in the aftermath of risks
TRANSCRIPT
MSc Thesis Marije Rothuizen
IN THE AFTERMATH OF RISKS COPING WITH RISKS BY MEANS OF CREDIT IN MEXICO
October 2010
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MSc Thesis Development Economics DEC-80433
In the Aftermath of Risks
Risk Coping by Means of Credit in Mexico
Master Thesis for the chair group Development Economics,
submitted for partial fulfilment of the requirements of the
Master of Science degree in
International Development Studies
At Wageningen University,
The Netherlands
Marije Rothuizen
Supervisors:
Dr. Ir. Marrit van den Berg
Development Economics Group
Wageningen University
Msc Roselia Servin
PhD candidate Development Economics Group
Wageningen University
© Copy or recitation of this thesis is only allowed with permission of the supervisors.
IV
List of Figures and Tables
Figure or Table Name Page
Figure 1 Map of Mexico and its states Page 11
Table 1 Summary of variables used Page 20
Table 2 Explanatory Variables and Expected Causality Page 23/24
Table 3 Experienced shocks in 2004 and 2005 Page 27
Table 4 Total reported shocks per region and quantiles of land
(rural) and assets (urban)
Page 28
Table 5 Formal and Informal Credit Page 29
Table 6 Use of formal and informal credit as a coping strategy Page 30
Table 7 Simultaneous shocks and coping by means of formal and
informal credit
Page 31
Table 8 Estimation Results on the Probability of using Formal
Credit as Coping Strategy
Page 32/33
Table 9 Estimation Results on the Probability of using Informal
Credit as Coping Strategy
Page 35/36
List of Abbreviations
BANRURAL Public Agricultural Bank
BANSEFI National Bank of Savings and Financial Services
CNVB National Commission of Banking and Values
CPI Consumer Price Index
CURP Unique Key of Register of Population
GDP Gross Domestic Product
HH Household
IFE Electoral Federal Identification
LPM Linear Probability Model
MFI Microfinance Institutions
NAFTA North American Free Trade Accord
NGO Non-governmental Organization
SACP Sector of Savings and Public Credit
SAGARPA Secretary of Agriculture, Livestock, Rural Development, Fisheries and
Food
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Table of Contents
1. Introduction .................................................................................................................................................... - 1 -
1.1 Background ............................................................................................................................................... - 1 -
1.2 Problem Statement .................................................................................................................................. - 2 -
1.3 Objective and Research Questions ........................................................................................................... - 3 -
1.4 Approach .................................................................................................................................................. - 3 -
1.5 Overview of Study .................................................................................................................................... - 4 -
2. Theoretical Framework ................................................................................................................................... - 5 -
2.1 Risk ........................................................................................................................................................... - 5 -
2.2 Risk Response Strategies by rural households ......................................................................................... - 6 -
2.3 Formal and Informal Credit as a risk coping strategy ............................................................................... - 8 -
3 Mexico ............................................................................................................................................................ - 11 -
3.1 Description of the context: Mexico ........................................................................................................ - 11 -
3.2 Formal credit in Mexico .......................................................................................................................... - 12 -
3.3 Informal Credit in Mexico ....................................................................................................................... - 14 -
4 Models and Data ............................................................................................................................................ - 15 -
4.1 Theoretical models ................................................................................................................................. - 15 -
4.2 The Data ................................................................................................................................................. - 18 -
4.3 Construction workable data set ............................................................................................................. - 19 -
4.4 Empirical Model...................................................................................................................................... - 21 -
4.5 Estimation methods ............................................................................................................................... - 25 -
5 Analysis........................................................................................................................................................... - 27 -
5.1 Household characteristics ...................................................................................................................... - 27 -
5.1 Shocks ..................................................................................................................................................... - 27 -
5.3 Formal and Informal Credit .................................................................................................................... - 29 -
5.4 Formal and Informal Credit used as coping strategy .............................................................................. - 30 -
5.5 Formal Credit used as a Coping Strategy ................................................................................................ - 31 -
5.6 Informal Credit used as Coping Strategy ................................................................................................ - 34 -
5.7 Difference between Formal and Informal Credit ................................................................................... - 37 -
6 Conclusions .................................................................................................................................................... - 39 -
7 Discussion ....................................................................................................................................................... - 42 -
Bibliography ...................................................................................................................................................... - 44 -
Appendices ........................................................................................................................................................ - 50 -
Appendix I : Calculation of Variables ............................................................................................................ - 50 -
Appendix II: Answers coping strategies ............................................................................................................ 52
Appendix III: Robustness Checks ...................................................................................................................... 53
Appendix IV: Output Formal Credit as Coping Strategy ................................................................................... 54
Appendix V: Output Informal Credit as Coping Strategy .................................................................................. 58
1. Introduction
his first chapter will introduce the topic of this master thesis by giving a clear
background and problem statement. It provides the main research questions as well as
the method to be used.
1.1 Background
Rural and urban households in developing countries face substantial risks, such as climate
risks, economic fluctuations and other (idiosyncratic or systematic) shocks, which make
households very vulnerable.
The rural households are mostly farmers, who either work in agriculture for their own food
or have cash crops as an income source. It is also possible that they have other income
sources next to farming. Either way rural households are very vulnerable to risks (e.g.
weather related events such as drought as well as health related events such as illness of a
household member) in which cases they would have to use coping strategies. Some
examples of these coping strategies include taking children out of their schools prematurely
to work as well as selling cattle in order to gain some money. However these measures cause
the rural households to lose investments (in human capital) and make them even more
vulnerable to new risks.
Saving to be able to manage risks is a widely used management strategy, however very
difficult to do for the poorest households. The existing theoretical saving models do not
always reflect on the poor, or at least no consensus exists on this. The question rises on how
the poorest of the poor can save. These households require financial intermediation, on a
micro-level, for example in precautionary savings. As Deaton (1990) already stated saving is
not only about accumulation, but about consumption smoothing in the face of volatile
incomes, and about providing insurance for poor people, whose lives are difficult and
uncertain. Economic theory would suggest that the combination of income volatility and
borrowing constraints make it therefore necessary for the poor to use their assets, both
liquid and semi-liquid, as a buffer against income shocks. However, this is not always
possible and the poor households would have to find other ways of coping with risks.
These other ways of handling risks with risk-management and risk-coping strategies include
different credit mechanisms. Although meant for investment purposes, formal loans or loans
in microfinance programs often also finance consumption because these funds are very
fungible. Unfortunately, in many developing economies a problem of weak financial
institutions and inefficient markets for credit is present. Morduch (1995) already points out
that when households in developing countries have access to financial institutions, they
often face borrowing constraints when times are bad and less when times are good. This
means that it might be more difficult to access (formal or informal) credit when times are
bad, for example in case of a natural disaster. Another reason why high risks, like natural
disasters, are not easily insured by formal market mechanisms is because credit and
insurance markets are typically absent or incomplete in developing countries.
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Next to using formal credit, informal credit is frequently used as well when coping with risk.
Informal credit markets appear to adjust the best to high-risk environments. Repayment is in
many cases conditional on income outcomes of both borrowers and lenders, with negative
shocks translated into more favorable terms for the party experiencing them (Dercon, 2002).
1.2 Problem Statement
Climate risks, economic fluctuation and a large number of other (idiosyncratic or systematic)
shocks make households very vulnerable. They are exposed to a variety of risks like drought,
flood and frost which may cause harvest failure as well as health related risks. Many studies
have reported high income variability related to risks. These risks cause consumption
shortfalls for households. The households have different types of strategies that they could
use to try to avoid the consumption shortfalls. The amount of empirical literature on this
topic is enormous, for example Townsend (1994) and Kinsey and others (1998) who mainly
use panel data studies.
Even though much literature can be found on management and coping strategies all over the
world, only few studies are found that deal with the different coping strategies within
Mexico. Eakin (2005) found out for example that rural households in the communities of
Jesu´s Nazareno, Los Torres and Plan de Ayala used different kinds of strategies when coping
with risk. The main ones include selling livestock, intensifying their participation in non-farm
activities, seeking financial support through informal loans, and, when possible, replanting
their fields. However no specific literature is found on the use of formal and informal credit
within Mexico as a coping strategy.
Formal credit from e.g. microfinance institutions is supposed to be for investment purposes,
however, these funds are fungible and are used for consumption smoothing in many cases,
but especially after a shock occurred. As is formal credit, informal credit is also used often
when coping with risks. Both could contribute to risk resilience of households after the
shock, or disaster has happened. As stated earlier, not much research on this has been done
yet or was insufficient due to varying measures and definitions, but also to the lack of
reliable household data. As stated already in Paxton (2009), in many cases the component of
informal credit is absent in country level statistics.
Then, this study aims to fill this gap by using a novel dataset that includes both informal and
formal credit as a mechanism to cope with risk variables on household level in Mexico.
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1.3 Objective and Research Questions
For this research a main as well as several sub research questions have been formulated. The
main research question of this thesis will be:
“What are the roles of formal and informal credit for Mexican households in order to cope
with risks?”
This question can be divided into several sub-questions:
- What are the different risks households have to deal with?
- How big is the amount of used formal and informal credit in Mexico?
- Which external factors might influence the use of formal credit among Mexican
households and how do these factors influence the use of formal credit as a coping
strategy?
- Which external factors might influence the use of informal credit among Mexican
households and how do these factors influence the use of informal credit as a coping
strategy?
- What is the difference in roles and importance of both formal and informal credit for
Mexican households when coping with risks?
1.4 Approach
The research designed to answer these research questions will not involve any fieldwork. It
consists out of an analysis of existing data, gathered by the National Bank of Savings and
Financial Services in Mexico. As for research that does use primary data, this research will
also be based on desk research, by studying different articles and literature. This will be
done to get a good insight in the background of the topic and to be able to base the
empirical model on clear theory.
Households’ ability to cope with risks by using formal and informal credit can be researched
using both quantitative, such as household surveys, and qualitative approaches, such as
participatory assessment. I don’t believe that only qualitative methods can be used to study
the risk coping strategies and vulnerability to risks. Although qualitative studies have a local
nature which will be able to provide a more detailed understanding it is difficult to compare
the results across areas. I think national household surveys, such as the one I will use for my
research, will give a better and more objective view on these issues.
Following the above framework, this study attempts to quantify the impact of formal and
informal credit on households in Mexico by using existing data. In doing so, a sample of
5,000 households is analyzed, 50% of which (treatment group) is working with formal
financial institutions and the rest (50%) works only with informal sources of finance
(moneylenders, relatives, friends, etc.). The data gathers more detailed information about
the financial behavior of cooperative member households as well as households that choose
not to or cannot access financial services from these institutions. This survey started in 2004
with financial support from the Mexican Secretary of Agriculture, Livestock, Rural
Development, Fisheries, and Food (SAGARPA) and the National Bank of Savings and Financial
Services (BANSEFI). The project targeted several Mexican states identified as having high
levels of marginalization.
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1.5 Overview of Study
After this introduction, theoretical consideration will be addressed in chapter two through a
literature review on risks, risk response mechanisms and formal and informal credit. Within
chapter three some studies will be analyzed in the context of Mexico and risk coping
strategies to get some insights on available works. Chapter four will explain the methodology
of the statistical analysis as well as the construction of the data set from the available data.
After this, in chapter five, the conducted analysis will be addressed. Chapter six offers
conclusions and finally, this thesis comes to a closure by means of a discussion chapter.
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2. Theoretical Framework
his chapter will explain some theoretical concepts, which are used for this research,
more in detail. This is done to understand the importance of risk of rural households
and their coping strategies. First of all the basic concept of risk will be clarified. Formal
and informal credit is one way of coping with risk. However there are many different kinds of
risk management and risk coping strategies, therefore these will be discussed before a
detailed description of both formal and informal credit will be given.
2.1 Risk
Risks occur in variable situations in which the outcome is uncertain. Main characteristics of
risks include that they are unpredictable; it is unknown whether a risk will occur, when it
occurs and how often it occurs; and that they always have a negative outcome, so they will
involve a loss (Hung, 2007).
Some other characteristics of risks (either natural, health or economic related) include the
frequency and intensity and the persistence of their impacts (Murdoch, 1995). For example,
smaller shocks, like temporarily illness, may occur more frequent but they are easier to deal
with than large shocks, like the death of a household member.
In general, there are three sources of risk (Sebstad, Jennifer and Cohen, 2000):
1. Structural factors such as seasonality, inflation, or the weather;
2. Unanticipated crises and emergencies such as sickness or death of a family member,
unemployment, fire, or theft;
3. High-cost, life cycle events such as marriage, funerals, childbirth, homebuilding,
festivals, and educating children.
Risks can be divided into common, or covariant or systematic, and idiosyncratic risks
(Dercon, 2002). Common risks affect all members of a community, which means they are
aggregate, economy-wide and covariate. Individual risks, on the other hand, also called
idiosyncratic risks, only affect a particular individual or household. In reality almost no risk is
either purely idiosyncratic or common. Idiosyncratic shocks can be dealt with, or insured,
within a community. Since everybody is affected in case of a common shock, this is not the
case for common shocks, because the risk cannot be shared. Common shocks can only be
dealt with by means of formal or informal transfers (credit, insurance, public transfers) from
outside of the community.
Some risks may also have a persistent effect, which make them more difficult to cope with.
Especially for poor households examples like losing a life and becoming severely ill are a
serious threat. In these cases the income of the whole household will be affected. And since
most poor households include farmers or laborers (selling one’s labor to another party by
engaging in waged labor), other risks like floods and pests are risks these households are
extra exposed to as well. This implies that rural households already live in environments
that are more risky and they are also more vulnerable to these risks due to the persistent
effect (Dercon, 2002).
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A basic understanding of change in assets over time is necessary to be able to understand
the lack of these assets for rural households. Assets can be classified into human, physical,
social, financial and natural capital (Pearce, 1993). The concept of human capital is related to
the available knowledge, working capacity, skills and health of people. Physical capital is
about the basic infrastructure and producer goods, which households use. Social capital can
be of great importance to rural households since this includes social networks, membership
of (in)formal groups, safety nets (non-contributory transfer programs seeking to prevent the
poor or those vulnerable to shocks and poverty from falling below a certain poverty level)
and relationships with friends and family. Next to savings, financial capital also includes
credits and remittances for rural households. Natural capital is very important for rural
households since it includes their land and trees, but also things that are more intangible like
the weather.
One can conclude from all above theory that several types of risks exist (Sebstad, Jennifer
and Cohen, 2000):
1. Covariant risks affect all households in the same location at the same time;
2. Idiosyncratic risks affect only one or a few households at the same time;
3. Low frequency risks are one-time events that do not repeat;
4. High frequency risks are those that happen repeatedly;
5. Anticipated risks are quite predictable and expected;
6. Unanticipated risks are not expected;
7. Low-impact risks result in temporary shortfalls in income;
8. Severe risks result in sharp and less reversible drops in income.
2.2 Risk Response Strategies by rural households
As argued above, rural households are more exposed to risks, which may imply that the
consequences of risks are bigger than for non-rural households. Next to a difference
between urban and rural households, a difference between poor and rich households also
occurs when coping with risks. Richer households can often rely on their savings, something
that the poor households often do not have, even when they do have the potential.
Vulnerability to poverty linked to risks is high, even though poor households do use risk
management and risk-coping strategies, which involve the ability of households to cope with
shocks (Jabeen, 2009). A number of studies have examined income and consumption
patterns in the risky environments of developing economies lacking formal safety nets
(Agarwal, 1991; Deaton, 1990; Kinsey et al., 1998; Alderman &Paxson, 1992,; Platteau, 1991;
Udry, 1994).
Some of these studies (Deaton, 1990; Kinsey et al., 1998; Alderman & Paxson, 1992) have
indicated that poor and rich households may respond differently to various shocks. Rich
households have better access to insurance and credit facilities and a larger asset base,
which may help protect them against the impact of an income shock (Jalan and Ravallion,
2001). Moreover it is shown that the size of the loss has influence on the ability to cope with
the risk. A household is better able to cope with a temporarily illness of a household
member than a severe drought (Brown & Churchill 2000).
