DP2016-06

Consumption Sm oothing, Risk Sharingand Household Vulnerability
in Rural Mexic o*
Naoko UCHIYAMA
February 25, 2016

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Consumption Smoothing, Risk Sharing and Household Vulnerability
in Rural Mexico*
Naoko Uchiyama

Research Fellow, Japan Society for the Promotion of Science, andResearch Institute for Economics and Business Administration,
Kobe University, 2-1 Rokkodaicho, Nada-ku, Kobe 657-8501, Japan.
Telfax: +81(78)803-7001
E-mail: uchiyama@emerald.kobe-u.ac.jp*

I am grateful to Takahiro Sato, Nobuaki Hamaguchi, and Shoji Nishijima for their feedback
and support. I also thank Yuriko Takahashi, Yoshiaki Hisamatsu, Tomokazu Nomura, and thefloor participants at the 50th Annual Conference of the Japan Society of Social Science on Latin
America at IDE-JETRO on November 16, 2013, for their helpful comments. This research is
supported by JSPS KAKENHI (grant no: 26·40002). The author is responsible for all remaining
errors.

1

Abstract
This study empirically analyzes risk-sharing functioning in rural Mexico. It also
aims to examine the vulnerability of rural households and whether the conditional
cash transfer (CCT) program will reduce the vulnerability within the risk-sharing
framework. I adopt the two most recent Mexican rural household panel data for 2003
and 2007—although rich in information, the data have not been fully utilized given
the lack of pure control groups. Drawing on Townsend’s (1994) risk sharing model,
the empirical results reject the hypothesis of full risk sharing but confirm that risksharing functions serve better in securing basic needs such as food. In addition, therisk-sharing function, reinforced by longer exposures to the CCT program, serves tomitigate the liquidity constraints or vulnerability of poor households.

JEL Classification: O12, D12, O54
Keywords: risk sharing, household vulnerability, CCT

2

1. Introduction
Mexico is known as a middle income country, being the second largest country in Latin
America and the Caribbean (LAC) in terms of population as well as economy size after
Brazil. Its GDP per capita amounted to US$10,130 in 2011 (ECLAC, 2012) and its HDI was
0.775 (UNDP, 2013), ranked 61st among 187 countries in 2012 (Uchiyama, 2013b). It is
widely recognized that the LAC has achieved a steady macroeconomic situation in the 2000s
after 20 years of the so-called “lost decades” accompanied by the controversial economic
crises and adjustments. For the first time ever, the number of people belonging to the middle
class surpassed the number of poor people, a sign that LAC countries are progressing toward
becoming components of a middle-class region. Despite the impressive gains of the past
decade, the region remains unequal, with some 82 million people living on less than $2.5 per
day (World Bank, 2013). World Bank (2013) warns that creating opportunities for vulnerable
people is the priority issue to be addressed. Mexico is not the exception with more than halfof the population being classified as poor in 2010 at the national level according to thenational poverty line i. It is an unignorable fact that the poverty ratio reaches more than 60
percent in rural areas in the same year (Uchiyama, 2013b).
The term “vulnerability” is now used in many different literatures. However, its definition
varies according to the contexts and thus to the researchers. A general idea of vulnerability ineconomics should be “the probability of falling below a certain poverty line in the future.”(World Bank, 2000) The importance of measuring vulnerability is increasing in a sense that“vulnerability” is a dynamic concept closely related to poverty and encapsulating changes
between different points in time whereas “poverty” is a static concept representing a
condition at a particular point in time.
According to Dercon (2005), vulnerability can be explained by the sum of the welfare loss
from deviations of mean consumption relative to an agreed level (the cost of poverty) plus a3measure of the cost of risk—the difference of expected utility from the utility related to
expected consumption. In this respect, based on the theory of expected utility, Kamanou andMorduch (2005) argue that the expected utility of risk-averse individuals falls as thevariability of consumption rises. In addition, Dercon (2005) describes two strategies suchhouseholds exposed to income fluctuations use to reduce the impacts of shocks: riskmanagement strategies and risk-coping strategies. Risk-management strategies attempt toreduce the riskiness of the income process ex ante (income smoothing) through, in mostcases, diversification of income sources by combining different income generating activities,
including cultivation of various crops to reduce the harvest risk, even if the crops have alower average yield. In contrast, risk-coping strategies consist of self-insurance (throughprecautionary savings) and informal group-based risk sharing. They deal with theconsequences of income risk (consumption smoothing). Households can insure themselves bybuilding up assets in good years, which they can deplete in bad years. Also, informal
arrangements can be made among members of a group or village to support each other in caseof hardship, for example, among extended families, ethnic groups or neighborhoods.
When looking at rural areas of developing countries where poverty and vulnerability are
generally concentrated, it is widely known that rural people must often cope not only withsevere consumption poverty but also with extremely variable incomes, such as natural
disasters, illness, injury, involuntary unemployment and market price changes (Bardhan andUdry, 1999). Thus they have to rely on the above mentioned income and consumption
smoothing strategies, however, they are often hardly insured against these risks. Income
fluctuations can present an acute threat to people’s livelihoods even if, on average, incomesare high enough to maintain a minimal standard of living (Bardhan and Udry, 1999).
It is important to recall that the main obstacles to consumption smoothing should be theliquidity constraints caused by the market imperfection especially in rural areas. Bardhan and4Udry (1999) argue that liquidity constraints are more likely to be binding particularly amongpoor farmers, so that (agricultural) production and consumption cannot be financed fromsavings, and the costs (in terms of utility, health and even survival) of fluctuations in an
already low level of consumption are extreme. The poor farmers need to consume before
harvest, which depends on climate and many other external factors. Thus, when production isrisky and insurance markets are incomplete, credit transactions are required to play a key roleby permitting people to smooth consumption. However, it is quite usual that famers also facecredit constraints and thus fail to smooth their consumption, which inevitably forces them tofall into a poverty trap.
Among various econometric methods of measuring household vulnerability, the one that hasa theoretical foundation is the risk sharing model proposed by Townsend (1994) as is argued
in Kurosaki (2009). Townsend (1994) proposes a general equilibrium model of jointly
evaluating the actual risk-coping institutions of any kind at a time: in other words, toempirically analize how much the institutions could insure people in the villages against therisks they face, such as erratic rainfall, crop and human diseases, and severe income
fluctuations, by using ten-year panel data on three high-risk villages in the semi-arid tropics
of southern India ii. The model has been modified by Ravallion and Chaudhuri (1997) andapplied to various development countries.
In this study, an empirical analysis of risk-sharing functions in rural Mexico is conducted to
discuss the vulnerability of the rural poor using the most recent two periods of Mexican rural
household panel data, in 2003 and 2007. In this study, vulnerability is considered the inability
to smooth consumption because of liquidity constraints. First, I estimate a basic risk-sharing
model defined by Townsend (1994) to examine how the risk-sharing mechanism works inrural Mexico. Continuously, I extend the model to consider the effects of Mexico’s widely
known poverty reduction program: the Conditional Cash Transfer (CCT) program, within the5risk-sharing framework. I will also conduct the two-stage regressions with instrumental
variables to deal with endogeneity problems which any of the previous literatures could notovercome successfully. The empirical results will show that there exist partial risk-sharing
functions, especially for basic needs such as food, and that the risk-sharing function,
reinforced by longer exposures to the CCT program, serves to mitigate the liquidity
constraints or vulnerability of poor households.
The structure of this paper is as follows. Section 2 provides an overview of the poverty statusof the rural households in the sample. Section 3 presents a standard risk-sharing model.
Section 4 conducts empirical analyses to test the full risk-sharing hypothesis and examine theeffects of CCT. Section 5 concludes the paper.

2. Panel Data and Poverty Status
2-1. Data
The series of household panel data used in this study is called Encuestas de Evaluación de los
Hogares (ENCEL), or Household Evaluation Surveys, which is designed and conductedperiodically by the Social Development Secretary (Secretaría de Desarrollo Social:
SEDESOL) assisted by the International Food Policy Research Institute (IFPRI) for the
purpose of the external evaluation of the CCT program which is generally called
PROGRESA-Oporunindades iii. Nine rounds of rural household panel data are available todate from 1997 to 2007, including a baseline survey in 1997. A unique characteristic of the
ENCEL is that the randomized experiment was implemented at the beginning of the program
to evaluate the effects of the program accurately. The full sample of ENCEL consists ofrepeated observations collected for 24,000 households from 506 localities (villages) in the 7
states of Guerrero, Hidalgo, Michoacán, Puebla, Querétaro, San Luis Potosí and Veracruz. Of
those 506 localities, 320 localities were assigned to the treatment group (denominated as

6

“Treatment 1998” herein), and 186 localities were assigned as controls (denominated as“Treatment 2000” herein). The eligible households of the control localities could not receive
PROGRESA-Oportunidades benefits until 2000 (Skoufias, 2007).
An additional comparison group of 151 localities not yet incorporated into the program wasselected as a new control group using propensity score matching (PSM) for the seventh roundof the survey in 2003 (denominated as “Control 2003” herein) (Todd, 2004). They became
entitled to receive benefits through 2004. In total, eight rounds of surveys were conducted in
the most marginal rural areas by 2007, which enables researchers to make use of a long
period of micro-panel data.
I use rural samples of the two most recent rounds available: the years 2003 and 2007. ENCEL
2003 consists of 33,887 households and 205,306 individuals. ENCEL 2007 consists of 25,899
households and 176,809 individuals living in the seven sample states iv . When we drop
households whose consumption is unreported or reported as nil, 18,942 households remain as
a complete panel dataset in the case of food consumption, and 17,603 households in the caseof total consumption.

2-2. Poverty Status of PROGRESA-Oportunidades’ Sample Villages
In this section, I examine the poverty trend of PROGRESA-Oportunidades’ sample villages
from the seven pilot states for the years 2003 and 2007 using the household samples fromENCEL. I used the Foster–Greer–Thorbecke (FGT; Foster et al. 1984) poverty indices––themost popular indices in measuring poverty. The FGT indices are defined as

1

𝑐𝑐

𝑞𝑞

∝

𝑃𝑃∝ = 𝑛𝑛 ∑𝑖𝑖=1 �1 − 𝑧𝑧𝑖𝑖𝑡𝑡 � ,7(1)

where q represents the number of individuals identified by i, whose consumption, 𝑐𝑐𝑖𝑖𝑖𝑖 , at time

t is below a certain poverty line, z. n represents the total population. When α = 0, 1, and 2,

𝑃𝑃0 , 𝑃𝑃1 , and 𝑃𝑃2 represent the poverty head count ratio, poverty gap ratio, and squared poverty
gap ratio, respectively.

