat surprisingly, the effect ofinflation (after taking account of the effect of food prices) on real monthly average agricultural
wage rate is positive and significant in most cases. A positive and significant association between
log real average agricultural wage and log inflation is robust to the change in the specifications,for instance in the case where (only) food price is dropped from equation (3) or in the case where
only log inflation is used as an explanatory variable – with or without year dummies. The reason
is not clear, but the related factors influencing this relation include: (i) The inflation is negatively
associated with CPI (with the correlation of coefficient -0.07 for sample countries); (ii) theformer is calculated based on the annual change of spot prices and subject to the sudden pricesurges/shocks while the latter is calculated on the accumulated basis (using monthly prices of
11

These elasticity estimates appear to be high because they are applied to the first difference, not levels.

27

multiple items); and (iii) the change in price levels is not necessarily positively correlated withthe price levels in general. In columns (7)-(12) of Table 3b, the first difference equation isestimated for real average monthly agricultural wage rate.
In Table 3c we have examined the relationship between the growth rate of nominal or real
agricultural wage and the growth rate of labour productivity. The pattern of the results is
unchanged once land productivity is replaced by labour productivity. It has been found that 1%
increase in labour productivity growth tends to lead to 3.22 % (2.85%) increase in the growthrate in nominal (real) agricultural wage, which is mostly explained by the relation of thesevariables after 2000. That is, the ILO’s (2012) argument for the widening gap between
agricultural wage and labour productivity in recent years - which was established by using thedata of developed countries (Figure 36 on p.48 in ILO, 2012) - is unlikely to hold for developing
countries. The results suggest that there has been a closer link between agricultural wage andlabour productivity in more recent years for developing countries. This is related to the possible
reasons for the relatively high growth rate of real wage for developing countries. For instance,
annual average real wage growth in recent years (2006-2011) has been very low in developed
countries ranging from -0.5% to 1%, while it has been high in developing countries (e.g. 0.5% to6.2% for Africa, 3.9% to 6.8% for Asia, -3.5% to 14.4% for Eastern Europe and Central Asia,
0.8% to 3.5% for Latin America and the Caribbean) (ILO, 2012, pp.8-9). While it may be true toargue that the labour income share has been declining in developing countries as in developed
countries, e.g., due to the negative effect of financialisation on the labour income share (ibid.,
2012, pp.52-53), they should not use this as evidence for the widening gap between wages andproductivity. It may be rather conjectured that both real wages and labour productivity have risen28

in recent years (e.g. after 2000) and this must continue to be supported by appropriate
governmental policies.
In Table 4 we have estimated equations (1) and (2) simultaneously by 2SLS where in the firststage agricultural employment per hectare (log L) is estimated and in the second, after
controlling for the first stage results, agricultural employment per hectare (log Ya) is estimatedby log L predicted in the first stage together with other control variables.12 It is found that thecoefficient of log L is positive and significant in the second stage, that is, larger agricultural
employment tends to lead to higher agricultural productivity. Here 1% increase in agricultural
employment tends to raise agricultural productivity by 0.14% after taking into account theendogeneity of the former. Other results are mostly consistent with Gaiha and Imai (2008).
Table 4 Elasticity estimates for agricultural value added per hectare and agricultural
employment per hectare (IV Model for equations (1) & (2)); Dep. Variable in the First
Stage: log L = agricultural employment per hectare; Dep. Variable in the Second Stage: log
Ya = agricultural value added per hectare
First Stage
Second Stage
log L
log YaDep. Variables
Exp. Variables
logL

-

logAm

-5.026***(0.005)-8.081***(2.176)2.541**(1.034)-2.470***(0.848)3.903***(0.744)4.326***(1.357)2.353**(1.146)-0.285***(0.095)-35.749

logIrrlogFertlogAg R&D
logTradelogYa
Log NonCereal
Predicted logWage
Constant

0.141*(0.0730)
0.879***(0.132)
1.963**(0.101)-0.903***(0.191)0.163(0.214)-0.517*(0.280)10.75

12The result of 2SLS should be interpreted with caution as Sargan statistic is statistically significant, thatis, the null hypothesis that the over-identifying restrictions are valid is rejected. Moreover, the results of
2SLS based on the small sample should also be interpreted with caution (e.g. Hahn et al. 2004).