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The used risk response strategies by households vary, based on the following characteristics
(Sebstad, Jennifer and Cohen, 2000):
• The timing of the strategy;
• The nature of the risk itself (severity and frequency);
• The impact of the strategy on the long-term well-being of the household;
• Whether the strategy involves just the household itself (intra-household measures)
or draws upon resources outside of the household (inter-household measures).
(Intra-household measures are referred to as self-insurance. Inter-household or
group-based measures include informal and formal insurance.)
As said before, households use different risk response strategies in order to cope with
shocks. These include both risk management strategies and risk coping strategies. This
categorization is based on the characteristic of the timing of the strategy. The difference
between these two is that risk management strategies work on the income process ex ante,
which basically means income smoothing, while risk coping strategies are ex-post and deal
with the consequences of income risk by smoothing of consumption.
Ex ante management strategies are used for future risks. Examples on how to achieve
income smoothing by means of ex ante management strategies include income
diversification (combining activities with low covariance) and income skewing (more low-risk
activities at low returns) (Dercon, 2002). Individual households also can take other
preventative actions to mitigate the impacts of shocks. The households try to prevent risks
by means of agricultural-based preventative measures, since the most significant risks facing
farmers are production-related (Sebstad, Jennifer and Cohen, 2000). Here one can think of
for example, building canals and dikes for irrigation, modifying crop and livestock activities in
response to market and price fluctuations and diversifying crops and income earning
activities.
Another ex-ante management strategy involves increasing the assets in times of a shock for
example by savings. Buffer stock saving behavior can arise from two distinct assumptions.
Deaton (1990) explicitly imposes a no borrowing constraint but assumes that a household is
always employed and therefore always receives a positive income. Carroll (2001), on the
other hand, generates endogenously a no borrowing constraint by assuming that with a very
small probability, a household will receive a labor income shock, implying that the household
will optimally never want to borrow, given that the marginal utility of zero consumption is
infinite. In this formulation, the household may face an unemployment spell but receives
zero labor income in that period (Caroll, 2001).
This research focuses on the ex-post strategies, since the ex-ante risk management
strategies may fail to achieve an entirely smooth consumption path. In the cases of ex-post
risk coping strategies the household tries to deal with a shock after it happened. The
households try to raise extra income to compensate for the loss of income due to the shock.
They do so by modifying or reducing consumption, raising income and drawing on financial
resources, which is reached for example by increasing labor supply (child and woman labor
mostly), reducing consumption, migrating to a city (temporarily or not), engaging in other
activities, using cash savings, selling cattle, seeking gifts or loans from relatives and friends
or, as is stressed upon within this research, use formal and informal credit.
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Although all of these coping strategies will have a positive outcome on the short term
survival, some of these coping strategies will threat the long-term survival or security of the
household. For example, selling key productive assets like cattle will reduce a household’s
ability to generate income.
All risk response strategies, both risk management and risk coping strategies, have a social,
cultural or economic aspect (Durante, 2009). The social aspect often has an organizational
aspect as well; this is because poor households will organize themselves to support each
other when a disaster happens. Every member of these formed groups has the same rights
as well as obligations. This is observed within extended families, ethnic groups as well as
professional networks.
The economic category is most present when talking about income diversification. Income
diversification means combining activities with low positive covariance. The last aspect, the
cultural one, describes the way people view the disaster. This can be very different from
time to time, think about for example a religious point of view, in which disaster is just
something that is supposed to happen.
The choice of a household regarding which risk response strategy to use depends for a great
extent on the risk itself. For example if the risk is low-impact (resulting in a temporary
shortfall in income) or severe (resulting in a sharp and less reversible drop in income). In the
case of a low impact risk, strategies like mobilizing labor, reducing expenditures, modifying
consumption, drawing down physical stocks, using savings or insurance, borrowing, selling or
pawning assets and seeking help from friends and relatives will occur more often than in
cases with severe risks (Lekprichakul, 2007). Furthermore strategies like intensifying income-
generating activities, postponing marriage or other social obligations, entering asymmetric
interpersonal dependencies, migrating and turning to drastic measures such as illegal
activities or abandoning children are used more often.
2.3 Formal and Informal Credit as a risk coping strategy
Since using formal and informal credit as an ex post coping strategy is the topic of this thesis,
these concepts will be discussed into detail here.
In a developing country context, credit is important for enhancing productive capacity
through financing investment by the rural households in their human and physical capital.
This credit can be either formal (e.g. provided by formal institutions such as banks, credit
and savings associations, cooperatives of credit and savings, microfinance institutions (MFIs),
NGOs, etc.) and informal (e.g. provided by family members, relatives, friends, moneylenders,
etc.). In many developing countries formal and informal credit markets coexist (Hoff and
Stiglitz, 1990).
Governments in developing countries have often imposed certain regulations, for example
an interest rate ceiling, to promote formal borrowing. However this has created the informal
credit sector in a way (Hoff and Stiglitz, 1990), because lending to the poor is riskier and
therefore less profitable to lend to them at rates which are below the interest rate ceiling.
Another reason for the fact that formal and informal credit coexist is related to rationing,
due to asymmetric information between lenders and borrowers.
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Because of the adverse selection and moral hazard problems, formal lenders are only willing
to lend when collateral is available, while informal lenders are also willing to lend without
this. This is because informal lenders have better information about borrowers (from the
inside) and they use different mechanisms to make sure the money will be repaid
(Christiaensen & Subbarao, 2005).
So to say, poor households lack access to financial services for efficient risk coping because
of the fact that traditional formal institutions typically have no interest in lending to the
poor. The households have a lack of viable collateral and high transaction costs associated
with the small loans that suit them. The excluded households may then find other options,
perhaps by turning to more expensive sources of informal finance to smooth their
consumption (Conning & Udry, 2007). Indeed, in the rural areas of developing countries,
households use informal financial services (provided by relatives and friends, deposit
collectors and moneylenders, and informal credit and savings associations or clubs, known in
Mexico as tandas).
Informal credit is accessible to poor households through self-enforcing informal contracts
among friends, neighbors and members of the extended family and these contracts are
arranged within networks of informal institutions of diverse natures (Fafchamps & Lund,
2001). These non-market informal institutions have often been found to outperform the
financial institutions, which governments have set up to serve the rural population, and even
some semi-formal (unregulated) institutions which emerged to provide financial services to
the poor. Thereby, informal credit markets adjust to high risk environments. Hence,
repayments are conditional on the income of the borrowers.
On the contrary, formal credit markets cannot easily insure these high risk environments.
Moreover, consumption loans are rare in formal markets (Dercon 2002) although recently
this is increasing and made available to poor households which may have some implications
in respect to their coping strategies.
Microfinance, as a form of formal credit, is the provision of financial services to low-income
clients, including consumers and the self-employed, who traditionally lack access to banking
and related services (Tietze, 2007). Contrary to formal banks, MFIs normally provide
comprehensive packages, including small loans, saving facilities, payment services, money
transfers and in some cases insurance, to poor and low-income households that have no
access to formal banks. Microfinance can have impacts on the household level (for example
income, assets, children education, food and housing), the individual level (for example
empowerment and confidence in the future) as well as the enterprise level (for example
assets, employment, revenue and transaction costs) (Hauville, 2005). Some other impacts
that are caused by MFIs lay in the fields of child mortality, environmental impacts, social
capital and training the clients. Even though packages MFIs offer were originally mended for
investment purposes; the credits are now also used as a way to smooth consumption and
cope with risks.
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Microfinance institutions (MFIs) can provide clients with an important risk coping strategy to
meet cash flow needs associated with consumption. This is true for idiosyncratic shocks,
such as sickness, birth, marriage as well as for covariate shocks, where vulnerability can be
reduced through the emergency assistance during periods of acute natural disasters. The
fact that these MFIs can turn into de-facto relief agencies is crucial in sustaining households
in case of a natural disaster. But the post-disaster rehabilitation assistance, both financial
and other services, might be even more important (Zaman, 1999). Microfinance services
can help to build up assets that can be depleted in times of stress. However, when shocks
begin to occur more frequently, those households that receive credit are more likely to cope
with risks by reducing productive assets. This means that credit may have a negative impact
on households’ coping strategies, which will be greater among the poorer households
(Hauville, 2005). With already low income levels, they have fewer options for coping with
the shock while continuing to make payments on the loan. Then, the liquidation of a
productive asset may be one of the few options they have for protecting their credit record.
Demand and supply for both formal and informal credit depends on a number of factors. For
example total savings or total value of liquid assets relating to production or consumption
(Kochar, 1997), but also obstacles like high interest rates, bureaucratic loan process
associated with formal loans, collateral risk (Boucher, Carter and Guirkinger, 2007) and
asymmetric information. What is very interesting for this research is that the demand for
credit is also dependent on covariate and/or idiosyncratic shocks.
To conclude, informal arrangements (agreements that are not bound by legislation or
formalized by contract) can be beneficial for poor people. Moreover, with the microfinance
revolution and the expansion of the frontier of rural financial markets, some attention is set
to the role that access to formal credit transactions may play in improving the set of
household instruments used to cope with risk. Formal loans or loans in microfinance
programs do often finance consumption because the funds they provide are fungible.
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3 Mexico
his chapter will introduce the Mexican context, as the area of research. It will also
elaborate on both formal and informal credit within this Mexican context.
3.1 Description of the context: Mexico
To start, Mexico is a country with 1,964,375 sq. km and 112 million inhabitants which ranks
15th on the global population list. The country is located in North America, bordering the
Caribbean Sea and the Gulf of Mexico, between Belize and the United States and bordering
the North Pacific Ocean, between Guatemala and the United States.
Mexico City is the capital of the country. The United Mexican States, as Mexico is officially
called, consist of thirty-two states and these are also divided into municipalities, the smallest
administrative political entity in the country.
Figure 1: Map of Mexico and its states
Source: CIA, 2010
The Mexican economy, with a GDP of $1.482 trillion (CIA, 2010) contains a mixture of
modern and outmoded industry and agriculture, increasingly dominated by the private
sector. A devaluation of the peso in late 1994 triggered the worst recession in over half a
century. The nation had been making an impressive recovery until the global financial crisis
hit in late 2008. Ongoing economic and social concerns include low real wages,
underemployment for a large segment of the population, inequitable income distribution,
and few advancement opportunities for the largely “Amerindian” population in the
impoverished southern states.
In 1994, Mexico entered the North American Free Trade Accord (NAFTA) with the United
States and Canada. Under NAFTA Mexico was allowed to continue to protect the domestic
maize market for example (Nadal, 2000).
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After Mexico entered the NAFTA rapid growth in exports were experienced and the
restructuring of the macroeconomic finances had initiated significant results in the reduction
of the poverty rate: according to the World Bank, poverty was reduced from 24.2% in 2000
to 17.6% in 2004. Most of this reduction was achieved in rural communities whose rate of
poverty declined from 42% to 27.9% in the 2000–2004 period, although urban poverty
stagnated at 12%. According to the World Bank, in 2004, 17.6% of Mexico's population lived
in extreme poverty, while 21% lived in moderated poverty. The CIA Factbook, on the other
hand, reported that 13.8% of the population was under the poverty line, as measured in
food-based poverty.
What is very remarkable is that the incidence of the poverty ranges from 35 percent in the
northern region to 81 percent in the southern region (Taylor, Mora, Adams & Lopez-Feldman
2005). Households living in the southern states are very poor, and certainly much poorer
than households living in the rest of Mexico. The high level of poverty in the south is
confirmed by both the Marginality Index (based on access to basic infrastructure services,
housing conditions, education attainment, and wage earnings) from the National Population
Council (Consejo Nacional de Población-CONAPO) and the Human Development Index (HDI)
(based on per capita gross domestic product [GDP], educational achievement and
enrollment, and life expectancy) from the United Nations Development Programme (UNDP).
The production environment in Mexico today is different from that of the early 1980s. The
ways in which smallholder farmers were previously able to accumulate resources have been
severely constrained. There is only a limited supply of credit, insurance, and technical
support, especially for well-off farmers (those with large land ownership, with liquid
collateral, etc.) and given current patterns of social change in rural Mexico, the most viable
adaptation strategies may be changing their production strategies (crop diversification) or
outside the agricultural sector, via self-employment in the service or manufacturing sector .
Over the past years some major changes in agriculture have been noted. Whereas
previously, systems of food production and consumption policies in many Latin American
countries were determined largely by national political and socioeconomic objectives,
nowadays increasingly national agricultural policies are driven by transnational conditions,
such as multilateral regulations on food product quality, regulations on trade and
protectionism, quality and quantity demands of transnational agro-industries, and
cosmopolitan consumer tastes and preferences (McMichal, 1994 as found in Eakin, 2005).
Adaptation to these conditions has been difficult for rural households in Mexico. Integrating
into the global economy only started with the evolution of neo-liberalism, policies designed
to liberalized markets and minimized government regulation of resource distribution.
3.2 Formal credit in Mexico
In the early 1990s, the public agricultural bank, BANRURAL, was restructured to meet the
needs of farmers identified as commercially viable (Eakin, 2005) eliminating all but a handful
of Mexico’s 27,470 ejidos (the agrarian communities who received land in the land
distribution program after the 1910 Revolution) from its portfolios (Wayne & Myhre, 1998).
With the loss of credit, the smallholders also lost access to crop insurance and other
services.
- 13 -
However, as discussed in the theoretical framework, access to formal credit can be achieved
in several ways. It can be done via various formal financial institutions such as cooperatives
of credit and savings, credit unions, MFIs, commercial banks and private initiatives. Some
offer only credit and savings, while others make cash transfers of remittances and subsidies.
Two structures can be identified within these formal institutions which are in most cases
regulated entities by the central bank of financial authorities as the National Commission of
Banking and Values (CNBV). These include privately owned and publicly owned institutions.
All institutions require a proof of income as collateral. The total amount of savings can serve
as collateral when small credit requirements take place but additional collaterals are
required when the amounts are higher. In addition to the proof of income, the majority of
institutions are requiring official identification for instance, the IFE (Electoral Federal
Identification), CURP (Unique Key of Register of Population), the birth certificate and the
marriage certificate and others.
In cases of group loans within MFIs collateral is more about social capital than about the
other capitals. Because rural communities have tight-knit hierarchical structures information
about borrowers is accessible and the enforcement of sanctions via social networks makes
collateral unnecessary.
Privately owned
This category exists out of mostly commercial banks who grant credit with reasonable
interest rates. These banks have high collateral requirements for the demanding households,
which are income related and many documents have to be in order. This sector serves about
25 percent of Mexico’s urban adult population, whereas these figures for rural areas are
unknown, and next to credit it also facilitates access to savings accounts. Another aspect of
this category is that MFIs, who mainly focus on woman in rural areas, set high interest rates
but less collateral requirements.
Another aspect of the privately owned institutions is the institutions that are working from
the grass-roots. These institutions have been created by local people in urban and rural
areas where it is difficult for households to have access to formal credit. Examples of these
institutions are those which belong to the Sector of Savings and Public Credit (SACP). In this
category are various organizations such as Cajas de Ahorro y Credito Popular (Savings and
Credit Associations), Cajas Solidarias, Credit Unions, among others who work at the local
level in several dimensions of welfare of their members (not only credit, but also savings,
health, education, productive-related aspects, etc.). Here the collateral requirements are
less important because the social collateral is most relevant since each member is able to
support other members with its own savings in case of idiosyncratic shocks as is the case in
rural regions where most of these institutions are operating.
- 14 -
Publicly owned
Within this category a difference can be made between the bigger financial institutions, who
serve as first-tier institutions, and the second-tier smaller financial intermediaries, through
which the first group grand credit. First-tier financial institutions correspond to this category
such as the Development Bank institutes (e.g. Financiera Rural, FIRA, FIRCO) and the Social
Bank (BANSEFI - National bank of financial services) that are financing households in both
semi-urban and rural areas. Also second-tier smaller financial intermediaries such as a
Cooperatives of Credit and Savings or Credit Unions are part of this category. The objective
of all of these institutions is to provide agricultural credit for productive use and to facilitate
access to saving accounts and other financial services such as transfers of remittances and to
directly work with the households, so that less collateral is required.