In this study, each household’s per capita weekly total consumption—the sum of reportedmonetary expenditures and self-consumption in the week prior to the interview—is used toestimate the FGT indices. The official rural food basket v (canasta básica alimentaria rural),published by the National Council for the Evaluation of Social Development Policy (Consejo
Nacional de Evaluación de la Política de Desarrollo Social (CONEVAL)), is used as thepoverty line. The per capita total consumption and poverty line are deflated by the state-level
general CPI and food CPI (Banco de México).

8

TABLE 1
CHANGES IN FGT INDICES IN THE SAMPLE VILLAGES OF PROGRESA-OPORTUNIDADES,
2003–2007
Per-Capita Total Consumption
Treatment
Treatment
Overall Sample
Poverty Indices
1998
2000
2003
2007
2003 2007 2003 2007
Headcount ratio
0.87
0.91
0.88
0.92
0.89
0.92
Poverty gap ratio
0.46
0.53
0.48
0.55
0.48
0.55
Squared poverty gap ratio
0.29
0.35
0.30
0.37
0.30
0.37
Number of obs.
17,603 17,603 8,373 8,373 5,864 5,864
Source.—Author’s calculation based on ENCEL 2003 and 2007.

9

Control 2003
2003
0.82
0.39
0.23
3,366

2007
0.84
0.43
0.27
3,366

0

cumulative percentage
.2
.4
.6
.8

1

FIGURE 1.
STOCHASTIC DOMINANCE OF PER-CAPITA TOTAL CONSUMPTION, 2003 AND 2007

0

2
4
6
Log of per-capita real consumption (total)tcons 2003
poverty line 1
poverty line 3

tcons 2007
poverty line 2

Source: Author’s elaboration based on Encel 2003 and 2007.

108

Table 1 presents changes in the three types of FGT indices for 2003 and 2007. The indices arecalculated using the overall sample and the three subsamples: original treatment groups(villages), in which the eligible households began receiving benefits in 1998 (Treatment
1998); the original control villages, accruing benefits since 2000 (Treatment 2000); and the
new control villages, which were integrated into the ENCEL in 2003 and began receiving
benefits by 2004 (Control 2003).
A striking result in Table 1 is that the three poverty indices in all three subsamples, which
received different periods of program exposure, worsened. Both the overall poverty
headcount ratio and the Treatment 1998 ratio increased by 4 percentage points, from 87
percent to 91 percent and from 88 percent to 92 percent. Treatment 2000 and Control 2003
both increased by 2–3 percentage points, from 89 percent to 92 percent and from 82 percent
to 84 percent vi. The overall poverty gap ratio, which indicates the “depth” of poverty,
worsened by 7 percentage points, that is, from 46 percent to 53 percent. Treatment 1998 andTreatment 2000 worsened by the same percentage and Control 2003 worsened by 4
percentage points. In addition, the squared poverty gap ratio, which represents the “severity”
of poverty, increased by 6 percentage points in total and 4–7 percentage points in thesubsamples. The aggravated poverty gap ratio and squared poverty gap ratio confirm that the
distribution of poverty among the poor worsened; in other words, the poorest of the poor
became even poorer than the rest of the population.
The level of poverty indices for Control 2003 is modest compared to those for the twotreatment groups for 2003 and 2007, suggesting that the Control 2003 profile should be lesspoor, even though this group was added to serve as a new control for the original samples.
Furthermore, the magnitude of changes in the FGT indices is less in the poverty gap andsquared poverty gap ratios for Control 2003, again confirming the hypothesis that the mostvulnerable households, which are most easily affected by unexpected shocks, should be the11poorest of the poor.
I depict the cumulative distribution graph in Figure 3, with the log of per capita realconsumption in ascending order on the horizontal axis, and the cumulative percentage on thevertical axis. As shown, the three vertical lines represent: the rural food basket (poverty line1), the 75% line of the rural food basket (poverty line 2), and the 50% line of the rural foodbasket (poverty line 3). Since the consumption for 2007 is higher than that of 2003, at any
point on the graph, it can be concluded that stochastic dominance is exhibited. In other words,
we can confirm that for any level, the consumption for 2007 falls below that of 2003 (thatpoverty unquestionably worsened).

3. Risk-Sharing Model vii
3-1. General Model
We assume a village economy wherein the Pareto-efficient allocation of risk is achieved, butthere is no access to credit and/or insurance markets or even storage. Now, 𝑖𝑖 = 1, … , 𝑁𝑁

indexes households in the village. There are T periods indexed by t. Further, 𝑠𝑠 = 1, … , 𝑆𝑆

indexes the states of nature with the probability of occurrence 𝜋𝜋𝑠𝑠 . In state s, each household i

receives an income of 𝑦𝑦𝑖𝑖𝑖𝑖𝑖𝑖 > 0 viii. Let 𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖 represent household i’s consumption if state s
9F

occurs in period t. Suppose each household has a separable utility function, then

𝑈𝑈𝑖𝑖 = ∑𝑇𝑇𝑡𝑡=1 𝜌𝜌𝑡𝑡 ∑𝑆𝑆𝑠𝑠=1 𝜋𝜋𝑠𝑠 𝑢𝑢𝑖𝑖 (𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖 ),(2)

where 𝜌𝜌𝑡𝑡 is a discount factor. 𝑢𝑢( ∙ ) is twice continuously differentiable with 𝑢𝑢′ > 0, 𝑢𝑢′′ < 0and lim 𝑢𝑢′ (𝑥𝑥) = +∞. A Pareto-efficient allocation of risk within the village can be found by
𝑥𝑥→0

maximizing the weighted sum of utilities for each of the N households, where the weight ofhousehold i in the Pareto efficiency is 𝜆𝜆𝑖𝑖 , subject to the aggregated resources available in the12village at each point in time in each state of nature:𝑁𝑁∑𝑁𝑁
𝑖𝑖=1 𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖 ≤ ∑𝑖𝑖=1 𝑦𝑦𝑖𝑖𝑖𝑖𝑖𝑖 , ∀𝑠𝑠, 𝑡𝑡.

(3)

𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖 ≥ 0, ∀𝑠𝑠, 𝑡𝑡.

We obtain the first-order conditions with respect to 𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖 and 𝑐𝑐𝑗𝑗𝑗𝑗𝑗𝑗
𝑢𝑢′ (𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖 )𝑢𝑢′ �𝑐𝑐𝑗𝑗𝑠𝑠𝑠𝑠 �𝜆𝜆= 𝜆𝜆𝑗𝑗 , ∀𝑖𝑖, 𝑗𝑗, 𝑠𝑠, 𝑡𝑡.
𝑖𝑖(4)This equality extends across all N households in the village in any state at any point in time.
The marginal utilities and, therefore, consumption levels of all the households in the village,
move together.
Now, I assume that all the households in the village have an identical constant absolute risk
averse utility function: 𝑢𝑢𝑖𝑖 (𝑐𝑐𝑖𝑖 ) = −(1⁄𝜎𝜎)𝑒𝑒 −𝜎𝜎𝑐𝑐𝑖𝑖 . Applying this utility function to the first-ordercondition of equation (4), taking logs and the sum of the N equalities, we obtain

1

1

𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖 = 𝑐𝑐̅𝑠𝑠𝑠𝑠 + 𝜎𝜎 �ln(𝜆𝜆𝑖𝑖 ) − 𝑁𝑁 ∑𝑁𝑁
𝑗𝑗=1 ln(𝜆𝜆𝑗𝑗 )�,(5)1

where 𝑐𝑐̅𝑠𝑠𝑠𝑠 = 𝑁𝑁 ∑𝑁𝑁
𝑗𝑗=1 𝑐𝑐𝑗𝑗𝑗𝑗𝑗𝑗 .
Therefore, household consumption is equal to the average level of consumption in the village
plus a time-invariant household fixed effect, which depends on the relative weight of thehousehold in the Pareto program. Equation (5) implies that the change in household
consumption between any two periods is equal to that in average community consumption
13

between the two periods. After controlling for average consumption, a household’s
consumption is unaffected by its own income. In a Pareto-efficient allocation of risk within acommunity, households face only aggregate risk. Idiosyncratic income shocks are completely
insured within the community.
The existence of ex post risk-pooling mechanisms within various communities in lessdeveloped countries suggests that some communities may have developed insurance systems
that allocate risk to approach Pareto efficiency. This line of reasoning has motivatednumerous quantitative studies on risk sharing ix. For example, Townsend (1994) and Ravallion
and Chaudhuri (1997) examine consumption outcomes rather than specific risk-poolingmechanisms in ICRISAT’s case villages. Within this set of villages, there is a high degree of
co-movement in consumption across households, despite a substantial amount of
idiosyncratic income variation. Nevertheless, a fully Pareto-efficient allocation of risk is notachieved in these villages.

3-2. Empirical Model
A reduced form of Equation (5) based on Townsend (1994) is

𝑐𝑐𝑖𝑖𝑖𝑖 = 𝑏𝑏𝑖𝑖 + 𝑎𝑎𝑖𝑖 𝑐𝑐̅𝑡𝑡 + 𝛽𝛽𝑖𝑖 𝑦𝑦𝑖𝑖𝑖𝑖 + 𝑣𝑣𝑖𝑖𝑖𝑖 ,(6)

where 𝑐𝑐̅𝑡𝑡 is the average consumption in the village, 𝑦𝑦𝑖𝑖𝑖𝑖 is a household’s idiosyncratic
variables—in this case, income—and 𝑣𝑣𝑖𝑖𝑖𝑖 is an i.i.d. error term with mean zero.

We assume measurement errors in 𝑦𝑦𝑖𝑖𝑖𝑖 :𝑦𝑦𝑖𝑖𝑖𝑖 = 𝑦𝑦𝑖𝑖𝑖𝑖∗ + 𝜖𝜖𝑖𝑖𝑖𝑖 .
14(7)

Then, Equation (8) becomes𝑐𝑐𝑖𝑖𝑖𝑖 = 𝑏𝑏𝑖𝑖 + 𝑎𝑎𝑖𝑖 𝑐𝑐̅𝑡𝑡 + 𝛽𝛽𝑖𝑖 𝑦𝑦𝑖𝑖𝑖𝑖 + 𝑣𝑣𝑖𝑖𝑖𝑖 − 𝛽𝛽𝑖𝑖 𝜖𝜖𝑖𝑖𝑖𝑖 .

