Community Electrification and Labour Market Development
Louise Grogan
May 28, 2008
Abstract
Does community electrification change labour markets? This paper identifies the effects of
community electrification on the earnings, labour market participation, occupations, and industry of employment of the first generation of individuals to grow up in electrified communities.
Community and household-level data from Guatemala is combined to identify these effects. It
is found that individuals whose communities are electrified before age ten have much higher
earnings, and lower probabilities of working in unskilled occupations or agriculture, than do
individuals who have begun their working lives before the advent of community electricity.
Community electrification causes labour market diversification.
JEL codes: H41, I2, O12, O5, R11
Key words: labour demand, community electrification, earnings, labour market diversification.
——————————————————————————————————————————
Comments are very welcome. I am very grateful to the University of Maryland Population Research Center,
which hosted me during the early stages of this paper in October 2005, and where I received very helpful
comments on an early draft. Correspondence to: Louise Grogan, Department of Economics, University of
Guelph, MacKinnon Building Rm.743, Guelph ON. Tel. 1 (519) 824 4120 ext. 53473. Fax. 1 (519) 763
8497. Email: lgrogan@uoguelph.ca
1
1
Introduction
In 1935 Franklin Roosevelt established the Rural Electrification Administration, and launched a
large-scale initiative to bring electricity to what was then the Great Dust Bowl1 . This Act facilitated
millions of dollars worth of loans to private, public and small cooperative utility ventures. Some of
these new power companies paid folk singers to roam the Western United States and write songs which
would promote rural electrification. The resulting Woody Guthrie song Grand Coulee Dam (1941)
is perhaps the most specific about the positive effects of electrification on labour markets: “Now in
Washington and Oregon you can hear the factories hum, Making chrome and making manganese and
light aluminum.”. Guthrie, neither environmentalist nor economist, was one of many who believed
that electrification would bring jobs and economic diversification, and have positive impacts on the
rural standard of living2 .
Folk wisdom aside, a large literature now examines the effects of post-war technological changes
on the US labour market. Rising US female labour force participation is attributed to technological
improvements, and particularly to technologies that relaxed the time constraints from childbearing
(see, for example Angrist and Evans (1998), Goldin and Katz (2002), Greenwood, Seshadri, and
Vandenbrouke (2005), and Bailey (2006)). A major explanation of rising wage inequality in the US
since the 1980s is that technological changes have been disproportionally beneficial to high-skilled
workers (Acemoglu (1998), Acemoglu (2002), and Galor and Moav (2006)). Surprisingly, despite this
extensive literature on the labour market effects of technological change, there is still very little work
relating rural electrification to subsequent changes in the structure of the US labour market3 .
An extensive literature in development economics documents the effects of technological improvements such as the Green Revolution on human capital (Foster and Rosenzweig (1996)) and agricultural productivity (Murgai, Ali, and Byerlee (2001)), but very few papers have addressed the effects
of community electrification on labour market diversification. Yet, according to the World Bank
1
In the late 1930s, rural electrification rates were about 12% in the US (Wolman (2006)). The high cost of connecting
sparsely-populated areas meant that rural electrification could not be profitable for the existing large power companies
that served cities. This was not the only way in which the situation prior to the founding of the Rural Electrification
Administration resembled what is currently observed in some developing countries. In the mid 1930s, low agricultural
prices, an agricultural depression which had begun in the 1920s, and desertification and outmigration from the Midwest,
were features of US rural life (Wolman (2006)). The prevalence of sharecropping, particularly in the South, may have
discouraged investments in household electrification.
2
Woody Guthrie wrote for the Bonneville Power Authority of Oregon in 1941 the song Bonneville Dam, which
summed up his hopes for impoverished agricultural areas:
“I’ll turn my stone and till my land
Waiting for the big Bonneville Dam
That Bonneville Dam is a sight to see
Makes that e-lec-a-tric-i-ty”
.
3
Malone (2008) and Schurr, Burwell, Devine, and Sonenblum (1990) credit rural electrification for large increases
in demand for home appliances and an accompanying development of the electric and plumbing trades in rural
communities.
2
(2007), at least 1.6 billion people, about a quarter of the world’s population, still do not have access
to electricity. Countries and regions with low electrification rates tend to be rural and agricultural,
and to be extremely vulnerable to supply and demand shocks in agriculture. The lack of literature
examining community electrification effects appears to be primarily attributable to a previous lack
of data from developing countries on community level infrastructure provisions.
Two recent papers identify causal effects of electrification on labour market outcomes. Dinkleman
(2008) examines the initial effects of a massive rural electrification program in Kwa-Zulu Natal,
South Africa, on household fuel use and employment. The substantial observed effects on these two
margins are attributed to changes in home production because of household electrification. Grogan
and Sadanand (2008) model the effects of improved home production technology on fertility and time
use, and test the model using household and community-level data from Guatemala. The positive
causal effects of household electrification on the time women spend in market activities and on their
earnings are found to be very large4 .
Diversification of local labour markets is clearly a favorable policy outcome. Sectoral shocks, such
as drought, may have less dramatic impacts on poverty when a smaller fraction of the local working
population is employed in this sector. New industries and occupations which use electricity may
provide greater returns to experience and permit individuals greater improvements in the marginal
product of their labour. As well, communities with more diverse economic bases are less vulnerable
to negative shocks in demand for their output. If community electrification does have causal effects
on labour market diversification, these should thus be taken account of in cost-benefit studies of
electrification projects.
Electrification and other infrastructural provisions may be viewed as specific mechanisms through
which government institutions may promote diversification away from agriculture. While the importance of both formal and informal institutions to the development process is now widely accepted, the
literature on the means through which such institutions promote development is more recent5 . One
specific means through which well-functioning institutions facilitate economic growth may be that
4
In contrast with Dinkleman (2008), who identifies the combination of household and community-level effects,
Grogan and Sadanand isolate the effects of household electrification on time use and earnings of women. For women,
community electrification status is a valid instrument for both of these outcomes. The data show that, conditional on
an extensive range of other community-level variables, there are no direct effects for women of community electrification
status on either earnings or time spent in most activities. For men, household electrification is found not to have a
direct association with earnings, but community electrification status is found to have a large positive association with
earnings. This indicates that, at least for men, community electrification may have strong effects on subsequent local
labour market development.
5
The economics literature has devoted considerable attention recently to the link between institutions and development outcomes. A growing literature examines the impact of formal, state institutions on development using
aggregate cross-country data (see for example,Acemoglu (2002), and Acemoglu, Johnson, and Robinson (2004)).
Micro-data studies of informal institutions such as rotating savings and credit cooperatives (see for example Banerjee, Besley, and Guinnane (1994), and Anderson and Baland (2002)), social networks (see for example Jackson and
Watts (2002), Jackson and Calvas-Armengol (2004)), and social capital (see for example Guiso, Sapienza, and Zingales
(2004)) have contributed to an understanding of the role of social norms and sanctioning mechanisms in upholding
non-state economic actors.
3
they provide infrastructure which makes labour markets less dependent on agriculture. Certainly
this appears to have been one of the results of the Rural Electrification Administration in the US.
Several recent papers have quantified the impact of infrastructure provisions on a range of development outcomes. Esfahani and Ramı́rez (2003) use aggregate level data to estimate a structural
model of infrastructural investment and GDP growth. This model suggests that infrastructural provisions (telecommunications and power production) can have important impacts on long-run growth.
Banerjee, Duflo, and Qian (2007) estimate the effect of access to railroad and major lines of communication on wages, inequality and economic growth in China using methodology that accounts for
endogenous infrastructure placement. Miguel and Roland (2005) estimate the long-term effects of
the US bombing campaign in Vietnam on population density, poverty rates, the access of households
to electricity, and literacy. Whereas Banerjee, Duflo and Qian find positive effects of transportation infrastructure on local wages and outmigration, Miguel and Roland find that the destruction of
infrastructure in the the US bombing campaign had no statistically-significant long run effects on
the observable measures of development. Thus the debate on the importance of infrastructure to
long-run growth remains open.
This paper focuses on the identification of longer-run effects of community electrification on
local labour markets. It is clear that community electrification might facilitate the emergence of
new productive activities which were infeasible or unprofitable without power. This means that,
regardless of a household’s electrification status, community electrification status may impact the
wage rates and types of activities in which individuals engage. Because the emergence of these new
economic activities takes time, the initial labour market effects of community electrification may
be very different from the longer term effects. This means that cost-benefit analyses of community
electrification schemes which measure benefits based on data collected soon after electrification might
underestimate the true impact of community electrification on local labour markets6 .
In this paper I examine the labour market effects of community electrification from the perspective
of the first generation of individuals to make labour market choices under electrification. Using the
2000 Guatemala LSMS data it is possible to control for household electrification status, and also
for the nature and vintage of other community infrastructure that might impact labour markets.
Because of this, I can distinguish both between community versus household electrification effects
and also between the effects of age at electrification versus those of the age at the introduction
of other household infrastructure. Four labour market outcomes are examined: earnings, labour
force participation, the probability of being in a low-skilled occupation, and the probability of being
engaged in agriculture. Large effects of being younger versus older at community electrification are
found. This effects are attributed to changes in local labour demand as a result of electrificatoin.
The paper proceeds as follows. Section 2 provides background information and introduces the
data to be employed. Section 3 demonstrates key differences in the labour markets of more and
6
There is also a competing possibility that initial impact assessments might overestimate how community electrifi-
cation and other infrastructure provisions change local labour markets. This is because the process of improving local
infrastructure might have dramatic, but temporary effects on labour demand and household incomes.
4
less recently electrified communities. I show that the effects of community electrification on labour
demand grow over time. Section 4 discusses the identification strategy, and estimates the effect of
an individual’s age at electrification on each of the four labour market outcomes considered. Both
fully parametric and semi-parametric estimators are employed. Section 5 concludes.
2
Background and Data
Guatemala is amongst the poorest countries in the Americas. More than 60% of the Guatemalan
population resides in rural areas, and more than one third of the Guatemalan labour force is engaged
primarily in agriculture (see Vakis (2003)). According to the World Bank Development Indicators for
2001, per capita GDP in Guatemala was 3630 US dollars, measured at purchasing power parity. By
this measure its’ citizens are better off than those in neighbouring Honduras (GDP per capita income
of 2270 US dollars in 2001), but less well off than those in El Salvador (4260 dollars) or those in
Panama (5450 dollars). However, this measure obscures the fact that Guatemala is also characterised
by one of the most unequal income distributions in the world. According to Vakis (2003), almost
half of the wealth of Guatemala is concentrated in the Guatemala city region, whereas only 22%
of the national population live in this area. The 1989 National Socio-Demographic Survey suggest
that 65.5% of Guatemalans lived below the poverty line, and more than 38% were below the extreme
poverty line (see Gragnolati (2004)). According to the World Health Organization Global Database
on Child Growth, nearly sixty percent of Guatemalan children under aged three were stunted in 1987,
the highest rate in Latin America. In 2003, life expectancy in Guatemala was the lowest in Central
America, at 65 years, and infant mortality was the highest in the region, at 45 per thousand (see
Gragnolati and Marini (2003)). Until a peace accord signed in 1996, Guatemala had experienced 36
years of continuous civil war.