3.3 Informal Credit in Mexico
Informal credit can be provided by family members, relatives and friends as well as by
moneylenders and other input suppliers operating in the local market. These transactions
are not bound by legislation or formalized by contract and can actually reach more people
than formal credit does. One explanation of this is the fact that most rural communities are
isolated locations with inexistent formal financial institutions operating in their
surroundings. This implies that as soon as a household requires credit, they need to travel
and incur in high transaction costs before getting a formal loan. For that reason, in Mexico,
households use informal financial services provided by friends and family members, but also
by deposit collectors, moneylenders and informal credit and savings associations, known in
Mexico as ‘’tandas’’ or ‘’roscas’’. These associations differ from the formal institutions as
they require prelimary (in)formal contact and interaction.
- 15 -
4 Models and Data
his chapter will explain in which way the analysis will be dealt with. First the theory
behind the empirical model will be discussed, after which the data and the used
variables will be discussed. To be followed by the empirical model and the used
estimation methods.
4.1 Theoretical models
Within this research the use of credit as a coping strategy is investigated. The use of credit is
determined by both supply and demand, therefore it is important to look at the factors
influencing the supply and demand side of credit. The household model, the credit market
model as well as the livelihood capital model help to understand the theory behind the
supply and demand side of credit and the factors influencing these sides.
4.1.1 Household Model
Credit can be used by households for a wide variety of purposes, e.g. for productive or
purely consumption reasons. The demand of formal and informal credit when coping with
risks is a decision made within the household itself, therefore the farm household model will
be the basic theory of this research. 1
Chayanov (1925) and Nakajima (1986) are among the first who believed that behaviors of
households were best understood in a household framework, where potentially important
interactions exists between nonfarm labor markets, the farm operation, and household
consumption (Findeis, 2005). Becker’s (1981) unitary household model forms the foundation
for the agricultural household model (Singh, Squire and Strauss, 1986), through its
assumptions on household decision-making through a single household head. However, the
agricultural household model recognizes that agricultural producers both produce and
consume the agricultural output produced by the household, which means the model
assumes that farm output is consumed by producing households, with the surplus being
marketed, a reality for most farm households in developing countries (Singh, Squire and
Strauss, 1986). Further, the model incorporates a farm production function, reflecting the
returns to farm self-employment. The agricultural household model assumes a nonlinear
farm production function, assuming that the marginal returns to labor decline with increases
in production. The model is a combination of consumer and producer models into a single
model. A household has preferences on an agricultural good Xa, a manufactured good Xm,
and leisure Xl defined by the utility function U(Xa;Xm;Xl). The household also has a
production technology represented by the production function Q(L;A) where L is labor and A
is land. This means that when one of the factors influencing the basic model would change in
any possible way, like in case of a shock, the household would be unbalanced and this could
lead to an increased demand of credit in order to return to the original state.
1 For this research not only farmers are taken into account but also people living in urban areas who follow the
same household model
T
- 16 -
However, this approach poses significant demands for data reflecting production,
consumption and labor decisions. Due to the stability preferences households may use
different strategies like the credit market to maximize utility, especially when shocks occur.
This may change over time, which means the household model needs to be extended by an
inter-periodical model.
Therefore, extensions on the basic household model in order to fit credit within the model,
pose the need for data on sources of formal and informal credit among other behaviors or
decisions of particular interest over time. At the same time, household-firm models have the
ability to provide answers to important questions (Findeis, 2005), because they explain how
the different concepts within them might change in times of shock, which could influence
the demand for credit specifically.
4.1.2 Credit market
In developing countries, like for this research, access to formal credit is often rationed
(Stiglitz and Weiss, 1981) and access to informal credit is commonly linked to kinship ties,
again a function of the household (Swaminathan, 1992).
The supply side of credit is very much characterized by constraints. Credit market
imperfections (from the supply side) such as interest rate ceilings, large transaction costs for
borrowers in applying for loans, and moral hazard problems increase potential for credit
rationing (Carter, 2007; Foltz, 2004). A household is credit rationed if it demands more credit
than its supply. On the supply side, lenders may restrict loans to households that can signal
their credit worthiness via observable wealth criteria, such as collateral. On the demand
side, large transaction costs may impede application for loans. Also, under the interaction
between imperfect formal credit institutions and households’ risk aversion behavior, rural
households will be discouraged to depress their formal credit demand or replace it with
informal credit, which is called demand-side credit constraints.
Determinants of the limited access to credit and other financial services available to
households of developing countries, such as Mexico, are not well known. Some models do
exists on access to credit but they have their shortcomings. The first method tries to detect
credit constrained households through tests of violation of the life-cycle or permanent
income hypothesis. One of the testable implications of this hypothesis is that in the absence
of liquidity and borrowing constraints, transitory income shocks should not affect
consumption (Deaton, 1990). Empirical evidence from this methodology regarding presence
or absence of credit constraint has been inconclusive, mostly because violation of the
implications of the hypothesis is neither a sufficient nor necessary condition for being credit
constrained (Diagne et al, 2000). The second method of measuring access and detecting
credit constraint collects household-level credit market information directly from household
surveys to determine whether or not households are credit constrained. 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 effects of this likelihood on
various household outcomes. The formulation of credit constraint in this literature focuses
on household assets, income, and consumption variables, and abstracts from all the
variables related directly to the credit market. The former set of variables does affect credit
transactions, but only indirectly (except the case of an asset being used as collateral).
- 17 -
This means that assets, income and consumption variables should be taken into account
when analyzing credit as a coping strategy as well.
The credit market can be analyzed as any other commodity market would be, using the
concept of demand and supply of credit, with the interest rate being the price of credit. In
this framework, the supply and demand curve represents, respectively, the amount the
lender is willing to lend and the amount the borrower is willing to borrow at exogenously
given interest rates. This simplistic Walrasian equilibrium view of the credit market is,
however, not useful for understanding the complete nature of credit transactions (Freixas
and Rochet, 1997), because the assumption of price-taking behavior on the part of the
lender is hardly realistic, especially in a developing country context and other problems
related to information asymmetries and contract enforcement make it difficult to
understand everything. Another problem is that interest rates can have a negative effect as
well as the real poor households will be only accepted in programs with high interest rates
like microfinance programs, which then results in a default within the microfinance program
itself.
4.1.3 Livelihood Capital
Both the household model as well as the credit market model emphasize on the importance
of assets when dealing with formal and informal credit. In chapter two the classification of
assets into the different capitals was already introduced. When clustered, the assets form
the livelihood capital of a household. Livelihood capital is the wide range of individual,
household and community assets poor people utilize to cope with different levels of
vulnerability associated with poverty (Gifford, 2004). They represent an essential component
and a starting point for livelihood analysis as they correspond to what the poor have, rather
than what they do not have (Moser, 1998). What households have reflects on their capacity
to respond to shocks. Accordingly, assets are the basic factors of production and
consumption that are controlled by the household (Fofona, 2010). As discussed in the
conceptual framework, Scoones (1998) classified assets into natural, physical, human,
financial and social capital. In later analysis also the capital of location has been included. In
the case of this research, the livelihood capitals fit the contextual framework of credit,
where financial capital is the asset that comprises access to formal loans and social capital to
informal credit.
Natural capital refers to natural resources such as land, water, forest, and other biological
resources used by households to meet their needs for survival. In rural areas, land is one of
the most important production factors. In connection to formal and informal credit, land can
serve as collateral for lending. Location capital refers to the place the household is located
at, e.g. in a rural or urban area or in a specific region.
Human capital refers to the labor available to households and it is related to the education
level, skills and health status of people that together enable them to pursue different
livelihood strategies and achieve their livelihood outcomes. Labor is a very important factor
for the household and it determines its capability to generate a livelihood (Fofana, 2010).
Psychical capital includes agricultural infrastructures like irrigation canals and infrastructures
such as roads, electricity, and clean water supplies. These assets are important for the
support of livelihood and the development of both rural and urban communities.
Financial capital refers to the stocks of cash that can be accessed by households in order to
purchase either production or consumption goods. This also includes the provision of credit.
- 18 -
Lastly, social capital refers to the social networks, associations and relations, in which
households participate, and from which they can derive support.
4.2 The Data
From 2004 until 2007, an annual household panel survey was implemented by the Mexican
Ministry of Agriculture (SAGARPA) and the National Savings and Financial Services Bank
(BANSEFI) through the project ‘Program for strengthening Savings, Social Credit and Rural
Micro-finance’.
Four waves of the survey have been undertaken (2004, 2005, 2006 and 2007). The total
sample size for the survey is about 5,700 households and it gathers detailed information
from households, both rural and urban, on assets and liabilities (physical and financial,
formal and informal), expenditures, level of income (from labor and non-labor sources),
productive activities, insurance and remittances, among other variables (Zapata-Alvarez,
2007). Most interesting here is that the survey also includes information on shocks
experienced by different types of households and the type of coping strategies used to deal
with these shocks.
By ways of stratified sampling techniques the data was collected on the household level. The
technique of probability proportional to the number of clients for each financial institution
has been used according to the size of the MFIs. Then, the clients for each organization were
selected at random by a client’s records which were made available by the institutions. The
selection criteria permitted a link to them with equal probability of being selected. Within
the group of households several group divisions have been made. First, all the Mexican
states have been divided into three groups or regions, according to their geographic location
(north, center and south). Each region was then divided into four strata, according to the
estimated number of clients of financial institutions. Finally the institutions have been
grouped, in turn, into very small (0-1,500 clients), small (1,501-10,000 clients), medium
(10,0001-100,000 clients), and large (100,001 or more clients) institutions.
Also, all the interviewed households have been divided into two groups according to their
access to financial institutions. This meant a difference between households that have at
least one member in the family who is a client of a financial institution (treatment group)
and households that do not have any member in the family who is a client of a financial
institution (control group). Both groups display similar socio-economic characteristics.
During the period of the survey 1,721 households went from the control group to the
treatment group and 1,424 left the survey, which means that some attrition occurred.
The survey was designed to gather more detailed information about the financial behavior of
cooperative member households as well as households that choose not to access, or are
unable to access financial services from institutions. The detailed information of the panel
survey allows changes to be tracked over time at the household level in 27 states.
- 19 -
4.3 Construction workable data set
As discussed above the used panel set has been made available by SAGARPA and BANSEFI.
However, as a direct result from the fact that the panel set is unbalanced, or in other words,
contains households that drop from one year to the other and that not all questionnaires are
the same for every year, it is difficult to merge the data over the different years. Therefore
only data from 2004 and 2005 has been used.
The chosen variables will be explained into further detailin the next section of the empirical
model; however it should be noted that some variables have monetary values. Because data
from both 2004 and 2005 is used, it was necessary to correct the monetary values of the
data for inflation. This is accomplished by dividing these variables by the Consumer Price
Index (CPI). The year of 2004 has been used as the start year and for 2005 the CPI deflator of
0.96165192 is used.
In some variables, missing values have been found and corrected by recoding or only the
sample without the missing values has been used. Also, it was found that in the year 2004
some observations were included in the survey as repeated. Therefore, these observations
have been taken out of the data. Then new variables were created by making calculations
with the data provided by SAGARPA and BANSEFI. How these were exactly calculated can be
found in appendix I.
From this work a summary was made of all statistics used for the analysis in chapter five,
extended information on how all variables have been calculated can be found once again in
appendix I.
- 20 -
Table 1: Summary of variables used
Variable Label Obs Mean Std. Dev. Min Max
Formal credit coping
strategy
Dummy 10385 .0145402 .1197087 0 1
Informal credit coping
strategy
Dummy 10385 .0327395 .1779626 0 1
Formal credit Amount, deflated 10385 306260.6 2237639 0 1.22e+08
Dummy 10385 .1603274 .3669271 0 1
Informal credit Value, deflated 10385 147349 1300750 0 7.58e+07
Dummy 10385 .2358209 .4545312 0 1
# shocks As reported by the HH 10385 .6161772 .836043 0 6
Death HH Shock death HH member 10379 .0366124 .1878173 0 1
Illness Shock illness HH member 4550 .2114286 .4083665 0 1
Disaster Shock natural disaster 4551 .1711712 .3766999 0 1
Low Prices Shock lower sale price 4549 .0683667 .2524018 0 1
Low Quantity Shock drop sales 4549 .2484062 .4321361 0 1
Joblessness Shock joblessness 4549 .4688943 .4990864 0 1
Job lost Shock losing job 4549 .1026599 .3035476 0 1
Equipment Shock equip breakdown 4549 .0505606 .2191227 0 1
Savings * Log of savings, deflated 10385 3.459897 5.2388 0 16.7
Productive assets * Log and deflated 10385 122400.3 1503404 0 5.09e+07
Animals * Log of the value, deflated 10385 3.562415 5.61626 0 19.4
Dependency ratio # dependent HH members / #
total members
10384 .3720341 .2671335 0 1
Working # working HH members 10385 2.703996 1.456088 0 12
Education # years education head 10385 5.84805 4.678145 0 23
# years education adults 10378 6.493951 3.866126 0 21
Age Of the head 10380 47.91859 15.60216 17 99
Average of the HH 10380 31.99148 16.36948 7.5 97
Gender 1 if head is male 9909 .8395398 .3670508 0 1
Civil status 1 if head married 10385 .6440058 .4788365 0 1
Indigenous 1 if indigenous language 10354 .2387483 .4263392 0 1
Water 1 if access to water 10360 .9189189 .2729728 0 1
Electricity 1 if access to electricity 10290 .9687075 .1741156 0 1
Telephone 1 if access to telephone 10074 .5053603 .6406442 0 1
Land # hectares 10382 1.342478 10.13125 0 502
South 1 if living in south region 10385 .4961001 .5000089 0 1
Central 1 if living in central region 10385 .324988 .4683929 0 1
Urban 1 if urban 10385 .5223881 .4995226 0 1
Year 1 if 2004 10385 .5410688 .4983345 0 1
Income Total income, deflated 10385 1.06e+07 1.21e+08 0 9.17e+09
Expenditures Total expenditures, deflated 10385 9035144 9179182 0 3.25e+08
Consumption Total consumption, deflated 10385 8164417 8370761 0 3.24e+08
Insurance # insured HH member/ # total 10258 .1307519 .3083297 0 1
Tandas Being part of 10384 .1714176 .3768916 0 1
Remittances International, deflated 10385 28613.93 224324.6 0 1.15e+07
HH memb # HH members 10384 4.40678 1.981805 1 18
* the log of these variables was constructed to be able to fit them into all the estimation models
- 21 -
4.4 Empirical Model
This research has been designed to estimate the amount of formal and informal credit, used
as a coping strategy to deal with risks in Mexico. Therefore formal credit and informal credit
are the dependent variables for this estimation. From the theoretical models discussed in
4.1, the conclusion can be drawn that several sets of variables determine the dependent
variables. The concepts of all three models discussed there have been combined by dividing
them under the five capitals of Scoones (1998) (financial, human, natural, physical, social
and the extra location capital) as well as some shock related variables, to be able to model
these for this research.
The variables defining financial capital include financial savings as liquid assets, productive
assets and value of animals. All these variables are important because they determine the
possibilities a household has in order to deal with risks. The more assets a household
possesses the more possibilities to sell capital in case of shocks. This could mean that having
financial capital makes credit unnecessary. On the other hand, financial capital is also used
as collateral requirement by credit institutions. A poor household, with fewer assets, has
fewer options to obtain (formal) credit because it cannot provide collateral.
Other financial variables, which cannot be accounted for capital of a household, like total
household income, remittances, expenditures and consumption have endogeneity and inter-
correlation problems. These problems are the cause of the fact that the earlier mentioned
variables will not be estimated within the empirical model. Endogeneity can arise as a result
of measurement error, auto regression with auto-correlated errors, simultaneity, omitted
variables, and sample selection errors (Baum, 2006). In this case it means that a loop of
causality between the independent and dependent variables leads to endogeneity. Because
this research talks about data in time series, some independent variables are actually
dependent for their value in period t on the values of other variables in period t-1. Within
the analysis this will result in the fact that these particular variables cannot be used as
independent variables.
At the same time, remittances can become complicated because this variable might be a risk
coping strategy itself and influence the amount of formal and informal credit used as a
coping strategy at the same time.
