Time Allocation of Agricultural Households under Economic Recession:
Lessons from Japanese Agriculture in 1930s *
September 2014
Motoi Kusadokoro †, Takeshi Maru ‡, and Masanori Takashima §
Abstract
When developing economies face economic recession, rural sectors are expected to absorb
unemployed returning persons who lost their jobs in urban sectors and to retain potential
excess laborers who would have flowed in urban sectors if the economy had been in a period
of expansion. This paper examines this work sharing hypothesis by using historical data of
Japanese farm households in the 1930s when the Japanese economy suffered from the Great
Depression. In the 1930s of Japan, adjusting the labor intensity of farm management had
played important role to achieve the work sharing. This result is contrast to the evidences
obtained from contemporary China. Also, the work sharing strategies taken by the farm
households differed depending on their land ownership status.
Key words
Work sharing, farm household economy, economic recession, prewar Japan
JEL classification
N55, O12, Q12
*
The authors gratefully acknowledge the financial support of Grants-in-Aid (#25245047, #22243030, and
#22223003) from the Japan Society for the Promotion of Science and by the Japanese Ministry of
Education, Culture, Sports, Science, and Technology through the Research Unit for Statistical and
Empirical Analysis in Social Sciences Center of Excellence Program. The authors are also grateful for
helpful comments on the earlier version of this study to Chiaki Moriguchi, Tuan-Hwee Sng, and Dongwoo
Yoo.
†
Institute of Agriculture, Tokyo University of Agriculture and Technology. E-mail: [email protected]
‡
The Institute of Economic Research, Hitotsubashi University. E-mail: [email protected]
§
The Institute of Economic Research, Hitotsubashi University. E-mail: [email protected]
1
Time Allocation of Agricultural Households under Economic Recession:
Lessons from Japanese Agriculture in 1930s
1. Introduction
As the classical theory of dual economy suggests, rural economies have contributed to the
economic development of many countries by providing the surplus labors in the economies to
the emerging non-primary sectors such as industry and service at the early phase of economic
development (Fei and Ranis, 1963; Lewis, 1958). However, the course of economic
development is not a linear process, and the economy generally repeats boom and bust cycles.
In the time of economic recession, the rural economy may absorb unemployed persons who
had migrated from rural area to urban area or may retain potential surplus laborers who would
have flowed in urban sectors if the economy had been in a period of expansion. Thus, rural
economies may also contribute to mitigate the fluctuation of labor market under dual
economic structure (Zhang, et al., 2001). In other words, the rural economy under dual
economic structure may perform the function of work sharing. This function of rural economy
is especially important to avoid the direct effect of economic recession on the lives of the
people in the economy where unemployment insurance and social security are not fully
established.
The process of the economic development of the Japanese economy has been focused as
a leading example of dual economy (Fei and Ranis, 1963; Jorgenson, 1966; Lewis, 1958;
Minami, 1968, 1970). It is widely believed that the Japanese economy had finally passed the
turning point in the early 1960s (Minami, 1968, 1970). The rural economy in the prewar
period of Japan constituted more or less subsistence economy under dual economic structure
and held overemployment in the economy.
The Japanese economy had experienced economic stagnation and recession during the
inter-war period between World War (WW) I and II. The end of WW I the recovery of
European economy terminated the economic boom in the late 1910s, because the contraction
2
of European economy by the war had afforded Japan to increase exports in the world market.
Low rate of economic growth had continued throughout 1920s, and finally, the Showa
Depression (the Depression) which had been induced by the Great Depression in 1929
attacked both urban and rural economies of Japan. Despite of the negative effect of the
Depression, the historical statistics showed small impact of the Depression on the
unemployment rate (Odaka, 2003). Also, Odaka (2003) and Odaka and Yuan (2006) found
that the agricultural wage rate in the prewar period were close to the average labor
productivity and higher than the marginal productivity. From these observations they
suggested the existence of work sharing in the rural economy of the prewar period of Japan.
Unemployment insurance had not been well established in this period of Japan (Kase, 2006).
Thus, the work sharing in the rural economy might have played essential role to mitigate the
direct effect of the Depression on wage wokers.
Although the agricultural labor market existed in the prewar period of Japan, the main
function of it was to provide casual workers to the farm households who were short of
household labor force in the peak season of farming. The prominent economic agent
performing the function of work sharing in the rural economy would have been each
individual farm household. Some space for absorbing or retaining the surplus laborers in the
farm economy is prerequisite for the mechanism working effectively. As described in some
detail below, however, the negative impact of the Depression was worse in the rural economy
than it in the urban or modern economy. This feature of the Depression throws doubt on the
ability of farm household under the Depression to perform work sharing in the individual
economy. We need to examine the work sharing using micro level data of farm households.
The Ministry of Agriculture and Forestry, Japan had conducted detail survey on the farm
household economy in the inter-war period (MAF survey). Fortunately, the individual data of
the sample farm households of the MAF survey has partly become accessible. The dataset
includes information about time allocation of each household member as well as the farm
management and the farm economy. The aim of this paper is to examine the work sharing
3
through careful empirical analyses utilizing this dataset. Although the work sharing in rural
economy has been widely recognized, the formal investigations are still lacking except a few
examples (Kong, et al., 2010; Zhang, et al., 2001). As well as the contribution on the
economic history of Japan, this paper will help our understandings on the role of rural
economy as a buffer for stabilizing the fluctuation of labor market.
This paper is organized as follows. In the next section, to provide the background of our
analyses, the Japanese economy in inter-war period and the impact of the Showa Depression
on the economy is discussed. In Section 3, the data utilized in our econometric analyses is
explained. In Section 4, we propose the empirical strategies to test the work sharing
mechanism with the theoretical backgrounds. Section 5 discusses our empirical results.
Section 6 is a conclusion.
2. Japanese economy in the inter-war period and impact of the Showa Depression
In this section, the trends of Japanese economy in inter-war period (between WW I and WW
II) and the impact of the Depression on the economy are described with some statistics and
reviews on the related literature.
According to Ohkawa (1965) and Ohkawa and Rosovsky (1973), the inter-war period
constitutes the second phase of modern economic development of Japan lasting from the late
1900s to the early 1950s. In the beginning of the second phase, private nonagricultural
investment had increased especially during the economic boom caused by WW I in the late
1910s (Ohkawa and Rosovsky, 1973). Although the economic boom had ended with an end of
the war, the investment during this period had brought different pattern of development
between modern economy and subsistence (rural) economy since 1920s.
<Figure 1>
Figure 1 shows growth of the Net Domestic Products (NDP) of primary (including
agriculture) and secondary sector during 1901-1940. By the end of 1910, the primary and
non-primary sectors had showed similar growth patterns with rapid growth in nominal term in
4
the latter half of 1910s. In 1920s, however, these sectors had started to show different patterns.
Despite of the end of the economic boom in 1920, the real value of output of non-primary
sector itself had continued to increase throughout 1920s. In contrast, the primary sector
experienced negative growth in both of nominal and real terms in the early twenties and
returned to the level of 1920 in the middle of the decade. The output in nominal term,
however, again started to decrease in the latter half of the decade.
The stagnation of primary sector, especially of agriculture, has been analyzed by several
researchers. The most influential argument is that the stagnation is a result of the colonial rice
policy which encouraged the supply of rice from colonial Taiwan and Korea, and the
exhaustion of rono techniques (veteran farmers’ techniques) that had been accumulated in
separated rural societies under the feudal political system in the Tokugawa Shogunate and had
diffused across the country after the Meiji Restoration by the improvements of transportation
and information technology (Hayami and Ruttan, 1971; Hayami and Yamada, 1991; Ohkawa
and Rosovsky, 1960) 1).
The Depression and the removal of the gold embargo in 1930 caused severe deflation
and hit the overall Japanese economy as observed from the drop of the nominal NDP of
primary and secondary sectors in 1930. The abandonment of Gold Standard in 1931 and the
deficit spending policy by Korekiyo Takahashi (Japan’s Finance Minister) permitted earlier
recovery from the recession than other countries (Nanto and Takagi, 1985). The degree of
damage and the speed of recovery, however, were not equal between primary and secondary
sectors. Although, even in nominal term, the output of secondary sector recovered in 1933 to
the level before the Depression, the recovery of the primary sector should wait until the latter
half of the decade. Since the rapid recovery and growth of secondary sector was at least partly
the result of the militarization of Japan toward WW II, heavy industrialization had
increasingly progressed throughout 1930s (Ohkawa, 1965; Teruoka, 1984).