29

Number of obs(10.270)15(1.666)

Joint Significant Tests
F( 8, 6) = 194.89
Sargan statistic (overidentification test of all instruments):
13.296:

Chi-sq(2) P-val =0.0013

Weak identification test: Ho: equation is weakly identified
Cragg-Donald Wald F-statistic
4.41
Weak-instrument-robust inference: Tests of joint significance of endogenous regressors B1 in main equation
Ho: B1=0 and overidentifying restrictions are valid
Anderson-Rubin Wald test F(3,6)= 4.3e+12 P-val=0.0000
Anderson-Rubin Wald test Chi-sq(3)=3.2e+13 P-val=0.0000
Stock-Wright LM S statistic Chi-sq(3)=15.00 P-val=0.0018

Model 2: Model for agricultural and non-agricultural employment growth
We have then estimated the model where agricultural employment growth and non-agricultural
employment using the dynamic panel model (SGMM) for each variable in Table 5. The firstcolumn shows the result where D.logL is estimated. First, the lagged dependent variables arenegative and significant, implying the adjustment process where the higher agricultural
employment growth tends to be followed by the lower agricultural employment growth, or viceversa. The coefficient estimate for D.logYa(-1) (lagged agricultural land productivity growth) is
positive and significant, implying that agricultural productivity growth leads to the larger
agricultural employment (per hectare) over time.13
The result for D.logLN (non-agricultural employment growth) is reported in the second
column. Here an important result is that the second lag of agricultural employment growth which is treated as endogenous in SGMM - is positive and significant in explaining nonagricultural employment growth. That is, if agricultural employment growth increases by 1%,
non-agricultural employment growth will increase by 0.0172%, other things being equal, and
13

The estimation in Case 1 of Table 5 is based on only 44 observations for 9 countries and the results will
have to be interpreted with caution due to the small number of observations as well as small n. As thenumber of observation is small, we have adopted SGMM with the finite sample correction of the two-stepstandard errors proposed by Windmeijer (2005). It should be noted that with the small sample the possible
bias of the SGMM estimator is much smaller than that of the first differencing estimator as shown by anumber of simulations carried out by Hayakawa (2007) for 50 observations. Hence SGMM with the finite
sample correction by Windmeijer has been used in this analysis.

30

after adjusting for the endogeneity of agricultural employment growth. That is, agricultural
employment growth tends to lead to non-agricultural employment growth over time with somelags. The Arellano-Bond test for zero autocorrelation in first-differenced errors as well as theSargan test of overidentifying restrictions have been carried out for each case. On Arellano-Bond
test, the null for zero autocorrelation is rejected for the order 1 (Cases 1 and 2) and for the order
2 (case 2) at the 10% significance level, broadly justifying our use of dynamic panel model. The
Sargan test statistic is statistically insignificant at the 10% level in both Cases 1 and 2, which will
broadly validate the instruments in SGMM.
Table 5 Estimates for log agricultural employment and non-agricultural employment
growth (Blundell and Bond’s (1998) System GMM model for the dynamic panel_
Dependent Variable: Case 1: D.log L (first difference of agricultural employment per
hectare) Case 2: D.log LN (first difference of non-agricultural employment)
Dep. Variable

Case 1Case 2

D.logL

D.logLN

Exp. Variables
D.logL(-1)

Exogenous-0.539***D.logL(-2)

Exogenous-0.595***(0.0935)(0.171)
D.logWage(-1)

Endogenous

D.logYa(-1)

Exogenous

0.00866(0.0136)0.431**(0.205)D.logNonCereal(-1)

Exogenous

D.logLN(-1)

Exogenous

0.264(0.249)-0.0582***(0.0121)D.logLN(-2)

Exogenous-0.00941(0.0181)D.logL(-1)

Endogenous

0.00152(0.00549)

D.logL(-2)

Endogenous

0.0172***(0.00520)

Constant-0.03480.0144(0.0209)(0.00295)

