Introduction1
Achieving strong, sustainable, and inclusive growth has been an important priority for policymakers across the world since at least the global financial crisis. The COVID-19 pandemic has made inclusive growth all the more important and achieving the 2030 Sustainable Development Agenda even more challenging, as the economic costs of the crisis fell disproportionately on the most vulnerable segments of the population.
Even before the pandemic, countries in the Middle East and North Africa (MENA) region were facing lackluster growth and were relying on a model of development that had been distributing the benefits of economic growth to only a few segments of the population, which had fueled discontent and, in some cases, led to political and economic instability. For these countries, the pandemic represents an opportunity to rethink their model of development and accelerate reforms that allow the sharing of economic gains more equally across the population in order to raise living standards for all.
Making growth more inclusive is first and foremost a moral and ethical imperative. However, are more inclusive economies also able to grow faster and show more resilience against negative shocks? This question is the focus of this chapter, and to address it, we follow a two-pronged strategy. First, we briefly and selectively review recent economic literature on the channels through which more inclusiveness could boost economic activity and strengthen economic stability. Ten, we present a general equilibrium model, which captures a few key distortions that affect inclusiveness in both factor and product markets in the MENA region, and quantify the impact of removing such distortions on both growth and economic stability.
Does more Inclusiveness Lead to Economic Growth and Stability?
Inclusiveness is a multifaceted phenomenon that encompasses many socioeconomic dimensions and could affect economic growth and stability through many channels. The causality link can also be reversed, as the degree of inclusiveness can be a consequence of economic growth and stability (see Cerra, Lama, and Loayaza 2021). In this section, we focus on the potential implications of inclusiveness for growth and stability and organize our selective literature survey around the four main pillars of inclusiveness highlighted in Chapter 1: benefit sharing, opportunity, participation, and empowerment.
Growth
Benefit sharing
Severe inequality in income distribution threatens economic growth through at least four channels. First, it could increase the risk of political instability and reduce incentives for domestic and foreign investment (Barro 2000; Banerjee and Dufo 2005; Bloom 2009; Aguiar and Amador 2011; Azzimonti 2018). Second, it could prevent achieving a broad consensus on economic policies (Alesina and Rodrik 1994; Azzimonti 2011; Jaimovich and Rebelo 2017). Third, it may distort incentives by causing individuals to divert their efforts toward securing favored treatment or protection, resulting in resource misallocation, corruption, and nepotism (Stiglitz 2012). Finally, to the extent that poorer households are unlikely to generate demand for new products, it can hinder the development of domestic industrial sectors (as producers will not be able to take full advantage of opportunities from returns to scale) (Murphy, Shleifer, and Vishny 1989; Matsuyama 2002; Holmes and Stevens 2014).
Other papers have noted mechanisms through which some degree of income inequality could favor growth. “Excessive” income redistribution policies could harm economic growth by discouraging labor supply (Heckman, Lochner, and Taber 1998), investment (Chamley 1986; Bénabou 2002; Krueger and Ludwig 2016), or research and development activities (Bloom, Grifth, and Van Reenen 2002; Agrawal, Rosell, and Simcoe 2020). As household saving rates rise with the level of income, a strong redistribution of resources may also lower the aggregate investment rate (Barro 2000). Moreover, large setup costs for investment implies that a certain degree of concentration of asset ownership could be necessary for business development and economic growth (Barro 1997, 2000). Others have made the argument that some degree of income inequality may make it easier for developing economies to accumulate the human capital necessary to successfully adopt frontier technology or to access international markets (Barro 1997, 2000; Porzio 2017).
It is therefore not surprising that cross-country evidence on the causal link between income distribution and economic growth does not yield definitive conclusions. The omitted variable bias (as many factors tend to drive both inequality and growth simultaneously, including tax and transfer systems, technological change, product market barriers, access to finance) adds to the challenge of establishing a causal relationship between the two (Banerjee and Dufo 2003). In general, cross-sectional regressions are more likely to find a negative relationship between income inequality and growth than panel regressions. Results from crosscountry empirical analyses tend to depend on the sample of countries, the period of coverage, and the methodology used. For instance, Alesina and Rodrik (1994) and Perotti (1996) find an overall tendency for income inequality (proxied by Gini coefficient) to cause lower economic growth. Barro (2000) instead shows a negative effect of inequality on growth for poor countries and a positive one for rich countries (for which high-quality institutions make it easier to channel higher savings into productive investment). Among papers that use panel regressions, Forbes (2000) and Cin-gano (2014) find that higher inequality leads to higher economic growth.
Besides its impact on growth rate, another strand of the literature has investigated the relationship between inequality and the persistence of economic growth. Berg and Ostry (2017) and Berg and others (2018) find that longer growth spells are robustly associated with more equality in the income distribution. This relation holds even when other determinants of growth duration— external shocks, level of development, institutional quality, openness to trade, and macroeconomic stability—are considered. Consistently, Dabla-Norris and others (2015) find an inverse relationship between the income share accruing to the top 20 percent of the distribution and economic growth over a five-year horizon, as the higher saving associated with greater inequality does not lead to more investment in the medium term.
