long-run determinants of intergenerational job status mobility · 2015-07-09 · causation.” in...

Long-run determinants of intergenerational job status mobility

John A. Bishop

Department of Economics (East Carolina University, US). Tel: +01 2523286756 E-mail: [email protected]

Haiyong Liu

Department of Economics (East Carolina University, US). Tel: +01 2523286006 E-mail: [email protected]

Juan Gabriel Rodríguez1

Departamento de Análisis Económico I (Universidad Complutense de Madrid, Spain). Tel: +34 91 3942515. E-mail: [email protected]

Abstract

There are conflicting views of the primary role of inequality in economic development. Many believe that higher income shares at the top reflect substantial economic contributions while others think that these increases in top shares have not translated into higher economic growth. Others have pointed out that, regardless of the origin of total inequality, higher inequality could hurt economic performance by decreasing intergenerational mobility. We contribute to this debate on inequality and intergenerational mobility by examining the relationship between job status mobility and past inequality. We find a robust negative effect of lagged inequality on upward intergenerational job status mobility and a robust positive effect of lagged inequality on downward intergenerational job status mobility. In addition, we find that the quality of political institutions and religious fractionalization both contribute positively to job status mobility. Higher levels of past GDP result in less upward job status mobility and more downward job status mobility.

Keywords: Intergenerational mobility; job status; income inequality;

JEL codes: J62, D31, I30

1 Responsibility for any error is the authors’ alone. Professor Rodríguez acknowledges funding from the Ministerio de Ciencia e Innovación under project ECO2013-46516-C4-4-R and Fundación Caja Canarias (Spain).

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1. Introduction

The recent debate between those who think that incomes at the top have grown much faster than

average because they have made significant economic contributions (Mankiw, 2013) and those

who believe that top income share increases in the U.S. since the 1970s have not translated into

higher economic growth (Piketty et al., 2011) has spun around the primary driving force behind

inequality. However, some scholars have pointed out that, regardless of the origin of total

inequality, higher inequality could hurt economic performance by increasing the intergeneration

earnings elasticity (IGE), i.e., the capacity of parent's earnings for predicting their child's future

earnings (Krueger, 2012; Corak, 2013a and 2013b). If this is true, the significant rise of

inequality in the aftermath of the Great Recession will cause an undesirable decrease on social

mobility in the future and, consequently, a deterioration of economic performance and

opportunity. To throw some light on this debate, we analyze the long-run determinants of

intergenerational mobility. In particular, we study the main factors affecting long-run upward

mobility, downward mobility and immobility in job status with special attention given to the

impact of inequality.

Corak (2013b) has recently noted that there is a tendency of “countries with higher income

inequality to be countries where a greater fraction of economic advantage and disadvantage is

passed on from parents to children.” This relationship has been coined the Great Gatsby Curve

(GGC) by Alan Krueger (2012) and a heated debate about this relationship has spread to the

blogs of Paul Krugman and Greg Mankiw as well as the popular press (NPR News Hour,

Economist Magazine and the National Review). We address here the Job Status Mobility Great

Gatsby Hypothesis– higher inequality leads to less intergenerational job status mobility. Status

mobility may be a more appropriate measure of social mobility than earnings mobility. In fact,

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Clark (2014) argues that status is a better measure of social mobility as high income parents may

have children that choose relatively low paying high status jobs.

In addition to past inequality, we consider other potential factors that might impact on

intergenerational mobility. First, following Glaeser et al. (2004), we evaluate the capacity of

human capital accumulation to increase intergenerational mobility. By providing more

opportunities, a larger accumulation of human capital should benefit the upward mobility of

talented and hard-worked individuals. Second, following the institutional economics literature

(Hall and Jones, 1999; Acemoglu et al., 2002; Easterly and Levine, 2003), we estimate the

importance of the quality of institutions to improve social intergenerational mobility. If the

political institutions of limited government are not implemented properly, a self-perpetuating

process of wealth inheritance could be established due to rent-seeking and corruption activities.

Social mobility could also be influenced by social division or fractionalization (Alesina et al.,

2003; Barro and McCleary, 2003). To recognize this possibility we consider ethno linguistic

fractionalization and religious fractionalization. To evaluate the role of government for social

intergenerational mobility, we consider the level of government expenditure. Finally, we

consider the antiquity of state because it has been shown that this variable is a good proxy of

social infrastructure (Bockstette et al., 2002).

Our analysis is focused on the long-run; all the factors influencing mobility are lagged (circa) 20

years. To measure mobility we exploit the International Social Survey Program’s Social

Inequality Survey (Waves 3-5) to construct an unbalanced panel of countries for 1992, 1999 and

2009. This database allows us to compute three distinct variables of mobility: upward

intergenerational job status mobility; intergenerational job status immobility; and downward

intergenerational job status mobility. In principle, these three measures of intergenerational

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mobility should be explained by the same factors which call for the estimation of a system of

three equations, one for each type of mobility.

The dispersion between countries is larger than dispersion within countries because the time

dimension is short, and there are explicative factors that are time invariant (ethno linguistic and

religious fractionalization and the antiquity of state). For these reasons, we employ a random

effects technique. Finally, the panel is unbalanced and our estimation procedure explicitly takes

this into account. Given all these requirements, we apply the Random Effects SUR Method for

Unbalanced Panels proposed in Biorn (2004). The basic model includes the lag of Gini (circa 20

years) and two variables representing the degree of development (lag of real GDP per capita in

logarithms and lag of total population in logarithms) (see Acemoglu et al., 2005). Then, the rest

of variables are added to the previous specification one by one in order to see if the effect of past

inequality goes away when other factors that could be considered potential channels of

transmission are included. We complete our analysis with robustness checking.

