Page 1 of 23
Advances in Social Sciences Research Journal – Vol. 8, No. 4
Publication Date: April 25, 2021
DOI:10.14738/assrj.84.10031. Orach, H., Pu, C., Qianling, S., Shiying, W., Ssewajje, H., & Wigmore, R. (2021). The Effect of Financial Constraint on Farmers’ Health:
Evidence from Rural China. Advances in Social Sciences Research Journal, 8(4). 415-437.
Services for Science and Education – United Kingdom
The Effect of Financial Constraint on Farmers’ Health: Evidence
from Rural China
Henry Orach
Sichuan Agricultural University, Chengdu, 211 Huimin Road
Wenjiang District, 611130, China
Chen Pu
Sichuan Agricultural University, Chengdu, 211 Huimin Road
Wenjiang District, 611130, China
Shen Qianling
Sichuan Agricultural University, Chengdu, 211 Huimin Road
Wenjiang District, 611130, China
Wei Shiying
Sichuan Agricultural University, Chengdu, 211 Huimin Road
Wenjiang District, 611130, China
Hassan Ssewajje
Peking University, Beijing, 5 Yiheyuan Rd, Haidian District, 100871, China
Rosie Wigmore
Peking University, Beijing, 5 Yiheyuan Rd, Haidian District, 100871, China
ABSTRACT
Health is an important tool to farmers. However, percentage of farmers are unable
to obtain good health due to inadequate capital and inadequate access to credit
from financial institutions. Using China’s rural household income survey (CHIP)
database conducted in 2014, this study contributes to the literatures by analyzing
the effect of credit rationing on rural farmers’ health status. Ordered probit model
was used to evaluate the impact of credit rationing on farmers’ health status. Credit
rationing was found to play the negative role of hindering rural farmers from
accessing good health status. This study definitely answers the question regarding
the negative effect of credit rationing on the health status of rural household
farmers. Further study to establish causal relationships using time-variants/panel
datasets.
Keyword: health status; credit rationing; rural household farmers
INTRODUCTION
Health is defined as a commodity which consumers are willing to invest in (Grossman, 1972).
As a result, health investment always increases in parallel with the nature and danger of a
Page 2 of 23
416
Advances in Social Sciences Research Journal (ASSRJ) Vol. 8, Issue 4, April-2021
Services for Science and Education – United Kingdom
disease. But when the price of the medical care increases, health demand decreases. Therefore,
with higher credit access, reduction in income disparity, better insurance arrangements, and
more wide spread health center can improve the health of a population, and consequently, also
improve people’s productivity level. Preston(2007), show that there is a cross-country
relationship between life-expectancy and income per head. In the Least Developed Countries
(LDCs), an increase in income are even more noticeably associated with an increase in life
expectancy? However, in LDCs, poor people cannot afford quality health care. Opportunities to
borrow from financial institutions on reasonable terms are also limited and prevents many
poor people from investing in their health.
China’s spectacular economic development and poverty reduction since the late 1970s has
received much attention, but what has received less attention, is the accompanied deterioration
of health care for China’s large, rural population which account for 40.85%. Although, China has
famously reduced extreme poverty rate to 1.7% (Wang, Zhao, Bai, Zhang, & Yu, 2020),
inequalities, such as income disparity, has increased and rural poverty remains a problem,
including a luck of quality healthcare compared to urban areas. This is arguably, the most
serious socio-economic problem that China now faces. According to Chow,(2006), the Chinese
government allocated 16 to 17% of the health budget for rural households, therefore 50 to 60%
of health expenditures are individual expenditures as compared to the government assistant to
the urban population through the provision of insurance. Many poor and uninsured farmers
received less healthcare since their income was not sufficient to pay healthcare at much higher
prices. A luck of investment in rural healthcare has resulted in healthcare accounting for 60%
of a rural farmer’s expenditure. Low government expenditure on rural healthcare has led to
increase in adverse shock such as health shock and climatic shock, and mortalities in rural
areas. Other studies have indicated that as well as low government expenditure on healthcare,
a low proportion of medical insurance reimbursement and the farmers’ low income also
contributed to farmers being unable to pay for medical (Cheung & Padieu, 2015). Furthermore,
Chinese farmers are among the least subsidized compared to farmers in all other developed
countries in term of credit access by financial institutions, which make them helpless in control
of several adverse shocks (e.g., health shock, climatic shocks). In addition, the recent informal
credit market has become inadequate in addressing the rural credit needs due to high demand
for large amount of credit by the farmers which cannot be fulfill by the informal credit market.
Because agricultural production, and therefore the rural economy, is unstable in rural China, as
well as being unable to fully insure themselves, rural households tend to support one another
(Binswanger & Rosenzweig, 1993). The limited access to credit from formal financial institution
and insurance mechanisms, combined with their limited ability to save, makes rural poor
farmers vulnerable to various covariate shocks (e.g., extreme weather and, disease epidemic,)
and/ or idiosyncratic shocks (e.g., sickness and funerals). Therefore, access to credit is essential
in poor rural households. Thus in order to reduce the impact of these shocks and make
production, investment and consumption decisions, rural farmers must have access to credit
(Jia, 2010).
Credit can help stabilize the rural economy in a variety of ways. Firstly, access to credit can
significantly increase the ability of households with limited savings to make investments in
agricultural inputs to increase productivity. Secondly, access to credit can help poor farmers
adopt new technologies that raise both income and reduce riskiness of income (Tang & Guo,
Page 3 of 23
417
Orach, H., Pu, C., Qianling, S., Shiying, W., Ssewajje, H., & Wigmore, R. (2021). The Effect of Financial Constraint on Farmers’ Health: Evidence from
Rural China. Advances in Social Sciences Research Journal, 8(4). 415-437.
URL: http://dx.doi.org/10.14738/assrj.84.10031
2017). Finally, access to credit allows rural households to increase their consumption in case
of an adverse events. The important role of credit in improving the rural economy is well
supported by empirical evidence. For example, (Diagne et al., (2000) found a positive
relationship between credit access and the welfare of rural household members. (Khanam,
Nghiem, & Connelly, 2009), also found that wealthier households tend to have healthier
children because they can afford to access better healthcare facilities.