(8)

By taking first differences, we obtain

Δ𝑐𝑐𝑖𝑖𝑖𝑖 = 𝑎𝑎𝑖𝑖 Δ𝑐𝑐̅𝑡𝑡 + 𝛽𝛽𝑖𝑖 Δ𝑦𝑦𝑖𝑖𝑖𝑖 + 𝑢𝑢𝑖𝑖𝑖𝑖 ,(9)𝑢𝑢𝑖𝑖𝑖𝑖 = Δ𝑣𝑣𝑖𝑖𝑖𝑖 − 𝛽𝛽𝑖𝑖 Δ𝜖𝜖𝑖𝑖𝑖𝑖 ,(10)

where Δ𝑦𝑦𝑖𝑖𝑖𝑖 represents a household’s idiosyncratic income shocks.

Full risk sharing can be achieved when the null hypothesis of 𝛽𝛽𝑖𝑖 = 0 is accepted across all

households within the village x. In other words, if the economy (e.g., village) achieves Pareto
optimal risk sharing among villages, Δ𝑐𝑐𝑖𝑖𝑖𝑖 should respond only to the village level shock Δ𝑐𝑐̅𝑡𝑡

so that the size of 𝛽𝛽𝑖𝑖 shows excess sensibility of consumption to idiosyncratic income shocks.
A relatively large positive value of 𝛽𝛽𝑖𝑖 indicates that individual i is less able to cope with suchshocks.

Ravallion and Chaudhuri (1997) thoroughly examined Townsend’s (1994) model to provethat his estimates of 𝛽𝛽𝑖𝑖 had a downward bias. They insisted that the first term of Equation (9)(𝑎𝑎𝑖𝑖 Δ𝑐𝑐̅𝑡𝑡 ) should be replaced with time village dummies, ∑𝑡𝑡 𝛿𝛿𝑡𝑡 𝐷𝐷𝑡𝑡 , when assuming uniform time

preference and risk aversion across households in the village. The parameter 𝛽𝛽𝑖𝑖 can moreaccurately reflect the effects of idiosyncratic income shocks, Δ𝑦𝑦𝑖𝑖𝑖𝑖 , because the time village

dummies absorb all the aggregate shocks that occur within the village economy. In addition,
restrictions on the parameters, 𝑏𝑏𝑖𝑖 = 𝑏𝑏, 𝑎𝑎𝑖𝑖 = 𝑎𝑎, 𝑎𝑎𝑎𝑎𝑎𝑎 𝛽𝛽𝑖𝑖 = 𝛽𝛽, ∀𝑖𝑖, are usually imposed byassuming uniform time preferences and risk aversion across households when using
15

developing countries’ panel data because the available period is typically short.

Δ𝑐𝑐𝑖𝑖𝑖𝑖 = 𝑏𝑏 + ∑𝑡𝑡 𝛿𝛿𝑡𝑡 𝐷𝐷𝑡𝑡 + 𝛽𝛽Δ𝑦𝑦𝑖𝑖𝑖𝑖 + 𝑢𝑢𝑖𝑖𝑖𝑖 ,(12)

Parameter 𝛽𝛽 is also called the “excess sensitivity” parameter because it becomes positive
when liquidity constraints and imperfect credit and/or insurance market mechanisms areobserved. It can be argued that parameter 𝛽𝛽 represents how much households in a certain

group (village) are vulnerable to idiosyncratic income shocks on average, showing the extent
to which consumption decreases (increases) when income marginally decreases (increases)
for a household.
Since this study uses panel data for two periods (2003 and 2007), the model to be estimatedtakes the form of a cross-section:

Δ𝑐𝑐𝑖𝑖 = 𝑏𝑏 + 𝑎𝑎𝑣𝑣 + 𝛽𝛽Δ𝑦𝑦𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖 + 𝑢𝑢𝑖𝑖 , (13)where 𝑎𝑎𝑣𝑣 are village dummies, 𝑋𝑋𝑖𝑖 is a vector of household-specific factors that affectconsumption change, and 𝑢𝑢𝑖𝑖 is an i.i.d. error term with mean zero.

4. Empirical Analyses and Results
4-1. Summary Statistics
Table 2 presents the summary statistics of variables used in this study’s regression xi. The
manner in which the variables are created is summarized in Appendix A. A significant drop in
consumption and income was observed between 2003 and 2007. Weekly per capita realconsumption decreased on average by 8.8 Mexican pesos for food alone and by 10.8 pesos in
16total. Furthermore, per capita real income decreased by 6.3 pesos on average in the same
period. In contrast, per capita income between 2001 and 2002 increased by 1.2 pesos. Thisphenomenon can be attributed to the welfare loss in poor households owing to the increase in
price for international and domestic food during the period (Valero-Gil and Valero 2008;
Attanasio et al. 2009, 2013; Wood et al. 2009; Uchiyama 2013a). Table 2 shows that half ofthe sampled households experienced an income decline.
With respect to household characteristics in the base year (2003), 24 percent of the household
heads received no education. Those with a primary education accounted for 65 percent, while
9.5 percent had a secondary education and only 2 percent received a high school or higher
education. The author’s calculation based on the data revealed that more than half of thehousehold heads who enrolled in primary school did not graduate, indicating a high dropout
rate. Ten percent of the households were headed by women. The average age of household
heads was 46 years, and 88 percent were married and 10 percent divorced or separated. The
average household size was 5.3 people and the dependency ratio was 42.7. About 31 percentof the households were indigenous, that is, the household heads speak indigenous languages.
About 60 percent of the households received benefits under the CCT program in 2003. This
percentage increased to 78.5 percent in 2007 because by then, the Control 2003 households
began receiving benefits. About 6.3 percent households reported self-consumption during theinterview week. Approximately 63 percent households owned or cultivated 4.8 hectares ofland on average, but the median farming household only owned or cultivated 2 hectares ofland, indicating a high number of small poor farmers and low numbers of large farmers. Land
with full or partial irrigation accounted for 9.3 percent of those who owned/cultivated a landin 2003, suggesting that most of the land was rain-fed with poor yields. About 26 percent of
the households received personal transfers (remittances) in cash or kind. In addition, 32
percent of the households had members older than 15 years and lived away from home17

(migrants). The local tortilla price increased by 2.52 pesos on average, which indicates prices
in 2007 were 1.3 times higher than those in 2003 xii. This perfectly corresponds to the
Mexican national rate of increase in tortilla prices during the period, as indicated in
Uchiyama (2013a).
Highly marginal pilot villages (localities) were selected from seven states of Mexico: four inCentral Mexico (Hidalgo, Michoacán, Puebla, and Querétaro) and one each from the North(San Luis Potosí), the Gulf of Mexico (Veracruz), and the Southern Pacific (Guerrero). The
three treatment or control groups in Table 2 correspond to the aforementioned village
categories.

18

TABLE 2
SUMMARY STATISTICS
Variables
ΔC_i: Food
ΔC_i: Total
ΔY_i
ΔY_i: 2001-02
Food Consumption03
Food Consumption07
Total Consumption03
Total Consumption07
Income03
Income07
noedu03
primary03
secondary03
highschool03
technical03
univ03female03
age03
married03
divorced03
total_member03
depratio03
indigenous03
CCT_dum03
selfcons_dum03
land_dum03
total_land_ha03*
irrigation03**
hhremit03
hhmig_over15_dum03
local tortilla price change0307
State12: Guerrero
State13: Hidalgo
State 16: MichoacánState 21: PueblaState 22: Querétaro
State 24: San Luis PotosíState 30: Veracruz
Treatment 1998
Treatment 2000
Control 2003

Obs.
12349
11483
12349
12349
12349
12349
12349
11483
12349
12349
12218
12218
12218
12218
12218
12218
12240
12236
12349
12349
12349
12349
12349
12349
12349
12349
8119
7774
12349
12349
12365

Mean
−8.819−10.787−6.2591.223
71.11662.29790.73680.24528.94622.687
0.236
0.649
0.095
0.011
0.007
0.002
0.100
46.268
0.877
0.099
5.246
42.678
0.308
0.592
0.063
0.627
4.776
0.093
0.256
0.324
2.520

Std. Dev.
47.25761.17747.072
21.48043.96839.79156.46954.38643.76421.473
0.424
0.477
0.293
0.105
0.082
0.048
0.300
14.620
0.329
0.299
2.315
23.848
0.462
0.492
0.244
0.484
10.048
0.290
0.437
0.468
1.550

Min
−338.668−391.588−378.942−604.357
10.4358.421
9.885
10.892
0.607
0.964
0
0
0
0
0
0
0
3
0
0
1
0
0
0
0
0
1
0
0
0
−1.587Max
267.406
487.897137.707831.271
391.298294.724415.187505.691392.972
146.838
1
1
1
1
1
1
1
98
1
1
19
100
1
1
1
1
200
1
1
1
7.935

12349
12349
12349
12349
12349
12349
12349
12349
12349
12349

0.079
0.104
0.168
0.146
0.077
0.162
0.263
0.477
0.335
0.188

0.270
0.306
0.373
0.353
0.267
0.369
0.440
0.499
0.472
0.390

0
0
0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1
1
1

Source.—Author’s elaboration based on ENCEL 2003 and 2007.
19

Note.—Consumption and income are shown in local currency in real terms (June 2011 = 100). ΔC_i and
ΔY_i denote changes in consumption and income for 2003 and 2007.
* Includes those who reported hectares of their land.
** Includes those who owned or cultivated the land in 2003.

20

4-2. Basic ModelI assume that the observed changes in income are endogenous in estimating Equation (13)mainly because of measurement errors. Thus, the explanatory variables are replaced by fitted
values using instrumental variables. As instruments in the first stage, I use the changes inlagged income between 2001 and 2002 (ΔY_i: 2001-02) xiii based on the argument ofRavallion and Chaudhuri (1997) to correct the downward bias in Townsend’s (1994)
estimations, I also selected additional instrumental variables: the land holding dummy(land_dum03), and the migrant household dummy (hhmig_over15_dum03) in 2003, tocomplement the shortcomings of the lagged income variable as is discussed in Appendix A.
These variables are expected to correlate with the income changes in 2003 and 2007, but notwith the consumption variation in the same period directly. Food consumption and total
consumption are used as explained variables in all models herein.