31Observations

44

656

Number of Countries

956

Arellano-Bond test for

Order

Prob>zProb>zzero autocorrelation in

1

0.09080.0973first-differenced errors

20.90050.0985

H0: no autocorrelation
Sargan test ofoveridentifying
restrictionschi2(59)
70.54

H0: overidentifying restrictions

Prob > chi2 =Prob > chi2 =are valid0.14440.8553=chi2(705)= 665.32

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Model 3: Model for the effect of agricultural value added per capita growth non-agricultural
per capita growth on the change in inequality
Table 6 extends the analysis in Imai and Gaiha (2014, Table 4). It has been found that bothpredicted agricultural growth and predicted non-agricultural growth tend to decrease the overall
inequality over time (Case 1, taken from Imai and Gaiha, 2014). The result is robust to the choiceof different estimation methods, such as, dynamic panel model, or CCEMG model (Imai and
Gaiha, 2014).
Table 6: Effect of Predicted Agricultural/Non-Agricultural Growth on Inequality Change
Dependent Variable: D.Inequality (Based on Annual panel (Fixed Effects Model (Robust
Estimators)) (Cases 1, 3 and 4) and Blundell and Bond (1998) SGMM estimator (Case 2)
Case 1Case 2

Case 3

Case 4

FE

SGMM

FE

FE

-3.947**-6.364

0.318

3.091

(1.808)(6.008)(18.18)(17.84)4.813*-16.99-17.34*(2.481)(10.58)(10.25)

Explanatory Variables
D.Log Agricultural Value Added per capita [Predicted]
D.Log Non-Agricultural Value Added per capita[Predicted]-9.782***(3.133)

D.Inequality (-1)

0.867***(0.0414)

D.log L [First Difference of log Agricultural Employment
Per Hectare] [Predicted]-21.98*(11.71)

D.log LN [First Difference of log
Non-Agricultural Employment] [Predicted]-90.7532

(70.96)
D.log L (-1) [Lagged First Difference of log Agricultural
Employment Per Hectare] [Predicted]
D.log L (-1) [Lagged First Difference of log Agricultural
Employment Per Hectare] [Predicted]-13.95-15.12(10.02)(9.236)-82.28-83.14*(48.02)(50.03)

0.331

6.559

44.97

45.36

(0.0791)(2.098)(0.565)(1.169)Observations

932

100

118

118

Number of countries

49

19

21

21

Constant

R-squared
0.014
0.047
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Statistically significant coefficient estimates are shownin bold.

Here we have carried out a few extensions. In Case 2 of Table 6, Blundell and Bond’s (1998)
SGMM model has been estimated to include the lagged dependent variable (or lagged inequality)and predicted values of the first difference of agricultural employment per hectare and the firstdifference of non-agricultural employment. Here we can confirm that change in agricultural
employment per hectare is negatively associated with change in overall inequality. That is, if
agricultural employment increases over time at a higher pace, inequality reduces over time at ahigher pace, which is consistent with the role of agricultural employment in reducing overall
inequality.

Model 4: Model for agricultural productivity and Poverty
Table 7 presents the results of the determinants of the long-term relation between agricultural
productivity and poverty based on 3SLS, which has been carried out as an extension of Imai and
Gaiha (2014). Four different definitions of poverty have been tried according to whether poverty
is defined as poverty gap or poverty headcount ratio, based on US$1.25 or US$2 poverty
threshold. An important finding is that agricultural productivity (log Ya) - which is treated as
endogenous by including the equation for (lagged) log Ya – increases GDP per capita, which33decreases poverty regardless of its definition.
For instance, in case where poverty gap based on US$2.00 is estimated (Case 1), 1% increasein agricultural productivity raises GDP per capita by 0.45%, which reduces poverty by 0.51%,
over time, other things being equal. That is, 1% increase in agricultural productivity (treated as
an endogenous variable in the system) will reduce poverty by 0.23% (0.45 * -0.51), other factors
being equal. Similarly in Case 2, 1% increase in agricultural productivity tends to reduce poverty
gap based on US$1.25 by 0.23% (0.45*-0.51). In Case 3, 1% increase in agricultural productivity
will lead to the decline in poverty headcount by 0.25% ( 0.44*-0.56). In Case 4, log GDP per
capita is not statistically significant in the poverty equation. Overall, the results are consistent
with the significant role of raising agricultural productivity in reducing poverty amongdeveloping countries.

