metadata: authors: 0: author: Adoho,Franck M.
1: author: Tien,Ndjore Bienvenue
count: Africa
docna: 0: docna: Inequality in earnings and adverse shocks in
            early adulthood
docty: Policy Research Working Paper
subtopic: Educational Sciences,Gender and Development,Primary Education,Labor Markets,Poverty Reduction Strategies
teratopic: Social Protections and Labor,Education,Poverty Reduction,Gender
abstracts: cdata!: The inequality of opportunity theory
            postulates that achievement gaps arising because of factors
            beyond the individual's control are morally
            unacceptable and should therefore be compensated by society.
            These factors or circumstances range from the
            individual's social background to adverse shocks. Most
            studies have focused on the contribution of social
            background and genetic and other childhood-related
            circumstances to inequality of opportunity. Borrowing
            insights based on the impressionable years hypothesis in
            social psychology, this paper tests how exposure to adverse
            shocks, such as war, in early adulthood (ages 18-25) affects
            the individual's future labor earnings and subsequently
            contributes to earnings inequality. The application to the
            Democratic Republic of Congo is associated with two
            significant takeaways. First, all else equal, individuals
            who experience intensely violent conflict at a young age
            earn significantly less than their counterparts. Second,
            after controlling for the individual's social
            background, the share of overall inequality in earnings
            accounted for by the experience of adverse shocks in early
            adulthood is not negligible, ranging from 2.5 to 3.5
            percent. These insights broaden our understanding in the
            discussion on inequality of opportunity and represent a new
            path in the design of allocation policies that seek to
            reduce inequality and poverty.
repnme: repnme: Inequality in earnings and adverse shocks
            in early adulthood
display_title: Inequality in earnings and adverse
            shocks in early adulthood
listing_relative_url: /research/2018/08/30325353/inequality-earnings-adverse-shocks-early-adulthood
topicv3: Primary Education m1326139 480,Education m1327048 470,Gender m1327621 615,Jobs m1503931 2670,Labor Earnings m1504041 2738
publishtoextweb_dt: 2018-08-03T00:00:00Z
historic_topic: Social Protections and Labor,Education,Poverty Reduction,Gender
no_of_pages: 32
author_keys: 0: author_key: 944874
1: author_key: 1224057
model_extractions: WPS8538 Policy Research Working Paper 8538 Inequality in Earnings and Adverse Shocks in Early Adulthood _Bienvenue N. Tien Franck M. Adoho_ Poverty and Equity Global Practice August 2018
Policy Research Working Paper 8538

# **Abstract**

- The inequality of opportunity theory postulates that achieve ment gaps arising because of factors beyond the individual’s control are morally unacceptable and should therefore be compensated by society. These factors or circumstances range from the individual’s social background to adverse shocks. Most studies have focused on the contribution of social background and genetic and other childhood-related circumstances to inequality of opportunity. Borrowing insights based on the impressionable years hypothesis in social psychology, this paper tests how exposure to adverse shocks, such as war, in early adulthood (ages 18–25) affects the individual’s future labor earnings and subsequently

contributes to earnings inequality. The application to the Democratic Republic of Congo is associated with two significant takeaways. First, all else equal, individuals who experience intensely violent conflict at a young age earn significantly less than their counterparts. Second, after controlling for the individual’s social background, the share of overall inequality in earnings accounted for by the experience of adverse shocks in early adulthood is not negligible, ranging from 2.5 to 3.5 percent. These insights broaden our understanding in the discussion on inequality of opportunity and represent a new path in the design of allocation policies that seek to reduce inequality and poverty..

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/research. The authors may be contacted at fadoho@worldbank.org and btien@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
## **Inequality in Earnings and Adverse Shocks in Early Adulthood**

Franck M. Adoho World Bank Bienvenue N. Tien World Bank

# The World Bank

**JEL Codes:** D31, D63, Z13, N37 **Keywords:** Inequality of opportunity, Adverse shocks, Early Adulthood, Africa
# **I. Introduction**

The question addressed in this study is as follows: To what extent do adverse shocks, such as violent conflicts, experienced in early adulthood (18–25 years) contribute to inequality of opportunity in earnings?

Equality of opportunity as formulated by Roemer (1998) requires that the opportunities of individuals in society be independent of the circumstances of these individuals.<sup>1</sup> Circumstances are defined in this context as matters imposed on individuals in ways that they could not have influenced or controlled (Roemer 1993, 1998). This includes, for instance, race, gender, place of birth, family background, and adverse shocks. Thus, there is equality of opportunity if the positions of individuals in an outcome distribution are independent of individual circumstances and are influenced only by personal effort (Roemer 1993, 1998). The implication is that, for the purposes of justice or equity, the achievement gap resulting from the circumstances of individuals is ethically unacceptable and must be compensated by society (Anerson 1989; Ferreira and Gignoux 2008, 2011; Ferreira and Peragine 2015; Lefranc, Pistolesi, and Trannoy 2009; Peragine 2004; Roemer 1998). Although stimulating, this implication is associated with two essential practical issues that pundits on the subject must face. The first is related to the way to implement equality of opportunity, and the second revolves around the way to measure the degree of inequality in society (Kanbur and Wagstaff 2016; Pignataro 2012).

On the first issue, Roemer (1998) proposes in his seminal work that, for meaningful allocation policy, social planners must partition the influences on the chosen outcomes individuals face into circumstances and effort; effort is the only choice variable over which an individual may be held accountable. On the second issue, Ferreira and Peragine (2015) find that, over the years, the array of contributions on the subject is sizable, and the issue has been raised in various spheres of human activity and across various domains of public policy. Brunori, Ferreira, and Peragine (2013); Ferreira and Peragine (2015); Fleurbaey and Peragine (2013); Ramos and Van de gaer (2016); and Roemer and Trannoy (2016) provide substantial reviews of the literature on the subject.

Overall, emerging evidence across studies, mostly on income inequality in advanced economies, concludes that the share of inequality attributable to circumstances ranges from 10 percent to 30 percent (Ferreira and Peragine 2015; Hufe et al. 2017). Some authors characterize this share of inequality arising because of circumstances as surprisingly small and argue that one reason for this is the lack of data on circumstance variables in empirical studies (Hufe et al. 2017; Roemer 2017). This data limitation may explain why most studies have examined parental backgrounds (the educational attainment and labor status of parents), sex, race, and birthplace as circumstances:

> 1 Ferreira and Peragine (2015) state that this discussion has its root in political philosophy and in normative economics through such notable thinkers as John Rawls (1971), Ronald Dworkin (1981a, 1981b), and Amartya Sen (1980). However, Roemer (1998) was the first to formalize this issue in applied economics.

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information on these variables is usually available in household survey data (Bourguignon, Ferreira, and Menéndez 2007; Checchi and Peragine 2010; Cogneau and Mesplé-Somps 2008; Ferreira and Gignoux 2008, 2011; Hassine 2012; Lefranc, Pistolesi, and Trannoy 2008; Pistolesi 2009; Singh 2012).