Informal credit has generally been the main source of credit for rural farmers as it has the
advantage of low or even zero interest rates, flexible borrowing terms, and few restrictions on
how the loans should be used in comparison to formal financial institutions that demand high
interest rates, inflexible borrowing terms and high restrictions on loan used due to the fear of
information asymmetry and moral hazard. However, as China’s economy has grown, the type
of economic activities (e.g., high valued crops and non-farm business activities) that rural
farmers need to engage with to support themselves has also diversified and the credit needed
to be successful in these activities has increased. Accordingly, rural farmers now need to access
larger amounts of credit from formal financial institutions. However, due to a lack of
government intervention, rural households are usually excluded from formal financial
institutions due to their high transaction costs and asymmetry information (J. Stiglitz & Hoff,
1990). In addition, the formal financial institutions themselves are discouraged from offering
rural farmers credit due to their weak credit contract enforcement, lack of insurance
(Weerasuriya & Goonaratne, 1992). Thus, the low interest rate of credit that subsidized
financial institutions offer, is an alternative solution which many developing countries use to
help ensure that farmers in rural areas can access credit. However, such forms of credit can also
distort the rural financial market, often fail to reach the most vulnerable and exacerbates
income inequality (Conning & Udry, 2005; Meyer & Nagarajan, 2000). Thus, the poorest and
smallest farm households are still constrained from formal credit regardless of how intensive
and broad government interventions have been.
Credit constraints can seriously impact the welfare of a rural farmer household and its ability
to continue agricultural production. (Guirkinger & Boucher, (2008) have shown that in Peru,
credit constraints can reduce the allocation of agricultural resources for production, which
reduces agricultural productivity and outputs by 26%. (Dong et al., (2012) also had similar
conclusion of the negative effect of credit constraints on productivity input. (Tran, 2017) study
reveals the negative impact of credit constraints on rural households’ income in Vietnam. (Lin,
Wang, Gan, & Nguyen, 2019) result also suggest that credit constraint does have significant
impact on rural farmers’ consumption and welfare in Fujian province in China.
To address all of these problems, the Chinese government has set up credit programs to help
rural household access credit. These programs are similar to what other countries such as
Bangladesh have been doing for the past 40 years. However, most of these programs have not
been successful because formal financial institutions consider granting loans to rural
households in China a risk due to collateral requirements and asymmetry information. For
instance, the Agricultural Development Bank (ADB) offers credit with subsidized interest rates
but is still not serving the rural poor especially those that are geographically located very far
from the bank access and those mostly vulnerable group that the credit could have reach them
first. The Rural Credit Cooperative (RCC) is the main source of credit for households in rural
China and account for 87.5% of all loans obtained by rural farmers in 2005 (He, 2004). By 2005,
Page 4 of 23
418
Advances in Social Sciences Research Journal (ASSRJ) Vol. 8, Issue 4, April-2021
Services for Science and Education – United Kingdom
the total deposits in RCCs had reduced 30694 million RMB although the amount of loans offered
only totaled 21968 million. Thus, even the largest rural financial institution is not providing
adequate financial services, especially in terms of loans to rural households. The Agricultural
Bank of China (ABC), despite being the biggest commercial agricultural bank, actually focuses
mainly on city market up then until now. Thus, rural households in China face an enormous
amount of credit constraints.
Despite the clear relationship between access to formal credit and welfare of rural households
in China, there are limited studies into the impact of credit constraints on these households.
Furthermore, the studies that do exist have only considered full quantity rationing (when
households apply for loan and are rejected) and fail to address partly quantity rationing (when
the loan obtained by the borrowers is less than what they applied for) and self-rationing (when
households fail to apply for loan for the fear of rejection). Consequently, this study aims to build
upon previous studies and measure the impact of credit constraints on the health status of rural
households in China. If the impact is significant, then an adjustment of the government policy
on rural credit for farm households is necessary to improve their health status.
This study uses the self-reported health status through interviews of rural households as an
indicator of their health. To reveal the effect of credit constraints on health status accurately,
the self-reported health status indicator is the dependent variable and is analyzed in the results
section of this paper to accurately show the impact of credit constraints on health status. The
findings show that the difficulty of obtaining credit from financial institutions, directly affected
the ability of rural farmers to access medical treatment.
In order to show the quantitative relation and marginal coefficient between credit constraint
and health status, the econometric model should be constructed and the inevitable endogeneity
needs to be controlled (Lindeboom & Kerkhofs, 2009). We leveraged instrumental variable (IV)
strategy to address the endogeneity concern (Zhang, Uddin, Cheng, & Huang, 2018). However,
this method is limited by the availability and reasonability of variables. Therefore, the best
approach is to circumvent endogeneity using Randomized Controlled Trial (RCT) normalized
from the source of data collection at the social experiment design stage, although this is difficult
to carry out (Loyalka et al., 2015). Other strategies were also used to check the accuracy of the
results, such as choosing another suitable econometric model (Hille & Möbius, 2019). Firstly,
the CHIP survey dataset was used. Secondly, the econometric approaches of ordered probit
regression (Oprobit) and ordinary least squares (OLS) were constructed, and the quantitative
correlation between credit constraint and health status was detected and discussed. The results
will be used to provide suggestions on how to reduce the negative impact of credit constraint
on rural households’ health status in China.
The rest of the paper is organized as follow: Section 2 reviews related literatures followed by a
discussion of the research methodology used and the data in Section 3. Empirical results and a
further discussion will be presented in Section 4. Finally, section 5 will offer recommendations
drawn from the results of this study.
Page 5 of 23
419
Orach, H., Pu, C., Qianling, S., Shiying, W., Ssewajje, H., & Wigmore, R. (2021). The Effect of Financial Constraint on Farmers’ Health: Evidence from
Rural China. Advances in Social Sciences Research Journal, 8(4). 415-437.