<Table 1>
Table 1 shows the rate of non-working persons and the number of workers in each
5
sector in the inter-war period. In this table, the rate of non-working persons (proxy for
unemployment rate) was calculated based on the method by Odaka (2003). The rate of
non-working male persons had slightly increasing trend, but kept extremely low level
throughout twenties and thirties.
The small impact of the Depression on the unemployment rate, however, does not mean
that the labor market of modern sector had not been affected by the Depression. As shown in
Table 1, the trend of decreasing number of male workers in primary sector during 1920s had
reversed in the first half of 1930s. The number of female workers in primary sector continued
to increase throughout the inter-war period, despite of the decrease of the number in
secondary sector in the first half of 1930s. Also, an excess of the number of laid-off factory
workers to that of newly employing factory workers was observed in 1930 and 1931 (Teruoka,
1984). The rate of laid-off factory workers who returned to the rural area also increased in the
early 1930s. (Nojiri, 1942). Also, some literature reports the aggravation of overpopulation
problem in the rural economy in the middle of the Depression and the struggle of rural
economy against the problem (Teruoka, 1984, 2003). These observations may also support the
existence of work sharing mechanism in the inter-war period of Japan.
It is worthwhile to note the effect of heavy industrialization on the labor market of the
modern economy in the 1930s. Heavy industries required well educated and skilled labor
compared to light industries such as textiles. Kobayashi (1961) pointed an increase of
educational status of heavy industry workers in the 1930s and longer years of continuous
employment in the industries with high capital intensity such as metallurgical and chemical
industries than those in the industries with low capital intensity such as mining and
construction. Since large part of labor force outflowed from rural area was unskilled and
less-educated, the benefit from the rapid recovery and growth of heavy industry in the 1930s
on the off-farm job opportunities had been limited. Indeed, contributions of the outflow of
labor force from agriculture to the total increase of labor force of modern sector had sharply
decreased in 1930s (Ohkawa and Rosovsky, 1973).
6
The impacts of the Depression on farm household economies were also not equal. In the
prewar Japan, a landlord–tenant farmer system had prevailed and more than half of paddy
field and nearly 40% of upland field were tenanted land just before WW II (Kawano, 1965).
Large-scale landowners were few in prewar period of Japan and most of landlords lived in
rural area where social ties could regulate the landlord–tenant relationship (Francks, 2006;
Sakane, 2010). The ties in rural area allowed collective action between landowners and tenant
farmers over matters such as the custom of state-contingent rent reductions (Arimoto, 2005).
The Depression and the following deflation hit both tenant farmers and landlords
because the rent of land was generally paid in kind. Some landowners, who had difficulty to
continue their landlord managements, forced their tenants to return the land and tried to
cultivate by themselves or to rent out the returned land to other tenants who could pay more
rent. On the other hand, tenant farmers had to keep cultivating their rented land for their
subsistence under limited opportunity of off-farm jobs. Thus, the response of landlords to the
Depression raised tenancy disputes more frequently, and landlords and tenant farmers mainly
contested the tenancy rights (Sakane, 2010; Teruoka, 2003).
3. Data
In the analyses, we use a panel data constructed from a subsample of the survey of the farm
household economy which was conducted by The Japanese Ministry of Agriculture and
Forestry (MAF). The survey is known as the MAF survey. The MAF survey in prewar Japan
collected individual records using single-entry bookkeeping designed for farm household
economies. According to the revision of the bookkeeping design and sampling method, Inaba
(1953) categorized these surveys into four stages: the first-period MAF survey (1921-1923),
the second-period MAF survey (1924-1930), the third-period MAF survey (1931-1941), and
the fourth-period MAF survey (1942-1948).
In this paper, we mainly use sub-sample of the third-period MAF survey and collected
individual data of farm households from 16 prefectures (Akita, Fukushima, Ibaragi, Tokyo,
7
Niigata, Yamanashi, Nagano, Shizuoka, Aichi, Toyama, Osaka, Shimane, Hiroshima,
Tokushima, Fukuoka, and Miyazaki) over 11 years. The first eight prefectures are in the east
of Japan, and the other eight are in the west. In principle, the survey included six or nine
households in each prefecture every year. Main reasons we choose the third-period MAF
survey for our analyses are that, first, this survey covers 1930s when the Japanese economy
had suffered from the Depression and had started to gradually recover and, second, panel data
of farm household and individual household member can be constructed by using information
about the names of household head and household members.
One drawback of using the third-period MAF survey is that this survey does not cover
the period before the Depression. To overcome this problem, we complementary use
individual data of the second-period MAF survey which covers the period before the
Depression. However, the survey has the different bookkeeping design and the sampling
method with the third period MAF survey. The results obtained from these surveys should be
carefully compared. More importantly, it is difficult to construct panel data of households and
household members from the survey2). The analytical methods which can be applied to the
data of the second-period MAF survey are also restricted.
There are other issues which should be considered when using the MAF survey. The
MAF surveys in the prewar period did not employ random sampling methods. By the time of
the third-period MAF survey, the MAF survey had bias toward farmers with larger farms
(Inaba, 1953). Because of the revision of sampling criteria at the third-period MAF survey, the
upward bias was reduced, but still remained. The average management land size in the
second-period MAF survey was 1.7 cho (1 cho is nearly equal 1 ha), but decreased to 1.3 cho
in the third-period MAF survey, whereas the average value in prewar japan was around 1 cho.
The third-period MAF survey also considered the situation of land ownership in prewar Japan.
Compared with other representative surveys of farm household economies in that time, the
survey appropriately collects data from landed-tenant and tenant farmers3).
Another issue is related to the completeness of panel data. Our sample collected from the
8
third-period MAF survey includes 224 farm households and the total number of observations
used in the analyses is 1,070. Although the survey in principle continued to collect the data
from same farm household, only five households in our sample are surveyed every year
throughout the survey period and the median years of surveyed were 4 years. Our panel data
is therefore highly incomplete and has a possibility of attrition bias. The results obtained from
the MAF survey should be interpreted with some care.
<Table 2>
Table 2 shows descriptive statistics of some key variables by the initial condition of land
ownership. Here, the farmer is classified into a landed farmer, a landed-tenant farmer, or a
tenant farmer depending on the land ownership condition in the year when the farmer
appeared to the survey. Landed farmers are defined as farmers owning land not less than 80%
of the operational land used, and tenant farmers are defined as farmers borrowing land not
less than 80% of the operational land used, and others are classified in landed-tenant farmers.
In this table, the labor power is the adjusted number of household members by considering the
actual contribution of each household member to the labor forces (Nojiri, 1942; Tomobe,
2007)4).
We can observe that the landed-tenant and the tenant farmers have less male and female
adult members, but have more elderly members than the landed farmers. Similarly, the landed
farmers keep more labor power in the household than the landed-tenant and the tenant farmers.
The tenant famers let more household members engage in off-farm work than the landed
tenant farmers, and the total hours of landed-tenant and tenant farmers engaged in
non-agricultural work are longer than that of landed farmers. However, the non-agricultural
income does not differ among these groups. The landed farmers might have a good
opportunity of off-farm work with higher wage rate even in the rural economy5). We cannot
find systematic differences in labor intensity and hours of agricultural labor per household
member by land ownership structures. The labor productivity of landed-tenant farmers is
slightly higher than that of landed farmers. The agricultural income is decreasing in order of
9
landed, landed-tenant, and tenant farmers because of the burden of land rent. The family
income and expenditure are also decreasing in this order.
<Figure 2>
Figure 2 shows yearly fluctuation of real household income per household member by
land ownership status. These are calculated using the second and third period MAF survey.
The tenant farmers had generally recorded the lowest farm income among the groups
throughout almost all period. Although we can observe the recovery of landed and
landed-tenant farmers from 1934, we cannot observe clear recovery of tenant farmers until
1939. The Depression gave severe impact on the farm economy of tenant farmers.
4. Analytical framework
4.1. Theoretical framework
Before going to our empirical strategies, the theoretical framework is briefly discussed. The
theoretical model applied here is a modified version of household model to illustrate the time
allocation of farm household under dual economies originally analyzed by Sen (1960, 1966).