21

TABLE 3
REGRESSION RESULTS OF EQUATION (13)Model 1
Variablesβ (OLS)β (2SLS)

β (OLS)β (2SLS)
Tortilla price change
Village dummies(A)
No. of Obs.
R-squared (OLS)

0.1445***(0.0131)0.1819***(0.0163)
yes
yes
12320
0.1372Model 2(A) Food Consumption
0.1402***0.1476***(0.0131)(0.0130)
0.6423***(0.0834)(B) Total Consumption
0.1816***0.1841***(0.0162)(0.0162)0.7509***(0.1048)
no
yes
yes
yes
12349
0.137612320
0.13310.1438***(0.0130)
0.5886***(0.0732)0.1837***(0.0162)0.6794***(0.0925)
no
yes
12349
0.1335Chi2 (2SLS)
216028.91
132799.98(B)
No. of Obs.
11414
11442
11414
11442
R-squared (OLS)
0.16110.16130.15890.1592Chi2 (2SLS)
279507.62
923638.75
Note.—Huber–White heteroscedasticity consistent standard errors are in parentheses.
Tortilla price changes were automatically omitted from the 2SLS regressions because ofcollinearity.
* p<0.1.
** p<0.05.
*** p<0.01.

22

Table 3 presents the results for both the OLS and 2SLS regressions for food consumption andtotal consumption. 𝑋𝑋𝑖𝑖 includes household size; dependency ratio; age, sex, and marital status;

ethnicity; education level of the household head; total hectares of land owned or cultivated by
the household; and change in the local tortilla price. The instruments comprise per capita
wage income changes for 2001–2002, a land dummy (1 if a household owns or cultivates
land, 0 otherwise), and migrant dummy (1 if a household has any member aged 15 years orolder and has migrated to another place, 0 otherwise). Different specifications are applied
using irrigation (1 if a household has any irrigated land, 0 otherwise), CCT (1 if a household
receives money benefits under CCT, 0 otherwise), self-consumption (1 if a household has
self-consumption, 0 otherwise), and remittance (1 if a household receives personal transfers,
0 otherwise) dummies as 𝑋𝑋𝑖𝑖 (Model 1) or not (Model 2) to check for robustness. Table 3 islimited to the present estimated coefficients of β. The complete results are provided inAppendices B and C (Tables B1, C1 and C2) xiv.

The OLS estimation coefficients of β are about 0.14 for food consumption and about 0.18 fortotal consumption in both models, which is consistent with previous studies in significanceand magnitude. This implies that real food and total consumption increase (decline) by about
0.14 and 0.18 Mexican pesos when real income rises (declines) by 1 Mexican peso. The null
hypothesis of full risk sharing is rejected at the 1 percent level. It is noteworthy that thecoefficients for food consumption are smaller than those for total consumption, which implies
that food consumption is better insured than total consumption. This result is consistent withmost previous studies on developing countries xv. To this effect, Skoufias (2007) explains thatwhen household income increases, the demand for luxury goods (non-food) rises more thanthat for necessities (e.g., food), and the opposite is true in case of a decrease in householdincome. The 2SLS coefficients are much larger than the OLS coefficients for both food andtotal consumption, which confirms the downward bias in β owing to endogeneity, as the23theory predicts xvi. The estimated values of β remain almost the same, regardless of model
specifications (except in the case of 2SLS for total consumption) and controlled tortilla pricechange xvii.

4-3. Model with CCT Effects
In this section, the discussion centers on whether the Mexican CCT program has contributed
to reduce households’ current vulnerability to poverty in the treatment villages by reinforcing
the existing risk-sharing functioning through cash transfers. CCT programs in general, which
are implemented in many developing countries in the world as a new targeting poverty
reduction strategy, were generally undertaken with two clear objectives. First, they provide
poor households with a minimum consumption floor (to reduce current poverty). Second, inmaking transfers conditional, they encourage the accumulation of human capital to break a
vicious cycle whereby poverty is transmitted across generations (to reduce future poverty)(Fiszbein and Schady, 2009). Whether CCT can enhance the risk-sharing mechanism
corresponds to the first objective (to reduce current poverty). The PROGRESAOportunidades which srated in 1997 and is well known for being the very first CCT programin the world. The objectives and contents are the almost same as the general CCTs mentioned
above.
Fiszbein and Schady (2009) point out that income volatility can provide an additional
argument for targeted cash transfer programs when fixing the credit and/or insurance markets
themselves is too costly and complicated to reach many of the poor families. However, to mybest knowledge, not many studies focus on the relationship between consumption smoothing
and the CCT, which corresponds to the first objective, whereas there are plenty of literature
regarding the second objective (the effects of human capital investments for the future
poverty reduction) xviii. One previous study that examined risk-sharing effects of the CCT in24rural Mexico is Skoufias (2007). He conducted an empirical analysis of the risk insurance
model using three rounds of the ENCEL panel data for 1998–1999 and rejected full risk
sharing in all the specifications. The effect of PROGRESA-Oportunidades on the
improvement of pre-existing risk sharing within villages was not statistically significant in all
the models, except for a few cases of subsample regressions based on household
characteristics (e.g., cases where the household head had less than a primary education or was
not eligible for PROGRESA). He attributed this result to the short duration (1.5 years) after
the program’s implementation. He also conducted regressions using shock variables as
instrumental variables to deal with the attenuation bias of income variables and found thecoefficients to be insignificant and the sign of the coefficient of the interaction terms (effectsof PROGRESA) reversed (positive and insignificant) because of weak instruments.
To examine the CCT’s effects on risk sharing, I insert the interaction terms of per capita
income change with subgroup dummies in Equation (13). The model specification is asfollows:

Δ𝑐𝑐𝑖𝑖 = 𝑎𝑎𝑣𝑣 + 𝛿𝛿1 𝑇𝑇1998 + 𝛿𝛿2 𝑇𝑇2000 + 𝛽𝛽Δ𝑦𝑦𝑖𝑖 + 𝜃𝜃1 𝑇𝑇1998 Δ𝑦𝑦𝑖𝑖 + 𝜃𝜃2 𝑇𝑇2000 Δ𝑦𝑦𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖 + 𝑢𝑢𝑖𝑖 ,Δ𝑐𝑐𝑖𝑖 = 𝑎𝑎𝑣𝑣 + 𝛿𝛿𝑇𝑇98&00 + 𝛽𝛽Δ𝑦𝑦𝑖𝑖 + 𝜃𝜃𝑇𝑇98&00 Δ𝑦𝑦𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖 + 𝑢𝑢𝑖𝑖 , (14)where 𝑇𝑇1998 and 𝑇𝑇2000 represent the Treatment 1998 and Treatment 2000 subgroup dummies,
and the 𝛿𝛿𝑇𝑇98&00 dummy includes both the Treatment 1998 and Treatment 2000 subgroups.
The parameters of the interaction terms are expected to be negative if PROGRESA-

Oportunidades can improve the existing risk-sharing function, and consequently, households’
vulnerability. The remainder of the variables is the same as those in Equation (13).
Table 4 presents the regression results for Equation (14). Both OLS and 2SLS estimates are25

shown. The coefficient of the interaction terms with Treatment 1998 becomes negative andsignificant only in the OLS estimates for food consumption when using Treatment 1998 andTreatment 2000 dummies separately (Panel (A)). The OLS coefficients for total consumption
are negative but insignificant, while those for 2SLS are positive and insignificant. In using
the Treatment 1998 and 2000 dummy to compare the first two groups with the newest
Control 2003 group (Panel (B)), all coefficients for both OLS and 2SLS become negative, but
only the OLS estimates for food consumption are significant at 5 percent. These results
suggest that longer exposure to the program might have certain effects on the existing risksharing mechanism. The full results of the regression are shown in Tables D1 and D2(Appendix D).
Subsequently, considering the possibility of different household profiles across the
subgroups, I conducted separate regressions of Equation (13) for each of the threesubgroups—Treatment 1998, Treatment 2000, and Control 2003—allowing different returns
to household characteristics across the subsamples. The results for the estimated coefficientsof β are presented in Table 5 (OLS) and Table 6 (2SLS). The full results are shown in Tables
E1 and F1 (Appendices E and F). Each model’s specifications are the same as those in theprevious sections.

26

TABLE 4
REGRESSION RESULTS OF EQUATION (14)Food Consumption

Total Consumption

Variables
Model 1Model 2Model 1Model 2

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

OLS

2SLS

OLS

2SLS

OLS

2SLS

OLS

2SLS

(A): interaction with Treatment 1998 and Treatment 2000 dummies
βT1998*βT2000*β0.2036***0.6546***0.2057***0.5908***0.2155***0.8806***

0.2168***0.7901***(0.033)(0.108)(0.033)(0.105)(0.045)(0.143)(0.045)(0.139)−0.0785**0.041−0.0784**

0.051−0.0420.010−0.0420.041(0.037)(0.095)(0.037)(0.100)

0.049

0.125

0.049

(0.129)−0.063−0.119−0.061−0.112−0.041−0.165−0.039−0.165(0.039)(0.100)(0.039)(0.104)

0.052

0.132

0.052

(0.137)(B): interaction with Treatment1998&2000 dummyβ0.2036***

0.6513***0.2057***0.5891***0.2155***0.8771***0.2168***0.7884***(0.033)(0.108)(0.033)(0.105)(0.045)(0.143)(0.045)(0.139)−0.0723**−0.023−0.0713**−0.015-0.042-0.060-0.041-0.043(0.036)(0.090)(0.036)(0.094)(0.048)(0.119)(0.048)(0.123)

no

no

no

no

no

no

no

no

No. of Obs.

12349

12349

12349

12349

11442

11442

11442

11442

R-squared

0.138

0.127

0.134

0.122

0.161

0.151

0.159

0.148

No. of Obs.

12349

12349

12349

12349

11442

11442

11442

11442

R-squared

0.138

0.126

0.134

0.121

0.161

0.150

0.159

0.148

T1998&2000*βTortilla pricechange(A)(B)

Note.—Huber–White heteroscedasticity consistent standard errors are in parentheses.
* p<0.1.
** p<0.05.
*** p<0.01.

27

Table 5 shows that coefficient β is the smallest for the Treatment 1998 groups, followed byTreatment 2000, and is the largest for the Control 2003 groups for food (0.11–0.12, 0.15–
0.16, and 0.20–0.21, respectively) and total consumption (0.17, 0.19, and 0.21–0.22,
respectively). The table also shows the t-values of the differences between each group’s
coefficients. Notably, the t-values of the differences between Treatment 1998 and Control
2003 become significant at the 5 percent level in both models of food consumption, which is
the most important aspect of risk insurance. The result clearly supports the inference that
longer exposure to the CCT program results in a better insured household. At the very least,
the remarkable difference between the group with the longest exposure (Treatment 1998) andthat with the shortest (Control 2003) supports the hypothesis that CCT mitigates poverty andvulnerability by securing a minimum income level. Moreover, the results imply that the risksharing function is enhanced more in food consumption than in total consumption: thedifferences in the magnitude of the β coefficients between food consumption and total
consumption are greater in Treatment 1998 (0.12 vs. 0.17) and Treatment 2000 (0.15–0.16 vs.
0.19) than in Control 2003 (0.20–0.21 vs. 0.21–0.22). This implies that longer exposure to
CCT may be more effective in securing basic needs, especially food consumption.