URL: http://dx.doi.org/10.14738/assrj.84.10031
REVIEW OF RELATED LITERATURE
Definition of credit constraints
Credit constraint are classified into three different categories and their definitions are based on
these categories. (Barham, Boucher, & Carter, 1996) defined quantity constraint as the inability
of the borrower to obtain full quantity of credit demanded by him/her from the financial
institutions at the prevailing contract rate, the extent of credit constraint is in the gap between
the amount demanded and received. Transaction cost constraint; this is where the low-wealthy
households have positive demand for credit, but due to high transaction cost, low repayment
capacity especially under negative income shock, high in egalitarian societies, led to hindering
them from obtaining the loan from the lender since their investment might become non- profitable. Risk rationed constraint on the other hand refers to a situation where there is
positive demand for credit by the household but do not participate in loan application for the
fear of rejection, collateral lost etc. The fear of losing their most valuable collateral such as land,
machinery which they solely depend on in production due to unsuspected idiosyncratic shocks,
always discourages farmers from borrowing loan.
Conventionally, rural household who obtained the full loan or did not apply for a loan are
categorized to be credit unconstrained. However, failure in considering not only the supply side
but also the demand side constraint may lead to underestimating the demand for credit as well
as credit constraint situation in rural area. (Kochar, 1997) for instance concluded in his studies
that the demand for credit by rural household in India is low, however, he did not considered
the internal rationing as a hindrance to household participation in the credit market. (Barham
et al., 1996) identify internal rationing in Guatemala is more serious than external rationing.
(Bashir & Azeem, 2008) in Pakistan have suggested that the main constraints farmers always
face are the loan procedure and delay in loan provisions which is normally similar to internal
rationing. (Fenwick & Lyne, 1998) also showed that there is a significant relationship between
high transaction cost and farm households’ likelihood to borrow. (Nardi, 2006) argues that the
demand side constraint is as serious as the supply side constraint in China. (Boucher, Carter, &
Guirkinger, 2008) found that risk rationing account for 20% to 40% of non-price rationing
cases in Peru, Honduras and Nicaragua.
Causes of credit constraint
(D. M. Jaffee & Modigliani, 1969; D. Jaffee & Stiglitz, 2000; J. E. Stiglitz & Weiss, 1981) studies
shows that in a world of imperfect information, when there’s excessive demand of credit over
supply by the borrowers, financial institutions will be reluctant to increase their interest rate
from the currently prevailing interest rate that may lead to equating demand and supply hence
leading to increase in loan defaults as a results of adverse selection and moral hazard behavior.
Maintaining the prevailing interest rate excludes lots of risk-adverse borrowers who mainly
invest in safe but low return projects leading to a pool of borrowers becoming worse off (J. E.
Stiglitz & Weiss, 1981). Higher interest rate may also encourages borrowers to invest in high
risk projects leading to high probability of defaults (J. E. Stiglitz & Weiss, 1981). As a result, at
the prevailing interest rate, some borrowers are excluded by the financial institutions hence
credit constraint.
(Azzi, Cox, Azzi, & Cox, 1976) studies shows that borrowers are credit constrained if they are
unable to meet the amount of not only interest rate but also the amount of collateral and equity
required by the bank as a requirement for loan access. Since the amount of credit demanded by
Page 6 of 23
420
Advances in Social Sciences Research Journal (ASSRJ) Vol. 8, Issue 4, April-2021
Services for Science and Education – United Kingdom
borrowers is a function of interest rate, collateral, and equity terms of the loan contract, lots of
rural households are unable to meet these requirements hence making them unable to access
credit from financial institutions.
In rural area, information asymmetry as well as high transaction cost and high risk always
prevent formal financial institutions from providing credit to the market (Meyer & Nagarajan,
2000; Petrick, 2005). Geographical dispersal and small loan size are the causes of high
transaction cost. (Hoff & Stiglitz, 1990; Meyer & Nagarajan, 2000) shows that due to
idiosyncratic shocks in agricultural production, inadequate collaterals, and the absence of
insurance market in rural area led to bank being reluctant to lend to rural households. Although
subsidized credit in developing countries aims at relaxing credit constraints for rural farm
households, it is blamed to favor better off farmers because high cost of obtaining information
from small-scale borrowers discourages banks to offer loan to them (Carter, 1988; Conning &
Udry, 2007).
Methods to identify and measure credit constraint
(Diagne, Zeller, & Sharma, 2001) identifies two methods of measuring credit constraint as
indirect method and direct method. Indirect method tries to detect credit constrained
households through tests of violation of life-cycle/permanent income hypothesis. Empirical
evidence from this methodology regarding presence or absence of credit constraint has been
inconclusive mostly because violation of the implication of life-cycle/permanent income
hypothesis is neither a sufficient nor necessary condition for being credit constrained (Kimball,
1990; Zeldes, 1989b). The second method for detecting the presence of credit constraint uses
information gained directly from the household members on their participation and
experiences in the credit market to determine if they are credit constrained. In practice,
households are classified as credit constrained base on their response to several qualitative
questions regarding loan applications and rejections during a given recall period. This
classification is then used in reduced-form regression equations to analyze the determinants of
the likelihood of a household being credit constrained and the effect of this likelihood on
various household outcomes (Buchenrieder & Heidhues, 1995; Feder, Lau, Lin, & Luo, 1990;
Jappelli, 1990; Zeller, 1994).
However, despite Direct method representing a substantial improvement in comparison to the
indirect method, it is still incapable of providing the framework that allows one to quantify the
extent to which households are credit constrained or to satisfactory assess the impact of access
to credit on household welfare outcomes. Therefore, In order To correct the short coming of
direct method, a conceptual frame work is develops in the next section of methodology which
aims to measure the extent to which households are credit constrained. Methods to identify
credit constrained households will be discussed in greater detail in section 3.
Impact of credit constraints on rural households
Household productivity and welfares are all back up by the availability of credit in the hand of
rural households. But due to the obstacle faced during the borrowing of credit from formal
financial institution, they are always credit constrained, hence unable to satisfy their
productivity and welfare needs such as health expenditure, input purchase, education
expenditure etc. (Guirkinger & Boucher, 2008) studies shows that productivity is independent
of endowments for unconstrained households, but is tightly linked to endowments for credit
Page 9 of 23
423
Orach, H., Pu, C., Qianling, S., Shiying, W., Ssewajje, H., & Wigmore, R. (2021). The Effect of Financial Constraint on Farmers’ Health: Evidence from
Rural China. Advances in Social Sciences Research Journal, 8(4). 415-437.