Nonetheless, recent analyses strive to incorporate other circumstances deemed relevant. A notable example is the inclusion of circumstances related to the childhood environment. In a recent paper, Hufe et al. (2017) argue that all measurable achievements and behaviors of children before the age of consent should be considered as the results of their circumstances.<sup>2</sup> Therefore, the children should not be responsible for any of their accomplishments before that age. Examples of circumstance variables related to childhood environment include school support from parents, playing with parents, perceived quantity of time with the mother, and so on. Using two data sets, the National Longitudinal Survey of Youth 1979 and the 1970 British Cohort Study that capture data on childhood environment, Hufe et al. (2017) find that, in the U.S. sample (the longitudinal survey), the share of income inequality attributable to circumstances rises from 27 percent to 43 percent, while, in the U.K. sample, it rises from 18 percent to 27 percent. Making a similar argument (but with no empirical application), Roemer (2017) posits that all achievements of children before the age of consent (14 or 16 years of age), such as birthweight, nutritional measures, health status, and test scores, should be attributed to nature and nurture and should therefore be categorized as circumstances.

Using microdata on Germany, Peichel and Ungerer (2016) analyze how inequality of opportunity estimates are affected if a partner or spouse’s circumstance or effort variables are factored in as if they were one’s own circumstances. They consider selected sets of circumstance variables (gender, year of birth, place of birth, and so on) and effort variables (work experience, working hours, education, industry) among individuals ages 25–55 in their analysis. Their results indicate that, including spouse’s variables can increase inequality of opportunity measures by more than 20 (35) percent of gross (net) earnings.

Across all these studies, a common trait is the extensive focus on the use of mostly genetic and social backgrounds (including childhood environment) as circumstance variables. This paper therefore examines whether, beyond genetic and social backgrounds, there are other circumstantial factors that may contribute to inequality of opportunity. Answering this question requires an understanding of various circumstantial factors that are relevant for inequality of opportunity analysis. Ramos and Van de gaer (2016) label factors that affect individual outcomes, say earnings, as luck and list four categories of luck: social background luck (factors related to the family or social background one happens to inherit), genetic luck (constituent characteristics of the individual), brute luck (situations in which the individual cannot alter the probability that an event

> 2 The authors define age of consent as the age at which a child is legally considered an adult, which they indicate may vary across countries.

3
takes place), and option luck (risks that individuals might deliberately take that are assumed to be calculated, isolated, anticipated, and avoidable).<sup>3</sup> This characterization of factors based on the concept of luck undoubtedly provides a useful orientation in gathering relevant circumstance variables for measuring the magnitude of inequality of opportunity in a given context. Yet, most empirical analyses of inequality of opportunity do not explicitly consider luck as a factor (Peichel and Ungerer 2016).<sup>4</sup> The current state of the empirical literature on inequality of opportunity has mostly and at best documented factors related to genetic and social backgrounds and, more recently, has included investigations of childhood environment.

However, taking the individual’s life as a continuum, Lundberg and Wuermli (2012) emphasize that the developmental stage at which certain events, such as adverse shocks, occur matters and may have long-lasting effects. This emphasis on the lasting impact of certain events that happen at a specific developmental stage supports the impressionable years hypothesis in social psychology. According to this hypothesis, economic and political beliefs are formed mostly during early adulthood (ages 18–25) and change only slowly thereafter (Krosnick and Alwin 1989). Krosnick and Alwin argue that the historical environment in which young people become active participants in society shapes their basic values, attitudes, and worldviews during that time span. Once this socialization process has been completed, the core orientations remain constant throughout the rest of their lives.<sup>5</sup> To the extent possible, the adversities that individuals experience during this sensitive period should therefore be factored into the analysis of future outcomes, such as earnings, in the inequality of opportunity debate because individuals do not have control over these experiences. However, the impressionable years hypothesis has not been tested in the economic literature on inequality of opportunity.

Against this background, this paper suggests two related hypotheses. First, it anticipates that individuals who have experienced intense adverse shocks at a young age will earn less than others in their age-group who have not had such experiences. Second, the intensity of the adverse shocks experienced during this sensitive period of life will contribute significantly to inequality in labor earnings. The study uses insights derived from social psychology to test the contribution of adverse shocks among the young to inequality in earnings. The relevant adverse shocks may include natural

> 3 The terms brute luck and option luck were coined by Dworkin (1981b). Ferreira and Peragine (2015) suggest that the brute feature of luck may be revealed either initially or later. According to them, initial brute luck encompasses social background luck and genetic luck, while brute luck that emerges later includes unexpected, random events that might take place later in life, such as natural disasters, wars, accidents, and so on. Throughout this paper, brute luck refers to events related to brute luck that emerge later, while initial brute luck, such as an individual’s social background, is explicitly considered in this paper among the separate circumstantial variables of inequality.

> 4 Lefranc, Pistolesi, and Trannoy (2009) and Lefranc and Trannoy (2016) provide a discussion on the inclusion of luck in the analysis of inequality of opportunity.

> 5 “The impressionable years hypothesis proposes that individuals are highly susceptible to attitude change during late adolescence and early adulthood and that susceptibility drops precipitously immediately thereafter and remains low throughout the rest of the life cycle” Krosnick and Alwin (1989, 416).

4
disasters (floods, earthquakes), economic recession, war, and so on. The analysis is applied to the Democratic Republic of Congo with a special focus on episodes of violent conflict in that country. Two main data sources are used. The paper relies on the Polity IV data set measuring the major episodes of political violence (MEPVs) in the country. The MEPV is used as a proxy to construct an index that captures the intensity of adverse shocks that individuals experienced in their early adulthood. To maintain consistency with earlier studies on inequality of opportunity, the socioeconomic characteristics of individuals are included using the 2012/13 wave of the 1-2-3 Household Survey.

Estimates from both parametric and nonparametric approaches offer important insights. The first takeaway is that intense exposure to adverse shocks, such as MEPVs, in young adulthood is negatively correlated with future labor earnings. These results are robust after controlling for other factors, such as social background, educational attainment, sector of occupation, and current area of residence. Second, experiencing violent conflicts in early adulthood contributes between 2.5 percent and 3.5 percent of the inequality in earnings in the Democratic Republic of Congo, and this is mostly prevalent among men in urban areas. These results expand the knowledge of other relevant circumstance variables that should be factored in during the discussion on inequality of opportunity. The findings also invite greater attention to adverse shocks that individuals experience during early adulthood, especially in designing social policies aimed at reducing inequality and poverty.

The reminder of the paper proceeds as follows. Section II sets out the analytical framework of the measurement of the intensity of adverse shocks experienced during young adulthood in an environment of recurrent MEPVs. Section III describes the data and estimation techniques. The estimation results are discussed in section IV, and section V concludes.

# **II. Shocks in early adulthood and inequality in labor earnings: An analytical framework**

The impressionable years hypothesis motivates the analysis on how shocks experienced in early adulthood affect inequality in earnings. In general, according to the hypothesis, adverse events, such as recession, civil wars, and natural disasters, can have long-lasting psychological impacts on affected populations, and the impacts depend on the development stage of the individuals affected (Lundberg and Wuermli 2012). Two sets of findings have emerged through this analysis. First, the emphasis is on the long-term impact of shock occurrences at specific points in time. Second, a focus of interest is the developmental stage of the individuals affected by the shocks.