28

TABLE 5
OLS REGRESSION RESULTS OF EQUATION (13) BY SUBGROUPS
Food Consumption
T1998

T2000

Total Consumption
C2003

T1998

T2000

C2003

Model 1βt-value

Tortilla price0.1170***0.1512***0.2044***0.1651***0.1863***0.2109***(0.018)(0.022)(0.034)(0.021)(0.028)(0.047)

T98 vs. T00

T00 vs. C03

T98 vs. C03

T98 vs. T00

T00 vs. C03

1.21

1.32

2.27***

0.61

0.46

0.90

no

no

no

no

no

no

T98 vs. C03

changeModel 2β0.1194***0.1552***0.2050***0.1653***0.1905***0.2159***(0.018)(0.022)(0.034)(0.021)(0.028)(0.046)

T98 vs.

T00 vs.

T98 vs.

T98 vs.

T00 vs. C03

T98 vs. C03

T00

C03

C03

T00

1.27

1.23

2.22***

0.73

0.47

1.00

no

no

no

no

no

no

No. of Obs.

5897

4130

2322

5450

3848

2144

R-squared

0.144

0.131

0.150

0.183

0.146

0.148

No. of Obs.

5897

4130

2322

5450

3848

2144

R-squared

0.140

0.126

0.147

0.179

0.144

0.147

t-value

Tortilla pricechange(Model 1)(Model 2)

Note.—Huber–White heteroscedasticity consistent standard errors are in parentheses. t-value represents thesignificance of the differences between each group’s coefficients.
* p<0.1.
** p<0.05.
*** p<0.01.

29

TABLE 6
2SLS REGRESSION RESULTS OF EQUATION (13) BY SUBGROUPS
Food Consumption
T1998

T2000

Total Consumption
C2003

T1998

T2000

C2003

Model 1βt-value

Tortilla price change

0.3746***0.7404***0.9217***

0.4610**'*0.8417***

1.3914***(0.109)(0.138)(0.205)(0.140)(0.169)(0.322)

T98 vs. T00

T00 vs. C03

T98 vs. C03

T98 vs. T00

T00 vs. C03

T98 vs. C03

2.08**

0.73

2.36***

1.73*

1.51

2.65***

no

no

no

no

no

no

Model 2β0.3340***0.7643***0.7312***

0.3926***0.8588***1.0717***(0.101)(0.129)(0.154)(0.128)(0.159)(0.225)

T98 vs. T00

T00 vs. C03

T98 vs. C03

T98 vs. T00

T00 vs. C03

T98 vs. C03

2.63***

0.17

2.16***

2.28***

0.77

2.62***

no

no

no

no

no

no

No. of Obs.

5897

4130

2322

5450

3848

2144

Chi2

199730.55

209185.88

450.9452926.70

778204.72

934.94

No. of Obs.

5897

4130

2322

5450

3848

2144

Chi2

46395.91

224529.67

597.0654330.17

30893.71

1106.87t-value

Tortilla price change(Model 1)(Model 2)

Note.—p-values are based on Huber–White heteroscedasticity consistent standard errors are provided. t-value
represents the significance of the differences between each group’s coefficients.
* p < 0.1.
** p < 0.05.
*** p<0.01.

30

The 2SLS regression results in Table 6 correct the downward bias of the OLS estimates andmostly confirm the OLS results by providing t-values significant at the 1 percent level for thedifference in β estimates between Treatment 1998 and Control 2003 both in food and total
consumptions, even though the magnitudes of coefficients vary more in 2SLS. In addition, I
obtained a significant difference between Treatment 1998 and Treatment 2000 at the 10
percent level in Model 1 and at the 1 percent level in Model 2. However, the β estimates forthe total consumption of Control 2003 exceeded 1, which contradicts the theory. In addition,
the Model 2 estimates for food consumption are not as expected, that is, the coefficients ofTreatment 2000 are higher than those of Control 2003, but the t-value shows no significant
difference.

5. Concluding Remarks
In this study, I examine the risk-sharing functions and effects of the CCT program on
household vulnerability in rural Mexico using the most recent two-period rural household
panel data. First, I focus on testing the full risk sharing hypothesis by using the general risksharing model as most he the previous studies did, and the empirical results confirmed thatthe existing risk sharing is incomplete, which is consistent with previous studies. I also
revealed that the risk-sharing functions work better for food consumption smoothing than for
total consumption smoothing. Continuously, I included CCT effects on risk sharing indifferent specifications and confirmed that CCT reinforces the risk-sharing functions of thetreatment villages, especially when a village has longer exposure to the CCT program. The
CCT effects were more apparent in securing the basic needs of poor households, notably foodconsumption. The risk-sharing functions reinforced by CCT, in turn, could serve to mitigate
the vulnerability (liquidity constraints) of rural households.

31However, one should take into consideration the possibility of the downward rigidity of fooddemand, that is, vulnerable households living below subsistence levels cannot further
decrease their consumption when hit by income shocks. Furthermore, I have not clarified themechanism through which longer exposure to the CCT program enhances the existing risksharing functioning, thereby reducing household vulnerability. It is possible that securing aminimum consumption floor, which is stated as one of the CCT’s main objectives, might
gradually change the consumption behavior of a household. There are two possible
assumptions in this respect: the ease of liquidity constraints and the increasing migration.
Drawing on Angelucci and De Giorgi (2006, 2009), the exogenous positive income shocks tovillages by eligible household receiving the CCT regularly should increase available
resources within the village, which without doubt enables better consumption smoothing.
They also argue that not only eligible but also ineligible households may be able to change
their behaviors through networks within the village—a phenomenon defined as the spillover
effect. Also Angelucci (2012) points out that the regular transfers are likely to be used ascollaterals to increase the loan size. In addition, Angelucci (2012, 2015) and Aonuma (2009)
empirically confirm the effect of PROGRESA-Oportunidades on the increasing number of
migration, both international (Angelucci, 2015) and domestic (Aonuma, 2009), using ENCEL
data. However, these inferences of how the CCT reinforce the risk-sharing mechanisms
should yet to be carefully examined with more detailed analyses of quantitative andqualitative evidences, which remains for future study.
Finally, we should note the fact that the increase of food prices peaked in 2008 and 2011 dueto the international food price crisis, which I could not cover with the ENCEL data available
so far. It is required to expand the sample data to examine more thoroughly the influence offood price shocks and the Lehman shock, when new data become available.

32

APPENDIX A: Variables

Household real per capita food consumption: First, I construct each household’s weekly foodconsumption by summing up the reported amount of weekly food consumption and theestimated weekly self-consumption. Then, I divide the household’s weekly food consumption
by the number of household members to ascertain the per capita weekly food consumption. In
estimating self-consumption, I first calculate the median state price of each item using eachhousehold’s reported weekly purchase and the expenditure on the item. Then, I multiply thevalue of reported self-consumption by the estimated unit median price of the state. Per capita
food consumption is deflated by the annual average food CPI xix.

Household real per capita total consumption: I construct a household’s real per capita total
consumption in the same way as food consumption, using the reported weekly total
consumption of food and non-food items. Per capita total consumption is deflated by theannual average general CPI.

Household real per capita income in 2003 and 2007: This includes all household members’
wages, pensions, bonuses, monetary institutional transfers (including CCT), agriculturalsales, and non-agricultural sales. It excludes personal transfers (including remittances), nonlabor or irregular incomes, such as the sales of assets (e.g., houses, cars, and homeelectronics), inheritance, lottery, gifts, and donations. Personal transfers are excluded because
they are more likely to reflect ex post adjustments to shocks, as Skoufias (2007) argues. The
reported units for each income source vary from daily, weekly, and monthly to annual. Thus, I
estimate the weekly amount of each income source and sum these up to estimate weekly
household income. Then, I divide the weekly total income by the number of household
33

members and deflate it by the annual average general CPI. Households that have any type of
unreported income sources are dropped from the sample.

Household real per capita income in 2001 and 2002: This consists of the sum of thehousehold head and spouse’s retrospective weekly wage income divided by the number ofhousehold members, which is deflated by the average annual general CPI.

Education dummies: Primary, secondary, or highschool refers to those who have enrolled ina primary, secondary, or high school, regardless of whether they graduated. Technicaleducation refers to those who have enrolled in any technical or vocational school, including
teacher’s college. University education includes those who have enrolled for a university andhigher education (including those who graduated from university and have entered into or
graduated from the post-graduate level).

Household demographic variables: The total number of household members refers to themembers who live in the same house. It excludes those who live separately for more than one
year, whose stay is temporary, and who have expired. The dependency ratio is the proportionof household members under 14 years and over 65 years of age (non-labor force) to thenumber of household members aged 15–64 years (labor force).

Local tortilla price: Drawing on Attanacio et al. (2009, 2013), I first calculate the medianvillage (locality) price of tortilla using each household’s unit price (per kilogram), which isderived by applying the same method used to estimate self-consumption. I exclude medianvillage prices above 20 pesos per kilogram for 2003 and 25 pesos for 2007. Then, I calculate
the mean and standard deviation for the remaining median village tortilla prices, which are34

4.7 and 2.1 for 2003 and 7.9 and 1.8 for 2007. I use each village’s median price as a local
price of tortilla if the median price is between the mean value ± the standard deviation (2.6–
6.9 for 2003 and 6.1–9.7 for 2007). The median price of a village with less than threehouseholds reporting the purchases of tortilla is also automatically dropped. Local prices that
do not meet the criteria are replaced by the corresponding upper level (municipal) median
prices, which are calculated using the same method as that for the village median price. I use
the state median price as the local tortilla price in case the municipal median prices do not
fulfill the criteria mentioned above. Of the sample, 30 percent was replaced by municipal
median prices and 17 percent by state median prices in 2003, and 24 percent and 16 percentof the sample was replaced by municipal and state median prices in 2007. The means(weighted by the number of households) of the local tortilla prices are 4.9 pesos per kilogramfor 2003 and 8.1 pesos per kilogram for 2007. These estimated prices are quite reasonable
since the means of the state median prices (weighted by the number of households) are 5.2
and 8.2 pesos. The local price changes for 2003 and 2007 are deflated by the food CPI.