Opportunity
While there is a strong consensus on the positive link between education and economic growth, identifying and quantifying a causal relationship between the two is far from obvious. First, government investment in education is not random. More developed economies have greater institutional and financial resources to foster investment in health and education, which point to a reverse causality issue. Second, empirical research is often based on crude proxies for education, such as average years of schooling, which weigh equally an extra year of primary school and one at a more advanced level of education. Two countries with the same average years of schooling might thus grow at different rates if the underlying education levels (in primary, secondary, and tertiary education) are different (Aghion and others 2009). Acemoglu, Aghion, and Zilibotti (2006) show that higher education might be more growth-enhancing in countries close to the technological frontier, while countries with low technological capacity may benefit more from primary and early secondary education and vocational programs closely linked to industrialization strategies (since their growth relies more on importing technology from the former).2
Participation in economic life
Enhancing participation in labor markets should allow an economy to fully utilize its human resources and help maximize its growth potential. In many emerging and developing countries, and in the MENA region, one obstacle to labor market participation is that high-quality jobs in the private sector are scarce (Purfeld and others 2018; Ahn and others 2019). This may reflect the persistence of widespread barriers to private firms’ entry and expansion, including from unequal access to factors of production, especially compared to state-owned enterprises (Song, Storesletten, and Zilibotti 2011; Cavalcanti and Santos 2021); underdeveloped financial systems (Buera, Kaboski, and Shin 2011; Buera and Shin 2013; Dabla-Norris and others 2021); or the absence of good governance and efficient institutions, which encourages rent-seeking and distorts incentives (Murphy, Shleifer, and Vishny 1991; Acemoglu, Johnson, and Robinson 2001; Stiglitz 2012; Bai and others 2019). Barriers to private sector activity affect growth potential by inducing misallocation of production factors (physical, entrepreneurial, and human capital). For instance, Buera, Kaboski, and Shin (2011) find that the difference in financial development can explain almost 80 percent of the difference in output per worker between Mexico and the United States in 2002. Midrigan and Xu (2014) find that financial frictions that prevent firms’ entry in China explain about 40 percent of its gap in total factor productivity level compared to South Korea, with another 5 to 10 percent of the difference being accounted for by the impact of such frictions on incumbent firms.
Low participation in labor markets is generally found among women, young workers, and minorities. Hsieh and others (2019) find that between 20 percent and 40 percent of the growth in US output per person between 1960 and 2010 can be explained by the greater participation of women and minorities in labor markets. Obstacles to labor markets can also impair growth on account of the underinvestment in education by the most disadvantaged categories and the human capital depreciation suffered during unemployment spells, especially of long periods (see, for instance, Kaitz 1970; Mincer and Ofek 1982; Acemoglu 1995; Mroz and Savage 2006; Görlich and de Grip 2008; Abraham and others 2019).
Another obstacle to full participation in economic life is represented by the large share of informal activity and employment, which is also relevant in the MENA region (IMF 2021). Informal workers lack access to social protection systems, have little incentive or opportunity to build their human capital, and generally work in worse conditions than in formal jobs. At the same time, informal firms tend to operate on a smaller scale and with limited access to credit; their owners are also less educated, all of which curbs their investment and expansion prospects (La Porta and Shleifer 2014). The misallocation of resources associated with segmented labor markets provides one explanation for why a number of studies find that the size of the informal sector accounts for a significant portion of the difference in output per capita between rich and poor countries. Prado (2011), for instance, finds that informality due to regulations and taxation of formal activities accounts for at least 5 percent of the output per capita difference between the richest and poorest countries.
Among all dimensions of labor market segmentation, gender is perhaps the most widespread form, particularly in the MENA region (see also Chapter 5 of this book). From a simple growth accounting perspective, increasing female participation in labor markets implies higher economic growth (Lagarde 2019). Aguirre and others (2012) suggest that raising the female labor force participation rate to male levels would raise GDP by 5 percent in the United States, 9 percent in Japan, 12 percent in the United Arab Emirates, and 34 percent in Egypt. Ostry and others (2018) estimate that closing the gender gap in labor force participation could increase GDP by between 10 percent and 80 percent, depending on the initial value of female participation. Additional growth dividends from reducing gender gaps arise from reducing the overall misallocation of talents (Cuberes and Teignier 2016; Hsieh and others 2019; Cools, Fernandez, and Patacchini 2020). In particular, Cuberes and Teignier (2016) find that GDP losses from the underused economic potential of women ranges from between 10 percent in Europe and Central Asia to 40 percent in the Middle East and North Africa. In addition, closing the gender gap boosts human capital accumulation in the long term, as greater inclusion and empowerment of women have positive implications on the development outcomes of their children (Dufo 2012).
Empowerment in social and political life
An important dimension of empowerment is governance. The quality of governance plays a key role in the provision of public services and goods, and in promoting inclusiveness. As highlighted by Ivanyna and Salerno (2021), poor governance harms growth by undermining the business climate, creating distrust in institutions, and limiting revenue collection as well as distorting expenditure (see also IMF 2016; North and others 2008). Advanced economies that rank in the top quartile of the corruption index estimated by IMF (2019) collect on average 4.5 percent of GDP more in tax revenues than those in the bottom quartile. In emerging market economies, the difference is 2.75 percent of GDP and it is about 4 percent of GDP in low-income countries. Moreover, the share of the budget dedicated to education and health is one third lower in countries that are perceived as having higher levels of corruption (IMF 2019).