On the former, Burgess et al. (2003), Kahn (2010), Oreopoulos, Vonwachter, and Heisz (2012), and Stevens (2008), for instance, indicate that entering the labor force during a recession can result in long-term unemployment and lower lifetime earnings. Oyer (2006, 2008) also finds that adverse 5
initial labor market conditions can have important long-term effects on the earnings of college graduates. In addition, some studies look at the link between historical events, such as economic shocks, and beliefs. The underlying idea is that historical events shape people’s beliefs and perceptions. For instance, Friedman and Schwartz (1963) find that the Great Depression basically shattered the depression generation’s confidence in the future of capitalism. In similar fashion, Cogley and Sargent (2008) define macroeconomic shocks, such as the Great Depression, as belieftwisting events. According to them, the effects of these shocks can be long-lasting because it takes time to correct the pessimistic perceptions induced precisely by the shocks. On taxation, Piketty (1995) argues that shocks can alter people’s beliefs about the relative importance of luck versus effort as a driver of success and therefore on the amount of taxes they vote for. Most importantly, on income inequality, Piketty (1995) shows that, when people are young and start with the same beliefs, they put forth the same effort. Therefore, inequality only results from the shocks people experience. Piketty basically argues that, as time passes, people who have experienced negative shocks may become discouraged and, as a consequence, supply less effort. Less effort may take the form, for instance, of less labor supply, which eventually translates into low earnings.

In addition to the long-term impact of shocks, other studies emphasize the developmental stage of the affected individuals at the time the shocks occur. Thus, Elder (1999), in an analysis of the Great Depression in the United States, finds that the effect depended partly on the developmental stage of the individual at the time of the economic shock. Furthermore, in applying the impressionable years hypothesis to economics, Giuliano and Spilimbergo (2014) examine whether experiencing a recession during early adulthood permanently alters one’s preference for redistribution.<sup>6</sup> They use pooled cross-sectional data from the 1972–2010 General Social Survey, along with other data sets, and find that individuals who experienced a recession when young (ages 18–25) come to believe that success in life depends more on luck than effort.<sup>7</sup> These individuals also had a statistically significant preference for redistribution. Following Guiliano and Spilimbergo (2014), one may wonder how exposure to adverse shocks in early adulthood may contribute to inequality in outcomes among individuals, say income.

Taken together, these findings imply not only that adverse experiences shape people’s beliefs and have long-lasting impacts on outcomes among individuals, but also the development stage of these individuals at the time of the shocks is equally important. As a consequence, shocks associated with brute luck, such as civil wars, and the development stage of the affected individuals should be jointly accounted for in analyses of inequality of opportunity. The paper aims to document the extent to which the experience of violent conflict (associated with brute luck) in early adulthood contributes to inequality in labor earnings in the Democratic Republic of Congo, which represents

> 6 Giuliano and Spilimbergo (2014) are among the first to explicitly apply the impressionable years hypothesis to economic issues.

> 7 The other data sets are the National Longitudinal Survey of the High School Class of 1972 and the WVS (World Values Survey) (database), King's College, Old Aberdeen, United Kingdom, http://www.worldvaluessurvey.org/wvs.jsp.

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an ideal case because the country has, since its independence on June 1, 1960, undergone several tumultuous periods, from “independence to anarchy” (Turner 2009, 346). Data from the Polity IV data set indicate that MEPVs occurred in 36 of the years between 1960 and 2012. Figure 1 shows the various MEPVs registered in the country between 1960 and 2012. Over these years, there were three distinct periods of MEPVs: 1960–67, 1977–84, and 1992–2012; each episode can be assigned a value of intensity on a scale from 1 (lowest) to 10 (highest). The durations of the MEPVs also varied, ranging from 7 years in both 1960–67 and 1977–84 to 20 years in 1992–2012.<sup>8</sup>

**Figure 1:** Magnitudes of (Societal and Interstate) MEPVs, **Figure 2:** Circumstances and Developmental Stages the Democratic Republic of Congo, 1960–2012

<!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1960 64 68 72 76 80 84 88 92 96 2000 04 08 2012<br><!-- End of picture text --> <!-- Start of picture text --> 2012<br><!-- End of picture text --> <!-- Start of picture text --> 0 year 18 years 25 years<br>Circumstances in childhood Circumstances in early Adulthood<br>up to age of consent: e.g.: adulthood: e.g.:<br>social backgrounds adverse shocks<br><!-- End of picture text --> _Source:_ Polity IV data, MEPV version, May 2016. _Note:_ Scale: 0 = no episode; 1 (lowest) to 10 (highest).

Analyzing these MEPVs in the light of the impressionable years hypothesis suggests that individuals in the 18–25 age-group may experience different frequencies and degrees of MEPV in the Democratic Republic of Congo. For instance, some individuals may have experienced MEPVs during one to seven years, and, in extreme cases, some may never have experienced MEPVs, while others may have experienced MEPVs over their entire early adulthood, that is, for up to eight years.

To put this into perspective in terms of circumstances relevant to discussions on the inequality of opportunity, figure 2 displays developmental stages among individuals ages 0–25, along with probable circumstances associated with each stage. The first segment portrays the stage from childhood to the age of consent (up to age 18). The literature on inequality of opportunity mostly emphasizes circumstances related to genetic and social backgrounds during this stage. The second segment, the focus of this paper, is early adulthood (ages 18–25). During this phase, factors related to later brute luck are of interest. An example is the exposure of individuals to adverse shocks, such as violent conflicts. The third phase, adulthood, is beyond the scope of this analysis.

> 8 The cutoff year in the sample is 2012 because the macrodata can be linked with the household survey that was conducted in 2012. Nonetheless, this should not be taken to mean the country has not registered any MEPVs since then. For more on MEPVs in recent years, see “INSCR Data Page,” Center for Systemic Peace, Vienna, VA, http://www.systemicpeace.org/inscrdata.html.

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That the experience of exposure to MEPVs during early adulthood differs among individuals leads one to conclude that the intensity of the shocks experienced also varies. The frequency of shocks is therefore an important parameter in modeling the dynamics of the intensity of MEPVs. It therefore matters whether an individual experiences eight years or only one year of MEPVs during this period.

Likewise, following the literature on resilience and coping mechanisms in fragile contexts, one must factor in the possibility that individuals who are experiencing MEPVs in early adulthood may become more resilient or used to shocks (Cherewick et al. 2016; Russo et al. 2012; Rutter 2012; Southwick and Charney 2012; Wu et al. 2013). Because the aim is to approximate, to the extent possible, the intensity of shocks, one must not fail to include the resilience factor.

Against this background, a model is now outlined that captures the intensity of MEPVs experienced in early adulthood. One aim is to distinguish across individuals who experienced MEPVs in early adulthood by the intensity of their exposure. Some individuals may have experienced more exposure than others. This will help in the construction of the typology of individuals in early adulthood according to the intensity of their respective exposure to MEPVs when they were young in the Democratic Republic of Congo, thereby contributing to the inequality decomposition analysis.

It is assumed that the intensity of MEPV exposure **_I_** to violent conflicts �, is increasing in MEPV �� �� frequency **_N_** , �0 , and decreasing in individual’s age **_A_**<sup>9</sup> **_,_** �0. Moreover, the intensity of �� �� MEPV exposure should be 0 if an individual never experienced an MEPV during early adulthood.

Formally, the intensity of MEPV exposure **_I_** is approximated as follows: Therefore, (1) is the approximation of the intensity of MEPV exposure during young adulthood, �� where � � can be understood as a resilience factor. Furthermore, the intensity of MEPV in (1) experienced in early adulthood is normalized for the remaining analysis<sup>10</sup> . This index ranges from

> 9 The fact that the outcome among individuals, actual earnings for example, is associated with age **_A_** of the individuals at the time of the survey leads to the use of this age as a discounted factor. Actual age in the sample is calculated based on the survey year, 2012. The median magnitude of violent conflict, _S_ , during the early adulthood of each individual �� is used. This ensures that the assumption �� �0 holds for every individual.

> 10 �� �����������⁄ ��� ������ where ���� is the maximum of I and ����is the minimum of I.