35

APPENDIX BTABLE B1
REGRESSION RESULTS OF THE FIRST STAGE OF EQUATION (13)Variables
primary03
secondary03
highschool03
technical03
univ03
total_member03
depratio03
female03
age03
married03
divorced03
indigenous03
total_land_ha03
irrigation03
CCT_dum03
selfcons_dum03
hhremit03
Instruments

Food Consumption
Total Consumption
Model 1Model 2Model 1Model 2
Instrumented: Δy_i, t (2003–2007)−3.021***−3.195***−3.263***−3.347***−2.964*−2.565−2.985*−2.835−15.311***−14.91***−14.798***−14.374***−10.714−9.841−11.192−10.241−17.271−17.086−15.144−14.896
1.184***
1.101***
1.248***
1.112***
0.172***
0.165***
0.166***
0.160***
2.756
2.151
2.670
2.313
−0.436***−0.448***−0.436***−0.444***
3.147
2.775
4.631
4.118
1.400
1.535
2.868
2.622
4.456**
4.473**
4.269**
4.271**−0.368***−0.374***−0.334***−0.339***−9.623***−10.078***−4.630***−4.490***−9.907***−9.781***−0.196−0.049Δy_i ,t-1 (2001–2002)−0.077**
land_dum03−11.477***
hhmig_over15_dum03−6.177***
Constant
33.554Village dummies
yes
No. of Obs.
12349
R-squared
0.163
F statistics
599.11
Note.—Δy_i stands for changes in income.
* p<0.1.
** p<0.05.
*** p<0.01.

−0.073**−12.566***−6.548***35.341***
yes
12349
0.159
66.10

36

−0.075**−12.242***−5.833***34.506***
yes
11442
0.170
165.84−0.071**−13.521***−6.114***35.226***
yes
11442
0.164
59.62

APPENDIX CTABLE C1REGRESSION RESULTS OF EQUATION (13)Dependent Variable: Food Consumption
Model 1
(1)
(2)
(3)
(4)
OLS
OLS
2SLS
OLS
0.1445***0.1402***0.6423***0.1476***−0.9733−1.1055
0.6891−1.1013−2.8077−2.4718−1.1952−2.983−0.1772−1.0253
6.6416−0.3017−9.5205−11.4713*−6.2134−9.4409−18.8005−19.8707−12.0768−19.1923
1.2951***
1.3817***0.8856***1.3234***0.1602***0.1578***0.0544*0.1583***1.0167
1.1467−0.36170.2389−0.3054***−0.3079***−0.0191−0.3226***

married03
divorced03−1.1928−9.537**−1.6523−10.026**−3.1846−11.294***−1.351−9.4233**

Model 2
(5)OLS
0.1438***−1.2637−2.6685−1.1275−11.2992*−20.2393
1.4000***0.1552***0.2995−0.3278***−1.837−9.907**indigenous03
total_land_ha03
irrigation03
CCT_dum03
selfcons_dum03−1.59420.03771.022
1.4207−11.8695***−3.1392***
3.8972*−14.2081**
yes
12320
0.1372-−2.1373
0.03840.4371
1.1779−12.9203***−4.0437*0.2931***
7.7785***3.7368***−7.7668***−1.58390.0331−2.15310.0326−3.0017−10.959***−3.7969*
0.2718***0.0378yes
12349
0.1389-−15.5295
Yes
12349
66.10
132799.98-

46.001243.3731-71.9595-

160.708

Variablesβ
primary03
secondary03
highschool03
technical03
univ03
total_member03
depratio03
female03age03

hhremit03−3.4085***−2.9343**
tortilla price change0307
3.3049Constant
1.4002−15.6875−13.155**
Village dummies
yes
yes
yes
No. of Obs.
12349
12349
12320
R-squared
0.13760.1331
F statistics
599.11
Chi2216028.91
Robust Durbin–Wu–
Hausman test ofendogeneity
Chi2
43.8441
F statistics
40.8796
Weak instrument tests
F statistics
61.5707
Test of overidentifying
restrictions
Chi2
142.941
Note.—p-values are based on Huber–White heteroscedasticity consistent standard errors.
*p < 0.1.
**p < 0.05.
***p < 0.01.

37

(6)
2SLS
0.5886***0.4277−1.56725.4696−7.2412−13.3902
1.0419***0.0653**−0.937−0.0576

TABLE C2
REGRESSION RESULTS OF EQUATION (13)Dependent Variable: Total Consumption
Model 1
Variables

Model 2

(1)

(2)

(3)

(4)

OLS

OLS

2SLS

OLS

β
0.1819***0.1816***0.7509***0.1841***
primary03−3.0582**−3.1118**−0.9731−3.1785**
secondary03−3.8574−3.897−2.2347−4.0471*highschool03
0.62990.57598.8971
0.423
technical03
9.48199.406115.3861*9.4935
univ03−2.3128−2.39215.2763−2.8723
total_member03
2.1314***2.1321***
1.5756***
2.1947***
depratio03
0.2408***0.2425***0.1281***0.2380***female03−3.7456−3.7037−5.7328*−4.5651
age03−0.4042***−0.4049***−0.0774−0.4272***
married03
6.51996.3441
3.78956.4929divorced03
2.42352.16750.13662.6424indigenous03
1.20891.1975−0.80541.2308
total_land_ha03
0.00520.00510.2801***−0.0001
irrigation03
0.4124
0.58679.3690***
CCT_dum032.20392.17644.9942***
selfcons_dum03−9.1156***−9.0792***−3.4248hhremit03−4.2398***−4.1940***−3.5689**
Tortilla price13.3782***
12.8063***
change0307
Constant−22.5192***
28.3717**9.2848−21.5963***
Village dummies
yes
yes
yes
yes
No. of Obs.
11414
11442
11442
11414
R-squared
0.161082671
0.16130.1589F statistics
165.84Chi2279507.62
Robust Durbin–Wu–
Hausman test ofendogeneity
Chi2
38.1502
F statistics
36.0017
Weak instrument
tests
F statistics
61.489Test ofoveridentifying
restrictions
Chi2
139.746
Note.— p-values are based on Huber–White heteroscedasticity consistent standard errors.
*p < 0.1.
**p < 0.05.
*** p < 0.01.

38

(5)

(6)

OLS

2SLS

0.1837***−3.2256**−4.0774*
0.38539.4212−2.9251
2.1950***0.2397***−4.5211−0.4273***
6.30662.377
1.20780.00030.6794***−1.2724−2.71317.413113.97123.74181.7981***0.1420***−6.1791*−0.1258*
4.32170.7203−0.50210.2558***26.9945**yes
11442
0.1592-

9.9755
yes
11442
59.62
923638.75-

35.510333.7585-74.066-149.333

APPENDIX DTABLE D1
REGRESSION RESULTS OF EQUATION (14)Food Consumption
Model 1

Total Consumption
Model 2Model 1Model 2

(1)
OLS

(2)
2SLS

(3)
OLS

(4)
2SLS

(5)
OLS

(6)
2SLS

(7)
OLS

(8)
2SLS

Second-Stage
Regression
βT1998*βT2000*βprimary03
secondary03
highschool03
technical03
univ03
total_member03
depratio03
female030.2036***−0.0785**−0.063−1.0126−2.7973−0.1739−9.647−19.2515
1.3108***0.1611***
1.06580.6546***
0.0414−0.11850.7033−1.3826
7.1924−4.5107−11.2374
0.8395***0.0602**−0.5978

0.2057***−0.0784**−0.0607−1.1356−2.9661−0.2764−9.5664−19.5971
1.3383***0.1591***0.28030.5908***0.051−0.11210.431−1.7973
5.946−5.6662−12.6357
0.9961***0.0724***−1.07270.8806***0.0102−0.1647−0.8913−2.0869
10.4327
15.8937*
8.17651.5392***0.1069***−5.9780*

0.2168***−0.042−0.0386−3.2098**−4.0706*
0.34369.4215−3.1067
2.2021***
0.2394***−4.49490.7901***0.0409−0.1651−1.2041−2.58828.736814.27216.30151.7722***0.1227***−6.4010*age03−0.307***−0.026−0.324***−0.0632−0.0343−0.428***−0.0828

married03−1.2943−1.462

6.31275.0317−9.7677**

2.13491.03262.35681.4653indigenous03
total_land_ha03
irrigation03
CCT_dum03
selfcons_dum03
hhremit03
T1998_dum
T2000_dum_consFirst Stage
Regression
Instruments:
Δy_i ,t-1 (2001–
2002)
land_dum03
hhmig_over15_dum03−1.54440.03771.3302
1.3563−11.838***−3.1132***−0.0832
1.218−0.2022−1.5498
0.0336−2.679−10.628**−3.03610.2706***4.6424

divorced03−2.8422−10.904***−3.3000*
0.2921***
8.3924***3.7698***−6.974***−2.5222**40.681***38.782***−15.5612

0.2155***−0.042−0.0409−3.0975**−3.8896
0.52189.4032−2.6017
2.1394***0.2422***−3.666−0.4053***
6.34791.22240.00480.76762.148−9.088***1.22770.0003−0.75240.3183***−4.2110***−1.1183
0.3440***10.287***5.3728***−2.597−3.3609**

2.58134.2784−1.565435.812***34.143***−15.184−99.433***−102.19***−122.51***−128.94***−94.632***−97.149***−108.82***−115.27***27.6653**

6.461826.3024**

7.4518-−0.0774**-−0.0735**-−0.0774**-−0.0735**-−11.484***-−12.68***-−11.484***-−12.680***-−6.1763***-−6.489***-−6.1763***-−6.4892***

no

no

no

yes
11442
0.151

yes
11442
0.159

yes
11442
0.148

−9.655**Tortilla price
no
no
no
no
no
changeVillage dummies
yes
yes
yes
yes
yes
No. of Obs.
12349
12349
12349
12349
11442
R-squared
0.138
0.127
0.134
0.122
0.161
Note.—p-values are based on Huber–White heteroscedasticity consistent standard errors.
* p < 0.1.
** p < 0.05.
*** p < 0.01.