Empowerment refers also to active involvement of women and minorities in the political life of a country (Pande and To -palova 2013). Evidence from the literature shows that mandating exposure to female leaders, such as by imposing electoral quotas, helps voters appreciate that women can be competent leaders and lessens the bias against female leadership after repeated exposure (Beaman and others 2009). Tis, in turn, raises the aspirations that parents have for their daughters, and teenage girls have for themselves, which leads to improvements in educational attainment (Beaman and others 2012).
Stability
A large literature documents that lack of inclusion can cause macroeconomic instability, both as a source of instability in itself and because it could reduce an economy’s resilience to shocks:
Source of instability. A low degree of inclusion can generate frustration and discontent about the status quo, causing a loss of confidence in current institutions, eroding social cohesion, and clouding future economic prospects (Stiglitz 2012; Chetty and others 2017). In extreme cases, this could create significant turmoil and undermine the overall functioning and stability of a society. Events related to the 2011 Arab Spring show that MENA countries are vulnerable in this regard (Purfeld and others 2018). The economic challenges emerging from the COVID-19 pandemic may make such risks even more concrete (IMF 2021). Lack of inclusion can also lead to extremely polarized societies, where political changes through the electoral cycle are more likely to be accompanied by sharp turnarounds in public policies, which could destabilize the economy (Azzimonti and Talbert 2014).
Reduced resilience to shocks. Economies with more unequal distribution of income also usually have a greater concentration of wealth and debt, both of which amplify the impacts of shocks (Gertler and Gilchrist 1994; Kiyotaki and Moore 1997). Kumhof, Ranciere, and Winant (2015) present a theoretical model where higher leverage and financial crises are the endogenous result of a growing share of income accruing to rich households. The model is consistent with US data showing that the periods preceding the Great Depression (1920–29) and the global financial crisis (1983–2008) both exhibited a large increase in income inequality. Significant income inequality could also increase the volatility of aggregate demand, given that poorer households tend to have higher marginal propensity of consumption (Carroll and Kimball 1996; Carroll 1997; Heathcote and Perri 2018). More generally, since the global financial crisis, new business cycle literature has developed that, by incorporating income and wealth heterogeneity among households, shows the policies that reduce these inequalities can reduce macroeconomic fluctuations (Ahn and others 2018; Kaplan, Moll, and Violante 2018). Greater female labor force participation could help economic stability. A number of studies show that female labor supply is less cyclical than male labor supply, as women tend to work more in recessions to compensate for their partner’s job loss (Parker and Skoufas 2004; Shore 2010; Albanesi 2020; Guner, Kulikova, and Valladares-Esteban 2020). Or-tigueira and Siassi (2013) find that, while wealth-rich households use mainly savings to smooth consumption across unemployment spells, wealth-poor households rely on increased labor supply from a spouse (generally women). These results are consistent with the female labor supply providing a household insurance mechanism also known as the “added worker effect.” Sahay and Cihak (2018) find that greater representation of women in banking leadership is associated with more financial stability. Finally, Kazandjian and others (2019) find that reducing gender gaps helps low-income and developing countries diversify their exports, and therefore better cope with the effects of idiosyncratic, negative economic shocks.
Inclusion and the Macroeconomy: A Quantitative Example
In this section, we present a general equilibrium model that captures some of the most important barriers to inclusion in the MENA region. As discussed in Chapter 1 and Purfeld and others (2018), among those barriers are those that prevent the development of the private sector and the lack of access to financial services. The model is calibrated to an average MENA economy along a few key dimensions, such as the firm size distribution and private sector credit-to-GDP ratio. It is then used to assess how inclusion-enhancing reforms that remove market distortions, improve governance capacity, and deepen the financial sector could lead to higher and more resilient economic growth.
The Model
We consider a model in which an efficient and distortion-free private sector plays a key role in creating jobs and achieving an efficient allocation of resources. The model contains individuals that are heterogeneous in their income, wealth, and entrepreneurial talent (e). Individuals can choose between working as an employee or becoming an entrepreneur. If individuals choose to be employees, their source of income is their wage w; if they decide to become entrepreneurs, they will run a firm that operates with a production function that combines entrepreneurial talent e, rental capital k, and hired labor l:
where α is the capital income share, and 1 – v governs the returns to scale at the firm level (Lucas 1978). The higher 1 – v is, the more production will be concentrated among more productive firms. Notice that in the context of our model, entrepreneurial talent is equivalent to firm productivity.
Without any friction, firms choose the size (the amount of capital and labor) which maximizes profits π based on their productivity and factor prices (w and r):
Because profits increase with entrepreneurial talent, people with higher e always choose to become entrepreneurs, while those with lower e will enter the labor market as employees. More productive firms will also have a larger size.