8
0 (no or low-intensity exposure) to 1 (high-intensity exposure). The next section outlines the estimation methods as well as the variable construction for the analysis.

# **III.Estimation Methods, Data, and Variable Construction**

# **a) Estimation Methods**

This section describes the methodology used to compute measures of inequality of opportunity in this paper. The framework upon which the choice of estimation method rests is first briefly described. Roemer’s idea of opportunity egalitarianism, which focuses on reducing inequalities that are deemed unfair (leveling the playing field), guides the choice of estimation techniques in this paper.<sup>11</sup> The literature highlights that two welfare criteria are within the egalitarian view: the compensation principle and the reward principle. Basically, the reward principle addresses how to reward efforts among individuals experiencing identical circumstances, while the compensation principle demands that inequalities associated with circumstances be eliminated (Ferreira and Peragine 2015; Fleurbaey 1995; Ramos and Van de gaer 2016). The compensation principle can be formulated according either to an ex post version or an ex ante version (Roemer 1993; Van de gaer 1993). The ex ante version argues that there is inequality of opportunity if individuals face different sets of opportunities because of their circumstances. The ex post view is concerned with differences among individuals exerting the same level of effort, independently of their circumstances. In other words, in the ex ante version, compensation is due prior to the realization of effort, while, in the ex post version, compensation is due after the efforts are realized (Ferreira and Peragine 2015; Ramos and Van de gaer 2016).

Taken separately, the ex ante and ex post views of compensation each embody two measurement approaches (Ferreira and Peragine 2015). In the ex ante view, these are the between-types inequality approach and the direct unfairness approach, while the within-tranche inequality approach and the fairness gap approach make up the pair in the ex post view of compensation. The authors extensively discuss the pros and limits of these measurement approaches. Brunori, Ferreira, and Peragine (2013) and Ferreira and Peragine (2015) also document that, in practice, although empirical applications of all four approaches exist, most measures of inequality of opportunity have followed the ex ante between-types approach; except Checchi and Peragine (2010), who compute ex ante between-types measures as well as ex post within-tranche measures. Brunori, Ferreira, and Peragine (2013) review eight papers that use the ex ante between-types measure. These papers cover 41 countries, ranging from Guinea to Luxembourg, and therefore allow an international comparison. In line with the standard practice of estimating the degree of inequality of opportunity in an outcome, such as labor earnings, this paper therefore follows the frameworks of Bourguignon, Ferreira, and Menéndez (2007) as extended by Checchi and Peragine (2010) and Ferreira and Gignoux (2008, 2011). Thus, the paper relies on both ex ante between-

> 11 This section does not formally review existing approaches in the literature. Ferreira and Peragine (2015) and Ramos and Van de gaer (2016) provide extensive discussion about all existing approaches.

9
types measures and ex post within-tranche measures.<sup>12</sup> Roemer (1998) proposes that factors affecting outcomes such as earnings be categorized into circumstances and effort variables. Accordingly, the earning function can be specified as follows: where _yi_ are an individual’s earnings; _Ci_ denotes a vector of circumstance variables; _Ei_ denotes a vector of effort variables; and _ui_ are unobserved factors. Recall that the circumstance variables are outside an individual’s control and are therefore exogenous. The effort variables, however, are endogenous because they may depend on circumstances (that is, parental background, ethnicity, region of birth, and so on) and other unobserved determinants. Equation (2) can therefore be rewritten as follows:<sup>13</sup> Romer’s concept of equality of opportunity requires that F(y|C) = F(y).<sup>14</sup> Therefore, to measure inequality of opportunity is to measure the extent to which F(y|C) ≠ F(y). The following subsections describe how to compute inequality of opportunity using both nonparametric and parametric approaches.

# **Nonparametric Approach**

The nonparametric approach, as elaborated by Checchi and Peragine (2010), is based on two alternative partitions of the entire sample that are based on two alternatives for computing inequality of opportunity. The first partition divides the population into groups by circumstance categories; the members of each group, named types, are endowed with similar circumstances. The second partition, based on effort, splits the population into subsets (tranches) of individuals who exert the same degree of effort. Because effort cannot be observed, a person’s effort is measured, following Roemer (1998), by his or her quantile in the income or earnings distribution of the subgroup of the individual’s type. So, all individuals in the same quantile of the type distribution of earnings are considered to exert the same level of effort. The present analysis provides estimates using both methods. The decomposition using both the types and the tranche methods follow. **Types.** In this partition, inequality of opportunity is given by inequality between types. This inequality can be assessed by applying a smoothing transformation using a constant reference value of effort, �<sup>�</sup> , namely, ����, �<sup>�</sup> � ∀�. The smoothed distribution can be represented by the

> 12 Recent studies, such as Hassine (2012) and Singh (2012), use the frameworks of Bourguignon, Ferreira, and Menéndez (2007) and extended by Checchi and Peragine (2010) and Ferreira and Gignoux (2008, 2011). The elaboration and notation here follow these papers closely. This paper takes no credit for the exposition of the framework hereafter. 13 For simplicity, subscripts for individual elements of the circumstance and effort vectors are omitted, but are kept whenever necessary to avoid misunderstanding.

> 14 Ferreira and Gignoux (2008, 6) discuss the conditions that this concept implies.

10
{uc} {uc} {Uc} {uc} I gd _ ~~Iu)~~ types 1(F(y)) ye ~~ey~~ e yt Olypes =1- 1({z7}) I(F(y)) rT _—4_ ~~T({te})~~ % =1 <u>1(F(y))</u> {He} , co <u>iu</u> , Ofranches = U({z;})/I(FW))
w — In(y) = Ca+EB+u E=BC+v In(y) = C(a+fB)+ vB +u In(y) =Cot+e + = exp[C@ + &] 9, — ~~eel~~ ! oval i) = exp[C/@! +c] Gi! + &]
Therefore, the circumstance _J_ -specific share of inequality of opportunity can be given as follows:

# **b) Data, Variable Construction, Identification, and Summary Statistics**

# **Data, Variable Construction, and Identification**

Two data sources are used for the analysis. First, the paper relies on the 2012/13 wave of the nationally representative 1-2-3 Household Survey, which covered all 11 of the provinces into which the Democratic Republic of Congo was divided until 2015. It provides information on individual characteristics (gender, education, occupation, sector of activity, earnings, and so on) and the family background of individuals, such as father’s occupation, sector of activity, and education.

The sample is restricted to working men and women in the 20–64 age-group in urban and rural areas with known sector of employment and positive total monthly labor earnings.<sup>16</sup> In addition, the sample is further restricted to those individuals whose fathers were working when they were 15 years old and who had never migrated outside the country. The latter restriction is added to ensure that the results are not driven by migration effects. This resulted in a final sample of 11,609 individuals of whom 6,561 (5,048) were men (women), and 6,219 (5,390) were living in urban (rural) areas.

Following Cogneau and Mesplé-Somps (2008), information on the occupation and educational attainment of fathers is combined to define four social origins of the individual, as follows: (i) farmers with no education or with primary education, (ii) farmers with secondary or tertiary education, (iii) nonfarmers with no education or with primary education, and (iv) nonfarmers with secondary or tertiary education. The positions of individuals (the sons and daughters) are also defined in the same way as those of the fathers. However, contrary to Cogneau and Mesplé-Somps (2008), the unemployed, students, and retirees are not included because the outcome variable here is the labor earnings of individuals, that is, the sample captures only individuals who, at the time of the survey, reported they were employed.