We find a robust negative effect of lagged inequality on upward intergenerational mobility and a

robust positive effect of lagged inequality on downward intergenerational mobility. Moreover,

the impact of lagged inequality on intergenerational immobility is not significant in most

specifications. The quality of institutions and religious fractionalization show a robust impact on

intergenerational mobility. In particular, and in accordance with previous literature, better

institutions and higher religious fractionalization favor upward mobility, while deter downward

mobility. Additionally, higher levels of past GDP result in less upward job status mobility and

more downward job status mobility. The rest of factors are not generally significant.

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The rest of the paper is organized as follows. A brief overview of the current debate on the

relationship between inequality and intergenerational mobility is provided in Section 2. In

Section 3 we discuss the sources of data we use and offer an examination of the mobility indices.

Section 4 presents the model we estimate. The empirical results, and a discussion of their

significance, are presented in Section 5. Some robustness checks are provided in Section 6.

Finally, Section 7 concludes.

2. The impact of inequality on intergenerational mobility

It is often argued that income inequality is innocuous as long as there is income mobility. The

reason lies on the fact that there is a significant cost to the economy and society if children from

low-income families do not have anything close to the opportunities to develop and apply their

talents as their more fortunate counterparts from better-off families, who attend better schools,

receive college prep tutoring, and draw on a network of family connections in the job market.

Consequently, if the Great Gatsby curve is true--higher inequality reduces intergenerational

mobility—then countries cannot compensate for income inequality through income mobility.

The greater the distance in a country between a high and a low income, the harder it would be to

go from the latter to the former or vice versa.

The relationship between income inequality and social mobility has been found to be positive

and significant not only at the country level (Prieto-Rodríguez et al., 2008) but also at the

regional level (Prieto-Rodríguez et al., 2010). Unfortunately, obtaining observations of the

intergenerational elasticity of earnings (at any level) is more difficult than measuring social

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mobility. In fact, the study of the GGC has typically allowed only a correlation analysis.2 Despite

this, Corak believes it is “too glib to dismiss it by simply saying correlation does not imply

causation.” In his view the Great Gatsby Curve summarizes the “whole host of ways that

inequality of incomes affects children” (2013b; p.7).

Unfortunately, the scarcity of data is not the only problem. The IGE estimates usually come from

independent studies where databases offer different levels of reliability and measures of income

are defined in a different way across countries. Worse still, many countries do not conduct

studies tracking children’s income as they grow older, so researchers must estimate childhood

income using an algorithm obtained from a separate data set and compare the result against

actual adult-child income. In addition, some researchers such as Clark (2014) suggest that pure

earnings mobility may not accurately capture social mobility.

Perhaps more importantly, as the Great Gatsby Curve postulates that past economic inequality

diminishes current opportunities, the temporal gap between inequality and intergenerational

mobility should be at least one generation (around 20 years). Otherwise, the GGC would simply

reflect a contemporary relationship and, therefore, inequality and mobility are related to each

other when they share common factors (Björklund and Jantti, 1997; Solon, 2004). For example,

the GGC in Corak (2013a) and Krueger (2013) is calculated from inequality data for about 1985,

and intergenerational mobility measures for cohorts of children born during the early to mid-

1960s with adult outcomes measured in the mid to late 1990s. That is, the temporal gap

considered is approximately one decade. This gap does not seem to be sufficiently large to avoid

2 Corak (2013a) analyzes the existence of the GGC based on the observation of 22 countries, while Bishop et al. (2014) use 39 countries.

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the fact that common factors to both variables might drive the estimation of the Great Gatsby

curve.3

Finally, there is the problem of omitted variables. Apart from the impact of lagged inequality on

intergenerational mobility, there could be other causal mechanisms by which inequality can

cause less mobility. For example, Manzi (2012) finds that alternative explicative variables like

country population (bigger countries contain more variety) and religious fractionalization are

also good candidates to explain the GGC.

To address these shortcomings we focus on the measurement of intergenerational mobility in job

status. First, we take the International Social Survey Program (ISSP) that originates from a cross-

national collaboration so the databases are homogeneous and the measures of income are similar

across countries. In this manner, mobility estimates are based on individual perceptions of job

status mobility, rather than models. In addition, the use of the ISSP allows us a large sample of

observations, enough to construct an unbalanced panel of countries for 1992, 1999 and 2009, and

to estimate not only intergenerational immobility but also upward mobility and downward

mobility. Second, we use the Gini coefficient lagged (circa) 20 years to guarantee that the

temporal gap between inequality and intergenerational mobility corresponds to one generation.

Third, we consider a battery of additional controls. We attempt not only to reduce the omitted

variable problem but also to study other potential long-run factors of intergenerational mobility.

In particular, we consider: the degree of development represented by the lag of real GDP per

capita (in logarithms) and the lag of total population (in logarithms); human capital represented

by the lag of average years of total schooling; the quality of institutions represented by the

3 Clark (2014) using surname data suggests that the relationship between inequality and intergenerational mobility disappears over sufficiently long time periods.

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lagged Polity IV index (better institutions will increase mobility by launching meritocracy and

limiting nepotism in society); (ethno linguistic and religious) fractionalization; the role of

government (lagged public expenditure); and social infrastructure represented by the antiquity of

state.

3. The ISSP Social Inequality Modules

The International Social Survey Program is a cross-national collaboration of surveys covering

topics of interest to social science research. To date, the data consists of 30 modules, beginning

with Role of Government (1985) and extended to topics including religion, citizenship, health,

environment, and social inequality, among others. Our interest is in the social inequality modules

which began in 1987 and were repeated for 1992, 1997 and 2009. Each of these modules asks the

respondents to report their current job status as well as that of their father. The resulting

difference provides a measure of intergenerational job status mobility for our study. To enable a

broader set of cross-country comparisons, the literature has typically focused on the outcomes of

fathers and sons since the analysis needed to address the changing role of women in the labor

force is more complicated. Following previous literature, we restrict our analysis to fathers and

sons.

