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Regional differences in overweight rates: The case of Italian regions
Economics and Human Biology 12 (2014) 20–29
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Economics and Human Biology journal homepage: http://www.elsevier.com/locate/ehb
Regional differences in overweight rates: The case of Italian regions Giorgio Brunello a,*, Giovanna Labartino b a b
University of Padova, IZA, Cesifo and ROA, Italy Centro Studi Confindustria, Italy
A R T I C L E I N F O
A B S T R A C T
Article history: Received 27 October 2011 Received in revised form 27 September 2012 Accepted 9 October 2012 Available online 30 October 2012
Southern regions in Italy are characterized by higher overweight rates than Northern and Central regions. This gap is higher for young males than for females. We fully account for the differences in overweight rates with a relatively parsimonious set of covariates, and show that the key factors accounting for these differences vary substantially by gender. There is a strong association between regional differences in educational attainment, labor market outcomes and overweight rates for females, and a strong association between regional differences in parental and peer BMI, the regional percentage of primary schools with a canteen and overweight rates for males. We are grateful to two anonymous referees and to Danilo Cavapozzi for help with the data. Financial support by Fondazione Cariparo is gratefully acknowledged. The usual disclaimer applies. ß 2012 Elsevier B.V. All rights reserved.
1. Introduction Obesity and overweight are increasingly becoming a major problem not only in the US but also in Europe. Compared to the US and the UK, continental Europe has lower rates, but these rates are increasing over time, albeit at lower pace than in Anglo-Saxon countries (see Brunello et al., 2009). The prevalence of these phenomena varies substantially not only between but also within countries. According to Ezzati et al. (2006), in the year 2000 the percentage of obese American men was higher than 30% in Texas and lower than 20% in Colorado. These large spatial differences have motivated a small but growing literature which looks at the effects of urban sprawl on obesity (see for instance Eid et al., 2008). Compared to the other major European countries, Italy fares relatively well (see Brunello et al., 2009), especially for females. According to OECD data, in 2008 the percentage of overweight Italian females was 27.1%, higher than in France but lower than in Germany, Spain, Greece and The Netherlands. On the other hand, the percentage of overweight
* Corresponding author. E-mail address: [email protected] (G. Brunello). 1570-677X/$ – see front matter ß 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ehb.2012.10.001
males was 44.7%, higher than in France but very similar to that in Germany and Spain. National averages hide substantial within – country heterogeneity: the difference in the percentage of overweight females1 between Italy and France (about 5 percentage points) is much lower than the difference in the same percentage between Lombardia in the North of Italy (20.8%) and Basilicata in the South (30.8%). Similarly, the difference in the percentage of overweight males between Northern Liguria (39.1%) and Southern Campania (48.1%) is close to the difference between Italy and France (44.7 versus 32.2).2 Fig. 1 illustrates the regional dispersion in the share of overweight males and females aged 18 to 80 for the period 2001–2009. While all Southern regions – with the single exception of Sardinia – cluster in the upper right corner of the figure, most Northern regions are located in the lower left corner. This pattern is confirmed when we focus on the youngest cohort aged 18–34 (see Fig. 2). Given that Italian
1 We follow international conventions and define overweight as the individuals with a body mass index equal or larger than 25. 2 Regional percentages for Italy are from the Survey on Daily Lifestyles of Italian Households (Indagine sugli aspetti della vita quotidiana delle famiglie italiane).
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
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PUG MOL SIC
.28
CA
.26
CA L
AB R
FV G
.24 4
ERO
LA Z MA R T OS
UMB
.22
T RE VE N PIE
LIG
SA R
LOM
.2
% overweight; females - age 18-80
.3
BA S
.4
.42
.46
.44
.48
% overweight; males - age 18-80
Note: PIE=Piemonte-Valle d’Aosta, LOM=Lombardia, TRE=Trentino Alto-Adige, VEN=Veneto, FVG=Friuli-Venezia-Giulia, LIG=Liguria, ERO=Emilia Romagna, TOS=Toscana, UMB=Umbria, LAZ=Lazio, ABR=Abruzzo, MOL=Molise, CAM=Campania, PUG=Puglia, MAR=Marche, BAS=Basilicata, CAL=Calabria, SIC=Sicilia, SAR=Sardegna
.16
CA M
BA S
.14
PUG ERO AB R
SIC CA L MOL
.12
UMB MA R LA Z FV G PIE T OS VE N T RE LOM SA R
.1
% overweight - females aged 18-34
.18
Fig. 1. Overweight rates by region. Age groups 18–80. Note: PIE = Piemonte-Valle d’Aosta, LOM = Lombardia, TRE = Trentino Alto-Adige, VEN = Veneto, FVG = Friuli-Venezia-Giulia, LIG = Liguria, ERO = Emilia Romagna, TOS = Toscana, UMB = Umbria, MAR = Marche, LAZ = Lazio, ABR = Abruzzo, MOL = Molise, CAM = Campania, PUG = Puglia, BAS = Basilicata, CAL = Calabria, SIC = Sicilia, SAR = Sardegna.
LIG
.25
.3
.35
.4
% overweight - males aged 18-34
Fig. 2. Overweight rates by region. Age groups 18–34. Note: See Fig. 1.
regions share the same national institutional setup, the natural question to ask is what are the factors accounting for the substantial difference in overweight rates between the North and the South of the country.3 Due to the long standing structural differences in culture, social norms and economic development (see for instance Tabellini, 2010; De Blasio and Nuzzo, 2009), the North–South classification is the natural way to group Italian regions. Fig. 3 illustrates this by showing regional differences in economic well-being and civic capital. The former is computed by extracting the principal component from a vector of variables which include the unemployment rate and value added per capita. Social or civic capital
is a notion that is difficult to pin down exactly. Following the literature,4 we extract the principal component from the following three variables: the share of civil marriages, the percentage of people who read daily newspapers at least once a week and blood donation per capita.5 The figure shows that Southern regions have less social capital and lower well-being than Northern regions. Using the methodology proposed by Cutler and Lleras Muney (2010), this paper proposes an accounting exercise which associates regional differences in overweight rates to differences in education, income, parental background, labor market status, daily diet and calorie expenditure. Our contribution is twofold. First, we are able to fully account for the existing differences in overweight rates with a relatively parsimonious set of covariates. Second, we show that the key factors accounting for these differences vary substantially by gender. We use a rich dataset – the Survey of Daily Life of Italian Households (DAILIH) – and regress overweight rates on a set of demographics and the dummy S, equal to one if the individual resides in a Southern region and to zero otherwise. Since residence is the result of individual choice and of migration patterns, we instrument the dummy S with variables capturing regional differences in ‘‘amenities’’. Next, we add to this regression an increasingly broad vector of individual, group and regional covariates, which include education, parental background, income and labor market status, eating habits and calorie expenditures, peer effects and the regional percentage of overweight children and schools with canteens. For each
4
Guiso et al., 2004; Buonanno et al., 2009; Putnam et al., 1993. Sources: for blood donation (Guiso et al., 2004), for civil marriages and daily newspapers ISTAT, 2008. 5
3
We focus on the overweight but results are similar for the obese.
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The paper is organized as follows: Section 2 presents the empirical strategy and briefly reviews the literature, Section 3 introduces the data and Section 4 shows the results. Conclusions follow.
3
22
2
TRE
1
TOSPIE LIG
0
MAR LAZ UMB
2. The empirical strategy
-1
ABR MOL SAR
BAS PUG CAL CAMSIC
-3
-2
Index of social capital
LOM ERO VEN FVG
-3
-2
-1
0
1
2
3
Index of economic well being
Fig. 3. Economic well-being and social capital, by region. Note: See Fig. 1.
regression, we compute the percentage decline in the coefficient of the dummy S. This decline measures our ability to account for regional differences in the overweight rate using observables.6 Given the importance of parental background for individual BMI, we choose to focus our analysis on the cohorts of Italians aged between 18 and 34 at the time of the interview, for whom we can construct measures of parental education and BMI. Our set of covariates fully account for the regional differences in female and male overweight rates. Regional differences in education, labor market status and measures of income/wealth account for more than 60% of the South–North gap for females and for only 2.3% of the gap in the case of males. On the other hand, the BMI of parents and peers – which we interpret as measure of long-run cultural factors and social norms – account for about 48% of the regional gap for males, compared to about 29% for females.7 Regional differences in the percentage of schools with a canteen also matter for the male gap. We find that improvements in education and labor market outcomes in Southern Italy are negatively correlated to the overweight gap between young females living there and those living in Northern regions, but uncorrelated to the larger regional gap for young males. There is also evidence that the overweight gap for males is positively correlated to the gap in parental and peer BMI, and negatively correlated to the regional gap in the percentage of primary schools with a canteen. Since we estimate associations rather than causal relationships, we refrain from drawing policy implications from our results. The importance of parental BMI and the lower share of primary schools with a canteen do suggest, however, that boys in the South are less exposed to different dietary habits and are therefore more prone than boys in the North to be affected by household lifestyles.
6 We use ‘‘account for’’ rather than ‘‘explain’’ to emphasize that we are looking at associations, not at causal relationships. 7 These results are qualitatively robust to changes in the order in which these covariates enter the regressions.
Only a few papers investigate within-country differences in obese or overweight rates, as we do in this paper. Ezzati et al., 2006, document the significant increase in obesity in the US during the 1990s and show that this increase was not homogeneous across states. Eid et al., 2008, focus on the relationship between urban sprawl and obesity and investigate the hypothesis that living in a sprawl might increase the probability of being obese, because in places where buildings are more spread out people tend to use more theirs cars and walk less. Using US data, they find no evidence of a causal relationship between living in a sprawl and the probability of being obese. Barone and O’Higgins (2009), explore the impact of being overweight on the probability that young Italians living in the South and particularly in Salerno, near Naples, drop out of school early. They compare young males and females and find that the former are more likely to be overweight or obese. They argue that while young females are more influenced by peer pressure and by concerns about physical appearance, young males are influenced mainly by parental background. Following Cutler and Lleras Muney (2010), we start from the following baseline specification OWi ¼ a0 þ a01 Southi þ X i b þ ei
(1)
where OWi is a dummy equal to one if the individual is overweight and to zero otherwise, South is a dummy equal to one if the individual resides in a Southern region and to zero if she resides in the North or Centre, Xi is a vector of individual personal characteristics such as age, age squared and height, and the coefficient of interest is a01 . We re-estimate (1) by adding the set of covariates Z 1i OWi ¼ a0 þ a1 1 Southi þ X i b þ Z i 1 g þ vi
(2)
and compute 1 a11 =a01 , the percentage change in the coefficient associated to South from adding these covariates. We further proceed by sequentially adding vectors of covariates Zi2 ; . . . ; ZiN , and by computing in each step the reduction in the estimated effect of residing in a Southern region on the probability of being over-weight 1 ak1 =a01 , where k = 2,. . .N. We use a probit model, but our key results do not change when we use a linear specification.8 An important concern with this estimation strategy is that residence in a Southern region is not random but follows from individual choice. Individuals choose the region of residence by comparing the net benefits of staying in their region of birth with the net benefits of moving to another region. Expected benefits include both monetary
8
Results available from the authors upon request.