34

Table 7 Determinants of the Long-term relation between Agricultural Productivity and Poverty based on 3SLSVARIABLES
conflict_int

Case 1
Poverty Gap US$2.00
With Equation: L.logYa
(1)
(2)
(3)
povertyloggdppc
L_logYa

Case 2
Poverty Gap US$1.25
With Equation: L.logYa
(4)
(5)
(6)
povertyloggdppc
L_logYa

Case 3
Poverty Headcount US$2.00
With Equation: L.logYa
(7)
(8)
(9)
Povertyloggdppc
L_logYa

Case 4
Poverty Headcount US$1.25
With Equation: L.logYa
(10)
(11)
(12)
povertyloggdppc
L_logYa-0.053(0.0863)-0.387***(0.0907)0.176***(0.0680)0.223**(0.0949)

L.logYa
tot_non_ofg

0.0057(0.00479)

L.inequality_D
Loggdppc

0.0611(0.0476)
0.449***(0.0848)0.00645**(0.00274)
0.0121(0.00751)-0.512**(0.224)L.logFert
L.logTrade

0.0248(0.0389)0.440***(0.0799)0.00402*(0.00237)
0.0141**(0.00653)

0.0118**(0.00549)-0.559***(0.207)0.0857***(0.0247)-0.273***(0.0400)
0.280***(0.0361)
0.736***(0.0575)

L.logIrr

Year Dummies-9E-04(0.00393)-0.512**(0.235)

L.logAm

Regional Dummies

0.0051(0.00503)

0.0624(0.0476)
0.453***(0.0848)0.00617**(0.00274)
0.0098(0.00750)

0.0203(0.0388)
0.415***(0.0786)
0.0036(0.00236)
0.0130**(0.00634)-0.093(0.285)0.0857***(0.0247)-0.272***(0.0400)
0.280***(0.0362)0.737***(0.0575)0.0891***(0.0194)-0.299***(0.0402)0.269***(0.0341)0.707***(0.0550)0.0872***(0.0193)-0.301***(0.0402)0.268***(0.0341)0.706***(0.0549)





































1980
-2009

1980
-2009

1980
-2009

1980
-2009

1980
-2009

1980
-2009

1980
-2009

1980
-2009

1980
-2009

1980
-2009

1980
-2009

1980
-2009

246
0.921

246
0.932

246
0.846

265
0.888

265
0.941

265
0.876

265
0.862

265
0.941

265
0.876

Observations
246
246
246
R-squared
0.913
0.932
0.846
Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

35

VI.

The Way Forward: Involving Young People in Agricultural Sector

The last part of econometric analyses in this paper has addressed how important it will be toincrease agricultural productivity in reducing poverty (Table 7). In a separate estimation in
Table 4, we have shown the importance of increasing agricultural employment in improving
agricultural productivity. However, one of the limitations of these analyses is that we treat
employment as an aggregate category, while disaggregated effect (e.g. age groups, gender) is
likely to be important in analysing the link between agricultural productivity, employment andpoverty. For instance, the young people are likely to play an important role in improving
agriculture. While it is not feasible to carry out regressions to estimate the role of the youth (e.g.
due to the absence of age-disaggregated agricultural employment), this section delineates a few
useful policy implications regarding the role of young people in agriculture drawing upon recent
works, such as, Proctor and Lucchesi (2012), and Paisley (2014) and Bi (2014).
First, labour is highly underutilised at younger generations across all the regions. As shown in
Table 8, youth unemployment rates are much higher globally and across all the regions (Proctor
and Lucchesi, 2012), implying that aggregate productivity could be improved by better
utilisation of the labour force comprising younger generations.
Table 8.Youth and adult unemployment rate in key regions in 2010
Youth unemployment rate (%)

Adult unemployment rate (%)