The Social Inequality modules include 93 country-years with 43 unique countries. Sample sizes

vary from 1335 responses (China, 2009) to 203 responses (East Germany, 2009) with the median

number of responses for a country in each survey module being approximately 500 responses. As

a legacy of earlier surveys, “East” and “West” Germany were surveyed and are treated

separately. We treat the United Kingdom and Great Britain as the same entity.

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Figure 1 provides the actual ISSP job mobility question. Respondents (sons, in our case) are

asked to compare the status of their current job to that of their father during their early teenage

years. There are 5 useable responses, ranging from much higher status to much lower status. We

code job mobility as “upward” (responses 1 and 2), “downward” (responses 4 and 5) or

“immobile” (response 3). We separate the data in to these three categories to recognize that

lagged inequality may have different effects on upward and downward mobility. For example,

the higher lagged Ginis may be associated with both greater downward mobility and decreased

upward mobility.

Table 1 provides the summary statistics for job mobility, ordered from most mobile (least

immobile) to least mobile (most immobile) country-years. For the entire data set (unweighted by

population), 33.26 percent of son’s respond that their job status is about equal with that of their

father, 47.76 percent respond that their job status is higher or much higher than their father’s, and

18.98 percent respond that their job status is lower or much lower than their father’s. The

average 20 year lagged Gini is 30.38. Our sources for the Gini coefficient and the other controls

are described in the Data Appendix.

Examining the mobility data we find that Portugal (1999) and China (2000) show the greatest job

mobility, each with less than 20 percent of son’s reporting no change in job status from that of

their father. Curiously, these finding may be related to dramatic changes in the political structure

a decade earlier; Portugal joining the European Union and China embracing the market

economy. At the other end of the scale (lowest mobility) we find an assortment of Central and

Eastern European Countries.

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Hungary in particular deserves comment—it was among the least immobile in 1987 (20.92

percent), ranked near the mean in 1992 (37.18 percent) and 1999 (33.89 percent), but was then

ranked among the highest in job immobility in 2009 (49.88 percent). However, most of the

countries in our sample do not show such a pattern of variability. Our three observations on job

immobility for Slovenia (1992, 1999, and 2009) vary by only one percentage point (39.57 to

40.56). Other examples include Japan (1999, 2009) which varies between 30.00 to 31.45 percent,

the US (1987, 1992, 1999, 2009) which varies between 23.12 and 28.56 percent, and Australia

(1987, 1992, 1999, 2009) which varies between 27.42 and 31.57 percent. While we include the

1987 mobility statistics in Table 1 we exclude the 1987 data from the estimation results below

due to a small number of countries included in the sample, the difficulty of finding reliable data

for some explicative factors in 1967 and the problem of mixing very different temporal gaps (the

temporal gap between the first two waves, 1987 and 1992, is 5 years while the gap between the

last two waves, 1999 and 2009, is 10 years).

4. The model and econometric strategy

The elasticity of intergenerational mobility measures the persistence in the values of relevant

variables like income, earnings, job status or education attainment over generations between

parents and children. Thus, following Galton (1869), the intergenerational elasticity is derived

from a regression-to-the-mean model, usually as the OLS estimate of the coefficient b in the

following equation:

ititit ybay 1 (1)

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where the constant term a captures the secular trend in outcome growth across generations due to

changes in general labor market conditions and productivity, and the error term it captures other

determinants of the child’s labor market outcome not correlated with parental outcome. The

coefficient b may be referred to as intergenerational elasticity, and measures the degree of

persistence in job status across generations.4 Empirically, the main complication associated with

estimating equation (1) stems from the fact that it is very unlikely that the unobserved

determinants of a child’s job status in the above error term are uncorrelated with her parental

outcome. An instrumental variable approach or other identification method is needed to recover

an unbiased estimate for b.

Alternatively, one may adopt the value-added format for equation (1) by specifying the

difference in job status between parents and children, itm , as a function of the observed

characteristics of parents ( 1itx ). Here, it is important to note that the intergenerational elasticity

in (1) offers an overall average measure of the degree of immobility without imposing any

restriction about the direction of change. However, cross-country heterogeneity may reflect

differences not only in the degree of upward mobility, but also in the magnitude of downward

mobility. For this reason, our model is the following:

Uit

Uit

UUit bxam 1 (2a)

Iit

Iit

IIit bxam 1 (2b)

Dit

Dit

DDit bxam 1 (2c)

4 See Mulligan (1997) for a detailed description of this model.

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where we distinguish between the three distinctive mobility measures: upward job status

mobility ( Uitm ), job status immobility ( I

itm ), and downward job status mobility ( Ditm ). These

dependent variables are, in principle, explained by the same set of independent variables.

However, the key regressor of interest in our study is the income inequality (lagged Gini) so we

utilize the following aggregate form of the specification above:

Ukt

Ukt

UUkt uXM 1 (3a)

Ikt

Ikt

IIkt uXM 1 (3b)

Dkt

Dkt

DDkt uXM 1 (3c)

where UktM , I

ktM and DktM are the portions of respondents of country k in time period t that were

moving upward, immobile or moving downward, respectively. The vector 1ktX represents the

set of country-specific characteristics such as GDP, population, education, institutions,

fractionalization, public expenditures and social infrastructure, as well as income inequality in

time t-1 (parents’ generation).

