The Labor Productivity Gap between the - 麻省理工学院人工智能研究专业

The Labor Productivity Gap between the
Agricultural and Nonagricultural Sectors, 和
Poverty and Inequality Reduction in Asia
Katsushi Imai, Raghav Gaiha, and Fabrizio Bresciani∗

The objective of this paper is to examine how agricultural and nonagricultural
labor productivities have grown over time and whether the growth pattern
affected poverty in low- and middle-income economies in Asia. We ﬁrst
examine whether labor productivities in the agricultural and nonagricultural
sectors have converged, ﬁnding evidence that they did not as the latter have
grown faster. We then conﬁrm that both agricultural and nonagricultural labor
productivities have converged across economies and that the convergence effect
is stronger for the nonagricultural sector. We have also observed that, 尽管
the relatively slower growth in agricultural labor productivity, the agricultural
sector played an important role in promoting nonagricultural labor productivity
and thus in nonagricultural growth. 最后, we have found some evidence
that the labor productivity gap reduces rural and urban poverty, 也
national-level inequality.

关键词: agricultural labor productivity, 亚洲, 不等式, labor productivity
gap, 贫困
JEL codes: C23, I32, J24, O13

我. 介绍

The objective of this paper is to examine (我) how labor productivities in
the agricultural and nonagricultural sectors in Asia have grown over time, 和 (二)
whether the growth pattern—proxied by the labor productivity gap between the two
sectors—affected poverty and inequality in low- and middle-income economies
in Asia. We focus on these economies because the interaction between the
agricultural and nonagricultural sectors has become increasingly important as these

∗Katsushi Imai (corresponding author): Associate Professor, 经济系, School of Social Sciences,
曼彻斯特大学, 英国. 电子邮件: Katsushi.Imai@manchester.ac.uk; Raghav Gaiha: Honorary
Professorial Research Fellow, Global Development Institute, 曼彻斯特大学, United Kingdom and Visiting
学者, Population Studies Centre, 宾夕法尼亚大学, 美国; Fabrizio Bresciani: Lead Economist,
Asia and the Paciﬁc Division, International Fund for Agricultural Development, 意大利. 电子邮件: f.bresciani@ifad.org.
This study is funded by the Asia and the Paciﬁc Division of the International Fund for Agricultural Development.
The opinions expressed in this publication are those of the authors and do not necessarily represent those of the
International Fund for Agricultural Development. The authors would like to thank the managing editor and three
anonymous referees for helpful comments and suggestions. The usual ADB disclaimer applies.

Asian Development Review, 卷. 36, 不. 1, PP. 112–135
https://doi.org/10.1162/adev_a_00125

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Labor Productivity Gap, and Poverty and Inequality Reduction 113

economies have experienced structural transformation. We will ﬁrst investigate the
convergence of labor productivity in the agricultural and nonagricultural sectors
with a focus on both intersectoral convergence and within-sector convergence
across different economies over time.

The issue of intersectoral convergence versus divergence is reviewed in
the literature, which investigates allocations or misallocations of inputs into the
agricultural and nonagricultural sectors. 例如, using microlevel data, Gollin,
Lagakos, and Waugh (2013) found that a large gap between the two sectors persists,
suggesting the misallocation of labor at the macro level. 然而, the extent of
the gap and how it has changed over time differs across economies depending on
their initial capital and labor endowments, the stage of economic development, 和
the nature of their public policies. As the degree of the misallocation of resources
in dual-economy settings explains variations in national income and productivity
生长 (Vollrath 2009), it is important to examine how the gap has changed over
时间.

To investigate whether the growth pattern impacts poverty and inequality
in low- and middle-income economies in Asia, we draw upon the large empirical
literature to test the convergence hypothesis in line with the neoclassical growth
模型: 那是, whether poorer economies or regions grow faster than richer
economies or regions (Barro 1991, Barro and Sala-i-Martin 1992, Barro et al.
1991). 例如, Barro et al. (1991) and Barro and Sala-i-Martin (1992) 用过的
state-level data on personal income for 48 states in the United States (我们) 期间
1940–1963 and found clear evidence of convergence. As for convergence across
经济体, while the earlier literature suggests that there was convergence across
a wide range of economies (Barro [1991] observes 98 economies during 1960–
1985) and that the convergence was also observed for productivity growth (Baumol,
纳尔逊, and Wolff 1994), it has been debated whether the convergence occurred
for a subset of economies or for different speciﬁcations (Levine and Renelt 1992,
Quah 1996). The results partly depend on the extent to which the economies are
integrated, 例如, through international trade (Ben-David 1996). 鉴于
East and South Asian economies are becoming more integrated, an interesting
question is whether productivity converged among Asian economies.

We will also investigate whether the gap is associated with poverty and
inequality reduction in rural and urban areas. While the literature has focused on the
poverty-reducing effect of agricultural sector income or productivity growth, little is
known about whether the gap between agricultural and nonagricultural productivity
inﬂuences poverty or inequality.1 A point of departure is that we treat the labor
productivity gap as endogenous by using the ﬁxed-effects instrumental-variable
(FE-IV) 模型, where the cropping pattern is used as an instrument. 最后, 我们

1See Imai, Gaiha, and Bresciani (2016) for the evidence for Asia.

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114 Asian Development Review

will discuss whether the labor productivity gap will dynamically affect the labor
allocation between rural and urban sectors.

Our paper draws upon the following three strands of the literature. 第一个
is the literature on the empirical investigations of the gap between agricultural
and nonagricultural productivities in the dual-economy model, consisting of the
traditional and modern sectors. A seminal work in this strand of the literature
is Gollin, Lagakos, and Waugh (2013), who used both national accounts and
household data to show that value added per worker is much higher in the
nonagricultural than agricultural sector in developing economies. They call this
the “agricultural productivity gap.” As Gollin, Lagakos, and Waugh (2013, p. 942)
笔记, the investigation of the agricultural productivity gap has been viewed as an
important topic in the early literature on development economics as it can offer
valuable insights into the analysis of economic growth and inequality in developing
经济体 (例如, Lewis 1955, Kuznets 1971). 最近几年, the agricultural and
nonagricultural sectors have become more integrated within economies through
structural transformation, while the agricultural (or nonagricultural) sector of one
economy has become more closely linked with the same sector of other economies
under globalization. Given the nature of the data that Gollin, Lagakos, and Waugh
(2013) 用过的, their analysis is essentially static. 然而, it is important to analyze
the gap in a dynamic context. Drawing upon the panel data of Asian economies, 这
present study focuses on how agricultural and nonagricultural labor productivities
已经长大, with their interactions taken into account. It also estimates the effect of
the gap on poverty and inequality.

第二, our study is closely related to the large body of the literature on
the role of the agricultural sector in development and the reduction of poverty and
不等式 (例如, Christiaensen, Demery, and Kuhl 2011). A point of departure of the
recent literature (Christiaensen, Demery, and Kuhl 2011; Imai, Cheng, and Gaiha
2017) is that the role of agriculture is captured by dynamic interactions between the
agriculture and nonagricultural sectors. The present study extends these arguments
and focuses on the effect of the labor productivity gap between the two sectors on
poverty and inequality.