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
and non-monetary returns, and depend on regional amenities as well as on economic conditions. We capture regional differences in amenities and economic conditions with three variables: the percentage of hospital beds per 100 thousand inhabitants, the kilometers of motorways per 1000 km squared and the share of public employment. Since public employment in Italy has typically provided both very good employment security and relatively good hourly pay, the availability of public jobs is expected to reduce the probability of moving out of the region (see for instance Brunello et al., 2000). The percentage of hospital beds and the kilometers of motorways, on the other hand, measure the quality of services available in the region. Better quality is expected to reduce the probability of moving to another region. We use these three variables as sources of exogenous variation in the probability of living in the South of Italy. The identifying assumption is that they affect overweight only indirectly by affecting the choice of the region of residence.9 Table 1 shows that these variables vary significantly across regions and over time.
3. The data We use several waves of the Italian DAILIH survey, which has been conducted since 1993 on a nationally representative sample of the Italian population to collect information on different aspects of daily life. We consider only the waves from 2001 to 2009, because previous surveys did not include information on weight and height.10 Given the importance of parental background for individual BMI, we restrict our attention to individuals aged 18–34, for whom we can construct indicators of parental education and BMI based on the available information on co-habiting parents. Following standard practice, we measure the body mass index BMI as the ratio of the self-reported weight to the square of the self-reported height, and classify each individual as severely underweight (BMI below 16.5), normal weight (BMI over 16.5 up to 24.9), overweight (BMI above or equal to 25) and obese (BMI above or equal to 30). In this paper, we focus on the overweight. Table 2 shows the share of overweight males and females and average BMI in the South and in the Centre– North (North from now on) of the country.11 Average BMI is higher in the South and the prevalence of overweight is 21–27% higher in the South than in the North, depending on gender. Table 3 presents the same information by region. While the North/South divide is a clear feature of
9 One may object that people in areas with fewer roads are more inclined to walk, which affects overweight rates. Yet the substitution of driving with walking seems unlikely in the case of motorways, which are typically used for medium or long distance transportation. 10 The year 2004 is missing because the survey was not carried out in that year. 11 The South consists of the following regions: Abruzzo, Molise, Campania, Puglia, Basilicata, Calabria, Sicilia and Sardegna. The Centre– North consists instead of Piemonte-Val d’Aosta, Lombardia, Trentino AltoAdige, Veneto, Friuli Venezia Giulia, Liguria, Emilia-Romagna,Toscana, Umbria, Marche and Lazio.
23
the data, we notice that both the South and the North are heterogeneous with respect to the prevalence of overweight: Sardegna in the South is more similar to Northern than to Southern regions and Emilia-Romagna in the North is closer to Abruzzo in the South than to Lombardia.12 The means by gender of the covariates in vectors X and Z 2 ½Z1 . . . Z7 are shown in Table 4. The vector X includes age, age squared and individual height. The vector Z1 consists of a dummy equal to one if the individual is single and to zero otherwise. The vector Z2 contains two education dummies (primary, secondary schooling, with tertiary education in the constant), the education of parents, the dummy books, which indicate whether the individual has read at least a book in the last 12 months, and the variable newspapers, which indicates how often he/she reads newspapers. Since the information on the education of parents is only available for co-habiting parents – the percentage living with their parents is 71.9% in Southern regions and 62.7 in Northern regions for males are 60.2 and 51 respectively for females – we construct average parental years of education by region, gender and cohort of birth and assign to each individual aged 18–34 in the sample the mean of his/her cell. The indicators in this vector show that individuals residing in the South have lower education than in the North. The vector Z3 includes the average BMI of the mothers and the fathers in the relevant cell, which we construct in a similar fashion as parental education. As expected, both take higher values in the South than in the North. The vector Z4 contains the indicators of labour market status employed and unemployed (out of the labor force in the constant); the occupational dummies senior and junior white collar, manager, self-employed and firm-owner (blue collar jobs in the constant), and the sector dummies industry, construction, retail, transportation, services, public administration, education and medical services and other services (agriculture in the constant). These dummies are good proxies of earnings, which are not available in our data. To further control for differences in income, we use individual assessments of economic conditions, which can be low, not sufficient, fair and excellent (the last two in the constant), and individual evaluations of the economic situation in the household compared to the previous year (much better, better, unchanged, worse, much worse, with the former two in the constant). Additional proxies of household income are: vacations, which counts the number of vacation episodes in the past year; paid sport and sport club, two dummies indicating whether the individual paid for sports or joined sport and recreation clubs; the number of the rooms in the house, and whether someone in the family has a private health insurance or a pension fund. We also add a measure of household wealth, which we construct from the Survey on the Income and Wealth of Italian Households (Bank of Italy). Using micro-data from 4 waves, 2000, 2002, 2006 and 2008, we generate average wealth by gender, cohort,
12 We have repeated our empirical analysis by dropping Sardinia from the sample, with marginal effects on the results.
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
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Table 1 Hospital beds per 100 thousand inhabitants, kilometers of motorway per 1000 km2 and share of public employment. By region. Hospital beds
Year
Region Piemonte-Valle d‘Aosta Liguria Lombardia Trentino Alto-Adige Veneto Friuli Venezia Giulia Emilia Romagna Toscana Umbria Marche Lazio Abruzzo Molise Campania Puglia Basilicata Calabria Sicilia Sardegna
2001 477.9 513.3 482.3 537.4 465.2 499.6 486.4 441.6 379.4 471.1 587 415.6 464.7 357.9 425.7 408.3 503.7 383 461.1
Motorways
Year
Region Piemonte-Valle d‘Aosta Liguria Lombardia Trentino Alto-Adige Veneto Friuli Venezia Giulia Emilia Romagna Toscana Umbria Marche Lazio Abruzzo Molise Campania Puglia Basilicata Calabria Sicilia Sardegna
2001 32 70.6 25.3 27 27 27.7 26.7 18.7 7.1 17.6 28.1 33 8.2 33.1 16.3 3 20 23.3 0
Share of public employment
Year
Region Piemonte-Valle d‘Aosta Lombardia Trentino Alto-Adige Veneto Friuli Venezia Giulia Liguria Emilia Romagna Toscana Umbria Marche Lazio Abruzzo Molise Campania Puglia Basilicata Calabria Sicilia Sardegna
2001 0.23 0.28 0.2 0.27 0.21 0.26 0.22 0.24 0.27 0.24 0.35 0.28 0.30 0.36 0.30 0.29 0.37 0.38 0.34
2002 435.5 505.1 454.7 472.1 448.2 477.8 479.5 450.2 368.8 458.8 566.9 461.5 477.1 347.6 402.4 403 416.1 392.5 462.7
2003 428.1 364.6 431.6 427.9 421.3 436 469.3 391.1 334.6 378.7 539.7 407.3 503 303.9 397.4 318.5 501.1 375.8 443.9
2004 413 366.2 419.3 408.2 398.3 388.6 443.2 383.8 331.9 404.9 522.6 353.5 499.5 309.4 368 319.4 423.3 350.1 434.8
2005 400.3 448.4 414.9 458.1 384.8 368.1 447.1 380.6 310.4 400.2 514.7 421.3 521.2 322.4 377.3 313.3 387 342.6 432.3
2006 393.5 405.2 412.4 448.8 384.4 361.4 432.1 363.4 300 373.4 514.8 437.9 516 325.5 375 339.3 387.7 329.4 431.9
2007 390.3 399.7 403.1 438.3 378 357.7 430.7 354.8 311.2 370.9 479.4 421.4 512.4 321.3 368.7 326.5 378.3 325.1 395.7
2008 379.8 372.6 395.6 431.2 365.6 345.4 418.4 342.9 299.4 371 478.8 370.1 465 310.3 356.6 310.8 336.1 310.2 392.3
2009 385.3 381.5 394.2 419.7 358.3 350.7 406.5 320.7 295.8 363.9 455 345.3 444.5 301.2 346.4 309.2 321 293.2 376.4
2002 31.6 70.2 24.6 26 26 27.4 29.4 18.2 7.8 20.9 28.3 29.9 11.9 33.2 14.6 4.1 18.9 22.9 0
2003 31.9 70.2 24.6 26 26 27.4 29.4 18.2 7.8 20.9 28.3 30 11.9 33.2 14.6 4.1 18.9 22.9 0
2004 32.7 70.4 25.3 27 27 27.8 26.4 18.7 7.1 17.6 27.8 33.1 8.2 33 16.3 3 20 23.3 0
2005 32.8 70.4 25.3 27 27 27.8 26.4 18.7 7.1 17.6 27.8 33.1 8.2 33 16.3 3 20 24.9 0
2006 33.1 70.4 25.3 27 27 27.8 26.4 18.7 7.1 17.6 27.8 33.1 8.2 33 16.3 3 20 24.9 0
2007 33.1 70.4 25.3 27.6 27.6 27.8 26.4 18.7 7.1 17.6 27.8 33.1 8.2 33 16.3 3 20 25 0
2008 33.1 70.4 25.3 27.6 27.6 27.8 26.4 18.7 7.1 17.6 27.8 33.1 8.2 33 16.3 3 20 25.3 0
2009 33.2 70 26 28 28 28 26 19 7 18 28 33 8 33 16 3 20 26 0
2006 0.22 0.31 0.2 0.29 0.19 0.24 0.21 0.25 0.27 0.23 0.36 0.28 0.30 0.33 0.28 0.30 0.35 0.37 0.31
2007 0.23 0.31 0.21 0.29 0.2 0.26 0.22 0.25 0.28 0.23 0.34 0.28 0.31 0.32 0.29 0.31 0.35 0.37 0.33
2008 0.23 0.31 0.21 0.3 0.21 0.26 0.22 0.25 0.28 0.23 0.34 0.26 0.30 0.31 0.29 0.31 0.34 0.36 0.33
2009 0.24 0.3 0.22 0.3 0.21 0.26 0.22 0.24 0.29 0.22 0.35 0.28 0.30 0.31 0.29 0.32 0.34 0.36 0.32
2002 0.23 0.28 0.21 0.26 0.21 0.26 0.23 0.25 0.27 0.23 0.35 0.30 0.32 0.35 0.31 0.29 0.37 0.38 0.34
2003 0.23 0.29 0.22 0.28 0.21 0.26 0.22 0.25 0.27 0.23 0.34 0.31 0.32 0.35 0.31 0.30 0.36 0.37 0.32
2004 0.23 0.28 0.21 0.28 0.2 0.27 0.22 0.25 0.27 0.23 0.35 0.30 0.32 0.34 0.30 0.29 0.35 0.37 0.33
2005 0.23 0.3 0.2 0.28 0.2 0.24 0.21 0.24 0.29 0.23 0.35 0.29 0.31 0.32 0.28 0.29 0.34 0.37 0.30
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
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Table 2 Overweight rates and BMI in South and North–Centre Italy. Age: 18–34. Regions
Females
Males
Overweight rate
BMI
Obs.