The second data source is the Polity IV data set measuring the MEPVs in the country. The MEPV variable of interest is the magnitude of all societal MEPVs. This includes episodes of civil and ethnic violence as well warfare involving the country during a year. The scale of the magnitude

> 16 Total labor earnings is defined here as the sum of all income derived from primary and secondary employment and self-employment for individuals. In the construction of the labor earnings aggregates, the bottom 1 percent and top 1 percent were trimmed to correct for outliers.

13
ranges from 1 (lowest) to 10 (highest), and is represented by 0 if, in a year, there was no MEPV registered. The data set covers 1960 to 2012.

One aim is to categorize individuals, in a reasonable fashion, based on the intensity of their exposure to MEPVs in early adulthood using equation (1). However, the paper first addresses the fact that the intensity of MEPV exposure in young adulthood, by construction, may be endogenous. The median share of the urban population in the country is therefore used over the early adulthood years of individuals as an instrument to circumvent this potential endogeneity.<sup>17</sup> (This issue is discussed in appendix A.) The data for the urban population share in the country are from the World Development Indicators database.<sup>18</sup> Once the endogeneity issue has been addressed, the intensity of MEPV exposure index is computed residually.<sup>19</sup> To categorize individuals based on the intensity of their exposure, the sample is split into terciles, and t-tests are performed. The t- statistics indicate there is no significant difference between the two lower terciles. Therefore, individuals with (residually computed) MEPV exposure indexes in the upper tercile are considered to have experienced MEPVs at a high intensity in young adulthood, while the result is a 0 (no or low-intensity MEPV exposure in young adulthood) among those in the two lower terciles. The residually defined MEPV index for those in the upper tercile is between 0.74 and 1.00.<sup>20</sup> For the parametric and nonparametric analysis, the circumstance variables include the social background of the fathers of the individuals and a dummy for the intensity of MEPV exposure in early adulthood. The set of circumstances therefore identifies eight types, that is, the results of four father social backgrounds, times two MEPV exposure intensities. As noted by Ferreira and Gignoux (2008, 2011), the number of categories for each circumstance (here, the social background of fathers and the intensity of MEPV exposure in early adulthood) is restricted to four or fewer to reduce the number of types exhibiting zero or few observations in the sample. This is highly relevant for the nonparametric analysis, which relies on the precision of the estimates of conditional means for each type (or cell of the partition). A description of the sample, along with summary statistics on earnings (the main dependent variable) and the circumstance variables, is presented in the next subsection.

# **Summary Statistics**

Appendix B, table B.1 presents the breakdown of the final sample by the social backgrounds of fathers, the social characteristics of the individuals, and the circumstance categories. A look at the

> 17 The rationale for using the share of the median urban population over the individual’s early adulthood years is in �� line with the argument laid out above, that is, to ensure that �� �0 in equation (1) holds for every individual.

> 18 See WDI (World Development Indicators) (database), World Bank, Washington, DC, http://data.worldbank.org/products/wdi.

> 19 The fractional logit estimation technique is used because the dependent variable, the intensity of MEPV exposure, is bounded between 0 and 1 (Papke and Wooldridge 1996).

> 20 Rankings of individuals in the initial MEPV index compared with rankings using the residually determined MEPV index, with the respective cutoff measures (upper tercile versus two lower terciles), are stable. The kappa coefficient is about 0.9, indicative of an almost perfect agreement (Viera and Garett 2005).

14
social background of fathers reveals that most fathers were farmers with no education or with only primary education (41.1 percent), followed by nonfarmers with no education or with only primary education (29.3 percent). Most in the children’s generation are nonfarmers with secondary or tertiary educational attainment (45.5 percent), and about a quarter (24.8 percent) are farmers with no education or with some primary education. The increased share of nonfarmers among the children’s generation may indicate an overall rise in school enrollment in the country. Regarding the circumstance categories, the sample is skewed toward individuals with parents who did not have any formal education and who experienced low exposure to MEPVs when young (table B.1). For instance, 48.2 percent of the individuals had parents with no formal education and who had experienced low MEPV exposure. Independently of the social background of fathers, the share of individuals who experienced a high level of MEPV exposure in early adulthood ranged between 3.4 percent and 13.4 percent (table B.1).

Figure 3, panels a and b display, respectively, the distribution of labor earnings conditional on the background of the father and the intensity of MEPV exposure in early adulthood. Two patterns may be observed. First, the earnings of individuals rise with the social status of the fathers. Thus, individuals with fathers who were in nonfarm occupations and who had secondary or tertiary educational attainment earn more than their counterparts whose fathers undertook agricultural activities and who had no formal education. In other words, having a farmer father is always the most disadvantageous social background. Cogneau and Mesplé-Somps (2008) document similar findings in five countries (Côte d’Ivoire, Ghana, Guinea, Madagascar, and Uganda) in SubSaharan Africa. Moreover, individuals who experienced high-intensity MEPVs in early adulthood appear to earn less in the labor market than their counterparts who had no or little experience with even low-intensity MEPVs.

**Figure 3, panel a: Distribution of earnings conditional on the background of the father**

**Figure 3, panel b: Distribution of earnings conditional on intensity of MEPV exposure in early adulthood**

<!-- Start of picture text --> 1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 Farmers with No/Prim Edu .2<br>Farmer with Sec/Ter Edu<br>Nonfarm No/Prim Edu No/low exposure<br>0 Nonfarm with Sec/Ter Edu 0 High exposure<br>8 10 12 14 16 8 10 12 14 16<br>Log(Earnings) Log(Earnings)<br>Cumulative Probability Cumulative Probability<br><!-- End of picture text --> 15
Appendix B, table B.2 displays the mean labor earnings across areas of residence and gender within each of the eight types (the various social backgrounds of fathers and intensity of MEPV exposure in early adulthood), along with the respective number of observations. On average, the earnings of individuals increase as the social background of fathers improves and decrease with the intensity of MEPV exposure. For instance, at the national level, the highest earnings (CGF 138,820) are among individuals whose fathers were in nonfarm activities, were more well educated, and had experienced low MEPV exposure in young adulthood. By contrast, the lowest average earnings (CGF 33,823) were among individuals whose fathers were in farm activities, had no formal education, and had experienced high MEPV exposure in early adulthood. Thus, having a father with low social background, combined with a high intensity of MEPV exposure in early adulthood, is always the most disadvantageous circumstance. This pattern holds across the gender and residence dimensions (table B.2) and is also in line with the description in figure 3, panels a and b.

# **IV. Results**

Both the nonparametric and parametric methods are applied to measure the degree of inequality of opportunity in labor earnings in the Democratic Republic of Congo. Given the difference between the two estimation methods, the results are presented separately. The nonparametric analysis and parametric analysis have also been done separately in urban and rural areas. The rationale behind this disaggregation is that the nature of jobs and subsequent sources of earnings in these two areas may differ. Indeed, results at the aggregate level, which show the averages in the two areas, may fail to capture the contrast between the areas. Tables illustrating each estimation technique display the results on overall earnings inequality and the degree of inequality of opportunity using the mean log deviation (MLD), which is the only inequality measure characterized by a pathindependent decomposition.

# **a) Nonparametric Results**

Table 1 reports the nonparametric results. The level of overall earnings inequality in the Democratic Republic of Congo, based on the MLD measure, is 63.5 percent. Analysis based on both the tranche approach and the types approach indicates that more than 14 percent of this inequality is associated with only two circumstance variables, namely, the social background of the fathers of individuals in the sample and the intensity of the individual’s exposure to MEPV in early adulthood.