URL: http://dx.doi.org/10.14738/assrj.84.10031
Rural household members were credit constrained (CC) if he/she was rejected loan requests
from bank or other financial institution. Or partly credit constrained (partly CC) if he/she was
partially approved credit request. When rural household members obtained total loan amount
approved, or he/she was not interested in loan application. We classify those individual
household members as credit unconstrained (No CC).
One difficulty of measuring credit constraint, is finding the ratio of credit constraint of rural
households. For instance, credit limits may not be determined by the amount of loan from the
formal financial institution. Only because, households that did not borrow do not mean they are
credit unconstrained. These households may be latent credit constrained with the greatest
demand for formal credit. The current study attempt to identify credit constrained households
by the way of direct elicitation following Boucher et al. (2009), Huang and Liu (2007), Cheng
and Luo (2010) studies. Figure2 present a conceptual framework that we used to identify credit
constraint through a series of questions
Descriptive evidence
Data source: Authors’ own computation based on own survey data.
Figure1 presents the distribution of the self-reported health status among China rural
household members measured in five categories as Dependent variable. As seen from the
Figure, 31% of households’ member with excellent health status , 46% with Good health status,
17% with Average health status, 6% with poor health status, and 1% with very poor health
status. Most correspondents’ responses are “Good”, with their health status. The respondents,
“Average”, and “poor”, and “very poor” account for 23%, while the other respondents- Excellent
Good
Average
Poor
Very poor 30.52%
45.69%
16.88%
5.526%
1.369%
(Rural Household Self-reported Health Status Respondents)
FIGURE 1 Distribution of Self-Reported Health Status
Page 10 of 23
424
Advances in Social Sciences Research Journal (ASSRJ) Vol. 8, Issue 4, April-2021
Services for Science and Education – United Kingdom
“Excellent” and “Good” account for only 77%. The above result still indicate that the rural
household member’s health status still need to be accounted for by the policy maker.
Table1 presents the basic statistics of the key variables used in our analysis. The descriptive
evidence show 28% of rural household member were credit rationed. The household head of
rural household member in our sample is a middle-aged male with an average level of education
around 7 years. The household member is endowed with more than 4 laborers, and more than
62% of the house hold members are married. Only 5% of rural household members in our
sample were members of various political parties. 42% of the household members were
firstborn, and 8% of the members were minority in our sample. Financial assets of the
households account for only 8% and household debts were above 15000 Yuan.
A simple cross-tabulation of the key variables in table1 allows us to observe the household
characteristics in response to the outcome of the health status of household farmers. A few
interesting findings are worth highlighting here: first we note that the heads’ level of education
is low (8years). Few household members were in any political affiliation. The debts level owed
by household members from various credit sources is so high. The number of household size is
low which contribute to lesser farm productivity and output. The households’ financial assets
and disposable income is also low. All this household members characteristics contribute
towards the higher credit rationing on farmers, which in turn hinder them from having good
health status. To rigorously verify this possible causal relationship, we will rely on the ordered
probit model analysis the results of which are presented in the following section.
Page 11 of 23
425
Orach, H., Pu, C., Qianling, S., Shiying, W., Ssewajje, H., & Wigmore, R. (2021). The Effect of Financial Constraint on Farmers’ Health: Evidence from
Rural China. Advances in Social Sciences Research Journal, 8(4). 415-437.
URL: http://dx.doi.org/10.14738/assrj.84.10031
Table1
Descriptive statistics of Sample Households
Variables Description
Observation
Mean Std.
Dev. Min
Max
Dependent variable
Health Health
status(1=excellent,
2=good, 3=average,
4=poor, 5=very poor)
38924 2.015 0.905 0 5
Explanatory variables
Rationing Whether household have
been
rationed(1=rationed, 0
otherwise)
39065 0.282 0.45 0 1
Gender Gender of households
head(1=male, 0
otherwise)
39063 0.521 0.5 0 1
Age Age of household
head(years)
39062 38.517 20.206 0 105
Age2 Age of household head
squared(years)
39062 1891.782 1635.563 0 11025
Married Marital status of
household
head(1=married, 0
otherwise)
39001 0.623 0.485 0 1
Education Education level of
household head(years of
schooling)
35750 7.417 3.504 0 21
Political Whether members are in
any political
affiliation(1=communist
party
38767 0.048 0.214 0 1
Pension Whether household head
has pension(1=pension, 0
otherwise)
38375 0.689 0.463 0 1
Firstborn Whether household
member is
firstborn(1=firstborn, 0
otherwise)
39065 0.422 0.494 0 1
Minority Whether household
members are minority
(1=minority 0 otherwise)
39044 0.079 0.27 0 1
Income Total Household
members disposable
income(yuan)
38896 50003.16 47178.94 600 1600000
Finance Total balance of
household financial
assets(yuan)
39065 7.863 2.587 0 14.153
Debt Debts being owed by the
households members
24833 14824.86 46746.67 0 900000
Household size Numbers of
household/family
members
39065 4.363 1.487 1 13
Data source: Authors’ own computation based on own survey data.
Page 12 of 23
426
Advances in Social Sciences Research Journal (ASSRJ) Vol. 8, Issue 4, April-2021
Services for Science and Education – United Kingdom
Figure 2: A conceptual framework of rural household members’ credit constraints. The
researchers define Credit Constraint (CC) as a rejection of a loan request from the Bank or
other financial institution, Partly Credit Constrained (Partly CC) as a partially approved credit
request. And No Credit Constraints if the rural household obtained the full loan or did not apply
for a loan, we classify those individual as not suffering from credit constraint (No CC).
METHODOLOGY
Ordered probit model
As previously mentioned, the main objective is to examine the causal effect of credit rationing
on rural household members’ health status. Since health status variable is an ordered variable,
this study employs the ordered probit method to explore the impact of credit constraints on
Chinese rural household members’ health status in China, as shown below.