Overweight rate
BMI
Obs.
South North
0.144 0.113
21.99 21.47
12,581 17,399
0.351 0.29
24.33 23.91
12,126 17,171
Table 3 Overweight rates and BMI by region; age: 18–34. Regions
Piemonte-Valle d‘Aosta Lombardia Trentino Alto-Adige Veneto Friuli Venezia Giulia Liguria Emilia Romagna Toscana Umbria Marche Lazio Abruzzo Molise Campania Puglia Basilicata Calabria Sicilia Sardegna
Females
Males
Overweight rate
BMI
Obs.
Overweight rate
BMI
Obs.
0.115 0.104 0.107 0.114 0.115 0.078 0.131 0.115 0.122 0.117 0.123 0.135 0.137 0.177 0.143 0.151 0.137 0.145 0.104
21.41 21.27 21.54 21.44 21.5 21.12 21.68 21.5 21.59 21.54 21.72 21.72 21.91 22.5 22.11 21.99 21.92 22.04 21.23
2514 2627 1704 1959 932 835 1560 1570 888 1313 1497 1257 832 2337 2119 991 1491 2127 1427
0.28 0.281 0.281 0.294 0.3 0.26 0.319 0.278 0.303 0.299 0.31 0.314 0.346 0.41 0.357 0.359 0.336 0.384 0.25
23.79 23.77 23.78 23.92 23.98 23.67 24.16 23.87 24.19 24.1 23.98 24.06 24.38 24.74 24.36 24.38 24.34 24.5 23.57
2481 2651 1521 1967 899 913 1588 1499 948 1302 1402 1222 829 2165 2090 1044 1371 2002 1403
level of education and region and match this information with the individuals in our dataset. To control for family size, we also add the number of household members (Size HH). The vectors Z5 and Z6 contain variables that capture calorie intake and consumption. The dummy sport indicates whether the individual has played any sport during the last 12 months; the variables TV hours and Watches TV measure the number of hours spent watching TV and whether the person watches television every day respectively. To capture differences in calorie intake, we use the variable lunch at home, which indicates whether the person has usually lunch at home, and a set of variables measuring the consumption of pork, fruit, cheese and beef.13 We also add to this vector the average number of cigarettes smoked per day, an indicator of whether the individual drinks alcohol14 and an indicator of whether she has health problems. Finally, the vector Z7 captures peer effects, which we define as the average BMI of individuals who live in the same region and belong to the same gender and birth cohort. Conditional in income and employment, regional differences in these effects are likely to measure long term differences in cultural and social norms. We measure regional differences in amenities that directly affect overweight with the percentage of primary
13 Each variable indicates how often the individual consumes a certain type of food: never, once a week, twice a week, or everyday. 14 The dummy takes value 1 if the individual drinks at least 1–2 glasses of wine, beer or other alcoholic beverages during a week.
schools with a canteen and the percentage of overweight children. The former variable refers to 2005 (source: the Italian Minister of Education). It turns out that the percentage of primary schools with a canteen ranges from close to 100% in the Northern regions of Piemonte and Lombardia to 70% in Southern Sicily.15 The percentage of overweight children (aged between 8 and 9) is for the year 2008 and is drawn from the project ‘‘Okkio alla salute’’, carried out by the Italian Minister of Health and the Minister of Education. The data show substantial regional variation, with 27.5% of male children being overweight in Southern Campania, compared to only 14.5% in Northern Trentino. 4. Results We use an instrumental variable (IV) strategy to estimate the effect of residing in the South on the probability of being overweight. We start by estimating first stage regressions by gender, where the dependent variable – the dummy S – is regressed on the three selected instruments and the additional covariates, using a linear probability model. Since each instrument varies both by region and over time, we cluster standard errors by region and year. As expected, we find that the number of hospital beds per 100 thousand inhabitants and the kilometers of
15 The percentage of primary schools with a canteen is above 90 percent in all Italian regions, with the exception of Puglia (86.9%) and Sicily (70.2%).
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G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
Table 4 Descriptive statistics. Males
Females
Overweight rate Demographics Age Height Z1: Marital status Single Z2: Education Primary Secondary Tertiary Books Newspapers Education of fathers Education of mothers Z3: Parental BMI BMI of Fathers BMI of Mothers
South
North
South
North
Mean
Mean
Mean
Mean
0.144
26.33 163.83
0.113
26.99 165.24
0.351
26.23 174.88
0.29
26.97 177.43
0.646
0.646
0.794
0.781
0.344 0.533 0.123 0.558 1.977 8.7 8.35
0.255 0.584 0.161 0.713 2.367 9.01 8.67
0.41 0.505 0.085 0.337 2.373 8.47 8.12
0.346 0.546 0.108 0.481 2.728 8.86 8.55
26.78 25.8
26.48 25.05
26.87 25.65
26.48 25
Z4: Labour market and income Unemployed Employed Blue-collar Junior white collar Senior white collar Manager Self-employed Firm-owner Agriculture Industry Construction Retail Transportation Services Public administration Education, medical service Other services Econ. resources excellent Econ. resources fair Econ. resources low Econ. resources not suff. Econ.sit. much better Econ.sit. better Econ.sit.not changed Econ.sit. worse Econ.much worse Vacations Paid sport Private sport Sport-club Number of room in the house Health insurance Pension fund Wealth Size HH
0.214 0.281 0.152 0.157 0.009 0.006 0.07 0.006 0.03 0.049 0.006 0.122 0.009 0.02 0.021 0.061 0.081 0.01 0.54 0.374 0.077 0.01 0.072 0.494 0.31 0.114 0.635 0.188 0.136 0.1 4.502 0.062 0.082 3.73 3.73
0.081 0.605 0.25 0.353 0.017 0.008 0.095 0.01 0.022 0.152 0.016 0.183 0.019 0.049 0.04 0.11 0.143 0.012 0.665 0.287 0.036 0.01 0.098 0.505 0.299 0.089 1.18 0.325 0.206 0.171 4.628 0.186 0.2 7.072 3.287
0.235 0.524 0.323 0.144 0.011 0.004 0.122 0.017 0.042 0.123 0.099 0.125 0.035 0.028 0.052 0.023 0.093 0.01 0.534 0.374 0.082 0.009 0.071 0.489 0.31 0.121 0.66 0.269 0.133 0.116 4.506 0.098 0.116 3.874 3.7
0.071 0.737 0.401 0.208 0.022 0.005 0.147 0.021 0.029 0.27 0.102 0.13 0.049 0.05 0.038 0.026 0.11 0.014 0.66 0.292 0.033 0.01 0.087 0.498 0.317 0.088 1.096 0.357 0.147 0.2 4.694 0.246 0.241 6.822 3.28
Z5: Calorie intake Lunch at home Consumption of pork Consumption of fruit Consumption of cheese Consumption of beef Drink often Number of cigarettes smoked
0.881 1.541 3.186 2.091 1.772 0.009 1.669
0.669 1.469 3.01 2.156 1.763 0.009 1.993
0.752 1.669 3.027 2.115 1.868 0.064 4.799
0.566 1.662 2.792 2.206 1.933 0.058 4
Z6: Calorie expenditure
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
27
Table 4 (Continued ) Males
Females South
North
South
North
Mean
Mean
Mean
Mean
Sport Hours watching TV Watches TV Health problems
0.28 2.871 0.89 0.193
Z7: Peer effects BMI peer Observations
0.439 2.42 0.822 0.217
21.98 12,581
0.489 2.408 0.852 0.164
21.501 17,399
0.602 2.206 0.81 0.208
24.27 12,126
23.88 17,171
See Appendix A for the definition of each variable in the Table.
Table 5 Overweight males. Age: 18–34. IV Probit estimates. Marginal effects. South
Demographics Marital status Education Parental BMI Labour market and income Calorie intake Calorie expenditure Peer effects Percentage of canteens Percentage of overweight children Observations
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
0.086
0.082
0.086
0.059
0.059
0.059
0.056
0.036
0.009
(0.016)***
(0.015)***
(0.018)***
(0.023)***
(0.037)
(0.039)
(0.039)
(0.039)
(0.048)
Yes No No No No No No No No No 29,297
Yes Yes No No No No No No No No 29,297
Yes Yes Yes No No No No No No No 29,297
Yes Yes Yes Yes No No No No No No 29,297
Yes Yes Yes Yes Yes No No No No No 29,297
Yes Yes Yes Yes Yes Yes No No No No 29,297
Yes Yes Yes Yes Yes Yes Yes No No No 29,297
Yes Yes Yes Yes Yes Yes Yes Yes Yes No 29,297
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 29,297
Reduction coeff.