We consider a farm household who consumes consumption goods and leisure and earns
income from agricultural management and wage labor. There are 𝑚 members who consume
consumption goods in the farm household. Also, 𝑛 members out of the 𝑚 members can
work to earn income. We assume that the amount of consumption 𝐶 is divided equally by all
members and the amount of leisure 𝑅 is divided equally by all working members. Thus, the
amount of consumption per member and the amount of leisure per working member are equal
to 𝑐 = 𝐶/𝑚 and 𝑟 = 𝑅/𝑛, respectively.
The production technology of agriculture is given by 𝐹(𝐿𝑎 ) and 𝑝 is the price of
agricultural goods. For simplicity, we assume that the all working household members have
equal abilities to do work. Under this assumption, the amount of agricultural labor 𝐿𝑎 and
the amount of wage labor 𝐿𝑤 are divided equally by the all household labor forces;
𝐿𝑎 = 𝑛𝑙𝑎 and 𝐿𝑤 = 𝑛𝑙𝑤 . To consider the rural economy under the dual economies and the
10
effect of economic recession, we assume that supplying a part of the household labor forces to
labor market at a given wage rate 𝑤 is optimum for the farm household, but the demand side
of market restricts the total amount of wage labor that the household can supply to 𝐿�𝑤 .
Although the farm household should maintain the level of consumption per member at not
less than the subsistence level 𝑐̅, there is a possibility that the constraint is binding because of
low productivity of agriculture, low wage rate, and restricted opportunity for wage labor.
In these settings, the utility maximization problem of the farm household is given as
follows:
max 𝑈(𝐶, 𝑅) = 𝑈(𝑚𝑚, 𝑛𝑛)
(1)
𝑠. 𝑡. 𝑚𝑚 ≤ 𝑝𝑝(𝑛𝑙𝑎 ) + 𝑤𝑤𝑙𝑤
𝑛𝑙𝑎 + 𝑛𝑙𝑤 + 𝑛𝑛 ≤ 𝑛𝑛
𝑛𝑙𝑤 ≤ 𝐿�𝑤
𝑐̅ ≤ 𝑐.
The Lagrangian function can be defined as,
ℒ = 𝑈(𝑚𝑚, 𝑛𝑛) + 𝜆1 {𝑝𝑝(𝑛𝑙𝑎 ) + 𝑤𝑤𝑙𝑤 − 𝑚𝑚} + 𝜆2 {𝑛𝑛 − 𝑛𝑙𝑎 − 𝑛𝑙𝑤 − 𝑛𝑛}
+𝜆3 {𝐿�𝑤 − 𝑛𝑙𝑤 } + 𝜆4 {𝑐 − 𝑐̅}.
We set the shadow price of consumption goods and wage labor as 𝑞 ∗ = 1 − 𝜆4 /𝑚𝜆1 and
𝑤 ∗ = 𝑤 − 𝜆3 /𝜆1, respectively. The full income constraint can be defined as,
𝑞 ∗ 𝐶 + 𝑤 ∗ 𝑅 = 𝑝𝑝(𝐿𝑎 ) − 𝑤 ∗ 𝐿𝑎 + 𝑤 ∗ 𝑇 + (𝑤 − 𝑤 ∗ )𝐿�𝑤 − (1 − 𝑞 ∗ )𝑚𝑐̅ = 𝛱 ∗ .
Using the price of agricultural goods, the shadow prices, and the full income, we can express
the solutions of the problem as the demand system of consumption goods, leisure, and
11
agricultural labor (de Janvry, et al., 1991):
𝐶 = 𝑚𝑚 = 𝐶(𝑞 ∗ , 𝑤 ∗ , 𝛱 ∗ )
𝑅 = 𝑛𝑛 = 𝑅(𝑞 ∗ , 𝑤 ∗ , 𝛱 ∗ )
𝐿𝑎 = 𝑛𝑙𝑎 = 𝐿𝑎 (𝑝, 𝑤 ∗ ).
The shadow wage 𝑤 ∗ is not larger than the real wage rate 𝑤 because of the non-negativity
of Lagrangian multipliers 𝜆1 and 𝜆3 . Thus, the marginal productivity is not exceeding the
wage rate and the amount of agricultural labor is not less than the efficient level that equates
marginal productivity and wage rate.
We consider the case where both of the constraints on subsistence and wage labor are
binding. In this case, the total amount of agricultural labor is exclusively decided so that the
subsistence level of consumption is satisfied,
𝑝𝑝(𝐿𝑎 ) = 𝑚𝑐̅ − 𝑤𝐿�𝑤 .
(2)
Let assume that the number of household labor force 𝑛 increases while the number of
household member 𝑚 remains constant. This situation may occur when a young household
member reaches a productive age along with life-cycle of the household and he/she stays in
the household. When the increase in household labor force cannot release the constraint of the
subsistence level of consumption, eq. (2) is still valid. The shadow wage and the total amount
of agricultural labor remain unchanged and the agricultural labor is shared by the all working
members. Thus, an increase in 𝑛 will decrease the amount of agricultural labor per working
member.
Next, let assume that both of the number of household members 𝑚 and the number of
family labor forces 𝑛 increase at the same time, that is d𝑚 = d𝑛. This case can occur if a
household member who had migrated to other area for work returned to the household
12
because of dismissal in the migrated area, or if new adult became a household member
because of marriage, adoption, and etc. By differentiating eq (2), we obtain
d𝑤 ∗
𝜕𝐿
= 𝑐̅⁄(𝑤 ∗ 𝜕𝜕𝑎 ) ≤ 0.
d𝑚
(3)
Thus, the increase of the number of household members as well as the number of family labor
forces decreases the shadow wage and increases the total amount of agricultural labor.
We can also consider the case that the constraint on wage labor is binding, but the
constraint on consumption is not. The shadow price of consumption goods is fixed at 𝑞 ∗ = 1.
In this case, we can show that an increase in the number of labor forces may decrease the
shadow wage regardless of whether the change involves increases of the number of household
members (see Appendix). Therefore, the total amount of agricultural labor may also increase
according to the decrease of shadow wage.
<Figure 3>
The simple household model we referred suggests that three cases of response may exist
against an increase in the household labor forces. Figure 3 illustrates these cases. Case 1: If
the increase does not affect the shadow wage, the total agricultural labor input may be held
constant and shared by the all household labor forces including the new labor force. In this
case, the agricultural labor hours per working member will decrease from tangent a to tangent
a’ in Figure 3. However, if the increase in the household labor forces decreases the shadow
wage, the total amount of agricultural labor will increase. Case 2: If the effect is moderate, the
total agricultural labor input will increase from A to B as illustrated in Figure 3, and also the
labor intensity of farm management, that is the ratio of total agricultural labor input to the
managed land size, will also increase. In this case, the agricultural labor hours per family
worker will decrease from tangent a to tangent b. Case 3: If the shadow wage largely
decreases and the total agricultural labor input also largely increase (from A to C in Figure 3),
the agricultural labor hours per family worker may not change or may increase.
13
Case 1 and 3 are two extreme cases. Case 1 may occur in our model if the farm
household lives at the subsistence level, and also faces an increase in household labor forces
without change in the number of household members. Other possibilities are that, first, the
household already inputs the agricultural work to the level that the marginal productivity
becomes zero and that, second, the utility function has flat region in the sense that the
marginal utility of consumption is zero (Sen, 1966). In contrast, Case 3 may occur if the
subsistence consumption level is larger than the shadow income of agricultural labor per
working member with binding constraints on subsistence and wage work. Also, high price
elasticity of labor demand and low price elasticity of demand for leisure may lead to Case 3,
when only the constraint on wage work is binding (see Appendix). Negative gap between
marginal productivity and wage rate exists when the opportunity of wage work is restricted
from demand side. If the farm household chooses Case 2 or 3 with an increase in the
household labor forces, it will result in further loss of efficiency of agricultural production in
the sense that the negative gap between marginal productivity and wage rate will be widened.