In principle, we should follow Zellner (1962) and estimate the system of equations above by the

SUR technique in order to take account of the correlation between the errors of the three

equations. Moreover, the sample of countries is more or less random, dispersion between

countries is larger than dispersion within countries because the time dimension is short and there

are some explanatory variables that are time invariant (ethnic-linguistic fractionalization,

religious fractionalization and the antiquity of states). For these three reasons, we should apply a

random effects technique. In addition, the estimation procedure has to consider the fact that our

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panel is unbalanced. Given all these requirements, we employ the random effects SUR method

for unbalanced panels proposed by Biorn (2004).

Observations in our unbalanced data set are observed in at least one and at most 3 periods. We

denote the number of countries observed in l periods, where l = 1, 2, 3, by lN and rearrange the

data set in the way that the 1N countries come first, the 2N countries come second and the 3N

countries come third. Formally, the first set of countries, 1N , will be a cross-section, while the

sets of 2N and 3N countries will be balanced panels. Following Biorn (2004), the error term is

decomposed into

DIUjevu jkt

jk

jkt ,,, (4)

where are unobserved individual country-level effects in the j-th equation and are the

observation-specific errors in the j-th equation at time t. Then, the GLS estimates of the model

are obtained for the countries observed l times (l = 1, 2, 3). The overall GLS estimator is the

matrix weighted average of the group specific estimators, with weights equal to the inverse of

their respective covariance matrices. However, to get efficient estimates of the SUR system, a

second step is necessary. Taking the previous estimates as given, we maximize the log likelihood

with respect to the covariance matrices of v and e. This multistep algorithm is stopped when

overall estimates converge.5

In the next section we present our main results and in Section 6 we carry out some robustness

analysis.

5 We implement this Multistep Maximum Likelihood estimation by using the xtsur command in Stata (Nguyen and Nguyen, 2010).

jkv

jkte

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5. Estimation Results

We begin our analysis by presenting the Job Status Great Gatsby Curves for upward

intergenerational mobility, intergenerational immobility and downward intergenerational

mobility. Figures 2a-c present the scatter plots of job mobility and lagged inequality after

controlling both by the explicative variables under consideration (income, population, education,

institutions, fractionalization, government expenditure and social infrastructure).6 As it is readily

apparent from the figures a higher lagged Gini coefficient is negatively correlated with upward

mobility and positively correlated with downward job mobility. Interestingly, the upward and

downward coefficients are nearly the same magnitude, differing only in their sign. There is no

relationship between the degree of job immobility and the lagged Gini.

Table 2 (the basis for Figures 2a-c) allows us to examine the influence of the confounding

factors. In most cases the coefficients on the confounding factors are insignificant, with the

exceptions being population, government expenditures, and religious fractionalization.

However, as noted above the OLS estimates are inefficient and the following section provides

estimates from the more appropriate random effects SUR method.

Random Effects SUR Method

This section expands our analysis beyond simple OLS to a random effects SUR model for

unbalanced panels. Our approach is to begin with the lagged of Gini (circa 20 years) and the two

variables representing the degree of development (lag of real GDP per capita in logarithms and

lag of log total population). Then, the additional variables are added one by one in order to see if

the effect of past inequality is eliminated when other possible channels of transmission are

6 All estimations in this Section include time dummies and regional dummies (Eastern Europe, America, Asia and Oceania, and Africa and others).

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included. As in the OLS analysis we run three separate equations for upward job mobility,

downward job mobility, and job immobility. When comparing the upward and downward results

we expect symmetry –if past inequality reduces upward mobility then it should also enhance

downward mobility. Of course, we anticipate the same to be true for the confounding factors.

Our results are presented in Tables 3a-c. In all seven specifications the lagged Gini is negative

and significant for upward mobility, in six of the seven specifications the lagged Gini has no

statistically significant effect on job immobility, and in all seven specifications the lagged Gini is

positive and significant for downward mobility. These results are consistent with our OLS

findings. In sum, greater past inequality is associated with less upward mobility and more

downward mobility.

Next we consider the influence on job mobility of the confounding factors, considering both

statistical significance and symmetry. Using this criterion we identify three possible factors that

influence job mobility: lagged real GDP per capita; polity; and religious fractionalization.

Lagged real GDP per capita lowers the degree of upward job mobility and increases the degree

of downward job mobility. Polity (the quality of institutions) increases the amount of upward job

mobility and lowers the amount of downward job mobility. Poor institutions also result in greater

job stasis (immobility). Finally, the literature has found that ethnic-linguistic fractionalization

and religious fractionalization have different effects on growth: the former is bad for growth,

while the second enhances growth (Alesina et al., 2003). In a similar way, we find different

effects for these two types of fractionalization: ethnic-linguistic fractionalization has no impact

on job mobility, while higher levels of religious fractionalization are associated with greater

upward mobility and lower downward mobility.

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Other confounding variables of interest include (lagged) government expenditures, education,

and population size. Higher government expenditures result in greater upward mobility but do

not significantly affect downward mobility. Higher levels of education result in both greater

upward mobility and lower downward mobility in models excluding religious fractionalization.

Our findings for population size are somewhat unusual in that we have sign changes in

alternative models.

6. Robustness analysis

In this section we briefly present several robustness checks of our results. In particular, we

estimate the model with an alternative sample of the Gini coefficients, a shorter time span of the

panel, and by replacing the variable “antiquity of the state” with a “colony” variable.

A major finding of our paper is that there is a relationship between the level of lagged inequality

and job mobility. Is this finding robust to the data source for the Gini employed? The data

sources for the Gini coefficients in Table 1 were the World Bank, OECD and Areppim (see

Appendix). We substitute the Gini coefficients from Areppim with the Gini coefficients from the

UNU-WIDER World Income Inequality Database (WIID). In Table 4 we show that the results

for the three mobility measures are maintained and the significance and sign for the quality of

institutions and religious fractionalization coincide with the results of the previous section. In

addition, it appears that the degree of development represented by the lagged of real GDP per

capita has a negative effect on upward mobility while this effect is positive for downward

mobility.