4.70% 7.00% 31.40% 31.40% 34.10% 34.90% 58.10% 100% 100% 29,297
Clustered robust standard errors in parentheses. F-test first stage 50.84. *p < 0.1; **p < 0.05; ***p < 0.01.
motorways per 1000 km squared have a negative effect, and that the share of public employment has a positive effect on the probability of living in the South. Each instrument is statistically significant at least at the 5% level of confidence. Furthermore, the F-test of the hypothesis that the three instruments are jointly statistically significant is clearly above the rule of thumb value of 10 (50.84 for males and 57.11 for females), suggesting that the selected instruments are not weak.16 Tables 5 and 6 present our estimates of the IV probit model. Both tables are organized in nine columns. In each column, we present the estimated marginal effect of the dummy S on the probability of being overweight and the set of covariates being used.17 In the last column, we report the marginal reduction in the coefficient a10. In the case of males, the baseline specification, which includes demographics as well as year dummies, indicates that the South–North gap in the percentage of overweight individuals is equal to 8.6 percentage points. After controlling for all available covariates and
16 In the case of males, we estimate that a 1 percent increase in the share of public employment increases the probability of living in a southern region by 2.4%, and that a 1 percent increase in the number of beds per 100 thousand inhabitants and the kilometers of motorways per 1000 km squared reduce the probability of living in the South by 0.98 and 0.23 percent respectively. Similar values are estimated for females. 17 Since the instruments vary by region and year, we cluster standard errors accordingly (region year).
for differences in amenities, we find that this gap is reduced to zero (0.009 percentage points). The marginal contribution of each vector of controls is high for peer and parental BMI, which jointly account for close to 48% of the North–South difference in overweight rates. The remaining gap is accounted mainly by the regional differences in the percentage of primary schools with a canteen (close to 40%).18 These findings suggest that the marked differences in education and labor market conditions in the two areas of the country – the diffusion of tertiary education, the probability of being employed and household wealth are significantly higher in the North than in the South19 – contribute little to the regional dispersion in male overweight rates, which show instead a strong association both with cultural factors – measured by parental and peer overweight rates – and with regional amenities. In the case of females, the combination of demographics, marital conditions, education, parental BMI and labor market conditions is sufficient to account for the entire gap, which is equal to 3.4 percentage points in the baseline specification. Interestingly, differences in education and labor conditions account for close to 63% of the
18 While the coefficient associated to the percentage of overweight children is statistically significant, this variable contributes only marginally to the gap. 19 See Table 4.
28
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
Table 6 Overweight females. Age: 18–34. IV Probit estimates. Marginal effects. South
Demographics Marital status Education Parental BMI Labour market and income Calorie intake Calorie expenditure Peer effects Percentage of canteens Percentage of overweight children Observations
0.034
0.031
0.021
0.011
0.007
0.012
0.014
0.022
0.006
(0.010)***
(0.010)***
(0.011)***
(0.013)*
(0.019)
(0.02)
(0.021)
(0.02)
(0.02)
Yes No No No No No No No No No 29,980
Yes Yes No No No No No No No No 29,980
Yes Yes Yes No No No No No No No 29,980
Yes Yes Yes Yes Yes No No No No No 29,980
Yes Yes Yes Yes Yes Yes No No No No 29,980
Yes Yes Yes Yes Yes Yes Yes No No No 29,980
Yes Yes Yes Yes Yes Yes Yes Yes No No 29,980
Yes Yes Yes Yes Yes Yes Yes Yes No Yes 29,980
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 29,980
Reduction coeff.
8.82% 38.24% 67.65% 100% 100% 100% 100% 100% 100% 29,980
Clustered robust standard errors in parentheses. F-test first stage 57.11. *p < 0.1; **p < 0.05; ***p < 0.01.
gap, a much larger contribution than in the case of males. Parental and peer BMI also are important, but much less than in the case of males (29 versus 48%). Conditional on education, parental BMI and income, regional differences in measured calorie intake and expenditure account for a very small part of the gap, both for females and for males.20
5. Conclusion In this paper, we have investigated the factors are accounting for the important variation in overweight rates between the North and the South of Italy. Using the methodology proposed by Cutler and Lleras Muney, 2010, we have found that observed differences in education, labor market status and income/wealth are strongly associated to the female South–North gap, but not associated at all to the male gap. In contrast, we have found that the BMI of parents and peers, which we interpret as measuring long – term and slow changing cultural effects, is strongly associated to the male gap but weakly associated to the female gap. Finally, there is evidence that overweight rates are higher in regions where the percentage of primary schools with a canteen is lower. An interpretation of this result is that in regions where there are fewer primary schools with a canteen, boys are less exposed to alternative dietary habits and more prone to follow household lifestyles. Our findings show that the overweight gap between Southern and Northern Italy is sizeable, especially among young males, and that improvements of education and labor market outcomes in Southern Italy are negatively correlated to the overweight gap between young females living there and those living in Northern regions, but uncorrelated to the larger regional gap of young males. Since education, labor market outcomes, parental background and overweight rates are affected by common genetic and environmental effects, these correlations are unlikely to reflect causal effects. Similarly, regional variations in the share of primary schools with a canteen
20 For both gender groups, the decomposition of the gap is substantially unaltered when we change the order of entry of each vector of covariates in the regressions.
may correlate both with overweight rates and with unobserved regional effects, which affect BMI. We therefore refrain from drawing policy implications from our results. Our paper is a preliminary investigation of the sources of regional differences in overweight rates. The next step requires that we identify causal relationships. While there is abundant research which estimates the causal effect of education on health – including overweight and obesity – less has been done so far to estimate causal effects when there are several endogenous variables – for instance education and labor market variables.21 This is a difficult task, which we leave to future research.
Appendix A. Description of the variables Demographics includes demographic variables: Age, Age squared and Height. Z1 marital status: Single is 1 if the individual is married and 0 otherwise. Z2 education: Primary is 1 if the individual completed primary school and 0 otherwise. Secondary is 1 if the individual completed also the secondary school. Books is a dummy variable that indicates whether the individual has read at least a book in the last 12 months. Newspapers indicates how often the individuals read daily newspapers (never, once or twice a week, three or four days a week, five or six days a week, everyday). Education of fathers is constructed as the average by region, cohort and sex of the years of education of the cohabiting fathers of the individuals aged between 18 and 34 in the sample. Education of mothers is constructed as the average by region, cohort and sex of the years of education of the cohabiting mothers of the individuals aged between 18 and 34 in the sample. Z3 parental BMI: BMI of fathers is the average of BMI for the fathers in the sample, computed by region, year of birth and sex.
21
An example is Brunello, Fort, Schneeweis and Winter Ebmer, 2012.
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
BMI of mothers is the average of BMI for the mothers in the sample, computed by region, year of birth and sex. Z4 labor market and income: Employed takes value 1 if the individual is employed and 0 otherwise. Unemployed is 1 when the individual is unemployed and 0 otherwise. Sectors of activity: industry, construction, retail, transportation, services, public administration, education and medical service and other services; agriculture is the excluded dummy Position in the labor market: senior and junior white collar, manager, self-employed and firm-owner; blue collar is the excluded category. Economic resources are low is 1 if the individual thinks that current economic resources are low for the annual needs of the family, 0 otherwise. Economic resources are low not sufficient is 1 if the individual thinks that current economic resources are not sufficient for the annual needs of the family, 0 otherwise. Economic resource are excellent or fair are the excluded dummies. Economic situation did not changed is 1 if the individual thinks that the economic situation of the family did not change compared to the once of the previous year, 0 otherwise. Economic situation is worse is 1 if the individual thinks that the economic situation of the family is worse compared to the once of the previous year, 0 otherwise. Economic situation is much worse is 1 if the individual thinks that the economic situation of the family is much worse compared to the once of the previous year, 0 otherwise. Economic situation is better and Economic situation is much better are the excluded dummies. Vacations indicates the number of vacations lasting at least 4 nights during the year. Paid sport is 1 if the individual practices some form of paid sport, 0 otherwise. Private sport is 1 if the individual takes private sport lessons, 0 otherwise. Sport club is 1 if the individual is member of any sport or recreation club and 0 otherwise. Number of rooms in house indicates the number of rooms in the family house. Health insurance is 1 if the individual has a private health insurance and 0 otherwise. Pension fund is 1 if the individual has a private pension fund and 0 otherwise. Wealth indicates real household wealth divided by the size of household. It varies by cohort, region sex and level of education. The variable is divided by 100. For Basilicata and Molise we use data for Calabria and Abruzzo respectively.
29
Size HH is the size of the family. Z5 Calorie Intake: Lunch at home is 1 if the individual usually consumes his lunch at home and 0 otherwise, Consumption of pork, Consumption of fruit, Consumption of cheese and Consumption of beef indicates how often a certain food is consumed: never, once a week, twice a week and every day. Drink often takes value 1 if the individual drinks at least 1– 2 glasses of wine, beer or other alcoholic beverages during a week, 0 otherwise. Number of cigarettes smoked indicates the average number of cigarettes smoked per day. Z6 Calorie Expenditure: Sport is 1 if the individual played some sports in the last 12 months and 0 otherwise. Hours watching TV is number of hours spent in front of the TV every day. Watches TV is 1 in the individual watches TV every day. Health problems is the number of chronic diseases. Z7 peer effects: BMI peer is the peer BMI, computed in the same fashion as parental BMI.
References Barone, A., O’Higgins, N., 2009. Fat and out in Salerno and province: Adolescent obesity and early school leaving in southern Italy. IZA Discussion Papers 4229, Institute for the Study of Labor (IZA). Brunello, G., Lupi, C., Ordine, P., 2000. Regional disparities and the Italian NAIRU. Oxford Economic Papers 52 (January (1)), 146–177. Brunello, G., de Galdeano, A.S., Michaud, P.C., 2009 July. The rise of obesity in Europe: an economic perspective. Economic Policy. Brunello, G., Fort, M., Schneeweis, N., Ember, R.W., 2012. The causal effect of education on health. What is the role of health behaviors? IZA Discussion Paper. Buonanno, P., Montolio, D., Vanin, P., 2009. Does social capital reduce crime? Journal of Law and Economics 52 (1), 145–170. Cutler, D.M., Lleras-Muney, A., 2010. Understanding differences in health behaviors by education. Journal of Health Economics 29, 1–28. De Blasio, G., Nuzzo, G., 2009. Historical traditions of civicness and local economic development. Journal of Regional Science 50 (4), 833–857. Ezzati, M., Martin, H., Skjold, S., Vander Hoorn, S., Murray, C.J.L., 2006. Trends in national and state-level obesity in the USA after correction for self-report bias: analysis of health surveys. Journal of Royal Society of Medicine 99 (250-257). Guiso, L., Sapienza, P., Zingales, L., 2004. The role of social capital in financial development. American Economic Review 94 (3), 526–556. Eid, J., Overman, H.G., Puga, D., Turner, M.A., 2008. Fat city: questioning the relationship between urban sprawl and obesity. Journal of Urban Economics 63, 385–404. Putnam, R.D., Leonardi, R., Nanetti, R., 1993. Making Democracy Work: Civic Traditions in Modern Italy. Princeton University Press, Princeton, New Jersey. Tabellini, G., 2010. Culture and institutions: economic development in the regions of Europe. Journal of the European Economic Association 8 (4), 677–716.