4.2 Empirical strategies
In order to examine the work sharing by the farm household in the prewar Japan, this paper
uses both of household level measures and an individual measure. The household level
measures are the agricultural labor hours per working member and the labor intensity of farm
management. The individual measure is the agricultural labor hour of each household labor
force.
First, we estimate the following equation using panel data constructed from the third
period MAF survey:
′
′
𝐴𝐴𝐴𝑊𝑗𝑗 𝑜𝑜 𝐿𝐿𝐿𝑇𝑗𝑗 = 𝐹𝐹𝐹𝑀𝑗𝑗′ 𝛽𝑓𝑓𝑓𝑓 + 𝐹𝐹𝐹𝑅𝑗𝑗
𝛽𝑓𝑓𝑓𝑓 + 𝑀𝑀𝑀𝑅𝑗𝑗
𝛽𝑓𝑓𝑓𝑓 +
𝑊𝑊𝑊𝐸𝑗𝑗′ 𝛽𝑤𝑤𝑤𝑒 + 𝑇𝑇𝑇𝐸𝑡′ 𝛽𝑡𝑡𝑡𝑡 + 𝜇𝑗 + 𝜖𝑗𝑗
14
(4)
Here, the subscript 𝑗 shows each farm household and 𝑡 shows year. 𝑢𝑗 is fixed effect of the
household 𝑗. 𝐴𝐴𝐴𝐴 and 𝐿𝐿𝐿𝐿 are the agricultural labor hours per family worker and the
labor intensity, respectively. 𝐹𝐹𝐹𝐹 is a vector of demographic structures of the farm
household. Two series of demographic structures are considered. The first series consists of
the number of male aged 14-59, female aged 14-59, elderly aged over 60, children aged 0-6,
children aged 7-13, and engaging in off-farm jobs. The second series consists of the number
of labor power, children aged 0-6, and engaging in off-farm jobs. The first series consider the
different effects of the demographic structures on the work sharing strategies by sex of the
productive aged members. The second series, rather, focuses on the effects of labor forces the
farm household actually holds on the work sharing strategies.
Other variables are included to control heterogeneity of farm household and regions.
𝐹𝐹𝐹𝐹 shows other family structures and constitutes of age of household head, square of age
of household head, and a dummy variable identifying nuclear family. 𝑀𝑀𝑀𝑀 shows farm
management structures and constitutes of managed land size, value of productive stock, and
debt. 𝑊𝑊𝑊𝑊 constitutes of wage rates of male and female agricultural daily worker. The data
of wage is not household level data, but prefectural7). 𝑇𝑇𝑇𝑇 is a vector of time dummies.
By applying fixed effects estimator to estimate eq. (4), only information of within
variation of time varying variables is utilized. The coefficient reflects the impact of the
deviation of the corresponding time varying variable from the average level in the household.
As discussed in the previous section, we can suppose three cases as the responses of the farm
household against the change of the size of household labor forces. Case 1: If the shadow
wage of labor does not decrease with an increase in the size of household labor forces, the
number of male aged 14-59, female aged 14-59, and labor power will have negative impact
on the agricultural labor hours per family worker, but will not have any impact on the labor
intensity. Case 2: If the shadow wage of labor moderately decreases, these variables will have
negative impact on the agricultural labor hours per family worker, and will have positive
impact on the labor intensity. Case 3: If the shadow wage of labor largely decreases, these
15
variables will have no or positive impact on the agricultural labor hours per family worker,
and will have positive impact on the labor intensity.
The demographic structures may not change so frequently, and the change if occurs may
have long term effects on the time allocation of the farm household. The fixed effects
estimator which utilizes the within variation may capture the long term effects. The ability,
however, highly depends on the time the change occurs and the structures of long term effect
(Laporte and Windmeijer, 2005). More importantly, there is a possibility that the short term
effect is under estimated depending on these factors. To check the robustness of the fixed
effects estimator, we also apply the difference estimator which captures only the short term
effect.
The third period MAF survey covers from 1931 to 1941, when the Japanese economy
had been the midst of the Depression and had gradually recovered. The work sharing strategy
might be different between the period before and after the Depression. In order to investigate
the possibility, we compare the results obtained from the third period MAF survey with the
results obtained the second period MAF survey, which covers from 1926 to 1930. The
estimated equation is as follows:
𝐴𝐴𝐴𝑊𝑗𝑗 𝑜𝑜 𝐿𝐿𝐿𝑇𝑗𝑗
(5)
′
= 𝐹𝐹𝐹𝑀𝑗𝑗′ 𝛾𝑓𝑓𝑓𝑓 + 𝑀𝑀𝑀𝑅𝑗𝑗
𝛾𝑓𝑓𝑓𝑓 + 𝑇𝑇𝑇𝐸𝑡′ 𝛾𝑡𝑡𝑡𝑡 + 𝑃𝑃𝑃𝑃𝑗′ 𝛾𝑝𝑝𝑝𝑝 + 𝜖𝑗𝑗 .
As discussed in Section 3, panel data set cannot be constructed from the second period MAF
survey. Thus, the sample data is pooled by each survey and OLS estimator is applied to eq. (5).
Also, some variables are omitted from eq. (5) compared with eq. (4) because of the data
availability. Alternatively prefectural dummies (𝑃𝑃𝑃𝑃 ) are included to control regional
heterogeneity. The estimation results will be influenced by between variation such as
difference in the average size of demographic structure and unobserved fixed effects. The
results should be carefully interpreted.
16
There is a possibility that a household member participates into the labor force, but any
agricultural work is not allocated to the member. In other words, the agricultural work is not
shared by all labor forces. Even in this case, the agricultural labor hours per working member
will decrease with an increase in the size of the household labor forces. Also, the change of
the size of the household labor forces may have different impacts on the agricultural labor
hours of individual members depending on their characteristics. The third period MAF survey
provides the information of each household member. The response of agricultural labor hours
of individual member to the change in the demographic structure is also investigated. By
constructing the panel data of individual household members, we estimate the following
equation:
′
′
′
𝐼𝐼𝐼𝐻𝑖𝑖𝑖 = 𝐹𝐹𝐹𝑀𝑖𝑖𝑖
𝛼𝑓𝑓𝑓𝑓 + 𝐼𝐼𝐼𝑅𝑖𝑖𝑖 𝛼𝑖𝑖ℎ𝑟 + 𝐹𝐹𝐹𝑅𝑖𝑖𝑖
𝛼𝑓𝑓𝑓𝑓 + 𝑀𝑀𝑀𝑅𝑖𝑖𝑖
𝛼𝑓𝑓𝑓𝑓
(6)
′
+ 𝑊𝑊𝑊𝐸𝑖𝑖𝑖
𝛼𝑤𝑤𝑤𝑤 + 𝑇𝑇𝑇𝐸𝑡′ 𝛼𝑡𝑡𝑡𝑡 + 𝜇𝑖𝑖 + 𝜖𝑖𝑖𝑖 .
Here, the subscript 𝑖 shows an individual household member of household 𝑗. 𝐼𝐼𝐼𝐼 is
agricultural labor hours of individual member. Although 𝐹𝐹𝐹𝐹 shows the demographic
structures of the household the member belongs to as with eq. (4) and (5), the member 𝑖 is
excluded from the calculation of these variables. 𝐼𝐼𝐼𝐼 is a vector of individual
characteristics and constitutes of age, square of age, dummy for marital status, dummy for
participation into off-farm labor, and dummy for returned member which takes 1 if the
member returned to the household from outside in the given year.
If some part of agricultural work of member 𝑖 is allocated to the new labor force, an
increase in the number of other productive aged members (male aged 14-59, female aged
14-59, and labor power) will have negative impact on the agricultural labor hours of the
member. However, the positive impacts on some members are plausible if the shadow wage
decreases according to an increase in the number of other productive aged members. Eq. (6 is
estimated separately by sex and age group (14-29 and 30-59) of the members.
17
In the next section, firstly, we will see the results using all observations. After that, in
order to consider the effects of land ownership status on the work sharing strategies, eq. (4)
and (6) are estimated separately by land ownership group.
5. Empirical Results
5.1 Results using all observations
Table 3 shows the estimation results of eq. (4). The results are obtained by using all
observations from the third period MAF survey which covers the midst of the Depression and
the recovery period (1931 - 1941).