第三, the present study is also closely related to the literature on structural
转型, in particular rural transformation (or agricultural transformation),
and its effect on development and/or poverty in low- and middle-income economies
in Asia and elsewhere (例如, Reardon and Timmer 2014, Dawe 2015, Barrett
等人. 2017). As the structural transformation implies a closer and more intricate
relationship between the agricultural and nonagricultural sectors, our empirical
investigation of the gap between agricultural and nonagricultural productivity can
provide useful insight into the literature on structural transformation.

The rest of the paper is organized as follows. In the next section, we brieﬂy
summarize the theoretical foundations underlying our empirical investigation. 在
section III, we examine the convergence of labor productivity in the agricultural and

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Labor Productivity Gap, and Poverty and Inequality Reduction 115

nonagricultural sectors. Section IV estimates the effects of the labor productivity
gap on poverty, 不等式, and the sectoral population share. The ﬁnal section offers
our concluding observations.

二. Theoretical Foundations

我们的

empirical

investigation of

the gap between agricultural

和
nonagricultural labor productivity is associated with a large body of theoretical
literature on the dual-economy model, which originated from Arthur Lewis (1954)
and was later developed by many authors (例如, Dixit 1973, Mundlak 2000).
最近, Vollrath (2009) constructs a dual-economy model in which the
productivity differences between the two sectors arise endogenously. In Vollrath’s
模型, agricultural production is a constant returns to scale function of labor effort
and land (Vollrath 2009, p. 8). Total agricultural production is denoted as

Y A
t

= AA

t F

(西德:3)

(西德:2)

右, EA
t

(1)

where Y A
is agricultural production at time t (and superscript A denotes the
t
agricultural sector), AA
is total factor productivity of the agricultural sector, R is
t
the total amount of land (or resources in general) in the agricultural sector, 和
= statLt. F is a well-behaved function with
EA
t
constant returns to scale. Net income for a representative farmer in the agricultural
sector is

is the total labor effort: 那是, EA
t

I A
t

= pA

t AA

t F (rt,st ) − ρtrt

(2)

where rt is the land employed by the farmer at time t. Each individual has a unit
时间的, with the share st ∈ (0, 1) allocated to productive work in the agricultural
sector and the remaining 1 − st spent in nonfarm activity at time t. ρt is the rental
price of land, and pA
is the price of agricultural goods relative to manufactured
t
商品.

The manufacturing (nonagricultural) sector is assumed to be perfectly
competitive so that labor effort is paid its marginal product (Vollrath 2009, p. 9).
The wage rate per unit of effort in the nonagricultural sector is speciﬁed as

wM
t

= AM
t

w (在 )

(3)

where the wage rate depends on the productivity of nonagricultural sector, 是
,
t
as well as on a well-behaved function w of the number of people in agriculture,
在 (w(西德:4) > 0 and w(西德:4)(西德:4) > 0), given the assumption that the nonagricultural sector is
competitive, while the agricultural sector is not. These properties imply that the

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116 Asian Development Review

nonagricultural wage increases as the number of people in the nonagricultural sector
(1 − at ) decreases. Net income for nonagricultural workers is simply deﬁned by

I M
t

= wM

t st

(4)

Under these settings, Vollrath (2009, p. 11) showed that in equilibrium a dual
economy exists where nonagricultural workers allocate more time to productive
work than agricultural workers, and the marginal product of a worker is higher
for nonagricultural (manufacturing) workers.2 As a result, 国内生产总值
(GDP) per capita can be increased by a transfer of labor from the agricultural
sector to the nonagricultural sector. Vollrath’s model (2009, p. 13) also implies that
sustained increases in agricultural productivity will help industrialize the economy,
but this will be accompanied by a growing disparity in productivity between sectors.
相反, increases in nonagricultural productivity will not only industrialize
the economy but also induce agricultural workers to work more efﬁciently.3 This
model prediction is intuitively valid given close interactions between the two sectors
through migration, particularly in emerging economies such as India, the People’s
Republic of China, and Viet Nam.

The above model would predict, in our empirical context, that the gap in labor
productivity between the agricultural and nonagricultural sectors expands as the
economy grows. As the gap in labor productivity between the two sectors implies
an improvement in relative productivity of the nonagricultural sector, it is likely to
reduce poverty. 所以, we will test the hypotheses directly related to Vollrath (2009)
那 (我) the labor productivity gap between the agricultural and nonagricultural
sectors expands over time, 和 (二) the labor productivity gap between the two
sectors reduces poverty. As we will discuss later, our empirical results are broadly
consistent with Vollrath (2009).

Vollrath’s (2009) model also implies that agricultural productivity and
nonagricultural productivity interact in a complicated way. 然而, the model does
not explicitly consider the interactions with factors outside the economy. Assuming
the concavity of the production function in both sectors, we will empirically
investigate whether agricultural productivity will converge or not across Asian
economies by taking account of the effect of the lagged nonagricultural productivity
on agricultural productivity. The convergence of nonagricultural productivity will
also be examined by incorporating the effect of agricultural productivity on
nonagricultural productivity. This empirical model is oriented in the literature to
test the convergence of economic growth (Barro 1991, Barro and Sala-i-Martin
1992, Barro et al. 1991).

Vollrath (2009) predicts that in the long term the agricultural sector’s
productivity growth will exacerbate the inefﬁciencies of a dual economy and

2See Vollrath (2009, PP. 8–11) for details on how equations (1)–(4) will lead to the results.
3See Vollrath (2009, PP. 12–13 and the Appendix) for more details.

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Labor Productivity Gap, and Poverty and Inequality Reduction 117

produce slower overall growth than will nonagricultural sector productivity
improvements, and therefore the dual economy will disappear. This is consistent
with empirical observations of developed Asian economies such as Japan and the
Republic of Korea. While both of these economies improved their agricultural
productivity in the late 20th century, the GDP share of the agricultural sector
declined as they industrialized and eventually achieved higher overall productivity.
同时, the overall inequality of these economies remained relatively low
and stable.4 However, Vollrath (2009) lacks two aspects. 第一的, the effect of the
persistence of the dual economy on income distribution is not explicitly analyzed.
第二, focusing on the long-term effect, Vollrath’s model may not fully capture the
positive role of agriculture on economic growth and the reduction of poverty and
不等式, which is important in most low- and middle-income economies in Asia
such as India. 例如, Ravallion and Datt (1996) 用过的 35 household surveys
of India between 1951 和 1991 and found that the growth of the primary sector
(mainly agriculture) and the tertiary sector (mainly services) reduced national,
rural, and urban poverty signiﬁcantly, while growth of the secondary sector (mainly
manufacturing) increased national poverty. They also showed that rural growth is
more important for poverty reduction than urban growth. It is evident that a separate
theoretical model is necessary to analyze the effect of a dual economy on income
distribution and poverty.