In comparison with other countries in Latin America, a relatively large fraction of the Guatemalan
population is indigenous. According to the 2000 LSMS data, 43% of the population identifies themselves as indigenous7 . Many indigeneous people do not speak Spanish, a fact which undoubtably
hinders their labour market activities and access to information.
Because of the distribution of wealth in Guatemala, there are stark geographical differences
in standards of living, and large fractions of the population without access to basic infrastructure
provisions such as piped water, telephones, sanitation, and electricity. In several areas, infrastructure
provisions arrived only following the end of the civil war. Infrastructure provisions were not part of
a massive, rapid ‘industrialisation package’, occurring rapidly and simultaneously across the country
during a short period. They occurred on a piecemeal basis, largely due to the logistical obstacles
posed by the long-running civil war. These historical features of the introduction of infrastructure
in Guatemala are important for the identification of the effects of electrification on the economic
outcomes considered.
7
Amongst this population, there are three major ethnic groups: the Maya, the Garifuna, and the Xinca. The
largest indigenous language groups is Quiche, which is spoken by more than one fifth of indigeneous people.
5
Due to the civil war, the collection of nationally-representative household survey data was begun
relatively late in Guatemala. However, the 2000 LSMS data constitute one of the largest and most
comprehensive of all of the World Bank LSMS surveys. More than 37 700 individuals took part,
and answered detailed questions on education, labour supply, time use, agricultural work, health,
fertility, and participation in community activities. At the community level, another detailed survey
was administered to obtain information on the infrastructure and services provided in the community.
This survey, unlike many LSMS surveys containing community-level information, records information
on the timing of the introduction of water, sanitation, telephone, and electricity infrastructure. It is
this information which is crucial to obtaining unbiased estimates of the effects of age at electrification
on the labour market outcomes of individuals. Two-thirds of communities sampled by the LSMS
individual-level and household questionnaires also completed this community-level survey, giving a
total of 485 communities8 . More information on the LSMS survey is available on the World Bank
Living Standards Monitoring Survey website, www.worldbank.org/lsms.
The next section is devoted to motivating the examination of the effects of age at electrification
on the labour market outcomes of individuals. It is shown that earnings, labour force participation,
the distribution of occupations, and the distribution of individuals across industries is very different
in communities who have had electricity for longer versus shorter periods. It is also demonstrated
that, conditional on an extensive list of individual observables, household electrification status, and
community and region fixed effects, these labour market outcomes differ substantially for individuals
in communities that have been electrified for longer (versus shorter) periods. These findings underline
the important association between community electrification and the development of labour markets,
particularly the diversification out of agriculture and unskilled work.
3
Time Since Electrification and Local Labour Markets
Finding that there are differences in labour market outcomes between communities with and without
electricity would not be surprising. Richer communities are more likely to have electricity because
they can pay for it and because they likely have more political say in the provision of infrastructure.
For similar reasons, one cannot make a claim that communities who have had electricity for longer
periods are otherwise identical to those who have had electricity for shorter periods. Still, the
comparison of the labour market outcomes of individuals in these two types of communities can
provide some stylised facts about the associations between community electricity vintage and the
state of local labour markets.
I first examine the two most basic labour market outcomes, individual monthly incomes and labour
force participation propensities. Table 1 presents means for these variables by sex, disaggregated by
the time since community electrification took place. Only electrified communities are included, and
8
While the incomplete coverage of the community-level survey is certainly a cause for concern, the identifica-
tion strategies employed here do not rely on strong assumptions about the nature of these missing community-level
observations.
6
these are divided amongst those who have been electrified for less than ten years, and for ten or more
years, respectively. T-tests of the equality of means across these electrification vintages are presented
in the final column of Table 1.
Women earn about four times more in communities which have been electrified for at least ten
years. For men, earnings differences are not statistically significant. As well, female labour force
participation rates are about two thirds higher (0.34 versus 0.52) in communities which have been
electrified for at least ten years. For men, labour force participation rates are actually about 0.025
lower (statistically significant at the 5% level) in communities that have been electrified for longer.
If community electrification makes it profitable to engage in new types of occupations, then the
distribution of workers across occupations should be quite different in places that were electrified earlier versus later. Table 2 shows that, for both women and men, there are significant differences in the
types of work performed, by community electrification vintage. In communities that were electrified
earlier, fewer individuals are engaged in unskilled labour (ILO 1-digit occupational code ‘9’), and
more are engaged in professional occupations (ILO 1-digit occupational code ‘2’). More individuals
are engaged in mid-level technical and professional work (ILO 1-digit occupational code ‘3’), as office
clerks (ILO 1-digit occupational code ‘4’), and as service workers (ILO 1-digit occupational code ‘5’)
and much less are engaged in qualified agricultural work (ILO 1-digit occupational code ‘6’). More
men work as skilled heavy equipment operators (ILO 1-digit occupational code ‘8’) in communities
that have been electrified for longer.
These stark differences in the distribution of workers across occupations are mirrored in the
distribution of workers across industries. Only about one third as many women, and about one
half as many men, are employed in agriculture in recently electrified communities as in those who
have been electrified for ten or more years. More men are employed in manufacturing, construction,
and commerce in communities that have been electrified for longer. Amongst both sexes, longer
electrification is associated with greater fractions of individuals employed in transportation and food
processing, services and commerce, public administration, teaching, and social and health sector
employment.
Clearly, there are many other factors which may differ across individuals and communities that
could be causing the strong observed association between the time since electrification and these
labour market outcomes. However, in Data Appendix A, I demonstrate that these associations remain strong in multivariate analyses, even after including an extensive array of individual observables
(including educational attainment and current household electrification status), other communitylevel infrastructure information and departamento (provincial) fixed effects. To summarise the results of these specifications, there remains a strong association between time since electrification and
earnings of men and women (positive), time since electrification and female labour force participation (positive), time since electrification and the probability of working in an unskilled occupation
(negative), and time since electrification and the probability of working in agriculture (negative). Although time since electrification is still not exogenous in these multivariate analyses, these findings
do strongly suggest that causal effects of community electrification may accumulate over time.
7
This section has presented evidence that individuals in communities that have been electrified for
more than 10 years have very different working lives from those whose communities were electrified
more recently. This suggests that community electrification might cause labour market development.
To prove that community electrification causes the earnings, participation, and occupational and
industrial structure changes observed, I note that individuals generally chose their occupation and
industry early in their lives. As well, I note that the increase in the marginal productivity of
human capital from work experience should be greater in more skilled versus unskilled labour. Skill
increases productivity and skills are improved on the job. If this is true, the age of individuals at the
advent of community electrification should impact their entire working lives, even after controlling
for an extensive array of individual and community level observables. By identifying the effect of
community electrification on the labour market experiences of the first generation of individuals
to grow up in ‘enlightened’ communities, I can trace out the effects of labour market change on
individuals9 . Although different communities were electrified at different times, and this timing is
likely not exogenous to the outcomes of interest, the age of an individual at the time of electrification
is exogenous. Because I also have data on the existence and timing of other major infrastructural
improvements, as well as household electrification status and educational attainment, I can be sure
that I am isolating the effect of the age of an individual at electrification on his or her current labour
market outcomes. The next section identifies these causal effects.
4
Age at electrification and working life
I estimate the following equation:
LM KTijc = β0 + β1 ∗ ELEC5T O9ij + β2 ∗ ELEC10T O14ij + β3 ∗ ELEC15T O19ij
+β4 ∗ ELEC20T O24ij + β5 ∗ ELEC25T O29ij + β6 ∗ ELEC30T O34ij
+β7 ∗ ELEC35T O39ij + β8 ∗ ELEC40T O44ij + β9 ∗ ELEC45T O50ij
(1)
+γ ∗ CON T ROLSijc + µd + ijcd
Here i refers to the individual, j to their household, c to their community, and d to their province
or departamento. Four labour market outcomes are examined: LMKTijc ∈ {ln(monthly earnings),
labour force participation, working in an unskilled occupation, and working in agriculture}. OLS
estimation is used for identification of the effects of age at electrification on monthly earnings, while
probit estimation is used to estimate the remaining three outcomes. Only currently-employed individuals are included in the estimation of the effects of age at electrification on the probability of
being in an unskilled occupation, and of working in agriculture.
The age at electrification dummies are as follows: Under age 5 at electrification, 5 to 9 at
electrification (ELEC5TO9), age 10-14 at electrification (ELEC10TO14), age 15-19 at electrification (ELEC10TO15), age 20-24 at electrification (ELEC20TO24) (6.) age 25-29 at electrification
(ELEC25TO29), age 30-34 at electrification (ELEC30TO34), age 35-39 at electrification (ELEC35TO39),
9
In many languages, electricity is referred to as ‘light’. When the electricity is not working, people commonly say
that there is no ‘light’. In Russian, ‘Sveta niet-o’, in Spanish ‘No hay luz’.
8
age 40-44 at electrification (ELEC40TO44), and age 45-49 at electrification (ELEC45TO49). Those
who were under age five at the advent of electrification are the reference group. Only individuals
aged 20 to 50 are included in the sample10 . Variation in the age of individuals at the time of community electrification, community level information on the nature and timing of other infrastructure
improvements, and controls for household electrification status, isolates local labour market effects.
Because there is a strong direct effect household electrification on earnings in these data (see
Grogan and Sadanand (2008)), I control in all specifications for household electrification status. I
also include 5-year age cohort dummies and control for the highest grade completed in school. There
is some migration in these data, so I use a dummy to indicate that an individual was born in the
current community11 . As well I control for the number of adults and children under age 18 in the
household.
The community level survey that accompanied the household-level LSMS permits extensive controls for community level factors that might potentially be correlated with the timing of electrification.
I include dummies for the presence of the following community level characteristics: rural, community has piped water, community has improved sanitation, community has no preschool, community
has no elementary school, community has no secondary school, community was affected by Hurricane
Mitch, community has a paved highway, and community has access to markets. Because the timing
of piped water and improved sanitation is known, I also control for the age of the individual at the
time of these provisions using 5-year age cohort dummies. Since I am able to control for the timing
of other infrastructure which might impact labour market outcomes, I can be relatively confident
that the age at electrification variables do not reflect a convolution of coincident improvements in
community infrastructure12 . Robust standard errors are employed in all estimation.
If labour markets undergo a process of diversification in the years following electrification, as
the previous section suggests, we should observe that the coefficients on the age at electrification
dummies exhibit a systematic trend. This is both because older individuals are less likely to make
new occupational choices following community electrification and because larger fractions of their
work experience (and therefore human capital) will have been created doing tasks that do no not
require electricity.
I first examine the causal effect of the age of an individual at electrification on labour market
earnings.
10
11
According to the WHO (2003), life expectancy in Guatemala was 55 for men and 60 for women in 2002.