Asia and the Pacific

11.2

3.1

East Asia

8.4

3.3

South-East Asia and the Pacific

14.8

2.9

South Asia

10.3

3.2

Sub-Saharan Africa

23.8

6.5

Middle East and North Africa

23.8

6.4

Middle East

23.7

6.2

North Africa

23.8

6.5

Latin America and the Caribbean

15.8

5.9

Developed Economies and EU

19.1

7.4

36

World13.1
4.8
Source: Table 1 of Proctor and Lucchesi (2012, based on ILO, 2010). Note: ‘Youth unemployment rate’ and ‘adult unemployment
rate’ for ‘Asia and Pacific’ and for ‘Middle East and North Africa’ are calculated as average of the ‘sub-regions’; South-East Asia andthe Pacific are combined as reported in ILO (2011b).

The data on rural or agricultural employment or unemployment disaggregated by age- groupare generally limited and thus it is difficult to use them in the cross-country regressions or to
draw general conclusions. However, Table 9 below shows that for selected countries, youth
unemployment rate is generally much higher than (aggregate) rural unemployment. Whether
voluntary or involuntary unemployment dominates in the total rural unemployment rate has to becarefully examined with more detailed data, but Table 9 suggests that younger generations areunderutilised in rural areas. Thus, given the importance of agricultural employment in improving
agricultural productivity and reducing poverty, there is a need for making better use of rural
youth in agricultural sector.
Table 9. Total rural employment and youth rural employment in selected countries
Rural Unemployment Rate (%)

Rural Youth Unemployment Rate (%)

Zambia

1990

22

41

Benin

2002

0.3

0.4

Ghana

2000

9

12

Indonesia

2000

4

14

Lao

1995

1

2

Urguay1996

48Venezuela
1990
10
17
Source: Table 2 of Proctor and Lucchesi (2012, based on ILO Rural Labour Statistics Dataset, 2011).

Proctor and Lucchesi (2012, p.50) argues that given the important role of agriculture in
employment and the sheer number of youth in rural areas, new models to enhance decent
employment and livelihood in the agriculture sector must be developed, including support toemployment opportunities along the entire agrifood market chain and the associated service
sectors and calls for supportive policy and new investments, for instance, through public–private
sector partnerships.

37

Paisley (2014, pp.1-2) points out that based on the study in Uganda the tendency for the rural
youth to look for non-agricultural professions may have a negative effect on agriculturalinvestment in rural areas. He points to the need for addressing mismatch between education andemployment in the agricultural sector to ensure that young people can obtain the skills andcompetencies required for modern agriculture through education. For this, there is a need to work
across different disciplines, in partnership with different stakeholders and understand the value
chain and potential for profit and entrepreneurship at different stages, and to integrate this new
way of thinking into educational institutions and agricultural curricula (ibid., 2014, p.2).
In terms of policy for more effective use of young people in agricultural sector, formalized
and regular processes that bring together youth and decision makers are seen as more effective
than informal and irregular consultations (ibid., 2014, p.2). These may include youth 'seats' indecision-making bodies and advisory groups. Youth representatives face similar challenges as
those who represent other constituent groups, in that they represent the interests and concerns of
all youth in agriculture, including those with different aims and interests in both urban and rural
areas (ibid., 2014, p.2). Strategies for engaging the youth in agriculture recommended by Paisley(2014, p.4) include use of local media and social media to share the success stories/ideas
experience and to reduces isolation.
A survey by YPARD of its network indicated that 93.5 per cent of its members wereinterested in joining a mentoring programme. It is thus something that many young people value,
but few have access to. It is critical for young professionals for enhancing their skills andunderstanding of the sector, their confidence and the drive to excel. As the youth population rises
and employment prospects become more limited, rural families will need to consider the farm as