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Next, in Table 5 we reduce the span of the panel by dropping the first wave of the Social

Inequality module (1992). The sign and significance of the coefficients for the lagged Gini

coefficient are as expected. The quality of institutions enhances upward mobility and harms

immobility, while it is irrelevant for downward mobility. For religious fractionalization, the

results remain the same and for the lagged of real GDP per capita we find again a negative effect

on upward mobility and a positive impact on downward mobility.

Finally, we substitute one variable, the antiquity of the state, by another variable, colony. In

Table 6 we show that the results for the lagged Gini coefficient remain qualitatively the same.

The quality of institutions has a positive effect on upward mobility but harms not only downward

mobility but also immobility. Religious fractionalization, however, only has a (positive)

significant effect on upward mobility. Again, we find that the degree of development represented

by the lagged of real GDP per capita has a negative effect on upward mobility while a positive

impact on downward mobility.

7. Conclusion

The literature has not reached a consensus about the primary role of inequality in economic

development. Specifically, many believe that higher inequality reflects substantial economic

contributions of top incomes while others think that the increase in inequality since the 1970’s

has not translated into higher economic growth. Some scholars have pointed out, however, that

regardless of the origin of total inequality, higher inequality could hurt economic performance by

decreasing social mobility. We contribute to this debate on inequality and social mobility by

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examining the impact of past inequality on job status mobility; i.e., a Job Status Great Gatsby

Curve.

To test our hypothesis we exploit the International Social Survey Program’s Social Inequality

Survey and compute three distinct measures of mobility for an unbalanced panel of countries for

1992, 1999 and 2009. Moreover, we do not consider only past inequality (the lagged Gini) but

also other possible factors (the degree of development, the level of human capital, the quality of

institutions, fractionalization, public expenditures and social infrastructure). We find a robust

negative effect of lagged inequality on upward intergenerational job status mobility and a robust

positive effect of lagged inequality on downward intergenerational job status mobility. In

addition, we find that the quality of political institutions and religious fractionalization both

contribute positively to job status mobility. Higher levels of past real GDP per capita result in

less upward job status mobility and more downward job status mobility.

Future improvements of the available databases should allow researchers to explore these results

in more detail. In this respect, two avenues seem to be promising. First, Palomino et al. (2014)

have recently found that the IGE shows a U-shape for the US, with maximum values at the tails

(0.66 at the 10th percentile and 0.48 at the 90th percentile) and a minimum value of 0.37 at the

70th percentile. Accordingly, it would be interesting to analyze the long-run determinants of

intergenerational mobility by quintiles in order to evaluate if their impact depends on the

distribution. Second, the high persistence of intergenerational mobility and inequality calls for a

dynamic model specification. By estimating a dynamic panel data model we could take into

account the potential reverse causation from intergenerational mobility to inequality.

Nevertheless, we believe that our proposal constitutes a useful first approach to analyze the

factors behind the evolution of intergenerational mobility in the long-run.

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Prieto-Rodríguez, J., Rodríguez, J. G. and Salas, R. (2008): “A study on the relationship between economic inequality and mobility”, Economics Letters, 99, 111-114.

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22

Figure 1. Job Status Question.

Q11: Please think about your present job (or your last one if you don’t have one now). If you compare this job to the job your father had when you were <14/15/16>, would you say that the level of status of your job is (or was)… (please click one box.)

Much high than your father’s 1

Higher 2

About Equal 3

Lower 4

Much lower that your father’s 5

I never had a job 6

I don’t know what my father did/ father never had a job/ never knew 7 father/ father deceased

Note: we code job mobility as “upward” (responses 1 and 2), “downward” (responses 4 and 5) or “immobile” (response 3).

23

Figure 2a. Lagged inequality and upward mobility (adjusted pooled data).

Figure 2b. Lagged inequality and immobility (adjusted pooled data).

Figure 2c. Lagged inequality and downward mobility (adjusted pooled data).

upward mobility (adj.) = 1E-07 - 0.378·L_Gini (adj.)

R2 = 0.0809

-25

-20

-15

-10

-5

0

5

10

15

20

25

-15 -10 -5 0 5 10 15 20 25

L_Gini (adjusted)

Up

wa

rd m

ob

ilit

y (

ad

jus

ted

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job immobility (adj.) = 1E-07 + 0.0055·L_Gini (adj.)

R2 = 6E-05

-20

-15

-10

-5

0

5

10

15

-15 -10 -5 0 5 10 15 20 25

L_Gini (adjusted)

Jo

b i

mm

ob

ilit

y (

ad

jus

ted

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downward mobility (adj.) = -1E-07 + 0.3725·L_Gini (adj.)

R2 = 0.1733

-20

-15

-10

-5

0

5

10

15

20

-15 -10 -5 0 5 10 15 20 25

L_Gini (adjusted)

Do

wn

wa

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24

Table 1. Job Mobility Percentages and Gini Coefficients.