<Table 3>
When we look the results for the agricultural labor hours per working member estimated
by fixed effects estimator (columns 1 and 2), regardless of the choice of the series of
demographic structures, the variables which represent the number of family labor forces (male
and female aged 14-59 or labor power) have significantly negative impact on the labor hours
per working member. The difference estimators (columns 3 and 4) provide similar results with
respect to the corresponding variables. In contrast, when we look the results for the labor
intensity (columns 5-8), the variables which represent the number of family labor forces have
positive impact on the labor intensity. Choices of the estimators and the series of the
demographic structures do not have any influence on this result.
These results suggest that the response of the farm household in the 1930s against the
change of household labor forces may apply to Case 2 as discussed in Section 4. An increase
of the family labor forces may moderately decrease the shadow wage, and then, increase the
labor intensity. The increment, however, is not enough to keep the agricultural labor hours per
working member constant.
The fixed effects estimator and the difference estimator provide similar results with
respect to the variables discussed above. The long term effects of these variables on the work
sharing strategies, even if existed, the bias of fixed effect estimator may not be large. When
18
panel data is utilized in the following analyses, fixed effects estimator will be applied because
of the efficiency compared with difference estimator (Wooldridge, 2010).
Table 3 also reports the coefficients of time effects. From the results estimated by fixed
effects estimator, we can see that the coefficients of time dummies on the agricultural labor
hours per working member switch over from positive to negative as times go by, but none of
these coefficients are significant. In addition to the same trend in the results for the labor
intensity, all of the coefficients after 1936 are significant. In the process of recovery from the
Depression, the farm households may have decreased the labor intensity.
<Table 4>
Next, we estimate eq. (5) to discuss the change of household behaviors regarding to the
work sharing strategies before and after the Depression. The results are summarized in Table
4, and these are estimated by OLS estimator because of the difficulty constructing panel data
from the second period MAF survey that covers the period before the Depression
(1926-1930).
From the results for the agricultural labor hours per working member, we can observe
some differences in the magnitude of the coefficients of the number of labor forces (male and
female aged 14-59 or labor power) between the period before and after the Depression
(column 1 vs. 3 and column 2 vs. 4). The absolute values in the period before the Depression
are larger than those in the period after the Depression. However, the signs of these
coefficients are significantly negative and same each other.
The results for the labor intensity show clear contrast. All of the coefficients of the
number of labor forces in the period before the Depression have positive signs, but the
magnitudes are small and not significant (columns 5 and 6). In contrast, all of the coefficients
of these variables in the period after the Depression have significantly positive signs (columns
7 and 8).
Despite of the differences in estimators and control variables, the coefficients of the
number of labor forces in the period after the Depression are compatible with the estimation
19
results of eq. (4). The possible bias of eq. (5) mainly caused by applying OLS estimator, even
if existed, did not change the implications that the farm household after the Depression
decreased the shadow wage according to an increase in the size of household labor forces. If
the bias is not so much in the estimation results of eq. (5) for the period before the Depression,
however, it can be said that the farm households before the Depression did not adjust the
shadow wage and that Case 1 discussed in the Section 4 might be true.
<Table 5>
Table 5 summarizes the estimation results of eq. (6). The results are obtained using the
third period MAF survey. The agricultural labor hours of male member aged 14-29 is affected
positively from an increase in the number of male aged 14-59 or labor power. However, that
of aged 30-59 is not affected from any change in the demographic structures. Similar
tendency can be observed from the results for female productive aged members. A young
female member increases her agricultural labor hours along with an increase in the number of
same sex members aged 14-59. Changes in the family labor forces may not prompt the
households to adjust the agricultural labor among the matured family labor forces. The
reallocation may be executed through the adjustment among the young members, especially
within same sex.
Investigations on the household level measures suggested that Case 2 might be true as
the response of farm households after the Depression against an increase in the size of
household labor forces. If so, some part of the agricultural work that the existing working
members bore would be reallocated to the new labor force. However, the estimation results of
eq. (6) implied that young members increased their agricultural working time along with the
increase in family labor forces, suggesting a decrease of the shadow wage of these members.
Reallocation of the existing agricultural work did not play an important role as the work
sharing strategies of the farm households in the 1930s. Rather, the farm households allocated
more work to the young members, although the amount was not enough to keep the
agricultural labor hours per working member constant.
20
5.2 Land ownership and work sharing strategies
In this section, we consider the land ownership structures and the effects on the work sharing
strategies.
<Table 6>
Table 6 summarizes the estimation results of eq. (4) and (6) by the land ownership group.
In this table, only the coefficients of the number of male and female members aged 14-59 or
labor power are reported, but same independent variables with the estimations using all
observations are included.
Both of the number of male and female members aged 14-59 and also labor power have
significantly negative impacts on the agricultural labor hours per working member of landed
farmers (columns 1 and 2 of panel A). The results for tenant farmers show similar results
except that the coefficient of female members aged 14-59 is not significant (columns 5 and 6
of Panel A). In contrast, all of these variables have no significant impacts on the agricultural
labor hours per working member of landed farmers (columns 3 and 4 of panel A). These
variables show significantly positive impacts on the labor intensity of all groups (panel B). It
is suggested that the landed farmers and the tenant farmers after the Depression choose Case 2,
but the landed-tenant farmers choose Case 3 as the response against the change of the size of
household labor forces. Thus, the landed-tenant farmers might adjust the labor intensity so as
to keep the agricultural labor hours per working member constant.
These results have relevance to the estimation results for agricultural labor hours of
individual member (panel C to F). The young male and female members of landed-tenant
farmers increase their agricultural labor hours along with an increase in the labor power or, at
least, the number of labor forces with same sex (columns 3 and 4 of panels C and E).
However, only the young male members of landed farmers (columns 1 of panels C) and the
female members of tenant farmers (columns 5 of panels E) showed similar responses when
the number of male and female members aged 14-59 are utilized. The positive response of the
21
young female members of tenant farmers lost the significance when the labor power is
utilized (column 6 of panel E). The landed-tenant farmers, who mainly choose Case 3, may
have particularly adjusted the shadow wage of young members along with changes in the
number of labor forces.
When the numbers of male and female members aged 14-59 are used as the independent
variables (Table 6), we can observe a few cases that these variables have weakly significant
and negative impacts on the agricultural labor hours of individual members: these are the
response of the female members aged 14-29 of tenant farmers to the number of female
members aged 14-59 and the response of male members aged 30-59 of landed farmers to the
number of female members aged 14-59. These results might suggest that some part of the
agricultural work of these members is reallocated to the new labor forces. However, these
responses lose the statistical significances, if the labor power is alternatively used. The
evidences are not robust against the choices of variables.
5.3 Discussion
Main findings of the econometric analyses in Section 5.1 and 5.2 can be summarized as
follows. First, the analyses utilizing the household measures suggested that the farm
households in the 1930s (after the Depression) adjusted the shadow wage according to the
size of household labor forces, but the amount was not enough to keep agricultural labor
hours per working member constant. In contrast, the shadow wag was not adjusted in the late
1920s (before the Depression). Second, from the analyses utilizing the agricultural labor hours
of each household member, we could not find any evidence that some part of the agricultural
work that the existing working members bore was reallocated to the new labor force. Rather,
the agricultural labor hours of young members increased with an increase in the size of
household labor forces. Third, the landed-tenant farmers actively adjusted the shadow wage
and the labor intensity. The increment of the labor intensity was enough to keep the
agricultural labor hours per working member constant.
22
<Figure 4>
Regarding to the first finding, why the labor intensity was adjusted in the 1930s, but not
in the late 1920s? One explanation might be that our theoretical model tells us that when the
living of the household falls to the subsistence level, the household may not be able to adjust
flexibly the shadow wage, and therefore, the labor intensity. The gradual recovery from the
Depression in the 1930s may have released the farm households from living at subsistence
level. However, since opportunities for good off-farm jobs had been still limited, the farm
households adjusted the labor intensity. In other words, the labor intensity of the farm
households in the late 1920s had reached to the highest level to keep the subsistence.