Migration would tend to bias observed effects of the age at electrification towards zero. While it would be preferable
to know about electrification status in an individual’s original community and his or her reason for migration, the
LSMS does not collect this information. Since migration may be correlated with community electrification, individuals
who have migrated are not excluded from the sample. About 71% of the estimation sample were born locally.
12
An extreme example would be that piped water, improved sanitation, the building of schools, and electrification
were undertaken simultaneously in many communities. In this case it would be impossible to distinguish the effects of
different infrastructure improvements on labour market outcomes. Luckily there is sufficient variation in the timing
of piped water and sanitation improvements in these data to distinguish the effects of the age at each provision on
labour market outcomes. While the timing of educational infrastructure improvements is not known, all estimation
controls for educational attainment.
9
4.1
Monthly income
It is clear that being younger, and particularly being under age 15 at the time of community electrification, has large positive effects on current earnings. As expected, being older at the advent
of electrification lowers an individual’s current earnings substantially. Table 4 presents the results
for the full sample and by sex. In the full sample, individuals who were between age 45 and 50 at
the advent of community electrification earn 181% less than those who were aged zero to five when
community electrification arrived. As predicted, there is a monotonic relationship between age at
electrification and monthly earnings, both in the aggregate and in the estimates for women. For
women, being older than 15 at community electrification causes much lower wages, and the extent
of the wage penalty is increasing in the lateness of electrification.
For men, results follow the general pattern of the full sample, with one exception. Individuals
who were aged 5 to 9 at community electrification actually earn about 70% more currently than do
the reference group of slightly younger individuals. This remains to be explained13 . As well, for
men, statistically significant earnings differences between those who were younger versus older at
electrification emerge only after age 30. This may be a sample size issue or it may reflect the fact
that men do successfully switch into new higher productivity occupations and industries until this
age.
To illustrate the meaning of these effects simply, I use locally-weighted least squares to plot the
relationship between age at electrification and the log of labour market earnings, LNEARN (see
Robinson (1988), and Yatchew (2003)). I allow the most flexible possible relationship between age
at electrification and earnings by specifying the following equation:
LN EARNijc = f (age at elec.) + X̄ β̂ + ijc
(2)
Here X̄ refers to the means of the controls used in the specification of Table 4, less the age at
electrification variables. I have estimated β̂ using OLS regressions which do not include the age at
electrification dummies. The residual of these regressions is then related to age at electrification using
locally-weighted least squares. The estimated relationship for f (age at elec.) is plotted in Figure 1.
This Figure shows that the age of an individual at community electrification has large causal effects
on the earnings of both women and men, and that these effects are particularly dramatic for women
who were children at the advent of electrification.
4.2
Labour force participation
Probit models are estimated to examine the causal effect of age at electrification on the probability
of being a labour force participant currently. Labour force participation is defined to include those
13
One might suspect that, if community electrification reduced fertility, changes in the ‘quality’ of individuals
across cohorts might have occurred. In fact, Grogan and Sadanand (2008) show that there are large causal effects of
community electrification on fertility in these data. Still, this is unlikely the cause of the present result since I control
for educational attainment of individuals.
10
working and those currently unemployed but searching.
The effects of age at electrification on current labour force participation differ strongly across
the sexes. For women, being less than 15 at the advent of community electrification causes large
increases in current participation probabilities. Only amongt women aged 45 to 50 at electrification
are participation propensities as high as for those who were less than 5 at electrification. This may be
because older women, whose children have grown up, have relatively high participation propensities
in any case, or because of a lack of observations (162 women are in this age at electrification category
and there are 67 regressors).
For men, the effects of age at electrification on labour force participation are of the opposite sign
and smaller magnitude to those for women. Men who were older versus younger at electrification,
particularly those who were above 20 at electrification, are more likely to participate currently. Given
that the male participation rate is about 91%, this likely reflects the wealth effect: Individuals who
were young at electrification have greater wealth as a result and so are less likely to stay in the labour
market as they get older.
To illustrate the causal effects of the age at electrification on current labour force participation
most simply, I estimate the same semi-parametric specification as for earnings. The causal effect
of age at electrification on current labour force participation is identified using locally-weighted
linear regression, and plotted in Figure 2. From this Figure it is apparent that women’s labour
force participation is very positively affected by being younger rather than older at the advent of
electrification. As with earnings, the negative relationship between age at electrification and current
labour force participation is strongest for those who were under 20 at the advent of electrification.
4.3
Work in low-skilled occupations
Using similar specifications to those for earnings and labour force participation, but including only
those currently working, I estimate the effect of age at electrification on the probability that an
individual works in an unskilled occupation. As was seen in Table 2, longer community electrification
is associated with less unskilled labour in these data.
Table 6 shows that there is a strong causal effect of the age of an individual at electrification on the
probability of an individual working in an unskilled occupation. A woman for whom electrification
occurred at age 45 or older is 19% more likely to work in an unskilled occupation than a woman who
was under 5 at the time of electrification. For men, results are broadly similar, with the men for
whom electricity occurred after age 45 being 11% more likely to work in an unskilled occupation.
Figure 5 shows the relationship between the probability of currently working in an unskilled
occupation and age at electrification using the same semi-parametric technique as for earnings and
labour force participation. For men, effects of being younger at electrification peak in the mid 20s
and are similar thereafter. This lends support to the notion that individuals choose occupations at
the beginning of their working lives and do not switch much afterwards. For women, however, the
relationship between age at electrification and the probability of working in an unskilled occupation
is strong and monotonic: the younger is a currently working woman at electrification the less likely
11
is she to currently work in an unskilled occupation.
4.4
Work in agriculture
Perhaps the most dramatic of the labour market effects observed is that of the effect of age at
electrification on the industry of current labour market activities. It appears that rural electrification
takes the ‘rural’ out of communities by moving employment out of agricultural activities. It is found
that the older an individual is at the time of electrification the more likely they are to work in
agriculture.
For working women, the causal effects of age at electrification on the probability of working in
agriculture are most dramatic. Those who were above age 45 at the advent of electricity are 73%
more likely to be engaged in agriculture than are those whose communities were electrified before
the age of 5. For men the results are less dramatic but similarly statistically significant.
In general the results for agriculture follow the predicted pattern that the higher the age of a
person at electrification, the greater the probability that their current industry is agriculture. One
anomaly, however, is that individuals who were aged 5 to 9 at the advent of electricity are in fact less
likely to work in agriculture than are those who were under age 5 at electrification. It may be the
case that those aged 5 to 9 at electrification benefited most from an unsustained increase in career
choice which occurred between 10 and 20 years following electrification. This might also explain why
men who were 5 to 9 at electrification were found to have about 78% higher earnings than those who
were zero to 5 at electrification. In Data Appendix A, it is shown that individuals in communities
that have been electrified for more than 10 years do have substantially higher earnings and female
labour force participation, and smaller probabilities of working in unskilled or agricultural work than
do those in more recently electrified communities. However, in general there is little difference in
these outcomes between communities electrified for 10 to 19 years versus those electrified for 20 or or
more years. This suggests that the rate of increase in labour demand following electrification might
initially be great but then level off.
Figure 6 plots the locally-weighted regression relationship between age at electrification and the
probability of working in agriculture. For both men and women, the relationship is monotonic. For
men, being under age 5 at electrification versus 45 or older causes a reduction in the probability of
working in agriculture from about 75% to less than 25%. For women this reduction is from about
45% to about 10%. This dramatic effect underlines how community electrification diversifies local
labour markets.
4.5
Sensitivity analysis
The major concern with the identification strategy employed is that the timing of community electrification systematically coincides with other community improvements which themselves lead to labour
market diversification. Although I control for the timing of piped water and sanitation provisions,
and for the educational attainment of individuals, there may still be other local factors which are
12
driving community labour market diversification around the advent of electricity. In Data Appendix
B I show that this cannot be the case.
Data Appendix B compares, for all four labour market outcomes considered, two estimates. The
first estimates are those presented here as causal effects because they control for all community level
factors plus the age of individuals at the advent of piped water and sanitation. The second set of
estimates excludes all community level variables.
By showing that the estimated coefficients on the 5-year age at electrification dummies are statistically the same across these two specifications I prove that omitted community level factors cannot
be driving the observed relationship. The rationale is as follows: First, I note that the extensive list
of community level observables and the timing of infrastructure improvements should be correlated
with any unobservables which threaten my identification strategy by occurring systematically around
the time of electrification and impacting labour markets. This correlation means that the estimation of models which exclude all community level information should result in dramatically different
estimates if indeed there were important unobservables which were previously partially accounted
for by the community controls. If estimates of the effect of age at electrification on the labour market outcomes do not differ when community level controls are excluded, it means that unobserved
heterogeneity at the community level could not have been driving the main estimates. Only in the
very unlikely case that the detailed community level controls are orthogonal to the unobservables
which are correlated with timing of electrification would this sensitivity test potentially deliver a
false negative outcome.
To summarise the results of this sensitivity analysis, there are no statistically significant differences
across the two types of specifications in the measured effects of age at electrification on any of the
four labour market outcomes.
The finding of this section that age of electrification has such large causal effects on the working
lives of individuals has large implications for cost-benefit analyses of rural electrification projects.
First, it is clearly incorrect to assume that the effects of electrification operate only through changing
home production processes and therefore labour supply decisions. Second, evaluations which take
place very soon after community electrification occurs may miss the eventual changes in the nature
of labour demand which have here been documented. Third, it is clear that the cost benefit analyses
should attempt to gauge the potential benefits to communities from reduced vulnerability to adverse
shocks as labour diversifies out of agriculture and unskilled work.
5
Conclusions
This paper examines the effects of community electrification on the development of local labour
markets. Because the short and long run effects of community electrification are likely very different,
these effects are identified from the perspective of individuals who were the first to begin their
working lives in electrified communities. Household and community survey data from Guatemala is
combined to identify the effects of the age of an individual at electrification on four key labour market
13
outcomes: monthly earnings, labour force participation, the probability of working in a low-skilled
occupation, and the probability of working in agriculture. These community electrification effects
are distinguished from those of household electrification, and from possible effects of community
electrification on the subsequent educational attainment of children.
It is shown that the age of individuals at community electrification has strong causal effects on
their current labour market earnings, occupation and industry of work. For women, being younger at
community electrification has strong positive effects on labour force participation, even conditional on
household electrification status. For both men and women, the probability of working in agriculture
is dramatically lowered by community electrification. These effects, because they are exogenous to
individuals, are interpreted as results of changes in labour demand.
These findings have implications for cost benefit analyses of community electrification projects.
They imply that community electrification may make communities less vulnerable to input or product
demand shocks. In this way, community electrification might reduce food insecurity or other measures
of vulnerability. It is clearly of substantial policy and research interest to examine whether or not
this is indeed the case.
The findings of this paper are also relevant to the large literature demonstrating the relationship
between institutions and development outcomes. This paper has explained how community electrification affects the first generation of individuals to begin their working lives under electrification.