The model features the following two sets of frictions and distortions:
- Governance capacity constraint and product market distortions. These frictions alter market neutrality and restrict the expansion of productive firms. It can be thought of as arising from (1) corruption and weak law enforcement (Cooley and Quadrini 2001; Clementi and Hopenhayn 2006; Akcigit, Alp, and Peters 2021); (2) dominant market positions of state-owned enterprises, which are often found to be less productive and benefit from more favorable access to credit or looser regulation (Kornai, Maskin, and Roland 2003; Song, Storesletten, and Zilibotti 2011); or (3) red tape, local protectionism, and high transportation costs that stife domestic competition (Eberhardt, Wang, and Yu 2016; Tombe and Zhu 2019). To keep the model tractable, following the standard practice in the literature (Restuccia and Rogerson 2008; Hsieh and Klenow 2009; Qi, Tang, and Xi 2021), we capture this friction as a generic “tax” τ (or “subsidy” when τ < 0) imposed on a firm’s revenue,
Because in reality, more productive and profitable firms are more likely to be the targets of these distortions, the model assumes that more productive firms are subject to a higher probability of being taxed, as in Buera and Shin (2013). Notice that a firm subject to the tax τ will earn less profits and hence run at a smaller scale compared to the level implied by its productivity e, while a firm that receives a subsidy will run at a larger scale. The result is a reallocation of resources from more productive firms to less productive ones, which hurts aggregate productivity, reduces wages, and limits the private sector’s ability to generate jobs.
Financial frictions: In the frictionless economy, firms can borrow freely from financial markets to finance their capital investment. Thus, the optimal allocation of capital and labor is independent of the distribution of wealth; it is determined fully by the underlying distribution of firm productivity. Financial frictions however, by restricting access to external finance, create a discrepancy between the distribution of wealth and entrepreneurial talent. Talented entrepreneurs with insufficient financing would need to accumulate capital over time to run their business at full capacity, making their market share inefficiently lower than in the frictionless benchmark. Similar to the other distortions, financial constraints also lead to a misallocation of resources and a larger presence of less productive firms. To implement financial constraints in the model, we assume that an entrepreneur with personal wealth a can rent capital k up to λa, where λ captures the degree of financial development. When λ = 1, k a ≤, meaning that all capital investment must be financed internally (financial autarky); as λ → ∞, entrepreneurs can rely entirely on external finance (financial markets are complete).3
Calibration and Simulations
The model is calibrated following the strategy in Guner, Ventura, and Xu (2008). The frictionless economy is calibrated to match that of the United States, assumed to best represent a relatively distortion-free benchmark economy.4 The governance and product market distortions and financial friction is calibrated so that the firm size distribution and external finance-to-GDP ratio match those of an average MENA economy.5,6 The underlying assumption is that the difference between the US and MENA economies is driven exclusively by the distortions and friction considered. To the extent that firms in MENA economies are on average less productive than those in the US, this could overestimate the size of the distortions and consequently their macroeconomic impact. Hence the results should be interpreted as an upper bound of the possible range of estimates, with smaller distortions leading to qualitatively similar, but quantitatively smaller, results.
Using the calibrated model, five sets of counterfactual simulations are conducted. The first two remove the governance and product market distortions (scenario one) and financial friction (scenario two) to analyze their macroeconomic impact separately. The next simulation removes both constraints together (scenario three) to examine the interaction between the two types of distortions. We then consider a scenario where authorities manage to engineer an exogenous increase in total factor productivity without remedying the underlying distortions (scenario four). Lastly, we look at the effect of removing the distortions in a more dynamic economic environment, one in which firms’ turnover rate doubles (scenario five).
Removing distortions
Removing governance and product market distortions in the MENA region by setting τ to zero leads to substantial improvement in the efficiency of the economy, with aggregate output, consumption, and capital stock increasing by 79, 69, and 125 percent, respectively (Table 2.1, column 1). As these distortions affect more productive firms disproportionately, removing them would allow productive firms to expand and take up a much larger share of the market, causing labor demand per firm to increase by 131 percent and wages by 67 percent across the region. Production concentrates more among productive firms, with the employment share of the top 10 percent firms (the largest and most productive ones) increasing by 30 percent. Higher wages, in turn, would drive the least productive firms out of the market, which can be seen from the 54 percent decline in number of firms, and further raises the average productivity of the remaining firms.
Removing Distortions (change relative to baseline, in percent)
Removing Distortions (change relative to baseline, in percent)
| Statistics | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| Output | 78.7 | 23.0 | 127.5 | 84.3 |
| Capital | 124.5 | 32.1 | 170.0 | 48.0 |
| Consumption | 69.3 | 22.7 | 120.0 | 44.4 |
| External finance to GDP | -47.4 | 422.5 | 471.2 | -30.0 |
| Number of firms | -53.8 | -9.7 | -62.2 | -1.9 |
| Average firm total factor productivity | 50.1 | 2.3 | 78.9 | 53.3 |
| Average income | 74.8 | -2.9 | 74.5 | 44.2 |
| Labor demand per firm | 130.7 | 11.3 | 181.4 | 2.0 |
| Wage | 67.2 | 25.1 | 113.4 | 44.9 |
| Interest rate | 5.9 | 181.2 | 157.6 | 4.7 |
| Labor share by top 10% firms | 30.3 | -8.7 | 22.6 | 3.6 |
| Labor share by top 20% firms | 19.2 | -6.8 | 15.2 | 2.5 |
| Income share by top 10% people | 5.0 | -33.4 | -25.8 | -0.5 |
| Income share by top 20% people | 3.9 | -26.3 | -20.4 | -0.4 |
Removing Distortions (change relative to baseline, in percent)
| Statistics | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| Output | 78.7 | 23.0 | 127.5 | 84.3 |
| Capital | 124.5 | 32.1 | 170.0 | 48.0 |
| Consumption | 69.3 | 22.7 | 120.0 | 44.4 |
| External finance to GDP | -47.4 | 422.5 | 471.2 | -30.0 |
| Number of firms | -53.8 | -9.7 | -62.2 | -1.9 |
| Average firm total factor productivity | 50.1 | 2.3 | 78.9 | 53.3 |
| Average income | 74.8 | -2.9 | 74.5 | 44.2 |
| Labor demand per firm | 130.7 | 11.3 | 181.4 | 2.0 |
| Wage | 67.2 | 25.1 | 113.4 | 44.9 |
| Interest rate | 5.9 | 181.2 | 157.6 | 4.7 |
| Labor share by top 10% firms | 30.3 | -8.7 | 22.6 | 3.6 |
| Labor share by top 20% firms | 19.2 | -6.8 | 15.2 | 2.5 |
| Income share by top 10% people | 5.0 | -33.4 | -25.8 | -0.5 |
| Income share by top 20% people | 3.9 | -26.3 | -20.4 | -0.4 |
The external finance-to-GDP ratio decreases, as the financial system cannot fully accommodate the greater capital demand from large firms. Instead, these firms will have to rely mostly on retained earnings for financing.