Some authors have explored the relationship between growth and income
distribution with a focus on the dual economy (例如, 罗宾逊 1976, Bourguignon
1990, Fields 1993, Bourguignon and Morrisson 1998). Bourguignon (1990) offers a
theoretical ground for Kuznets’ hypothesis in detail. The dual economy is modeled
in a general equilibrium framework by taking account of the entire distribution,
which generates a Lorenz curve rather than summary measures. Bourguignon
(1990, p. 219) ﬁrst derived a proposition that a “necessary and sufﬁcient condition
for growth to shift the Lorenz curve of the income distribution upward is that
the share of the traditional sector in GDP increases with growth.” That is, 一个
increase of the share of the agricultural sector in the growth process tends to reduce
不等式. 然而, as Bourguignon notes, it is unlikely that the agricultural sector
share increases with growth. Bourguignon (1990, PP. 226–27) then derives the
proposition that a “necessary condition for growth to be unambiguously egalitarian,
despite a fall in the GDP share of the traditional sector, is that capital–labor
substitution be inelastic in the modern sector,” implying that “observing a falling
GDP share of the traditional sector, together with elastic capital–labor substitution
in the modern sector, is sufﬁcient to rule out unambiguously egalitarian growth
in a dual economy.” That is, the model predicts that the disparity between the

4The income Gini coefﬁcient of the Republic of Korea declined from 0.34 在 1965 到 0.31 在 1993 (秋
1991) and that of Japan fell from 0.29 在 1966 到 0.28 在 1998 (based on the Family Income and Expenditure Survey
from Moriguchi and Saez 2008). Both economies experienced a decline in the GDP share of agriculture during the
respective review period.

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118 Asian Development Review

agricultural and nonagricultural sectors tends to increase inequality with elastic
capital–labor substitution in the nonagricultural (modern) sector. Bourguignon’s
model motivates our empirical analysis of the relationship between the agricultural–
nonagricultural labor productivity gap and inequality and poverty.

三、. Convergence of Labor Productivity in the Agricultural

and Nonagricultural Sectors

Drawing upon the theoretical discussion in the last section, this section
will examine the relationship between agricultural
labor productivity and
nonagricultural labor productivity with a focus on whether (我) these two converge
or diverge over time, (二) agricultural labor productivity converges across different
经济体, 和 (三、) nonagricultural labor productivity converges across different
经济体. 为了 (二) 和 (三、), the intersectoral effects are also taken into account
in one case. 那
labor productivity
on agricultural labor productivity is considered. 为了 (三、), the effect of lagged
agricultural labor productivity on nonagricultural labor productivity is taken into
帐户. For simplicity, the labor productivity of the agricultural (nonagricultural)
sector is deﬁned as value added in the agricultural (nonagricultural) sector divided
by the number of workers in the agricultural (nonagricultural) sector.

the effect of lagged nonagricultural

是,

桌子 1 compares labor productivity in these sectors by economy and region,
and for Asia as a whole. The comparison is also made for the entire period as
well as before and after the year 2000. 桌子 1 reports labor productivity growth
as well as the labor productivity gap as deﬁned by the gap between the logarithm of
agricultural value added per worker and the logarithm of value added per worker
in the nonagricultural sector. Consistent with earlier literature (例如, Martin and
米特拉 2001, Bernard and Jones 1996), nonagricultural labor productivity is higher
in all cases except the Federated States of Micronesia before 2000. 还, 这
labor productivity gap is higher after 2000 in all cases except Fiji. Our results
strongly conﬁrm the labor productivity divergence between the two sectors. 那是,
nonagricultural labor productivity was higher than agricultural labor productivity to
start with and that the gap has expanded over time.

the gap has only moderately increased, 但

然而, there is a great degree of heterogeneity in terms of the speed of
分歧. 例如, in a few economies (例如, Indonesia and the Federated
in other
States of Micronesia),
经济体 (例如, Bhutan, 印度, and the People’s Republic of China), the gap
dramatically increased after 2000. It is thus safe to conclude that there is no evidence
of labor productivity convergence between the agricultural and nonagricultural
部门. This is due to the fact that while agricultural labor productivity has grown
substantially since 2000, nonagricultural labor productivity has grown even faster
in many economies.

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Labor Productivity Gap, and Poverty and Inequality Reduction 119

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120 Asian Development Review

数字 1. The Gap between Nonagricultural Labor Productivity (nonagricultural value
added per worker) and Agricultural Labor Productivity (agricultural value added per worker)
in South Asia by Economy

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logagrivapw = logarithm of agricultural value added per worker, lognoagrivapw = logarithm of nonagricultural value
added per worker.
来源: Authors’ calculations based on World Bank. 2016. World Development Indicators 2016. https://
openknowledge.worldbank.org/handle/10986/23969.

人物 1 和 2 conﬁrm these results graphically. 数字 1 plots labor
productivity in the agricultural and nonagricultural sectors in South Asian
economies over time. The productivity gap was initially small in many economies
(in the 1960s and 1970s), but it has expanded over the years. 数字 2 表明
the above results are broadly similar for East and Southeast Asian economies. If we
aggregate these data, the divergence of labor productivity between the agricultural
and nonagricultural sectors can be conﬁrmed for all of Asia.

下一个, we will examine whether agricultural

labor productivity (或者
nonagricultural labor productivity) has converged across different economies based
on the following simple static model (FE model) and dynamic panel model (系统
generalized method of moments). The idea is similar to Ghosh (2006), WHO
examined the convergence of agricultural productivity among Indian states during
1960–2001. He found that there was signiﬁcant divergence in labor productivity,
particularly after the early 1990s, while there was no signiﬁcant convergence or
divergence in land productivity and per capita agricultural output. To take account

Labor Productivity Gap, and Poverty and Inequality Reduction 121

数字 2. The Gap between Agricultural Labor and Nonagricultural Labor Productivity
in East and Southeast Asia, by Economy

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FSM = Federated States of Micronesia, Lao PDR = Lao People’s Democratic Republic, logagrivapw = logarithm of
agricultural value added per worker, lognoagrivapw = logarithm of nonagricultural value added per worker, PRC =
People’s Republic of China.
来源: Authors’ calculations based on World Bank. 2016. World Development Indicators 2016. https://
openknowledge.worldbank.org/handle/10986/23969.

122 Asian Development Review

of the business cycle, we have taken the 5-year averages and estimate the same
models as follows. We have redeﬁned the time periods as t = 1 for 1960–1964,
t = 2 for 1965–1969, . . . , and t = 11 for 2010–2014. A selection of the
economies is guided by the availability of variables: 37 middle-income and low-
income economies have been chosen from Asia and the Paciﬁc.
第一的, the static model (FE model) is speciﬁed as

d log AGLPit = β0 + β 1 log AGLPit−1 + β2T + Xit · β3 + β4d log NAGLPit−1

+ μi + εit

(5)

where d log AGLPit stands for the annual agricultural labor productivity growth at
time t for economy i. log AGLPit−1 is the level of agricultural productivity one period
earlier in order to capture the convergence effect following the empirical literature
to test the Solow growth model. Our main hypothesis for convergence is to test
whether β1 is negative.

