metadata: authors: 0: author: Molina,Ezequiel
1: author: Narayan,Ambar
2: author: Saavedra Chanduvi,Jaime
count: World
docna: 0: docna: Outcomes, opportunity and development : why
            unequal opportunities and not outcomes hinder economic development
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Industrial Economics,Health Care Services Industry,Reproductive Health,Early Child and Children's Health,Nutrition,Educational Sciences,Economic Growth
teratopic: Information and Communication Technologies,Poverty Reduction,Health, Nutrition and Population,Macroeconomics and Economic Growth
abstracts: cdata!: This paper studies the relationship
            between inequality of opportunity and development outcomes
            in a cross-country setting. Scholars have long debated the
            impact of inequality on growth, development, and the quality
            of institutions in a society. The empirical relationships
            are however confounded by the notion that
            "inequality" can be seen as a composite of
            inequality arising from differences in effort and ability,
            which would tend to encourage competition and productivity,
            and inequality attributable to unequal opportunities,
            particularly in terms of access to basic goods and services,
            which might translate to wasted human potential and lower
            levels of development. The analysis in this paper applies a
            measure of educational opportunities that incorporates
            inequality between "types" or circumstance groups.
            Theories from economic history are used to instrument for
            this type of inequality in a large cross-country dataset.
            The results seem to confirm the hypothesis that this measure
            of inequality of opportunity is a better fit for structural
            inequality than the Gini index of income. The results
            suggest that inequality of  endowments at the outset of
            history led to unequal educational opportunities, which in
            turn affected development outcomes such as institutional
            quality, infant mortality, and economic growth. The findings
            are robust to several checks on the instrumental variable specification.
repnme: repnme: Outcomes, opportunity and development : why
            unequal opportunities and not outcomes hinder economic development
display_title: Outcomes, opportunity and
            development : why unequal opportunities and not outcomes
            hinder economic development
listing_relative_url: /research/2013/12/18730827/outcomes-opportunity-development-unequal-opportunities-not-outcomes-hinder-economic-development
topicv3: Economic Growth m1326241 471,Infant and Child Mortality m1326544 1446,Education m1327048 470,Health m1504661 3480,Malnutrition m1627088 4737
publishtoextweb_dt: 2013-12-30T00:00:00Z
historic_topic: Information and Communication Technologies,Poverty Reduction,Health, Nutrition and Population,Macroeconomics and Economic Growth
no_of_pages: 46
author_keys: 0: author_key: 929781
1: author_key: 167324
2: author_key: 436201
model_extractions: WPS6735 Policy Research Working Paper 6735 Outcomes, Opportunity and Development Why Unequal Opportunities and Not Outcomes Hinder Economic Development _Ezequiel Molina* Ambar Narayan Jaime Saavedra-Chanduví_ The World Bank Poverty Reduction and Economic Management Network Poverty Reduction and Equity Unit December 2013
Policy Research Working Paper 6735

# **Abstract**

This paper studies the relationship between inequality of opportunity and development outcomes in a crosscountry setting. Scholars have long debated the impact of inequality on growth, development, and the quality of institutions in a society. The empirical relationships are however confounded by the notion that “inequality” can be seen as a composite of inequality arising from differences in effort and ability, which would tend to encourage competition and productivity, and inequality attributable to unequal opportunities, particularly in terms of access to basic goods and services, which might translate to wasted human potential and lower levels of development. The analysis in this paper applies a measure of educational opportunities that incorporates inequality between “types” or circumstance groups. Theories from economic history are used to instrument for this type of inequality in a large cross-country dataset. The results seem to confirm the hypothesis that this measure of inequality of opportunity is a better fit for structural inequality than the Gini index of income. The results suggest that inequality of endowments at the outset of history led to unequal educational opportunities, which in turn affected development outcomes such as institutional quality, infant mortality, and economic growth. The findings are robust to several checks on the instrumental variable specification.

This paper is a product of the Poverty Reduction and Equity Unit, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at ezequielmolina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Outcomes, Opportunity and Development Why unequal opportunities and not outcomes hinder economic development

Ezequiel Molina<sup>*</sup> Ambar Narayan Jaime Saavedra-Chanduví JEL Classification Codes: O11, O15, D63 Key words: inequality; equity; opportunity; growth; institutions; education Sector Board: Poverty Reduction (POV)

* Corresponding author: ezequielmolina@worldbank.org; The World Bank Group, 1818 H Street NW, Washington DC 20433, USA. The authors would like to thank Alejandro Hoyos Suarez and Claudio Montenegro for providing access to data from all over the world, and Joao Pedro Azevedo, Jose Molinas and Amer Hassan for advice and support in the computation of Human Opportunity Index. Insightful comments on earlier drafts and presentations were received from Martin Ardanaz, Carles Boix, John Londregan, Pedro Olinto, Eliana Rubiano, Carolina SanchezParamo, and participants at the World Bank Equity Conference of 2012. The findings, interpretations, and conclusions are entirely those of the authors. They do not necessarily represent the view of the World Bank Group, its Executive Directors, or the countries they represent.
# **1. Introduction**

Do unequal opportunities among different members or groups in a society hinder economic and social development? Using recently developed distributional measures of opportunity, this paper studies the relationship between opportunities for a country’s citizens and its development outcomes.

For years scholars have discussed whether the way wealth is distributed has an impact on the level of economic development of society as well as the quality of its institutions. Aided by increasing availability of data on income inequality, many researchers have studied this relationship empirically. The relationship is however complicated by the notion that inequality of outcomes can be thought of as combining elements of “good” and “bad” inequality (World Bank, 2005). “Good” inequality is the one traced to differences in effort or talent, which drives investment and competition that play a key role to increase productivity. “Bad” inequality is the one caused as a consequence of some individuals not having basic opportunities to develop their human potential, due to circumstances beyond their control. If this were true, one could argue that _net_ of the effect of opportunities, inequality of outcomes can even be good for economic development. In other words, once the effect of “bad” inequality is accounted for, inequality of outcomes could drive competition and efficiency, and encourage economic growth.

In this paper we argue that most of the existing empirical studies have been missing an important variable – given that the theoretical link between inequality and development is based on the concept of unequal opportunities rather than unequal outcomes. We present results that appear to confirm this hypothesis – once we account for the effect of unequal opportunities, inequality of outcomes does not appear to play any role in hindering economic development.

The World Development Report 2006 on Equity and Development (World Bank, 2005) focused on equality of opportunity and concluded that institutions and policies that promote a level playing field, where all members of society have similar chances to become socially active, politically influential, and economically productive, contribute to sustainable growth and development. However, as there were no indicators that could be computed for a large number of countries with the data that were available, there was no rigorous empirical evidence to show that higher equality of opportunity causes social and economic development at the country level. The WDR 2006 was followed by several World Bank reports measuring equality of opportunity for different countries and regions. These analytical studies developed several indicators to measure how countries rank in terms of distribution of opportunities among groups in each society.

Whether there is a causal link between inequality of opportunity in a country and its overall level of development remains an open question that has been examined only to a limited extent in existing literature. This paper intends to make a contribution to this literature, by using the recently developed measures of opportunities to investigate the causal link between (the level and distribution of) opportunities and development outcomes, such as gross domestic product per capita and institutional quality. It examines the extent to which empirical evidence supports a hypothesis that relates historical factors to the distribution of opportunities, in explaining a country’s development trajectory. The hypothesis, drawn from seminal literature on the history of economic development of nations, is based on the idea that factor endowments at the outset of history influenced the type of human capital institutions 2
that each country developed. The quality of a country’s institutions was manifested in the distribution of opportunities among its population, which in turn has had an effect on its level of development.

# **2. Motivation and links to recent literature**

The empirical literature at the individual level is unambiguous. People who face better opportunities in life, attend better schools, have a more customized educational experience, and enjoy a better family environment are more likely to be able to develop their full human potential, achieving better outcomes in terms of income, education, and so on. Furthermore, increasing opportunities for disadvantaged groups has proved to increase cognitive as well as non-cognitive skills, including improvements in personality, social and emotional traits. This general set of skills has also been proved to be essential in producing social and economic success (Herrnstein et al, 1994, Heckman et al, 2006 and Borghans et al, 2008).

Interventions to equalize opportunities earlier in life are also found to be significantly more costeffective and successful than those attempted later in life. Research shows that preschoolers with low levels of cognitive development have lower school achievement and earn lower wages in adulthood (Currie and Thomas, 1999; Case and Paxson, 2006). More recent studies suggest that early childhood education has substantial long-term impacts, ranging from adult earnings to retirement savings (Chetty et al., 2010). Reynolds et al. (2003) present a comprehensive review of the impact of early childhood programs directed toward disadvantaged children. Their findings also support a causal link between opportunities and productivity. Lost opportunities during childhood cannot always be compensated for. Child malnutrition, for example, can generate life-long learning difficulties, poor health and lower productivity and earnings over a lifetime (Alderman et al, 2006 and Hoddinott et al, 2008). But do these findings at the individual level extend to the aggregate level? Our paper attempts to address this question by empirically analyzing whether countries where opportunities are more widely available and distributed independent of an individual’s circumstances at birth have better development outcomes.

Extrapolating from the micro-level evidence mentioned above, one would expect that increased opportunities in childhood in a country would have positive impacts on its growth prospects. The rich cross-country empirical literature linking economic growth with human capital has relevance to this hypothesis. For example, Barro (2001) finds growth to be positively related to the average years of school attainment of adult males at the secondary and higher levels at the beginning of the period, in a panel of 100 countries observed from 1965 to 1995. While “quantity” of schooling is important, quality of schooling as measured by internationally comparable test scores is even more so. A number of studies in recent years have shown the effect of health on economic growth to be important (see Grimm, 2011 for an overview).

Thus improving opportunities for children – by improving coverage and reducing inequality of opportunity – is not just about “fairness” and building a “just society”, important as these principles are, but also about realizing a society’s aspirations of economic prosperity. Notably, the dividends of investing in opportunities among children, micro and macro level evidence seems to suggest, are likely to accumulate over time and across generations.

The empirical literature on _inequality_ and economic development has largely neglected the role of opportunities and focused on the relationship between inequality of outcomes and economic development, 3
with inconclusive findings (see Banerjee and Duflo, 2003 and Boix, 2010). In contrast, our paper combines the literature on equality of opportunity – brought into prominence by John Roemer’s seminal 1998 book “Equality of Opportunity”<sup>1</sup> – with recent theoretical and empirical work on the effect of the historical distribution of wealth and its effects on economic development. Our paper is also related to preceding work by scholars who have argued for a change in focus from inequality of outcomes to inequality of opportunities. In particular, Ferreira and Walton (2006) argue for inequality of opportunity as a more suitable concept to test the leading theories on economic development.<sup>2</sup> They provide a detailed account of the literature where the empirical tests rely on measures of inequality of outcomes, even though the best theoretical fit would be a measure of opportunities.

A case in point is the theory suggested by Engerman and Sokoloff (1997). In their seminal study, they argue that historical dominance of some crops like wheat in agriculture lead to more equal societies over time; while that of others, like sugarcane, which have large economies of scale, are associated with the growth of large plantations and structural inequality. These inequalities later translate to inferior education systems, weaker institutions and less democratic societies. Weak institutions, which are persistent, have a negative impact on economic growth and development. Easterly (2007) tested the Engerman and Sokoloff theory empirically, measuring structural inequality as the Gini coefficient of a country averaged over the period 1960–98. Using an agricultural endowment instrument for factor endowments, he assesses the causal effect of inequality of outcomes on development outcomes, namely GDP per capita, institutional quality and secondary enrollment.