38

part of their children's future and this is thus how this critical legacy will be passed on (ibid.,
2014, p.5).
Bi (2014) focuses on the role of the rural youth in China where with accelerating urbanizationand industrialization, more and more of rural labour is migrating to urban areas. According to theannual statistics from the National Bureau of Statistics of China (2014), 268.9 million rurallabourers, or 35.0 per cent of the total employed labour force, migrated to urban areas in 2013,
and about 60 per cent of the migrants were less than 40 years old. A survey in 10 provinces ofChina revealed that the average age of farmers was 57 in 2010, while another survey asking“who will plant in the next 10 years in your family?” found that almost none of the parents
interviewed expect their children to be engaged in farming (ibid., 2014, p.6).
Bi (2014, p.7) highlights the importance of considering market oriented production andindustrialization because, driven by increasing labour, land and other input costs, together withincreasing demand for safe and high quality food, both the government and private sector are
accelerating the transition from traditional agriculture to market oriented agriculture integrated
with domestic and international markets. Also, the food processing industry is growing very
rapidly, food transportation is becoming quicker because of the development of better logistic
systems, and food supply and value chains are becoming more efficient in China. Production,marketing, and distribution will be integrated and developed as part of a consolidated supply andvalue chain. Quality standards and branded production and processing will also attract more
attention. The application of new information and communication technology has the potential toempower farmers and production cooperatives to access technical and market information. Thesefeatures are making agriculture a more market-oriented, technology and capital-intensive, large

39scale, integrated, multifunctional and creative sector that is more attractive for innovative youth(ibid., p.7).
New patterns for youth participation in agriculture have emerged (Bi, 2014, pp.7-8): (i) In
spite of the current rural-urban migration trends, more and more talented young people who aregood at farming and who earn their first fortunes in the cities are shifting their focus toagriculture and going back to rural areas; (ii) Many well-educated graduates are similarly
engaged in specialized businesses in the areas of agricultural production or related logistics
fields; (iii) In the eyes of innovative youth, attractive opportunities are emerging (e.g organicallygrown fruits and vegetables or eco-friendly agriculture); and (iv) the unpolluted naturalenvironment becomes an asset and a means of generating additional profit through tourism.
These may be still specific to China, but these point to emergence of bright prospects for youth
engagement in agriculture.
On the other hand, constraints hindering youth engagement in agriculture (Bi, 2014, p.8)
include (i) farmers’ lack of ability to resist risks (both natural and market risks) and shocks; (ii)limited application of technological innovation and low rate of technology transfer constrain thedevelopment of modern agriculture; (iii) lack ofagribusiness management capacity andentrepreneurship; (iv) current land policy and lack of financial support as constraints for growthof modern, large scale agricultural production; (v) The generally low income levels in traditional
small scale farming compared to other sectors and unfavourable notions about agriculture related
careers resulting in lack of interest amongst youth in agricultural farming and research; and (iv)
uncomfortable living conditions in rural areas tend to make youth prefer to live in cities.
Priorities and strategies for enhancing youth engagement in agriculture (ibid., p.8) include (i)national foresight and a vision for modern agriculture (as a “roadmap”) with e.g. a five-year plan40

for youth to engage in agriculture; (ii) need for attaining a balance between urbanization andemergence of the 'new countryside', especially with respect to rural development and provisionof a social safety net system; and (iii) a broad based advocacy network.

VII.

Concluding Observations

This paper has extended our earlier study (Gaiha and Imai 2014) to re-examine the relationships
among agricultural productivity, employment, wage rates and poverty, based on econometricanalysis of cross-country panel data. We have updated the datasets, added a few additionalvariables, such as, gender indices, and estimated poverty by system equations where agricultural
productivity is treated as an endogenous variable.
First, we have identified a number of important factors affecting agricultural productivity,
such as agricultural R&D expenditure, irrigation, fertilizer use, agricultural tractor/machinery use,
reduction in inequality of land distributions, or reduction in gender inequality. In policy terms, it
is important for the government to allocate larger share of public expenditure to agricultural
R&D, to implement policies enabling the landless or smallholders to have access to land in orderto reduce inequality in land distributions, and to reduce gender inequality by installing
institutions and programs to enforce laws and policies that promote equal access for men andwomen in education, health, economic/employment opportunities and property rights.
Second, while agricultural wage rate is negatively associated with agricultural productivity
and food price in levels, the growth in agricultural wage rate is positively correlated with thegrowth in agricultural productivity and with the growth in food price, particularly after 2000.
That is, agricultural wages have become more sensitive to, or more responsive to, change in foodprices and agricultural land or labour productivity in recent years. Contrary to ILO’s (2012)41