Therefore, they could not adjust it when they faced a change in the size of household labor
forces. Figure 4.A illustrates the yearly average of labor intensity. The labor intensity had
increasing trend in the late 1920s, but turned to decreasing trend from the midst of 1930s.
As discussed in Section 4, if the farm household decreases the shadow wage with an
increase in the size of household labor forces, the efficiency of agricultural production will get
worse. Figure 4.A also illustrates the yearly average of labor productivity. The decreasing
trend of the labor intensity from the midst of 1930s might be related to the increasing trend of
the labor productivity in this period. Furthermore, clear negative correlation between them
can be observed from Figure 4.B which shows a result of lowess regression of the land
productivity on the labor intensity. The farm households might have decreased the labor
intensity to improve the efficiency of agricultural production in the process of the recovery
from the Depression. However, they needed to adjust the labor intensity according to the size
of household labor forces because of the restricted opportunity for off-farm jobs.
Regarding to the second finding, the young household members generally increased their
working time with their ages up to almost 30 years old. The agricultural labor hours of them
also depict same patterns. The matured members might work so hardly that their working time
could not be adjusted according to the size of household labor forces. Because of this, the
farm households mainly adjusted the agricultural working time of young members.
23
Finally, regarding to the third finding, the landed-tenant famers actively adjusted the
labor intensity compared with the landed farmers and the tenant farmers. Our theoretical
analyses suggested that high price elasticity of labor demand raises the possibility that the
agricultural labor hours per working member are held constant. Some researchers pointed out
the progress of landed-tenant farmers and the market oriented style of their farm management
in the inter-war period (Kurihara, 1948; Shoji 1987). The price elasticity of landed-tenant
farmers might be higher than those of landed farmers and tenant farmers.
Although the landed farmers and the tenant farmers showed similar responses against
the change of the size of household labor forces, the background of their responses might be
different. If the price elasticity of demand for leisure is high, the effect of the size of
household labor forces on the shadow wage and the labor intensity are small. The landed
farmers were generally richer than other groups of farmers. It might be possible that the
landed farmers had higher price elasticity of demand for leisure than other farmers. In contrast,
the tenant farmers were generally poor and the recovery from the Depression of their
household economies was delayed compared to the other groups. Their living might be close
to the subsistence level, so that flexible adjustment of the shadow wage along with their utility
schedule was difficult.
6. Conclusion
This paper investigated time allocation of Japanese farm households in the inter-war period to
test work sharing mechanism of farm household under economic recession. In the 1930s of
Japan, adjusting the labor intensity of farm management had played important role to achieve
the work sharing. This result is contrast to the finding of Zhang, et al. (2001) who examined
the work sharing mechanism of Chinese farm households in the 1990s. Their study provided
the evidence that household members reduced their working time if the household labor
forces increased. Odaka and Yuan (2006) conducted comparative analyses by mainly using
aggregate statistics of several Asian countries in different times to examine the work sharing
24
in the rural economies. Our study cleared the needs for the similar comparative analyses but
using micro-data.
Also, the work sharing strategies taken by the farm households differed depending on
their land ownership status. It might come from differences in the extent of market oriented
farming and the living standard. Some structural approaches which assumed particular
behaviors of farm households may help test these possibilities and to get more insight about
work sharing in rural economy.
Notes
1. Brandt (1993) alternatively emphasized the difficulties in introducing labor-saving
technologies under increasing flow of population from rural to urban as the reasons of the
agricultural stagnation in1920s.
2. The MAF published statistical books named Noka Keizai Chosa (Survey of the Farm
Household Economy) every year by aggregating the individual data collected from the MAF
survey. During the second-period MAF survey, these published books included individual
data in the appendices. The individual data of the second-period MAF survey was obtained
from the appendices and digitalized by Kyoto University. On the other hand, most of the
original individual records of the MAF survey have been stored in Kyoto University.
Hitotsubashi University has advanced projects constructing database of the individual records
of the MAF survey. These projects are still going on and only part of the full sample is
currently available.
3. See Kusadokoro et al., (2012) and Senda and Kusadokoro (2009) for more detailed
discussion on the issues of sampling methods and potential bias of the MAF survey.
4. The contributions of male household members aged 6 or under, 7-13, 14-24, 25-39, 40-59,
and over 60 to the labor power are assumed to be 0, 0.3, 0.85, 1, 1, and 0.4, respectively. In
the same manner, those of female members are 0, 0.3, 0.75, 0.8, 0.8, and 0.4, respectively.
5. Nojiri (1942) found from the original survey that migrants of richer farmers in the inter-war
25
period had better educational status.
7. The data until 1937 is obtained from Nosaku Yatoi Chingin Tokeihyo and the data from
1938 is obtained from Norinsho Tokeihyo, and both statistics were published by MAF.
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29
Table 1. Rate of non-working persons and number of workers in each sector, 1920-1939
Number of workers (1,000 persons)
Rate of non-working
persons
Male
Female
Male
Female
Primary
Secondary
Tertiary
Primary
Secondary
Tertiary
1920-1924
2.56
45.50
8,193
4,271
4,867
6,358
1,686
2,263
1925-1929
2.97
48.25
8,116
4,520
5,707
6,445
1,619
2,405
1930-1934
3.28
50.21
8,386
4,686
6,420
6,532
1,456
2,831
1935-1939
3.35
49.04
7,882
5,640
6,675
6,917
1,764
3,282
Source: Long Term Economics Statistics (LTES) Database
Note: All figures are five-year averages. The rate of non-working persons was calculated as
follows. The number of non-working persons was obtained by substituting the number of
working persons aged 25-59 from the population of the corresponding ages, and then, the
number of non-working persons was divided by the population.
30
Table 2. Descriptive statistics of selected variables of the third period of the MAF survey
All
obs.
No. of obsevations
No. of HH. members
Male aged 14-59
Female aged 14-59
Elderly 60Children aged 0-6
Children age 7-13
Labor power
Engage in off-farm work
Managed land size (tan=10a)
Household labor (hours)
Agricultural
Non-agricultural
Household chores
Agricultural per working member
Hired labor (hours)
Farm income (Yen)
Agricultural
Non-agricultural
Family expenditure (Yen)
Labor intensity (hours/tan)
Labor productivity (Yen/hours)
Group by land ownership
Landed
LandedTenant
tenant
350
399
321
6.43
6.15
6.30
1.76
1.49*
1.67
1.78
1.60*
1.50*
0.54
0.64*
0.70*
1.20
1.32
1.30
1.15
1.10
1.14
3.62
3.27*
3.39*
1.15
1.26
1.54*
13.19
13.37
12.79
10,918
10,500
10,883
5,984
5,650
5,746
751
956 *
1,167 *
3,606
3,351
3,380
1,544
1,636 *
1,584
294
405 *
226
1,202
1,124
906 *
953
866
676 *
195
190
173
915
843 *
731 *
508
483
519
0.15
0.16*
0.15
1,070
6.29
1.63
1.63
0.63
1.28
1.13
3.42
1.31
13.14
10,753
5,788
952
3,443
1,590
315
1,084
837
187
833
502
0.16
Source: Sample data from the third-period MAF survey
Note: Figures with * in columns of landed-tenant and tenant farmers differ from those of
landed farmers at 5% significant levels.