T is the linear time trend. Xit

is a vector of control variables, 例如
the logarithm of schooling years, the logarithm of share of the mining sector in
GDP (in order to capture the economy’s resource dependency), and the lagged
level of inequality (based on the Gini coefﬁcient). A selection of explanatory
variables draws upon the recent literature, which investigated the interactions
between agricultural growth and nonagricultural growth (Christiaensen, Demery,
and Kuhl 2011; Imai, Cheng, and Gaiha 2017). The average years of total schooling
is based on the Barro–Lee data, which has been commonly used in the empirical
macroeconomics literature as it is a broad measure of the human capital stock of
the economy.5 It is assumed that as the economy’s educational attainment improves,
agricultural or nonagricultural labor productivity improves. The GDP share of the
mining sector captures the extent to which the economy relies on natural resources,
which may undermine sectoral labor productivity. The degree of inequality in
various ways inﬂuences the sectoral labor productivity. 例如, if there exists
a threshold (based on the nutritional requirement) below which workers cannot
work efﬁciently in the labor market, a high level of inequality may undermine
either agricultural or nonagricultural labor productivity. d log NAGLPit−1 is the
lagged annual nonagricultural productivity growth to capture the transmission
effect of labor productivity growth in the nonagricultural sector. This draws upon
Vollrath’s (2009) 模型, which showed that nonagricultural labor productivity
enhances agricultural labor productivity over time in a dual-economy setting.
μi is the economy’s unobservable ﬁxed effect (例如, cultural or institutional factors).
εit is an error term. We estimate this model with and without control variables, 或者

5For more details, see Barro–Lee Educational Attainment Dataset. http://www.barrolee.com/.

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Labor Productivity Gap, and Poverty and Inequality Reduction 123

the nonagricultural labor productivity growth term, while the results are robust to
包容性 (exclusion) of a few other explanatory variables.

As an extension, 方程 (1) has been estimated using the dynamic panel
模型 (system generalized method of moments) drawing upon the Blundell and
Bond (1998) robust estimator:

d log AGLPit = β0 + β1d log AGLPit−1 + β2 log AGLPit−1 + β3T

+ β4d log NAGLPit−1 + εit

(6)

这里, d denotes the ﬁrst difference. The lagged dependent variable captures the
persistent effect of agricultural labor productivity growth. Control variables have
been dropped as they are statistically insigniﬁcant.

Exactly the same models can be estimated for nonagricultural
劳动
productivity growth by static and dynamic panel models as in equations (7) 和
(8). The same models have been applied to subsamples for South Asia and for East
and Southeast Asia:

d log NAGLPit = β0 + β1 log NAGLPit−5 + β2T + Xit · β3 + β4d log AGLPit−1

+ μi + εit

d log NAGLPit = β0 + β1d log NAGLPit−1 + β2 log NAGLPit−5 + β3T

+ β4d log AGLPit−1 + μi + εit

(7)

(8)

表中 2, the above models are estimated by using the 5-year average
数据. 这里, the presence of convergence effect can be tested by checking whether
the lagged agricultural labor productivity (agricultural value added per worker
[t − 1]) is negative and statistically signiﬁcant in Cases 1–4, and whether lagged
nonagricultural labor productivity (nonagricultural value added per worker [t − 1])
is negative and statistically signiﬁcant in Cases 5–8. The result of a positive effect of
agricultural productivity on nonagricultural productivity (Cases 1–4) 很重要
as this is consistent with the prediction of Vollrath’s (2009) model that there is
diffusion from the agricultural sector. This is important in terms of the literature
on structural transformation in Asia (Reardon and Timmer 2014), which suggests
that the transformation of the agricultural sector (例如, commercialization and
product diversiﬁcation) is becoming closely linked to changes in dietary patterns;
supply chain and retail revolution; and integrated labor, 土地, and credit markets.
这里, the whole process of structural transformation implies a positive diffusion
effect of agricultural labor productivity on nonagricultural labor productivity.
然而, contrary to Vollrath’s prediction, a positive effect of nonagricultural labor
productivity on agricultural labor productivity was not observed as many Asian

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124 Asian Development Review