Country Year Immobility Upward Downward L_Gini Portugal 1999 17.82 68.92 13.26 33.00

China 2009 19.33 72.05 8.62 32.40

Israel 1999 19.46 58.96 21.58 36.40

Hungary 1987 20.92 67.36 11.72 22.70

France 1999 21.21 61.42 17.37 40.30

United States 1992 23.12 58.58 18.30 40.10

Poland 1999 24.24 49.64 26.12 23.10

Italy 1992 24.83 65.43 9.74 38.00

Canada 1992 24.89 56.34 18.77 31.60

France 2009 24.90 57.30 17.80 29.00

Portugal 2009 25.66 60.06 14.28 32.90

United States 1999 25.73 53.35 20.92 40.40

United Kingdom/GB 1987 26.18 55.77 18.05 24.60


Philippines 1999 26.83 40.52 32.65 45.40

Spain 1999 26.86 62.25 10.89 26.80

Canada 1999 26.94 49.99 23.07 31.00

Australia 1999 27.42 57.66 14.92 19.00

United States 2009 27.89 49.92 22.19 34.80

Australia 1992 28.33 57.97 13.70 22.50

Croatia 2009 28.47 48.42 23.11 22.80

United States 1987 28.56 56.93 14.51 41.60

South Africa 2009 28.57 49.73 21.70 63.00

Netherlands 1987 29.39 55.26 15.35 35.40

Japan 2009 30.00 30.59 39.41 30.40

New Zealand 1992 30.32 51.62 18.06 54.30

Czech Republic 1992 30.43 47.24 22.33 21.00

Slovak Republic 1992 30.43 47.24 22.33 21.00

Poland 1987 30.50 56.15 13.35 26.00

Italy 1987 30.60 56.58 12.82 40.00

Poland 1992 30.91 53.34 15.75 23.20

Poland 2009 31.03 46.33 22.64 26.90

Philippines 2009 31.05 38.29 30.66 40.60

New Zealand 1999 31.06 47.82 21.12 34.80

Australia 2009 31.13 52.90 15.97 26.60

Italy 2009 31.25 52.46 16.29 29.70

Japan 1999 31.45 26.04 42.51 33.90

Israel 2009 31.46 48.13 20.41 32.90

Australia 1987 31.57 53.48 14.95 24.20

United Kingdom 2009 32.01 47.14 20.85 35.40

Russian Federation 1999 32.34 43.01 24.65 24.50


Slovak Republic 1999 32.44 43.90 23.66 19.60


Korea, Rep. 2009 32.46 42.94 24.60 33.60

Switzerland 2009 33.14 54.23 12.63 33.80

Norway 2009 33.28 50.39 16.33 22.20

Bulgaria 1999 33.60 51.52 14.88 25.80

Hungary 1999 33.89 47.61 18.50 20.70

Spain 2009 33.98 49.56 16.46 33.70

25

Austria 2009 34.38 49.27 16.35 23.60

Ukraine 2009 34.58 39.66 25.76 23.30

Chile 2009 34.60 41.87 23.53 55.30

Finland 2009 34.75 48.81 16.44 20.90

Turkey 2009 34.76 35.82 29.42 43.60

West Germany 1992 34.82 51.11 14.07 38.30

Norway 1999 34.84 48.07 17.09 31.40

New Zealand 2009 34.87 46.67 18.46 31.80

Slovak Republic 2009 35.14 48.58 16.28 19.50

Austria 1992 35.19 54.18 10.63 26.70

Czech Republic 1999 35.27 41.22 23.51 19.60

Denmark 2009 35.27 51.24 13.49 22.60

Philippines 1992 35.41 39.15 25.44 49.40

Sweden 2009 35.45 46.63 17.92 20.90

Sweden 1992 35.47 42.44 22.09 42.80

West Germany 2009 35.94 48.17 15.89 26.70

Estonia 2009 36.30 39.26 24.44 23.00

Chile 1999 36.31 34.45 29.24 47.00

Argentina 2009 36.31 39.11 24.58 46.60

Austria 1999 36.69 51.16 12.15 31.40

Norway 1992 36.72 44.50 18.78 34.80

Taiwan 2009 36.86 35.24 27.90 31.20

Hungary 1992 37.18 46.58 16.24 23.60

Bulgaria 1992 37.59 45.38 17.03 21.80

Sweden 1999 38.26 41.23 20.51 19.70


Venezuela, RB 2009 38.72 39.48 21.80 43.80

East Germany 1992 39.00 51.46 9.54 20.40

Slovenia 1992 39.57 42.20 18.23 22.80

Slovenia 1999 39.59 39.37 21.04 22.00

Latvia 1999 39.86 37.29 22.85 24.80

Slovenia 2009 40.56 38.01 21.43 23.60

Belgium 2009 40.76 45.33 13.91 27.40

East Germany 2009 40.89 43.34 15.77 18.50

Latvia 2009 42.12 34.25 23.63 22.50

Austria 1987 42.18 48.60 9.22 29.30

Cyprus 2009 43.41 52.50 4.09 29.00

Bulgaria 2009 46.28 40.69 13.03 23.40

West Germany 1999 46.62 40.84 12.54 36.60

Iceland 2009 47.32 26.80 25.88 33.10

Czech Republic 2009 47.54 34.23 18.23 23.20

West Germany 1987 48.65 42.20 9.15 38.60

Hungary 2009 49.88 30.63 19.49 27.30

MEAN 33.26 47.76 18.98 30.38

26

Table 2. Long-run determinants of intergenerational mobility.

(Pooled-OLS)

Upward mobility Immobility Downward mobility

L_Gini ‐0.3780** 0.0055 0.3725***

(‐2.01) (0.05) (3.11)

L_ln_rgdp ‐4.3487 0.1239 4.2248

(‐0.98) (0.05) (1.49)

L_ln_pop 2.2806* ‐2.5394*** 0.2589

(1.90) (‐3.68) (0.34)

L_edu ‐0.3091 0.5608 ‐0.2517

(‐0.24) (0.75) (‐0.30)

L_polity 4.8129 ‐4.3932 ‐0.4197

(0.58) (‐0.93) (‐0.08)

L_ethnic ‐5.5478 ‐0.3540 5.9017

(‐0.58) (‐0.06) (0.97)

L_religion 7.3251 1.8088 ‐9.1338*

(0.89) (0.38) (‐1.73)

L_Gov_exp 0.1828 ‐0.3824** 0.1996

(0.72) (‐2.61) (1.23)

L_statehist ‐11.1645 2.3478 8.8167

(‐1.26) (0.46) (1.56)

R20.34 0.56 0.46

Obs. 62 62 62

Note: t statistics in parentesis; * p < 0.1, ** p < 0.05, *** p < 0.01.