(1) HS C X i k i i i i , 1,2,3,4,......, * =a + b +e =
Have you or any member of your household applied for loan?
Yes No
Was your loan request approved? Didn’t you need a loan?
Yes No Yes No
Did you obtain full loan approval? Did you obtained partial loan
approval
Why you did not need a
loan?
Why you did need a
loan
Yes No Yes No Have enough credit Have insufficient credit
No CC Partly CC Partly CC CC No CC CC
Page 13 of 23
427
Orach, H., Pu, C., Qianling, S., Shiying, W., Ssewajje, H., & Wigmore, R. (2021). The Effect of Financial Constraint on Farmers’ Health: Evidence from
Rural China. Advances in Social Sciences Research Journal, 8(4). 415-437.
URL: http://dx.doi.org/10.14738/assrj.84.10031
(2)
Here, refers to the health status of the respondents and denotes the latent variable of
health status in the equation. 1. The key explanatory variable, is a binary variable that
indicates whether or not any of the households’ rural members have been credit rationed. is
the coefficient of interest, which shows the estimated effects of credit rationing on rural farmer
household members is a vector of demographic and socioeconomic variable. The main
demographic factors we control for are age, marital status, and gender. Controlled
Socioeconomic factors include the educational level of the individual household members,
household level factors such as household disposable income, household finance, household
debts, whether or not the households face credit constraints, whether or not the household
head is male, and the number of household members. Is a normally distributed error term in
the equation and denotes an individual respondent, are the thresholds (cut-off
points) to be estimated with the restriction of . The base model of the ordered
probit estimates of the determinant of health status are presented in Table3, which indicate,
not surprisingly, that credit rationing negatively affect health status of rural farmer household
members. We also report OLS estimates for comparison purposes.
Endogeneity and instrumental variables
One of the problems with modelling health status is the inherent endogeneity, which is likely to
make our estimates biased. Endogeneity might occur for two reasons: First, as a result of
variables omitted from the model on the reverse causality problem (Wearing & Fernandez,
2015). At provincial, household, and individual level, the health status can also be affected by
some other omitted variables. For example, individual, genetic illness could make the rural
farmers sick. Furthermore, some exposure or environmental factors such as stress, exposure to
toxins, pathogens, radiation, and chemical found in almost all personal care products are all
possible cause of genetic illness. Variations in economic activities or how much a micro-credit
coverage a person receive at provincial and regional levels can also influence both credit
constraints and a person’s health status. Additionally, there could be household level variations
that we were not able to capture in our variables, as they are not easy to measure. Therefore,
when conducting our analysis, we corrected them (omitted variables) by including household
and provincial level fixed effects, which will capture the impact of household and provincial
factors that are not explicitly controlled for in our model. Thus, we included an estimates of
provincial fixed effects to determine any bias found in the omitted variables, as well as the
household fixed effects which will control for time invariant household specific factors.
Secondly, a cause of endogeneity is likely to be reverse causality from health status to credit
rationing. Due to the potential endogeneity of credit rationing and health status, we included
the instrumental variable in the ordered probit model (IV ordered probit) for our analysis. We
adopted the variable of ‘average number of rural household members rationed’ as our
ï
ï
ï
î
ï
ï
ï
í
ì
<
< £
< £
< £
£
=
*
*
*
*
*
i
i
i
i
i
i
if r HS
if r HS r
if r HS r
if r HS r
if HS r
HS
5,
4,
3,
2,
1,
4
3 4
2 3
1 2
1
HSi
* HSi
Ci
a
Xi
i e
i r ( j =1,2,3,4) j
1 2 3 4 r < r < r < r
Page 17 of 23
431
Orach, H., Pu, C., Qianling, S., Shiying, W., Ssewajje, H., & Wigmore, R. (2021). The Effect of Financial Constraint on Farmers’ Health: Evidence from
Rural China. Advances in Social Sciences Research Journal, 8(4). 415-437.
URL: http://dx.doi.org/10.14738/assrj.84.10031
whole sample which is shown in the Table4 below. We found evidence of a significant
relationship between credit rationing and health status when controlling for the endogeneity
using the IV ordered probit in Column 4 and IV 2sls for OLS in Column 6. The self-reported
health status was defined as ‘1=Excellent’, ‘2=Good’, ‘3=Average’, ‘4=Poor’, ‘5=Very Poor’, thus,
a negative coefficient of credit rationing indicate a negative effect on the health status of
farmers. As indicated in Column 4 and 6, credit rationing significantly reduced the health status
of the rural household farmers in China (coefficient -0.7367, P<0.05, and coefficient -0.5034,
P<0.005). The estimated results of other control variables were not in general, unexpected. The
variable of age, marital status and education all played an important role in determining health
status. Household health status deteriorated with age, while it improve with educational
attainment and marital status. Being in a political party, being in a high income bracket and a
large amount of financial assets also significantly improve farmers health status. Finally, having
a greater number of family member, the more likely was that health status could be improved.