One of the major roles of institutions is that of the provision of basic public goods such as piped
water, sewage treatment, telephone connections, electricity, and schools. Because of the large fixed
costs associated with these services, it is infeasible for isolated households to obtain them. This
paper shows how, by massing the resources of individuals and households to create infrastructure,
institutions may have strong effects on later market development. Large infrastructural provisions
may have effects on the local economy which reduce vulnerability to shocks, thus facilitating private
investments and a virtuous cycle of growth.
14
References
Acemoglu, D. (1998). Why do new technologies complement skills? directed technical change and
wage inequality. Quarterly Journal of Economics 113 (4), 1055–1089.
Acemoglu, D. (2002). Technical change, inequality, and the labor market. Journal of Economic
Literature 40 (1), 7–72.
Acemoglu, D., S. Johnson, and J. Robinson (2004). Institutions as the fundamental cause of longrun growth. NBER Working Paper No. W10481 .
Anderson, S. and J.-M. Baland (2002). The economics of ROSCAS and intrahousehold resource
allocation. Quarterly Journal of Economics 117 (2), 693–695.
Angrist, J. and W. Evans (1998). Children and their parents’ labor supply: Evidence from exogenous variation in family size. American Economic Review 88 (1), 450–477.
Bailey, M. J. (2006). More power to the pill: The impact of contraceptive freedom on women’s life
cycle labor supply. Quarterly Journal of Economics 121 (1), 289–320.
Banerjee, A., T. Besley, and T. Guinnane (1994). Thy neighbor’s keeper: The design of a credit
cooperative with theory and a test. Quarterly Journal of Economics 84 (2), 491–515.
Banerjee, A., E. Duflo, and N. Qian (2007). The railroad to success: The effect of infrastructure
on growth. MIT (mimeo).
Dinkleman, T. (2008). The effects of rural electrification on employment: New evidence from South
Africa. University of Michigan (mimeo).
Esfahani, H. S. and M. T. Ramı́rez (2003). Institutions, infrastructure, and economic growth.
Journal of Development Economics 70 (1), 443–447.
Foster, A. and M. Rosenzweig (1996). Technical change and human capital returns and investments:
Evidence from the Green Revolution. American Economic Review 86 (4), 931–953.
Galor, O. and O. Moav (2006). Ability-biased technological transition, wage inequality, and economic growth. Quarterly Journal of Economics 115 (2), 469–497.
Goldin, C. and L. Katz (2002). The power of the pill: Oral contraceptives and women’s career and
marriage decisions. Journal of Political Economy 110 (1), 730–770.
Gragnolati, M. (2004). Children’s growth and poverty in rural Guatemala. Latin America and the
Caribbean Region Human Development Sector Unit, World Bank .
Gragnolati, M. and A. Marini (2003). Health and poverty in Guatemala. World Bank Policy
Research Working Paper no. 2966 .
Greenwood, J., A. Seshadri, and G. Vandenbrouke (2005). The baby boom and baby bust. American Economic Review 95 (1), 183–207.
Grogan, L. and A. Sadanand (2008). Electrification and the household. University of Guelph
(mimeo).
15
Guiso, L., P. Sapienza, and L. Zingales (2004). The role of social capital in financial development.
American Economic Review 94 (3), 526–556.
Jackson, M. O. and A. Calvas-Armengol (2004). The effects of social networks on employment and
inequality. American Economic Review 94 (3), 426–454.
Jackson, M. O. and A. Watts (2002). The evolution of social and economic networks. Journal of
Economic Theory 106 (2), 265–295.
Malone, L. (2008). The Rural Electrification Administration. Economic History Net Encyclopedia,
edited by Robert Whaples. March 16, 2008 .
Miguel, E. and G. Roland (2005). The long-run impact of bombing Vietnam. University of California Berkeley (mimeo).
Murgai, R., M. Ali, and D. Byerlee (2001). Productivity growth and sustainability in post-green
revolution agriculture: The case of the Indian and Pakistan Punjabs. World Bank Research
Observer 16 (2), 199–218.
Robinson, P. (1988). Root-n-consistent semi-parametric regression. Econometrica 56.
Schurr, S., C. Burwell, W. Devine, and S. Sonenblum (1990). Electricity in the American economy:
Agent of technological progress. Contributions in Economics and Economic History. Greenwood
Press, Westport CT. 117.
Vakis, R. (2003). Guatemala: Livelihoods, labour markets, and rural poverty. Guatemala Poverty
Assessment Program (GUAPA) Technical Paper No. 1 .
Wolman, P. (2006). Rural electrification in the united states,19301950. an institutional, political,
and social perspective. World Bank Presentation for Energy Week 2006,.
World Bank (2007). Global data monitoring, results measurement system. http://ddpext.worldbank.org/ext/GMIS/gdmis.do?siteId=1targetId=9menuId=LNAV01LST3SUB4 (accessed Nov. 26th 2007).
Yatchew, A. (2003). Semiparametric regression for the applied econometrician. Cambridge University Press: Cambridge UK .
Table 1: Time since community electrification, incomes
Community electricity since:
Less than 10 years
Individual Income
All
792.7805
(143.403)
Women 179.2081
(12.730)
Men
1474.0790
(301.293)
Labour Force Participation
All
0.6190
(0.009)
Women 0.3397
(0.012)
Men
0.9291
(0.007)
and labour
10+years
1082.7890
(106.499)
708.1938
(160.410)
1518.9260
(134.324)
0.6984
(0.008)
0.5218
(0.012)
0.9039
(0.007)
force participation
|t|-test
1.62
3.29
0.14
6.66
10.87
2.49
Source: Guatemala ENCOVI data 2000. Income refers to total monthly incomes in Quetzales. Standard errors in parentheses.
Source: Guatemala ENCOVI data 2000. Only employed individuals are included. ILO 9-digit employment codes refer to primary occupation. Standard errors in parentheses.
Table 2: Time since community electrification and the occupational distribution
All
|t|-test
Women
|t|-test
Men
Community elec > 10 years
Community elec > 10 years
Community elec
≤ 10yrs
> 10 years
≤ 10yrs
> 10 years
≤ 10yrs
Director, head of public admin.
0.0205
0.0195
0.22
0.0315
0.0245
0.81
0.0154
(0.003)
(0.003)
(0.007)
(0.005)
(0.003)
Professionals, scientists, intellectuals
0.0352
0.0824
6.87
0.0381
0.0934
4.66
0.0338
(0.004)
(0.005)
(0.008)
(0.009)
(0.005)
Technical professionals, midlevel profs. 0.0173
0.0383
4.34
0.0166
0.0283
1.61
0.0177
(0.003)
(0.004)
(0.005)
(0.005)
(0.004)
Office clerks
0.0178
0.0551
6.85
0.0149
0.0679
5.78
0.0192
(0.003)
(0.005)
(0.005)
(0.008)
(0.004)
Service and food workers
0.1385
0.1945
5.03
0.2434
0.2953
2.32
0.0899
(0.008)
(0.008)
(0.017)
(0.014)
(0.008)
Qualified agricultural workers
0.2434
0.1066
11.82
0.0712
0.0217
4.35
0.3233
(0.010)
(0.006)
(0.010)
(0.004)
(0.013)
Arts and crafts workers
0.1501
0.2125
5.43
0.1772
0.1755
0.09
0.1375
(0.008)
(0.008)
(0.016)
(0.012)
(0.010)
Skilled heavy equipment operators
0.0352
0.0574
3.57
0.0281
0.0321
0.45
0.0384
(0.004)
(0.005)
(0.007)
(0.005)
(0.005)
Unskilled labour
0.3395
0.2316
7.88
0.3791
0.2594
5.01
0.3210
(0.011)
(0.008)
(0.020)
(0.013)
(0.013)
> 10 years
> 10 years
0.0160
(0.003)
0.0747
(0.007)
0.0453
(0.005)
0.0460
(0.005)
0.1233
(0.008)
0.1667
(0.010)
0.2387
(0.011)
0.0753
(0.007)
0.2120
(0.011)
6.53
4.27
6.94
9.70
2.88
4.05
4.26
4.84
0.14
|t|-test
Source: Guatemala ENCOVI data 2000.
a Category includes agriculture, hunting, animal husbandry and fishing.
Only employed individuals are included. Refers to primary occupation. Standard errors in parentheses.
Table 3: Time since community electrification and the distribution of
All
|t|-test
Women
Community elec > 10 years
Community elec > 10 years
≤ 10yrs
> 10 years
≤ 10yrs
> 10 years
Agriculturea
0.4732
0.2165
18.29
0.2864
0.1008
(0.011)
(0.008)
(0.018)
(0.009)
Mining
0.0016
0.0004
1.20
0.0000
0.0000
(0.001)
(0.000)
(0.000)
(0.000)
Manufacturing
0.1180
0.1825
6.06
0.2036
0.2271
(0.007)
(0.008)
(0.016)
(0.013)
Electricity, gas, water works
0.0037
0.0043
0.33
0.0000
0.0009
(0.001)
(0.001)
(0.000)
(0.001)
Construction
0.0535
0.0676
1.97
0.0066
0.0028
(0.005)
(0.005)
(0.003)
(0.002)
Commerce
0.1836
0.2395
4.57
0.3162
0.3402
(0.009)
(0.008)
(0.019)
(0.015)
Transportation food processing 0.0257
0.0356
1.91
0.0017
0.0104
(0.004)
(0.004)
(0.002)
(0.003)
Services and commerce
0.0126
0.0293
3.98
0.0099
0.0254
(0.003)
(0.003)
(0.004)
(0.005)
Public administration
0.0231
0.0340
2.20
0.0050
0.0151
(0.003)
(0.004)
(0.003)
(0.004)
Teaching
0.0294
0.0610
5.17
0.0364
0.0811
(0.004)
(0.005)
(0.008)
(0.008)
Social and health sector
0.0729
0.1258
5.97
0.1325
0.1913
(0.006)
(0.007)
(0.014)
(0.012)
International organisation
0.0026
0.0035
0.54
0.0017
0.0047
(0.001)
(0.001)
(0.002)
(0.002)
workers across industries
|t|-test
Men
Community elec
≤ 10yrs
9.01
0.5599
(0.014)
0.0023
(0.001)
1.13
0.0783
(0.007)
1.00
0.0054
(0.002)
1.03
0.0753
(0.007)
1.01
0.1221
(0.009)
2.47
0.0369
(0.005)
2.46
0.0138
(0.003)
2.15
0.0315
(0.005)
3.94
0.0261
(0.004)
3.21
0.0453
(0.006)
1.14
0.0031
(0.002)
> 10 years
> 10 years
0.2984
(0.012)
0.0007
(0.001)
0.1509
(0.009)
0.0067
(0.002)
0.1135
(0.008)
0.1682
(0.010)
0.0534
(0.006)
0.0320
(0.005)
0.0474
(0.005)
0.0467
(0.005)
0.0794
(0.007)
0.0027
(0.001)
0.20
3.77
2.94
2.17
3.26
2.12
3.48
3.48
0.44
6.11
1.10
14.41
|t|-test
Table 4: The effect of age at
Dependent variable: log (monthly income)
All
Age at elec.≥5, <10
0.3840
(0.322)
Age at elec.≥10, <15
-0.1747
(0.326)
Age at elec.≥15, <20
-0.7603 ∗∗
(0.309)
Age at elec.≥20, <25
-0.8373 ∗∗
(0.307)
Age at elec.≥25, <30
-0.9617 ∗∗
(0.343)
Age at elec.≥30, <35
-1.0954 ∗∗
(0.388)
Age at elec.≥35, <40
-1.3079 ∗∗
(0.442)
Age at elec.≥40, <45
-1.6905 ∗∗
(0.540)
Age at elec.≥45, <50
-1.8137 ∗∗
(0.803)
Sex
-5.8194 ∗∗
(0.145)
Hhld has elec
0.6826 ∗∗
(0.203)
Highest ed grade
0.2426 ∗∗
(0.034)
rural
-0.4439
(0.309)
Born here
-0.4535 ∗∗
(0.183)
Community has water
0.4422 ∗
(0.265)
Community has improved sanitation 0.7665 ∗∗
(0.251)
Community has no preschool
0.1911
(0.169)
Community has no elementary school 0.0845
(0.193)
Community has no secondary school 0.2752
(0.191)
Community affected by Mitch
0.3807 ∗∗
(0.156)
Community has paved highway
-0.2677 ∗
(0.163)
Community has access to markets
-0.0680
(0.177)
no. kids under 18 in hhld
-0.0228
(0.057)
no. adults in hhld
-0.1913 ∗∗
(0.039)
Constant
4.9029 ∗∗
(0.969)
R2
0.2784
no. obs.