For human capital many variables could be identified. Average age of the household
members as well as the specific age of the head of the household, marital status, gender,
average education for all adults in the households as well as only for the head, dependency
ratio, amount of working family members, insurance ratio and speaking the indigenous
language. All these variables might have an influence on using formal and informal credit as
a coping strategy. Only the insurance ratio (amount of insured household members divided
by the total amount of household members) is experiencing endogeneity problems, which is
why it is not included in the empirical model. Education can cause some households
members to find other, better paid jobs. In this process age and gender do also have an
influence, older men or woman will encounter more difficulties in finding off farm jobs for
example. Poor households often tend to have more household members, which gives them
more labor but at the same time also more mouths to feed.
- 22 -
For natural capital only the amount of hectares of land a household owns is identified,
because this can be used as collateral when applying for a loan.
Access to water, electricity and owning a telephone are determinants for physical capital.
These are very important for the development of a household and so the ability to cope with
shocks. When a household has access to water and electricity it is a household which is
better-off in terms of welfare. When it has access to a telephone it is easier to communicate
with possible money lenders, both formal and informal. Also, in rare cases these variables of
physical capital can be used as collateral for obtaining a loan.
Social capital is very difficult to measure, in this case the variable of being part of roscas, or
tandas could be used. Of course this would give more opportunities of obtaining an informal
loan when this variable would be positive. However this causes also the endogeneity
problems encountered. Because of these problems social capital will be left out of this
empirical model.
The extra capital, the locational capital, includes both variables on living in a rural or urban
area as well as in the specific regions of southern and central Mexico. When a household
lives in an urban area it has easier access to formal credit institutions, however the MFIs are
more focused on rural areas. The southern region of Mexico is the poorest region of Mexico,
as discussed in chapter three; this implies that the households living on the south possess
few assets that could be used as collateral for credit.
Next to the different assets, also the shocks experienced itself have an influence on the
dependent variables of formal and informal credit used as a coping strategy. That is why the
total number of shocks a household reported as well as the eight different types of shocks is
included in the empirical model. These risks include frequent less severe risks as well as very
severe risks and risky events. The latter includes the death of a household member as well as
severe illness to one of the household members. These risks bring along extra costs for a
funeral or hospital admission and at the same time the household may be faced with a loss
of income. Another severe risk, but covariant instead of the earlier idiosyncratic risks, is the
risk of a natural disaster, this includes drought, excessive rainfall, floods as well as pest and
diseases in livestock and crop.
Some moderate severe risks include a lower price for products, a drop in the sales, being
joblessness and losing a job. All these risks result in income shortfalls, which can be
positively changed over time. An example of less severe risks includes the breakdown of
equipment or vehicle. Other risks, which are less interesting for this research, include lower
wages, birth, theft, divorce, fire and death of livestock.
As a control variable the time variable of the year 2004 will be investigated. Of course it is
important to look at the time-aspect, if the household reports in 2004 or 2005, because a
panel data set will be used to estimate the empirical model.
The variables that encounter endogeneity problems are left out of the empirical model,
however they will be calculated and estimated in order to achieve a better insight in the
characteristics of the households; they can be found in the summary of the variables in table
1 and how they are calculated and defined has been included in appendix I.
- 23 -
The variables that will be used as explanatory variables can be found, together with their
expected causality for both coping strategies in table 2. In appendix I a list with clear
definitions and specific concepts of all variables can be found. Apart from that the
dependent variables of formal and informal credit used as coping strategy have been
described.
Table 2: Explanatory Variables and Expected Causality
Variable Causality Expected
Correlation
Formal
Credit
Expected
Correlation
Informal
Credit
SHOCK RELATED2
Number of shocks as
reported by the HH
The higher the intensity of shocks the more all
coping strategies will be used.
+ +
Death HH member If this shock is experienced, more credit will be
asked for to pay for the funeral for example.
+ +
Illness /accident Hospital bills can be paid by credit. + +
Natural disaster Because this is covariant, less access to formal
credit and more to informal.
- +
Lower sale price Leads to fewer assets to be used as collateral,
informal credit can be used to cope.
- +
Drop of sales Leads to fewer assets to be used as collateral,
informal credit can be used to cope.
- +
Joblessness Leads to fewer assets to be used as collateral,
informal credit can be used to cope.
- +
Losing job Leads to fewer assets to be used as collateral,
informal credit can be used to cope.
- +
Equipment
breakdown
Money to fix or replace the equipment can
quickly be obtained by informal credit.
+/- +
FINANCIAL CAPITAL
Savings
(Stock Savings)
The more savings, the higher the probability of
using a formal loan as this can be used of
collateral, however the probability of informal
credit will reduce because it will first use savings
as coping strategy.
+ -
Productive assets
(equipment,
machinery, animals)
The fewer assets a HH has the fewer possibilities
to cope with risks, which implies a positive
correlation with both dependent variables.
+ +
Value of animals Animals can be used as collateral for both formal
and informal credit.
+ +
HUMAN CAPITAL
Dependency ratio The more family members are dependent, the
less flexible a HH is and is case of shocks it will
use less credit and instead lower consumption.
- -
Number of working
HH members
When many HH members work, the HH will have
more possibilities to cope with risk using other
strategies.
- -
2 The variables within this section were expected to also show endogeniety problems, however when taking
them out of the model, the other variables did not change as can be seen in both appendices IV and V.
- 24 -
Education (head) More human capital by means of education gives
a responsible image to the HH and enlarges the
access to formal and informal credit.
+ +
Education (adults) More human capital by means of education gives
a responsible image to the HH and enlarges the
access to formal and informal credit.
+ +
Age (head) If age increases, the HH will be seen as more
reliable for obtaining formal and informal credit.
+ +
Age (HH) If age increases, the HH will be seen as more
reliable for obtaining formal and informal credit.
+ +
Gender (head) If the head is female, it will be easier to access
formal credit since many of these target women
due to moral hazard problems. Informal credit is
more accessible to men.
- +
Civil status (head) Being married extends your informal network as
well as your reliability for a formal loan, because
the document can be showed.
+ +
Indigenous When only speaking the indigenous language
documentation cannot be provided or read which
is needed for loans.
- -
PHYSICAL CAPITAL
Access to water Water can be rare in rural areas, which means it
can be seen as an indicator for welfare which
increases the credit coping strategies.
+ +
Access to electricity Electricity can be rare in rural areas, which means
it can be seen as an indicator for welfare which
increases the credit coping strategies.
+ +
Access to telephone Communication means increase a formal and
informal network.
+ +
NATURAL CAPITAL
Number of hectares
of land
Land can be used as collateral, which will increase
both dependent variables.
+
+
LOCATION CAPITAL
Region: south In the south HH are poorer and face more shocks
but they are also more risk-averse, which leaves
the causality of both variables doubtful.
+/- +/-
Region: central In the central region, HH are richer, but also face
more shocks. Due to the possibilities of coming
with collateral, positive correlation is expected.
+ +
Urban HH in urban areas have more access to formal
institutions, while rural HH to informal ones.
This is because microcredit programs have
normally been implemented in rural areas of
developing countries
+ -
CONTROL
Year 2004 has a lot more reported shocks than 2005,
which implies more use of both coping strategies.
+ +
- 25 -
4.5 Estimation methods
As explained in paragraph 4.3, data will be used over the time span of 2004 and 2005.
Because a censored panel data set is available it would be very interesting to use a linear
probability model and a XT logit model, both holding for fixed effects. Unfortunately these
models only take these observations into account that change of time (go from 0 to 1 or
from 1 to 0) which left the analysis with a sample that was too small to get significant
outcomes.
It has also been attempted to get more interesting results by running a truncated model and
a double hurdle model on a censored sample of the data. However, only few observations
were counted for when analyzing formal and informal credit as a coping strategy. This is why
neither the truncated nor the double hurdle model was taken into the estimation methods.
A normal linear probability model (LPM) has been run, after which the data has been tested
and corrected for residuals, outliers and heteroskedasticity. The results of these robustness
checks can be found in appendix III.
In the end, the empirical model described above has been estimated while running two
probit models and a tobit model; on this will be elaborated in chapter five. In the following
paragraph a short insight in these methods is given.
4.5.1 Probit Model
Probit models are used to model a relationship between a binary dependent variable Y and
one or more independent variables X. The independent variables are presumed to affect the
choice or category or the choice maker, and represent beliefs about the causal or associative
elements of importance in the choice or classification process.
A probit model, as any other regression, has some basic assumption, namely:
• Y is caused by or associated with the X’s, and the X’s are determined by influences
‘outside’ of the model.
• There is uncertainty in the relation between Y and the X’s, as reflected by a scattering
of observations around the functional relationship.
• The distribution of error terms must be assessed to determine whether a selected
model is appropriate.
A probit model looks like this:
Pr(yi=1)=F(xi'b)
Here b is a parameter to be estimated, and F is the normal cdf. The logit model is the same
but with a different cdf for F.
The coefficients that are the result from this probit model need to be supplemented with the
marginal effects, to be able to see the change in probability. Also, the method of maximum
likelihood that selects values of the model parameters that maximize the likelihood function
need to be applied.
- 26 -
4.5.2 Tobit Model
The tobit model is an econometric model which is used to describe the relationship between
a censored dependent variable Y and an independent variable X. Within this model, the
dependent variable is censored, which means yi* is not observed. The model is:
yi*=bxi+ui where ui~N(0,s2)
yi=yi* if yi
*>y0, and yi=y0 otherwise yi is observed
The tobit and probit models are similar in many ways for this research. They both use the
same structural model, but they differ in measurement models i.e. how the y* is translated
into the observed y. In the tobit model, we know the value of y* when y*
> 0, while in the
probit model we only know if y* > 0.
- 27 -
5 Analysis
his chapter will elaborate on the data, the variables and the relationships between
them. It will start with a description of the data and the variables and conclude by
means of the actual analysis while referring to the background theories.
5.1 Household characteristics
Within the panel survey many different types of households have answered the
questionnaires. The households, with an average of 4.4 members, of which in average 2.1 is
man and 2.3 is woman and 2.7 working, live for 47% in rural areas as compared to 53% in
urban, of which 1,858 live in the northern region; 5,176 in the southern and 3,500 in the
central region. The average age of the head of the household (which is a man in 80% of the
observations) is 47.8 years, whereas the average age of all members is 31.9; the head of the
household has followed an average of 5.8 years of education and the average of all adults of
the household is 6.4 years.
The ability to cope with risks is highly related to an average of 0.13 insurance ratio, an
average of 28,614 pesos received international remittances and an income of 10,600,000
pesos per year. Also, an average expenditure of 9,035,144 pesos is experienced, of which
8,164,417 belongs to consumption, per year.
5.1 Shocks
As said before the survey was held under 5,700 household all over Mexico. As part of the
questionnaire the households were asked if they experienced any shocks during the past
twelve months. These shocks include the death of a household member, severe illness to
one of the household members, the risk of a natural disaster, a lower price for products, a
drop in sales, being joblessness, losing a job, breakdown of equipment or vehicle and other
risks. These other risks, which are less interesting for this research, include lower wages,
birth, theft, divorce, fire and death of livestock.
In table 3 the specific number of shocks that have been experienced can be found as well as
these experienced shocks as a percentage of the amount of observations of that variable.
Table 3: Experienced shocks in 2004 and 2005
Type of shock Frequency Total
‘04 ‘05
Death of HH member 195 (3,4%) 193 (4,1%) 388
Illness of/ accident with HH member 526 (19,4%) 456 (23,5%) 982
Natural disaster 451 (16,6 %) 338 (20,6%) 789
Lower sale price 198 (7,3%) 132 (6,8%) 330
Drop of sales 743 (27,4%) 439 (22,6%) 1,182
Joblessness 1312 (48,4%) 857 (44,2%) 2,169
Losing job 242 (8,9%) 243 (12,5%) 485
Equipment/ vehicle breakdown 163 (6,0%) 83 (4,3%) 246
Other 5 (0,2%) 3 (0,2%) 8
Total (N) 3,835 2,744 6,579
Percentage of amount of observations in parentheses
T
- 28 -
The most important risks to take into account in both 2004 and 2005 are drop of sales,
illness of a household member, natural disasters and by far joblessness.
Mexico is often threatened by natural phenomena such as earthquakes, flooding, volcanic
activity, hurricanes and tropical storms. In the year of 2005, Mexico was even in the top 10
of countries most hit by natural disasters (International Disaster Base). In 2004 and 2005 a
high unemployment rate was present in Mexico (Instituto Nacional de Estadística Geografía
e Informática) which is the direct cause of the high number of joblessness observations in
this data set.
The low GDP of 1% in 2004 and 1.3% in 2005 is the direct result of the fact that many
households face cases of drop in sales. When research would have been done on later years,
most probably this amount of cases of drop in sales would be less. If looked at illness over
the years, this risk is always reported high however it cannot be seen as a constant variable
since different households every year report this shock.
Some households reported more than just one shock for each year, in 2004 an average of
0.65 shocks were reported and in 2005 this was 0.58. However, when only taking the
observations that reported at least one shock into account, the average amount of shocks is
1.40 for 2004 and 1.41 for 2005.
An important notion is that the variables are based on responses of the households
themselves; it could be that a shock like equipment breakdown was important enough for
one household to report, while another (most probably richer) didn’t. Therefore a closer
look needs to be taken at the relationship of the total amount of shocks reported and the
welfare level of these households. Two factors are of influence when looking at this
relationship: poor household may report relatively small shocks sooner than rich households
do and poor households may be more exposed to shocks than rich households. Income
cannot be used here as an indicator for welfare due to the fact that this income has been
experienced after the shock, which could have caused big fluctuations. Since urban and rural
households behave differently according to their welfare level, a separation is made
between them. The following table looks at the relationship between the amount of shocks
and the quantiles of amount of hectares of land for the rural population and the quantiles of
assets for the urban households.
Table 4: Total reported shocks per region and quantiles of land (rural) and assets (urban)
#Shocks
‘04
# HH reporting Average
per HH
reporting
# Shocks
‘05
# HH
reporting
Average
per HH
reporting
North 505 364 (35.7%) 1.39 411 317 (37.8%) 1.30
South 2,014 1,440 (51.1%) 1.40 1,435 1,020 (43.7%) 1.41
Central 1,136 806 (45.2%) 1.41 898 604 (38.0%) 1.49
Urban 1,787 1,279 (43.1%) 1.40 1,318 1,022 (44.2%) 1.29
Rural 1,863 1,331 (50.2%) 1.40 1,426 919 (37.4%) 1.55
Q1 Land 999 736 1.36 800 593 1.35
Q4 Land 844 582 1.45 614 419 1.47
Q1 Assets 504 363 1.39 371 259 1.43
Q4 Assets 402 281 1.43 289 200 1.45
Percentage of amount of households in the sample in parentheses
- 29 -
For urban households, one can draw the conclusion that reporting shocks is not correlated
with welfare, since there is almost no difference between the average amount of shocks per
household as well as the total reported shocks for households in the first quantile of assets
and the fourth quantile. For rural households, where hectares of land are taken as a welfare
indicator, it turns out that the more land a household possesses, the more shocks it reports.
Main cause for this is that land is heavily exposed to shocks like natural disasters. However,
since this can be reasoned in other ways, it is not proven that welfare and number of
reported shocks are correlated and therefore the variable total number of shocks will be
seen as rather reliable.
It is remarkable that a high proportion of households that have faced shocks were
concentrated in the south of the country, where poverty is relatively higher and natural
disasters occur more frequent. Also remarkable, the difference in shocks reported between
rural and urban areas is not of a significant value, which means that not only rural areas are
vulnerable to shocks, which was expected at forehand.
5.3 Formal and Informal Credit
Within Mexico several institutions for both formal and informal credit exist in which the
households of this sample obtained a monetary value of credit.