As expected, removing financial constraints by setting λ →∞ also improves economic efficiency across the region, with aggregate output, consumption, and capital stock increasing now by 23, 22.5, and 32 percent, respectively (Table 2.1, column 2)7 The mechanisms, however, are different, which explains the much more moderate gains. Removing the financial friction improves access to financial resources, allowing talented but wealth-poor entrepreneurs to expand the scale of production. However, the most productive firms benefit less from the removal of financial distortions, compared to firms with medium-level productivity, for two reasons. First, because the governance and product market distortions are heavier on more productive firms, the extent to which they can benefit from deeper financial markets is limited. Second, more productive firms are also able to circumvent financial constraints by saving and self-financing (Moll 2014; Midrigan and Xu 2014). Overall, the magnitude of resource reallocation is limited. This can be seen by noticing that despite the 11 percent increase in average labor demand per firm, such an increase is driven mostly by mid-sized firms, as the employment share by the largest, most productive firms shrinks by 9 percent. Consequently, better access to financial resources causes only 10 percent of the less productive firms to drop out of the market, and average productivity increases marginally, by 2 percent.
To gauge the interaction between the two types of distortions, we remove both at the same time and the economy is set back to its frictionless benchmark (Table 2.1, column 3). This leads to higher efficiency gains than the sum of relaxing each distortion independently. For instance, aggregate output would increase by 128 percent, which is higher than the 102 percent from adding up individual gains. This implies that different distortions can intensify each other and inflict larger overall damage, and that a more balanced reform agenda, which improves policies and institutions on multiple fronts in tandem, has the potential to bear more fruit.
Pro-inclusive reforms versus direct productivity shock
In scenario four, aggregate productivity is increased by 50 percent (the same improvement of average firm-level productivity when governance and product market distortions are removed in scenario one) while keeping both distortions untouched. This scenario is used to proximate government intervention that successfully boosts growth (for example, by investing in public infrastructure) but without addressing distortions that limit private sector participation. This scenario (Table 2.1, column 4) is compared with scenario one to illustrate the additional gains from structural reforms that improve inclusiveness. The results show that while reforms in scenario one lead to similar gains in aggregate output, they improve various welfare metrics significantly; consumption, average income, and wage under scenario one all witness a greater-than-50-percent additional increase, pointing to the more efficient use of resources (especially capital) than in scenario four. Moreover, in scenario four, despite the significant increase in productivity, existing distortions prove to be a strong obstacle to job creation. The increase in investment associated with the productivity shock is only able to add 2 percent more jobs, as it mainly benefits the incumbents. In contrast, structural reforms that remove distortions and create better opportunities for talented entrepreneurs have the potential to generate 130 percent more jobs.
Adapting to a changing world
Establishing an economy that provides equal opportunity for everyone with talents can be even more important in a dynamic, fast-changing world. Limited capability to adapt to such changes may contribute to explaining why many developing countries were often able to ignite growth but failed to sustain it. To show how a more volatile external environment makes it even more important to implement reforms that improve inclusiveness, we consider a scenario in which firms’ turnover rates double, which can be thought of as firms facing a more volatile demand or new technologies becoming available to firms at higher frequency. In the model, it is assumed that each period entrepreneurs retain their current productivity e with a probability ψ. In scenario four, ψ is lowered and the experiments in scenarios one through three are repeated as before. The results are summarized in columns 1 to 3 of Table 2.2.