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< p * , 5 0 . 0 < p * * , 1 0 . 0 < p * * * . s e s e h t n e r a p n i s r o r r e d r a d n a t s t s u b o R : s e t o N = M M G S , t c u d o r p c i t s e m o d s s o r g = P D G Labor Productivity Gap, and Poverty and Inequality Reduction 125 economies were primarily dependent on the agricultural sector during our data period. In Table 2, we conﬁrm that labor productivity converges in both the agricultural and nonagricultural sectors, and the convergence effect is signiﬁcant in all the cases except Case 2. This implies “a catching-up effect” in which the economies with relatively low agricultural labor productivity tend to catch up with those having relatively high agricultural labor productivity. The catching up effect is also found for nonagricultural labor productivity. We have also found that lagged nonagricultural labor productivity growth deters agricultural labor productivity growth (Cases 3 and 4). This is consistent with the theoretical model of Vollrath (2009) that an improvement of nonagricultural productivity induces agricultural workers to work more efﬁciently. However, the result is reversed when we use the annual panel data in which nonagricultural labor productivity is lagged by 5 years. Here, lagged nonagricultural labor productivity growth is found to promote agricultural labor productivity growth as predicted by the theoretical model.6 On the other hand, we have found, based on the 5-year average panel, that lagged agricultural labor productivity growth promotes nonagricultural labor productivity growth (Cases 5, 7, and 8). In Case 8, the lagged agricultural productivity growth is treated as an endogenous variable. Other covariates are mostly statistically insigniﬁcant, but a large lagged inequality increases nonagricultural labor productivity growth in Case 7. We have estimated the same models using the 5-year average data only for South Asia. A statistically signiﬁcant convergence effect is found in the case of agricultural labor productivity growth. For the cross-sectoral effects, lagged agricultural labor productivity growth is found to promote nonagricultural labor productivity growth. For South Asia, a higher level of inequality tends to reduce overall agricultural labor productivity growth with some lag. Given that inequality can dampen the productivity of the disadvantaged group of agricultural workers or poor smallholders, this is a plausible result.7 When we replicate the same regressions for East and Southeast Asia, we ﬁnd that convergence effects are generally found to be signiﬁcant. For the cross-sectoral effect, lagged agricultural labor productivity growth positively affects nonagricultural labor productivity growth.8 6The results based on the annual panel will be provided on request. 7For South Asian economies, the Gini coefﬁcient is positively correlated with the agricultural commercialization index based on the extent to which an agricultural product is processed (Imai, Gaiha, and Bresciani 2016); the coefﬁcient of correlation is 0.067. For East and Southeast Asian economies, the correlation is negative with a coefﬁcient of –0.4. This could explain the negative correlation between inequality and agricultural labor productivity for South Asia, though the causality will have to be examined carefully in future studies. 8The disaggregated results will be provided on request. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 1 2 1 6 4 4 1 6 7 a d e v _ a _ 0 0 1 2 5 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 126 Asian Development Review IV. Effects of the Labor Productivity Gap between the Agricultural and Nonagricultural Sectors on Poverty, Inequality, and the Sectoral Population Share We have so far examined the pattern of (i) the convergence of labor productivity between the agricultural and nonagricultural sectors, and (ii) the convergence of agricultural or nonagricultural productivity across different economies. Overall, agricultural labor productivity growth has promoted nonagricultural productivity growth and the sectoral gap has widened, while the between-economy disparity of the sectoral labor productivity has narrowed. These ﬁndings are broadly consistent with the theoretical model of Vollrath (2009). is, a change toward higher nonagricultural An interesting empirical question is how this process will dynamically affect poverty and inequality as well as labor allocation across different sectors over time. As we discussed in section II, the theoretical model implies that an increase of the sectoral gap tends to be generally less egalitarian, or that there is an increase in inequality when both sectors grow (Bourguignon 1990). However, it is not straightforward to answer the question because of the difﬁculty in disentangling the complex causal links from the labor productivity gap between the agricultural and nonagricultural sectors to poverty (or inequality or the sectoral population share). For instance, an increase in the labor productivity gap may imply a divergence: labor productivity (reﬂecting that technological development) and/or lower or more stagnant agricultural productivity. On the other hand, a reduction in the gap may imply a change toward convergence due to stagnant nonagricultural labor productivity and/or an increase in agricultural labor productivity. However, while the larger gap affects poverty or inequality, the higher poverty rates or inequality might also inﬂuence the gap. For instance, poor people in rural areas cannot invest in a proﬁtable investment in agriculture that would require a certain amount of investment in physical and human capital (e.g., machinery or high-yielding crops), which hinders the growth of labor productivity in agricultural areas. Thus, there is a need for instrumenting the labor productivity gap because it may be endogenous. We have tackled the endogeneity by instrumenting the labor productivity gap by (i) the lagged agricultural product diversity index (Imai, Gaiha, and Bresciani 2016) and (ii) the lagged logarithm of the production share of the mining sector in GDP.9 The ﬁrst instrument is used as a proxy for agricultural transformation by 9This draws upon Remans et al. (2014), who use an index called the Shannon Entropy Diversity Metric to (cid:4) capture production diversity at the country level using FAOSTAT. It is deﬁned as H (cid:4) = − R i=1 pi ln pi where R is the number of agricultural products and pi is the share of production for the item, i, available from FAOSTAT. The production share, pi, is deﬁned in terms of the monetary value at a local price for each product, i. If the country produces more agricultural products, including processed and unprocessed crops, and the monetary value of all products is more evenly divided among different items, the diversity index, H (cid:4), takes a larger value. On the contrary, if the country produces a smaller number of agricultural products and the monetary value of one or two speciﬁc products is large, H (cid:4) is smaller. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 1 2 1 6 4 4 1 6 7 a d e v _ a _ 0 0 1 2 5 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Labor Productivity Gap, and Poverty and Inequality Reduction 127 Imai, Gaiha, and Bresciani (2016), and is supposed to affect the labor productivity gap, mainly by inﬂuencing agricultural labor productivity. However, the change of the production pattern itself cannot directly inﬂuence poverty or inequality. We cannot deny the possibility that the process of specialization could increase poverty, for instance, as there may be less demand for manual labor; but we can reasonably assume that poverty can change through adjustments in farm production or income (per worker). The second instrument could also reduce the labor productivity gap because dependence on the mining sector could deter the overall effort for technological progress in the industrial sector, without directly affecting poverty. The reliance on the mining sector could affect poverty directly (e.g., the impoverishment of manual workers in the mining sector), but we assume that this does not have a direct impact on poverty, particularly in rural areas. We assume that the productivity or income effect is larger than the direct effect on poverty, while we admit limitations in using the second instrument.10 We have applied the IV model in the panel framework using the FE-IV model, whereby the unobservable country effect is taken into account. Because we focus on the relatively longer-term effect, we use only the 5-year average data. In the ﬁrst stage, we will estimate the determinants of the labor productivity gap between the two sectors: Gapit−1 = β0 + β1d log AGLPit−1 + β2d log NAGLPit−1 + β3Sit−1 + β4Miningit−2 + μi + εit (9) + β5Product Diversityit−2 Here, t stands for each 5-year period: t = 1 for 1960–1964, t = 2 for 1965–1969, . . . , t = 11 for 2010–2014. Gapit−1 is the ﬁrst lag of normalized difference between nonagricultural value added per worker and agricultural value added per worker (at purchasing power parity [PPP] in US dollars divided by 1,000). d log AGLPit−1 is the lag of the ﬁrst difference in log of agricultural value added per worker: that is, the agricultural labor productivity growth during the preceding period. Likewise, d log NAGLPit−1 is the nonagricultural labor productivity growth during the preceding period. Sit−1 is the lag of schooling years. μi is the unobservable country ﬁxed effect and εit is an error term (independent and identically distributed). Instruments for the labor productivity gap between the agricultural and nonagricultural sectors are the second lag of the production share of the mining sector (Miningit−2) and the second lag of the agricultural product diversity index. These instruments, despite the limitations, are justiﬁed on the following grounds. Since the mining sector share is a variable closely associated with the (broadly predetermined) factor endowment of the economy, it will have a direct effect on the economy’s labor allocations across different sectors, including the 10These sets of instruments are the best candidates given the data availability. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 1 2 1 6 4 4 1 6 7 a d e v _ a _ 0 0 1 2 5 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 128 Asian Development Review rural agricultural sector, rural nonagricultural sector (nonmining or mining), and urban nonagricultural sector (nonmining or mining). Depending on the degree of dependence on mining resources, the allocation of labor across sectors and worker efforts in each sector are inﬂuenced directly. It is surmised here that the effect of the mining sector share ﬁrst inﬂuences sectoral labor productivity, rather than poverty. While the mining sector share may inﬂuence poverty directly (e.g., through the impoverishment of mining workers), we assume that it mainly inﬂuences the relative sectoral productivity. The second instrument, the product diversity index, affects agricultural labor productivity directly as more diversiﬁed production implies the economy’s adoption of proﬁtable and marketable agricultural products (e.g., vegetables, fruits, meat). The index also inﬂuences nonagricultural labor productivity as the introduction of these products inﬂuences the productivity of the food processing sector. However, it is unlikely that the product diversity index directly affects poverty or inequality. These instruments, despite the limitations, have been validated by speciﬁcation tests. In the second stage, poverty is estimated by the (instrumented) labor productivity gap as well as other determinants: (cid:5)Gapit−1 = γ0 + γ1 Povertyit + γ2d log AGLPit−1 + γ3d log NAGLPit−1 + γ4Sit−1 + θi + eit (10) Equations (9) and (10) are estimated using the FE-IV model. Poverty is deﬁned in various ways, including (i) the national poverty headcount, or poverty gap, based on the international poverty line of $1.9 (extreme poverty) or $3.1 (moderate poverty) per day at PPP in 2011 (World Bank 2016); (ii) the rural poverty headcount, poverty gap, or poverty gap squared, based on $1.25 (extreme poverty) or $2 (moderate poverty) at PPP in 2005; and (iii) the same urban poverty indexes in (ii), based on household data in rural areas.11 In one case, we have replaced poverty by the Gini coefﬁcient evaluated at the national or subnational level (for rural and urban areas separately). Finally, given the data limitations, we have derived the population share of the rural sector, nonagricultural sector, and urban sector, and used each share as a dependent variable in the second-stage regression (Imai, Gaiha, and Garbero 2017). This aims to examine how the labor productivity gap will inﬂuence the labor allocation in the middle to long run. In all cases, the endogeneity of the labor productivity gap is instrumented. First, we have estimated national poverty in the second stage (the upper left panel of Table 3).12 In the ﬁrst stage, one of the instruments, the agricultural product 11The difference in the deﬁnitions of rural, urban, and national poverty reﬂects the data availability. Poverty estimates for (ii) and (iii) have been provided by the Strategy and Knowledge Department of the International Fund for Agricultural Development. 12A full set of the regression results will be provided upon request. We provide only the second-stage results in Table 3. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 1 2 1 6 4 4 1 6 7 a d e v _ a _ 0 0 1 2 5 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Labor Productivity Gap, and Poverty and Inequality Reduction 129 y t i l a u q e n I d n a y t r e v o P n o s r o t c e S l a r u t l u c i r g a n o N d n a l a r u t l u c i r g A e h t n e e w t e b p a G y t i v i t c u d o r P r o b a L e h t f o s t c e f f E . 3 e l b a T ) l e d o m V I - E F e h t f o e g a t s d n o c e s ( l a r u R y t r e v o P p a G d e r a u q S ) 2 $ ( l a r u R y t r e v o P p a G ) 2 $ ( l a r u R y t r e v o P t n u o c d a e H ) 2 $ ( l a r u R y t r e v o P p a G d e r a u q S ) 5 2 . 1 $ ( l a r u R y t r e v o P p a G ) 5 2 . 1 $ ( * * 5 6 4 1 − . * * 7 5 3 1 − . * * 7 1 1 . 1 − 1 9 2 . 1 − * * 3 3 6 . 1 − ) 2 6 6 0 ( . 2 5 1 1 − . ) 3 9 5 0 ( . 8 0 0 1 − . ) 4 0 5 0 ( . 4 0 9 . 0 − ) 0 6 6 1 ( . ) 2 2 4 1 ( . ) 9 0 1 . 1 ( 4 1 2 0 . ) 0 4 0 1 ( . ) 9 9 7 0 ( . 2 3 9 0 − . 4 2 5 0 . 5 4 2 1 . d e u n i t n o C 2 7 1 0 . ) 6 8 8 0 ( . ) 4 0 7 0 ( . 4 8 5 0 − . 6 0 5 0 . 5 4 2 1 0 9 1 . 0 ) 9 7 6 . 0 ( ) 2 9 5 . 0 ( 4 7 1 . 0 − 5 5 4 . 0 5 4 2 1 ) 4 6 6 . 2 ( 1 6 9 . 2 ) 1 4 8 . 2 ( ) 7 9 7 . 0 ( 7 3 5 . 1 − ) 6 8 0 . 2 ( ) 2 7 1 . 1 ( 2 3 1 . 1 − 0 6 3 . 0 ) 3 1 3 . 1 ( * * 3 7 5 . 8 − ) 9 1 1 . 4 ( ) 1 3 9 . 0 ( 8 6 3 . 1 − 5 4 1 6 5 . 0 2 1 7 5 5 . 0 5 4 2 1 l a r u R y t r e v o P t n u o c d a e H ) 5 2 . 1 $ ( * * 0 2 6 . 1 − ) 4 3 7 . 0 ( 6 7 1 . 1 − ) 6 3 7 . 1 ( 9 5 1 . 0 ) 3 7 0 . 1 ( ) 8 8 8 . 0 ( 6 7 7 . 0 − 9 7 4 . 0 5 4 2 1 y t r e v o P y t r e v o P y t r e v o P y t r e v o P p a G ) 1 . 3 $ ( 4 9 3 . 0 − ) 9 5 4 . 0 ( 3 6 0 . 3 − ) 2 4 9 . 2 ( 3 7 3 . 0 ) 8 5 3 . 2 ( ) 6 5 0 . 1 ( 0 7 4 . 1 − 3 7 2 . 0 7 7 1 1 t n u o c d a e H ) 1 . 3 $ ( 4 7 0 . 0 − ) 1 0 6 . 0 ( 3 9 7 . 2 − ) 7 0 2 . 3 ( 4 5 1 . 0 ) 5 1 6 . 2 ( ) 4 2 2 . 1 ( 6 4 7 . 1 − 0 7 1 . 0 7 7 1 1 1 8 0 . 0 ) 4 6 4 . 0 ( 4 9 8 . 3 − ) 7 5 8 . 2 ( 5 9 3 . 0 ) 9 0 3 . 2 ( ) 3 2 0 . 1 ( 3 1 6 . 1 − 0 9 1 . 