TABLE D2
REGRESSION RESULTS OF EQUATION (14)Food Consumption
Model 1

Total Consumption
Model 2Model 1Model 2

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

OLS

2SLS

OLS

2SLS

OLS

2SLS

OLS

2SLS

β

0.2036***

0.6513***0.2057***0.5891***0.2155***0.8771***0.2168***0.7884***

T1998&2000*β−0.0723**−0.0228−0.0713**−0.0154−0.0416−0.0599−0.0407−0.0433

primary03−1.0099

0.7067−1.13240.4513−3.0949**−0.8846−3.208**−1.1721secondary03−2.7853−1.3877−2.9511−1.7764−3.8873−2.0728−4.0676*−2.5423highschool03−0.21077.2171−0.3147

5.99150.529110.51190.34288.8603technical03−9.6376−4.5469−9.5476−5.6758

9.396415.8988*9.413314.3167

univ03−19.2255−11.2742−19.5552−12.6442−2.596

8.1838−3.1027

6.3436

total_member03

1.3114***0.8436***

1.3383***1.0016***2.1403***

1.5452***2.2033***

1.7811***

depratio03

0.1613***0.0605**0.1592***0.0727***0.2422***0.1071***

0.2393***0.1230***female03

1.0667−0.57750.2754−1.0372−3.6722−5.9671*−4.5008−6.3696*age03−0.3074***−0.0268−0.324***−0.0633−0.405***−0.035−0.478***−0.0825

married03−1.3086−2.7467−1.4805−2.57136.35184.74196.31345.1589divorced03−9.7907**−10.806***−9.6791**−10.5284**

2.142

1.14892.3584

1.5953indigenous03−1.5176−3.3523*−1.5173−3.0886

1.1889−1.2416

1.1995−0.8863

total_land_ha03

0.03770.2861***0.03370.2646***0.005

0.3379***0.00050.3114***

irrigation03

1.34238.1807***0.765810.0334***

CCT_dum03

1.32843.8424***2.16035.4691***

selfcons_dum03−11.859***−6.9437***−9.0828**−2.5741

Second-Stage
Regression*

hhremit03−3.1258***−2.5076**−4.208***−3.3469**

T1998&T2000_du−0.1806−139.35***

2.4784−129.92***

17.1275

1.531620.0391*−5.6184−0.185−15.5857−1.5398−15.2992

27.6598**

6.414526.3004**7.2642-−0.0774**-−0.0734**-−0.0774**-−0.0734**m_consFirst-Stage
Regression
Instruments:
Δy_i ,t-1 (2001–
2002)

40

land_dum03-−11.477***-−12.672***-−11.477***-−12.672***-−6.1771***-−6.4935***-−6.1771***-−6.494***

no

no

no

no

no

no

No

no

Village dummies

yes

yes

yes

yes

yes

yes

yes

yes

No. of Obs.

12349

12349

12349

12349

11442

11442

11442

11442

R-squared

0.138

0.126

0.134

0.121

0.161

0.150

0.159

0.148

hhmig_over15_dum03
Tortilla pricechangeNote.—p-values are based on Huber–White heteroscedasticity consistent standard errors.
* p < 0.1.
** p < 0.05.
*** p < 0.01.

41

APPENDIX ETABLE E1
OLS REGRESSION RESULTS OF EQUATION (13) BY SUBGROUPS
Model 1
Food Consumption

Total Consumption

T1998

T2000

C2003

T1998

T2000

C2003

β

0.1170***0.1512***0.2044***0.1651***0.1863***0.2109***primary03−0.8358

0.1852−4.4889−1.6763−1.9039−10.3159***

secondary03−2.0505−0.9299−7.4195*−2.388−1.4179−12.1069**

highschool03

0.66898.436−12.1657−2.4082

23.0979**−21.9068

technical03−9.1394−3.2761−22.6581*

3.141319.4998

10.5354

univ03−44.5793***

5.3977−18.5092−26.5386***19.2509−5.4772

total_member03

1.5690***1.2929***0.59032.4549***2.1455***1.1453depratio03

0.1905***0.0727**0.2539***0.2623***0.1694***0.3289***female03−1.2811−0.15778.6485−3.3514−7.7932

4.8231age03−0.3682***−0.2483***−0.2366***−0.4673***−0.3237***−0.3681***

married03

6.0826−3.2409−13.3277

11.0242*2.86184.4769divorced03−0.3363−9.8002−29.1154***7.45710.8433−6.0789

indigenous03−4.23163.2263−3.2696−1.2497

4.53414.0494

total_land_ha03−0.02730.13070.1218−0.0966

0.15310.1208irrigation03

1.7561−2.1177

4.43364.3078−2.5239−2.8338

CCT_dum03

0.68062.43460.333

3.7031*

1.015−4.8429

selfcons_dum03−13.0368***−10.2925***−11.6963***−11.6666***−6.8446**−6.3393

hhremit03−2.4381−4.6840***−2.1228−4.3724**−5.0816**−2.2265

Constant−24.8410***31.8713***

11.2687

45.2987***−68.3045***35.0066*

Tortilla price change

no

no

no

no

no

no

Village dummies

yes

yes

yes

yes

yes

yes

t-value

T98 vs. T00

T00 vs. C03

T98 vs. C03

T98 vs. T00

T00 vs. C03

T98 vs. C03

1.21

1.32

2.27***

0.61

0.46

0.90

No. of Obs.

5897

4130

2322

5450

3848

2144

R-squared

0.144

0.131

0.150

0.183

0.146

0.148

Note.—p-values are based on Huber–White heteroscedasticity consistent standard errors. t-values represent the significance ofthe differences between each group’s coefficients. The results are based on the Model 1 estimation.
*p < 0.1.
**p < 0.05.
***p < 0.01.

42

APPENDIX FTABLE F1
2SLS REGRESSION RESULTS OF EQUATION (13) BY SUBGROUPS
Model 1
Food Consumption

Total Consumption

T1998

T2000

C2003

T1998

T2000

C2003

β

0.3746***0.7404***0.9217***

0.4610***0.8417***

1.3914***

primary03−0.2777

2.0674−0.4887−0.5862

0.5533−5.5082

secondary03−0.3515−1.0156−3.7433−1.317−1.1584−6.5447highschool03

2.527920.1321**−0.31121.129738.5871***−11.2545

technical03−8.8311

1.5847−15.6822

6.654426.6087

19.2708

univ03−38.3506**

15.2318−19.2628−19.1031*

31.7946−8.4041

total_member03

1.3927***0.505

0.72992.1619***1.2205**

1.1493depratio03

0.1355***−0.0463

0.07110.2049***0.05180.0366female03−2.1414−2.74

8.8046−4.9108−10.5525*

5.2037age03−0.2151***

0.12310.0763−0.2985***0.09040.1821

married03

3.571−3.0326−13.8416

8.99382.3201−0.5051

divorced03−2.3942−8.1869−31.1108***

5.702

3.2193−15.8834

indigenous03−5.0532*−0.9667−6.9733−1.7535

1.5854−2.3375

total_land_ha03

0.10750.4481***0.4982**0.03040.5396***

0.6724**

irrigation03

3.6961

4.113823.0640**8.0064*4.115830.8202**

CCT_dum03

1.60315.9968***

11.4168

4.7985**4.8523*14.6747

selfcons_dum03−12.0160***−3.8379−4.3927−9.5267***1.85026.5152hhremit03−3.0550**−3.0107−1.8826−4.3533**−2.8565−2.7419

Constant

36.0348**−31.4520*−15.2295−19.5675−68.0234***−3.8696Δy_i ,t-1 (2001–2002)−0.1069−0.0598−0.0595138−0.0977−0.0619−0.061

land_dum03−12.732***−11.105***

9.1502***−13.226***−11.928***−10.119***

hhmig_over15_dum03−3.299**−7.0626***−13.348***−2.7618*−7.1343***−12.947***First-Stage Regression
Instruments

Tortilla price change

no

no

no

no

no

no

Village dummies

yes

yes

yes

yes

yes

yes

t-value

T98 vs. T00

T00 vs. C03

T98 vs. C03

T98 vs. T00

T00 vs. C03

T98 vs. C03

2.08**

0.73

2.36***

2.28***

0.77

2.62***

No. of Obs

5897

4130

2322

5450

3848

2144

Chi2

199730.55

209185.88

450.9452926.70

778204.72

934.94

Chi2

4.63

24.78

23.21

4.90

20.68

31.55

F statistics

4.31

23.37

22.52

4.55

19.57

31.79

Robust Durbin–Wu–
Hausman test ofendogeneity

Weak instrument tests

43

F statistics

30.95

21.18

11.98

30.54

22.12

11.27

83.90

57.31

7.07

61.76

55.44

12.16

Test ofoveridentifying
restrictions
Chi2

Note.—p-values based on Huber–White heteroscedasticity consistent standard errors. t-values represent the significanceof the differences between each group’s coefficients. The results are based on the Model 1 estimation.
*p < 0.1.
**p < 0.05.
***p < 0.01.

44

REFERENCES
Amin, Sajeda, Ashoka S. Rai, and Giorgio Topa. 2003. “Does Microcredit Reach the Poor andVulnerable? Evidence from Northern Bangladesh.” Journal of Development Economics
70: 59–82.
Angelucci, Manuela. 2012. “Conditional Cash Transfer Programs, Credit Constraints, andMigration.” Labor 26 (1): 124–136.
— 2015. “Migration and Financial Constraints: Evidence from Mexico.” Review of Economics
and Statistics 97 (1): 224–228.
Angelucci, Manuela, and Giacomo De Giorgi. 2006. “Indirect Effects of an Aid Program: The
Case of Progresa and Consumption.” IZA Discussion Paper No. 1955.
— 2009. “Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles’
Consumption?” American Economic Review 99: 486–508.
Aonuma, Takashi. 2009. “Hinkon Bokumetu Puroguramu ga Imin ni Oyobosu Fukujiteki
Koka: Mekishiko Progresa-Oportunidades no Jirei,” (The Secondary Effect of a Poverty
Eradication Program on Migrants: The Case of Progresa-Oportunidades in Mexico) Latin
America Ronshu 43: 19–36. (in Japanese)
Attanasio, Orazio, Vincenzo DiMaro, Valérie Lechene, and David Phillips. 2009. “The Welfare
Consequence of Increases in Food Prices in Rural Mexico and Colombia.” mimeo.
— 2013. “The Welfare Consequence of Food Price Increases: Evidence from Rural Mexico.”
Journal of Development Economics 104: 136–151.
Banco de México. “Estadísticas” (Statistics).
http://www.banxico.org.mx/estadisticas/index.html. (Accessed on Dec. 1, 2013)
Bardhan, Pranab, and Christopher Udry. 1999. Development Microeconomics. Oxford
University Press: New York City.
Caridad Araujo, María, and Paula Suárez Buitrón. 2013. “Programa de Desarrollo Humano