6378
electrification on earnings
Women
-0.0282
(0.486)
-0.4330
(0.482)
-1.0004 ∗∗
(0.448)
-1.0729 ∗∗
(0.456)
-1.3327 ∗∗
(0.520)
-1.2263 ∗∗
(0.577)
-1.8490 ∗∗
(0.666)
-2.0820 ∗∗
(0.823)
-1.4657
(1.236)
Men
0.7861 ∗∗
(0.396)
0.1773
(0.427)
-0.5101
(0.419)
-0.5239
(0.401)
-0.6045
(0.430)
-0.9979 ∗∗
(0.504)
-0.7870
(0.566)
-1.2653 ∗
(0.706)
-2.0755 ∗∗
(1.029)
1.0159 ∗∗
(0.283)
0.2449 ∗∗
(0.051)
-0.8348 ∗
(0.471)
-0.2003
(0.265)
0.2870
(0.366)
1.2680 ∗
(0.374)
0.2439
(0.248)
-0.0935
(0.288)
0.3532
(0.283)
0.5058 ∗∗
(0.228)
-0.2014
(0.238)
0.1424
(0.265)
-0.0853
(0.084)
-0.2014 ∗∗
(0.055)
-0.2346
(1.716)
0.1382
3397
0.3566
(0.284)
0.2129 ∗∗
(0.045)
0.0413
(0.374)
-0.8423 ∗∗
(0.240)
0.6975 ∗
(0.377)
0.2003
(0.321)
0.1674
(0.225)
0.1500
(0.253)
0.1269
(0.249)
0.2304
(0.208)
-0.3354
(0.218)
-0.2816
(0.223)
0.0831
(0.076)
-0.1702 ∗∗
(0.053)
7.5522 ∗∗
(1.065)
0.0796
2981
Source: Guatemala ENCOVI data 2000. Income refers to total monthly incomes in Quetzales. Robust standard errors in parentheses.
Departamento fixed effects, 5-year age cohorts, and 5-year age at sanitation, and 5-year age at piped water dummies are included in all
specifications. ∗∗ significant at 5% level, ∗ significant at 10% level.
Table 5: The effect of age at electrification on labour force participation
Probit marginal effects
Dependent variable is labour force participation
All
Women
Age at elec ≥5, <10
0.0111
-0.0060
(0.028)
(0.039)
Age at elec≥10, <15
0.0035
-0.0097
(0.029)
(0.040)
Age at elec≥15, <20
-0.0346
-0.0643 ∗
(0.028)
(0.036)
Age at elec≥20, <25
-0.0337
-0.0845 ∗∗
(0.028)
(0.037)
Age at elec≥25, <30
-0.0273
-0.0870 ∗∗
(0.031)
(0.041)
Age at elec≥30, <35
-0.0499
-0.1145 ∗∗
(0.036)
(0.045)
Age at elec≥35, <40
-0.0642 ∗ -0.1819 ∗∗
(0.041)
(0.048)
Age at elec≥40, <45
-0.0427
-0.1654 ∗∗
(0.050)
(0.061)
Age at elec≥45, <50
0.0100
-0.0479
(0.067)
(0.098)
sex
-0.4817 ∗∗
(0.010)
Born here
-0.0127
-0.0120
(0.015)
(0.022)
Hhld has elec
0.0630 ∗∗ 0.1133 ∗∗
(0.018)
(0.025)
Highest ed. Grade
0.0155 ∗∗ 0.0213 ∗∗
(0.003)
(0.004)
Community has water
0.0118
0.0557 ∗
(0.023)
(0.034)
Community has improved sanitation 0.0441 ∗∗ 0.0664 ∗∗
(0.022)
(0.031)
Rural
-0.0266
-0.0461
(0.027)
(0.037)
Community has no preschool
-0.0011
0.0048
(0.015)
(0.022)
Community has no elementary school -0.0090
-0.0245
(0.017)
(0.024)
Community has no secondary school -0.0084
0.0231
(0.017)
(0.023)
Community affected by Mitch
0.0434 ∗∗ 0.0656 ∗∗
(0.014)
(0.020)
Community has paved highway
-0.0201
-0.0309
(0.014)
(0.020)
Community has access to markets
0.0375 ∗∗ 0.0394 ∗
(0.016)
(0.022)
no. kids under 18 in hhld
0.0045
-0.0004
(0.005)
(0.007)
no. adults in hhld
-0.0121 ∗∗ -0.0172 ∗∗
(0.003)
(0.005)
R2
0.26
0.09
no. obs.
6378
3397
obs. P
0.6624
0.4404
pred. P
0.7218
0.4317
Men
0.0185
(0.016)
0.0115
(0.018)
0.0029
(0.018)
0.0276 ∗
(0.015)
0.0334 ∗
(0.016)
0.0306
(0.018)
0.0558 ∗∗
(0.013)
0.0621 ∗∗
(0.013)
0.0490
(0.019)
-0.0109
(0.012)
0.0010
(0.014)
0.0022
(0.002)
-0.0185
(0.016)
0.0144
(0.016)
-0.0059
(0.019)
-0.0040
(0.011)
0.0026
(0.012)
-0.0320 ∗∗
(0.011)
0.0120
(0.010)
-0.0008
(0.011)
0.0209 ∗
(0.012)
0.0077 ∗∗
(0.004)
-0.0027
(0.002)
0.06
2950
0.9146
0.9281
Source: Guatemala ENCOVI data 2000. Robust standard errors in parentheses. Departamento fixed effects, 5-year age cohorts, and 5-year
age at sanitation, and 5-year age at piped water dummies are included in all specifications. ∗∗ significant at 5% level, ∗ significant at 10%
level.
Table 6: The effect of age at electrification on the probability
Probit marginal effects
Dependent var is ILO 1-digit code 9, unskilled
All
Women
Age at elec. ≥5, <10
-0.0098 ∗ -0.0418 ∗
(0.030)
(0.047)
∗
Age at elec.≥10, <15
0.0399
0.0627 ∗
(0.033)
(0.056)
Age at elec.≥15, <20
0.1104
0.1430
(0.033)
(0.057)
Age at elec.≥20, <25
0.0753
-0.0072
(0.033)
(0.052)
Age at elec.≥25, <30
0.1411
0.1522
(0.039)
(0.067)
Age at elec.≥30, <35
0.1312 ∗ 0.1490 ∗
(0.045)
(0.077)
Age at elec.≥35, <40
0.0862 ∗ 0.0874 ∗∗
(0.049)
(0.090)
∗∗
Age at elec.≥40, <45
0.1108
0.2362 ∗∗
(0.061)
(0.125)
Age at elec.≥45, <50
0.1087 ∗∗ 0.1928 ∗∗
(0.082)
(0.151)
sex
0.0492
-0.0926
(0.015)
(0.054)
Born here
0.0632
-0.1883
(0.017)
(0.049)
Hhld has elec
-0.0885
-0.0263
(0.023)
(0.006)
Highest ed. Grade
-0.0224
0.0113
(0.003)
(0.048)
Community has water
-0.0098
0.0531
(0.027)
(0.040)
Community has improved sanitation 0.0006
0.1946
(0.025)
(0.052)
Rural
0.1098
-0.0458
(0.030)
(0.030)
Community has no preschool
-0.0324
-0.0104
(0.016)
(0.032)
Community has no elementary school -0.0211
0.0026
(0.018)
(0.031)
Community has no secondary school 0.0144
0.0451
(0.018)
(0.028)
Community affected by Mitch
0.0194
-0.0080
(0.015)
(0.028)
Community has paved highway
0.0202
-0.0309
(0.016)
(0.030)
Community has access to markets
0.0048
0.0068
(0.017)
(0.010)
no. kids under 18 in hhld
0.0098
0.0084
(0.005)
(0.006)
no. adults in hhld
0.0057
0.2909
(0.004)
(0.259)
R2
0.08
0.13
no. obs.
4171
1478
obs. P
0.2702
0.2909
pred. P
0.2521
0.2590
of working in a low-skilled occupation
Men
0.0035 ∗
(0.039)
0.0335 ∗
(0.042)
0.0807
(0.041)
0.1041
(0.041)
0.1328
(0.049)
0.1387 ∗
(0.057)
0.1196 ∗
(0.062)
0.1055 ∗∗
(0.074)
0.1114 ∗∗
(0.102)
0.0207
(0.039)
-0.0474
(0.026)
-0.0220
(0.004)
-0.0102
(0.032)
-0.0407
(0.031)
0.0537
(0.037)
-0.0350
(0.020)
-0.0248
(0.022)
0.0142
(0.022)
0.0076
(0.019)
0.0284
(0.019)
0.0335
(0.021)
0.0122
(0.007)
0.0040
(0.004)
0.2588
(0.238)
0.09
2693
0.2588
0.2375
Source: Guatemala ENCOVI data 2000. Only currently-working individuals included. Robust standard errors in parentheses. Departamento
fixed effects, 5-year age cohorts, and 5-year age at sanitation, and 5-year age at piped water dummies are included in all specifications. ∗∗
significant at 5% level, ∗ significant at 10% level.