16
**<u>Table 1: Estimates of earning inequality and inequality of opportunity</u>**

||_Overall earnings_|_Tranches_|_approach_|_Types a_|_pproach_|
|---|---|---|---|---|---|
||_inequality_|_Opportunity_<br>_inequality_|_Opportunity_<br>_share_|_Opportunity_<br>_inequality_|_Opportunity_<br>_share_|
||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|
|Full sample||||||
|All|0.635<sup>***</sup>|0.0901<sup>***</sup>|0.142<sup>***</sup>|0.0911<sup>***</sup>|0.144<sup>***</sup>|
||(0.014)|(0.002)|(0.003)|(0.001)|(0.004)|
|Male|0.618<sup>***</sup>|0.0954<sup>***</sup>|0.154<sup>***</sup>|0.0932<sup>***</sup>|0.151<sup>***</sup>|
||(0.019)<br>|(0.002)<br>|(0.005)<br>|(0.002)<br>|(0.006)<br>|
|Female|0.567<sup>***</sup>|0.0828<sup>***</sup>|0.146<sup>***</sup>|0.0881<sup>***</sup>|0.155<sup>***</sup>|
||(0.022)|(0.002)|(0.005)|(0.002)|(0.007)|
|Urban sample<br>All|0.618<sup>***</sup>|0.0900<sup>***</sup>|0.146<sup>***</sup>|0.0560<sup>***</sup>|0.0907<sup>***</sup>|
||(0.016)<br>|(0.003)<br>|(0.004)<br>|(0.001)<br>|(0.004)<br>|
|Male|0.556<sup>***</sup>|0.0938<sup>***</sup>|0.169<sup>***</sup>|0.0561<sup>***</sup>|0.101<sup>***</sup>|
||(0.021)<br>|(0.005)<br>|(0.007)<br>|(0.002)<br>|(0.006)<br>|
|Female|0.581<sup>***</sup>|0.0838<sup>***</sup>|0.144<sup>***</sup>|0.0551<sup>***</sup>|0.0948<sup>***</sup>|
||(0.029)|(0.003)|(0.006)|(0.002)|(0.005)|
|Rural sample<br>All|0.393<sup>***</sup>|0.0641<sup>***</sup>|0.163<sup>***</sup>|0.00721<sup>***</sup>|0.0184<sup>***</sup>|
||(0.009)|(0.001)|(0.005)|(0.000)|(0.001)|
|Male|0.386<sup>***</sup>|0.0669<sup>***</sup>|0.173<sup>***</sup>|0.00731<sup>***</sup>|0.0189<sup>***</sup>|
||(0.011)|(0.002)|(0.007)|(0.000)|(0.001)|
|Female|0.333<sup>***</sup>|0.0603<sup>***</sup>|0.181<sup>***</sup>|0.00703<sup>***</sup>|0.0211<sup>***</sup>|
||(0.012)|(0.002)|(0.008)|(0.000)|(0.001)|

_Note:_ Bootstrapped standard errors in parentheses based on 99 replications.<sup>*</sup> _p_ < .10<sup>**</sup> _p_ < .05<sup>***</sup> _p_ < .001 Table 1 also shows that earnings inequality is greater in urban areas (61.8 percent) than in rural areas (39.3 percent). In urban areas, according to both the tranche and types approaches, inequality of opportunity as a share of overall earnings inequality ranges from 9.5 percent (table 1, column 5) to 16.9 percent (table 1, column 3) across the gender spectrum; the averages across gender in urban areas are 9.1 percent (table 1, column 5) and 14.6 percent (table 1, column 3). The difference in inequality of opportunity between men and women in urban areas is highly significant at the 1 percent level. In rural areas, however, the contribution of unequal opportunity to earnings inequality is greater among women according to the tranche approach. The nonparametric tranche approach yields relatively larger shares of inequality of opportunity than the nonparametric types approach. This might derive from the large sampling variance and the small cells (Ferreira and Gignoux 2008; Hassine 2012). Independently of the decomposition employed, the results should be treated as lower-bound estimates (Ferreira and Peragine 2015, Hassine 2012).

# **b) Parametric Results**

The second part of the analysis revolves around the parametric estimates. First, to obtain the overall opportunity share of earnings, the reduced form equation (10) has been estimated. Table 2 reports the results of the reduced form for all. Overall, the circumstance variables have the expected effects on earnings. Here again, the social background of fathers matters, especially for individuals with 17
fathers at a higher social status compared with fathers who were farmers with no education or only primary education. Most importantly and as conjectured, individuals who have experienced high MEPV exposure in early adulthood earn, on average, between 22 percent and 29 percent less than their counterparts who have not had this experience, after controlling for other circumstances and other relevant covariates. This confirms the descriptive patterns described in section 3.

**Table 2: OLS Reg. of individual earnings on observed circumstances and other covariates, dep. var.:** **<u>ln(income)</u>**

||_Reduced_|_-form spec_|_ification_|_Includin_|_g other co_|_variates_|
|---|---|---|---|---|---|---|
||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|Father social origin (Ref.: Farmer with No/Prim|||||||
|Edu)|||||||
|Farmers with Sec/Ter Edu|0.133<sup>***</sup>||0.193<sup>***</sup>|0.0224|0.00257|−0.0169|
||(0.0400)||(0.0397)|(0.0369)|(0.0367)|(0.0364)|
|Nonfarm No/Prim Edu|0.417<sup>***</sup>||0.400<sup>***</sup>|0.153<sup>***</sup>|0.0663<sup>**</sup>|0.0201|
||(0.0234)||(0.0233)|(0.0225)|(0.0227)|(0.0223)|
|Nonfarm with Sec/Ter Edu|0.766<sup>***</sup>||0.782<sup>***</sup>|0.405<sup>***</sup>|0.297<sup>***</sup>|0.215<sup>***</sup>|
||(0.0260)||(0.0256)|(0.0260)|(0.0261)|(0.0257)|
|Binary for MEPV intensity (1=high exposure)||−0.284<sup>***</sup>|−0.297<sup>***</sup>|−0.257<sup>***</sup>|−0.240<sup>***</sup>|−0.223<sup>***</sup>|
|||(0.0208)|(0.0203)|(0.0190)|(0.0188)|(0.0184)|
|Male||||0.410<sup>***</sup>|0.439<sup>***</sup>|0.430<sup>***</sup>|
|||||(0.0189)|(0.0188)|(0.0183)|
|Son social origin (Ref.: Farmer with No/Prim Edu)|||||||
|Farmers with Sec/Ter Edu||||0.124<sup>***</sup><br>(0.0279)|0.0898<sup>**</sup><br>(0.0277)|0.116<sup>***</sup><br>(0.0275)|
|Nonfarm No/Prim Edu||||0.273<sup>***</sup>|0.137<sup>***</sup>|0.121<sup>***</sup>|
|||||(0.0298)|(0.0306)|(0.0303)|
|Nonfarm with Sec/Ter Edu||||0.777<sup>***</sup>|0.595<sup>***</sup>|0.536<sup>***</sup>|
|||||(0.0238)|(0.0257)|(0.0256)|
|Urban area|||||0.386<sup>***</sup><br>(0.0213)|0.291<sup>***</sup><br>(0.0212)|
|Constant|10.15<sup>***</sup>|10.55<sup>***</sup>|10.24<sup>***</sup>|9.759<sup>***</sup>|9.688<sup>***</sup>|10.23<sup>***</sup>|
||(0.0137)|(0.0126)|(0.0155)|(0.0193)|(0.0195)|(0.0377)|
|Observations|11609|11609|11609|11609|11609|11609|
|Adj. R<sup>2</sup>|0.078|0.015|0.094|0.226|0.247|0.290|
|Province dummies (Ref.: Kinshasa)|No|No|No|No|No|Yes|

_Note:_ OLS = ordinary least squares. Robust standard errors in parentheses.<sup>*</sup> _p_ < .10<sup>**</sup> _p_ < .05<sup>***</sup> _p_ < .001 The results in table 2, column 3 represent the reduced form estimates (equation 10), whereby only circumstance variables are included. These coefficients capture not only the direct effect of observed circumstances on earnings, controlling for effort (such as the educational attainment of individuals), but also the indirect effect through effort. These estimates are used to calculate the overall share of (observed) opportunities in earnings inequality as defined in equation (12). Using these estimates, the counterfactual distribution specified in equation (11) is simulated.