Africa and others) are included.

Time dummies and regional dummies (Eastern Europe, America, Asia and Oceania,

27

Table 3a. Long-run determinants of upward intergenerational mobility. (Random Effects SUR Method for Unbalanced Panels)

(1) (2) (3) (4) (5) (6) (7)

L_Gini ‐0.5153*** ‐0.3521*** ‐0.3183*** ‐0.6267*** ‐1.6235*** ‐0.4751*** ‐0.5674***

(‐5.13) (‐3.50) (‐3.09) (‐4.12) (‐5.49) (‐3.63) (‐4.12)

L_ln_rgdp ‐2.6011** ‐8.8566*** ‐10.8661*** ‐3.144 ‐0.6232 ‐18.1663*** ‐18.337***

(‐2.34) (‐6.01) (‐6.61) (‐0.93) (‐0.09) (‐6.66) (‐6.40)

L_ln_pop ‐2.5088*** ‐0.3827 ‐0.5313 5.3241*** 5.8661** ‐0.6136 0.8333

(‐5.14) (‐0.67) (‐0.94) (4.07) (2.19) (‐0.63) (0.42)

L_edu 3.1083*** 2.7361*** 2.8852** ‐0.1304 ‐0.8741 ‐0.4949

(6.57) (5.72) (2.18) (‐0.05) (‐1.10) (‐0.57)

L_polity 11.2662*** 8.6811** 14.1286* 33.2605*** 31.9381***

(4.55) (2.17) (1.90) (9.31) (8.32)

L_ethnic 33.7113*** 33.9746** 4.7237 ‐5.6832

(4.37) (2.34) (0.92) (‐0.64)

L_religion 47.9817** 25.3775*** 28.8777***

(2.47) (3.17) (3.34)

L_Gov_exp 1.4332*** 1.1856**

(3.24) (2.36)

L_statehist ‐10.8430

(‐1.11)

Obs. 70 70 70 66 66 62 62

Note: z statistics in parentesis; * p < 0.1, ** p < 0.05, *** p < 0.01. Time dummies and regional dummies (Eastern Europe,

America, Asia and Oceania, Africa and others) are included.

Job Upward Mobility

28

Table 3b. Long-run determinants of intergenerational immobility. (Random Effects SUR Method for Unbalanced Panels)

(1) (2) (3) (4) (5) (6) (7)

L_Gini 0.3793 ‐0.0077 ‐0.0219 0.1433 0.8644*** ‐0.0447 0.0206

(1.10) (‐0.17) (‐0.47) (1.57) (4.01) (‐0.19) (0.08)

L_ln_rgdp ‐2.8178*** 0.0676 0.6395 1.9692 ‐1.1678 6.9881 7.2850

(‐9.30) (0.10) (0.83) (0.94) (‐0.23) (1.57) (1.62)

L_ln_pop ‐0.4104** ‐1.4543*** ‐1.2292*** ‐4.2724*** ‐6.6464*** ‐0.959 ‐2.0401

(‐2.55) (‐5.77) (‐4.85) (‐5.38) (‐3.40) (‐0.64) (‐0.74)

L_edu ‐1.5078*** ‐1.2379*** ‐1.5913** ‐0.41 0.9969 0.8178

(‐5.92) (‐5.04) (‐1.91) (‐0.20) (0.74) (0.56)

L_polity ‐3.6206*** ‐1.6188 ‐2.1559 ‐17.3954*** ‐16.898**

(‐3.50) (‐0.70) (‐0.41) (‐2.81) (‐2.57)

L_ethnic ‐15.6563*** ‐19.3605* ‐2.9934 1.5803

(‐3.26) (‐1.76) (‐0.37) (0.11)

L_religion ‐27.2771* ‐2.2578 ‐6.9210

(‐1.85) (‐0.19) (‐0.46)

L_Gov_exp ‐1.2021 ‐1.0178

(‐1.65) (‐1.07)

L_statehist 2.7065

(0.20)

Obs. 70 70 70 66 66 62 62



Job Immobility

29

Table 3c. Long-run determinants of downward intergenerational immobility. (Random Effects SUR Method for Unbalanced Panels)

(1) (2) (3) (4) (5) (6) (7)

L_Gini 0.3880*** 0.2234* 0.2294* 0.5496*** 0.9149*** 0.4519*** 0.5407***

(3.50) (1.81) (1.92) (4.41) (6.55) (3.59) (4.14)

L_ln_rgdp 5.9588*** 8.3687*** 9.8070*** 0.9864 1.0560 9.0939*** 9.8155***

(4.80) (4.65) (5.15) (0.37) (0.35) (3.20) (3.09)

L_ln_pop 3.1244*** 1.9656*** 2.1373*** ‐1.8455* ‐0.1911 1.8410** 1.2742

(5.83) (2.80) (3.23) (‐1.77) (‐0.15) (2.02) (0.44)

L_edu ‐1.4135** ‐1.4003** ‐1.8313* 0.3763 0.3001 0.1352

(‐2.47) (‐2.54) (‐1.89) (0.32) (0.36) (0.14)

L_polity ‐6.6530** ‐6.4645* ‐11.0142*** ‐13.2353*** ‐14.5806***

(‐2.27) (‐1.94) (‐3.00) (‐3.44) (‐3.62)