Contents lists available at SciVerse ScienceDirect
Economics and Human Biology journal homepage: http://www.elsevier.com/locate/ehb
Regional differences in overweight rates: The case of Italian regions Giorgio Brunello a,*, Giovanna Labartino b a b
University of Padova, IZA, Cesifo and ROA, Italy Centro Studi Confindustria, Italy
A R T I C L E I N F O
A B S T R A C T
Article history: Received 27 October 2011 Received in revised form 27 September 2012 Accepted 9 October 2012 Available online 30 October 2012
Southern regions in Italy are characterized by higher overweight rates than Northern and Central regions. This gap is higher for young males than for females. We fully account for the differences in overweight rates with a relatively parsimonious set of covariates, and show that the key factors accounting for these differences vary substantially by gender. There is a strong association between regional differences in educational attainment, labor market outcomes and overweight rates for females, and a strong association between regional differences in parental and peer BMI, the regional percentage of primary schools with a canteen and overweight rates for males. We are grateful to two anonymous referees and to Danilo Cavapozzi for help with the data. Financial support by Fondazione Cariparo is gratefully acknowledged. The usual disclaimer applies. ß 2012 Elsevier B.V. All rights reserved.
1. Introduction Obesity and overweight are increasingly becoming a major problem not only in the US but also in Europe. Compared to the US and the UK, continental Europe has lower rates, but these rates are increasing over time, albeit at lower pace than in Anglo-Saxon countries (see Brunello et al., 2009). The prevalence of these phenomena varies substantially not only between but also within countries. According to Ezzati et al. (2006), in the year 2000 the percentage of obese American men was higher than 30% in Texas and lower than 20% in Colorado. These large spatial differences have motivated a small but growing literature which looks at the effects of urban sprawl on obesity (see for instance Eid et al., 2008). Compared to the other major European countries, Italy fares relatively well (see Brunello et al., 2009), especially for females. According to OECD data, in 2008 the percentage of overweight Italian females was 27.1%, higher than in France but lower than in Germany, Spain, Greece and The Netherlands. On the other hand, the percentage of overweight
* Corresponding author. E-mail address: [email protected] (G. Brunello). 1570-677X/$ – see front matter ß 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ehb.2012.10.001
males was 44.7%, higher than in France but very similar to that in Germany and Spain. National averages hide substantial within – country heterogeneity: the difference in the percentage of overweight females1 between Italy and France (about 5 percentage points) is much lower than the difference in the same percentage between Lombardia in the North of Italy (20.8%) and Basilicata in the South (30.8%). Similarly, the difference in the percentage of overweight males between Northern Liguria (39.1%) and Southern Campania (48.1%) is close to the difference between Italy and France (44.7 versus 32.2).2 Fig. 1 illustrates the regional dispersion in the share of overweight males and females aged 18 to 80 for the period 2001–2009. While all Southern regions – with the single exception of Sardinia – cluster in the upper right corner of the figure, most Northern regions are located in the lower left corner. This pattern is confirmed when we focus on the youngest cohort aged 18–34 (see Fig. 2). Given that Italian
1 We follow international conventions and define overweight as the individuals with a body mass index equal or larger than 25. 2 Regional percentages for Italy are from the Survey on Daily Lifestyles of Italian Households (Indagine sugli aspetti della vita quotidiana delle famiglie italiane).
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
21
PUG MOL SIC
.28
CA
.26
CA L
AB R
FV G
.24 4
ERO
LA Z MA R T OS
UMB
.22
T RE VE N PIE
LIG
SA R
LOM
.2
% overweight; females - age 18-80
.3
BA S
.4
.42
.46
.44
.48
% overweight; males - age 18-80
Note: PIE=Piemonte-Valle d’Aosta, LOM=Lombardia, TRE=Trentino Alto-Adige, VEN=Veneto, FVG=Friuli-Venezia-Giulia, LIG=Liguria, ERO=Emilia Romagna, TOS=Toscana, UMB=Umbria, LAZ=Lazio, ABR=Abruzzo, MOL=Molise, CAM=Campania, PUG=Puglia, MAR=Marche, BAS=Basilicata, CAL=Calabria, SIC=Sicilia, SAR=Sardegna
.16
CA M
BA S
.14
PUG ERO AB R
SIC CA L MOL
.12
UMB MA R LA Z FV G PIE T OS VE N T RE LOM SA R
.1
% overweight - females aged 18-34
.18
Fig. 1. Overweight rates by region. Age groups 18–80. Note: PIE = Piemonte-Valle d’Aosta, LOM = Lombardia, TRE = Trentino Alto-Adige, VEN = Veneto, FVG = Friuli-Venezia-Giulia, LIG = Liguria, ERO = Emilia Romagna, TOS = Toscana, UMB = Umbria, MAR = Marche, LAZ = Lazio, ABR = Abruzzo, MOL = Molise, CAM = Campania, PUG = Puglia, BAS = Basilicata, CAL = Calabria, SIC = Sicilia, SAR = Sardegna.
LIG
.25
.3
.35
.4
% overweight - males aged 18-34
Fig. 2. Overweight rates by region. Age groups 18–34. Note: See Fig. 1.
regions share the same national institutional setup, the natural question to ask is what are the factors accounting for the substantial difference in overweight rates between the North and the South of the country.3 Due to the long standing structural differences in culture, social norms and economic development (see for instance Tabellini, 2010; De Blasio and Nuzzo, 2009), the North–South classification is the natural way to group Italian regions. Fig. 3 illustrates this by showing regional differences in economic well-being and civic capital. The former is computed by extracting the principal component from a vector of variables which include the unemployment rate and value added per capita. Social or civic capital
is a notion that is difficult to pin down exactly. Following the literature,4 we extract the principal component from the following three variables: the share of civil marriages, the percentage of people who read daily newspapers at least once a week and blood donation per capita.5 The figure shows that Southern regions have less social capital and lower well-being than Northern regions. Using the methodology proposed by Cutler and Lleras Muney (2010), this paper proposes an accounting exercise which associates regional differences in overweight rates to differences in education, income, parental background, labor market status, daily diet and calorie expenditure. Our contribution is twofold. First, we are able to fully account for the existing differences in overweight rates with a relatively parsimonious set of covariates. Second, we show that the key factors accounting for these differences vary substantially by gender. We use a rich dataset – the Survey of Daily Life of Italian Households (DAILIH) – and regress overweight rates on a set of demographics and the dummy S, equal to one if the individual resides in a Southern region and to zero otherwise. Since residence is the result of individual choice and of migration patterns, we instrument the dummy S with variables capturing regional differences in ‘‘amenities’’. Next, we add to this regression an increasingly broad vector of individual, group and regional covariates, which include education, parental background, income and labor market status, eating habits and calorie expenditures, peer effects and the regional percentage of overweight children and schools with canteens. For each
4
Guiso et al., 2004; Buonanno et al., 2009; Putnam et al., 1993. Sources: for blood donation (Guiso et al., 2004), for civil marriages and daily newspapers ISTAT, 2008. 5
3
We focus on the overweight but results are similar for the obese.
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
The paper is organized as follows: Section 2 presents the empirical strategy and briefly reviews the literature, Section 3 introduces the data and Section 4 shows the results. Conclusions follow.
3
22
2
TRE
1
TOSPIE LIG
0
MAR LAZ UMB
2. The empirical strategy
-1
ABR MOL SAR
BAS PUG CAL CAMSIC
-3
-2
Index of social capital
LOM ERO VEN FVG
-3
-2
-1
0
1
2
3
Index of economic well being
Fig. 3. Economic well-being and social capital, by region. Note: See Fig. 1.
regression, we compute the percentage decline in the coefficient of the dummy S. This decline measures our ability to account for regional differences in the overweight rate using observables.6 Given the importance of parental background for individual BMI, we choose to focus our analysis on the cohorts of Italians aged between 18 and 34 at the time of the interview, for whom we can construct measures of parental education and BMI. Our set of covariates fully account for the regional differences in female and male overweight rates. Regional differences in education, labor market status and measures of income/wealth account for more than 60% of the South–North gap for females and for only 2.3% of the gap in the case of males. On the other hand, the BMI of parents and peers – which we interpret as measure of long-run cultural factors and social norms – account for about 48% of the regional gap for males, compared to about 29% for females.7 Regional differences in the percentage of schools with a canteen also matter for the male gap. We find that improvements in education and labor market outcomes in Southern Italy are negatively correlated to the overweight gap between young females living there and those living in Northern regions, but uncorrelated to the larger regional gap for young males. There is also evidence that the overweight gap for males is positively correlated to the gap in parental and peer BMI, and negatively correlated to the regional gap in the percentage of primary schools with a canteen. Since we estimate associations rather than causal relationships, we refrain from drawing policy implications from our results. The importance of parental BMI and the lower share of primary schools with a canteen do suggest, however, that boys in the South are less exposed to different dietary habits and are therefore more prone than boys in the North to be affected by household lifestyles.
6 We use ‘‘account for’’ rather than ‘‘explain’’ to emphasize that we are looking at associations, not at causal relationships. 7 These results are qualitatively robust to changes in the order in which these covariates enter the regressions.
Only a few papers investigate within-country differences in obese or overweight rates, as we do in this paper. Ezzati et al., 2006, document the significant increase in obesity in the US during the 1990s and show that this increase was not homogeneous across states. Eid et al., 2008, focus on the relationship between urban sprawl and obesity and investigate the hypothesis that living in a sprawl might increase the probability of being obese, because in places where buildings are more spread out people tend to use more theirs cars and walk less. Using US data, they find no evidence of a causal relationship between living in a sprawl and the probability of being obese. Barone and O’Higgins (2009), explore the impact of being overweight on the probability that young Italians living in the South and particularly in Salerno, near Naples, drop out of school early. They compare young males and females and find that the former are more likely to be overweight or obese. They argue that while young females are more influenced by peer pressure and by concerns about physical appearance, young males are influenced mainly by parental background. Following Cutler and Lleras Muney (2010), we start from the following baseline specification OWi ¼ a0 þ a01 Southi þ X i b þ ei
(1)
where OWi is a dummy equal to one if the individual is overweight and to zero otherwise, South is a dummy equal to one if the individual resides in a Southern region and to zero if she resides in the North or Centre, Xi is a vector of individual personal characteristics such as age, age squared and height, and the coefficient of interest is a01 . We re-estimate (1) by adding the set of covariates Z 1i OWi ¼ a0 þ a1 1 Southi þ X i b þ Z i 1 g þ vi
(2)
and compute 1 a11 =a01 , the percentage change in the coefficient associated to South from adding these covariates. We further proceed by sequentially adding vectors of covariates Zi2 ; . . . ; ZiN , and by computing in each step the reduction in the estimated effect of residing in a Southern region on the probability of being over-weight 1 ak1 =a01 , where k = 2,. . .N. We use a probit model, but our key results do not change when we use a linear specification.8 An important concern with this estimation strategy is that residence in a Southern region is not random but follows from individual choice. Individuals choose the region of residence by comparing the net benefits of staying in their region of birth with the net benefits of moving to another region. Expected benefits include both monetary
8
Results available from the authors upon request.
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
and non-monetary returns, and depend on regional amenities as well as on economic conditions. We capture regional differences in amenities and economic conditions with three variables: the percentage of hospital beds per 100 thousand inhabitants, the kilometers of motorways per 1000 km squared and the share of public employment. Since public employment in Italy has typically provided both very good employment security and relatively good hourly pay, the availability of public jobs is expected to reduce the probability of moving out of the region (see for instance Brunello et al., 2000). The percentage of hospital beds and the kilometers of motorways, on the other hand, measure the quality of services available in the region. Better quality is expected to reduce the probability of moving to another region. We use these three variables as sources of exogenous variation in the probability of living in the South of Italy. The identifying assumption is that they affect overweight only indirectly by affecting the choice of the region of residence.9 Table 1 shows that these variables vary significantly across regions and over time.
3. The data We use several waves of the Italian DAILIH survey, which has been conducted since 1993 on a nationally representative sample of the Italian population to collect information on different aspects of daily life. We consider only the waves from 2001 to 2009, because previous surveys did not include information on weight and height.10 Given the importance of parental background for individual BMI, we restrict our attention to individuals aged 18–34, for whom we can construct indicators of parental education and BMI based on the available information on co-habiting parents. Following standard practice, we measure the body mass index BMI as the ratio of the self-reported weight to the square of the self-reported height, and classify each individual as severely underweight (BMI below 16.5), normal weight (BMI over 16.5 up to 24.9), overweight (BMI above or equal to 25) and obese (BMI above or equal to 30). In this paper, we focus on the overweight. Table 2 shows the share of overweight males and females and average BMI in the South and in the Centre– North (North from now on) of the country.11 Average BMI is higher in the South and the prevalence of overweight is 21–27% higher in the South than in the North, depending on gender. Table 3 presents the same information by region. While the North/South divide is a clear feature of
9 One may object that people in areas with fewer roads are more inclined to walk, which affects overweight rates. Yet the substitution of driving with walking seems unlikely in the case of motorways, which are typically used for medium or long distance transportation. 10 The year 2004 is missing because the survey was not carried out in that year. 11 The South consists of the following regions: Abruzzo, Molise, Campania, Puglia, Basilicata, Calabria, Sicilia and Sardegna. The Centre– North consists instead of Piemonte-Val d’Aosta, Lombardia, Trentino AltoAdige, Veneto, Friuli Venezia Giulia, Liguria, Emilia-Romagna,Toscana, Umbria, Marche and Lazio.
23
the data, we notice that both the South and the North are heterogeneous with respect to the prevalence of overweight: Sardegna in the South is more similar to Northern than to Southern regions and Emilia-Romagna in the North is closer to Abruzzo in the South than to Lombardia.12 The means by gender of the covariates in vectors X and Z 2 ½Z1 . . . Z7 are shown in Table 4. The vector X includes age, age squared and individual height. The vector Z1 consists of a dummy equal to one if the individual is single and to zero otherwise. The vector Z2 contains two education dummies (primary, secondary schooling, with tertiary education in the constant), the education of parents, the dummy books, which indicate whether the individual has read at least a book in the last 12 months, and the variable newspapers, which indicates how often he/she reads newspapers. Since the information on the education of parents is only available for co-habiting parents – the percentage living with their parents is 71.9% in Southern regions and 62.7 in Northern regions for males are 60.2 and 51 respectively for females – we construct average parental years of education by region, gender and cohort of birth and assign to each individual aged 18–34 in the sample the mean of his/her cell. The indicators in this vector show that individuals residing in the South have lower education than in the North. The vector Z3 includes the average BMI of the mothers and the fathers in the relevant cell, which we construct in a similar fashion as parental education. As expected, both take higher values in the South than in the North. The vector Z4 contains the indicators of labour market status employed and unemployed (out of the labor force in the constant); the occupational dummies senior and junior white collar, manager, self-employed and firm-owner (blue collar jobs in the constant), and the sector dummies industry, construction, retail, transportation, services, public administration, education and medical services and other services (agriculture in the constant). These dummies are good proxies of earnings, which are not available in our data. To further control for differences in income, we use individual assessments of economic conditions, which can be low, not sufficient, fair and excellent (the last two in the constant), and individual evaluations of the economic situation in the household compared to the previous year (much better, better, unchanged, worse, much worse, with the former two in the constant). Additional proxies of household income are: vacations, which counts the number of vacation episodes in the past year; paid sport and sport club, two dummies indicating whether the individual paid for sports or joined sport and recreation clubs; the number of the rooms in the house, and whether someone in the family has a private health insurance or a pension fund. We also add a measure of household wealth, which we construct from the Survey on the Income and Wealth of Italian Households (Bank of Italy). Using micro-data from 4 waves, 2000, 2002, 2006 and 2008, we generate average wealth by gender, cohort,
12 We have repeated our empirical analysis by dropping Sardinia from the sample, with marginal effects on the results.
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
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Table 1 Hospital beds per 100 thousand inhabitants, kilometers of motorway per 1000 km2 and share of public employment. By region. Hospital beds
Year
Region Piemonte-Valle d‘Aosta Liguria Lombardia Trentino Alto-Adige Veneto Friuli Venezia Giulia Emilia Romagna Toscana Umbria Marche Lazio Abruzzo Molise Campania Puglia Basilicata Calabria Sicilia Sardegna
2001 477.9 513.3 482.3 537.4 465.2 499.6 486.4 441.6 379.4 471.1 587 415.6 464.7 357.9 425.7 408.3 503.7 383 461.1
Motorways
Year
Region Piemonte-Valle d‘Aosta Liguria Lombardia Trentino Alto-Adige Veneto Friuli Venezia Giulia Emilia Romagna Toscana Umbria Marche Lazio Abruzzo Molise Campania Puglia Basilicata Calabria Sicilia Sardegna
2001 32 70.6 25.3 27 27 27.7 26.7 18.7 7.1 17.6 28.1 33 8.2 33.1 16.3 3 20 23.3 0
Share of public employment
Year
Region Piemonte-Valle d‘Aosta Lombardia Trentino Alto-Adige Veneto Friuli Venezia Giulia Liguria Emilia Romagna Toscana Umbria Marche Lazio Abruzzo Molise Campania Puglia Basilicata Calabria Sicilia Sardegna
2001 0.23 0.28 0.2 0.27 0.21 0.26 0.22 0.24 0.27 0.24 0.35 0.28 0.30 0.36 0.30 0.29 0.37 0.38 0.34
2002 435.5 505.1 454.7 472.1 448.2 477.8 479.5 450.2 368.8 458.8 566.9 461.5 477.1 347.6 402.4 403 416.1 392.5 462.7
2003 428.1 364.6 431.6 427.9 421.3 436 469.3 391.1 334.6 378.7 539.7 407.3 503 303.9 397.4 318.5 501.1 375.8 443.9
2004 413 366.2 419.3 408.2 398.3 388.6 443.2 383.8 331.9 404.9 522.6 353.5 499.5 309.4 368 319.4 423.3 350.1 434.8
2005 400.3 448.4 414.9 458.1 384.8 368.1 447.1 380.6 310.4 400.2 514.7 421.3 521.2 322.4 377.3 313.3 387 342.6 432.3
2006 393.5 405.2 412.4 448.8 384.4 361.4 432.1 363.4 300 373.4 514.8 437.9 516 325.5 375 339.3 387.7 329.4 431.9
2007 390.3 399.7 403.1 438.3 378 357.7 430.7 354.8 311.2 370.9 479.4 421.4 512.4 321.3 368.7 326.5 378.3 325.1 395.7
2008 379.8 372.6 395.6 431.2 365.6 345.4 418.4 342.9 299.4 371 478.8 370.1 465 310.3 356.6 310.8 336.1 310.2 392.3
2009 385.3 381.5 394.2 419.7 358.3 350.7 406.5 320.7 295.8 363.9 455 345.3 444.5 301.2 346.4 309.2 321 293.2 376.4
2002 31.6 70.2 24.6 26 26 27.4 29.4 18.2 7.8 20.9 28.3 29.9 11.9 33.2 14.6 4.1 18.9 22.9 0
2003 31.9 70.2 24.6 26 26 27.4 29.4 18.2 7.8 20.9 28.3 30 11.9 33.2 14.6 4.1 18.9 22.9 0
2004 32.7 70.4 25.3 27 27 27.8 26.4 18.7 7.1 17.6 27.8 33.1 8.2 33 16.3 3 20 23.3 0
2005 32.8 70.4 25.3 27 27 27.8 26.4 18.7 7.1 17.6 27.8 33.1 8.2 33 16.3 3 20 24.9 0
2006 33.1 70.4 25.3 27 27 27.8 26.4 18.7 7.1 17.6 27.8 33.1 8.2 33 16.3 3 20 24.9 0
2007 33.1 70.4 25.3 27.6 27.6 27.8 26.4 18.7 7.1 17.6 27.8 33.1 8.2 33 16.3 3 20 25 0
2008 33.1 70.4 25.3 27.6 27.6 27.8 26.4 18.7 7.1 17.6 27.8 33.1 8.2 33 16.3 3 20 25.3 0
2009 33.2 70 26 28 28 28 26 19 7 18 28 33 8 33 16 3 20 26 0
2006 0.22 0.31 0.2 0.29 0.19 0.24 0.21 0.25 0.27 0.23 0.36 0.28 0.30 0.33 0.28 0.30 0.35 0.37 0.31
2007 0.23 0.31 0.21 0.29 0.2 0.26 0.22 0.25 0.28 0.23 0.34 0.28 0.31 0.32 0.29 0.31 0.35 0.37 0.33
2008 0.23 0.31 0.21 0.3 0.21 0.26 0.22 0.25 0.28 0.23 0.34 0.26 0.30 0.31 0.29 0.31 0.34 0.36 0.33
2009 0.24 0.3 0.22 0.3 0.21 0.26 0.22 0.24 0.29 0.22 0.35 0.28 0.30 0.31 0.29 0.32 0.34 0.36 0.32
2002 0.23 0.28 0.21 0.26 0.21 0.26 0.23 0.25 0.27 0.23 0.35 0.30 0.32 0.35 0.31 0.29 0.37 0.38 0.34
2003 0.23 0.29 0.22 0.28 0.21 0.26 0.22 0.25 0.27 0.23 0.34 0.31 0.32 0.35 0.31 0.30 0.36 0.37 0.32
2004 0.23 0.28 0.21 0.28 0.2 0.27 0.22 0.25 0.27 0.23 0.35 0.30 0.32 0.34 0.30 0.29 0.35 0.37 0.33
2005 0.23 0.3 0.2 0.28 0.2 0.24 0.21 0.24 0.29 0.23 0.35 0.29 0.31 0.32 0.28 0.29 0.34 0.37 0.30
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
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Table 2 Overweight rates and BMI in South and North–Centre Italy. Age: 18–34. Regions
Females
Males
Overweight rate
BMI
Obs.
Overweight rate
BMI
Obs.
South North
0.144 0.113
21.99 21.47
12,581 17,399
0.351 0.29
24.33 23.91
12,126 17,171
Table 3 Overweight rates and BMI by region; age: 18–34. Regions
Piemonte-Valle d‘Aosta Lombardia Trentino Alto-Adige Veneto Friuli Venezia Giulia Liguria Emilia Romagna Toscana Umbria Marche Lazio Abruzzo Molise Campania Puglia Basilicata Calabria Sicilia Sardegna
Females
Males
Overweight rate
BMI
Obs.
Overweight rate
BMI
Obs.
0.115 0.104 0.107 0.114 0.115 0.078 0.131 0.115 0.122 0.117 0.123 0.135 0.137 0.177 0.143 0.151 0.137 0.145 0.104
21.41 21.27 21.54 21.44 21.5 21.12 21.68 21.5 21.59 21.54 21.72 21.72 21.91 22.5 22.11 21.99 21.92 22.04 21.23
2514 2627 1704 1959 932 835 1560 1570 888 1313 1497 1257 832 2337 2119 991 1491 2127 1427
0.28 0.281 0.281 0.294 0.3 0.26 0.319 0.278 0.303 0.299 0.31 0.314 0.346 0.41 0.357 0.359 0.336 0.384 0.25
23.79 23.77 23.78 23.92 23.98 23.67 24.16 23.87 24.19 24.1 23.98 24.06 24.38 24.74 24.36 24.38 24.34 24.5 23.57
2481 2651 1521 1967 899 913 1588 1499 948 1302 1402 1222 829 2165 2090 1044 1371 2002 1403
level of education and region and match this information with the individuals in our dataset. To control for family size, we also add the number of household members (Size HH). The vectors Z5 and Z6 contain variables that capture calorie intake and consumption. The dummy sport indicates whether the individual has played any sport during the last 12 months; the variables TV hours and Watches TV measure the number of hours spent watching TV and whether the person watches television every day respectively. To capture differences in calorie intake, we use the variable lunch at home, which indicates whether the person has usually lunch at home, and a set of variables measuring the consumption of pork, fruit, cheese and beef.13 We also add to this vector the average number of cigarettes smoked per day, an indicator of whether the individual drinks alcohol14 and an indicator of whether she has health problems. Finally, the vector Z7 captures peer effects, which we define as the average BMI of individuals who live in the same region and belong to the same gender and birth cohort. Conditional in income and employment, regional differences in these effects are likely to measure long term differences in cultural and social norms. We measure regional differences in amenities that directly affect overweight with the percentage of primary
13 Each variable indicates how often the individual consumes a certain type of food: never, once a week, twice a week, or everyday. 14 The dummy takes value 1 if the individual drinks at least 1–2 glasses of wine, beer or other alcoholic beverages during a week.
schools with a canteen and the percentage of overweight children. The former variable refers to 2005 (source: the Italian Minister of Education). It turns out that the percentage of primary schools with a canteen ranges from close to 100% in the Northern regions of Piemonte and Lombardia to 70% in Southern Sicily.15 The percentage of overweight children (aged between 8 and 9) is for the year 2008 and is drawn from the project ‘‘Okkio alla salute’’, carried out by the Italian Minister of Health and the Minister of Education. The data show substantial regional variation, with 27.5% of male children being overweight in Southern Campania, compared to only 14.5% in Northern Trentino. 4. Results We use an instrumental variable (IV) strategy to estimate the effect of residing in the South on the probability of being overweight. We start by estimating first stage regressions by gender, where the dependent variable – the dummy S – is regressed on the three selected instruments and the additional covariates, using a linear probability model. Since each instrument varies both by region and over time, we cluster standard errors by region and year. As expected, we find that the number of hospital beds per 100 thousand inhabitants and the kilometers of
15 The percentage of primary schools with a canteen is above 90 percent in all Italian regions, with the exception of Puglia (86.9%) and Sicily (70.2%).
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G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
Table 4 Descriptive statistics. Males
Females
Overweight rate Demographics Age Height Z1: Marital status Single Z2: Education Primary Secondary Tertiary Books Newspapers Education of fathers Education of mothers Z3: Parental BMI BMI of Fathers BMI of Mothers
South
North
South
North
Mean
Mean
Mean
Mean
0.144
26.33 163.83
0.113
26.99 165.24
0.351
26.23 174.88
0.29
26.97 177.43
0.646
0.646
0.794
0.781
0.344 0.533 0.123 0.558 1.977 8.7 8.35
0.255 0.584 0.161 0.713 2.367 9.01 8.67
0.41 0.505 0.085 0.337 2.373 8.47 8.12
0.346 0.546 0.108 0.481 2.728 8.86 8.55
26.78 25.8
26.48 25.05
26.87 25.65
26.48 25
Z4: Labour market and income Unemployed Employed Blue-collar Junior white collar Senior white collar Manager Self-employed Firm-owner Agriculture Industry Construction Retail Transportation Services Public administration Education, medical service Other services Econ. resources excellent Econ. resources fair Econ. resources low Econ. resources not suff. Econ.sit. much better Econ.sit. better Econ.sit.not changed Econ.sit. worse Econ.much worse Vacations Paid sport Private sport Sport-club Number of room in the house Health insurance Pension fund Wealth Size HH
0.214 0.281 0.152 0.157 0.009 0.006 0.07 0.006 0.03 0.049 0.006 0.122 0.009 0.02 0.021 0.061 0.081 0.01 0.54 0.374 0.077 0.01 0.072 0.494 0.31 0.114 0.635 0.188 0.136 0.1 4.502 0.062 0.082 3.73 3.73
0.081 0.605 0.25 0.353 0.017 0.008 0.095 0.01 0.022 0.152 0.016 0.183 0.019 0.049 0.04 0.11 0.143 0.012 0.665 0.287 0.036 0.01 0.098 0.505 0.299 0.089 1.18 0.325 0.206 0.171 4.628 0.186 0.2 7.072 3.287
0.235 0.524 0.323 0.144 0.011 0.004 0.122 0.017 0.042 0.123 0.099 0.125 0.035 0.028 0.052 0.023 0.093 0.01 0.534 0.374 0.082 0.009 0.071 0.489 0.31 0.121 0.66 0.269 0.133 0.116 4.506 0.098 0.116 3.874 3.7
0.071 0.737 0.401 0.208 0.022 0.005 0.147 0.021 0.029 0.27 0.102 0.13 0.049 0.05 0.038 0.026 0.11 0.014 0.66 0.292 0.033 0.01 0.087 0.498 0.317 0.088 1.096 0.357 0.147 0.2 4.694 0.246 0.241 6.822 3.28
Z5: Calorie intake Lunch at home Consumption of pork Consumption of fruit Consumption of cheese Consumption of beef Drink often Number of cigarettes smoked
0.881 1.541 3.186 2.091 1.772 0.009 1.669
0.669 1.469 3.01 2.156 1.763 0.009 1.993
0.752 1.669 3.027 2.115 1.868 0.064 4.799
0.566 1.662 2.792 2.206 1.933 0.058 4
Z6: Calorie expenditure
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
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Table 4 (Continued ) Males
Females South
North
South
North
Mean
Mean
Mean
Mean
Sport Hours watching TV Watches TV Health problems
0.28 2.871 0.89 0.193
Z7: Peer effects BMI peer Observations
0.439 2.42 0.822 0.217
21.98 12,581
0.489 2.408 0.852 0.164
21.501 17,399
0.602 2.206 0.81 0.208
24.27 12,126
23.88 17,171
See Appendix A for the definition of each variable in the Table.
Table 5 Overweight males. Age: 18–34. IV Probit estimates. Marginal effects. South
Demographics Marital status Education Parental BMI Labour market and income Calorie intake Calorie expenditure Peer effects Percentage of canteens Percentage of overweight children Observations
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
0.086
0.082
0.086
0.059
0.059
0.059
0.056
0.036
0.009
(0.016)***
(0.015)***
(0.018)***
(0.023)***
(0.037)
(0.039)
(0.039)
(0.039)
(0.048)
Yes No No No No No No No No No 29,297
Yes Yes No No No No No No No No 29,297
Yes Yes Yes No No No No No No No 29,297
Yes Yes Yes Yes No No No No No No 29,297
Yes Yes Yes Yes Yes No No No No No 29,297
Yes Yes Yes Yes Yes Yes No No No No 29,297
Yes Yes Yes Yes Yes Yes Yes No No No 29,297
Yes Yes Yes Yes Yes Yes Yes Yes Yes No 29,297
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 29,297
Reduction coeff.
4.70% 7.00% 31.40% 31.40% 34.10% 34.90% 58.10% 100% 100% 29,297
Clustered robust standard errors in parentheses. F-test first stage 50.84. *p < 0.1; **p < 0.05; ***p < 0.01.
motorways per 1000 km squared have a negative effect, and that the share of public employment has a positive effect on the probability of living in the South. Each instrument is statistically significant at least at the 5% level of confidence. Furthermore, the F-test of the hypothesis that the three instruments are jointly statistically significant is clearly above the rule of thumb value of 10 (50.84 for males and 57.11 for females), suggesting that the selected instruments are not weak.16 Tables 5 and 6 present our estimates of the IV probit model. Both tables are organized in nine columns. In each column, we present the estimated marginal effect of the dummy S on the probability of being overweight and the set of covariates being used.17 In the last column, we report the marginal reduction in the coefficient a10. In the case of males, the baseline specification, which includes demographics as well as year dummies, indicates that the South–North gap in the percentage of overweight individuals is equal to 8.6 percentage points. After controlling for all available covariates and
16 In the case of males, we estimate that a 1 percent increase in the share of public employment increases the probability of living in a southern region by 2.4%, and that a 1 percent increase in the number of beds per 100 thousand inhabitants and the kilometers of motorways per 1000 km squared reduce the probability of living in the South by 0.98 and 0.23 percent respectively. Similar values are estimated for females. 17 Since the instruments vary by region and year, we cluster standard errors accordingly (region year).
for differences in amenities, we find that this gap is reduced to zero (0.009 percentage points). The marginal contribution of each vector of controls is high for peer and parental BMI, which jointly account for close to 48% of the North–South difference in overweight rates. The remaining gap is accounted mainly by the regional differences in the percentage of primary schools with a canteen (close to 40%).18 These findings suggest that the marked differences in education and labor market conditions in the two areas of the country – the diffusion of tertiary education, the probability of being employed and household wealth are significantly higher in the North than in the South19 – contribute little to the regional dispersion in male overweight rates, which show instead a strong association both with cultural factors – measured by parental and peer overweight rates – and with regional amenities. In the case of females, the combination of demographics, marital conditions, education, parental BMI and labor market conditions is sufficient to account for the entire gap, which is equal to 3.4 percentage points in the baseline specification. Interestingly, differences in education and labor conditions account for close to 63% of the
18 While the coefficient associated to the percentage of overweight children is statistically significant, this variable contributes only marginally to the gap. 19 See Table 4.
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G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
Table 6 Overweight females. Age: 18–34. IV Probit estimates. Marginal effects. South
Demographics Marital status Education Parental BMI Labour market and income Calorie intake Calorie expenditure Peer effects Percentage of canteens Percentage of overweight children Observations
0.034
0.031
0.021
0.011
0.007
0.012
0.014
0.022
0.006
(0.010)***
(0.010)***
(0.011)***
(0.013)*
(0.019)
(0.02)
(0.021)
(0.02)
(0.02)
Yes No No No No No No No No No 29,980
Yes Yes No No No No No No No No 29,980
Yes Yes Yes No No No No No No No 29,980
Yes Yes Yes Yes Yes No No No No No 29,980
Yes Yes Yes Yes Yes Yes No No No No 29,980
Yes Yes Yes Yes Yes Yes Yes No No No 29,980
Yes Yes Yes Yes Yes Yes Yes Yes No No 29,980
Yes Yes Yes Yes Yes Yes Yes Yes No Yes 29,980
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 29,980
Reduction coeff.
8.82% 38.24% 67.65% 100% 100% 100% 100% 100% 100% 29,980
Clustered robust standard errors in parentheses. F-test first stage 57.11. *p < 0.1; **p < 0.05; ***p < 0.01.
gap, a much larger contribution than in the case of males. Parental and peer BMI also are important, but much less than in the case of males (29 versus 48%). Conditional on education, parental BMI and income, regional differences in measured calorie intake and expenditure account for a very small part of the gap, both for females and for males.20
5. Conclusion In this paper, we have investigated the factors are accounting for the important variation in overweight rates between the North and the South of Italy. Using the methodology proposed by Cutler and Lleras Muney, 2010, we have found that observed differences in education, labor market status and income/wealth are strongly associated to the female South–North gap, but not associated at all to the male gap. In contrast, we have found that the BMI of parents and peers, which we interpret as measuring long – term and slow changing cultural effects, is strongly associated to the male gap but weakly associated to the female gap. Finally, there is evidence that overweight rates are higher in regions where the percentage of primary schools with a canteen is lower. An interpretation of this result is that in regions where there are fewer primary schools with a canteen, boys are less exposed to alternative dietary habits and more prone to follow household lifestyles. Our findings show that the overweight gap between Southern and Northern Italy is sizeable, especially among young males, and that improvements of education and labor market outcomes in Southern Italy are negatively correlated to the overweight gap between young females living there and those living in Northern regions, but uncorrelated to the larger regional gap of young males. Since education, labor market outcomes, parental background and overweight rates are affected by common genetic and environmental effects, these correlations are unlikely to reflect causal effects. Similarly, regional variations in the share of primary schools with a canteen
20 For both gender groups, the decomposition of the gap is substantially unaltered when we change the order of entry of each vector of covariates in the regressions.
may correlate both with overweight rates and with unobserved regional effects, which affect BMI. We therefore refrain from drawing policy implications from our results. Our paper is a preliminary investigation of the sources of regional differences in overweight rates. The next step requires that we identify causal relationships. While there is abundant research which estimates the causal effect of education on health – including overweight and obesity – less has been done so far to estimate causal effects when there are several endogenous variables – for instance education and labor market variables.21 This is a difficult task, which we leave to future research.
Appendix A. Description of the variables Demographics includes demographic variables: Age, Age squared and Height. Z1 marital status: Single is 1 if the individual is married and 0 otherwise. Z2 education: Primary is 1 if the individual completed primary school and 0 otherwise. Secondary is 1 if the individual completed also the secondary school. Books is a dummy variable that indicates whether the individual has read at least a book in the last 12 months. Newspapers indicates how often the individuals read daily newspapers (never, once or twice a week, three or four days a week, five or six days a week, everyday). Education of fathers is constructed as the average by region, cohort and sex of the years of education of the cohabiting fathers of the individuals aged between 18 and 34 in the sample. Education of mothers is constructed as the average by region, cohort and sex of the years of education of the cohabiting mothers of the individuals aged between 18 and 34 in the sample. Z3 parental BMI: BMI of fathers is the average of BMI for the fathers in the sample, computed by region, year of birth and sex.
21
An example is Brunello, Fort, Schneeweis and Winter Ebmer, 2012.
G. Brunello, G. Labartino / Economics and Human Biology 12 (2014) 20–29
BMI of mothers is the average of BMI for the mothers in the sample, computed by region, year of birth and sex. Z4 labor market and income: Employed takes value 1 if the individual is employed and 0 otherwise. Unemployed is 1 when the individual is unemployed and 0 otherwise. Sectors of activity: industry, construction, retail, transportation, services, public administration, education and medical service and other services; agriculture is the excluded dummy Position in the labor market: senior and junior white collar, manager, self-employed and firm-owner; blue collar is the excluded category. Economic resources are low is 1 if the individual thinks that current economic resources are low for the annual needs of the family, 0 otherwise. Economic resources are low not sufficient is 1 if the individual thinks that current economic resources are not sufficient for the annual needs of the family, 0 otherwise. Economic resource are excellent or fair are the excluded dummies. Economic situation did not changed is 1 if the individual thinks that the economic situation of the family did not change compared to the once of the previous year, 0 otherwise. Economic situation is worse is 1 if the individual thinks that the economic situation of the family is worse compared to the once of the previous year, 0 otherwise. Economic situation is much worse is 1 if the individual thinks that the economic situation of the family is much worse compared to the once of the previous year, 0 otherwise. Economic situation is better and Economic situation is much better are the excluded dummies. Vacations indicates the number of vacations lasting at least 4 nights during the year. Paid sport is 1 if the individual practices some form of paid sport, 0 otherwise. Private sport is 1 if the individual takes private sport lessons, 0 otherwise. Sport club is 1 if the individual is member of any sport or recreation club and 0 otherwise. Number of rooms in house indicates the number of rooms in the family house. Health insurance is 1 if the individual has a private health insurance and 0 otherwise. Pension fund is 1 if the individual has a private pension fund and 0 otherwise. Wealth indicates real household wealth divided by the size of household. It varies by cohort, region sex and level of education. The variable is divided by 100. For Basilicata and Molise we use data for Calabria and Abruzzo respectively.
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Size HH is the size of the family. Z5 Calorie Intake: Lunch at home is 1 if the individual usually consumes his lunch at home and 0 otherwise, Consumption of pork, Consumption of fruit, Consumption of cheese and Consumption of beef indicates how often a certain food is consumed: never, once a week, twice a week and every day. Drink often takes value 1 if the individual drinks at least 1– 2 glasses of wine, beer or other alcoholic beverages during a week, 0 otherwise. Number of cigarettes smoked indicates the average number of cigarettes smoked per day. Z6 Calorie Expenditure: Sport is 1 if the individual played some sports in the last 12 months and 0 otherwise. Hours watching TV is number of hours spent in front of the TV every day. Watches TV is 1 in the individual watches TV every day. Health problems is the number of chronic diseases. Z7 peer effects: BMI peer is the peer BMI, computed in the same fashion as parental BMI.
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