31
Table 3. Estimation results for agricultural labor hours per family worker and labor intensity using all observations from the third period
MAF survey
Agricultural labor hours per family worker
FE
FE
Difference Difference
[1]
[2]
[3]
[4]
FDEM
Male age 14-59
Female age 14-59
Elderly age 60Children age 0-13
-66.825**
(30.58)
-85.706***
(27.61)
-132.035***
(37.36)
-48.491**
(18.96)
Labor power
-34.542**
(13.51)
Yes
Yes
Yes
No
-76.665***
(27.56)
-15.615
(18.73)
-31.761**
(13.40)
Yes
Yes
Yes
No
48.865
(50.40)
40.804
(64.38)
185.450
(202.79)
-75.859
(197.85)
Children age 0-6
Engage in off-farm work
FSTR
MSTR
WAGE
PRFC
TIME
Year1932
Year1933
-67.179**
(28.68)
-73.318**
(30.20)
-87.228*
(44.43)
-30.520
(18.76)
FE
[5]
Labor intensity
FE
Difference
[6]
[7]
57.966***
(9.47)
33.680***
(9.38)
35.834***
(9.95)
-0.005
(5.89)
-27.092**
(13.66)
Yes
Yes
Yes
No
-78.668***
(27.95)
-14.166
(21.45)
-27.718**
(13.76)
Yes
Yes
Yes
No
125.972*
(67.49)
82.275
(56.05)
127.105*
(67.59)
83.479
(55.75)
32
Difference
[8]
47.268***
(9.81)
43.005***
(11.55)
32.833**
(14.08)
12.069**
(5.39)
0.037
(5.78)
Yes
Yes
Yes
No
54.127***
(9.78)
7.778
(6.12)
0.211
(5.97)
Yes
Yes
Yes
No
-8.292
(6.23)
Yes
Yes
Yes
No
55.926***
(9.59)
6.237
(5.87)
-8.866
(6.22)
Yes
Yes
Yes
No
5.750
(17.63)
7.749
(22.85)
79.638
(74.89)
-19.688
(69.75)
10.916
(20.83)
15.153
(17.34)
11.288
(20.73)
14.743
(17.26)
Year1934
Year1935
Year1936
Year1937
Year1938
Year1939
Year1940
Year1941
Constant
No. of observations
R2
31.674
(73.78)
-8.745
(84.53)
32.143
(87.93)
-53.131
(94.91)
-93.536
(101.17)
-59.058
(98.16)
-129.469
(112.64)
-148.730
(119.82)
642.050
(567.91)
1070
0.087
17.503
(73.29)
-33.305
(84.45)
9.863
(88.18)
-77.178
(95.15)
-114.906
(100.84)
-75.914
(101.07)
-154.766
(114.71)
-177.103
(122.02)
576.285
(592.37)
1070
0.074
55.812
(57.54)
41.781
(58.46)
143.285**
(59.24)
-8.276
(57.21)
70.987
(60.42)
154.858**
(62.75)
-33.343
(74.06)
-
55.293
(57.40)
38.184
(58.35)
147.126**
(58.59)
-6.952
(56.77)
70.358
(60.19)
157.692**
(62.72)
-31.747
(73.73)
-
-84.131*
(46.44)
719
0.065
-85.368*
(46.07)
719
0.062
-26.636
(27.67)
-47.135*
(28.11)
-55.850*
(28.69)
-65.298*
(33.46)
-96.684***
(34.62)
-90.703**
(35.98)
-97.008**
(39.17)
-110.649***
(39.35)
264.188
(214.87)
1070
0.280
-23.548
(28.08)
-41.530
(28.55)
-50.407*
(28.88)
-62.423*
(33.76)
-92.150***
(35.17)
-86.760**
(36.49)
-91.940**
(39.71)
-107.588***
(39.88)
249.943
(211.79)
1070
0.243
-32.612*
(17.71)
-4.422
(16.16)
2.461
(16.94)
-6.678
(15.21)
-27.738
(18.63)
6.699
(16.76)
-11.785
(19.02)
-
-32.287*
(17.69)
-3.648
(16.11)
1.761
(16.71)
-8.118
(15.10)
-27.313
(18.61)
4.600
(16.70)
-11.978
(18.81)
-
-4.443
(12.34)
719
0.176
-3.948
(12.32)
719
0.178
Note: Figures in parentheses are robust standard errors. * Significant at 10%. ** Significant at 5%. *** Significant at 1%.
33
Table 4. Estimation results for agricultural labor hours per family worker and labor intensity using all observations from the second and
third period MAF survey (OLS estimators)
Agricultural labor hours per family worker
2nd survey
2nd survey
3rd survey
3rd survey
1926-30
1926-30
1931-41
1931-41
[1]
[2]
[3]
[4]
FDEM
Male age 14-59
Female age 14-59
Elderly age 60Children age 0-13
-166.354***
(27.14)
-177.026***
(27.12)
-131.981***
(32.61)
-15.110
(15.77)
Labor power
Children age 0-6
FSTR
MSTR
WAGE
PRFC
TIME
Constant
No. of observations
R2
No
Yes
No
Yes
Yes
2293.723***
(298.40)
958
0.205
-56.667***
(18.03)
-103.327***
(20.39)
-104.941***
(20.86)
-32.535***
(10.72)
-179.660***
(23.37)
-22.504
(23.31)
No
Yes
No
Yes
Yes
2362.684***
(287.37)
958
0.194
No
Yes
No
Yes
Yes
1086.370***
(88.94)
1070
0.293
2nd survey
1926-30
[5]
Labor intensity
2nd survey 3rd survey
1926-30
1931-41
[6]
[7]
0.204
(8.14)
9.596
(8.12)
14.256
(9.59)
-2.163
(4.02)
-98.495***
(16.05)
-6.529
(14.38)
No
Yes
No
Yes
Yes
1079.374***
(87.56)
1070
0.283
No
Yes
No
Yes
Yes
980.140***
(88.64)
958
0.406
3rd survey
1931-41
[8]
74.661***
(6.53)
41.950***
(7.05)
45.004***
(5.94)
-12.249***
(2.99)
4.738
(6.36)
-5.714
(7.27)
No
Yes
No
Yes
Yes
985.284***
(88.75)
958
0.405
No
Yes
No
Yes
Yes
494.835***
(29.24)
1070
0.476
64.211***
(5.81)
-11.863***
(4.23)
No
Yes
No
Yes
Yes
472.889***
(29.94)
1070
0.438
Note: Figures in parentheses are robust standard errors. * Significant at 10%. ** Significant at 5%. *** Significant at 1%.
34
Table 5. Estimation results for agricultural labor hours of individual member using all observations from the second and third period MAF
survey (FE estimator)
Age 14-29
[1]
FDEM
Male age 14-59
Female age 14-59
Elderly age 60Children age 0-13
247.598***
(70.26)
7.582
(78.65)
-93.961
(94.89)
52.136
(77.40)
Labor power
Children age 0-6
Engage in off-farm work
ICHR
FSTR
MSTR
WAGE
PRFC
TIME
Constant
No. of observations
R2
Male
Age 14-29
[2]
67.085
(48.91)
Yes
Yes
Yes
Yes
No
Yes
-7846.786***
(2434.92)
876
0.146
Age 30-59
[3]
Age 30-59
[4]
-5.455
(40.04)
-30.567
(36.95)
-25.297
(44.34)
23.669
(20.83)
174.508***
(64.58)
141.286*
(78.77)
69.955
(49.26)
Yes
Yes
Yes
Yes
No
Yes
-7912.373***
(2280.96)
876
0.135
-6.523
(17.25)
Yes
Yes
Yes
Yes
No
Yes
2557.320
(1872.69)
1017
0.064
Age 14-29
[5]
Female
Age 14-29
Age 30-59
[6]
[7]
-10.397
(75.99)
174.138**
(66.71)
24.549
(99.00)
29.825
(47.59)
-22.062
(38.91)
32.034
(23.15)
-6.254
(17.30)
Yes
Yes
Yes
Yes
No
Yes
1987.638
(1908.70)
1017
0.064
35.069
(26.67)
Yes
Yes
Yes
Yes
No
Yes
1696.827
(1524.79)
855
0.172
Age 30-59
[8]
9.305
(41.73)
21.418
(50.31)
35.279
(57.68)
-49.499
(37.06)
90.179
(55.48)
96.225*
(52.52)
43.650
(27.92)
Yes
Yes
Yes
Yes
No
Yes
1271.968
(1524.82)
855
0.162
4.668
(22.64)
Yes
Yes
Yes
Yes
No
Yes
65.561
(1966.43)
973
0.064
28.417
(42.18)
-60.442*
(32.95)
3.193
(21.84)
Yes
Yes
Yes
Yes
No
Yes
140.637
(1973.29)
973
0.064
Note: Figures in parentheses are robust standard errors. * Significant at 10%. ** Significant at 5%. *** Significant at 1%.