Page 18 of 23
432
Advances in Social Sciences Research Journal (ASSRJ) Vol. 8, Issue 4, April-2021
Services for Science and Education – United Kingdom
Table 4
Ordered probit, and IV estimates of the determinants of health status
(1) (2) (4) (5) (6)
IV Ordered probit 2SLS
VARIABLES Oprobit 1st Stage 2nd Stage 1st Stage 2nd Stage
Rationing -0.2489*** -0.7367*** -0.5034***
(0.0339) (0.0417) (0.0300)
Average rationing 3.0369*** 3.0369***
(0.0537) (0.0537)
Gender 0.0723*** 0.0255 0.0771*** 0.0255 0.0584***
(0.0106) (0.0155) (0.0120) (0.0155) (0.0086)
Age -0.0174*** 0.0086*** -0.0152*** 0.0086*** -0.0075***
(0.0027) (0.0025) (0.0019) (0.0025) (0.0014)
c.age#c.age -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0002***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Married 0.1016*** -0.0816*** 0.0857*** -0.0816*** 0.0806***
(0.0233) (0.0235) (0.0184) (0.0235) (0.0132)
Education 0.0349*** -0.0150*** 0.0317*** -0.0150*** 0.0226***
(0.0036) (0.0026) (0.0021) (0.0026) (0.0015)
Political 0.0729** -0.0979*** 0.0673** -0.0979*** 0.0537***
(0.0312) (0.0366) (0.0274) (0.0366) (0.0197)
Pension 0.0777** 0.0001 0.0724*** 0.0001 0.0472***
(0.0338) (0.0196) (0.0153) (0.0196) (0.0109)
Firstborn 0.0060 -0.0138 0.0058 -0.0138 0.0013
(0.0142) (0.0159) (0.0123) (0.0159) (0.0088)
Minority -0.0210 -0.0010 -0.0246 -0.0010 -0.0147
(0.0885) (0.0277) (0.0222) (0.0277) (0.0160)
Income2013 0.1550*** -0.0564*** 0.1390*** -0.0564*** 0.1034***
(0.0235) (0.0112) (0.0088) (0.0112) (0.0063)
Finance 0.0223*** -0.0350*** 0.0131*** -0.0350*** 0.0107***
(0.0072) (0.0032) (0.0026) (0.0032) (0.0019)
Debt -0.0080* 0.0680*** 0.0037* 0.0680*** 0.0006
(0.0042) (0.0021) (0.0020) (0.0021) (0.0015)
Household size 0.0153 -0.0034 0.0160*** -0.0034 0.0126***
(0.0130) (0.0057) (0.0044) (0.0057) (0.0032)
/cut1 -1.3010*** -1.6526***
(0.2386) (0.0959)
/cut2 -0.4434* -0.7993***
(0.2343) (0.0947)
/cut3 0.5155** 0.1575*
(0.2368) (0.0946)
/cut4 1.9328*** 1.5749***
(0.2388) (0.0948)
Constant -0.7173*** -0.7173*** 3.1998***
(0.1184) (0.1184) (0.0678)
Observations 34,972 35,003 34,972 35,003 34,972
R-squared 0.2235
Note: (1) Robust standard errors in parentheses (2) *** p<0.01, ** p<0.05, * p<0.1 (3)
Page 20 of 23
434
Advances in Social Sciences Research Journal (ASSRJ) Vol. 8, Issue 4, April-2021
Services for Science and Education – United Kingdom
being credit rationed. While, 29% and 52% are more likely to be in the excellent and good
health categories after they have been credit rationed. Overall, the negative significance effect
of credit rationing on the health of rural household farmers can results in heavy social and
economic burdens upon themselves, their families, and even their community as a whole. Thus,
the Department of Health, the Department of finance, the Department of education, as well as
the regional, and local governments should seriously consider removing these obstacles
financial obstacles. These institutions should also send a clear message of their commitment to
these efforts by introducing policy changes at local and national level, such as creation and
promotion of financial programs and institutions that are more supportive of rural household
farmers. Furthermore, institutions that offer financial services in specifically rural regions
would be greatly beneficial. Institutions include State banks, cooperatives, and any other
microfinance institutions that have recently emerged. Research regarding the negative impact
of credit constraints on rural farmer’s health should also be presented to the directors and staff
of financial institutions, and especially their credit officers, so that they are seen as credit
worthy and treated accordingly. This can also be done by conducting an analysis of the
institutions’ portfolios and assessing the quality of their loans; what percentage of their loans
are given to rural farmers, What are their repayment rates, and if the characteristics of those
loans differs between the rural household farmers, entrepreneurs etc., Some institutions can
use their IT systems to complete such an analyses with their computerized information
systems. However, others would need to use verbal reports from their credit officers. This could
help increase concerns for farmers and increase dialogue in regards to how to help farmers
amongst financial institution staffs.
Financial organizations that are more understanding of the impact of credit constraints on rural
household will need to conduct thorough reviews of the financial products that they currently
offer and of the processes needed to apply, receive, and repay loans. They will also need to
identify the steps and requirements during the loan process that directly or indirectly reduce
opportunities for rural farmers to receive credit. If there are currently no suitable products they
should consider creating financial products tailored to the needs of household farmer’s needs
or create incentives that encourage staff to reach out to rural farmers. Taking such measure will
enhance the credibility of the organizations’ intent to reach out to rural farmers, which will
provide them with a more in-depth understanding of the way current constraints operate, and
will offer workable insights into how these constraints can be overcome.
Before we conclude, it is worth reiterating some of the limitations of our analysis.
(1) These findings only apply to agricultural households and cannot be generalized to
households outside the scope of agricultural sectors where production and credit relationships
between the members may be different. (2) Our analysis is based on a survey rather than
experimental data. Because of the absence of a randomized control trial design, we have
followed the methodological literature on non-experimental database evaluation and used the
IV approach. The findings presented therefore should be revisited in the context of exogenously
assigned programs on credit rationing. (3) The CHIP data was a cross-sectional dataset, and the
question concerning respondents’ health status was only collected in waves between 2013 and
2014. Thus, we could not control for the time-variant individual heterogeneity using the fixed
effect. Second, we were not able to examine the credit rationing effect on health status across
Page 21 of 23
435
Orach, H., Pu, C., Qianling, S., Shiying, W., Ssewajje, H., & Wigmore, R. (2021). The Effect of Financial Constraint on Farmers’ Health: Evidence from
Rural China. Advances in Social Sciences Research Journal, 8(4). 415-437.
URL: http://dx.doi.org/10.14738/assrj.84.10031
genders and rationing types due to the data limitations. Future studies could explore these
issues when there is more information available.
Reference
Azzi, C. F., Cox, J. C., Azzi, B. C. F., & Cox, J. C. (1976). A Theory and Test of Credit Rationing : Comment A Theory
and Test of Credit Rationing : Comment, 66(5).
Barham, B. L., Boucher, S., & Carter, M. R. (1996). Credit constraints, credit unions, and small-scale producers in
Guatemala. World Development, 24(5), 793–806. https://doi.org/https://doi.org/10.1016/0305-
750X(96)00001-0
Bashir, M. K., & Azeem, M. M. (2008). Life and Social Sciences Agricultural Credit in Pakistan : Constraints and
Options, 6, 47–49.