Higher Firm Turnover Rate (change relative to baseline, in percent)
Higher Firm Turnover Rate (change relative to baseline, in percent)
| Statistics | 1 | 2 | 3 |
|---|---|---|---|
| Output | 63.4 | 30.8 | 136.8 |
| Capital | 103.8 | 43.3 | 211.0 |
| Consumption | 64.4 | 40.1 | 135.1 |
| External finance to GDP | -50.3 | 461.7 | 597.3 |
| Number of firms | -32.1 | -20.3 | -62.6 |
| Average firm total factor productivity | 25.0 | 2.9 | 71.6 |
| Average income | 68.4 | 11.3 | 101.0 |
| Labor demand per firm | 53.3 | 29.0 | 187.9 |
| Wage | 64.2 | 37.3 | 130.4 |
| Interest rate | 7.2 | 169.5 | 120.7 |
| Labor share by top 10% firms | 26.9 | -8.6 | 28.7 |
| Labor share by top 20% firms | 15.2 | -7.9 | 17.3 |
| Income share by top 10% people | 2.9 | -28.0 | -18.1 |
| Income share by top 20% people | 2.2 | -22.0 | -14.2 |
Higher Firm Turnover Rate (change relative to baseline, in percent)
| Statistics | 1 | 2 | 3 |
|---|---|---|---|
| Output | 63.4 | 30.8 | 136.8 |
| Capital | 103.8 | 43.3 | 211.0 |
| Consumption | 64.4 | 40.1 | 135.1 |
| External finance to GDP | -50.3 | 461.7 | 597.3 |
| Number of firms | -32.1 | -20.3 | -62.6 |
| Average firm total factor productivity | 25.0 | 2.9 | 71.6 |
| Average income | 68.4 | 11.3 | 101.0 |
| Labor demand per firm | 53.3 | 29.0 | 187.9 |
| Wage | 64.2 | 37.3 | 130.4 |
| Interest rate | 7.2 | 169.5 | 120.7 |
| Labor share by top 10% firms | 26.9 | -8.6 | 28.7 |
| Labor share by top 20% firms | 15.2 | -7.9 | 17.3 |
| Income share by top 10% people | 2.9 | -28.0 | -18.1 |
| Income share by top 20% people | 2.2 | -22.0 | -14.2 |
Comparing Table 2.2 with Table 2.1, the data shows that the loss of efficiency, caused by financial friction, becomes significantly larger. Two reasons are behind the results. First, with a higher turnover rate, reallocating resources from less productive (exiting and shrinking) to more productive (entering and expanding) firms becomes more important. Second, in a more volatile demand and productivity environment, it becomes more difficult for wealth-poor entrepreneurs to accumulate capital by retaining earnings before their productivity levels change. Economic activity is thus more reliant on a well-functioning financial market, which magnifies the cost of financial friction.8
Conclusions
A large literature has studied the link between inclusion and economic growth and stability. While identifying and quantifying such links remain difficult, and somewhat dependent on the data and research methodology adopted, there is a consensus that improving inclusiveness in its various dimensions could indeed allow an economy to tap into its full growth potential, achieve an efficient allocation of resources, and strengthen its resilience to shocks.
Modeling some of the distortions normally associated with lack of inclusiveness allows us to illustrate the channels through which inclusion-friendly structural reforms could improve macroeconomic outcomes. The model we build and calibrate for the MENA region in this chapter shows that removing key distortions that prevent the development and the efficient functioning of the private sector could lead to higher aggregate output, higher wages, and more jobs. Moreover, because different sources of distortions interact and intensify one another, a package of reforms that addresses them simultaneously promises to deliver better results than proceeding through a stepwise approach.
Annex 2.1
We use the model of Buera and Shin (2013). The basic structure of the model is as explained in the main text. The Annex provides additional mathematical details.
Preference and technology
Households have CRRA preference:
where t is the initial period, β is the discount rate, σ is the risk averse parameter, and ct is the consumption.
In each period t, each household can choose to work for wages wt or operate a firm according to their entrepreneur talent e. A firm then combines entrepreneur talent e (which is also equivalent to firm-level total factor productivity in our model), capital k and labor l to produce according to technology
We assume that e∈ε is stochastic. With probability ψ, households keep their productivity in t+1 . Otherwise, with probability 1 – ψ, they draw a new level of e from a time-invariant distribution. We use µe) to represent the probability density of this distribution.
Households’ saving can be converted into productive capital in a one-on-one manner. Capital depreciates at rate δ. The market rental rate is rt. We assume a continuum of perfectly competitive financial intermediaries that channel saving to capital, which implies that the rental price firms pay is rt + δ.
Distortions and friction
For financial friction, we assume that entrepreneurs’ capital rental k is limited by a collateral constraint k < λa, where a is their saving. When 1 λ = 1, capital investment can only be financed by retained earnings, while when λ →∞, the credit market is perfect.
We assume that the governance and product market distortions τ are a random variable that takes two values: τ+ (tax) and τ- (subsidy). The distortions are correlated with productivity positively by assuming that the probability of an entrepreneur with productivity e being taxed is
With q > 0, the probability that an entrepreneur with a higher e being taxed is higher.
Optimization problems
The Bellman equation of households is as follows. The state variables are wealth a, talent e, and distortion τ, with the latter two evolving exogenously. If a household chooses to be an employee, their choice variables are consumption c and saving a’. While if they choose to be an entrepreneur, they will first choose the optimal factor demand k and l, both are intratemporal. After that, they engage in standard consumption-saving decisions.
In the above Bellman equation,
The solution of the above problem consists of value function V(a;e,τ) decision rules for consumption-saving c(a;e,τ), a’(a;e,τ) and factor demand k(a;e,τ), l(a;e,τ).