Table 5: Formal and Informal Credit
Formal credit Informal Credit
# HH reported Average Amount* # HH reported Average Amount*
Total 1,665 (16.0%) 1,910,220 2,449 (23.6%) 624,834.3
Region: North 288 (15.5%) 2,097,857 463 (24.9%) 590,547.1
Region: South 783 (15.2%) 2,122,542 1,230 (23.9%) 667,632.6
Region: Central 594 (17.6%) 1,539,365 756 (22.4%) 576,200.8
Urban 820 (15.1%) 2,369,574 1,227 (22.6%) 756,905.6
Rural 845 (17.0%) 1,464,456 1,222 (24.6%) 492,222.6
Percentages of total households per variable are in parentheses
*amounts are in pesos and are deflated over time
Although the share of the population that has formal and informal credit do not differ that
much between urban and rural households, the amount is almost doubled for urban
households. This is partly due to the fact that urban households have access to formal
institutions which give out higher loans than the rural-oriented ones and partly to the fact
that the share of the population living in urban areas has a higher income level. 3 The latter
explains that the urban households have more income and abilities to lend informal credit of
a higher amount than in rural areas.
For all regions the amount of households that use informal credit is twice as big as the
amount of households that use formal credit. This stresses again on the easier access to
informal than formal credit.
3 For urban areas 32% of the households lay in the upper quartile of income, while 18% lays in the lower
quartile. For rural areas this is 17% versus 32% respectively.
- 30 -
This overview displays both formal and informal credit in general, however for this research
it is especially interesting to see this when both types of credits are used as a risk coping
strategy.
5.4 Formal and Informal Credit used as coping strategy
Earlier, the different shocks a household reported have been discussed. Within the survey,
the households have been asked what strategies they use to cope with risks. Possible
answers included getting a loan with interest (formal and informal), lowering consumption,
stop paying debts, use savings and other strategies. Since the latter needed to be specified, a
list of 48 answers was created, which has been placed in appendix II. Because this research is
about formal and informal credit as a coping strategy, and these factors had not been
directly asked for, these variables needed to be created. For informal credit the answers loan
without interest, credit of friends and family and participate in tandas have been combined.
Formal credit consists out of request from the cooperative as well as loan with interest. The
latter, loan with interest, had to be split up into informal and formal credit by means of the
dummy variables total formal credit and total informal credit. Households reporting formal
credit have been put under formal credit as a coping strategy and households reporting
informal credit have been put under informal credit as a coping strategy.
In table 6 the use of formal and informal credit as a coping strategy is displayed, which is
done by means of a number of observations that reported to have loans. Also, a percentage
is calculated of the number of observations that use credit to cope with a specific risk,
divided by the number of observations that have actually experienced that risk.
Table 6: Use of formal and informal credit as a coping strategy
Type of shock Formal Credit Informal Credit
‘04 ‘05 ‘04 ‘05
Death of HH member 17 (8,7%) 6 (3,1%) 50 (25,6%) 8 (4,1%)
Illness of/ accident with HH
member
69 (13,1%) 17 (3,7%) 152 (28,9%) 32 (7,0%)
Natural disaster 17 (3,8%) 5 (1,5%) 30 (6,7%) 6 (1,8%)
Lower sale price 8 (4,0%) 6 (4,5%) 11 (5,6%) 8 (6,0%)
Drop of sales 25 (3,4%) 14 (3,2%) 54 (7,3%) 11 (2,5%)
Joblessness 31 (2,4%) 10 (1,2%) 135 (10,3%) 20 (2,4%)
Losing job 6 (2,5%) 3 (1,2%) 22 (9,1%) 5 (2,1%)
Equipment/ vehicle breakdown 22 (13,5%) 6 (7,2%) 28 (17,2%) 4 (4,8%)
Other 1 (20%) - 1 (20%) -
Percentage of households reporting a specific shock in parentheses
As can be seen, informal credit is used more often than formal credit is. Also, both ways of
obtaining credit is mostly used in cases of temporary and immediate risk, for example the
illness of a household member.
The difference in used credit between 2005 and 2004 come as a direct result of simply
having more shocks experienced in 2004. In almost 30% of the observations, the same
household reported credit as a coping mechanism for more than one shock that the
household experienced, as can be seen in table 7.
- 31 -
Moreover a percentage is given of the amount of observations that have reported to use
credit as a coping strategy, compared to the amount of households which have reported to
have experienced shocks. Table 7: Simultaneous shocks and coping by means of formal and informal credit
# shocks
experienced
Formal Credit Informal Credit
‘04 ‘05 ‘04 ‘05
1 54 ( 3,0%) 20 (1,5%) 140 (7,8%) 31 (2,3%)
2 37 (5,9%) 9 (2,1%) 95 (15,1%) 14 (3,2%)
3 16 (11,3%) 5 (4%) 33 (23,4%) 6 (4,8%)
4 5 (13,2%) 1 (3,7%) 12 (31,6%) 3 (11,1%)
5 - 2 (25%) - 1 (12,5%)
6 - - 1 (100%) -
Percentage of amount of observations per row in parentheses
When a household faces one specific shock it has many options to cope with the results of
these, however when it will face more and more simultaneous shocks it will become more
difficult to cope with these, using other coping strategies. From this table, clearly, the
conclusion can be drawn that the more shocks a household faces, the more it will use both
types of credit as a coping strategy.
5.5 Formal Credit used as a Coping Strategy
Now, the relationship between the dependent variable formal credit and all the independent
variables, as indicated in the empirical model, will be analyzed by running two probit and
one tobit regression.
Before doing so, a normal linear probability model (LPM) has been run, after which the data
have been tested and corrected for residuals, outliers and heteroskedasticity. The results of
these robustness checks can be found in appendix III. By comparing the LPM to the probit
model, the estimation model has been checked for robustness as well.
Because it is interesting to see the change in formal credit in general, as well as formal credit
used as coping strategy, two probit models have been run. Another reason for running two
probit models has been to be able to account for the fact that reported use of formal credit
might not be the same as the observed use of this. The first probit model has been run by
taking the whole sample of observations, whereas for the second model, formal credit used
as coping strategy is taken as dependent variable for only those observations that report one
or more shocks. Only one tobit model has been run, in which the amount of credit has been
used as the dependent variable, as compared to the dummy of the first probit model. No
tobit model for amount of formal credit used as coping strategy could be run, because this
amount could not be calculated based on the information available.
The results of these models have been placed in table 8. For each model the marginal effects
as well as the standard errors have been included.
In appendix IV the complete output of these analyses can be found.
- 32 -
Table 8: Estimation Results on the Probability of using Formal Credit as Coping Strategy
Formal Credit Formal credit as coping
strategy
Variable Probit (dummy) Tobit (amount) Probit (dummy)
SHOCK RELATED
Number of shocks as
reported by the HH
-0.0719 -0.1077 -0.1486***
(0.1313) (0.0749) (0.03721)
Death HH member +0.0976 +0.1479 +0.8562***
(0.1860) (0.1319) (0.0234)
Illness /accident +0.1153 +0.1486 +0.7423***
(0.1765) (0.1164) (0.0566)
Natural disaster +0.0839 +0.1337 +0.6857***
(0.1695) (0.1169) (0.0735)
Lower sale price +0.1353 +0.1796 +0.7612***
(0.2048) (0.1419) (0.0875)
Drop of sales +0.1275 +0.1551 +0.5318***
(0.1755) (0.1138) (0.0714)
Joblessness +0.0663 +0.0980 +0.1824***
(0.1353) (0.0801) (0.0377)
Losing job +0.0885 +0.1859 +0.7467***
(0.1802) (0.1409) (0.0790)
Equipment breakdown +0.1298 +0.2085 +0.9260***
(0.2047) (0.1512) (0.0298)
FINANCIAL CAPITAL
Savings (log) +0.0187*** +0.0134*** +0.0040***
(0.0010) (0.0007) (0.0007)
Productive assets (log) +0.0027* +0.0020* +0.0009
(0.0014) (0.0011) (0.0007)
Value of animals (log) +0.0030*** +0.0012* +0.0010**
(0.0030) (0.0007) (0.0005)
HUMAN CAPITAL
Dependency ratio -0.0088 +0.0064 -0.0208
(0.0277) (0.0246) (0.0167)
Number of working HH
members
+0.0054 +0.0058 +0.0010
(0.0051) (0.0055) (0.0027)
Education (head) +0.0008 -0.0016 +0.0008
(0.0022) (0.0020) (0.0012)
Education (adults) +0.0061** +0.0092*** -0.0008
(0.0028) (0.0023) (0.0017)
Age (head) +0.0008 +0.00038 -0.0001
(0.0006) (0.0005) (0.0003)
Age (HH) -0.0006 -0.0002 -0.0004
(0.0007) (0.0005) (0.0004)
Gender (head) -0.0309 -0.0315 +0.0016
(0.0210) (0.0192) (0.0105)
Civil status (head) +0.0704*** +0.0516*** -0.0017
(0.0129) (0.0109) (0.0087)
Indigenous +0.0028 +0.0070 +0.0048
(0.0138) (0.0119) (0.0084)
PHYSICAL CAPITAL
Access to water +0.0268 +0.0223 +0.0108
(0.0167) (0.0115) (0.0078)
Access to electricity +0.0746*** +0.0614*** +0.0137
(0.0187) (0.0132) (0.0075)
- 33 -
Access to telephone +0.0364*** +0.0410*** +0.0065
(0.0097) (0.0069) (0.0051)
NATURAL CAPITAL
Number of hectares of
land
+0.0002 +0.0001 +0.0003
(0.0004) (0.0003) (0.0003)
CONTROLS
Region: south -0.0462*** -0.0238* -0.0201**
(0.0159) (0.0136) (0.0101)
Region: central -0.0093 -0.0126 -0.0095
(0.0162) (0.0128) (0.0072)
Urban -0.0572*** -0.0426*** -0.0097
(0.0118) (0.0092) (0.0068)
Year +0.1233*** +0.1117*** +0.0212***
(0.0101) (0.0091) (0.0067)
R2
0.2370 0.322 0.2776
N 4124 4124 1264
Standard errors in parentheses
* significant on 0.10 level ** significant on 0.05 level *** significant on 0.01 level
The amount of observations of the models defining formal credit equals 4,124 and the probit
model for formal credit as a coping strategy shows 1,264, which means the observations
with missing values for any of the variables have been taken out. Also all R2 show that the
models as a whole fit significantly better than an empty model.
Financial capital can be used as collateral for obtaining formal credit. This is the reason that
all variables have a significant impact on the amount of credit. A marginal change from the
average in savings, productive assets and value of animals is associated with a 1 % increase
in formal credit. For this formal credit to be used as a coping strategy, only animals and
savings have showed significant influence, as these will make access to the formal credit
institutions easier.
Human capital was expected to have a very big influence on formal credit, however from the
analysis can be concluded that in order to obtain a formal loan only the average amount of
years of education of all the adults in the household, as well as the civil status make a
difference in formal credit. Both indicators require documents: the more adults in the
household have at least obtained a high school diploma, the more reliable the household
becomes in the eyes of a credit supplier. Also a marriage certificate is an important
document as requirement to access to some specific formal institutions and this official
marriage is then preferred over the numerous civil status’s which are not legally written
down in a document. Furthermore, the demand for formal credit rises when education level
increases, since well-educated households have a preference of formal credit over informal
credit (Holden, 1998).
As expected, physical capital showed to have a high influence on formal credit. Having access
to a telephone and electricity showed a positive effect of 3.6% and 7.4% respectively on
formal credit. Electricity can be seen as an indicator for welfare and having a telephone
means making easier contact with the formal institutions as a way of communication. Also,
telecommunication brings institutions closer, which increases the demand for formal credit.
- 34 -
As discussed earlier, the southern region of Mexico is a very poor and vulnerable region. It
comes as no surprise that living in this region has a negative effect on formal credit, because
households living in this region have a lower welfare level and can therefore provide less
collateral. The demand for formal credit as a coping strategy in the south is lower than in
other regions, as other coping strategies, which cost less money, like reducing consumption
are easier to apply for the poor in the southern region.
However, it has been expected that urban areas would have easier access to formal credit in
comparison to rural households. The opposite turned out to be true, which would mean that
the focus of formal institutions on rural areas is very effective.
Furthermore, in the year 2004 more shocks occurred and were reported by the households
in the sample, which made the control Year very significant for both obtaining formal credit
as well as using this formal credit as a coping strategy.
The more shocks a household faces, the lower the probability that it will use formal credit as
a coping strategy. Nevertheless, all shocks turned out to have a positive effect on the
probability of using formal credit as a coping strategy. Immediate shocks, like death of a
household member and the breakdown of equipment are responsible for the biggest effects.
Joblessness, which is a more permanent shock, has a smaller significant influence on the
probability of using formal credit as a coping strategy.
5.6 Informal Credit used as Coping Strategy
Now, the relationship between the dependent variable informal credit and all the
independent variables, as indicated in the empirical model, will be analyzed by running two
probit regressions and one tobit regression.
Before doing so, a normal linear probability model (LPM) has been run, after which the data
has been tested and corrected for residuals, outliers and heteroskedasticity. The results of
these robustness checks can be found in appendix III. By comparing the LPM to the probit
model, the estimation model has been checked for robustness as well.
Because it is interesting to see the change in informal credit in general, as well as informal
credit used as coping strategy, two probit models have been run. Another reason for running
two probit models has been to be able to account for the fact that reported use of informal
credit might not be the same as the observed use of this. The first probit model has been run
by taking the whole sample of observations, whereas for the second model, informal credit
used as coping strategy is taken as dependent variable for only those observations that
report one or more shocks. Only one tobit model has been run, in which the amount of
credit has been used as the dependent variable, as compared to the dummy of the first
probit model. No tobit model for amount of informal credit used as coping strategy could be
run, because this amount is could not be calculated with the information available.
The results of these models have been placed in table 9. For each model the marginal effects
as well as the standard errors have been included.
In appendix V the complete output of these analyses can be found.
- 35 -
Table 9: Estimation Results on the Probability of using Informal Credit as Coping Strategy
Informal Credit Informal credit as coping
strategy
Variable Probit (dummy) Tobit (amount) Probit (dummy)
SHOCK RELATED
Number of shocks as
reported by the HH
-1.7893*** -1.3700*** -0.6169***
(0.0402) (0.3548) (0.0577)
Death HH member +0.8686*** +0.9034*** +0.9821***
(0.0055) (0.0520) (0.0036)
Illness /accident +0.9695*** +0.9748*** +0.9784***
(0.0023) (0.0171) (0.0061)
Natural disaster +0.9492*** +0.9607*** +0.9581***
(0.0032) (0.0253) (0.0139)
Lower sale price +0.8497*** +0.8882*** +0.9746***
(0.0055) (0.0578) (0.0001)
Drop of sales +0.9784*** +0.9821*** +0.8985***
(0.0020) (0.0116) (0.0262)
Joblessness +0.9883*** +0.9635*** +0.5821***
(0.0018) (0.0226) (0.0332)
Losing job +0.8882*** +0.9146*** +0.9724***
(0.0050) (0.0474) (0.0087)
Equipment breakdown +0.8256*** +0.8715*** +0.9774***
(0.0062) (0.0638) (0.0040)
FINANCIAL CAPITAL
Savings (log) -0.0068*** -0.0029*** -0.0004
(0.0016) (0.0002) (0.0015)
Productive assets (log) +0.0033* +0.0015*** +0.0015
(0.0020) (0.0007) (0.0019)
Value of animals (log) +0.0013 2.70e-06 +0.0009
(0.0014) (0.0003) (0.0013)
HUMAN CAPITAL
Dependency ratio +0.0218 -0.0040 -0.0375
(0.0381) (0.0083) (0.0408)
Number of working HH
members
-0.0056 -0.1009*** -0.0107
(0.0072) (0.0011) (0.0070)
Education (head) -0.0011 -0.0028*** -0.0019
(0.0032) (0.0001) (0.0035)
Education (adults) -0.0041 +0.0030* -0.0039
(0.0040) (0.0016) (0.0042)
Age (head) +0.0001 +0.0003 -0.0008
(0.0009) (0.0002) (0.0009)
Age (HH) -0.0054*** -0.0028*** -0.0014
(0.0009) (0.0006) (0.0010)
Gender (head) +0.0318* +0.0367** +0.0289
(0.0224) (0.0154) (0.0194)
Civil status (head) -0.0057 +0.0031 -0.0162
(0.0185) (0.0061) (0.0190)
Indigenous +0.0045 -0.0051 -0.0169
(0.0186) (0.0029) (0.0167)
PHYSICAL CAPITAL
Access to water +0.0342 +0.0195 +0.0391
(0.0233) (0.0113) (0.0166)
Access to electricity +0.0144 +0.0124 -0.0334
(0.0349) (0.0097) (0.0429)
- 36 -
Access to telephone +0.0163* +0.0196** -0.0058
(0.0143) (0.0099) (0.0140)
NATURAL CAPITAL
Number of hectares of
land
-0.0006 -0.0005 +0.0003
(0.0005) (0.0001) (0.0008)
CONTROLS
Region: south -0.0626** -0.0068* -0.0597***
(0.0213) (0.0039) (0.0226)
Region: central -0.0389* -0.0228*** -0.0441**
(0.0219) (0.0017) (0.0182)
Urban -0.0022 +0.0136 -0.0047
(0.0161) (0.0087) (0.0161)
Year +0.3428*** +0.2253*** +0.1198***
(0.0127) (0.0611) (0.0150)
N
4124 4124 1264
R2 0.1505 0.109 0.1921
Standard Errors in parentheses
* significant on 0.10 level ** significant on 0.05 level *** significant on 0.01 level
The amount of observations of the models defining formal credit equals 4124 and the probit
model for formal credit as a coping strategy 1264, which means the observations with
missing values for any of the variables have been taken out. Also all R2 show that the models
as a whole fit significantly better than an empty model.