Several other studies have complemented the theory suggested by Engerman and Sokoloff, suggesting alternate plausible channels through which inequality can affect economic development. Acemoglu et al. (2001, 2002 and 2005) highlighted the importance of unequal power in the process of economic development. Differences in settlers’ mortality led Europeans to establish different institutional set-ups, with extractive institutions in places where they could not settle and inclusive institutions in places they could. As a consequence, some societies enjoy equal distribution of power among members while others were ruled by the elites. These elites maximize their power by blocking enfranchisement and reforms to the education system that the rest of the population can benefit from. In contrast, Glaeser et al. (2004) argue that human capital is a more fundamental source of growth than political institutions, and human capital accumulation is the key factor that leads to institutional improvement and economic development.

Galor et al. (2009) formally model the mechanisms through which inequality in the distribution of land ownership negatively impacts the development of institutions that promote human capital (such as public schooling and child labor regulation), delaying the transition from agricultural to industrial society. Ferreira (2001) develops a model that highlights the political economy channel through which unequal educational opportunities may affect institutions and economic development. Educational opportunities are modeled as a function of wealth, and low educational opportunities translate into political inequality. Under some assumptions, the author finds opportunity traps, where low levels of wealth translate into low educational opportunities and political inequality, which in turn perpetuates inequalities in wealth, generating a vicious cycle.

> 1 For more on this literature, see, for example, Roemer (2002), Van der Gaer (1993) and Ferreira and Gignoux (2011).

> 2 Deininger and Olinto (2000) make a similar argument that what matters for growth is not inequality of outcomes but assets, which might be a better proxy for opportunity.

4
The above papers have something in common. Structural inequality, educational inequality and inequality of power are symptoms of a more encompassing concept: _inequality of opportunity_ .

The hypothesis we empirically test in this paper is compatible with the above literature. It is based on the idea that factor endowments at the outset of history influenced the type of human capital institutions that each country developed and as a result of that, affect education opportunities among its population. Low and unequal education opportunities tend to create a failure in the political market due to multiple agents (citizens) who vastly differ in their bargaining power, through no fault of their own. All agents do not have the same power to influence the principal (government), since the political power of influence is a function of information, resources and the capability to mobilize groups. In countries where broad segments of the population do not have information, resources or capabilities for mobilization, the politicians are likely to be less accountable, as they recognize that the population is not able to monitor and punish them for their misdeeds.

A recent paper (Jakiela et al. 2010), finds experimental evidence in favor of the idea that improving educational opportunities creates a better environment to design institutions. They combine data from a field experiment and a laboratory experiment to measure the causal impact of educational opportunity on respect for earned property rights. The paper finds that higher academic achievement reduces the willingness of young Kenyan women to appropriate others' labor income, and shifts players toward a 5050 split norm in the dictator game.

Thus it seems reasonable to suggest that greater equality of opportunity promotes more engaged citizens, who have more respect for property rights and more information and intellectual resources to “punish” corrupt principals. To empirically assess such an argument in a cross-country setting, we analyze the extent to which equality of opportunity, which we proxy with measures of educational opportunities, have an impact on development outcomes – namely, the level of economic development and institutional quality at the country level. We also assess the extent to which inequality of outcomes explains variation in GDP per capita or institutional quality, once the level and distribution of opportunities are controlled for (using recently developed measures that are new to the cross-country literature).

While research establishing a causal link between inequality of opportunity and growth in a crosscountry framework is at a nascent stage, the evidence so far seems to suggest that inequality of opportunity has an adverse impact on growth and development. Marrero and Rodríguez (2013), using income-based measures of inequality of opportunity that are quite different from ours, have found such evidence in an entirely different data set. They test the hypothesis that inequality of opportunity (attributable to two circumstances, namely parental education and race) and inequality of effort affect economic growth in different directions, using refined data of the PSID database for 26 states distributed throughout the United States in 1970, 1980 and 1990. They find robust support for a negative relationship between inequality of opportunity and growth and a positive relationship between inequality of effort and growth. While the results are encouraging, the authors acknowledge the need for caution because only two circumstances were used in the analysis, which implies that the results shed no light on the impact of other opportunity-related inequalities (for example, those attributable to parental occupation or 5
geographical location) on growth.<sup>3</sup> The likely underestimation of inequality of opportunity could also lead to their measure of inequality of effort containing some amount of inequality of opportunity. This would imply that their finding of a positive impact of inequality of returns to effort on growth merits more scrutiny through research, as the authors themselves point out.

Also of relevance is a paper by Grimm (2011), which assesses the effect of inequality in health on economic growth, using a cross-national panel data set of 62 low and middle-income countries between 1985 and 2007. The gradient in child mortality over mothers’ education groups, used as a proxy for disparity in health conditions to which different socioeconomic groups of a country are exposed, is closely related to inequality in health opportunities. Even with such a narrow measure, the paper finds a significant negative effect of health inequality (between different socioeconomic groups) on economic growth, which is robust to different specifications, estimation methods, and time spells.

# **3. Methodological approach**

## **Data and measures of equality of opportunity**

This paper incorporates new measures of opportunities. The first is the Human Opportunity Index (henceforth, HOI), which is a measure developed by Barros et al. (2009). The HOI is a composite indicator that combines two elements:

- (i) The level of coverage of _basic opportunities_ necessary for human development, such as primary education, water and sanitation, and electricity.

- (ii) The degree to which the distribution of those opportunities is conditional on circumstances exogenous to children, such as gender, parental income, or household characteristics.

The formula to compute this index is where p̅ is the average coverage of an opportunity and D is the Dissimilarity Index (henceforth, D-Index) that measures inequality in opportunity due to circumstances that are exogenous. D is defined by:<sup>4</sup> where pk is the coverage rate for group _k_ (where each group is defined by a set of circumstances unique to that group) _,_ and ak is the share of group _k_ in total population. The D-Index is then a measure of the weighted average of the distance between group access and population access. It ranges from 0 to 100, in percentage terms, and in a situation of perfect equality of opportunity, takes the value zero. The D-Index has an interesting interpretation as the fraction of all available opportunities that need to be reallocated

> 3 Also, the likely underestimation of inequality of opportunity could lead to their measure of inequality of effort containing some amount of inequality of opportunity. This would imply that their finding of a positive impact of inequality of returns to effort on growth needs to be subjected to more scrutiny through research.

> 4 We also make use of the geometric version of the D-Index, which is more sensitive to inequalities within each subgroup and is decomposable by subgroups.

6
from circumstance groups (or “types”, as defined in the inequality of opportunity literature) with coverage rate higher than p̅ to groups with coverage rate lower than p̅ to restore equal opportunity across all groups.

HOI has a number of desirable properties, including sensitivity to scale and inequality and its reward to Pareto improvements (see Barros et al. 2010). Of particular relevance is its sensitivity to inequality. HOI improves when inequality between types decreases with a fixed number of opportunities in a society, or when the number of opportunities increases and inequality among types stays constant.<sup>5</sup> The D-Index, on the other hand, improves if inequality among types, for a given number of total opportunities, declines. Importantly, while D is an indicator of inequality of opportunity, it is _not independent_ of the level of coverage. Clearly, as the coverage of an opportunity increases, the share of total opportunities allocated unequally among types is likely to fall, which is to say D is likely to fall. This is an inevitable consequence of the opportunity being a binary (zero-one) indicator, which implies that the coverage of an opportunity has an upper limit (100 percent). It also implies that the estimated effect of D on level of development will also reflect, in part, the effect of the level (coverage) of the opportunity, albeit to a much smaller extent than for the estimated effect of HOI.

Computing D and HOI when the number of circumstances is relatively large, as is the case here, requires an econometric exercise to obtain a prediction of HOI from observed access to the opportunity and circumstances among children (see Barros et al., 2010). This involves running a logistic regression model to estimate the relationship between access to a particular opportunity and the circumstances of the child, on the full sample of children for whom the HOI measure will be constructed. The estimated coefficients of the regression are used to obtain for each child his/her predicted probability of access to the opportunity, which is then used to estimate the coverage rate p̅, D and HOI (see Appendix 2).

We compute the HOI and the D-Index using data from the Program for International Student Assessment (PISA). PISA surveys 15-year-olds and assesses the extent to which students near the end of compulsory education have acquired the knowledge and skills essential for full participation in society. This is done through the use of standardized test scores. The scores range from 0 to 700, and six cut-off values or levels have been established by PISA. An “opportunity” is defined as achieving proficiency of Level 2 for the topics covered in all three tests (Mathematics, Reading and Science), which indicates a basic level of understanding of the topics covered.<sup>6</sup> The circumstances used are a measure of household wealth, calculated using principal component methodology based on asset ownership; gender of the child; parental education; school location;<sup>7</sup> and occupation of the head of the child’s household. For the child, these are all attributes s/he has no control over and are likely to have been determined at birth.

One of the drawbacks of using PISA is that the standardized tests are not carried out in all countries. To expand the sample of countries to a larger set, we also make use of Montenegro<sup>8</sup> et al. (2011)

> 5 The D-index and HOI are sensitive to the set of circumstances chosen for analysis. But this is mitigated by an additional property: the D-Index (HOI) will not be lower (higher) if more circumstances are added to the existing set of circumstances in the analysis.  This implies that the computed D-index serves as a lower bound to the “actual” inequality if all circumstances of interest could be included in the analysis.

> 6 The results are robust to the use of proficiency levels 3 or 4. See PISA 2009 for a description of what proficiency level 2 implies for each test.

> 7 The location of the school is classified as: a) Village, b) Small Town, c) Town, d) City or e) Large City.

> 8 Not all surveys had the necessary variables to compute the indicator. As a result, we end up with 80 surveys in which we could compute it.

7
Harmonized Micro data from Household Surveys that cover almost 100 developing countries to compute the HOI for another set of indicators: completing sixth grade on time, school attendance for children 10– 14 years old, and starting primary school on time for children between 6 and 7 years old. While these opportunities are not as attractive as PISA scores, since they cannot capture the quality of learning, they are reasonable proxies for educational opportunities. This is particularly true for timely completion of sixth grade (or six years of education), which correlates with variables like timely start to school and (absence of) grade repetition that are useful predictors for learning achievement. Taking advantage of the richer information available from household data, the circumstances considered here are: gender of the child, location, head of household education, per capita family income, number of children in the household and family structure (presence of both parents in the household).

Appendix 1 includes a description of the variables (with basic statistics included) for all the relevant measures used in the study, as well as a brief explanation of how these were constructed. Furthermore, Table A.2-2 in Appendix 2 shows correlations between all the opportunity measures computed for the study.

# **Identification strategy**

Below we present our structural equation model for identification.

Equation (1) denotes our outcome variable “Yi” indicating level of GDP, institutional quality, or infant mortality. These outcomes can be explained by different control variables ( _Xi_ ) as well as our main variable of interest ( _Ei_ ), which proxies for unequal opportunities.

The problem with estimating equation (1) is the possibility that the outcome variables also affect unequal opportunities, meaning that we have an endogenous variable. This would imply that if we replace Ei in Equation (1) we will end up with In order to be able to use an Ordinary Least Squares (OLS) approach, we need the classical assumptions to hold, which would require εi1 to be independent of εi2. Since we suspect that our variable is endogenous, we know the errors from Equations (1) and (2) will not be independent. As a result, OLS results would be biased. To address this problem we use Instrumental Variables (IV). In Equations (6) and (7) the instrument is represented by Zi .

8
For the IV approach to be valid and allow valid inferences of our variables of interest, we need two conditions to hold.

- a) _Exclusion Restriction_ : The instrument is uncorrelated with the error term, that is, Cov(Zi, εi1) = 0 . Or, to put it differently, the instrument has no direct effect on  Yi. This means that β1= 0 in Equation (6).

- b) Zi _is correlated with_ Ei, our opportunity measure. Cov(Zi, Ei) ≠0.

The literature on economic development suggests a few different potential instruments.

- a) _Log of Wheat Sugar Suitability Ratio_ :  Abundance of land suitable for growing wheat relative to that suitable for growing sugarcane (Easterly, 2007).