Table 7: The effect of age at electrification on the probability of working in agriculture
Probit marginal effects
Dependent variable is engaged in agriculture
All
Women
Men
Age at elec.≥5, <10
-0.0706 ∗ -0.0460 ∗ -0.0738 ∗
(0.029)
(0.031)
(0.046)
∗
∗
Age at elec.≥10, <15
0.0101
-0.0066
0.0169 ∗
(0.035)
(0.041)
(0.050)
Age at elec.≥15, <20
0.0768
0.1071
0.0556
(0.036)
(0.054)
(0.049)
Age at elec.≥20, <25
0.1019
0.0953
0.1249
(0.037)
(0.055)
(0.050)
Age at elec.≥25, <30
0.2116
0.2396 ∗ 0.2271
(0.045)
(0.082)
(0.055)
∗
∗
Age at elec.≥30, <35
0.1804
0.2383
0.1859 ∗
(0.052)
(0.097)
(0.064)
Age at elec.≥35, <40
0.2357 ∗ 0.3710 ∗∗ 0.2434 ∗
(0.060)
(0.130)
(0.070)
∗∗
∗∗
Age at elec.≥40, <45
0.2889
0.4668
0.2712 ∗∗
(0.072)
(0.157)
(0.081)
Age at elec.≥45, <50
0.3784 ∗∗ 0.7340 ∗∗ 0.2887 ∗∗
(0.096)
(0.132)
(0.111)
sex
-0.2568
0.0146
-0.0640
(0.013)
(0.040)
(0.052)
Born here
0.0995
-0.0324
-0.1668
(0.018)
(0.031)
(0.033)
Hhld has elec
-0.1241
-0.0146
-0.0484
(0.025)
(0.004)
(0.005)
Highest ed. Grade
-0.0339
0.0280
-0.1341
(0.004)
(0.029)
(0.042)
Community has water
-0.0652
0.0058
-0.1628
(0.030)
(0.033)
(0.037)
Community has improved sanitation -0.1079
0.1603
0.2684
(0.026)
(0.039)
(0.044)
Rural
0.2172
0.0374
-0.0116
(0.030)
(0.023)
(0.025)
Community has no preschool
0.0086
-0.0103
-0.1583
(0.017)
(0.025)
(0.027)
Community has no elementary school -0.1016
-0.0020
-0.0443
(0.019)
(0.027)
(0.030)
Community has no secondary school -0.0182
0.0551
-0.0545
(0.021)
(0.023)
(0.024)
Community affected by Mitch
-0.0131
0.0011
-0.0712
(0.016)
(0.021)
(0.024)
Community has paved highway
-0.0387
0.0254
0.0459
(0.017)
(0.021)
(0.027)
Community has access to markets
0.0335
0.0230
0.0003
(0.019)
(0.007)
(0.008)
no. kids under 18 in hhld
0.0106
-0.0017
0.0076
(0.006)
(0.005)
(0.006)
no. adults in hhld
0.0049
0.1932
0.4209
(0.004)
(0.102)
(0.368)
R2
0.33
0.30
0.31
no. obs.
4135
1206
2673
obs. P
0.3284
0.3284
0.1932
pred. P
0.2353
0.2353
0.1016
Source: Guatemala ENCOVI data 2000. Only currently-working individuals included. Robust standard errors in parentheses. Departamento
fixed effects, 5-year age cohorts, and 5-year age at sanitation, and 5-year age at piped water dummies are included in all specifications. ∗∗
significant at 5% level, ∗ significant at 10% level.
Data Appendix A: Years since community electrification and labour market outcomes
Table 8: The association between years since electrification and incomes
Dependent variable: ln(earnings)
All
Women
Men
Elec ≥ 10, < 20 years
0.8414 ∗∗ 0.8841 ∗∗ 0.7792 ∗∗
(0.199)
(0.297)
(0.257)
∗∗
∗∗
Elec ≥ 20 years
1.1065
1.5793
0.4978 ∗
(0.211)
(0.316)
(0.267)
Hhld has electricity
0.6695 ∗∗ 1.0210 ∗∗ 0.3249
(0.201)
(0.281)
(0.285)
∗∗
Aged 25-29
-0.8386
-0.3450
-1.3501 ∗∗
(0.214)
(0.311)
(0.286)
Aged 30-34
0.6129 ∗∗ 1.2344 ∗∗ -0.1032
(0.238)
(0.363)
(0.295)
∗∗
∗∗
Aged 35-39
0.5436
0.8739
0.2084
(0.245)
(0.368)
(0.310)
Aged 40-44
0.6340 ∗∗ 1.0430 ∗∗ 0.0130
(0.258)
(0.384)
(0.332)
∗∗
Aged 45-50
0.4317
1.2430
-0.5948 ∗
(0.280)
(0.426)
(0.360)
sex
-5.8236 ∗∗
(0.145)
Highest grade
0.2449 ∗∗ 0.2360 ∗∗ 0.2182 ∗∗
(0.034)
(0.051)
(0.045)
rural
-0.4735
-0.8242
-0.0472
(0.307)
(0.466)
(0.371)
Born here
-0.4487 ∗∗ -0.2034
-0.8208 ∗∗
(0.183)
(0.263)
(0.242)
Community has water
0.2569
0.2509
0.2422
(0.233)
(0.319)
(0.333)
Community has improved sanitation 0.6819 ∗∗ 1.0786 ∗∗ 0.2094
(0.208)
(0.307)
(0.272)
Community has no preschool
0.1624
0.2208
0.0822
(0.167)
(0.244)
(0.222)
Community has no elementary school 0.0866
-0.0872
0.1999
(0.192)
(0.285)
(0.250)
Community has no secondary school 0.2580
0.3803
0.1072
(0.185)
(0.273)
(0.243)
Community affected by Mitch
0.3930 ∗∗ 0.5562 ∗∗ 0.1878
(0.155)
(0.226)
(0.207)
Community has paved highway
-0.2229
-0.1990
-0.2251
(0.163)
(0.237)
(0.216)
Community has access to markets
-0.0276
0.1561
-0.2029
(0.175)
(0.261)
(0.222)
no. kids under 18 in hhld
-0.0305
-0.0792
0.0602
(0.056)
(0.084)
(0.076)
no. adults in hhld
-0.1927 ∗∗ -0.2043 ∗∗ -0.1731 ∗∗
(0.038)
(0.055)
(0.053)
Constant
4.0606 ∗∗ -2.1275 ∗ 6.9280 ∗∗
(0.866)
(1.276)
(0.780)
2
R
0.28
0.13
0.07
no. obs
6378
3397
2981
Source: Guatemala ENCOVI data 2000. Standard errors in parentheses. Departamento fixed effects included in all specifications.
significant at 5% level, ∗ significant at 10% level.
∗∗
Table 9: The association between years since electrification
Probit marginal effects
Dependent variable is labour force participant
All
Women
∗∗
Elec ≥ 10, < 20 years
0.0460
0.0939 ∗∗
(0.017)
(0.026)
Elec ≥ 20 years
0.0596 ∗∗ 0.1392 ∗∗
(0.018)
(0.026)
∗∗
Hhld has electricity
0.0615
0.1131 ∗∗
(0.018)
(0.025)
Aged 25-29
-0.0353 ∗ -0.0161
(0.019)
(0.027)
∗∗
Aged 30-34
0.0571
0.0992 ∗∗
(0.020)
(0.031)
Aged 35-39
0.0369 ∗
0.0435
(0.021)
(0.032)
∗∗
Aged 40-44
0.0655
0.0755 ∗∗
(0.021)
(0.032)
Aged 45-50
0.0645 ∗∗ 0.0866 ∗∗
(0.022)
(0.036)
∗∗
sex
-0.4817
(0.010)
Born here
-0.0135
-0.0118
(0.015)
(0.022)
Community has water
0.0138
0.0498 ∗
(0.020)
(0.029)
Community has improved sanitation 0.0351 ∗∗ 0.0511 ∗∗
(0.018)
(0.026)
Rural
-0.0226
-0.0403
(0.027)
(0.037)
Community has no preschool
-0.0009
0.0016
(0.015)
(0.021)
Community has no elementary school -0.0094
-0.0245
(0.017)
(0.024)
Community has no secondary school -0.0054
0.0308
(0.016)
(0.022)
∗∗
Community affected by Mitch
0.0463
0.0683 ∗∗
(0.014)
(0.020)
Community has paved highway
-0.0207
-0.0302
(0.014)
(0.020)
∗∗
Community has access to markets
0.0377
0.0408 ∗
(0.016)
(0.022)
Highest grade
0.0156 ∗∗ 0.0209 ∗∗
(0.003)
(0.004)
no. kids under 18 in hhld
0.0044
0.0000
(0.005)
(0.007)
no. adults in hhld
-0.0122 ∗∗ -0.0174 ∗∗
0.003
0.005
Observed p
0.6624
0.4404
pred. P
0.7214
0.4318
R-squared
0.26
0.09
no. obs
6378
3397
and labour force participation
Men
-0.0095
(0.015)
-0.0358
(0.015)
-0.0001
(0.014)
-0.0363
(0.016)
0.0062
(0.016)
0.0204
(0.015)
0.0280
(0.015)
0.0136
(0.017)
∗∗
∗∗
-0.0126
(0.012)
-0.0161
(0.015)
0.0041
(0.013)
-0.0055
(0.021)
-0.0031
(0.011)
0.0021
(0.013)
-0.0323 ∗∗
(0.011)
0.0114
(0.010)
-0.0009
(0.011)
0.0214 ∗
(0.012)
0.0023
(0.002)
0.0075 ∗∗
(0.004)
-0.0024
0.002
0.9146
0.9246
0.05
2950
Source: Guatemala ENCOVI data 2000. Standard errors in parentheses. Departamento fixed effects included in all specifications.
significant at 5% level, ∗ significant at 10% level.