From these estimation models it shows that for informal credit, having savings has a
negative impact on the probability of having informal credit, because people will not grant
households any money when they have savings themselves. Also, the demand for informal
credit will be lower when households have savings, because they simply do not need credit
in these cases.
However, having a marginal positive change in productive assets is associated with an
increase of informal credit. This can be explained by looking at the collateral requirements
for informal credit; assets are used in many cases as collateral (Das, 2009).
The average age of the household members has a significant negative influence on informal
credit. Older farmers have more difficulties to gain income and are thus less capable to pay
back loans. Another theory (Fafchamps, 2001) says that older farmers do not want to obtain
informal credit from family and friends because of age-related respect issues.
What is certain is that having a male as the head of the household positively influences the
amount of households that use informal credit by 3%. Males are being regarded as more
reliable in comparison to females, especially in rural areas, because generally, they will be
guarding the money and assets of monetary value.
- 37 -
Other variables within human capital (number of working household members, education of
all adults as well as the of the head) turned out to have a significant effect on the amount of
informal credit when looked at both the sample using any value of informal credit and the
ones that don’t use it all. However for this research it is most interesting to see that these
variables turned out to not be significant when looking at the households that do use
informal credit.
Having access to a telephone is an important factor in obtaining informal credit. This is due
to the fact that first of all it is easier for the borrowers to contact the lenders, which
increases the demand, but at the same time telecommunication makes it easier for the
lenders to track down the borrowers when repayment needs to be done, which increases
the supply.
Both the southern and the central region have a negative impact on informal credit in
comparison to the northern one. Households in the southern and central regions are poorer,
therefore they can also loan less money to other households. Poor households are also
more risk-averse, which decreases the demand of informal credit in these regions as well. As
for formal credit, 2004 was also a year in which informal credit was used more than in 2005;
2004 causes a 34% difference for informal credit in general and a 12% for it used as coping
strategy.
As could already be seen in the descriptive paragraph earlier in this chapter, informal credit
is used very often as coping strategy. Therefore all the risks have a positive impact on the
probability of using informal credit as a coping strategy. All risks, with an exception of
joblessness, have a percentage of over 90% higher probability to do so. However, when a
household faces more than one shock, the probability of using informal credit gets less. This
is because there is a limit to the amount of informal credit that can be borrowed. When the
credit of the first shock has not been repaid yet it is impossible to get new informal credit
when the second shock hits.
5.7 Difference between Formal and Informal Credit
Not only formal and informal credit used as coping strategy as independent factors are
interesting to investigate, but also the differences between those two factors. Of course the
correlation between the two has to be taken into account here as well. The interdependent
relationship has a significant pair wise correlation of 0.25, which means that using formal
credit is complementary to using informal credit as a coping strategy. The explanation of this
is that access to informal credit increases the probability of access to formal credit.
When comparing the use of formal credit to the use of informal credit, immediately all shock
variables draw the attention. Formal credit seems to be used for many other purposes than
to cope with risks, because the different shocks have no significant influence on formal
credit in general. This may be explained because that there is simply no need to use formal
credit as a coping strategy or because other coping strategies have been chosen.
Special attention should be given to the fact that an increase in the number of shocks
resulted in a decrease in the use of both formal and informal credit as a coping strategy.
- 38 -
Credit only has the character of coping with risks in small amounts: the more shocks a
household faces, the less collateral will be left over to be able to obtain loans. Also, a
household that faces many simultaneous shocks will have a lower welfare-level and a higher
risk-averse attitude associated with this.
All individual risks have a positive effect on informal credit in general, as well as informal
credit used as a coping strategy, as for formal credit used as a coping strategy. Joblessness
has the smallest influence on these factors, because this shock is very time-spread whereas
the other shocks have a more immediate character. The marginal effects of the individual
shocks on informal credit are higher than on formal credit, which implies that informal credit
is used more often as a coping strategy. This could already be concluded from the
descriptive part, earlier in this chapter.
Related to financial capital, savings is the most interesting variable to look at. Savings are
needed in order to obtain a formal credit but in order to receive informal credit, savings are
not always needed. This is how the different signs of the effect to the dependent variable
can be explained. When having savings, access to formal credit is easier (supply related) and
in case of informal credit this will not be asked for because the household will use its own
savings first (demand related).
Education, age, gender and civil status are the influencing factors of human capital. The
dependency ratio, amount of working household members and speaking the indigenous
language proved to have no significant effect on formal and informal credit. Gender was
expected to influence the demand for formal credit, as women often participate in MFIs,
however no significant influence for formal credit has been found. This does not necessarily
mean that in reality there really is no influence, because gender was only estimated for the
head of the household. A male-headed household can still have women participating in
MFIs. As for informal credit, males do seem to have a higher probability rate, but the same
rationing could be applied, which means that even though the household is male-headed it
could be the women participating in informal credit schemes.
The different types of physical capital have turned out to be of greater importance for
formal credit than for informal credit, as these can be used as collateral. Having access to a
telephone on the other hand has a positive effect on both types of credit as this makes it
easier for the household to communicate with the credit institute, as well as the other way
around.
Living in rural areas increases the probability of formal credit used as coping strategy, where
this was more expected for informal credit. For informal credit it does not matter whether
the household is located in an urban or a rural area.
Living in the south has a negative effect on the use of both types of credit, simply due to the
facts that people in the south are poor and cannot access credit easily and that these
household are more risk-averse which lowers the demand for credit. A last very remarkable
fact is that in 2004 both formal and informal credit has been used a lot more compared to
2005. More credit has been given out in that year.
- 39 -
6 Conclusions
The research discussed within this thesis contributes to the existing literature on risk coping
strategies and specifically on both formal and informal credit by giving special attention to
the Mexican context of these. This is very relevant given that no earlier studies have been
done on the relationship between the use of formal and informal credit as a coping strategy
and the factors of influence.
Within this thesis the factors influencing the use of credit as a coping strategy have been
explored and by these means also the role that these credits play in coping with risks within
Mexico. Households within Mexico, both rural and urban, encounter all types of risks, which
can be both covariant and idiosyncratic. To be able to investigate the role of credit when
attempting to cope with these risks, information of a database consisting out of 5,700
observations in Mexico has been used.
The specific researched risks include mostly idiosyncratic risks, namely the death of a
household member, illness of a household member, a lower price for sales, a drop in sales,
joblessness, losing a job and equipment or vehicle breakdown. Also cases of natural disaster
have been investigated. It turned out that over the time period of 2004-2005 the shocks that
were experienced the most were joblessness, drop of sales, illness of a household member
and natural disasters.
The figures on formal and informal credit in general, so not only used as a coping strategy,
could be derived from the database as well. As a sum of the observations of 2004 and 2005
1,665 households (16% of the total amount of households) had formal credit, with an
average monetary value of 306,261 pesos (compared to 10,600,000 pesos of average
income). Whereas for informal credit 2,449 households (24% of the total households) had
some type of informal credit with an average of 147,349 pesos (compared to 10,600,000
pesos of average income).
Most hypotheses on the influential factors were confirmed by the research. When looked at
formal credit in general, savings, productive assets, value of animals, education of the adults,
civil status and access to electricity and telephone have a positive effect on formal credit
because these are important factors to satisfy the requirements of collateral. Also from the
demand side these factors play a role, as households that possess more assets tend to
behave less risk-averse than households that possess fewer assets.
Interesting to see was that living in rural areas also had a positive effect on formal credit,
which was expected because formal institutions are focusing more and more on the rural
instead of the urban areas.
As for formal credit in general, savings and value of animals, also had a positive effect on
formal credit used as a coping strategy. Households with many of these financial capital
factors will demand more formal credit as a coping strategy, and these households will also
experience fewer supply-constraints.
- 40 -
But the most important variables that were influential on formal credit as a coping strategy
were the shock related ones. The occurrence of all individual shocks had a positive effect,
meaning that the probability of using formal credit as a coping strategy gets higher once one
of these shocks were experienced. Equipment breakdown as well as the death of a
household member had the biggest effect as these require specific funding for e.g. the
funeral or renewal of the equipment.
Talking about informal credit in general, variables like savings, productive assets, age, gender
and access to telephone had an influence. The demand for informal credit is lowered when a
household possesses savings, because there is simply no need for additional credit. Males
are seen as more reliable within many areas and access to telephone makes communication
easier as a way to control the relationship between lender and borrower. It is remarkable
however that the younger a household is, the more informal credit is used, not only in
general but also specific for the use of informal credit as a coping strategy.
However, by far the most influential variables on informal credit used as coping strategy are
the different shocks a household faces. Death of a household member, illness of a household
member, breakdown of equipment, lower sales price, losing a job, natural disaster, drop of
sales and joblessness (in that order) have a very high positive impact on informal credit used
as a coping strategy, even that much that this impact is also relatively high among informal
credit in general.
When comparing the use of both informal and formal credit in general, the most logical
differences are derived from the financial and physical capital aspects. These are used as
collateral for formal credit, whereas informal credit does not always need these. On the
contrary, when a household possesses savings, it will demand less informal credit because it
can use its own money. All these expected relations turned out to be significantly true.
Another interesting difference is that gender seems to only affect informal credit; males
have a higher probability of obtaining this. However it was very much expected that females
would have easier access to formal credit, due to the focus of MFIs on women, which turned
out to be false. Formal credit in Mexico consists out of several types of formal institutions,
which partly holds as the main reason for this hypothesis to not be true. Also, a male-headed
household can still have women within its household that obtain formal credit. No objective
conclusion can therefore be drawn based upon the relationship of the gender of the head of
the household and the use of informal and formal credit.
Region and time variables were proofed to be highly important as well. Living in the south
has a negative impact on both formal credit (in general and as a coping strategy) and
informal credit (in general and as a coping strategy), whereas the year 2004 has a positive
impact on all dependent variables. These are taken of the fact that the south region is
poorer and faces more shocks than other regions and more credit was supplied within 2004
as compared to 2005.
- 41 -
All individual shock variables turned out to have a high, significant positive effect on the use
of formal and informal credit as a coping strategy. Informal credit is used more often as a
coping strategy than formal credit, which is the cause of the higher effects on the probability
of the use of informal credit than on formal credit. Informal credit is an easy and quick way
of obtaining some money to be able to cope with risks, whereas formal credit needs more
work to fit the requirements and takes a longer period of time to really get a hold of the
credit.
In contrast to this very high positive impact of the individual shocks on both formal and
informal credit used as a coping strategy, there is a negative effect of the total number of
shocks experienced by a household. This means that the more shocks a household faces the
lower the probability that it will use informal or formal credit to cope with these
simultaneous shocks. First of all this is due to the fact that when the household already took
out credit for a first shock, it will be more difficult to obtain new credit when a new shock is
experienced. Also, when a household experiences simultaneous shocks this will involve a
lowering in assets and because of this a higher risk-averse attitude. Summing up, a high
amount of total shocks faced by the household involves lower demand as well as supply for
both types of credit used as a coping strategy.
- 42 -
7 Discussion
This research has increased the knowledge on the roles of formal and informal credit when
coping with risks. It looked at a big sample of Mexican households and the outcomes might
be interesting to policy makers, formal institutions, NGOs etc. working in Mexico; however
some questions remain to be unanswered.
Because a big sample of Mexican households was used as a whole by doing quantitative
research, differences between specific regions or areas were generalized. The southern
region turned out to highly affect all outcomes within this research. For further research it is
recommended to take only a sample with the households living in the south, because this
could give a totally different and very interesting outcome.
Also, for some outcomes the reasoning behind the answers to the questions has not been
clear. For example, when the average age of the household members showed a significant
negative effect on informal credit, it could not be said that older households will demand
less informal credit or that younger households have easier access to informal credit. This
cannot be guaranteed until qualitative research is conducted.
It was very interesting to see the different coping strategies on household level. However, as
argued in the thesis itself, then the data relies entirely on the responses of households itself.
For example, the amount of shocks a household reports could actually be an indicator for
welfare, as poor households may suffer more of a shock than richer households do. Also, on
this household level only few households reported to use credit as a coping strategy, which
is mainly due to the fact that the questionnaire on which the information used is based upon
was not designed for this purpose. Therefore it would be interesting to not only see the
shocks and coping strategies on a household level, but also on a municipality level, state
level or even regional level.
Another limitation to this research might have been the calculation of the variables.
Although this is done in the author’s best way possible, some arguments might exists on
including or excluding some concepts. For example, savings can also include both stock
savings in formal institutions and cash savings at home.
The different types of coping strategies are worth researching, although this has not been
the objective of this research, it is recommended to do so in the future. For this research the
focus has been on using formal and informal credit as a way to cope with risks. However it
could be very interesting to see the interaction between all used coping strategies. Now, it is
very difficult to say something about the impact of the coping strategies used and because of
this it is not possible to say something about the advantages of using formal or informal
credit as a coping strategy. As a result it is not feasible to measure the consequences of
these coping strategies.
- 43 -
The major limitation of this research has been that the data used has not been collected for
the purpose of this research. Therefore only few observations could be used and information
that could have been very interesting has been missing. Social capital, for example, would be
very interesting to look at, because it is assumed to have a big influence on informal credit.
However, no information was available on this, or it encountered endogeneity problems.
Also, only the gender of the head of the household has been known, where it would be more
interesting to see the gender of all household members. Also, it turned out that the
questionnaires changed from year to year a little bit. This made it difficult to use all available
information and merge the different data sets into one big panel set. For further research it
is therefore highly recommended to use data that does allow to see the difference over time
and that has all the information needed to fully conduct a research for the use of formal and
informal credit as a coping strategy.
- 44 -
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ZEF- Discussion Papers On Development Policy No. 19, pp. 30
- 50 -
Appendices
Appendix I: Calculation of Variables
Formal credit
Formal Credit is calculated by summing up all credit coming from the financial institutions
NAFINSA, FIRA, BANRURAL or FINANCIERA RURAL and CREDITO A LA PALABRA, SOFOL, all
commercial banks and the Cajas de Ahorro, Cajas Solidarias, Sociedades de Ahorro y
Préstamo and the Credit Unions.
Informal credit
Informal Credit is calculated by adding up all credits of family and friends, moneylenders,
informal credit supplier and others.
# Shocks
This is a sum of all the dummies of the separate shocks.
Savings
It includes stock savings in formal institutions such as credit and savings cooperatives, Cajas
Solidarias and commercial banks.
Productive assets
This corresponds to the opportunity cost (estimated value) of working animals (bulls, mules,
horses, donkeys and machinery and equipment being used in the different business the
household is operating in.
Dependency Ratio
Amount of dependent household member (<15 & >65)/ total amount of household
members.
Urban
According with the definition given by the INEGI (Instituto Nacional de Estadistica y
Geografia)/ National Institute of Geography and Informatics) a community is considered as
being rural when it has less than 2,500 inhabitants, whereas the urban is that where are
living more than 2,500 people.