35
Table 6. Summary of the estimation results of eq. (4) and eq. (6) by the group of land owner
ship status (FE estimators)
Landed
Landedtenant
[1]
[2]
A. Agricultural labor hours per family worker
Male, age 14-59
-112.879**
Female, age 14-59
-128.162***
Labor power
-128.368***
No. of observations
350
350
B. Labor intensity
Male, age 14-59
Female, age 14-59
Labor power
No. of observations
45.685***
22.803*
350
[3]
Tenant
[4]
-25.617
-61.292
399
399
C. Agricultural labor hours of individual member, male aged 14-29
Male, age 14-59
248.648**
413.408***
Female, age 14-59
47.551
-51.658
Labor power
202.294*
No. of observations
344
344
281
D. Agricultural labor hours of individual member, male aged 30-59
Male, age 14-59
6.761
-44.698
Female, age 14-59
-14.066
-82.861
Labor power
-1.399
No. of observations
324
324
367
-51.403
399
321
-99.408*
321
36.965**
38.290**
48.319***
399
321
38.875**
321
-49.171
-215.104*
299.484**
281
251
-102.778
251
-14.005
-14.668
-72.064
367
E. Agricultural labor hours of individual member, female aged 14-29
Male, age 14-59
-53.360
257.227**
Female, age 14-59
-10.376
294.968***
Labor power
-42.784
291.634***
No. of observations
325
325
303
303
F. Agricultural labor hours of individual member, female aged 30-59
Male, age 14-59
-33.213
49.468
Female, age 14-59
-109.510*
-3.245
Labor power
-31.070
No. of observations
317
317
356
[6]
-128.884**
-75.320
57.979***
22.722*
48.172***
350
[5]
326
-5.272
326
-181.514
508.582***
227
55.300
227
-85.765
181.314**
38.952
356
300
46.559
300
Note: * Significant at 10%. ** Significant at 5%. *** Significant at 1%. The t statistics are
calculated using robust standard errors.
36
500
450
400
350
300
250
200
150
100
50
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
0
Primary (Nominal)
Secondary (Nominal)
Primary (Real)
Secondary (Real)
Figure 1. Net domestic products (NDP) of primary and non-primary sectors, 1910-1940
(Index, 1920 = 100)
Source: Long Term Economics Statistics (LTES) Database
Note: Reference years of the price index utilized for the realization are 1934-1936.
37
Yen
250
200
150
100
50
Landed (3rd)
Landed-tenant (3rd)
Tenant (3rd)
Landed (2nd)
Landed-tenant (2nd)
Tenant (2nd)
Average (3rd)
Average (2nd)
Figure 2. Farm income per household member by land ownership
Source: Sample data from the second-period and the third-period MAF survey
38
1941
1940
1939
1938
1937
1936
1935
1934
1933
1932
1931
1930
1929
1928
1927
1926
0
Revenue
Shadow wage
A
B
C
Agricultural labor
No. of working members
a
c
b
a'
Figure 3. Work sharing strategies of farm household
Source: The authors with reference to the Diagram 1b of Sen (1960, p. 4)
39
700
0.2
600
Hours/tan
400
0.1
300
200
Yen/hours
0.15
500
0.05
100
1941
1940
1939
1938
1937
1936
1935
1934
1933
1932
1931
1930
1929
1928
1927
0
1926
0
Labor intensity (3rd)
Labor intensity (2nd)
Labor productivity (3rd)
Labor productivity (2nd)
-.2
Labor productivity (Yen/hours)
-.1
0
.1
.2
A. Yearly average (Source: The second and third period MAF survey)
-500
0
Labor intensity (hours/tan)
500
Observations = 1070, bandwidth = .8
B. Lowess smoothing of the within variation (Source: The second and period MAF survey)
Figure 4. Labor intensity and labor productivity
40
Appendix
In this appendix, we discuss the conditions that an increase in the number of household labor
forces decreases the shadow wage when the constraint on wage labor is binding, but the
constraint on consumption is not. The time constraint can be expressed as 𝑛𝑛 = 𝑛𝑙𝑎 + 𝐿�𝑤 +
𝑛𝑛 = 𝐿𝑎 (𝑝, 𝑤 ∗ ) + 𝐿�𝑤 + 𝑅(1, 𝑤 ∗ , 𝛱 ∗ ). By differentiating the time constraint equation, we
obtain the following equation after some manipulations:
𝑑𝑤 ∗
𝜕𝜕 ∗
𝜕𝐿𝑎
𝜕𝜕
𝜕𝜕
= �𝑡 −
𝑤 𝑡��� ∗ + � ∗ +
𝑅�� .
∗
𝑑𝑑
𝜕𝛱
𝜕𝑤
𝜕𝑤
𝜕𝛱 ∗
(A1)
To derive this equation, we used the relations of ∂𝛱 ∗ / ∂𝑛 = 𝑤 ∗ 𝑡 and ∂𝛱∗ / ∂𝑤 ∗ = 𝑛𝑛 −
𝐿𝑎 − 𝐿�𝑤 = 𝑅.
Since the price response of agricultural labor demand in the denominator of eq. (A1) is
negative as usually assumed, the sign of eq. (A1) is determined by the sign of the numerator
and the second term of the denominator. If the former is positive and the latter is negative, eq.
(A1) will have negative sign. The numerator represents the change of total labor supply of the
household along with the increase of labor forces. The sign will not be negative unless the
income effect on consumption goods is negative (i.e. consumption is normal goods): it is
unrealistic under the environment where the subsistence level of consumption might be
violated. It is reasonable to assume positive sign of the numerator.
The second term of the denominator of eq. (A1) shows the price response of demand for
leisure with the income effect. Using the elasticity form, the following condition should be
satisfied for the term having positive sign:
𝜂𝑅𝑅 𝑆𝑅 ≥ −𝜂𝑅𝑅 .
Here, 𝜂𝑅𝑅 and 𝜂𝑅𝑅 refer to the income elasticity and the own price elasticity of demand for
leisure, respectively. 𝑆𝑅 is the expenditure share of leisure. If consumption goods is normal,
41
𝜂𝑅𝑅 𝑆𝑅 is less than 1. If the cross price elasticity of demand for consumption goods is
non-negative, however, the absolute value of 𝜂𝑅𝑅 is not less than 1. The representative case
is that the utility function is given by the Cobb-Douglas function, that the cross price elasticity
is zero. It is also reasonable to assume negative sign of the second term of the denominator of
eq. (A1).
The above discussion shows that eq. (A1) may have negative under usual conditions. An
increase in the number of labor forces will usually decrease the shadow wage and also will
increase the total amount of agricultural labor. In the case where the constraint on
consumption is not binding, the total amount of consumption is not affected by the number of
household members. Because of this, whether a change in the number of household members
involves a change of the number of household members does not affect the above discussion.
Next, we examine the conditions that an increase in the size of household labor forces
involves the increase of agricultural labor hours per working member. This means that the
following inequality is satisfied:
𝜕𝐿𝑎 𝜕𝑤 ∗ 𝐿𝑎
≥ .
𝜕𝑤 ∗ 𝜕𝜕
𝑛
(A2)
First, we consider the case where both of the constraints on subsistence and wage labor are
binding. When both of the number of household members 𝑚 and the number of family labor
forces 𝑛 increase at the same time, by manipulating eq. (3), we can obtain the following
condition to satisfy eq. (A2):
𝑐̅ ≥
𝑤 ∗ 𝐿𝑎
.
𝑛
(A3)
This equation means that the subsistence level of consumption is larger than the shadow
income of agricultural labor per working member.
42
Second, we consider the case where only the constraint on wage labor is binding. From
eq. (A1), we can obtain the following condition to satisfy eq. (A2):
𝜂𝐿𝐿
(1 − 𝜂𝑅Π 𝑆𝑅 )𝑇 − 𝐿𝑎
≤ 𝜂𝑅𝑅 + 𝜂𝑅Π 𝑆𝑅 .
𝑅
(A4)
Here, 𝜂𝐿𝐿 is the price elasticity of labor demand in agriculture. Since 𝜂𝐿𝐿 and 𝜂𝑅𝑅 +
𝜂𝑅Π 𝑆𝑅 are negative, if the second term of the left side is negative, the inequality is not
satisfied. Thus, if the income elasticity of leisure or the consumption shares of leisure is high,
agricultural labor hours per working member will decrease with an increase in the size of
household labor forces. In addition to the positive sign of the second term, high price
elasticity of labor demand in agriculture, or low price elasticity of demand for leisure is
required to satisfy eq. (A4).
43