∗∗
Table 10: The association between years since electrification and the probability of working in a
low-skilled occupation
Probit marginal effects
Dependent variable is unskilled occupation (ILO 1-digit code 9)
All
Women
Men
Elec ≥ 10, < 20 years
-0.0830 ∗∗ -0.0897 ∗∗ -0.0768 ∗∗
(0.018)
(0.033)
(0.022)
Elec ≥ 20 years
-0.0829 ∗∗ -0.0942 ∗∗ -0.0783 ∗∗
(0.019)
(0.034)
(0.024)
Hhld has electricity
-0.0877 ∗∗ -0.1796 ∗∗ -0.0485 ∗∗
(0.023)
(0.048)
(0.025)
Aged 25-29
0.0397 ∗
-0.0038
0.0589 ∗∗
(0.022)
(0.038)
(0.026)
Aged 30-34
-0.0340
0.0046
-0.0518 ∗
(0.023)
(0.042)
(0.027)
Aged 35-39
-0.0253
0.0623
-0.0695 ∗∗
(0.024)
(0.046)
(0.027)
Aged 40-44
-0.0595 ∗∗ -0.0040
-0.0879 ∗∗
(0.023)
(0.043)
(0.027)
Aged 45-50
-0.0747 ∗∗ -0.0283
-0.0966 ∗∗
(0.023)
(0.044)
(0.027)
sex
0.0519 ∗∗
(0.015)
Born here
0.0606 ∗∗ 0.0887 ∗∗ 0.0400 ∗
(0.017)
(0.027)
(0.022)
Community has water
-0.0292
0.0020
-0.0351
(0.024)
(0.044)
(0.028)
Community has improved sanitation 0.0386 ∗
0.0746 ∗∗ 0.0097
(0.020)
(0.034)
(0.025)
Rural
0.1084 ∗∗ 0.1907 ∗∗ 0.0554
(0.029)
(0.051)
(0.037)
Community has no preschool
-0.0271 ∗ -0.0399
-0.0308
(0.016)
(0.029)
(0.019)
Community has no elementary school -0.0220
-0.0075
-0.0262
(0.018)
(0.032)
(0.022)
Community has no secondary school 0.0118
0.0107
0.0138
(0.017)
(0.030)
(0.021)
Community affected by Mitch
0.0233
0.0415
0.0116
(0.015)
(0.028)
(0.019)
Community has paved highway
0.0106
-0.0146
0.0205
(0.016)
(0.029)
(0.019)
Community has access to markets
0.0009
-0.0293
0.0280
(0.017)
(0.030)
(0.021)
Highest grade
-0.0225 ∗∗ -0.0242 ∗∗ -0.0219 ∗∗
(0.003)
(0.006)
(0.004)
no. kids under 18 in hhld
0.0088 ∗
0.0087
0.0103
(0.005)
(0.009)
(0.007)
no. adults in hhld
0.0061 ∗
0.0097
0.0043
(0.004)
(0.006)
(0.004)
Observed p
0.2702
0.2909
0.2588
pred. P
0.2531
0.2619
0.2395
R-squared
0.08
0.11
0.08
no. obs
4171
1478
2693
Source: Guatemala ENCOVI data 2000. Standard errors in parentheses. Departamento fixed effects included in all specifications.
significant at 5% level, ∗ significant at 10% level.
∗∗
Table 11: The association between years since electrification and the probability of working in agriculture
Probit marginal effects
Dependent variable is industry code 1, agriculture
All
Women
Men
Elec ≥ 10, < 20 years
-0.0683 ∗∗ -0.0435 ∗ -0.0958 ∗∗
(0.018)
(0.021)
(0.027)
∗∗
∗∗
Elec ≥ 20 years
-0.0861
-0.0973
-0.0901 ∗∗
(0.021)
(0.026)
(0.031)
Hhld has electricity
-0.1224 ∗∗ -0.0344
-0.1616 ∗∗
(0.025)
(0.032)
(0.033)
Aged 25-29
0.0036
-0.0036
0.0131
(0.023)
(0.028)
(0.033)
Aged 30-34
0.0101
-0.0471 ∗ 0.0550
(0.026)
(0.025)
(0.038)
∗
Aged 35-39
-0.0201
-0.0483
0.0036
(0.026)
(0.025)
(0.039)
Aged 40-44
0.0124
-0.0626 ∗∗ 0.0831 ∗∗
(0.028)
(0.024)
(0.041)
Aged 45-50
0.0292
-0.0299
0.0977 ∗∗
(0.030)
(0.029)
(0.045)
sex
-0.2563 ∗∗
0.013
Born here
0.0968 ∗∗ 0.0445 ∗∗ 0.1245 ∗∗
(0.018)
(0.020)
(0.027)
Community has water
-0.0121
0.0641 ∗∗ -0.0666 ∗
(0.024)
(0.021)
(0.035)
∗∗
Community has improved sanitation -0.0764
0.0267
-0.1394 ∗∗
(0.022)
(0.028)
(0.030)
Rural
0.2162 ∗∗ 0.1749 ∗∗ 0.2590 ∗∗
(0.030)
(0.040)
(0.042)
∗∗
Community has no preschool
0.0251
0.0519
0.0083
(0.018)
(0.024)
(0.025)
Community has no elementary school -0.1082 ∗∗ -0.0078
-0.1713 ∗∗
(0.019)
(0.027)
(0.027)
Community has no secondary school -0.0283
0.0051
-0.0562 ∗∗
(0.020)
(0.027)
(0.029)
Community affected by Mitch
-0.0125
0.0560 ∗∗ -0.0533 ∗∗
(0.016)
(0.024)
(0.023)
∗∗
Community has paved highway
-0.0465
0.0110
-0.0855 ∗∗
(0.017)
(0.022)
(0.024)
Community has access to markets
0.0182
0.0226
0.0246
(0.019)
(0.022)
(0.027)
Highest grade
-0.0340 ∗∗ -0.0138 ∗∗ -0.0476 ∗∗
(0.004)
(0.005)
(0.005)
no. kids under 18 in hhld
0.0116 ∗∗ 0.0231 ∗∗ 0.0023
(0.006)
(0.007)
(0.008)
no. adults in hhld
0.0042
-0.0020
0.0065
(0.004)
(0.005)
(0.006)
Observed p
0.3284
0.1932
0.4209
pred. P
0.2382
0.1137
0.3698
R-squared
0.32
0.27
0.3
no. obs
4135
1206
2673
Source: Guatemala ENCOVI data 2000. Standard errors in parentheses. Departamento fixed effects included in all specifications.
significant at 5% level, ∗ significant at 10% level.
∗∗
Data Appendix B: Sensitivity Analysis
Source: Guatemala ENCOVI data 2000. Standard errors in parentheses.
∗∗
significant at 5% level,
∗
significant at 10% level.
Table 12: Sensitivity analysis of the effect of age at electrification on earnings
Dependent variable: log (monthly income)
All
|t|-test
Women
|t|-test
Community infra. No community
Community infra. No community
Controls
Infra. Controls
Controls
Infra. Controls
Age at elec. ≥5, <10
0.3840
0.3775
0.01
-0.0282
0.0540
0.12
(0.322)
(0.300)
(0.486)
(0.463)
Age at elec.≥10, <15
-0.1747
-0.1209
0.12
-0.4330
-0.4686
0.05
(0.326)
(0.295)
(0.482)
(0.437)
Age at elec.≥15, <20
-0.7603 ∗∗
-0.7197 ∗∗
0.10
-1.0004 ∗∗
-1.1127 ∗∗
0.19
(0.309)
(0.267)
(0.448)
(0.386)
Age at elec.≥20, <25
-0.8373 ∗∗
-0.8594 ∗∗
0.06
-1.0729 ∗∗
-1.3240 ∗∗
0.42
(0.307)
(0.257)
(0.456)
(0.389)
Age at elec.≥25, <30
-0.9617 ∗∗
-1.0072 ∗∗
0.10
-1.3327 ∗∗
-1.6450 ∗∗
0.46
(0.343)
(0.290)
(0.520)
(0.441)
Age at elec.≥30, <35
-1.0954 ∗∗
-1.1110 ∗∗
0.03
-1.2263 ∗∗
-1.4235 ∗∗
0.26
(0.388)
(0.329)
(0.577)
(0.494)
Age at elec.≥35, <40
-1.3079 ∗∗
-1.3273 ∗∗
0.03
-1.8490 ∗∗
-1.9823 ∗∗
0.15
(0.666)
(0.582)
(0.442)
(0.377)
Age at elec.≥40, <45
-1.6905 ∗∗
-1.6957 ∗∗
0.01
-2.0820 ∗∗
-2.3369 ∗∗
0.23
(0.540)
(0.467)
(0.823)
(0.711)
Age at elec.≥45, <50
-1.8137 ∗∗
-1.3904 ∗∗
0.40
-1.4657
-1.0378
0.26
(0.803)
(0.700)
(1.236)
(1.067)
Controls for:
Community has piped water
Yes
No
Yes
No
Community has sanitation
Yes
No
Yes
No
No pre-primary school
Yes
No
Yes
No
No primary school
Yes
No
Yes
No
No secondary school
Yes
No
Yes
No
Affected by Mitch
Yes
No
Yes
No
Paved road access
Yes
No
Yes
No
Community has market access Yes
No
Yes
No
Age at piped water
Yes
No
Yes
No
(5 year age cohorts)
Yes
No
Yes
No
Age at sanitation
Yes
No
Yes
No
(5 year age cohorts)
Yes
No
Yes
No
R2
0.28
0.27
0.14
0.13
no. obs.
6378
6378
3397
3397
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.08
2981
No
No
No
No
No
No
No
No
No
No
No
No
0.07
2981
Men
Community infra. No community
Controls
Infra. Controls
0.7861 ∗∗
0.7011 ∗∗
(0.396)
(0.360)
0.1773
0.3075
(0.427)
(0.381)
-0.5101
-0.2719
(0.419)
(0.359)
-0.5239
-0.2330
(0.401)
(0.329)
-0.6045
-0.2765
(0.430)
(0.355)
∗∗
-0.9979
-0.6726
(0.504)
(0.416)
-0.7870
-0.5617
(0.566)
(0.466)
∗
-1.2653
-0.8348
(0.706)
(0.602)
-2.0755 ∗∗
-1.4207
(1.029)
(0.901)
0.48
0.46
0.31
-0.50
0.59
0.56
0.43
0.23
0.16
|t|-test
Source: Guatemala ENCOVI data 2000. Standard errors in parentheses.
∗∗
significant at 5% level,
∗
significant at 10% level.
Table 13: Sensitivity analysis of the effect of age at electrification on labour force participation
Probit mfx. Dependent variable is labour force participation)
All
|t|-test
Women
|t|-test
Men
Community infra. No community
Community infra. No community
Community infra. No community
Controls
Infra. Controls
Controls
Infra. Controls
Controls
Infra. Controls
Age at elec.≥5, <10
0.0111
0.0148
0.00
-0.0060
-0.0025
-0.07
0.0185
0.0180
(0.028)
(0.026)
(0.039)
(0.036)
(0.016)
(0.016)
Age at elec. ≥10, <15
0.0035
0.0020
0.00
-0.0097
-0.0110
0.02
0.0115
0.0122
(0.029)
(0.027)
(0.040)
(0.036)
(0.018)
(0.017)
Age at elec. ≥15, <20
-0.0346
-0.0331
0.00
-0.0643 ∗
-0.0632 ∗∗
-0.02
0.0029
0.0039
(0.028)
(0.024)
(0.036)
(0.031)
(0.018)
(0.016)
∗
∗∗
∗∗
∗
Age at elec.≥20, <25
-0.0337
-0.0412
0.00
-0.0845
-0.0950
0.22
0.0276
0.0257 ∗
(0.028)
(0.024)
(0.037)
(0.031)
(0.015)
(0.014)
Age at elec.≥25, <30
-0.0273
-0.0444 ∗
0.00
-0.0870 ∗∗
-0.1048 ∗∗
0.33
0.0334 ∗
0.0269
(0.031)
(0.027)
(0.041)
(0.034)
(0.016)
(0.016)
∗∗
∗∗
∗∗
Age at elec.≥30, <35
-0.0499
-0.0706
0.00
-0.1145
-0.1275
0.22
0.0306
0.0223
(0.036)
(0.032)
(0.045)
(0.038)
(0.018)
(0.019)
Age at elec.≥35, <40
-0.0642 ∗
-0.0899 ∗∗
0.00
-0.1819 ∗∗
-0.1933 ∗∗
0.18
0.0558 ∗∗
0.0488 ∗∗
(0.048)
(0.040)
(0.013)
(0.015)
(0.041)
(0.036)
∗∗
∗∗
∗∗
Age at elec.≥40, <45
-0.0427
-0.0628
0.00
-0.1654
-0.1746
0.12
0.0621
0.0612 ∗∗
(0.050)
(0.044)
(0.061)
(0.051)
(0.013)
(0.013)
Age at elec.≥45, <50
0.0100
0.0009
0.00
-0.0479
-0.0302
-0.13
0.0490
0.0364
(0.067)
(0.063)
(0.098)
(0.088)
(0.019)
(0.027)
Controls for:
Community has piped water
Yes
No
Yes
No
Yes
Community has sanitation
Yes
No
Yes
No
Yes
No
No pre-primary school
Yes
No
Yes
No
Yes
No
No primary school
Yes
No
Yes
No
Yes
No
No secondary school
Yes
No
Yes
No
Yes
No
Affected by Mitch
Yes
No
Yes
No
Yes
No
Paved road access
Yes
No
Yes
No
Yes
No
Community has market access Yes
No
Yes
No
Yes
No
Age at piped water
Yes
No
Yes
No
Yes
No
(5 year age cohorts)
Yes
No
Yes
No
Yes
No
Age at sanitation
Yes
No
Yes
No
Yes
No
(5 year age cohorts)
Yes
No
Yes
No
Yes
No
R2
0.26
0.26
0.09
0.08
0.06
0.04
no. obs.