L_ethnic ‐21.5097*** ‐17.8071*** ‐7.0555 ‐2.7390

(‐3.66) (‐2.79) (‐1.16) (‐0.22)

L_religion ‐24.2601*** ‐16.6162** ‐18.0886**

(‐2.86) (‐2.28) (‐2.59)

L_Gov_exp ‐0.4359 ‐0.3205

(‐1.37) (‐0.99)

L_statehist 7.1822

(0.48)

Obs. 70 70 70 66 66 62 62



Job downward mobility

30

Table 4. Long-run determinants of intergenerational mobility. (Gini coefficients from the WB/OECD/WIID)


L_Gini ‐0.8290*** 0.1049 0.7699***

(‐4.43) (0.31) (4.13)

L_ln_rgdp ‐20.0373*** 8.3611 10.9851***

(‐5.61) (1.53) (2.79)

L_ln_pop 0.8468 ‐2.5308 1.5697

(0.38) (‐0.81) (0.46)

L_edu ‐0.5790 0.6600 0.4507

(‐0.59) (0.42) (0.38)

L_polity 33.5783*** ‐18.8047** ‐15.9339***

(7.62) (‐2.63) (‐3.43)

L_ethnic ‐4.1473 3.2356 ‐7.9426

(‐0.42) (0.22) (‐0.55)

L_religion 37.4198*** ‐13.3874 ‐22.7536***

(3.74) (‐0.80) (‐2.96)

L_Gov_exp 1.0017* ‐0.9063 ‐0.1924

(1.92) (‐0.98) (‐0.56)

L_statehist ‐10.7921 4.1746 5.2694

(‐1.00) (0.28) (0.31)

Obs. 62 62 62

Note: z statistics in parentesis; * p < 0.1, ** p < 0.05, *** p < 0.01.



31

Table 5. Long-run determinants of intergenerational mobility. (waves 1999 and 2009)


L_Gini ‐0.3620** ‐0.0606 0.4600**

(‐2.40) (‐0.46) (2.16)

L_ln_rgdp ‐7.2074*** ‐1.9117 6.6059**

(‐3.01) (‐0.64) (1.98)

L_ln_pop ‐1.1853 ‐2.1901* 0.5167

(‐1.21) (‐1.70) (0.37)

L_edu ‐1.0879 0.4851 0.0410

(‐1.30) (0.45) (0.03)

L_polity 14.0537*** ‐5.6742* ‐5.2586

(3.28) (‐1.82) (‐0.83)

L_ethnic 7.5990 ‐4.1925 ‐3.7056

(1.34) (‐0.54) (‐0.47)

L_religion 13.7210*** ‐1.2279 ‐15.3448**

(2.74) (‐0.19) (‐2.15)

L_Gov_exp 0.4083*** ‐0.5292** 0.1194

(2.67) (‐2.21) (0.55)

L_statehist ‐0.4502 ‐1.7188 ‐4.0871

(‐0.07) (‐0.22) (‐0.48)

Obs. 51 51 51




32

Table 6. Long-run determinants of intergenerational mobility. (Colony instead of antiquity of state)


L_Gini ‐0.8003*** 0.1750 0.6993***

(‐5.71) (0.67) (5.04)

L_ln_rgdp ‐17.6813*** 5.0400 9.6991***

(‐6.51) (1.20) (3.66)

L_ln_pop 0.7192 ‐3.0780 1.7200

(0.29) (‐0.80) (0.67)

L_edu 0.1522 0.1661 0.1160

(0.14) (0.11) (0.10)

L_polity 30.4794*** ‐11.7765** ‐14.4321***

(8.58) (‐2.08) (‐4.02)

L_ethnic ‐10.8103 18.1069 ‐6.0217

(‐0.96) (0.96) (‐0.56)

L_religion 23.1385** ‐2.4226 ‐17.8501

(2.34) (‐0.14) (‐1.56)

L_Gov_exp 1.0801** ‐0.8656 ‐0.2989

(2.35) (‐1.00) (‐1.02)

Colony 16.8021* ‐21.5629 ‐3.9652

(1.71) (‐1.42) (‐0.38)

Obs. 62 62 62




33

Data Appendix

The data sources of the explicative variables are:

-L_Gini: World Bank/OECD/areppim (http://stats.areppim.com/listes/list_gini_1960x2012.htm). For the robustness analysis in Section 6 (Table 4) we substitute the Gini coefficients from areppim with the Gini coefficients from the UNU-WIDER World Income Inequality Database (WIID).

-L_ln_rgdp: the lag of real GDP per capita in logarithms (in 2005 US$) comes from the Penn World Table, version 8.0 (Feenstra et al., 2013).

-L_ln_pop: the lag of the logarithm of total population in thousands comes from the Penn World Table, version 8.0 (Feenstra et al., 2013).

-L_edu: the lag of the average years of total schooling (older than 25 years) comes from Barro and Lee_ v. 1.3, 4/2013.

-L_polity: to measure the quality of institutions we use the Polity IV index normalized between 0 and 1.

-L_ethnic: average of the variable ETHNIC obtained by Easterly and Levine (1997) from Muller (1964), Roberts (1962) and Gunnemark 1 and 2 (1991). This variable is time invariant.

-L_religion: religious fractionalization in 1970 (Barro and McCleary, 2003). This index is I=1-H, where H represents the Herfindahl index of religious concentration (religion shares).

-L_Gov_exp: general government final consumption expenditure (% of GDP) from the World Bank Indicators.

-L_statehist: state antiquity index from 1 to 1950 a.c. (Bockstette et al, 2002). The discount factor used is 5% (http://www.econ.brown.edu/fac/Louis_Putterman/antiquity%20index.htm).

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