Table 3 presents the MLD coefficients on the actual observed and counterfactual earnings distribution for the full sample as well for urban and rural areas. It also reports the corresponding estimates of overall inequality of opportunity. At the national level, the overall opportunity share 18
in total observed earnings inequality ranges from 14 percent to 16 percent across the gender spectrum; the national average is 15.6 percent (table 3, column 4). In urban areas, the overall opportunity share in total earnings inequality ranges from 7 percent to 10 percent across gender, and the average in urban areas is 9.4 percent (table 3, column 4). Based on the parametric method, it appears that inequality of opportunity in earnings in rural areas is marginal; the average in rural areas is less than 2 percent. Inequality of opportunity in earnings therefore appears, at least in the case of the Democratic Republic of Congo, to be an urban phenomenon.

**<u>Table 3: The contribution of unequal opportunities to earnings: MLD and Ratios</u>**

||_Total observed_<br>_inequality (MLD,_<br>_factual)_|_MLD estimate_<br>_(CFD)_|_Inequality of_<br>_opportunity level_|_Share in Total_<br>_observed_<br>_inequality_|
|---|---|---|---|---|
||_(1)_|_(2)_|_(3)_|_(4)_|
|Full sample|||||
|All|0.635<sup>***</sup>|0.535<sup>***</sup>|0.0991<sup>***</sup>|0.156<sup>***</sup>|
||(0.014)|(0.012)|(0.007)|(0.009)|
|Male|0.618<sup>***</sup>|0.515<sup>***</sup>|0.103<sup>***</sup>|0.167<sup>***</sup>|
||(0.019)<br>|(0.014)<br>|(0.010)<br>|(0.012)<br>|
|Female|0.567<sup>***</sup>|0.487<sup>***</sup>|0.0796<sup>***</sup>|0.140<sup>***</sup>|
||(0.022)|(0.016)|(0.009)|(0.013)|
|Urban sample<br>All|0.618<sup>***</sup>|0.559<sup>***</sup>|0.0583<sup>***</sup>|0.0944<sup>***</sup>|
||(0.016)|(0.016)|(0.008)|(0.011)|
|Male|0.556<sup>***</sup>|0.501<sup>***</sup>|0.0549<sup>***</sup>|0.0987<sup>***</sup>|
||(0.021)<br>|(0.020)<br>|(0.010)<br>|(0.016)<br>|
|Female|0.581<sup>***</sup>|0.537<sup>***</sup>|0.0446<sup>***</sup>|0.0767<sup>***</sup>|
||(0.029)|(0.028)|(0.010)|(0.016)|
|Rural sample<br>All|0.393<sup>***</sup>|0.386<sup>***</sup>|0.00659<sup>***</sup>|0.0168<sup>***</sup>|
||(0.009)|(0.010)|(0.002)|(0.004)|
|Male|0.386<sup>***</sup>|0.379<sup>***</sup>|0.00698<sup>**</sup>|0.0181<sup>**</sup>|
||(0.011)<br>|(0.012)<br>|(0.002)|(0.006)|
|Female|0.333<sup>***</sup>|0.331<sup>***</sup>|0.00211|0.00633|
||(0.012)|(0.012)|(0.002)|(0.007)|

_Note:_ MLD = mean log deviation. CFD = counterfactual distribution. Bootstrapped standard errors in parentheses based on 99 replications.<sup>*</sup> _p_ < .10<sup>**</sup> _p_ < .05<sup>***</sup> _p_ < .001 Table 4 assesses the roles of individual circumstance variables in the preceding results. It basically presents the MLD coefficients for factual earnings and counterfactual earnings, obtained by equalizing each circumstance variable in turn, while controlling for others as elaborated in equation (13). In a similar fashion as in the case of the estimates in table 3, the shares of each circumstance-specific opportunity in total observed inequality are also reported. The estimates of the shares of circumstance-specific opportunity in total inequality should be interpreted as descriptive evidence. Because of the fact that, if any unobserved circumstance variable is correlated with the individual (observed) circumstance variables, then the estimates are likely to be biased (Ferreira and Gignoux 2011; Singh 2012).

19
**<u>Table 4: Contribution of individual circumstance variables to earnings inequality: MLD (and Ratio)</u>**

||<br>_Total_<br>_observed_||<br>_Equalizing fath_<br>_social origin_|<br>_er_<br>|<br>_Equa_<br>|<br>_lizing MEPV e_<br>_in early adulth_|<br>_xposure_<br>_ood_|
|---|---|---|---|---|---|---|---|
||_inequality_<br>_(MLD,_<br>_factual)_|_MLD_<br>_estimate_<br>_(CFD)_|_Inequality of_<br>_opportunity_<br>_level_|_Share in_<br>_Total_<br>_observed_<br>_inequality_|_MLD_<br>_estimate_<br>_(CFD)_|_Inequality_<br>_of_<br>_opportunity_<br>_level_|_Share in_<br>_Total_<br>_observed_<br>_inequality_|
||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|_(7)_|
|Full sample<br>All|<br>0.635<sup>***</sup>|<br>0.550<sup>***</sup>|<br>0.0850<sup>***</sup>|<br>0.134<sup>***</sup>|<br>0.619<sup>***</sup>|<br>0.0159<sup>***</sup>|<br>0.0251<sup>***</sup>|
|Male|(0.014)<br>0.618<sup>***</sup>|(0.013)<br>0.530<sup>***</sup>|(0.006)<br>0.0880<sup>***</sup>|(0.008)<br>0.142<sup>***</sup>|(0.013)<br>0.600<sup>***</sup>|(0.003)<br>0.0178<sup>***</sup>|(0.004)<br>0.0287<sup>***</sup>|
|Female|(0.019)<br>0.567<sup>***</sup>|(0.015)<br>0.492<sup>***</sup>|(0.008)<br>0.0747<sup>***</sup>|(0.011)<br>0.132<sup>***</sup>|(0.017)<br>0.562<sup>***</sup>|(0.003)<br>0.00476|(0.005)<br>0.00839|
||(0.022)|(0.017)|(0.009)|(0.012)|(0.024)|(0.006)|(0.007)|
|Urban sample<br>All|0.618<sup>***</sup>|0.579<sup>***</sup>|0.0385<sup>***</sup>|0.0624<sup>***</sup>|0.599<sup>***</sup>|0.0191<sup>***</sup>|0.0310<sup>***</sup>|
|Male|(0.016)<br>0.556<sup>***</sup>|(0.018)<br>0.521<sup>***</sup>|(0.007)<br>0.0352<sup>***</sup>|(0.011)<br>0.0633<sup>***</sup>|(0.016)<br>0.537<sup>***</sup>|(0.004)<br>0.0193<sup>***</sup>|(0.007)<br>0.0347<sup>***</sup>|
|Female|(0.021)<br>0.581<sup>***</sup>|(0.022)<br>0.544<sup>***</sup>|(0.009)<br>0.0378<sup>***</sup>|(0.016)<br>0.0650<sup>***</sup>|(0.019)<br>0.576<sup>***</sup>|(0.004)<br>0.00520|(0.006)<br>0.00894|
||(0.029)|(0.027)|(0.009)|(0.015)|(0.032)|(0.008)|(0.014)|
|Rural sample<br>All|<br>0.393<sup>***</sup>|<br>0.390<sup>***</sup>|<br>0.00321<sup>**</sup>|<br>0.00816<sup>**</sup>|<br>0.390<sup>***</sup>|<br>0.00313<sup>**</sup>|<br>0.00796<sup>**</sup>|
||(0.009)|(0.010)|(0.001)|(0.003)|(0.009)|(0.001)|(0.003)|
|Male|<br>0.386<sup>***</sup>|<br>0.383<sup>***</sup>|<br>0.00354<sup>**</sup>|<br>0.00917<sup>**</sup>|<br>0.383<sup>***</sup>|<br>0.00322<sup>*</sup>|<br>0.00833<sup>**</sup>|
||(0.011)|(0.012)|(0.001)|(0.004)|(0.011)|(0.002)|(0.004)|
|Female|<br>0.333<sup>***</sup>|<br>0.332<sup>***</sup>|<br>0.00123|<br>0.00371|<br>0.332<sup>***</sup>|<br>0.000511|<br>0.00153|
||(0.012)|(0.012)|(0.001)|(0.004)|(0.012)|(0.002)|(0.006)|