Binswanger, H., & Rosenzweig, M. (1993). Wealth, Weather Risk and the Composition and Profitability of
Agricultural Investments. Economic Journal, 103, 56–78. https://doi.org/10.2307/2234337
Boucher, S. R., Carter, M. R., & Guirkinger, C. (2008). Risk Rationing and Wealth Effects in Credit Markets: Theory
and Implications for Agricultural Development. American Journal of Agricultural Economics, 90(2), 409–423.
https://doi.org/10.1111/j.1467-8276.2007.01116.x
Buchenrieder, G., & Heidhues, F. (1995). Reaching the poor through financial innovations, 34, 132–148.
Carter, M. R. (1988). Equilibrium credit rationing of small farm agriculture. Journal of Development Economics,
28(1), 83–103. https://doi.org/https://doi.org/10.1016/0304-3878(88)90015-6
Cheung, D., & Padieu, Y. (2015). Heterogeneity of the Effects of Health Insurance on Household Savings: Evidence
from Rural China. World Development, 66, 84–103. https://doi.org/10.1016/j.worlddev.2014.08.004
Conning, J., & Udry, C. (2005). Chapter 56 Rural Financial Markets in Developing Countries. Handbook of
Agricultural Economics, 3, 2857–2908. https://doi.org/10.1016/S1574-0072(06)03056-8
Conning, J., & Udry, C. (2007). Rural Financial Markets in Developing Countries. In R. Evenson & P. Pingali (Eds.)
(Vol. 3, pp. 2857–2908). Elsevier.
Diagne, A., Zeller, M., & Sharma, M. (2000). Empirical Measuremnets of Households’ Access to Credit and Credit
Constraints in Developing Contires: Methodological Issues and Evidence. Food Consumption and Nutrition
Division. International Food Policy Research Institute, 90(90), 1–73.
Diagne, A., Zeller, M., & Sharma, M. (2001). Empirical Measurements Of Households’ Access To Credit And Credit
Constraints In Developing Countries: Methodological Issues And Evidence.
Dong, F., Lu, J., & Featherstone, A. (2012). Effects of Credit Constraints on Household Productivity in Rural China.
Agricultural Finance Review, 72, 402–415. https://doi.org/10.1108/00021461211277259
Dong, F., Lu, J., & Featherstone, A. M. (2010). Effects of Credit Constraints on Productivity and Rural Household
Income in China. Working Paper 10-WP 516, (October).
Feder, G., Lau, L. J., Lin, J., & Luo, X. (1990). The Relationship between Credit and Productivity in Chinese
Agriculture: A Microeconomic Model of Disequilibrium. American Journal of Agricultural Economics, 72(5), 1151–
1157.
Fenwick, L. J., & Lyne, M. C. (1998). FACTORS INFLUENCING INTERNAL AND EXTERNAL CREDIT RATIONING
AMONG SMALL-SCALE FARM HOUSEHOLDS IN KWAZULU-NATAL. Agrekon, 37(4), 495–505.
https://doi.org/10.1080/03031853.1998.9523524
Freeman, H. A., Ehui, S. K., & Jabbar, M. (1998). Credit constraints and smallholder dairy production in the East
African highlands: application of a switching regression model. Agricultural Economics, 19(1-2), 33–44.
Grossman, M. (1972). On the Concept of Health Capital and the Demand for Health. Journal of Political Economy,
80(2), 223–255. https://doi.org/10.1086/259880
Guirkinger, C., & Boucher, S. (2008). Credit constraints and productivity in Peruvian agriculture. Agricultural
Economics, 39, 295–308. https://doi.org/10.1111/j.1574-0862.2008.00334.x
Page 22 of 23
436
Advances in Social Sciences Research Journal (ASSRJ) Vol. 8, Issue 4, April-2021
Services for Science and Education – United Kingdom
Gurmessa, N. E., Ababa, A., & Ndinda, C. (2017). Smallholder s ’ Access to a nd Demand for Credit and Influencing
Factors : Policy and Research Implications for Ethiopia, 4(3), 48–60.
Heckman, J. (1978). Dummy Endogenous Variables in a Simultaneous Equation System. Econometrica, 46(4),
931–959. Retrieved from https://econpapers.repec.org/RePEc:ecm:emetrp:v:46:y:1978:i:4:p:931-59
Hille, E., & Möbius, P. (2019). Do energy prices affect employment? Decomposed international evidence. Journal
of Environmental Economics and Management, 96(C), 1–21.
Hoff, K., & Stiglitz, J. E. (1990). FILE rnv, 235–250.
Jaffee, D. M., & Modigliani, F. (1969). A Theory and Test of Credit Rationing. American Economic Review, 59(5),
850–872.
Jaffee, D., & Stiglitz, J. (2000). Credit rationing. Handbook of Monetary Economics, II.
Jappelli, T. (1990). Who is Credit Constrained in the U. S. Economy? The Quarterly Journal of Economics, 105(1),
219–234.
Jia, X. (2010). Credit Rationing and Institutional Constraint : An Evidence from Rural China Xiangping Jia, 1–110.
Khanam, R., Nghiem, S., & Connelly, L. (2009). Child Health and the Income Gradient: Evidence from Australia.
Journal of Health Economics, 28, 805–817. https://doi.org/10.1016/j.jhealeco.2009.05.001
Kimball, M. (1990). Precautionary Saving in the Small and in the Large. Econometrica, 58(1), 53–73.
Kochar, A. (1997). An empirical investigation of rationing constraints in rural credit markets in India. Journal of
Development Economics, 53(2), 339–371. https://doi.org/https://doi.org/10.1016/S0304-3878(97)00020-5
Kumar, C. S., Turvey, C., & Kropp, J. D. (2013). The Impact of Credit Constraints on Farm Households: Survey
Results from India and China. Applied Economic Perspectives and Policy, 35(3), 508–527.