South region
States of Campeche, Chiapas, Guerrero, Oaxaca, Puebla, Quintana Roo, Tabasco, Tlaxcala,
Veracruz and Yucatán.
North region
States of Aguascalientes, Baja California, Baja California Sur, Chihuahua, Coahuila, Durango,
Nuevo León, San Luis Potosí, Sinaloa, Sonora, Tamaulipas and Zacatecas.
- 51 -
Central region
The states of Colima, Distrito Federal, Estado de México, Guanajuato, Hidalgo, Jalisco,
Michoacán, Morelos, Nayarit and Querétaro are included here.
Income
• sell or rent of house' durable goods:
washing machine, fridge, stove,
sewing machine, TV, radio, video,
bicycle, motorcycle, car, jewelry,
computer equipment,
• interests from savings accounts in
formal financial institutions
• interest from savings coming from
other persons who received the
money
• governmental program (subsidies):
- Oportunidades Program (welfare
program)
-Procampo Program (Agricultural
Program)
-Jovenes Portunidades (welfare
Program)
• Other public or welfare programs
• income compensation in case of
risk-related losses
• grants or gifts coming from
relatives or friends
• pensions
• inheritances, lottery, etc.
• sell or rent of land
• agricultural products
• rent of working animals
• agricultural byproducts income
(milk, eggs, meat, honey, wool)
• sell of cows, bulls, horses, mules,
donkeys, sheep, pigs, chickens,
honeycomb, other animals
• family business operation
• sell of business & offices
• rent of offices
• rent of machinery and equipment
• sell or rent of vehicle
• Income from several jobs 1, 2, 3,
etc.
• Income from rent or sell of house,
department
• computer equipment pawn income
• sell or rent of constructions
• sell or rent of house
• total remmittances (national and
International): USA, CANADA &
Mexico
• other means
• sell of land
• agricultural production
• tools
• animals
• animal byproducts: milk, eggs,
meat, honey, wool, cows, bulls,
horses, mules, donkeys, sheep,
pigs, goats, rabbits, poultry, other
animals
• business 1, 2, 3
• sell of business
• sell or rent of office
• sell of equipment, tools and
machinery
Consumption
This corresponds to the amount of money spent during the previous year in only household
consumption items (durables and non-durables). This concept does not include the
production side expenditure.
Remittances
Flow in funds coming from national and international destinations, mainly from migrants
living in USA, CANADA in some urban cities in Mexico.
52
Appendix II: Answers coping strategies
Respondents were asked for their coping strategies per shock, by taking some items the
variables of formal and informal credit used as coping strategy were calculated.
Used for formal credit Used for informal credit
Loan with interest X X
Reduce household consumption
Stop paying debts
Use savings
Temporary job
Loan without interest X
Lower prices
Buy where things are cheaper
Help in any way possible
Social security
Product promotion
Give better service
Search for a job
Debian X
Sell property
Cooperative Funeral X
Credit of the family X
Credit from friends X
Find a cheaper doctor
Close down business
Participate in tandas X
Introduce new products
Buying less merchandize
Help of the government
Cure myself
Work extra hours
Buy a new one
Fix it myself
Request money on the street
Compensation
Open a business
Change address
53
Appendix III: Robustness Checks
Normality test for residual of regression
6261 missing values generated
Skewness/Kurtosis tests for Normality ------- joint ------ Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -------------+--------------------------------------------------------------- res | 4.1e+03 0.000 0.000 . .
Leverage plot
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0.2
.4.6
Leve
rage
0 .002 .004 .006 .008Normalized residual squared
54
Appendix IV: Output Formal Credit as Coping Strategy
Probit formal credit
Iteration 0: log pseudolikelihood = -1922.1433 Iteration 1: log pseudolikelihood = -1486.0082 Iteration 2: log pseudolikelihood = -1466.8766 Iteration 3: log pseudolikelihood = -1466.5816 Iteration 4: log pseudolikelihood = -1466.5815 Probit regression, reporting marginal effects Number of obs = 4124 Wald chi2(29) = 722.87 Prob > chi2 = 0.0000 Log pseudolikelihood = -1466.5815 Pseudo R2 = 0.2370 ------------------------------------------------------------------------------ | Robust d_form | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ] ---------+-------------------------------------------------------------------- no_sho~h | -.0718981 .131929 -0.55 0.586 1.39985 -.330474 .186678 death_~b*| .0975692 .1859963 0.61 0.540 .082929 -.266977 .462115 d_illn~s*| .1153044 .1764786 0.75 0.455 .21096 -.230587 .461196 d_disa~r*| .0839131 .1695324 0.56 0.578 .177255 -.248364 .41619 d_low_~s*| .1353144 .204838 0.80 0.425 .066198 -.266161 .536789 d_low_~d*| .1275637 .1755454 0.83 0.407 .242726 -.216499 .471626 d_Jobl~s*| .0662724 .135337 0.50 0.619 .470902 -.198983 .331528 d_jobl~t*| .0884976 .1802285 0.57 0.572 .097963 -.264744 .441739 d_mach~n*| .1297508 .2047103 0.77 0.444 .049709 -.271474 .530976 log_sa~l | .0186906 .0010272 18.55 0.000 3.20591 .016677 .020704 LOG_pr~l | .0026581 .0013953 1.90 0.057 1.9582 -.000077 .005393 log_an~l | .0029571 .0009635 3.06 0.002 4.29566 .001069 .004846 no_has | .0001927 .0003729 0.52 0.605 1.58265 -.000538 .000924 depend~o | -.0088152 .0277178 -0.32 0.751 .36658 -.063141 .045511 n_work~y | .0053553 .0050682 1.06 0.291 2.81377 -.004578 .015289 head_e~l | .0007519 .0021993 0.34 0.732 5.39816 -.003559 .005062 averag~s | .0061204 .0028111 2.18 0.029 6.14423 .000611 .01163 age_head | .0008336 .000606 1.38 0.169 48.0558 -.000354 .002021 avg_ag~h | -.0005545 .0006711 -0.83 0.409 31.4564 -.00187 .000761 sex_head*| -.0309295 .020997 -1.55 0.120 .849418 -.072083 .010224 head_m~d*| .0704339 .0129024 5.01 0.000 .671435 .045146 .095722 indige~s*| .0028452 .0137983 0.21 0.836 .257032 -.024199 .029889 dwater~y*| .0267729 .0167223 1.49 0.136 .909311 -.006002 .059548 delect~m*| .0745628 .0187522 2.80 0.005 .963628 .037809 .111316 d_telp~e | .0363606 .0097188 3.75 0.000 .448594 .017312 .055409 regi~uth*| -.0461895 .0158516 -2.95 0.003 .553831 -.077258 -.015121 region~l*| -.009269 .0161866 -0.57 0.571 .29195 -.040994 .022456 urban*| -.0571803 .0118143 -4.81 0.000 .474054 -.080336 -.034025 d_2004*| .1232744 .0101549 10.79 0.000 .581959 .103371 .143178 ---------+-------------------------------------------------------------------- obs. P | .1765276 pred. P | .1208192 (at x-bar) ------------------------------------------------------------------------------ (*) dF/dx is for discrete change of dummy variable from 0 to 1 z and P>|z| correspond to the test of the underlying coefficient being 0
55
Tobit amount of formal credit
Tobit regression Number of obs = 4124 F( 29, 4095) = 3.18 Prob > F = 0.0000 Log pseudolikelihood = -13102.669 Pseudo R2 = 0.0322 (Std. Err. adjusted for 3280 clusters in folio) ------------------------------------------------------------------------------ | Robust formc_real | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- no_shocks_hh | -3621610 2581858 -1.40 0.161 -8683454 1440234 death_hhmemb | 3644629 2584326 1.41 0.159 -1422053 8711312 d_illness | 3923517 2568594 1.53 0.127 -1112322 8959357 d_disaster | 3530077 2581347 1.37 0.172 -1530766 8590921 d_low_prices | 4204165 2634205 1.60 0.111 -960309.3 9368639 d_low_quan~d | 4146102 2566393 1.62 0.106 -885421.9 9177627 d_Jobless | 3201487 2546196 1.26 0.209 -1790441 8193415 d_joblost | 4392131 2786212 1.58 0.115 -1070359 9854621 d_machiner~n | 4670267 2632849 1.77 0.076 -491547 9832082 log_saving~l | 451167.4 86841.03 5.20 0.000 280911.8 621423 LOG_prod_a~l | 68036.03 39661.68 1.72 0.086 -9722.42 145794.5 log_animal | 39250.68 23823.32 1.65 0.100 -7455.973 85957.34 no_has | 5037.569 9739 0.52 0.605 -14056.16 24131.3 dependency~o | 213819.3 841956.8 0.25 0.800 -1436874 1864512 n_workfamily | 194722.5 207736.8 0.94 0.349 -212554.5 601999.5 head_educ_~l | -53087.89 70393.88 -0.75 0.451 -191098.1 84922.36 average_ed~s | 309375.9 100214.2 3.09 0.002 112901.6 505850.2 age_head | 12688.52 15650.28 0.81 0.418 -17994.54 43371.58 avg_age_hh | -6414.814 17088.94 -0.38 0.707 -39918.42 27088.79 sex_head | -981157.5 664447.8 -1.48 0.140 -2283836 321521.3 head_married | 1861348 435543.3 4.27 0.000 1007447 2715250 indigenous | 230015.3 387002.3 0.59 0.552 -528719.5 988750.1 dwater_sup~y | 809738.6 458738.8 1.77 0.078 -89638.72 1709116 delectrici~m | 2813512 985597.5 2.85 0.004 881205 4745819 d_telphone | 1379366 365390.7 3.78 0.000 663001.2 2095730 region_south | -792368.1 409345.4 -1.94 0.053 -1594908 10171.4 region_cen~l | -432834.9 447002.4 -0.97 0.333 -1309203 443532.8 urban | -1442947 381330.2 -3.78 0.000 -2190561 -695332.4 d_2004 | 3985716 647716.6 6.15 0.000 2715840 5255593 _cons | -1.76e+07 3480371 -5.07 0.000 -2.45e+07 -1.08e+07 -------------+---------------------------------------------------------------- /sigma | 5739170 1059352 3662263 7816076 ------------------------------------------------------------------------------ Obs. summary: 3396 left-censored observations at formc_real<=0 728 uncensored observations 0 right-censored observations
56
Marginal effects after tobit y = Pr(formc_real>0) (predict, pr(0,.)) = .09623039 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- no_sho~h | -.1076711 .07493 -1.44 0.151 -.254523 .039181 1.39985 death_~b*| .1479193 .13199 1.12 0.262 -.11077 .406608 .082929 d_illn~s*| .1485878 .11638 1.28 0.202 -.07952 .376695 .21096 d_disa~r*| .1337235 .11692 1.14 0.253 -.095428 .362875 .177255 d_low_~s*| .179648 .14187 1.27 0.205 -.098404 .4577 .066198 d_low_~d*| .1551336 .11379 1.36 0.173 -.067895 .378162 .242726 d_Jobl~s*| .0980102 .08007 1.22 0.221 -.058924 .254944 .470902 d_jobl~t*| .1858789 .14099 1.32 0.187 -.09045 .462208 .097963 d_mach~n*| .208536 .15116 1.38 0.168 -.087728 .5048 .049709 log_sa~l | .0134133 .00074 18.19 0.000 .011968 .014858 3.20591 LOG_pr~l | .0020227 .00111 1.83 0.068 -.000147 .004192 1.9582 log_an~l | .0011669 .00067 1.73 0.083 -.000152 .002485 4.29566 no_has | .0001498 .00029 0.51 0.608 -.000422 .000721 1.58265 depend~o | .0063569 .02462 0.26 0.796 -.041903 .054617 .36658 n_work~y | .0057891 .00552 1.05 0.294 -.005024 .016602 2.81377 head_e~l | -.0015783 .00198 -0.80 0.426 -.005467 .00231 5.39816 averag~s | .0091978 .00229 4.01 0.000 .004701 .013695 6.14423 age_head | .0003772 .00047 0.80 0.424 -.000547 .001302 48.0558 avg_ag~h | -.0001907 .00052 -0.37 0.713 -.001206 .000824 31.4564 sex_head*| -.0314965 .01924 -1.64 0.102 -.069212 .006219 .849418 head_m~d*| .0515797 .0109 4.73 0.000 .030218 .072941 .671435 indige~s*| .0069257 .01188 0.58 0.560 -.016362 .030214 .257032 dwater~y*| .0223068 .01149 1.94 0.052 -.000214 .044827 .909311 delect~m*| .0614198 .01315 4.67 0.000 .03564 .087199 .963628 d_telp~e | .0410088 .0069 5.95 0.000 .027492 .054526 .448594 regi~uth*| -.0237987 .01358 -1.75 0.080 -.050421 .002824 .553831 region~l*| -.0126084 .01278 -0.99 0.324 -.037666 .012449 .29195 urban*| -.0426148 .00922 -4.62 0.000 -.060689 -.024541 .474054 d_2004*| .1116611 .00915 12.21 0.000 .093735 .129587 .581959 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1
57
Probit formal credit as a coping strategy
Iteration 0: log pseudolikelihood = -273.40184 Iteration 1: log pseudolikelihood = -206.15008 Iteration 2: log pseudolikelihood = -198.15444 Iteration 3: log pseudolikelihood = -197.50425 Iteration 4: log pseudolikelihood = -197.49628 Iteration 5: log pseudolikelihood = -197.49599 Iteration 6: log pseudolikelihood = -197.4959 Iteration 7: log pseudolikelihood = -197.49587 Iteration 8: log pseudolikelihood = -197.49586 Iteration 9: log pseudolikelihood = -197.49585 Iteration 10: log pseudolikelihood = -197.49585 Iteration 11: log pseudolikelihood = -197.49585 Iteration 12: log pseudolikelihood = -197.49585 Iteration 13: log pseudolikelihood = -197.49585 Iteration 14: log pseudolikelihood = -197.49585 Iteration 15: log pseudolikelihood = -197.49585 Iteration 16: log pseudolikelihood = -197.49585 Iteration 17: log pseudolikelihood = -197.49585 Probit regression, reporting marginal effects Number of obs = 1264 Wald chi2(28) = . Prob > chi2 = . Log pseudolikelihood = -197.49585 Pseudo R2 = 0.2776 ------------------------------------------------------------------------------ | Robust d_form~l | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ] ---------+-------------------------------------------------------------------- no_sho~h | -.1486302 .0372422 -17.55 0.000 2.30459 -.221624 -.075637 death_~b*| .8562157 .0234026 . . .141614 .810347 .902084 d_illn~s*| .7422886 .0565975 18.01 0.000 .349684 .631359 .853218 d_disa~r*| .6857466 .0735077 14.67 0.000 .274525 .541674 .829819 d_low_~s*| .7612025 .0874952 12.32 0.000 .174051 .589715 .93269 d_low_~d*| .5318272 .071368 16.50 0.000 .397943 .391948 .671706 d_Jobl~s*| .18242 .037656 14.85 0.000 .642405 .108616 .256224 d_jobl~t*| .7467144 .0790077 12.94 0.000 .207278 .591862 .901567 d_mach~n*| .9260253 .0297985 17.37 0.000 .116297 .867621 .984429 log_sa~l | .0040636 .0007616 6.27 0.000 3.44288 .002571 .005556 LOG_pr~l | .0009175 .000723 1.28 0.201 2.42263 -.0005 .002335 log_an~l | .0010324 .000497 2.05 0.040 4.64193 .000058 .002006 no_has | .0003142 .0002663 1.20 0.230 1.63994 -.000208 .000836 depend~o | -.020888 .0167238 -1.23 0.217 .360552 -.053666 .01189 n_work~y | .0010615 .0027007 0.39 0.696 2.93829 -.004232 .006355 head_e~l | .0007674 .001226 0.62 0.533 5.44146 -.001636 .00317 averag~s | -.0008423 .0016927 -0.49 0.627 6.26877 -.00416 .002475 age_head | -.0000502 .0003372 -0.15 0.882 48.129 -.000711 .000611 avg_ag~h | -.000371 .0004325 -0.87 0.386 31.0407 -.001219 .000477 sex_head*| .0016179 .0105114 0.15 0.882 .871044 -.018984 .02222 head_m~d*| -.0017453 .0087048 -0.20 0.838 .701741 -.018806 .015316 indige~s*| .0047695 .0084166 0.61 0.544 .254747 -.011727 .021266 dwater~y*| .0104804 .0078166 1.04 0.298 .909019 -.00484 .025801 delect~m*| .0137445 .0074665 1.16 0.246 .963608 -.00089 .028378 d_telp~e | .0065097 .0051222 1.23 0.220 .5 -.00353 .016549 regi~uth*| -.0200696 .0100655 -2.11 0.035 .563291 -.039798 -.000342 region~l*| -.0095371 .0071803 -1.19 0.235 .295886 -.02361 .004536 urban*| -.0097167 .0067545 -1.46 0.145 .47231 -.022955 .003522 d_2004*| .0212065 .0066959 3.22 0.001 .578323 .008083 .03433 ---------+-------------------------------------------------------------------- obs. P | .0561709 pred. P | .0177927 (at x-bar) ------------------------------------------------------------------------------ (*) dF/dx is for discrete change of dummy variable from 0 to 1 z and P>|z| correspond to the test of the underlying coefficient being 0