_Note:_ MLD = mean log deviation. CFD = counterfactual distribution. Bootstrapped standard errors in parentheses based on 99 replications;<sup>*</sup> _p_ < .10<sup>**</sup> _p_ < .05<sup>***</sup> _p_ < .001 The analysis of the effect of each individual circumstance starts with the individual’s social background. The sample estimates show that the social background of the individual’s father is associated with the largest share of inequality in earnings. Overall, the share of earnings inequality accounted for by the social background of the individual’s father is between 13 percent and 14 percent across gender; the overall average is 13.4 percent. In the urban sample, the social status of the father is associated with 6 percent to 7 percent of total earnings inequality. However, in rural areas, the social background of the individual’s father is associated with less than 1 percent of earnings inequality (table 4, column 4). On parental social background, the results are in line with earlier studies. For instance, using household income as outcome, Ferreira and Gignoux (2008, 2011) document that the mother’s educational attainment is associated with 9 percent to 12 percent of total inequality in six Latin American countries (Brazil, Colombia, Ecuador, Guatemala, Panama, and Peru). In the case of urban India, Singh (2012) finds that the father’s educational attainment is associated with 11 percent to 18 percent of total labor earnings inequality across the cohort. In the Egyptian context, Hassine (2012) documents that the relative shares of earnings 20
inequality associated with both the educational attainment and labor market backgrounds of the mother and the father are between a low of 0 percent to 4.1 percent.

The shares of earnings inequality accounted for by MEPV exposure in early adulthood ranges from 2.5 percent to 3.5 percent (table 4, column 7). These results are highly significant and reveal some key insights. First, the contribution of MEPV exposure in young adulthood to earnings inequality appears to be more prevalent in urban areas and among men. Second, these results are not negligible. In fact, the relative shares of other circumstances besides parental backgrounds (educational attainment or labor status), such as gender or birthplace by urban or rural area or region in earlier studies, are within similar ranges as the estimates of MEPV exposure in early adulthood here. For example, in Latin America, Ferreira and Gignoux (2011, 2008) find that inequality in income related to gender ranges from a low of 0 percent to 5 percent. In the case of the Arab Republic of Egypt, Hassine (2012) shows that inequality in earnings related to birthplace by urban or rural area or region is 1.7 percent to 3.0 percent.

# **V. Conclusion**

The concept of egalitarianism in opportunity argues that unequal outcomes resulting from the circumstances of individuals are unfair and should therefore be compensated by society. According to this view, circumstances are factors that are beyond the individual’s control, such as gender, place of birth, social background, and adverse shocks. In analyzing inequality of opportunity, most studies have extensively focused on circumstantial factors related to sex, gender, place of birth, social background, and childhood environment.

This paper therefore asks whether, beyond genetic factors and social background, there are other circumstantial elements that may contribute to inequality of opportunity that have not yet been documented empirically. For instance, the non-inclusion of factors related to adverse shocks, such as civil conflicts, represents the departure point of this paper. Borrowing insights based on the impressionable years hypothesis in social psychology, the paper argues that adverse shocks, such as wars, experienced by individuals in early adulthood (ages 18–25) should be considered as a consequence of circumstance. Social policies that seek to reduce inequalities should thus account for the exposure of individuals to shocks during this sensitive developmental stage.

Against this background, this paper develops an analytical framework to measure the intensity of adverse shocks experienced in early adulthood. Both parametric and nonparametric approaches are used to measure the inequality of opportunity in labor earnings in the country. The first takeaway is that intense exposure to adverse shocks, that is, to MEPV, in young adulthood is negatively correlated with future labor earnings. The estimates range from 22 percent to 29 percent. These results are robust after controlling for other factors, such as the social background of the fathers of individuals in the sample, the educational attainment of the individuals, the sector of 21
occupation, and the area of residence. The second takeaway is that the level of overall earnings inequality in the Democratic Republic of Congo, based on the MLD measure, is 63.5 percent.

Across the parametric and nonparametric decompositions, the two circumstance variables (the social background of the fathers of the individuals in the sample and the intensity of the individuals’ exposure to MEPV in early adulthood) account for between 14 percent and 16 percent of total earnings inequality among all workers in the country. Similarly, these two circumstance variables account for between 9 percent and 15 percent of total earnings inequality among urban workers. However, in rural areas, the two circumstance variables account for less than 2 percent of earnings inequality among workers.

The results on the circumstance-specific opportunity shares of inequality in labor earnings in the country offer insights. The estimates show that the social background of the fathers of the individuals in the sample is associated with the largest share of inequality in earnings, ranging from 13 percent to 14 percent across the gender spectrum. The overall average is 13.4 percent. These results are in line with the findings of earlier studies (Ferreira and Gignoux 2008, 2011; Singh, 2012 and Hassine, 2012).

The second important takeaway from the circumstance-specific analysis is the shares of the contribution of exposure to MEPVs in early adulthood. After controlling for the other circumstances (the social background of the individual’s father), the share of overall inequality in earnings accounted for by exposure to MEPVs in early adulthood is not negligible, ranging from 2.5 percent to 3.5 percent. To put this into perspective, the relative shares of other circumstances besides parental social backgrounds (educational attainment or labor status), such as gender, birth in urban or rural area, or by region in earlier studies are within similar ranges to the estimates of MEPV exposure in early adulthood here. For example, in Latin America, Ferreira and Gignoux (2008, 2011) find that inequality in income related to gender ranges from a low of 0 percent to 5 percent. In the case of Egypt, Hassine (2012) shows that inequality in earnings related to birth area and to birth region is between 1.7 percent and 3.0 percent.

This contribution, applied to a fragile state (but not limited to such a context), complements existing empirical studies on inequality of opportunity. It also invites greater attention on the shocks that individuals experience during early adulthood, especially in the design of social policies that seek to reduce inequality and poverty.

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