0 7 7 1 1 4 2 2 . 0 − ) 1 8 4 . 0 ( 5 4 4 . 3 − ) 0 4 9 . 2 ( 3 5 5 . 0 ) 6 6 3 . 2 ( ) 5 8 0 . 1 ( 0 6 6 . 1 − 1 5 2 . 0 7 7 1 1 p a G ) 9 . 1 $ ( t n u o c d a e H ) 9 . 1 $ ( l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 1 2 1 6 4 4 1 6 7 a d e v _ a _ 0 0 1 2 5 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 ) 1 − t ( p a G s e l b a i r a V l a r u t l u c i r g A ) 1 − t ( h t w o r g y t i v i t c u d o r p ) 1 − t ( h t w o r g y t i v i t c u d o r p l a r u t l u c i r g a n o N ) 1 − t ( s r a e y g n i l o o h c s g o L s n o i t a v r e s b O d e r a u q s - R f o r e b m u N s e i m o n o c e 130 Asian Development Review * 2 3 0 5 . n a b r U e r a h S ) 5 3 5 2 ( . 7 9 0 5 − . l a r u R . i r g a – n o N e r a h S * * * 1 8 0 3 . ) 1 8 2 8 ( . 0 4 9 5 . ) 1 9 4 7 ( . ) 0 5 6 7 1 ( . ) 0 0 9 1 2 ( . ) 2 4 1 . 0 ( ) 3 8 1 . 0 ( . * * 6 4 4 2 − ) 4 9 2 8 ( . . 2 4 2 1 − 5 1 1 0 . 0 − ) 1 7 9 0 . 0 ( 8 9 1 . 0 − 6 3 1 . 0 ) 7 4 1 . 0 ( 1 8 2 . 0 − * * * 6 3 6 . 4 − 4 5 8 . 6 − * * * 8 5 0 . 2 − * * * 4 6 8 . 1 − ) 4 7 1 . 1 ( 6 1 3 . 6 ) 5 2 9 . 3 ( * 9 0 9 . 8 ) 7 6 9 . 4 ( ) 4 8 0 . 5 ( ) 6 1 7 . 0 ( 0 5 2 0 . 0 − ) 7 8 2 . 1 ( ) 8 8 4 . 0 ( 8 8 1 . 0 − ) 2 5 0 . 1 ( ) 1 9 8 . 1 ( 4 7 0 . 2 − 6 1 3 . 0 ) 1 1 7 . 1 ( l a r u R e r a h S n a b r U i n i G l a r u R i n i G l a n o i t a N d e r a u q S i n i G ) 2 $ ( n a b r U y t r e v o P p a G n a b r U y t r e v o P p a G ) 2 $ ( n a b r U y t r e v o P n a b r U y t r e v o P p a G t n u o c d a e H d e r a u q S ) 2 $ ( ) 5 2 . 1 $ ( . d e u n i t n o C . 3 e l b a T n a b r U y t r e v o P p a G ) 5 2 . 1 $ ( * * 7 1 3 . 3 − ) 3 8 5 . 1 ( 5 2 5 . 0 − ) 2 0 7 . 1 ( n a b r U y t r e v o P t n u o c d a e H ) 5 2 . 1 $ ( 0 0 4 . 9 1 − ) 0 4 7 . 2 1 ( * 0 1 2 . 6 2 ) 8 1 . 4 1 ( * 6 8 5 8 . ) 8 4 0 5 ( . 3 5 9 9 − . ) 3 4 9 1 ( . 7 4 5 5 . ) 0 9 9 4 1 ( . 0 5 7 0 0 . 0 ) 6 8 0 . 0 ( 1 6 2 0 . 0 ) 6 0 1 . 0 ( * * 3 4 1 . 6 − * * 8 2 5 . 5 − ) 6 6 7 . 2 ( ) 0 7 0 . 2 ( ) 9 6 7 . 0 ( 0 5 4 . 0 − ) 8 5 6 . 0 ( 4 7 1 . 0 − ) 4 1 1 . 1 ( 9 4 7 . 0 − ) 4 6 0 . 1 ( 4 6 5 . 0 − ) 9 5 8 . 5 ( * * 8 6 . 5 1 − ) 5 0 4 7 ( . ) 5 7 8 8 ( . 6 5 6 8 − . * * * 7 3 9 3 − . * * * 4 3 6 4 . ) 9 7 6 8 ( . 8 6 4 3 0 0 . 0 1 9 2 6 0 . 4 2 6 6 8 6 0 . 4 2 6 * 4 3 2 . 0 ) 7 3 1 . 0 ( 3 4 6 5 3 . 0 2 1 9 9 2 0 . 0 − ) 7 7 1 . 0 ( 5 4 3 0 0 . 0 − 2 1 * * * 3 7 6 . 8 ) 1 5 8 . 2 ( ) 9 4 2 . 7 ( 5 6 2 . 5 − ) 3 1 1 . 1 ( 7 3 7 . 0 − ) 8 1 6 . 0 ( 5 3 1 . 0 − ) 0 9 0 . 2 ( 2 0 7 . 0 − 7 7 3 6 0 . 0 2 1 2 4 6 5 2 . 0 2 1 2 4 9 8 6 . 0 2 1 9 8 6 . 0 3 4 2 1 8 2 4 . 0 9 3 1 1 1 6 2 . 0 ) 1 3 6 . 1 ( 2 4 5 . 0 2 4 2 1 1 7 2 . 0 4 4 2 1 ) 7 9 . 3 1 ( 5 6 . 1 1 − ) 1 − t ( p a G s e l b a i r a V l a r u t l u c i r g A ) 1 − t ( h t w o r g y t i v i t c u d o r p ) 1 − t ( h t w o r g y t i v i t c u d o r p l a r u t l u c i r g a n o N ) 1 − t ( s r a e y g n i l o o h c s g o L s n o i t a v r e s b O d e r a u q s - R f o r e b m u N s e i m o n o c e y t r e v o p e h t e r a s g n i d a e h n m u l o c e h t w o l e b s e s e h t n e r a p e h t e d i s n i s e u l a V . 1 . 0 < p * , 5 0 . 0 < p * * , 1 0 . 0 < p * * * d e t i n U n i y t i r a p r e w o p g n i s a h c r u p t a ( r e k r o w r e p d e d d a e u l a v l a r u t l u c i r g a d n a r e k r o w r e p d e d d a e u l a v l a r u t l u c i r g a n o n . s e s e h t n e r a p n i s r o r r e d r a d n a t s t s u b o R . ) 0 0 0 , 1 y b d e d i v i d s r a l l o d s e t a t S . s e n i l n e e w t e b e c n e r e f f i d d e z i l a m r o n e h t o t s r e f e r ” p a G “ : s e t o N . e l b a i r a v l a t n e m u r t s n i s t c e f f e d e x ﬁ = V I - E F l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 1 2 1 6 4 4 1 6 7 a d e v _ a _ 0 0 1 2 5 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 . 9 6 9 3 2 / 6 8 9 0 1 / e l d n a h / g r o . k n a b d l r o w . e g d e l w o n k n e p o / / : s p t t h . 6 1 0 2 s r o t a c i d n I t n e m p o l e v e D d l r o W . 6 1 0 2 . k n a B d l r o W n o d e s a b s n o i t a l u c l a c ’ s r o h t u A : e c r u o S Labor Productivity Gap, and Poverty and Inequality Reduction 131 diversity in the preceding period, will reduce the labor productivity gap. That is, if the structural transformation in the rural sector progresses and agricultural production is more diversiﬁed, then the gap will be reduced, presumably because agricultural sector productivity will catch up with nonagricultural productivity. However, the ﬁrst lagged agricultural productivity growth increases the gap. This is counterintuitive, but if agricultural productivity growth promotes nonagricultural growth without a lag, the period with faster agricultural productivity growth may even match the period with faster nonagricultural growth. The coefﬁcient estimate labor productivity growth is negative, but not statistically of nonagricultural signiﬁcant.13 Education tends to increase the gap. The question arising from the analysis in the last section is why the labor productivity gap has grown in some economies and not in other economies. It is not easy to provide a deﬁnite answer, but our results imply that the agricultural transformation reduces the gap and that improved human capital widens the gap. In the second stage, we do not ﬁnd any evidence that the gap inﬂuences poverty at the national level with the coefﬁcient estimate being negative (except the second column) and statistically insigniﬁcant (the upper left panel of Table 3).14 We ﬁnd that the number of schooling years is negative and statistically signiﬁcant. The F-statistic of excluded instruments is 16.34, which is above the threshold of 10, and the Sargan overidentiﬁcation test of all instruments is not signiﬁcant (p-value of 0.331), validating the IV estimation. Next, we examine whether the labor productivity gap has affected poverty. Because the sample is reduced, the results from the ﬁrst stage have changed slightly. For instance, nonagricultural productivity growth is now negative and signiﬁcant, while one of the instruments, the productivity–diversity index, is now positive and signiﬁcant. So, with a smaller sample, the progress of the agricultural transformation tends to increase the labor productivity gap. The reason is not clear, but in this case the agricultural transformation may have an instant impact on improving both agricultural and nonagricultural labor productivity, with the magnitude of the latter being comparatively larger. In the second stage, the increase of the labor productivity gap tends to reduce poverty in the rural regions regardless of the choice of poverty thresholds and for all different measures of poverty (e.g., headcount, poverty gap, and poverty gap squared except the third column for extreme poverty gap squared as shown 13The correlation between the labor productivity gap and nonagricultural labor productivity growth is positive with a correlation coefﬁcient of 0.034. The correlation coefﬁcient between the gap and agricultural labor productivity growth is 0.036. Not surprisingly, the correlation coefﬁcient between the agricultural and nonagricultural sector growth terms is high at 0.614. The highest variance inﬂation factor of the ﬁrst-stage regression is 2.44, which is below the threshold of 10 and which would justify the inclusion of labor productivity growth in both sectors at the same time. 14We have also estimated the second-stage regressions by using the FE model without using IV. In this case, the sample size is larger, but we have found that the lagged labor productivity gap reduces signiﬁcantly both extreme and moderate poverty, for both the headcount ratio and poverty gap. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 1 2 1 6 4 4 1 6 7 a d e v _ a _ 0 0 1 2 5 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 132 Asian Development Review in the upper right panel of Table 3). That is, as nonagricultural labor productivity grows faster than agricultural labor productivity, rural poverty signiﬁcantly declines in every dimension, including the share of the poor, the depth of rural poverty, and inequality among the rural poor. This result may not be consistent with the theoretical prediction by Bourguignon (1990) as the model suggests that the gap between the agricultural and nonagricultural sectors tends to increase inequality given elastic capital–labor substitution assumed in the modern sector. However, Vollrath’s (2009) model implies that as nonagricultural labor productivity increases, the efﬁciency of workers in the agricultural sector improves. If this helps the rural poor escape from poverty, we expect that nonagricultural labor productivity growth has the effect of reducing rural poverty. Here, the test of excluded instruments (F-statistic) is 9.55, which is below the threshold of 10, partly because of the small sample size, and so the results need to be interpreted with caution. The Sargan statistic is not signiﬁcant, justifying the use of IV.15 We have also estimated urban poverty in the second stage of the FE-IV model. The results are shown in the lower left panel of Table 3. We have found that the size of the poverty-reducing effect is much larger for urban poverty than rural poverty. That is, as the gap between nonagricultural and agricultural labor productivity expands, both urban poverty and rural poverty decrease, but urban poverty tends to decline at a much faster rate. However, the results will have to be interpreted with caution, particularly in cases where the F-statistic for excluded instruments in the ﬁrst stage is low (columns 2 and 3). Finally, we have estimated the effect of the lagged labor productivity gap on the Gini coefﬁcient at the national, rural, and urban levels. As the sample sizes differ, the result in the ﬁrst column cannot be compared with the results in the second and third columns. However, after controlling for the endogeneity of the labor productivity gap, we have found evidence that the gap signiﬁcantly reduces the national Gini coefﬁcient (the lower right panel of Table 3). In this case, the ﬁrst-stage F-statistic is larger than 10. The result is robust if we do not instrument the labor productivity gap or if we use the smaller sample for which disaggregated inequality data are available. Using the disaggregated data, we have also estimated the effects of the lagged labor productivity gap on the sectoral population share, drawing upon Imai, Gaiha, and Garbero (2017). The results will have to be interpreted with caution, speciﬁcally in the ﬁrst and the second columns (due to the small sample size) where the speciﬁcation tests for IV do not validate the speciﬁcations. However, we have found some evidence that the labor productivity gap reduces the rural population share and increases the share of the rural nonagricultural sector. When we use a larger sample size, we have found that the lagged productivity gap 15The lagged labor productivity gap is no longer statistically signiﬁcant in explaining rural poverty for the larger sample in the FE model without IV. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 1 2 1 6 4 4 1 6 7 a d e v _ a _ 0 0 1 2 5 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Labor Productivity Gap, and Poverty and Inequality Reduction 133 increases the population share of the urban sector signiﬁcantly. These results are broadly consistent with the theoretical model of Vollrath (2009) where increases in nonagricultural productivity will help industrialize the economy and induce agricultural laborers to work more efﬁciently, while the share of the agricultural sector declines over time. If this process beneﬁts much of the population in rural and urban areas, inequality is likely to decline over time. However, our result is not consistent with Bourguignon’s (1990) model, which implies that the gap between the agricultural and nonagricultural sectors tends to increase inequality. In sum, we have found that the increase in the lagged labor productivity gap, which is treated as endogenous, will reduce both urban and rural poverty as well as national-level inequality. In particular, there is robust evidence conﬁrming that the labor productivity gap reduces urban poverty evaluated at the poverty threshold of $2 per day. IV. Conclusions First, we have examined whether labor productivities in the agricultural and nonagricultural sectors have converged by using the 5-year average panel dataset. We have found robust evidence that nonagricultural labor productivity and agricultural labor productivity did not converge; the former has grown faster and the gap has increased signiﬁcantly over time. We have also observed that within Asia (i) agricultural labor productivity has converged across economies, (ii) nonagricultural labor productivity has converged across economies, and (iii) the convergence effect is stronger for the nonagricultural sector. Agricultural labor productivity growth was found to promote nonagricultural productivity growth with some lag. That is, despite the slower growth in agricultural labor productivity, the agricultural sector played an important role in promoting nonagricultural labor productivity and thus in nonagricultural growth. As we used the 5-year average panel data, we can identify the middle- to long-term effects by controlling for short-term ﬂuctuations. In the second part of the study, we examined whether the labor productivity gap between the agricultural and nonagricultural sectors reduced poverty, inequality, and the sectoral population shares over time. While the results vary depending on the speciﬁcations, we have found some evidence that the labor productivity gap reduces both urban and rural poverty over time as well as national- level inequality. The gap also increases the share of the population in the urban sector. Our results provide the following policy implications. While improvement in agricultural labor productivity also brings about improvement in nonagricultural labor productivity, the latter has increased faster than the former over time, resulting in a gap between the two sectors. The widening gap was found to reduce poverty and inequality. These results are important in light of the literature on structural l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 1 2 1 6 4 4 1 6 7 a d e v _ a _ 0 0 1 2 5 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 134 Asian Development Review transformation in Asia (e.g., Reardon and Timmer 2014; Imai, Gaiha, and Bresciani 2016), which underscores diffusion from the agricultural sector. Our results suggest that as the agricultural sector experiences structural changes, it plays a central role in improving nonagricultural labor productivity and reducing poverty and inequality within an economy. Policy makers need to facilitate the process of structural transformation (e.g., commercialization and product diversiﬁcation of agriculture; revolutions in supply chain and retail networks; and integration of labor, land, and credit markets) to improve agricultural labor productivity and reduce poverty and inequality. 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World Development Indicators 2016. https://openknowledge.worldbank .org/handle/10986/23969. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 1 2 1 6 4 4 1 6 7 a d e v _ a _ 0 0 1 2 5 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 The Labor Productivity Gap between the image

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