Oportunidades: Evolución y Desafíos.” (Human Development Program Oportunidades:
Evolution and Challenges). Nota Técnica (Technical Note). IDB-TN-601. Banco
Interamericano de Desarrollo.
Consejo Nacional de Evaluación de la Política de Desarrollo Social (CONEVAL). 2011. NOTATÉCNICA: Instrucciones para Consultar del Contenido y Valor de la Canasta Básica
(Technical Notes: Instructions for the Content and Value of the Food Basket).
— 2012. Evolución de la Pobreza por Ingresos 1990–2010 (Changes of Income Poverty 1990–
2010). (http://www.coneval.gob.mx/Medicion/Paginas/Evolucion-de-las-dimensiones-dela-pobreza-1990-2010-.aspx) (Accessed on Dec. 1, 2013).
Deaton, Angus. 1992. “Saving and Income Smoothing in the Côte d’Ivoire.” Journal of African
Economies 1: 1–24.
Dercon, Stefan. (ed.) 2006. Insurance against Poverty. Oxford: Oxford University Press.
Fafchamps, Marcel. 1999. Rural Poverty, Risk and Development, Report submitted to Foodand Agriculture Organization (FAO).
Fiszbein, Ariel, Norbert Schady, and Francisco H.G. Ferreira. 2009. Conditional CashTransfers: Reducing Present and Future Poverty. Washington, D.C.: The World Bank.
Foster, James, Joel Greer, and Erik Thorbecke. 1984. “A Class of Decomposable Poverty
Measures.” Econometrica 52: 761–765.
Grimard, Franque. 1997. “Household Consumption Smoothing through Ethnic Ties: Evidence
from Côte d’Ivoire.” Journal of Development Economics 53: 391–422.
Jacoby, Hanan G., and Emmanuel Skoufias. 1997. “Risk, Financial Markets, and Human
Capital in a Developing Country.” Review of Economic Studies 64: 311–335.
Kamanou Gisele, and Jonathan Morduch. 2005. “Measuring Vulnerability to Poverty.” In
Dercon (2005), 155–175.
Kochar, Anjini. 1995. “Explaining Household Vulnerability to Idiosyncratic Income Shocks.”
46

American Economic Review 85: 159–164.
Kurosaki, Takashi. 2001. Kaihatsu no Mikuro Keizaigaku: Riron to Ouyou (Development
Microeconomics: Theory and Practice). Tokyo: Iwanami Shoten. (in Japanese).
— 2006. “Consumption Vulnerability to Risk in Rural Pakistan.” Journal of Development
Studies 42: 70–89.
— 2009. The Economic Analysis of Poverty and Vulnerability. Tokyo: Keiso shobo. (inJapanese).
Ravallion, Martin, and Shubham Chaudhuri. 1997. “Risk and Insurance in Village India:
Comment.” Econometrica 65: 171–184.
Secretaría de Desarrollo Social (SEDESOL). 2006. Nota Metodológica General Rural.
Instituto Nacional de Salud Pública Coordinación Nacional de Programa de Desarrollo
Humano Oportunidades.
Skoufias, Emmanuel. 2005. “PROGRESA and its Impacts on the Welfare of Rural Households
in Mexico.” Research Report 139, International Food Policy Research Institute (IFPRI).
— 2007. “Poverty Alleviation and Consumption Insurance: Evidence from PROGRESA in
Mexico.” Journal of Socio-Economics 36: 630–649.
Todd, Petra. 2004. “Design of the Evaluation and Method used to Select Comparison Group
Localities for the Six Year Follow-up Evaluation of Oportunidades in Rural
Areas.”International Food Policy Research Institute (IFPRI).
Townsend, Robert M. 1994. “Risk and Insurance in Village India,” Econometrica: Journal of
the Econometric Society 62: 539–591.
Uchiyama, Naoko. 2013a. “The Impacts of the CCT and Rising Food Prices on theConsumption of Rural Poor in Mexico.” Latin America Ronshu, 47: 43–60.
— 2013b. Empirical Studies of Poverty and Vulnerability in Latin America and the Caribbean.
Ph.D. Dissertation. Kobe University.
47

Udry, Christopher. 1994. “Risk and Insurance in a Rural Credit Market: An EmpiricalInvestigation in Northern Nigeria.” Review of Economic Studies 61: 495–526.
Valero-Gil, Jorge N., and Magali Valero. 2008. “The Effects of Rising Food Prices on Poverty
in Mexico.” Agricultural Economics 39: 485–496.
Wood, Ben, Carl Nelson, and Lia Nogueira. 2009. “Food Price Crisis: Welfare Impact on
Mexican Households.” Paper presented at the International Agricultural Trade ResearchConsortium (IATRC) Symposium, Seattle, Washington, June 22–23.
World Bank. 2000. World Development Report 2000/2001: Attacking Poverty, Washington,
D.C.: The World Bank.
World Bank. 2013. Annual Report 2013: End Extreme Poverty, Promote Shared Prosperity,
Washington, D.C.: The World Bank.

48

i

There are three national poverty lines: the food poverty line (Pobreza alimentaria, the levelthat covers the minimum food basket); the human capital poverty line (Pobreza decapacidades, the level that covers the minimum necessary expenditures for food basket,
education, and healthcare); and the asset poverty line (Pobreza de patrimonio, the humanpoverty line and the level that covers minimum expenditures pertaining to clothing, housing,and transportation) (CONEVAL, 2011). The asset poverty line is applied for this estimation.
ii

The Indian panel data, known as ICRISAT data, is named after the institution that conducted

the survey: the International Crops Research Institute for Semi-Arid Tropics.
iii

The program was named the Education, Health and Nutrition Program (Programa de

Educación, Salud e Alimentación (PROGRESA)) when it was firstly introduced in 1997,
which replaced all other existing poverty programs and was later renamed “Oportunidades”
when the government changed in 2000. Today, or since the return of the Institutional
Revolutionary Party (PRI) to the government, it is called “El Programa Oportunidades”(hereinafter PROGRESA-Oportunidades). As of late 2011, the program has managed to assist
5.8 million households in the states, covering all of Mexico’s municipalities (Caridad andSuárez, 2013).
iv

ENCEL 2007 also includes new samples of 18,052 households and 77,768 individuals

extracted from the original 7 states as well as from some other poor states. They are excluded
from the panel data because they are cross-sectional and their profiles are very different fromthose of ENCEL 2003.
v

The rural food basket used here is 23.37 Mexican pesos (approximately 1.98 US dollars) per

capita, per day, as of June 2011.
vi

The high percentage of the poverty headcount ratio is a result of the ENCEL choosing

sample villages from the most marginal rural areas throughout the country. The average rural
49

poverty ratios at national level, measured by the same food basket but calculated by percapita income, were 20.0 percent in 2002, 13.8 percent in 2006, and 18.4 percent in 2008
(CONEVAL 2012).
vii

This section draws on Bardhan and Udry (1999: Ch. 8) and Kurosaki (2001: Ch. 8, 2006,

2009: Ch. 9).
viii

ix

Here, I assume that income is exogenous.

Other representative empirical studies on risk sharing include Deaton (1992) and Grimard(1997) for Côte d’Ivoire, Udry (1994) for the rural credit market in Nigeria, and Amin et al.
(2003) for microfinance in Bangladesh. All of these studies rejected the full risk-sharing
hypothesis. See also Bardhan and Udry (1999: Ch. 8) and Kamanou and Morduch (2005) fora detailed literature review on empirical studies on risk sharing.
x

An alternative hypothesis implies a complete autarky or lack of risk-sharing mechanisms.

xi

After excluding zero or unreported income and the upper and lower one percent of thesample as outliers, the complete panel data for 2003 and 2007 comprised 12,243 households.
However, I found no attrition bias in the sample upon comparing the original unbalanced
samples with the balanced ones for regressions by applying the t-test to the means of thehousehold characteristics used in the regressions. The results can be made available uponrequest.
xii

Here, the price for tortilla is used as a proxy for food price changes in 2003 and 2007.

ENCEL data show that notable price changes were observed mainly in corn-related products,that is, tortilla and maize grain. Prices of other food products, such as wheat, rice, beans, fruitand vegetables, meats, and even eggs and milk, remained almost unchanged. Some foodprices even dropped during the period. Attanacio et al. (2009, 2013) pointed out a price rise inmeat and dairy products because of the rise in the price of feed grains; however, this priceincrease was not felt in the marginal rural areas, suggesting that villagers in these areas are50

self-sufficient in raising their own livestock. Detailed data can be made available uponrequest.
xiii

Details of the income change for 2001–2002 are explained in Appendix A. The sum of theretrospective wage earnings of household heads and spouses are used as a proxy of laggedhousehold income changes, because data for the newly added control group (Control 2003)
are not available for years prior to 2003.
xiv

We also regressed models with per capita consumption and income calculated using adultequivalent scales on the basis of Kurosaki (2009) and models without any control variables
𝑋𝑋𝑖𝑖 , but the results did not change in any of the specifications. The results can be madeavailable upon request.

xv

The estimates of three ICRISAT villages conducted by Ravallion and Chaudhuri (1997)

range from 0.209 to 0.462 when using flow accounting income data and from 0.120 to 0.336
when using observable transaction income data (Table IV). Kurosaki (2001: Tables 8–6) also
produced results similar to those of Ravallion and Chaudhuri (1997) using the same Indiandata. In the case of Côte d’Ivoire, the estimated excess sensitivity parameters range fromnegative (essentially zero) to 0.54. (Deaton 1993, Table 3) In addition, Grimard’s (1997)
estimation results using Côte d’Ivoire data are quite robust with coefficients values around
0.2 for different specifications and regression methods.
xvi

One can infer from the 2SLS regression results that a downward bias caused bymeasurement errors is greater than other possible biases that can be attributed to specification
errors or omitted variables.
xvii

The coefficient of the tortilla price change was positive but insignificant in OLS

estimations and was automatically omitted from 2SLS estimations, as is shown in Table 3.
Since the price change is an aggregate shock to the whole village, it is possible that the effectis absorbed by the village dummies (see Appendix C for details).
51

xviii See

Fiszbein and Schady (2009) for the summary of previous studies on the CCT

evaluations.
xix

Banco de México Estadísticas (http://www.banxico.org.mx/estadisticas/index.html ); June

2001 = 100.

52