- b) _Share of Tropical Land_ . Percentage of land area in geographical tropics (Sachs, 1997).<sup>9</sup>

- c) _Ethnic Fractionalization_ . Ranges from 0 and 1 and describes how ethnically segregated a society is (Alesina et al., 2003).

- d) _Legal Origin_ .<sup>10</sup>

- e) _Latitude_ of Country Centroid.<sup>11</sup>

- f) _Longitude_ of Country Centroid.<sup>12</sup>

Table A.2-3 in Appendix 2 presents a correlation table across all these measures. We choose agricultural endowments as our preferred instrument, which was first introduced by Easterly (2007). The variable “wheat–sugar suitability ratio” is defined as _LWHEATSUGAR=log[(1+share of arable land suitable for wheat) / (1+share of arable land suitable for sugarcane)]_ .

The idea behind this instrument is that the higher the ratio (share of arable land suitable for wheat to sugar in total arable land) in a society, the lower would be economies of scale in production, and the society would be less likely to need structural inequality (in its most extreme form, slavery) for efficiency. As a result, societies with a higher wheat-sugar ratio of arable land are likely to be more equal in terms of opportunities.

As Easterly points out, the wheat–sugar ratio could simply be proxying for whether the country is in the tropics. There is certainly a strong correlation (correlation= −0.66, t-statistic= −10.75), but also enough variation in this measure within tropical and non-tropical areas alike to suggest that the two measures are not the same. The measure comes from FAO and takes into account factors like soil, rainfall, temperature and elevation. Easterly (2007), using different studies, argues that this measure has

> 9 This instrument is similar to settler mortality, so we will not use it to avoid concerns about the validity of the historical data in the latter. See Acemoglu et al. (2012) and Albouy (2012) for a debate on the validity of settler mortality data.

> 10 Indicates which type of legal system the country ascribes to: British, French, Germany, Scandinavian or Communist.

> 11 Ranges from 0 at the Equator to 90 (North or South) at the poles and refers to a geographic coordinate that specifies the north-south position of a point on the Earth's surface.

> 12 Ranges from −180 to 180 and refers to the angle between a plane containing the Prime Meridian and a plane containing the North Pole, South Pole and the location in question.

9
remained constant over time. Hence, he can use current data on agricultural endowment to construct this measure, as the data would be highly correlated with historical agricultural endowments.

In order for it to qualify as a good instrument, we need to argue that the two necessary conditions stated above hold. The first condition, namely the _exclusion restriction_ , captures the notion that any effect of the instrument on the outcome must occur via the effect of the instrument on the treatment. In other words, we argue that conditional on the controls included in the regression, the wheat–sugar ratio has no effect on GDP per capita today, other than the effect through opportunities. The second condition, namely the _nonzero average causal effect of the instrument on the treatment_ , implies that there should be a relationship (linear in this case) between the wheat–sugar ratio and educational opportunity. If the relationship is weak, we would have weak instruments. In our case, the correlations are strong (see Figures 1 and 2) between the log wheat–sugar ratio and our measures of educational opportunities, namely the HOI and D-Index for PISA scores (Level 2 proficiency).

# **Figure 1**

**Figure 2**

<!-- Start of picture text --> Relation between Agricultural Endowments and Equality of Opportunity Relation between Agricultural Endowments and Inequality of Opportunity<br>r = 0.576*** r = -0.542***<br>0 20 40 60 80 100 0 10 20 30 40<br>Human Opportunity Index - PISA D-Index - PISA<br>Fitted values Americas Fitted values Americas<br>East Asia, South Asia & Pacific Europe & Central Asia East Asia, South Asia & Pacific Europe & Central Asia<br>Middle East and Africa Middle East and Africa<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>-.2 -.2<br>-.4 -.4<br>Log of Wheat Sugar Suitability Ratio Log of Wheat Sugar Suitability Ratio<br><!-- End of picture text --> Finally, for our theory to be consistent, we need to show that _firstly_ , factor endowments are related with historical measures of unequal educational opportunities; and _secondly_ , there is persistence of unequal educational opportunities from those historical measures to our current measures of unequal opportunities.

For historical data on educational achievement, we use Tutu Vanhanen’s data (in press). In particular, we use the Index of Knowledge Distribution, which is computed as the arithmetic mean of the percentage of students and literates in the country. The number of students refers to students in universities and other higher education institutions, per 100,000 inhabitants of the country.<sup>13</sup> Using this data comes at the cost of possible inaccuracies and methodological problems involved in using data from many different sources. However, the data are valuable for testing whether our story about a high endowment of wheat land relative to sugarcane land predicts a high value of the index of knowledge distribution. As this index is itself a measure of unequal opportunities, we can also perceive whether there is persistence over time, which is to say, whether today's educational opportunities are correlated with those in the past.

13 Two ways are used to calculate the percentage of Students (%): before the year 1988 the value 1000 of the variable “number of students” is equivalent to 100%; and between the years 1988-1998 the value 5000 of the same variable is equivalent to 100%. For percentage of literates we use all the adult population.

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Table 1 shows a positive and strong correlation between our measure of factor endowments and educational achievement, as measured by the aforementioned Index of Knowledge Distribution, over a long period of history between 1858 and 1998. Countries with a higher wheat-sugar ratio of arable land have had a higher percentage of students and literates throughout this period (measured with 10-year intervals). Tables 2A and 2B show strongly positive correlations between educational achievement during the period 1858-1998 (measured once every 10 years) and our measures of equality of opportunity (HOI and D-Index) using PISA data from 2009. Thus unequal educational opportunities appear to persist over time, all the way to our current measures of unequal opportunities. Taken together, the two types of correlation in Tables 1 and 2 seem to support the use of our measure of factor endowments, the wheatsugar ratio of arable land, as an instrument for educational opportunities.

<Tables 1, 2A and 2B here>

# **4. Empirical results**

We start by showing the strong correlations between our measures of Equality of Opportunity and GDP per capita. The HOI and D-Index (derived from PISA test scores or timely completion of primary school) have strong positive and negative correlation, respectively, with the log of GDP per capita (Figure 3) – consistent with our hypothesis. The correlations with GDP are weaker for measures derived from school attendance (10-14 years) and starting primary school on time (6-7 years) (Figure 4).<sup>14</sup>

## **Figure 3**

<!-- Start of picture text --> Opportunities and Economic Development Using PISA Test Scores<br>r = 0.766*** r = -0.713***<br>0 20 40 60 80 100 0 10 20 30 40<br>Human Opportunity Index - PISA Test Scores Dissimilarity Index - PISA Test Scores<br>Fitted values Americas<br>East Asia, South Asia & Pacific Europe & Central Asia<br>Middle East and Africa<br>These graphs show the strong economical and statistical correlation (p-value is equal to zero in both cases)<br>between our measures of equality of opportunity and economic development<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>Logaritm of GDP pc at Current USD (2005-2010) Logaritm of GDP pc at Current USD (2005-2010)<br><!-- End of picture text -->

> 14 See Appendix 2, Figures A-2.1 and A-2.2.

11
<!-- Start of picture text --> Figure 4<br>Opportunities and Economic Development - Completing Six Years on Time<br>r = 0.695*** r = -0.697***<br>0 20 40 60 80 100 0 20 40 60 80<br>HOI - Completing Six Years of Education On Time D-Index - Completing Six Years of Education On Time<br>Fitted values Americas<br>East Asia, South Asia & Pacific Europe & Central Asia<br>Middle East and Africa<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>Logaritm of GDP pc at Current USD (2005-2010) Logaritm of GDP pc at Current USD (2005-2010)<br><!-- End of picture text --> Before introducing our IV results, some results using OLS are helpful in setting the stage. Table 3 with the OLS results shows that once we take into account the effect of educational opportunity, measured by the HOI or D-Index using PISA data, inequality of outcomes (as measured by the Gini index of income) does not have a relationship with the level of economic development.<sup>15</sup> The next step is to assess the effect of unequal opportunities on development outcomes using the wheat–sugar ratio as an instrument for opportunities. The first stage regression shows a highly significant relationship between the wheat–sugar endowment ratio and the two measures of equality of opportunity. The F-statistics for the first stage regressions are above the critical values identified by Stock and Yogo (2005), which indicates a problem with weak instruments.<sup>16</sup> Table 4 shows the IV estimates of the effect of equality of opportunity (as measured by the HOI and D-Index) on economic development, measured by GDP per capita. Higher influence of circumstances on available opportunities is associated with lower degree of economic development. Once we account for the effect of opportunities, inequality of outcomes – measured by the Gini coefficient of income – has no causal effect on economic development. The relationship between opportunities and economic

> 15 The results are similar with equality of opportunity measures constructed with other variables (e.g. timely completion of primary school). But for the sample of countries for which we have those variables, the Gini coefficient does not have a significant negative relationship with GDP per capita in OLS regressions. Then we cannot make the case that accounting for equality of opportunity makes the Gini insignificant.

> 16 Staiger and Stock (1997) suggest that the F-statistic in the first stage regression should exceed 10. In most of our specification this value holds true for our case. For some specifications the F from the first stage are below 10, we also conduct sensitivity analysis for weak instruments and compute alternative confidence intervals following Chernozhukov and Hansen (2008). This confidence intervals are larger but produce the same insight the once we found using the small sample robust statistics we report in this paper.

12
development is also robust to inclusion of the Gini coefficient, which is averaged over 1960-2010 to mitigate the noise associated with the measure.<sup>17</sup> <Tables 3 and 4 here> Following up from the discussion earlier (in Section 3), the effect of HOI on GDP per capita is a combination of the effects of coverage _and_ inequality of educational opportunity. The effect of the D- Index on GDP, on the other hand, reflects the effect of inequality of opportunity, which is however also related (in part) to coverage in the sense that inequality by this measure is likely to be higher when coverage is lower and vice-versa. Given this feature, should the IV estimates of the D-Index be interpreted as the effect of _inequality_ of opportunity on economic development?

We would argue that such an interpretation is correct _,_ because of the way opportunities are defined here – as binary variables, where coverage is bounded above (at 100 percent). This is due to the nature of these variables – the rate of school completion or attainment of proficiency cannot exceed 100 percent – and the type of data available. Given the binary nature of these variables, an intuitive measure of inequality of opportunity cannot be fully independent of coverage. Rather the measure should capture the notion that inequality of educational opportunity in a country is likely to be higher when the basic proficiency level in education is not universal, and that there would be no inequality when every child attains the basic proficiency level.<sup>18</sup> Therefore, the estimated effect of the D-Index on per capita GDP _does_ reflect the effect of inequality of opportunity, even though the measure of the D-Index itself is partly dependent on coverage.

As in Easterly (2007), Table 4 also expands on the basic results by adding two quick robustness checks. The first check excludes the Western Hemisphere, to which Engerman and Sokoloff's original case study was limited. Excluding countries from this region, the hypothesis that inequality of opportunities inhibits development with the wheat–sugar ratio as an instrument holds “out of sample” for the rest of the world. The second check involves the inclusion of three regional dummy variables – a classification that is exogenous to the level of development of countries.<sup>19</sup> The results are robust to both the changes in specifications, although the results with the regional dummies have smaller F-statistics that could imply weak instruments.<sup>20</sup> Similar results are obtained when the HOI and D-Index are constructed using timely completion of six years of education as an alternative indicator of educational opportunity (see Table 5).<sup>21</sup> In this case, we use the “share of tropical land” as an instrument, which is related to the previously used wheat-sugar ratio, but allows for a larger number of observations for this particular opportunity measure. For all

> 17 As controls, we also use Gini coefficient adjusted to gain comparability across countries and surveys following Easterly (2007) and an alternative adjustment proposed by Solt (2009). Both of these measures produce very similar results as the one using the World Development Data.

> 18 Note that this would not apply when education proficiency is not a binary variable, but rather a continuous and unbounded variable reflecting that learning achievement is, in fact, unbounded.