6378
6378
3397
3397
2950
2950
Observed
0.6624
0.6624
0.4404
0.4404
0.9146
0.9146
Predicted P (at x-bar)
0.7218
0.7206
0.4317
0.4332
0.9281
0.9238
0.38
0.05
0.35
0.32
0.29
0.09
-0.04
-0.03
0.02
|t|-test
Source: Guatemala ENCOVI data 2000. Only working individuals included. Standard errors in parentheses.
∗∗
significant at 5% level,
∗
significant at 10% level.
Table 14: Sensitivity analysis of the effect of age at electrification on the probability of being
probit mfx. Dependent variable is low-skilled occupation (ILO 1-digit code 9)
All
|t|-test
Women
Community infra. No community
Community infra. No community
Controls
Infra. Controls
Controls
Infra. Controls
Age at elec.≥5, <10
-0.0098 ∗
-0.0167
0.17
-0.0418 ∗
-0.0634
(0.030)
(0.028)
(0.047)
(0.042)
∗
∗
Age at elec.≥10, <15
0.0399
0.0217
0.41
0.0627
0.0257
(0.033)
(0.030)
(0.056)
(0.049)
Age at elec.≥15, <20
0.1104
0.0825 ∗∗
0.64
0.1430
0.0827 ∗
(0.033)
(0.028)
(0.057)
(0.046)
∗∗
Age at elec.≥20, <25
0.0753
0.0537
0.51
-0.0072
-0.0571
(0.033)
(0.027)
(0.052)
(0.041)
Age at elec.≥25, <30
0.1411
0.1083 ∗∗
0.65
0.1522
0.1087 ∗∗
(0.039)
(0.032)
(0.067)
(0.054)
∗
∗∗
∗
Age at elec.≥30, <35
0.1312
0.1038
0.47
0.1490
0.0907
(0.045)
(0.037)
(0.077)
(0.062)
Age at elec.≥35, <40
0.0862 ∗
0.0495
0.57
0.0874 ∗∗
0.0187
(0.090)
(0.073)
(0.049)
(0.041)
∗∗
∗
∗∗
Age at elec.≥40, <45
0.1108
0.0827
0.35
0.2362
0.1630 ∗
(0.061)
(0.052)
(0.125)
(0.106)
Age at elec.≥45, <50
0.1087 ∗∗
0.0969
0.11
0.1928 ∗∗
0.1750
(0.082)
(0.073)
(0.151)
(0.127)
Controls for:
Community has piped water
Yes
No
Yes
No
Community has sanitation
Yes
No
Yes
No
No pre-primary school
Yes
No
Yes
No
No primary school
Yes
No
Yes
No
No secondary school
Yes
No
Yes
No
Affected by Mitch
Yes
No
Yes
No
Paved road access
Yes
No
Yes
No
Community has market access Yes
No
Yes
No
Age at piped water
Yes
No
Yes
No
(5 year age cohorts)
Yes
No
Yes
No
Age at sanitation
Yes
No
Yes
No
(5 year age cohorts)
Yes
No
Yes
No
R2
0.08
0.07
0.13
0.11
no. obs.
4171
4171
1478
1478
Observed p
0.2702
0.2702
0.2909
0.2909
Predicted p
0.2521
0.2544
0.2590
0.2635
0.09
0.45
0.59
0.59
0.51
0.75
0.83
0.5
0.34
|t|-test
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.0919
2693
0.26
0
No
No
No
No
No
No
No
No
No
No
No
No
0.0782
2639
0.2588
0.2406
Men
Community infra. No community
Controls
Infra. Controls
0.0035 ∗
0.0072
(0.039)
(0.037)
∗
0.0335
0.0193
(0.042)
(0.037)
0.0807
0.0630 ∗
(0.041)
(0.035)
0.1041
0.0973 ∗∗
(0.041)
(0.035)
0.1328
0.1040 ∗∗
(0.049)
(0.041)
∗
0.1387
0.1181 ∗∗
(0.057)
(0.048)
0.1196 ∗
0.0854 ∗
(0.062)
(0.052)
∗∗
0.1055
0.0751
(0.074)
(0.063)
0.1114 ∗∗
0.0881
(0.102)
(0.091)
in a low-skilled occupation
0.17
0.31
0.42
0.28
0.45
0.13
0.33
0.25
-0.07
|t|-test
Source: Guatemala ENCOVI data 2000. Only working individuals included. Standard errors in parentheses.
∗∗
significant at 5% level,
∗
significant at 10% level.
Table 15: Sensitivity analysis of the effect of age at electrification on the probability of being employed in agriculture
Probit marginal effects. Dependent variable is industry category 1, agriculture
All
|t|-test
Women
|t|-test
Men
Community infra. No community
Community infra. No community
Community infra. No community
Controls
Infra. Controls
Controls
Infra. Controls
Controls
Infra. Controls
Age at elec.≥5, <10
-0.0706 ∗
-0.0777 ∗∗
0.18
-0.0460 ∗
-0.0771 ∗∗
0.79
-0.0738 ∗
-0.0784 ∗
(0.029)
(0.028)
(0.031)
(0.025)
(0.046)
(0.044)
∗
∗
∗
Age at elec. ≥10, <15
0.0101
-0.0006
0.23
-0.0066
-0.0277
0.4
0.0169
0.0063
(0.035)
(0.031)
(0.041)
(0.034)
(0.050)
(0.045)
Age at elec.≥15, <20
0.0768
0.0706 ∗∗
0.13
0.1071
0.0671 ∗
0.59
0.0556
0.0557
(0.036)
(0.031)
(0.054)
(0.041)
(0.049)
(0.042)
∗∗
Age at elec.≥20, <25
0.1019
0.0811
0.43
0.0953
0.0238
1.06
0.1249
0.1193 ∗∗
(0.037)
(0.031)
(0.055)
(0.038)
(0.050)
(0.041)
Age at elec.≥25, <30
0.2116
0.1832 ∗∗
0.48
0.2396 ∗
0.1399 ∗∗
0.99
0.2271
0.2053 ∗∗
(0.045)
(0.038)
(0.082)
(0.060)
(0.055)
(0.046)
∗
∗∗
∗
∗∗
∗
Age at elec.≥30, <35
0.1804
0.1467
0.50
0.2383
0.1130
1.06
0.1859
0.1668 ∗∗
(0.052)
(0.043)
(0.097)
(0.068)
(0.064)
(0.054)
Age at elec.≥35, <40
0.2357 ∗
0.2250 ∗∗
0.14
0.3710 ∗∗
0.2115 ∗∗
0.97
0.2434 ∗
0.2524 ∗∗
(0.130)
(0.100)
(0.070)
(0.058)
(0.060)
(0.051)
∗∗
∗∗
∗∗
∗∗
∗∗
Age at elec.≥40, <45
0.2889
0.2476
0.44
0.4668
0.2416
1.13
0.2712
0.2640 ∗∗
(0.072)
(0.060)
(0.157)
(0.122)
(0.081)
(0.068)
Age at elec.≥45, <50
0.3784 ∗∗
0.3083 ∗∗
0.54
0.7340 ∗∗
0.4798 ∗∗
1.17
0.2887 ∗∗
0.2551 ∗∗
(0.096)
(0.087)
(0.132)
(0.172)
(0.111)
(0.094)
Controls for:
Community has piped water
Yes
No
Yes
No
Yes
No
Community has sanitation
Yes
No
Yes
No
Yes
No
No pre-primary school
Yes
No
Yes
No
Yes
No
No primary school
Yes
No
Yes
No
Yes
No
No secondary school
Yes
No
Yes
No
Yes
No
Affected by Mitch
Yes
No
Yes
No
Yes
No
Paved road access
Yes
No
Yes
No
Yes
No
Community has market access Yes
No
Yes
No
Yes
No
Age at piped water
Yes
No
Yes
No
Yes
No
(5 year age cohorts)
Yes
No
Yes
No
Yes
No
Age at sanitation
Yes
No
Yes
No
Yes
No
(5 year age cohorts)
Yes
No
Yes
No
Yes
No
R2
0.33
0.30
0.30
0.26
0.31
0.27
no. obs.
4135
4135
1206
1206
2673
2673
Observed p
0.3284
0.3284
0.33
0.1932
0.19
0.4209
Predicted p
0.2353
0.2445
0.24
0.1160
0.1
0.3787
0.23
0.07
-0.10
0.23
0.30
0.09
0.00
0.16
0.07
|t|-test
−3
ln(inc)
1
5
Figure 1: Predicted ln(income) by age at electrification
0
10
20
30
Age at community electrification
female
40
50
male
Source: Guatemala 2000 LSMS/ENCOVI data. Semiparametric locally−weighted least squares.
Figure 2: Predicted labour force participation probability
0
.25
prob.
.5
.75
1
by age at electrification
0
10
20
30
Age at community electrification
female
40
50
male
Source: Guatemala 2000 LSMS/ENCOVI data. Semiparametric locally−weighted least squares.
Figure 3: Predicted probability of working in unskilled occupations
0
.25
prob.
.5
.75
1
by age at electrification
0
10
20
30
Age at community electrification
female
40
male .
Source: Guatemala 2000 LSMS/ENCOVI data. Semiparametric locally−weighted least squares.
50
Figure 4: Predicted probability of working in agriculture
0
.25
prob.
.5
.75
1
by age at electrification
0
10
20
30
Age at community electrification
female
40
50
male
Source: Guatemala 2000 LSMS/ENCOVI data. Semiparametric locally−weighted least squares.