Competitive equilibrium
A competitive equilibrium consists of equilibrium prices {w,r}, value function V(a,e,τ), household decision rules {c(a,e,τ), a(a,e,τ),k(a,e,τ),l(a,e,τ)} and cumulative distributions of households G(a,e,τ) for any given policies {λ,τ} such that
1. Given {w,r}, V (a,e,τ) and {c(a,e,τ), a’ (a,e,τ),k(a,e,τ),l(a,e,τ)} solve the optimization problem of households.
2. Prices {w,r} clear all markets:
- Labor market:
- Capital market:
Goods market is automatically cleared due to Walras’ Law.
- 3. Aggregate consistency of the joint distribution G(a,e,τ):
- and
REFERENCES
Abraham, K. G., J. Haltiwanger, K. Sandusky, and J. R. Spletzer. 2019. “The Consequences of Long-Term Unemployment: Evidence from Linked Survey and Administrative Data.” ILR Review 72 (2): 266–99.
Acemoglu, D. 1995. “Public Policy in a Model of Long-Term Unemployment.” Economica: 161–78.
Acemoglu, D., P. Aghion, and F. Zilibotti. 2006. “Distance to Frontier, Selection, and Economic Growth.” Journal of the European Economic Association 4 (1): 37–74.
Acemoglu, D., S. Johnson, and J. A. Robinson. 2001. “The Colonial Origins of Comparative Development: An Empirical Investigation.” American Economic Review 91 (5): 1369–401.
Aghion, P., L. Boustan, C. Hoxby, and J. Vandenbussche. 2009. “The Causal Impact of Education on Economic Growth: Evidence from US.” Brookings Papers on Economic Activity 1 (1): 1–73.
Agrawal, A., C. Rosell, and T. Simcoe. 2020. “Tax Credits and Small Firm R&D Spending.” American Economic Journal: Economic Policy 12 (2): 1–21.
Aguiar, M., and M. Amador. 2011. “Growth in the Shadow of Expropriation.” Quarterly Journal of Economics 126 (2): 651–97.
Aguirre, D., L. Hoteit, C. Rupp, and K. Sabbagh. 2012. “Empowering the Third Billion: Women and the World of Work in 2012.” New York: Booz and Company.
Ahn, S., G. Kaplan, B. Moll, T. Winberry, and C. Wolf. 2018. “When Inequality Matters for Macro and Macro Matters for Inequality.” NBER Macroeconomics Annual 32 (1): 1–75.
Ahn, J., Z. An, J. C. Bluedorn, G. Ciminelli, Z. Koczan, D. Malacrino, D. Muhaj, and P. Neidlinger. 2019. “Work in Progress: Improving Youth Labor Market Outcomes in Emerging Market and Developing Economies.” IMF Staff Discussion Note 19/02, International Monetary Fund, Washington DC.
Akcigit, U., H. Alp, and M. Peters. 2021. “Lack of Selection and Limits to Delegation: Firm Dynamics in Developing Countries.” American Economic Review 111 (1): 231–75.
Albanesi, S. 2020. “Changing Business Cycles: The Role of Women’s Employment.” NBER Working Paper 25655, National Bureau of Economic Research, Cambridge, MA.
Alesina, A., and D. Rodrik. 1994. “Distributive Politics and Economic Growth.” Quarterly Journal of Economics 109 (2): 465–90.
Azzimonti, M. 2011. “Barriers to Investment in Polarized Societies.” American Economic Review 101 (5): 2182–204.
Azzimonti, M. 2018. “The Politics of FDI Expropriation.” International Economic Review 59 (2): 479–510.
Azzimonti, M., and M. Talbert. 2014. “Polarized Business Cycles.” Journal of Monetary Economics 67: 47–61.
Bai, J., S. Jayachandran, E. J. Malesky, and B. A. Olken. 2019. “Firm Growth and Corruption: Empirical Evidence from Viet-nam .” Economic Journal 129 (618): 651–77.
Banerjee, A. V., and E. Dufo. 2003. “Inequality and Growth: What Can the Data Say?” Journal of Economic Growth 8 (3): 267–99.
Banerjee, A. V., and E. Dufo. 2005. “Growth Theory through the Lens of Development Economics.” In Handbook of Economic Growth, Vol. 1A, edited by P. Aghion and S. N. Durlauf, Chapter 7, 473–552. North Holland: Elsevier B. V.
Barro, R. J. 1997. Determinants of Economic Growth: A CrossCountry Empirical Study. Lionel Robbins Lectures. Cambridge, Massachusetts: MIT Press.
Barro, R. J. 2000. “Inequality and Growth in a Panel of Countries.” Journal of Economic Growth 5 (1): 5–32.
Beaman, L., R. Chattopadhyay, E. Dufo, R. Pande, and P. Topalova. 2009. “Powerful Women: Does Exposure Reduce Bias?” Quarterly Journal of Economics 124 (4), 1497–540.
Beaman, L., E. Dufo, R. Pande, and P. Topalova. 2012. “Female Leaders Raise Aspirations of Girls.” Science 335 (6068): 582–86.
Bénabou, R. 2002. “Tax and Education Policy in a Heterogeneous-Agent Economy: What Levels of Redistribution Maximize Growth and Efficiency?” Econometrica 70 (2): 481–517.
Berg, A. G., and J. D. Ostry. 2017. “Inequality and Unsustainable Growth: Two Sides of the Same Coin?” IMF Economic Review 65 (4): 792–815.