> 19 The dummies correspond to Americas (includes Latin America & Caribbean region, Canada and United States); Europe and Central Asia (includes Western Europe), and Middle East & Africa; the omitted region is East Asia & Pacific (includes Japan, Australia and New Zealand).

> 20 All specifications compute robust standard errors to account for potential heteroscedasticity. See also footnote 13 regarding weak instruments.

> 21 This is interesting as the sample of countries is not the same between PISA data and Montenegro et al. (2011).

13
instruments that are correlated with our measure, we find a significant positive effect on the level of GDP per capita, with similar coefficients in every case.<sup>22</sup>

# <Table 5 here>

Next, we examine the relationship between equality of opportunity and other development outcomes, namely institutional quality and infant mortality.<sup>23</sup> Table 6A presents the results. Higher equality of opportunity (measured using PISA scores) predicts higher institutional quality of a country (see Appendix 2 for how this variable was created). Controlling for inequality of outcomes with the Gini index of income, whose coefficient is not statistically significant, the coefficients of both the HOI and the D-Index remain statistically significant. These coefficients also increase in size once we take into account the Gini of income. Interestingly, the magnitude of the effects is higher using IVs than for OLS, suggesting that the causal effect of inequality of opportunity on development outcomes is actually understated by the OLS estimation.

Table 6B shows the results from the same analysis as above, re-defining the “opportunity” as timely completion of six years of education. Equality of opportunity measured with this variable does not have a clear relationship with institutional quality, with some specifications having significant results and others not. For the other development outcomes, infant mortality and malnutrition, the results are clear: equality of opportunity measured by the HOI or D-Index of timely completion of six years of education predicts lower infant mortality and malnutrition rates.<sup>24</sup> <Tables 6A and 6B here>

# **Estimated magnitude of economic impacts**

How much does equality of opportunity matter as a driver of development? Using the data from PISA, a one standard deviation increase in the Human Opportunity Index (33 percentage points) increases income by 0.718 standard deviations, increases institutional quality by 0.814 standard deviations, and reduces infant mortality by almost one standard deviation (Table 7). An increase of one point (9.4 percentage points) in inequality of opportunity or the D-Index reduces per capita income by almost 10 percentage points and institutional quality by 2.3 percentage points, and increases infant mortality by almost 3 percentage points. By these estimates, the extent to which inequality of opportunity hinders development is economically meaningful, in addition to being statistically significant.

We also compute the elasticity of GDP per capita to HOI and find that a 1 percent higher HOI for a given country would lead to 2.3 percent higher GDP per capita. This means, for example, that if the current HOI for Argentina were higher by 1 percent (around 0.4 points), its GDP per capita would be higher by 150 USD.

> 22 With school attendance (10-14 years) or starting primary school on time (6-7 years) as the indicator of opportunity, there appears to be no causal relation. We believe this result is due to the fact that attendance in school and timely start to schooling are not good proxies for education achievement, which is what determines opportunities. Therefore, in the rest of the paper we use only the measure derived from PISA test scores and timely completion of six years of schooling.

> 23 For this specification we do not use malnutrition as a development outcome as only few observations were available to estimate the first stage.

> 24 As results with D-Index are very similar, we omit them from Table 6B to reduce the number of tables. Results are available upon request.

14
Using our alternative opportunity variable of completing sixth grade on time, we find that increasing the Human Opportunity Index by one point increases GDP per capita by 3.8 percentage points, reduces infant mortality rate by 0.6 percentage points, and reduces malnutrition by 0.8 percentage points. A one point increase in inequality of opportunity or the D-Index (4 percentage points) reduces per capita income by 6.7 percentage points, and increases infant mortality by 1.12 percentage points and malnutrition by 1.46 percentage points.

<Table 7 here>

# **5. Robustness of the results**

## **Testing different model specifications**

To test the robustness of our results in more detail, we first explore how sensitive the findings are to the choice of different model specifications. As we suggested when introducing the instrumental variable approach, there are some plausible competing alternatives to our theory of equality of opportunity being a key variable in explaining development outcomes. In particular, the literature has highlighted the role of inequality of outcomes, ethnic fractionalization, legal origins, latitude and longitude as well as tropical location (see Section 3). To test the alternative variables against our hypothesis, we adopt the approach of controlling for each variable in turn (taking them as exogenous), while continuing to run an IV regression of development outcomes on the inequality measures with the wheat–sugar endowment ratio as an instrument for the PISA test scores data.

Tables 8A and 8B show the estimated effects of the HOI and D-Index (based on PISA test scores) on log of GDP per capita, after controlling for the effects of ethnic fractionalization, legal origin, share of tropical land, latitude and longitude, in succession. When the effects of ethnic fractionalization are controlled for, the coefficients on the HOI and D-Index drop modestly but still have high statistical and economic significance. Introducing dummies for British, French and German or Scandinavian legal origin (Socialist legal origin is the omitted category) also leaves the significance of the HOI and D-Index unchanged, and actually increases the magnitude of the coefficients. The tropical location measure – the share of a country's cultivated land area in tropical climate zones<sup>25</sup> – was introduced by Sachs and coauthors. The tropics variable is not significant and its inclusion does not affect the significance of the HOI and D-Index. The inclusion of latitude and longitude variables reduces the coefficients on the HOI modestly and increases the coefficients on the D-Index.

Therefore, the effects of equality of opportunity – measured by the HOI and D-Index of PISA test scores – are robust to the exclusion of different variables that provide competing explanations for economic development. Interestingly, three of these variables – ethnic fractionalization, share of tropical land and latitude – have strong and significant effects on GDP per capita when the opportunity variables are not included in the specification, which disappear upon the inclusion of the HOI or D-Index.

<Tables 8A and 8B here>

> 25 Tropical climate zone is characterized as “hot and humid with no winter”, which is the most precise measure of tropical conditions.

15
As before, the same exercise is repeated defining the opportunity as timely completion of sixth grade (results available upon request). The coefficients on the HOI and D-Index are always statistically and economically significant with only modest reductions seen when ethnic fractionalization is controlled for.

Tables 9A and 9B show the estimates for the effects of the HOI and D-Index PISA test scores on institutional quality using the same controls as before. As in Tables 8A and 8B, the coefficients on the HOI and D-Index are always statistically and economically significant, with modest reductions in the coefficients seen when ethnic fractionalization is controlled for.<sup>26</sup> < Tables 9A and 9B here>

# **Overidentification test**

To check the robustness of our results, we also need to test the validity of our instruments in more detail. For an IV approach to work, the instrument must be: (i) uncorrelated with the unobserved error term and (ii) correlated with the endogenous explanatory variable. As shown in Section 3, our instrument, the wheat-sugar suitability ratio in arable land, appears to satisfy the second condition quite well.

The first requirement, however, cannot be tested definitively as it arises from a correlation between the instrument and an unobserved component of the error. One of the most problematic parts of any IV exercise is establishing that the selected instrument meets this so-called exclusion restriction. In our case it would imply that conditional on the pre-treatment controls, the wheat–sugar ratio affects the outcome, i.e. GDP per capita today, infant mortality or institutional quality, only through its effect on educational opportunities and not through any other variable. And it would imply that the share of tropical land does not directly affect the level of development, infant mortality and institutional quality, other than through its effect on the HOI and D-Index of completing sixth grade on time.

If we have more than one instrumental variable, we can effectively test whether these are uncorrelated with the structural error, with the null hypothesis being that all instruments are uncorrelated with the error term. The usual econometric approach is to run a test of overidentification. These tests are far from definitive, as “passing the test” merely implies a failure to reject the exclusion restriction. To run the test, we need an alternative instrument for equality of opportunity. The variable “share of tropical land” described earlier is a good candidate, as it is used as an instrument for the variable completing sixth grade on time. There is considerable consensus in the literature that the tropics variable affects income through social and political institutions rather than directly. For the tropics variable to be of use here as an instrument, its effects on institutions also must go through equality of opportunity rather than through any other mechanism.

The results on the over-identification tests fail to reject the exclusion restriction, by a considerable margin in all of the specifications, when the outcome variable is GDP per capita, institutional quality or malnutrition (see Table 10). When the outcome variable is infant mortality, the over-identification tests fail to reject the exclusion restriction in all cases but one.<sup>27</sup> Thus in almost all cases the over-identification

> 26 Results are qualitatively similar using other development measures as well as the different measures of equality of opportunity used in this paper. These regression outputs are available upon request.

> 27 The one where the hypothesis of exclusion restriction is rejected is for the case for infant mortality using the HOI and D-Index for PISA test scores data. However, the overidentification test fails to reject the hypothesis when the opportunity is defined as completing sixth grade on time, using data from household surveys.

16
tests, even as they are less than definitive due to the nature of the test, yield results favorable to the validity of the instruments.

<Table 10 here>

# **6. Final Remarks**

The findings of this paper suggest that the conflicting results in the literature on inequality of outcomes and economic growth can be attributed to the omission of an important variable – what really matters is inequality of opportunities and not of outcomes. A recent paper (Marrero and Rodriguez, 2013) finds evidence in favor of this hypothesis using a measure of inequality of opportunity that is derived from a decomposition of income. Our innovation is to apply a measure of educational opportunities that incorporates inequality between “types” or circumstance groups, using theories from economic history to instrument for this type of inequality. We argue that our measure of inequality of opportunity is a better fit for structural inequality than the Gini index of income, and the results seem to support this argument.

Consistent with the hypothesis of the literature, our paper supports the prediction that agricultural endowments – specifically the relative abundance land suitable for wheat to that suitable for sugarcane – predict unequal educational opportunities and this, in turn, predicts development outcomes. After controlling for the measures for unequal opportunity we construct, the Gini index that measures income inequality has no explanatory power. We test the robustness of our results using different country samples and sources of data, measures of opportunity and development outcomes, and controlling for the effects of other variables that have been shown in earlier literature to influence economic development. For almost all cases, our results remain statistically and economically significant.

Although the IV approach using cross-country data may not be the best identification strategy to establish causality, the results in this paper support a well-defined a priori hypothesis at the individual and aggregate levels, whereby unequal educational opportunities hinder development. The validity of the instrument, the wheat-sugar land ratio or the share of tropical land, both of which have been used in past literature, is typically a subject of debate in work such as ours. The evidence we offer on the instrument satisfying the condition of the exclusion restriction, which is the failure to reject the overidentifying restrictions in the system, is subject to important caveats about the power of such tests. In spite of these tests being less than definitive by their very nature, we find it encouraging that they fail to find evidence of the wheat–sugar land ratio having any effect on underdevelopment other than its effect through the opportunity variables defined on the basis of PISA test scores. The tests also fail to find evidence that the share of tropical land has any effect on underdevelopment other than through equality of opportunity, measured by timely completion of sixth grade from household survey data.

Future research could focus on disentangling the effects of higher coverage of opportunities (the level) from pure inequality of opportunities (which is independent of the level), which is not possible in the model we estimate. A related question to examine will be whether the relative importance of reducing inequality or increasing coverage is related to the existing level of opportunities in a society or the level of development. It is possible to argue, for example, that inequality in educational opportunity is relevant for development only after a country reaches a certain threshold level of education and/or income level.

17
Whether such an argument has any merit is an empirical question that could be a subject of future research.

18
# **Tables**

|**TABLE 1**<br>**Historical Equality of Opportunity and A**|**gricultural E**|**ndowmen**|**ts**|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Regression||||||Depe|ndent Variable|:Log of Whea|t Sugar Ratio|Sustainability||||||
||OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|
|Index of Knowledge Distribution (1858)|0.00913***<br>(0.00)|||||||||||||||
|Index of Knowledge Distribution (1868)||0.00847***<br>(0.00)||||||||||||||
|Index of Knowledge Distribution (1878)|||0.00889***|||||||||||||
||||(0.00)|||||||||||||
|Index of Knowledge Distribution (1888)||||0.00894***||||||||||||
|||||(0.00)||||||||||||
|Index of Knowledge Distribution (1898)|||||0.00913***|||||||||||
||||||(0.00)|||||||||||
|Index of Knowledge Distribution (1908)||||||0.00887***||||||||||
|||||||(0.00)||||||||||
|Index of Knowledge Distribution (1918)|||||||0.00816***|||||||||
||||||||(0.00)|||||||||
|Index of Knowledge Distribution (1928)||||||||0.00776***||||||||
|||||||||(0.00)||||||||
|Index of Knowledge Distribution (1938)|||||||||0.00681***|||||||
||||||||||(0.00)|||||||
|Index of Knowledge Distribution (1948)||||||||||0.00602***||||||
|||||||||||(0.00)||||||
|Index of Knowledge Distribution (1958)|||||||||||0.00610***|||||
||||||||||||(0.00)|||||
|Index of Knowledge Distribution (1968)||||||||||||0.00438***||||
|||||||||||||(0.00)||||
|Index of Knowledge Distribution (1978)|||||||||||||0.00538***<br>(0.00)|||
|Index of Knowledge Distribution (1988)||||||||||||||0.00405***<br>(0.00)||
|Index of Knowledge Distribution (1998)|||||||||||||||0.00435***|
||||||||||||||||(0.00)|
|Number of Observations|35|37|39|40|40|43|46|53|53|62|71|92|100|101|116|
|R-squared|0.341|0.323|0.379|0.409|0.443|0.425|0.398|0.409|0.355|0.321|0.357|0.288|0.14|0.197|0.202|
|* p<0.05, ** p<0.01, *** p<0.001||||||||||||||||

19
|**TABLE 2A- Persistance of Equality of O**<br>|**pportunit**|**y in Histo**<br>|**ry**<br>|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Regression||Dep|endent Va|riable: Hu|man Oppor|tunityInd|ex - Averag|e Mathem|atics,Rea|ingand Sc|ience Out|comes Lev|el 2 - PISA|2009||
||OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|
|Index of Knowledge Distribution (1858)|0.782***<br>(0.18)|||||||||||||||
|Index of Knowledge Distribution (1868)||0.774***||||||||||||||
|||(0.17)||||||||||||||
|Index of Knowledge Distribution (1878)|||0.756***<br>(0.16)|||||||||||||
|Index of Knowledge Distribution (1888)||||0.735***<br>(0.16)||||||||||||
|Index of Knowledge Distribution (1898)|||||0.762***|||||||||||
||||||(0.15)|||||||||||
|Index of Knowledge Distribution (1908)||||||0.855***||||||||||
|||||||(0.14)||||||||||
|Index of Knowledge Distribution (1918)|||||||0.890***<br>(0.14)|||||||||
|Index of Knowledge Distribution (1928)||||||||0.891***<br>(0.14)||||||||
|Index of Knowledge Distribution (1938)|||||||||0.881***<br>(0.14)|||||||
|Index of Knowledge Distribution (1948)||||||||||0.632***||||||
|||||||||||(0.14)||||||
|Index of Knowledge Distribution (1958)|||||||||||0.765***|||||
||||||||||||(0.13)|||||
|Index of Knowledge Distribution (1968)||||||||||||0.723***<br>(0.14)||||
|Index of Knowledge Distribution (1978)|||||||||||||0.725**|*||
||||||||||||||(0.15)|||
|Index of Knowledge Distribution (1988)||||||||||||||0.822***<br>(0.15)||
|Index of Knowledge Distribution (1998)|||||||||||||||0.801***|
||||||||||||||||(0.19)|
|Number of Observations|24|26|28|29|29|33|34|39|38|41|44|45|51|51|59|
|R-squared|0.455|0.46|0.463|0.455|0.493|0.55|0.56|0.543|0.52|0.339|0.457|0.397|0.334|0.387|0.242|
|* p<0.05, ** p<0.01, *** p<0.001||||||||||||||||
|*Average Mathematics, Reading and Scien|ce Outcome|s Level 2 -|PISA 2009|||||||||||||

20
|**Table 2B -Persistance of Equality of Opp**<br>Regression|**ortunity in**|**History**|Dependent|Variable|: Dissimilari|tyIndex -|Average M|athematic|s,Reading|and Scienc|e Outcom|es Level 2|- PISA 2009|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|
|Index of Knowledge Distribution (1858)|-0.258**<br>(0.07)|||||||||||||||
|Index of Knowledge Distribution (1868)||-0.253**<br>(0.07)||||||||||||||
|Index of Knowledge Distribution (1878)|||-0.246***<br>(0.06)|||||||||||||
|Index of Knowledge Distribution (1888)||||-0.235***<br>(0.06)||||||||||||
|Index of Knowledge Distribution (1898)|||||-0.243***<br>(0.06)|||||||||||
|Index of Knowledge Distribution (1908)||||||-0.292***<br>(0.06)||||||||||
|Index of Knowledge Distribution (1918)|||||||-0.305***<br>(0.06)|||||||||
|Index of Knowledge Distribution (1928)||||||||-0.302***<br>(0.06)||||||||
|Index of Knowledge Distribution (1938)|||||||||-0.297***<br>(0.06)|||||||
|Index of Knowledge Distribution (1948)||||||||||-0.207***<br>(0.05)||||||
|Index of Knowledge Distribution (1958)|||||||||||-0.254***<br>(0.05)|||||
|Index of Knowledge Distribution (1968)||||||||||||-0.234***<br>(0.05)||||
|Index of Knowledge Distribution (1978)|||||||||||||-0.258***<br>(0.05)|||
|Index of Knowledge Distribution (1988)||||||||||||||-0.275***<br>(0.06)||
|Index of Knowledge Distribution (1998)|||||||||||||||-0.266**<br>(0.08)|
|Number of Observations|24|26|28|29|29|33|34|39|38|41|44|45|51|51|59|
|R-squared|0.362|0.361|0.367|0.354|0.382|0.448|0.466|0.446|0.423|0.268|0.387|0.323|0.318|0.325|0.161|
|* p<0.05, ** p<0.01, *** p<0.001<br>*Average Mathematics, Reading and Science|Outcomes L|evel 2 - PIS|A 2009|||||||||||||

21
# **Table 3 - Basic Results for Development Outcomes ( Ordinary Least Squares) Equality** **<u>of Opportunity Measures versus Inequality of Outcomes Measures</u>**

|Regression|Dep|endent Variable: Loga|ritm of Per Capita Income|,Average 2005-|2010|
|---|---|---|---|---|---|
||OLS|OLS<br>OL|S(Comparison Sample)|OLS|OLS|
|Human Opportunity Index*|0.038***|||0.038***|0.042***|
||(0.004)|||(0.005)|(0.004)|
|Gini Index, Average 1960-2010||-0.050***|-0.047**|0.005|-0.005|
|||(0.013)|(0.016)|(0.013)|(0.012)|
|Americas - LAC plus Canada and United States|||||0.656**|
||||||(0.191)|
|Europe and Central Asia|||||0.273|
||||||(0.182)|
|Middle East and Africa|||||1.101<br>(0.738)|
|constant|7.163***|10.140***|11.308***|6.915***|6.664***|
||(0.287)|(0.538)|(0.580)|(0.675)|(0.664)|
|R-sqr|0.587|0.093|0.135|0.599|0.647|
|Number of Observations|63|152|59|59|59|
||0.000|0.000|0.000|0.000|0.000|
|Regression|Dep|endent Variable: Loga|ritm of Per Capita Income|,Average 2005-|2010|
||OLS|OLS<br>OL|S(Comparison Sample)|OLS|OLS|
|Dissimilarity Index*|-0.094***|||-0.090***|-0.097***|
||(0.012)|||(0.013)|(0.013)|
|Gini Index, Average 1960-2010||-0.050***|-0.047**|-0.004|-0.018|
|||(0.013)|(0.016)|(0.013)|(0.013)|
|Americas - LAC plus Canada and United States|||||0.624*|
||||||(0.271)|
|Europe and Central Asia|||||0.159|
||||||(0.225)|
|Middle East and Africa|||||0.863|
||||||(0.832)|
|constant|10.672***|10.140***|11.308***|10.706***|11.030***|
||(0.156)|(0.538)|(0.580)|(0.444)|(0.468)|
|R-sqr|0.508|0.093|0.135|0.522|0.557|
|Number of Observations|63|152|59|59|59|

* p<0.05, ** p<0.01, *** p<0.001

*Average Mathematics, Reading and Science Outcomes Level 2 - PISA 2009

22
**Table 4 - Basic results for Development Outcomes  (Instrumental Variables - Log of Wheat Sugar Sustainability)** **<u>Human Opportunity Index</u>**

|Regression|Depende|nt Variable: Logaritm of Per Capita Income,Average 200|5-2010|
|---|---|---|---|
||IV|IV<br>IV ExcludingAmericas|IV|
|Human Opportunity Index*|0.035***|0.033**<br>0.040***|0.034***|
||(0.006)|(0.012)<br>(0.009)|(0.008)|
|WDI Gini Index - Average for 1960-2010||-0.008||
|||(0.021)||
|Americas - LAC plus Canada and United States|||0.465|
||||(0.302)|
|Europe and Central Asia|||0.357|
||||(0.259)|
|Middle East and Africa|||0.136|
||||(0.433)|
|constant|7.243***|7.696***<br>6.852***|6.997***|
||(0.425)|(1.477)<br>(0.610)|(0.621)|
|Number of Observations|49|46<br>39|49|
|F-statistics from first stage|23.43|20.58<br>14.51|7.87|
|**D-Index**||||
|Regression|Depende|nt Variable: Logaritm of Per Capita Income,Average 200|5-2010|
||IV|IV<br>IV ExcludingAmericas|IV|
|D-Index*|-0.097***|-0.086**<br>-0.122***|-0.091***|
||(0.019)|(0.034)<br>(0.028)|(0.023)|
|WDI Gini Index - Average for 1960-2010||-0.012||
|||0.019||
|Americas - LAC plus Canada and United States|||0.517|
||||(0.375)|
|Europe and Central Asia|||0.424|
||||(0.315)|
|Middle East and Africa|||0.013|
||||(0.504)|
|constant|10.590***|10.910***<br>10.770***|10.151***|
||(0.219)|0.423<br>(0.267)|(0.348)|
|Number of Observations|49|49<br>39|49|
|F-statistics from first stage|19.6|19.95<br>10.33|6.37|
|* p<0.05 ** p<0.01 *** p<0.001||||
|, ,<br>*Average Mathematics, Reading and Science Outco|mes Level 2 - PISA 2009|||
|**Gini Index**||||
|Regression|Depende|nt Variable: Logaritm of Per Capita Income,Average 200|5-2010|
||IV|IV<br>IV ExcludingAmericas|IV|
|Gini*|-0.157***|0.078<br>-0.246***|-0.295**|
||(0.030)|(0.071)<br>(0.044)|(0.099)|
|Human Opportunity Index*||0.061**||
|||(0.018)||
|Americas - LAC plus Canada and United States|||4.286**|
||||(1.335)|
|Europe and Central Asia|||-0.126|
||||(0746)|
|Middle East and Africa|||.<br>0.872|
||||(0.957)|
|constant|14.526***|2.732<br>17.418***|19.001***|
||(1.199)|(3.682)<br>(1.656)|(3.763)|
|Number of Observations|112|48<br>89|112|
|F-statistics from first stage|57.27|15.09<br>37.44|42.47|
|* p<0.05 ** p<0.01 *** p<0.001||||
|, ,<br>*Average 1960-2010||||

23
**Table 5 - Basic results for Development Outcomes . (Instrumental Variables - Share of Tropical Land)** **<u>Human Opportunity Index</u>**

|Regression|Depen|dent Variable: Logaritm|of Per Capita Income,Average 200|5-2010|
|---|---|---|---|---|
||IV|IV|IV ExcludingAmericas|IV|
|Human Opportunity Index*|0.038***|0.050***|0.049**|0.039***|
||(0.01)|(0.01)|(0.02)|(0.01)|
|WDI Gini Index - Average for 1960-2010||0.039*|||
|||(0.02)|||
|Americas - LAC plus Canada and United States||||0.901**|
|||||(0.31)|
|Europe and Central Asia||||0.294<br>|
|||||(0.75)|
|Middle East and Africa||||0.133|
|||||(0.29)|
|constant|6.154***|4.126***|5.704***|5.797***|
||(0.29)|(0.66)|(0.32)|(0.37)|
|Number of Observations|70|68|53|70|
|F-statistics from first stage|11.776|9.648|9.19|6.055|
|**D-Index**|||||
|Regression|Depen|dent Variable: Logaritm|of Per Capita Income,Average 200|5-2010|
||IV|IV|IV ExcludingAmericas|IV|
|D-Index*|-0.067***|-0.084***|-0.075***|-0.083***|
||(0.02)|(0.02)|(0.02)|(0.02)|
|WDI Gini Index - Average for 1960-2010||0.039**|||
|||(0.01)|||
|Americas - LAC plus Canada and United States||||0.992**|
|||||(0.30)|
|Europe and Central Asia||||-0.053|
|||||(0.69)|
|Middle East and Africa||||0.379|
|||||(0.26)|
|constant|8.789***|7.485***|8.791***|8.734***|
||(0.41)|(0.80)|(0.49)|(0.50)|
|Number of Observations|70|68|53|70|
|F-statistics from first stage|18.386|15.004|17.007|8.216|
|* p<0.05, ** p<0.01, *** p<0.001|||||
|<br>*Completing Six Years of Education on Time - HHS|||||
|**Gini Index**|||||
|Regression|Depen|dent Variable: Logaritm|of Per Capita Income,Average 200|5-2010|
||IV|IV|IV ExcludingAmericas|IV|
|Gini*|-0.178***|-0.028|-0.265***|-0.487|
||(0.03)|(0.04)|(0.04)|(0.28)|
|Human Opportunity Index*||0.031***|||
|||(0.01)|||
|Americas - LAC plus Canada and United States||||7.026|
|||||(3.76)|
|Europe and Central Asia||||-1.133|
|||||(177)|
|Middle East and Africa||||.<br>2.467|
|||||(2.17)|
|constant|15.285***|7.576***|18.116***|26.001*|
||(1.12)|(1.65)|(1.62)|(10.52)|
|Number of Observations|142|68|116|142|
|F-statistics from first stage|78.67|6.656|49.503|3.01|
|* p<0.05, ** p<0.01, *** p<0.001|||||
|<br>*Average 1960-2010|||||

24
**Table 6A - Basic results for Development Outcomes . (OLS and Instrumental Variables using  Log of Wheat Sugar Sustainability)** **<u>Human Opportunity Index controlling for Gini Index</u>**

|Regression|OLS|OLS(Ci Sl)<br>Dependent V|OLS<br>ariable: Instituti|IV<br>onal Index,Average W|IV<br>BGI 2009|IV|
|---|---|---|---|---|---|---|
|Human Opportunity Index*|0.775***<br>|omparson ampe|0.783***<br>|0.806***<br>|<br>0.842***<br>|<br>0.890***<br>|
|Gini Index*|(0.07)|-1.014**|(0.09)<br>0.052|(0.12)|(0.23)<br>0.108|(0.14)|
|||(0.31)|(0.23)||(0.46)||
|Americas - LAC plus Canada and United States||||||12.512*<br>(6.06)|
|Europe and Central Asia||||||3.288<br>(4.90)|
|Middle East and Africa||||||7.808<br>|
|constant|18.992***|106.242***|16.461|15.907*|9.649|(5.88)<br>5.27|
||(5.00)|(11.28)|(12.32)|(7.84)|(30.37)|(10.17)|
|R-sqr|0.659|0.162|0.658||||
|Number of Observations<br>|63|59|59|49<br>|49<br>|49<br>|
|Wald F-statistics from first stage<br>Reression||D|eendent Varia|21.945<br>le: Infant Mortalit|6.304|9.514|
|g|OLS|OLS(Comparison Sample)<br>|OLS<br>p|IV<br> y|IV|IV|
|Human Opportunity Index*|-0.358***<br>||-0.355***<br>|-0.436***<br>|-0.538***<br>|-0.510***<br>|
|Gini Index*|(0.03)|0.515***|(0.04)<br>0.006|(0.06)|(0.09)<br>-0.308*|(0.08)|
|||(014)|(011)||(014)||
|Americas - LAC plus Canada and United States||.|.||.|-3.469<br>(4.11)|
|Europe and Central Asia||||||1.704<br>|
|Middle East and Africa||||||(3.33)<br>-4663|
|||||||.<br>(4.15)|
|constant|34.486***|-7.145|34.234***|40.159***|57.996***|44.950***|
||(2.38)|(5.16)|(6.03)|(4.70)|(10.37)|(6.57)|
|R-sqr<br>Number of Observations|0.649<br>61|0.194<br>58|0.645<br>58|49|49|49|
|<br>Wald F-statistics from first stage||||21.945|6.304|9.514|
|**D-Index controlling for Gini Index**<br>Regression||Dependent V|ariable: Instituti|onal Index,Average W|BGI 2009||
||OLS|OLS(Comparison Sample)<br>|OLS<br>|IV<br>|IV<br>|IV|
|Dissimilarity Index*|-1.853***<br>(022)||-1.762***<br>(026)|-2.221***<br>(046)|-2.219**<br>(076)|-2.383***<br>(056)|
|Gini Index*|.|-1.014**|.<br>-0.165|.|.<br>-0.002|.|
|Ai - LAC l Cd d Uitd Stt||(0.31)|(0.26)||(0.47)|13884|
|mercas pus anaa an ne aes||||||.<br>(6.97)|
|Europe and Central Asia||||||5.049<br>|
|Middle East and Africa||||||(5.27)<br>4.586<br>624|
|constant|89.681***|106.242***|94.410***|92.457***|92.523***|(.)<br>87.858***|
|R-sqr|(2.87)<br>0.544|(11.28)<br>0.162|(8.56)<br>0.544|(4.82)|(10.73)|(6.69)|
|Number of Observations|63|59|59|49|49|49|
|Wld Fii f fi||||14806|4999|6916|
|a -statstcs rom rst stage<br>Regression||D|ependent Variab|.<br>le: Infant Mortality|.|.|
||OLS|OLS(Comparison Sample)<br>|OLS<br>|IV<br>|IV|IV|
|Dissimilarity Index*|0.866***||0.805***|1.203***|1.420***|1.367***|
|Gini Index*|(0.10)|0.515***|(0.12)<br>0.108|(0.25)|(0.39)<br>-0.237|(0.34)|
|||(0.14)|(0.12)||(0.20)||
|Americas - LAC plus Canada and United States||||||-4.256<br>|
|Europe and Central Asia||||||(3.73)<br>0.693<br>|
|Middle East and Africa||||||(2.39)<br>-2.815<br>393|
|constant|1.776|-7.145|-1.243|-1.3|4.976|(.)<br>-2.432|
|R-sqr|(1.36)<br>0.551|(5.16)<br>0.194|(3.97)<br>0.553|(1.94)|(4.72)|(3.52)|
|Number of Observations<br>|61|58|58|49<br>|49<br>|49<br>|
|Wald F-statistics from first stage<br>* p<0.05, ** p<0.01, *** p<0.001||||14.806|4.999|6.916|

*Average Mathematics, Reading and Science Outcomes Level 2 - PISA 2009 *Gini Index: Average 1960-2010

25
**Table 6B - Basic results for Development Outcomes. (OLS and Instrumental Variables using Share of Tropical Land)** **<u>Human Opportunity Index controlling for Gini Index</u>**

|Regression||Depende|nt Variable: Instituti|onal Index,Average W|BGI 2009||
|---|---|---|---|---|---|---|
||OLS|OLS(Comparison Sampl|e)<br>OLS|IV|IV|IV|
|Human Opportunity Index*|0.348***||0.378***|0.303|0.536*|0.373|
||(0.06)||(0.08)|(0.21)|(0.22)|(0.24)|
|Gini Index*||0.34|0.32||0.35||
|||(0.23)|(0.20)||(0.23)||
|Americas - LAC plus Canada and United States||||||10.483|
|||||||(6.28)|
|Europe and Central Asia||||||0.768<br>|
|||||||(13.76)|
|Middle East and Africa||||||3.378<br>|
|||||||(4.68)|
|constant|25.313***|19.555|11.044|25.728***|5.237|19.492**|
||(2.57)|(10.26)|(9.05)|(5.15)|(10.37)|(6.44)|
|R-sqr|0.268|0.03|0.283||||
|Number of Observations|82|74|74|70|68|70|
|Wald F-statistics from first stage||||11.776|9.648|6.055|
|Regression|||Dependent Variab|le: Infant Mortality|||
||OLS|OLS(Comparison Sampl|e)<br>OLS|IV|IV|IV|
|Human Opportunity Index*|-0.999***||-1.178***|-1.036**|-1.183***|-0.791*|
||(0.11)||(0.14)|(0.35)|(0.33)|(0.39)|
|Gini Index*||(0.47)|(0.39)||(0.42)||
|||(0.51)|(0.36)||(0.49)||
|Americas - LAC plus Canada and United States||||||-21.276|
|||||||(13.41)|
|Europe and Central Asia||||||-8.194<br>|
|||||||(20.76)|
|Middle East and Africa||||||20.969*<br>|
|||||||(8.94)|
|constant|87.795***|82.214***|108.719***|88.882***|111.057***|77.130***|
||(4.40)|(22.94)|(16.53)|(9.17)|(18.81)|(9.46)|
|R-sqr|0.506|0.012|0.512||||
|Number of Observations|82|74|74|70|68|70|
|Wald F-statistics from first stage||||11.776|9.648|6.055|
|Regression|||Dependent Varia|ble: Malnutrition|||
||OLS|OLS(Comparison Sampl|e)<br>OLS|IV|IV|IV|
|Human Opportunity Index*|-0.275***||-0.278***|-0.365**|-0.453***|-0.424*|
||(0.05)||(0.05)|(0.11)|(0.12)|(0.17)|
|Gini Index*||-0.384*|-0.402**||-0.430*||
|||(0.16)|(0.13)||(0.16)||
|Americas - LAC plus Canada and United States||||||-14.360**|
|||||||(5.21)|
|Europe and Central Asia||||||-8.778<br>(8.82)|
|Middle East and Africa||||||-11.196**|
|||||||(3.53)|
|constant|25.389***|35.754***|43.137***|27.308***|48.249***|38.653***|
||(1.75)|(7.24)|(6.12)|(2.95)|(7.79)|(4.36)|
|R-sqr|0.296|0.082|0.385||||
|Number of Observations|66|65|65|62|61|62|
|Wald F-statistics from first stage||||11.805|11.248|5.528|

* p<0.05, ** p<0.01, *** p<0.001

- *Completing Six Year of Education on Time, Household Survey Data

*Gini Index: Average 1960-2010

26
|**Table 7 - Magnitude of Effect on Develo**<br>**Human Opportunity Index- Average M**<br>|**pment Outcomes**<br>**athematics, Reading and Scienc**<br>|**e Outcomesfor Level 2- PISA 200**<br>|**9**<br>|||
|---|---|---|---|---|---|
|Dependent Variable|Coefficient on HOI in IV<br>regression (for whole sample<br>without regional dummies)|Change in dependent variable<br>in response to 1 standard<br>deviation change in HOI|Ratio to 1 standard deviation<br>dependent variable|% change as a result of<br>a 1 point increase in HOI|% change as a result of<br>a 1 % increase in HOI|
|Log of GDP per capita, Average 2005-2010|0.035|0.773|0.718|3.50|2.31|
|Institutional Index, Average WBGI 2009|0.806|17.8|0.814|0.82|0.54|
|Infant Mortality|-0.436|-9.6|-0.956|-1.03|-0.68|
|**D-Index - Average Mathematics, **|**Reading and Science Outco**|**mesfor Level 2 - PISA 2009**||||
|Dependent Variable|Coefficient on HOI in IV<br>regression (for whole sample<br>without regional dummies)|Change in dependent variable<br>in response to 1 standard<br>deviation change in HOI|Ratio to 1 standard deviation<br>dependent variable|% change as a result of<br>a 1 point increase of D-Index|% change as a result of<br>a 1 % increase in HOI|
|Log of GDP per capita, Average 2005-2010|-0.097|-0.827|-0.767|-9.70|-1.02|
|Institutional Index, Average WBGI 2009|-2.221|-18.930|-0.865|-2.27|-0.24|
|Infant Mortality|1.203|10.253|1.017|2.85|0.30|
|<br>**Human Opportunity Index - Compl**|**eting Six Year of Education**|**on Time**||||
|Dependent Variable|Coefficient on HOI in IV<br>regression (for whole sample<br>without regional dummies)|Change in dependent variable<br>in response to 1 standard<br>deviation change in HOI|Ratio to 1 standard deviation<br>dependent variable|% change as a result of<br>a 1 point increase in HOI|% change as a result of<br>a 1 % increase in HOI|
|Log of GDP per capita, Average 2005-2010|0.038|0.910|0.803|3.76|0.99|
|Infant Mortality|-1.036|-25.079|-0.646|-0.63|-0.16|
|Malnutrition|-0.365|-8.836|-0.773|-0.84|-0.22|
|**~~D-Index - Completing Six Year of E~~**|**~~ducation on Time~~**|||||
|Dependent Variable|Coefficient on HOI in IV<br>regression (for whole sample<br>without regional dummies)|Change in dependent variable<br>in response to 1 standard<br>deviation change in Dindex|Ratio to 1 standard deviation<br>dependent variable|% change as a result of<br>a 1 point increase of D-Index|% change as a result of<br>a 1 % increase in HOI|
|Log of GDP per capita, Average 2005-2010|-0.067|-0.894|-0.789|-6.70|-1.65|
|Infant Mortality|1.846|24.641|0.635|1.12|0.28|
|Malnutrition|0.63|8.409|0.736|1.46|0.36|

27
# **Table 8A - Robustness Checks: Effect of Equality of Opportunity on Economic Development Human Opportunity Index - Average Mathematics, Reading and Science Outcomesfor Level 2 - PISA 2009** **<u>Controlling for Ethnic Fractionalization</u>**

|Regression<br>Instrument: Logof Wheat Sugar Sustainability|OLS<br>Dependent Variab|OLS<br>IV<br>le: Logaritm of Per Capita Income,Average 2005-2010|
|---|---|---|
|Index of Ethnic Fractionalization (0-Low to 1-high)|-2.505***|0.343<br>-1.075|
||(0.43)|(0.47)<br>(0.66)|
|Human Opportunity Index*||0.040***<br>0.028**|
|||(0.01)<br>(0.01)|
|constant|9.420***|6.942***<br>8.054***|
||(0.22)|(0.44)<br>(0.81)|
|R-sqr|0.158|0.577|
|Number of Observations|183|60<br>48|
|Wald F-statistics from first stage||13.591|
|**Controlling for Legal Origin**|||
|Regression|Dependent Variable: Lo|garitm of Per Capita Income,Average 2005-2010|
|Instrument: Logof Wheat Sugar Sustainability<br>|OLS|OLS<br>IV<br><br>|
|Human Opportunity Index*||0.038***<br>0.043***|
|||(0.00)<br>(0.01)|
|British Legal System|-0.344|0.658***<br>0.673**|
||(0.36)|(0.17)<br>(0.22)|
|French Legal System|-0.467|0.579***<br>0.626***|
||(0.32)<br>|(0.14)<br>(0.15)<br>|
|Germany Legal System|2.267**|0.692**<br>0.606|
||(0.72)|(0.22)<br>(0.33)|
|Scandinavian Legal System|2.653***|1.082***<br>0.975***|
||(0.72)|(0.22)<br>(0.25)|
|constant|8.246***|6.545***<br>6.252***|
||(0.26)|(0.20)<br>(0.35)|
|R-sqr|0.199|0.875|
|Number of Observations|142|55<br>48|
|Wald F-statistics from first stage||21.485|
|**Controlling for Share of Tropical Land**<br>Regression|Dependent Variable: Lo|garitm of Per Capita Income,Average 2005-2010|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS<br>IV|
|% land area in geographical tropics|-1.927***|0.075<br>-0.237|
||(0.23)|(0.33)<br>(0.64)|
|Human Opportunity Index*||0.038***<br>0.031*|
|||(0.01)<br>(0.02)|
|constant|9.116***|7.133***<br>7.536***|
||(0.15)|(0.36)<br>(1.12)|
|R-sqr|0.315|0.58|
|Number of Observations|158|59<br>49|
|Wald F-statistics from first stage<br>||3.306|
|**Controlling for Latitud**<br>Regression|Dependent Variable: Lo|aritm of Per Capita IncomeAverage 2005-2010|
|Instrument: Logof Wheat Sugar Sustainability|<br>OLS|, <br>OLS<br>IV|
|latitude of country centroid|0.030***<br>|0<br>0.002<br><br>|
||(0.01)|(0.00)<br>(0.00)|
|Human Opportunity Index*||0038***<br>0034***|
|||.<br>.<br>(0.01)<br>(0.01)|
|constant|7.565***|7.179***<br>7.253***|
||(0.15)|(0.30)<br>(0.44)|
|R-sqr|0.207|0.58|
|Number of Observations|158|59<br>49|
|Wald F-statistics from first stage||17.839|
|**Controlling for Longitude**|||
|Regression|Dependent Variable: Lo|garitm of Per Capita Income,Average 2005-2010|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS<br>IV|
|longitude of country centroid|-0.002|-0.003<br>-0.003**|
||(0.00)|(0.00)<br>(0.00)|
|Human Opportunity Index*||0.039***<br>0.036***|
|||(0.00)<br>(0.01)|
|constant|8.250***|7.154***<br>7.242***|
||(0.14)<br>|(0.29)<br>(0.37)<br>|
|R-sqr<br>Number of Observations|0.006<br>158|0.599<br>59<br>49|
|<br>Wald F-statistics from first stage||21.201|

* p<0.05, ** p<0.01, *** p<0.001

*Average Mathematics, Reading and Science Outcomes Level 2 - PISA 2009

28
# **Table 8B - Robustness Checks: Effect of Equality of Opportunity on Economic Development Dissimilarity Index - Average Mathematics, Reading and Science Outcomesfor Level 2 - PISA 2009** **<u>Controlling for Ethnic Fractionalization</u>**

|Regression<br>|Dependent Variab|le: Logaritm of Per Capita In|come,Average 2005-2010|
|---|---|---|---|
|Instrument: Logof Wheat Sugar Sustainability<br>Index of Ethnic Fractionalization (0-Low to 1-high)|OLS<br>-2.505***|OLS<br>0.192|IV<br>-1.004|
||(0.43)|(0.51)|(0.73)|
|Dissimilarity Index*||-0.095***|-0.079*|
|||(0.01)|(0.03)|
|constant|9.420***|10.622***|10.714***|
|R-sqr|(0.22)<br>0158|(0.20)<br>0496|(0.19)|
|Number of Observations|.<br>183|.<br>60|48|
|Wald F-statistics from first stage|||11.346|
|**Controlling for Legal Origin**||||
|Regression|Dependent Variable: Lo|garitm of Per Capita Income|,Average 2005-2010|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS|IV|
|Dissimilarity Index*||-0.092***|-0.115***|
|||(0.01)|(0.02)|
|British Legal System|-0.348|0.679**|0.600*|
||(0.36)|(0.20)|(0.29)|
|French Legal System|-0.494|0.489**|0.548**|
||(0.32)|(0.16)|(0.19)|
|Germany Legal System|2.213**|0.852**|0.682*|
||(0.72)|(0.25)|(0.31)|
|Scandinavian Legal System|2.599***<br>|1.193***<br>|1.016***<br>|
||(0.72)|(0.25)|(0.25)|
|constant|8.300***|10.070***|10.342***|
||(0.27)|(0.16)|(0.24)|
|R-sqr|0.2|0.82||
|Number of Observations|142|55|48|
|Wald F-statistics from first stage|||12.814|
|**Controlling for Share of Tropical Land**||||
|Reression|Deendent Variable: Lo|aritm of Per Caita Income|Averae 2005-2010|
|g<br>Instrument: Logof Wheat Sugar Sustainability|p <br>OLS|p <br>OLS|, g <br>IV|
|% land area in eorahical troics|-1927***|-0119|-0207|
|ggp p|.<br>(0.23)|.<br>(0.35)|.<br>(0.66)|
|Dissimilarity Index*||-0.090***|-0.088*|
|||(0.01)|(0.04)|
|constant|9.116***|10.623***|10.520***|
||(0.15)|(0.17)|(0.37)|
|R-sqr<br>|0.315|0.503||
|Number of Observations|158|59|49|
|Wald F-statistics from first stage|||2313|
|<br>**Controlling for Latitude**|||.|
|Regression|Dependent Variable: Lo|garitm of Per Capita Income|,Average 2005-2010|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS|IV|
|latitude of country centroid|0.030***|0.002|0.003|
||(0.01)|(0.00)|(0.00)|
|Dissimilarit Index*||-0089***|-0093***|
|y||.<br>(0.01)|.<br>(0.02)|
|constant|7.565***|10.530***|10.451***|
|R|(0.15)<br>0207|(0.26)<br>0505|(0.35)|
|-sqr<br>Number of Observations|.<br>158|.<br>59|49|
|Wald F-statistics from first stage|||11.074|
|**Controlling for Longitud**<br>Ri|Dd Vibl L|i f P Ci I|A20052010|
|egresson<br>Instrument: Logof Wheat Sugar Sustainability|epenent arae: o<br>OLS|gartm o er apta ncome<br>OLS|,verage -<br>IV|
|longitude of country centroid|-0.002<br>(0.00)|-0.002<br>(0.00)|-0.003*<br>(0.00)|
|Dissimilarit Index*||-0094***|-0099***|
|y||.<br>(0.01)|.<br>(0.02)|
|constant|8.250***|10.684***|10.650***|
||(0.14)|(0.17)|(0.22)|
|R-sqr|0.006|0.517||
|Number of Observations<br>l f f|158|59|49<br>|
|Wad F-statistics rom irst stage<br>* p<0.05, ** p<0.01, *** p<0.001|||14.576|

*Average Mathematics, Reading and Science Outcomes Level 2 - PISA 2009

29
# **Table 9A - Robustness Checks: Effect of Equality of Opportunity on Institutional Quality Human Opportunity Index - Average Mathematics, Reading and Science Outcomesfor Level 2 - PISA 2009** **<u>Controlling for Ethnic Fractionalization</u>**

|Regression<br>|Depende|nt Variable: Institutions,A|verage WBGI 2009|
|---|---|---|---|
|Instrument: Logof Wheat Sugar Sustainability<br>Index of Ethnic Fractionalization (0-Low to 1-hih)|OLS<br>-44247***|OLS<br>0073|IV<br>-9338|
|g|.<br>(6.76)|.<br>(8.06)|.<br>(11.02)|
|Human Opportunity Index*||0.783***<br>|0.740***<br>|
|||(0.09)|(0.17)|
|constant|67.794***|18.444*|23.131|
|R-sqr|(3.44)<br>0188|(7.60)<br>0658|(14.37)|
|Number of Observations|.<br>187|.<br>60|48|
|Wald F-statistics from first stage|||13.591|
|**Controlling for Legal Origin**||||
|Regression<br>|Depende|nt Variable: Institutions,A|verage WBGI 2009|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS|IV|
|Human Opportunity Index*||0.762***|0.957***|
|||(0.08)|(0.12)|
|British Legal System|2.821|9.493*|7.547|
||(5.75)|(4.29)|(4.46)|
|French Legal System|-6.042|9.046*|11.288*|
||(5.14)|(3.54)|(4.92)|
|Germany Legal System|42.788***|10.966|7.406|
||(11.44)|(5.54)|(8.22)|
|Scandinavian Legal System|51.321***|19.569***|16.394**|
||(11.44)|(5.53)|(4.95)|
|constant|44.444***|11.598*|-1.385|
||(4.21)|(5.37)|(9.17)|
|R-sqr|0.239|0.79||
|Number of Observations|144|55|48|
|Wald F-statistics from first stage|||21.485|
|**Controlling for Share of Tropical Land**<br>Ri|Dd|Vibl IiiA|WBGI 2009|
|egresson|epene|t arae: nsttutons,|verage|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS|IV|
|% land area in geographical tropics|-26.318***|1.676|12.03|
||(3.92)|(5.77)|(9.74)|
|Human Opportunity Index*||0.794***|1.005***|
|||(0.09)|(0.23)|
|constant|58.603***|17.599**|1.051|
||(2.65)|(6.33)|(16.23)|
|R-sqr|0.219|0.655||
|Number of Observations|163|59|49|
|Wald F-statistics from first stage|||3.306|
|**Controlling for Latitud**||||
|<br>Regression|Depende|nt Variable: Institutions,A|verage WBGI 2009|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS|IV|
|latitude of country centroid|0.376***|-0.09|-0.119|
||(0.08)|(0.07)|(0.10)|
|Human Opportunity Index*||0.830***|0.878***|
|||(0.08)|(0.15)|
|constant|37.923***|18.646***|15.343|
|R|(2.63)<br>012|(5.13)<br>0665|(7.67)|
|-sqr<br>Number of Observations|.<br>163|.<br>59|49|
|Wald F-statistics from first stage|||17.839|
|**Controlling for Longitude**<br>|d|bl||
|Regression<br>Instrument: Logof Wheat Sugar Sustainability|OLS<br>Depene|OLS<br>t Variae: Institutions,A|IV<br>verage WBGI 2009|
|longitude of country centroid|-0.043|-0.055*|-0.082*|
||(0.04)|(0.03)|(0.03)|
|Human Oortunit Index*||0803***|0821***|
|ppy||.<br>(0.07)|.<br>(0.11)|
|constant|46.779***|18.086***|15.866*|
||(2.21)|(5.04)|(7.11)|
|R-sqr|0.008|0.678||
|Number of Observations<br>ld ii f fi|163|59|49<br>|
|Wa F-statstcs rom rst stage<br>* p<0.05, ** p<0.01, *** p<0.001|||21.201|

*Average Mathematics, Reading and Science Outcomes Level 2 - PISA 2009

30
**Table 9B - Robustness Checks: Effect of Equality of Opportunity on Institutional Quality Dissimilarity Index - Average Mathematics, Reading and Science Outcomesfor Level 2 - PISA 2009** **<u>Controlling for Ethnic Fractionalization</u>**

|Regression<br>|Depend|ent Variable: Institutions,Av|erage WBGI 2009|
|---|---|---|---|
|Instrument: Logof Wheat Sugar Sustainability<br>Index of Ethnic Fractionalization (0-Low to 1-high)|OLS<br>-44.247***|OLS<br>-4.193|IV<br>-7.488|
||(6.76)|(9.35)|(13.41)|
|Dissimilarity Index*||-1.794***|-2.069**|
|||(0.26)|(0.61)|
|constant|67.794***|90.581***|93.098***|
||(3.44)|(3.57)|(4.32)|
|R-sqr|0.188|0.541||
|Number of Observations|187|60|48|
|Wald F-statistics from first stage|||11.346|
|**Controlling for Legal Origin**<br>Regression|Depend|ent Variable: Institutions,Av|erage WBGI 2009|
|Instrument: Logof Wheat Sugar Sustainability<br>Dissimilarity Index*|OLS|OLS<br>-1.744***|IV<br>-2.573***|
|||(0.23)|(0.55)|
|British Legal System|2.821|10.711*|5.979|
||(5.75)|(5.10)|(5.98)|
|French Legal System|-6.042|7.527|9.748|
||(5.14)|(4.19)|(6.76)|
|Germany Legal System|42.788***|15.047*|9.146|
||(11.44)|(6.50)|(8.12)|
|Scandinavian Legal System|51.321***|22.728**|17.319**|
||(11.44)|(6.53)|(5.42)|
|constant|44.444***|79.935***|89.522***|
||(4.21)|(4.08)|(6.41)|
|R-sqr|0.239|0.703||
|Number of Observations|144|55|48|
|Wald F-statistics from first stage|||12.814|
|**Controlling for Share of Tropical Land**<br>Regression|Depend|ent Variable: InstitutionsAv|erage WBGI 2009|
|Instrument: Logof Wheat Sugar Sustainability|OLS<br>|OLS<br> ,|IV<br>|
|% land area in geographical tropics|-26.318***|-3.104|12.987|
||(3.92)|(6.55)|(17.30)|
|Dissimilarity Index*||-1.792***|-2.815**|
|||(0.26)|(1.04)|
|constant|58.603***|89.347***|96.860***|
||(2.65)|(3.14)|(8.55)|
|R-sqr|0.219|0.538||
|Number of Observations|163|59|49|
|Wald F-statistics from first stage<br>|||2.313|
|**Controlling for Latitude**<br>Reression|Deen|ent Variable: InstitutionsA|erae WBGI 2009|
|g<br>Instrument: Logof Wheat Sugar Sustainability|OLS<br>p|OLS<br> ,|IV<br> g|
|latitude of country centroid|0.376***|-0.041|-0.102|
||(0.08)|(0.08)|(0.11)|
|Dissimilarity Index*||-1.901***|-2.392***|
|||(0.25)|(0.58)|
|constant|37.923***|91.473***|97.848***|
||(2.63)|(4.83)|(8.69)|
|R-sqr|0.12|0.539||
|Number of Observations|163|59|49|
|Wald F-statistics from first stage|||11.074|
|**Controlling for Longitud**<br>||||
|Regression<br>Instrument: Logof Wheat Sugar Sustainability|OLS<br>Depen|OLS<br>ent Variable: Institutions,A|IV<br>erage WBGI 2009|
|longitude of country centroid|-0.043|-0.048|-0.087*|
||(004)|(003)|(004)|
|Dissimilarity Index*|.|.<br>-1.893***|.<br>-2.267***|
|||(0.23)|(0.42)|
|constant|46.779***|90.692***|93.971***|
||(2.21)<br>|(3.17)<br>|(4.34)|
|R-sqr|0.008|0.554||
|Number of Observations|163|59|49|
|<br>Wald F-statistics from first stage|||14.576|

* p<0.05, ** p<0.01, *** p<0.001

*Average Mathematics, Reading and Science Outcomes Level 2 - PISA 2009

31
**Table 10 - Overidentification  Test**

# **_<u>A) GDP per capita</u>_**

# **_B)_** **_<u>Institutional Quality</u>_**

|Dependent Variable: Logaritm of Per Capita Income, Average 20|IV<br>05-2010|Dependent Variable: Institutions, Avera|IV<br>ge WBGI 2009|
|---|---|---|---|
|Human Opportunity Index* - PISA Test Scores|0.036***<br>0.005|Human Opportunity Index* - PISA Test Scores|0.749***<br>0.109|
|Constant|7.17***|Constant|19.61**|
||0.354||7.107|
|Number of Observations<br>|49<br>|Number of Observations<br>|49<br>|
|Wald F Statistic (First Stage)|46.515|Wald F Statistic (First Stage)|46.515|
|Overidentification test p-value:||Overidentification test p-value:||
|Hansen J Statistic|0.668|Hansen J Statistic|0.203|
|D-Index* - PISA Test Scores|-0.099***|D-Index* - PISA Test Scores|-2.053***|
||0.021||0.4308713|
|Constant|10.62***|Constant|90.682***|
||0.222||4.725435|
|Number of Observations|49|Number of Observations|49|
|Wald F Statistic (First Stage)<br>|16.832|Wald F Statistic (First Stage)<br>|16.832|
|Overidentification test p-value:||Overidentification test p-value:||
|Hansen J Statistic|0.740|Hansen J Statistic|0.329|
|||* p<0.05, ** p<0.01, *** p<0.001||
|||*Average Mathematics Reading and Science Outcomes|Level 2 - PISA 2009|
|Human Opportunity Index** - Six Grade on Time|0.042***|,<br>**Completing Six Year of Education on Time, Household|<br>Survey Data|
|Constant|0.010<br>6.06***|**_C) Infant Mortality_**||
||0.298<br>|Dependent Variable: Infant Mortality||
|Number of Observations|53||IV|
|Wald F Statistic (First Stage)|4.853|Human Opportunity Index* - PISA Test Scores|-0.367***|
|<br>Overidentification test p-value:|||0.057|
|Hansen J Statistic|0.286|Constant|35.61***<br>4141|
|Dissimilarity Index** - Six Grade on Time|-0.07***|Number of Observations|.<br>49|
||0.012|Wald F Statistic (First Stage)|46.515|
|Constant|8.98***|Overidentification test p-value:||
||0.280|Hansen J Statistic|0.012|
|Number of Observations<br>Wald F Statistic (First Stage)|53<br>10.635|||
|Overidentification test p-value:||D-Index* - PISA Test Scores|1.00***|
|Hansen J Statistic|0.933||0.203|
|* p<0.05, ** p<0.01, *** p<0.001||Constant|0.8|
|*Average Mathematics, Reading and Science Outcomes Level 2 - PISA 2009|||1.815|
|**Completing Six Year of Education on Time, Household Survey Data||Number of Observations|49|
|Note: two-stage least square regressions of development outcomes on inequ|ality|Wald F Statistic (First Stage)|16.832|
|with share of tropical land intrument in addition to log of wheat sugar suitabi|lity ratio|Overidentification test p-value:||
|**_D) Malnutrition_**||Hansen J Statistic|0.038|
|Dependent Variable: Malnutrition||||
||IV|||
|Human Opportunity Index** - Six Grade on Time|-0.49***|Human Opportunity Index** - Six Grade on Time|-1.16***|
||0.146||0.315|
|Constant|30.24***<br>4053|Constant|89.69***<br>9675|
|Number of Observations|.<br>48|Number of Observations|.<br>53|
|Wald F Statistic (First Stage)<br>|4.405|Wald F Statistic (First Stage)<br>|4.853|
|Overidentification test p-value:<br>Hansen J Statistic|0.303|Overidentification test p-value:<br>Hansen J Statistic|0.128|
|Dissimilarity Index** - Six Grade on Time|0.843***|Dissimilarity Index** - Six Grade on Time|2.00***|
||0.145||0.465|
|Constant|-3.83944<br>|Constant|8.813<br>|
|Number of Observations|3.226|Number of Observations|10.326<br>53|
|Wald F Statistic (First Stage)|8.649|Wald F Statistic (First Stage)|10.635|
|Overidentification test p-value:<br>Hansen J Statistic|0.979|Overidentification test p-value:<br>Hansen J Statistic|0.589|
|* p<0.05, ** p<0.01, *** p<0.001||* p<0.05, ** p<0.01, *** p<0.001||
|*Averae Mathematics Readin and Science Outcomes Level 2 - PISA 2009||*Averae Mathematics Readin and Science Outcomes|Level 2 - PISA 2009|
|g , g<br>**Completing Six Year of Education on Time, Household Survey Data||g , g<br>**Completing Six Year of Education on Time, Household|<br>Survey Data|

32