claim that the gap has widened recently, our results suggest narrowing of the gap between wage
and labour productivity once we focus on the conditional relationship between the two. Third,
agricultural employment per hectare tends to increase agricultural productivity after taking
account of the endogeneity of the former. Also, the growth in agricultural employment per
hectare tends to increase the growth in non-agricultural employment over time with theadjustment for the endogeneity of the former. We have also reviewed the recent literature andemphasised the importance of enhancing agricultural productivity and employment.14
Fourth, both agricultural growth and non-agricultural growth tend to lead to reduction in
overall inequality. Finally, increase in agricultural productivity which is treated as endogenous
will reduce poverty significantly through overall economic growth. Overall, a number of
econometric results in the present study imply that policies to increase agricultural productivity
and agricultural employment are likely to increase non-agricultural growth, overall growth andreduce poverty.

References
Arellano, M., and Bond, S. (1991) ‘Some tests of specification for panel data: Monte Carloevidence and an application to employment equations’ Review of Economic Studies, 58, 277297.
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14

Future studies should investigate the differential effect of employment in different age groups or genderon agricultural productivity or poverty using the household datasets for selective developing countries
given the lack of international data of employment or wages of rural youth.

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Appendix: Descriptive Statistics
Variable

logYa

logAm

logIrrlogFerr

logLan~ilogTradelogAgRD

Mean

Std. Dev.