58
Appendix V: Output Informal Credit as Coping Strategy
Probit Informal Credit Iteration 0: log pseudolikelihood = -2509.6421 Iteration 1: log pseudolikelihood = -2142.0078 Iteration 2: log pseudolikelihood = -2132.0352 Iteration 3: log pseudolikelihood = -2131.9029 Iteration 4: log pseudolikelihood = -2131.8773 Iteration 5: log pseudolikelihood = -2131.8699 Iteration 6: log pseudolikelihood = -2131.8676 Iteration 7: log pseudolikelihood = -2131.8669 Iteration 8: log pseudolikelihood = -2131.8667 Iteration 9: log pseudolikelihood = -2131.8666 Iteration 10: log pseudolikelihood = -2131.8666 Iteration 11: log pseudolikelihood = -2131.8666 Iteration 12: log pseudolikelihood = -2131.8666 Iteration 13: log pseudolikelihood = -2131.8666 Iteration 14: log pseudolikelihood = -2131.8666 Iteration 15: log pseudolikelihood = -2131.8666 Iteration 16: log pseudolikelihood = -2131.8666 Iteration 17: log pseudolikelihood = -2131.8666 Iteration 18: log pseudolikelihood = -2131.8666 Probit regression, reporting marginal effects Number of obs = 4124 Wald chi2(28) = . Prob > chi2 = . Log pseudolikelihood = -2131.8666 Pseudo R2 = 0.1505 ------------------------------------------------------------------------------ | Robust d_inf | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ] ---------+-------------------------------------------------------------------- no_sho~h | -1.78933 .0402439 -60.99 0.000 1.39985 -1.86821 -1.71045 death_~b*| .8685557 .0054667 45.13 0.000 .082929 .857841 .87927 d_illn~s*| .9695183 .0022694 55.08 0.000 .21096 .96507 .973966 d_disa~r*| .9492152 .0032307 48.25 0.000 .177255 .942883 .955547 d_low_~s*| .8497048 .0054839 . . .066198 .838957 .860453 d_low_~d*| .978414 .0019456 47.84 0.000 .242726 .974601 .982227 d_Jobl~s*| .9882891 .0018137 53.65 0.000 .470902 .984734 .991844 d_jobl~t*| .8881698 .0050341 49.98 0.000 .097963 .878303 .898036 d_mach~n*| .8256102 .0063251 40.70 0.000 .049709 .813213 .838007 log_sa~l | -.0068391 .0015577 -4.38 0.000 3.20591 -.009892 -.003786 LOG_pr~l | .0032998 .0020046 1.64 0.100 1.9582 -.000629 .007229 log_an~l | .0012676 .0013702 0.92 0.355 4.29566 -.001418 .003953 no_has | -.0005591 .0005429 -1.03 0.303 1.58265 -.001623 .000505 depend~o | .0218433 .0381201 0.57 0.567 .36658 -.052871 .096557 n_work~y | -.0055959 .0072248 -0.77 0.439 2.81377 -.019756 .008564 head_e~l | -.0010939 .0032342 -0.34 0.735 5.39816 -.007433 .005245 averag~s | -.0041017 .0040115 -1.02 0.306 6.14423 -.011964 .003761 age_head | .0000626 .000869 0.07 0.943 48.0558 -.001641 .001766 avg_ag~h | -.0053701 .0009404 -5.68 0.000 31.4564 -.007213 -.003527 sex_head*| .031784 .0224346 1.38 0.167 .849418 -.012187 .075755 head_m~d*| -.0057265 .0185154 -0.31 0.757 .671435 -.042016 .030563 indige~s*| .0044547 .0186317 0.24 0.811 .257032 -.032063 .040972 dwater~y*| .0342155 .0233106 1.42 0.155 .909311 -.011473 .079903 delect~m*| .0144138 .0349238 0.41 0.684 .963628 -.054035 .082863 d_telp~e | .0163154 .0142742 1.14 0.253 .448594 -.011662 .044292 regi~uth*| -.052636 .0212851 -2.48 0.013 .553831 -.094354 -.010918 region~l*| -.0388864 .0218575 -1.75 0.081 .29195 -.081726 .003954 urban*| -.0022357 .0160507 -0.14 0.889 .474054 -.033695 .029223 d_2004*| .3427984 .0126759 22.88 0.000 .581959 .317954 .367643 ---------+-------------------------------------------------------------------- obs. P | .2972842 pred. P | .257738 (at x-bar) ------------------------------------------------------------------------------ (*) dF/dx is for discrete change of dummy variable from 0 to 1 z and P>|z| correspond to the test of the underlying coefficient being 0
59
Tobit Informal Credit Tobit regression Number of obs = 4124 F( 29, 4095) = 4.22e+07 Prob > F = 0.0000 Log pseudolikelihood = -21224.114 Pseudo R2 = 0.0109 (Std. Err. adjusted for 3280 clusters in folio) ------------------------------------------------------------------------------ | Robust infc_real | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- no_shocks_hh | -1.80e+07 46606.38 -386.51 0.000 -1.81e+07 -1.79e+07 death_hhmemb | 1.88e+07 58710.36 320.61 0.000 1.87e+07 1.89e+07 d_illness | 1.88e+07 46754.94 401.27 0.000 1.87e+07 1.89e+07 d_disaster | 1.82e+07 46583.18 390.77 0.000 1.81e+07 1.83e+07 d_low_prices | 1.90e+07 55428.49 342.86 0.000 1.89e+07 1.91e+07 d_low_quan~d | 1.86e+07 76026.62 245.18 0.000 1.85e+07 1.88e+07 d_Jobless | 1.82e+07 51733.38 350.89 0.000 1.81e+07 1.83e+07 d_joblost | 1.85e+07 45870.36 402.55 0.000 1.84e+07 1.86e+07 d_machiner~n | 1.92e+07 53281.44 361.21 0.000 1.91e+07 1.94e+07 log_saving~l | -38587.41 7346.947 -5.25 0.000 -52991.42 -24183.4 LOG_prod_a~l | 19217.37 4217.576 4.56 0.000 10948.63 27486.11 log_animal | 35.52737 4233.008 0.01 0.993 -8263.468 8334.523 no_has | -6322.507 1211.217 -5.22 0.000 -8697.151 -3947.863 dependency~o | -52263.67 122887.4 -0.43 0.671 -293189.7 188662.4 n_workfamily | -144080.8 22805.32 -6.32 0.000 -188791.6 -99369.98 head_educ_~l | -37018.72 9369.304 -3.95 0.000 -55387.65 -18649.79 average_ed~s | 39522.2 10855.69 3.64 0.000 18239.15 60805.26 age_head | 3561.158 1754.065 2.03 0.042 122.2377 7000.079 avg_age_hh | -37239.87 2529.888 -14.72 0.000 -42199.83 -32279.92 sex_head | 503711.3 78516.48 6.42 0.000 349776.3 657646.3 head_married | 40281.38 70253.95 0.57 0.566 -97454.54 178017.3 indigenous | -67399.13 54863.59 -1.23 0.219 -174961.6 40163.32 dwater_sup~y | 263112.6 84464.06 3.12 0.002 97517.12 428708 delectrici~m | 166250 87693.99 1.90 0.058 -5677.894 338177.9 d_telphone | 257606.3 63580.51 4.05 0.000 132953.9 382258.6 region_south | -89583.69 72918.71 -1.23 0.219 -232544 53376.61 region_cen~l | -304254.8 67410.7 -4.51 0.000 -436416.4 -172093.2 urban | 178240.6 68847.59 2.59 0.010 43261.88 313219.3 d_2004 | 3175971 42874.92 74.08 0.000 3091913 3260029 _cons | -5018092 88678.15 -56.59 0.000 -5191949 -4844234 -------------+---------------------------------------------------------------- /sigma | 3599423 21354.51 3557557 3641290 ------------------------------------------------------------------------------ Obs. summary: 2898 left-censored observations at infc_real<=0 1226 uncensored observations 0 right-censored observations
60
Marginal effects after tobit y = Pr(infc_real>0) (predict, pr(0,.)) = .19273574 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- no_sho~h | -1.370023 .35484 -3.86 0.000 -2.0655 -.674551 1.39985 death_~b*| .9034191 .05202 17.37 0.000 .801471 1.00537 .082929 d_illn~s*| .9748461 .01708 57.07 0.000 .941367 1.00833 .21096 d_disa~r*| .9606623 .02528 38.01 0.000 .911123 1.0102 .177255 d_low_~s*| .8882436 .05784 15.36 0.000 .774886 1.0016 .066198 d_low_~d*| .9820718 .01164 84.37 0.000 .959257 1.00489 .242726 d_Jobl~s*| .9635147 .02264 42.55 0.000 .919134 1.0079 .470902 d_jobl~t*| .9146366 .04747 19.27 0.000 .821588 1.00769 .097963 d_mach~n*| .871516 .06379 13.66 0.000 .746493 .996539 .049709 log_sa~l | -.0029348 .00024 -12.14 0.000 -.003409 -.002461 3.20591 LOG_pr~l | .0014616 .00067 2.17 0.030 .000144 .002779 1.9582 log_an~l | 2.70e-06 .00032 0.01 0.993 -.00063 .000635 4.29566 no_has | -.0004809 .00006 -8.73 0.000 -.000589 -.000373 1.58265 depend~o | -.0039749 .00831 -0.48 0.632 -.02026 .01231 .36658 n_work~y | -.010958 .00114 -9.65 0.000 -.013183 -.008733 2.81377 head_e~l | -.0028154 .0001 -28.39 0.000 -.00301 -.002621 5.39816 averag~s | .0030059 .00161 1.87 0.062 -.000148 .006159 6.14423 age_head | .0002708 .0002 1.33 0.185 -.000129 .000671 48.0558 avg_ag~h | -.0028323 .00055 -5.15 0.000 -.003911 -.001754 31.4564 sex_head*| .0366693 .01543 2.38 0.017 .00643 .066908 .849418 head_m~d*| .0030585 .00611 0.50 0.617 -.008922 .015039 .671435 indige~s*| -.0051058 .0029 -1.76 0.078 -.010786 .000574 .257032 dwater~y*| .0194885 .01128 1.73 0.084 -.00262 .041597 .909311 delect~m*| .0124082 .00969 1.28 0.201 -.006593 .031409 .963628 d_telp~e | .0195922 .00991 1.98 0.048 .000168 .039016 .448594 regi~uth*| -.0068211 .00385 -1.77 0.077 -.01437 .000728 .553831 region~l*| -.0227845 .00173 -13.14 0.000 -.026184 -.019386 .29195 urban*| .0135708 .00871 1.56 0.119 -.003499 .03064 .474054 d_2004*| .2253051 .06112 3.69 0.000 .105511 .345099 .581959 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1
61
Probit Informal Credit used as Coping Strategy Iteration 0: log pseudolikelihood = -458.42643 Iteration 1: log pseudolikelihood = -375.55832 Iteration 2: log pseudolikelihood = -370.49212 Iteration 3: log pseudolikelihood = -370.37658 Iteration 4: log pseudolikelihood = -370.37483 Iteration 5: log pseudolikelihood = -370.37432 Iteration 6: log pseudolikelihood = -370.37415 Iteration 7: log pseudolikelihood = -370.3741 Iteration 8: log pseudolikelihood = -370.37408 Iteration 9: log pseudolikelihood = -370.37408 Iteration 10: log pseudolikelihood = -370.37407 Iteration 11: log pseudolikelihood = -370.37407 Iteration 12: log pseudolikelihood = -370.37407 Iteration 13: log pseudolikelihood = -370.37407 Iteration 14: log pseudolikelihood = -370.37407 Iteration 15: log pseudolikelihood = -370.37407 Iteration 16: log pseudolikelihood = -370.37407 Iteration 17: log pseudolikelihood = -370.37407 Probit regression, reporting marginal effects Number of obs = 1264 Wald chi2(28) = . Prob > chi2 = . Log pseudolikelihood = -370.37407 Pseudo R2 = 0.1921 ------------------------------------------------------------------------------ | Robust d_inf_~l | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ] ---------+-------------------------------------------------------------------- no_sho~h | -.6168814 .0577043 -26.10 0.000 2.30459 -.72998 -.503783 death_~b*| .9821683 .003555 22.71 0.000 .141614 .975201 .989136 d_illn~s*| .9784736 .0061075 28.41 0.000 .349684 .966503 .990444 d_disa~r*| .9580944 .0138636 20.73 0.000 .274525 .930922 .985267 d_low_~s*| .9745506 .000054 . . .174051 .974445 .974657 d_low_~d*| .8985401 .0261811 19.31 0.000 .397943 .847226 .949854 d_Jobl~s*| .5821181 .0332132 23.43 0.000 .642405 .517021 .647215 d_jobl~t*| .9724864 .0087478 21.01 0.000 .207278 .955341 .989632 d_mach~n*| .9773746 .0039628 22.49 0.000 .116297 .969608 .985141 log_sa~l | -.0004494 .0014906 -0.30 0.763 3.44288 -.003371 .002472 LOG_pr~l | .0015397 .0018747 0.82 0.414 2.42263 -.002135 .005214 log_an~l | .0009536 .0012636 0.75 0.451 4.64193 -.001523 .00343 no_has | .000308 .0007932 0.39 0.699 1.63994 -.001247 .001863 depend~o | -.0374702 .0408341 -0.91 0.361 .360552 -.117504 .042563 n_work~y | -.0107386 .0070389 -1.53 0.127 2.93829 -.024535 .003057 head_e~l | -.0018842 .0034922 -0.54 0.589 5.44146 -.008729 .00496 averag~s | -.0038634 .0041707 -0.93 0.354 6.26877 -.012038 .004311 age_head | -.0008445 .0009708 -0.87 0.385 48.129 -.002747 .001058 avg_ag~h | -.0013734 .0010273 -1.34 0.181 31.0407 -.003387 .00064 sex_head*| .0288844 .0193724 1.30 0.193 .871044 -.009085 .066854 head_m~d*| -.0162243 .019009 -0.88 0.376 .701741 -.053481 .021033 indige~s*| -.0169463 .0167164 -0.97 0.330 .254747 -.04971 .015817 dwater~y*| .03906 .016649 1.91 0.056 .909019 .006429 .071691 delect~m*| -.0334335 .0429179 -0.89 0.376 .963608 -.117551 .050684 d_telp~e | -.0058096 .0140494 -0.41 0.680 .5 -.033346 .021727 regi~uth*| -.0596825 .0225796 -2.75 0.006 .563291 -.103938 -.015427 region~l*| -.0440694 .0182152 -2.19 0.028 .295886 -.079771 -.008368 urban*| -.0046752 .0161401 -0.29 0.772 .47231 -.036309 .026959 d_2004*| .1197757 .0149822 7.15 0.000 .578323 .090411 .14914 ---------+-------------------------------------------------------------------- obs. P | .1178797 pred. P | .0734175 (at x-bar) ------------------------------------------------------------------------------ (*) dF/dx is for discrete change of dummy variable from 0 to 1 z and P>|z| correspond to the test of the underlying coefficient being 0