Li, R., Li, Q., Huang, S., & Zhu, X. (2013). The credit rationing of Chinese rural households and its welfare loss: An
investigation based on panel data. China Economic Review, 26(C), 17–27.
Li, R., & Xi, Z. (2010). Econometric analysis of credit constraints of chinese rural households and welfare loss.
Applied Economics, 42, 1615–1625. https://doi.org/10.1080/00036840701721604
Lin, L., Wang, W., Gan, C., & Nguyen, Q. (2019). Credit Constraints on Farm Household Welfare in Rural China:
Evidence from Fujian Province. Sustainability, 11, 1–19. https://doi.org/10.3390/su11113221
Lindeboom, M., & Kerkhofs, M. (2009). Health and Work of the Elderly _ Subjective Health Measures, Reporting
Errors and Endogeneity in the Relationship between Health and Work. Journal of Applied Econometrics, 24,
1024–1046. https://doi.org/10.1002/jae.1077
Loyalka, P., Huang, X., Zhang, L., Wei, J., Yi, H., Song, Y., ... Chu, J. (2015). The Impact of Vocational Schooling on
Human Capital Development in Developing Countries: Evidence from China. The World Bank Economic Review,
30, lhv050. https://doi.org/10.1093/wber/lhv050
Meyer, R., & Nagarajan, G. (2000). RURAL FINANCIAL MARKETS IN ASIA: FLAGSHIPS AND FAILURES.
Nardi, D. (2006). Entrepreneurship and Credit Constraints : Evidence from Rural Households in China, (55), 1–
11.
Newey, W. K. (1987). Efficient estimation of limited dependent variable models with endogenous explanatory
variables. Journal of Econometrics, 36(3), 231–250. https://doi.org/https://doi.org/10.1016/0304-
4076(87)90001-7
Osterreich, S. (2002). China’s Retreat from Equality: Income Distribution and Economic Transition: Carl Riskin,
Zhao Renwei, and Li Shi, editors; Armonk, New York, M.E. Sharpe Inc., 2001, 376 pages, $32.95, ISBN 0-7656-
0691-7. Journal of Asian Economics, 13(4), 565–567. Retrieved from
https://econpapers.repec.org/RePEc:eee:asieco:v:13:y:2002:i:4:p:565-567
Petrick, M. (2005). Empirical measurement of credit rationing in agriculture: A methodological survey.
Agricultural Economics, 33, 191–203. https://doi.org/10.1111/j.1574-0862.2005.00384.x
Page 23 of 23
437
Orach, H., Pu, C., Qianling, S., Shiying, W., Ssewajje, H., & Wigmore, R. (2021). The Effect of Financial Constraint on Farmers’ Health: Evidence from
Rural China. Advances in Social Sciences Research Journal, 8(4). 415-437.
URL: http://dx.doi.org/10.14738/assrj.84.10031
Phimister, E. (1995). Farm Consumption Behavior in the Presence of Uncertainty and Restrictions on Credit.
American Journal of Agricultural Economics, 77(4), 952–959.
Preston, S. H. (2007). The changing relation between mortality and level of economic development. International
Journal of Epidemiology, 36(3), 484–490. https://doi.org/10.1093/ije/dym075
Stiglitz, J. E., & Weiss, A. (1981). Credit Rationing in Markets with Imperfect Information. The American Economic
Review, 71(3), 393–410.
Stiglitz, J., & Hoff, K. (1990). Introduction: Imperfect Information and Rural Credit Markets-Puzzles and Policy
Perspectives. In “The Symposium Issue on Imperfect Information and Rural Credit Markets.” World Bank
Economic Review, 4, 235–250. https://doi.org/10.1093/wber/4.3.235
Tang, S., & Guo, S. (2017). Formal and informal credit markets and rural credit demand in China. 4th
International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017.
https://doi.org/10.1109/IEIS.2017.8078663
Tran, H. (2017). Credit constraint and household income: A quantile analysis approach. Economic Annals-ХХI,
164, 49–52. https://doi.org/10.21003/ea.V164-11
Wang, H., Zhao, Q., Bai, Y., Zhang, L., & Yu, X. (2020). Poverty and Subjective Poverty in Rural China. Social
Indicators Research. https://doi.org/10.1007/s11205-020-02303-0
Wearing, M., & Fernandez, E. (2015). Wearing, M. and Fernandez, E. (2015) Why are Poor Children Always With
Us? Theory, Ideology and Policy for understanding Child Poverty in Fernandez et al (Eds) Theoretical and
Empirical Insights into Child and Family Poverty: cross national perspectives, New York, Springer, pp71-96.
https://doi.org/10.1007/978-3-319-17506-5_5
Weerasuriya, K., & Goonaratne, C. (1992). Rationing in developing countries. Bmj, 304(6839), 1440–1440.
https://doi.org/10.1136/bmj.304.6839.1440
Whyte, M. K. (1994). The Distribution of Income in China. Edited by Keith Griffin and Zhao Renwei. [New York: St
Martin’s Press, 1993. 359 pp. $47.50. ISBN 0-312-10022-1.]. The China Quarterly, 140, 1176–1179.
https://doi.org/DOI: 10.1017/S0305741000053194
Winter-Nelson, A., & Temu, A. (2005). Liquidity constraints, access to credit and pro-poor growth in rural
Tanzania. Journal of International Development, 17, 867–882. https://doi.org/10.1002/jid.1175
Zeldes, S. (1989a). Consumption and Liquidity Constraints: An Empirical Investigation. Journal of Political
Economy, 97(2), 305–346.
Zeldes, S. (1989b). Optimal Consumption with Stochastic Income: Deviations from Certainty Equivalence. The
Quarterly Journal of Economics, 104(2), 275–298.
Zeller, M. (1994). Determinants of credit rationing: A study of informal lenders and formal credit groups in
Madagascar. World Development, 22(12), 1895–1907.
Zhang, Z., Uddin, M. J., Cheng, J., & Huang, T. (2018). Instrumental variable analysis in the presence of
unmeasured confounding. Annals of Translational Medicine, 6(10), 182.
https://doi.org/10.21037/atm.2018.03.37