Min

Max

9.876515

1.349567

5.231017

13.8112N=

3686

between1.318882

6.2710213.37533

n=114

within0.3094331

8.530314

11.04979

T = 32.3333

2.162559-5.437993

8.794895

N=

3487

between2.186574

8.611008

n=110

within0.6816546-2.898907
0.0740063

6.090359

T=

31.7

1.982855-7.518917

4.330534

N=

2916

between1.993302

4.199683

n=54

within0.1752255-7.23736
0.8995174

3.268996

T=

54

1.896681-4.327538

7.371776

N=3480

between1.856429-1.784782

6.890302

within0.4257769-2.497335

5.967347

n = 100
T-bar =
34.8

overalloveralloveralloveralloverall

3.184665

0.8930554

3.4024850.2462856

3.637586

4.465908

N=

1026

between0.2529111

3.637586

4.465908n=19within04.066477

4.066477

T=

54

4903

overall

4.066477

Observations

0.5826389

1.670378

5.636078

N=

between

0.4699554

2.869118

4.966186

within0.3337833

2.123924

5.536276

n = 119
T-bar =
41.20171.653559-2.040221

6.494419

N=

1130

between1.611059

6.314017

n=58

within0.3643778-1.513208
0.5520698

3.647747

T = 19.4828

overall

4.104422

2.317224

45

GenderNsw

frankel

logL

logLN

logWage

logRWage

logNon~llogFoo~elogAgr~w

logpri~soverall

0.636812

2

5

between

0.6099564

2.09375

4.71875

within0.2028181

2.545538

4.045538

overall

3.402681

1007

n=
64
T-bar =
15.73440.4927661

0

1

N=

2296

between0.4956906

0

1

n=

82

within00.4146341

0.4146341

T=

28

overall

0.4146341

N=

12.54785

2.3

68.83

N=

2156

between12.6272

2.3

68.83

n=

77

within015.93779

15.93779

T=

28

overall

15.93779

2.333406-16.43894

0.7190586

N=995

between2.411109-14.47385

0.6295472

n=90

within0.5690946-11.79891-6.34625

T = 11.0556

overall-8.165094

0.3887504

2.415914

5.135798

N=

between0.4457629

2.805248

5.048069

within0.2317907

2.612994

5.399674n=
97
T-bar =
13.3608

3.899703

0.7377972

31.93626N=

314

between4.693386

4.622152

31.93626n=64within1.481767

1.210787

14.1857

T = 4.90625

5.824899-3.379543

56.4354N=

287

between

8.616522

0.2806707

56.4354n=

58

within2.055797-4.070332

17.58275

T = 4.94828

0.9635001-9.173892

N=

between

0.6606344-2.55807-1.55E-06
0.2144091

within0.6890075-7.367968

1.37128n = 120
T-bar =
54.5667

1.032275-4.60517

5.907702

N=

1038

between0.4131813

3.228835

4.975382n=52

within0.9499068-4.13123

6.889

1.145068

4.398173

11.63057

N=

between1.16137

4.507414

10.77959

within0.2524113

6.210769

8.122969

n = 114
T-bar =
27.18420.204939

1.098612

2.079442

N=

overalloveralloveralloveralloveralloverall

4.504785

8.282192

5.001004

0.7521458

4.290067

7.117318

1.694504

46

1296

6548

T = 19.9615

3099

4994

popula~hloginf~nlognoa~clogagr~c

Inequa~llog~g200

log~g125log~c200log~c200

loggdppc

between0.1950136

1.13971

1.94591n=within0.0656541

1.2931412.0517

T = 41.9664

1.252397

11.18066

N=

6039
119

overallbetween

0.9303525-7.533252
0.1853683

4.70327n=within0.853747-8.252432

9.201019

T = 50.7479

2.312227

10.19474N=

4.270372

overall

1.974947

119

between

0.9850017-13.43871
0.3428978

within2.101953-13.03498

10.18986

n = 119
T-bar =
42.1765

overall

1.568645

2.70524-14.29116

9.782739

N=

4061

between

2.338038-13.85616

9.443203n=114

within0.4051493

4.052078.898143

T = 35.6228

overall

6.412387

5019

2.281737-14.3312

6.631798

N=

4088

between1.909991-14.23328

6.256322

n=115

within0.2044626

3.970452

5.824751

T = 35.5478

overall

4.840795

5.896689

20.57831

59.95359N=

between

4.292248

25.23815

51.37882

within3.172033

31.43002

60.12818

n = 119
T-bar =
23.4874

2.074055-4.60517

4.325985

N=783

between1.935846-3.606593

4.213312n=

119

within0.8227494-4.799615

4.6758

T = 6.57983

2.262646-4.60517

4.148517

N=

783

between

2.150242-3.632215

3.965753n=

119

within0.898589-4.332395

3.861336

T = 6.57983

1.924219-4.60517

4.589549

N=

830

between

1.789706-3.392642

4.555455n=119within0.8498869-4.75455.615557

T = 6.97479

1.924219-4.60517

4.589549

N=

830

between

1.789706-3.392642

4.555455n=119within0.8498869-4.75455.615557

T = 6.97479

1.108446

4.056728

10.27328

N=

between1.033475

4.871583

9.13772

within0.3806555

4.981512

9.020123n = 119
T-bar =
43.1513overalloveralloveralloveralloverall

44.4115

1.500566

0.5347658

2.605432

2.605432

6.797822

47

2795

5135

con~_int

tot_no~g

SSA

LAC

EAP

SA

ECA

0.5074446

0

2

N=

6426

betweenoverall

0.3095017

01.407407n=

119

within0.4031115-1.206816

2.145036

T=

54

overall0.2005913

32.13955

35.13227

532.8226

N=

4574

between

17.95782

73.79354

166.2942n=109

within26.70446

11.786473.1789

T = 41.9633

overall

106.6506

0.4827739

0

1

N=

6426

between

0.4847775

0

1

n=

119

within00.3697479

0.3697479

T=

54

overall

0.3697479

0.3948993

0

1

N=

6426

between

0.3965382

0

1

n=

119

within00.1932773

0.1932773

T=54

overall

0.1932773

0.2896644

0

1

N=

6426

between0.2908665

0

1

n=

119

within00.092437

0.092437

T=

54

overall

0.092437

0.2188274

0

1

N=

6426

between0.2197356

0

1

n=

119

within00.0504202

0.0504202

T=

54

overall

0.0504202

0.3948993

0

1

N=

6426

between

0.1932773

0.3965382

0

1

n=

119

within00.1932773

0.1932773

T=

5448