Berg, A., J. D. Ostry, C. G. Tsangarides, and Y. Yakhshilikov. 2018. “Redistribution, Inequality, and Growth: New Evidence .” Journal of Economic Growth 23: 259–305.
Bloom, N. 2009. “The Impact of Uncertainty Shocks.” Econometrica 77 (3): 623–85.
Bloom, N., R. Grifth, and J. Van Reenen. 2002. “Do R&D Tax Credits Work? Evidence from a Panel of Countries 1979–1997.” Journal of Public Economics 85 (1): 1–31.
Buera, F. J., and Y. Shin. 2013. “Financial Frictions and the Persistence of History: A Quantitative Exploration.” Journal of Political Economy 121 (2): 221–72.
Buera, F. J., J. P. Kaboski, and Y. Shin. 2011. “Finance and Development: A Tale of Two Sectors.” American Economic Review 101 (5): 1964–2002.
Carroll, C. D. 1997. “ Buffer-Stock Saving and the Life Cycle/Permanent Income Hypothesis.” Quarterly Journal of Economics 112 (1): 1–55.
Carroll, C. D., and M. S. Kimball. 1996. “On the Concavity of Consumption Function.” Econometrica 64 (4): 981–92.
Cavalcanti, T., and M. Santos. 2021. “(Mis)Allocation Effects of an Overpaid Public Sector.” Journal of the European Economic Association 19 (2): 953–99.
Cerra, V., R. Lama, and N. Loayaza. 2021. “Links Between Growth, Inequality, and Poverty: A Survey.” IMF Working Paper 21/68, International Monetary Fund, Washington, DC.
Chamley, C. 1986. “Optimal Taxation of Capital Income in General Equilibrium with Infinite Lives.” Econometrica 54 (3): 607–22.
Chetty, R., D. Grusky, M. Hell, N. Hendren, R. Manduca, and J. Narang. 2017. “The Fading American Dream: Trends in Absolute Income Mobility since 1940.” Science 356 (6336): 398–406.
Cingano, F. 2014. “Trends in Income Inequality and Its Impact on Economic Growth.” Manuscript.
Clementi, G. L., and H. A. Hopenhayn. 2006. “A Theory of Financing Constraints and Firm Dynamics.” Quarterly Journal of Economics 121 (1): 229–65.
Cooley, T. F., and V. Quadrini. 2001. “Financial Markets and Firm Dynamics.” American Economic Review 91 (5): 1286–310.
Cools, A., R. Fernandez, and E. Patacchini. 2020. “The Asymmetric Gender Effects of High Flyers.” Manuscript.
Cuberes, D., and M. Teignier. 2016. “Aggregate Effects of Gender Gaps in the Labor Market: A Quantitative Estimate.” Journal of Human Capital 10 (1): 1–32.
Dabla-Norris, E., K. Kochhar, N. Suphaphiphat, F. Ricka, and E. Tsounta. 2015. “Causes and Consequences of Income Inequality: A Global Perspective.” IMF Staff Discussion Notes 15/13, International Monetary Fund, Washington, DC.
Dabla-Norris, E., R. M. Townsend, Y. Ji, and D. Filiz Unsal. 2021. “Distinguishing Constraints on Financial Inclusion and Their Impact on GDP, TFP, and the Distribution of Income.” Journal of Monetary Economics 117: 1–18.
Dufo, E. 2012. “Women Empowerment and Economic Development.” Journal of Economic Literature 50 (4): 1051–79.
Eberhardt, M., Z. Wang, and Z. Yu. 2016. “From One to Many Central Plans: Drug Advertising Inspections and Intra-National Protectionism in China.” Journal of Comparative Economics 44 (3): 608–22.
Forbes, K. J. 2000. “A Reassessment of the Relationship between Inequality and Growth.” American Economic Review 90 (4): 869–87.
Gertler, M., and S. Gilchrist. 1994. “Monetary Policy, Business Cycles, and the Behavior of Small Manufacturing Firms.” Quarterly Journal of Economics 109 (2): 309–40.
Görlich, D., and A. De Grip. 2008. “Human Capital Depreciation during Hometime.” Oxford Economic Papers 61 (1): 98–121.
Guner, N., G. Ventura, and Y. Xu. 2008. “Macroeconomic Implications of Size-Dependent Policies.” Review of Economic Dynamics 11 (4): 721–44.
Guner, N., Y. Kulikova, and A. Valladares-Esteban. 2020. “Does the Added Worker Effect Matter?” IZA Discussion Paper 12923, Institute of Labor Economics, Bonn.
Heathcote, J., and F. Perri. 2018. “Wealth and Volatility.” Review of Economic Studies 85 (4): 2173–213.
Heckman, J. J., L. Lochner, and C. Taber. 1998. “Explaining Rising Wage Inequality: Explorations with a Dynamic General Equilibrium Model of Labor Earnings with Heterogeneous Agents.” Review of Economic Dynamics 1 (1): 1–58.
Holmes, T. J., and J. J. Stevens. 2014. “An Alternative Theory of the Plant Size Distribution, with Geography and Intra-and International Trade.” Journal of Political Economy 122 (2): 369–421.
Hsieh, C., and P. J. Klenow. 2009. “Misallocation and Manufacturing TFP in China and India.” Quarterly Journal of Economics 124 (4): 1403–48.
Hsieh, C., E. Hurst, C. I. