It seems obvious to state that schooling makes a difference in individual developmental and life outcomes, and that it strongly influences who will contribute to society at large and what kind of contributions will be made (Lutz & KC, 2011). In developed (i.e., high income) countries (The World Bank, 2013a), it is generally taken for granted that formal schooling is closely linked to both individual (Bronfenbrenner, McClelland, Wethington, Moen, & Ceci, 1996) and social (Glaeser, Laibson, & Sacerdote, 2002) capital—that is, it contributes significantly to an individual’s worth both to him- or herself and to society at large. Correspondingly, schooling is viewed both as the primary developmental task and the main accomplishment of such countries’ young members prior to entry into adulthood. These societies require schooling and stipulate the minimum number of years their youngsters are expected to spend in school; but these requirements and stipulations are not universal. In post-industrial economies, 96% of their school-aged members are engaged in regular or special education, whereas in emerging economies (less and least developed countries) these percentages are about 85% and 65%, respectively (UNICEF, 2009). The role of formal schooling in developing (i.e., low and middle income) economies is much more tangential to both social (Godoy et al., 2008) and individual (Serpell, 1993) capital; it is not uniformly viewed as either a requirement or as an accomplishment, so parents decide which of their children (if any), should or should not go to school (Brock & Levers, 2007). Primarily due to the uncertainty of the role of formal schooling in developing countries (Grigorenko et al., 2001; Grigorenko, Hein, & Reich, in press; Serpell & Jere-Folotiya, 2008), but also due to other characteristics of these societies (e.g., shortage of funds, societal conflict, and economic instability), many children in low and middle income countries are not enrolled in formal education. For example, in 2010, about 132 million children of primary and lower-secondary school age were not being schooled formally (UNICEF, 2013); approximately half of these out-of-school children lived in sub Saharan Africa (UNESCO, 2008). Yet, the impact of schooling, regardless of the magnitude of its main effect, is not homogeneous; individual differences in classrooms among both students and teachers remain the object of investigation of large subfields in psychology and education. To explain the presence of these differences, chiefly, three different hypotheses have been investigated. First, it has been assumed that these differences are due to the impact of underlying genetic factors that influence either general cognitive (e.g., Plomin et al., 2004) or scholastic (e.g., Martin et al., 2011) performance, although recent large-scale re-investigations of the previously published data suggest that, if they exist, these effects are of very small magnitude and cannot, when conceived additively, explain the observed broad range of variability of individual differences in the classroom (Chabris et al., 2012; Rietveld & et al., in press; for intelligence and educational attainment, correspondingly). Second, it has been argued that classroom-based individual differences arise differentially, depending on the quality of instruction, so that genetic influences are pronounced more in higher and less in lower quality classrooms (Taylor, Roehrig, Soden Hensler, Connor, & Schatschneider, 2010). It has also been assumed that genetic factors shape the parameters of brain structure and function (Kwan, Sestan, & Anton, 2012), which, in turn, determine the parameters of basic information processing functions (Rowe et al., 2007); these, in turn, underlie the high (.45–.80) correlations between cognitive and scholastic performance (Luo, Thompson, & Detterman, 2003). It is this third hypothesis that seems to be particularly interesting for an investigation in societies where schooling is not a given, where the impact of schooling can be differential for individuals differentiated by characteristics of basic information processing functions and, in turn, by the parameters of their brain functioning, and, correspondingly, by the variation in the genome which defines these parameters. To quantify the relevant genetic variation, we genotyped selected markers in the catechol-O-methyltransferase (COMT) gene (MIM 116790, 22q11). The enzyme produced by this gene is involved in the degradation of catecholamines (dopamine, epinephrine, and norepinephrine) and, through its participation in dopamine turnover, is related to neural functioning. COMT is said to account for >60% of the dopamine degradation in the prefrontal cortex (PFC); thus, it plays an important role in regulating dopamine levels in the PFC and, correspondingly, in psychological processes, both lower (e.g., reaction and inspection time) and higher (e.g., metacognitive functions) that engage the PFC (Karoum, Chrapusta, & Egan, 1994). Two isoforms of COMT exist: the soluble isoform (S-COMT) and the membrane-bound isoform (MB-COMT). These are regulated by different promoters, are of different lengths (221 and 271 aminoacid residues, respectively), and have different functions. Structural DNA variants within this gene and in its near vicinity within noncoding regions have been associated with individual differences in a number of cognitive and affective processes, indicators of brain activity, and neuropsychiatric conditions (Dickinson & Elvevag, 2009). The pleiotropic nature of the COMT gene action has been related to the complex dynamics of cognitive and effective functions (Mier, Kirsch, & Meyer-Lindenberg, 2010; Papaleo et al., 2008; Tunbridge, Harrison, & Weinberger, 2006). The most studied polymorphic variant of the COMT gene is a single nucleotide change G/A (also known as the rs4680 single nucleotide polymorphism, SNP), resulting in an amino-acid substitution of valine (Val) with methionine (Met) at codon 108 for S-COMT and codon 158 for MB-COMT (Val108/158Met) generating alternative forms of COMT with different functional properties (Lachman et al., 1996). Yet, the gene has other numerous polymorphisms that have been extensively studied in isolation, in combination with the rs4680 polymorphism, and in haplotype structures representing of the gene (Witte & Floel, 2012). Of note also is that the variation in the COMT gene has been previously associated with individual differences in IQ (Payton, 2009), academic attainment (Enoch, Waheed, Harris, Albaugh, & Goldman, 2009; Yeh, Chang, Hu, Yeh, & Lin, 2009), and numerous PFC-rooted processes (Lundwall, Guo, & Dannemiller, 2012; Stormer, Passow, Biesenack, & Li, 2012). Here we investigated the effect of schooling on levels of nonverbal intelligence in a sample of children from a developing country. Our specific hypothesis is that, assuming that schooling is the major causal factor in levels of intelligence around the world, genetic variation (specifically, genetic variation in the COMT gene) might modulate the impact of schooling and help explain, at least partially, the presence of individual differences in classrooms. To verify this hypothesis, we ascertained a sample of children with a graded amount of exposure to schooling and assessed them behaviorally and genotypically.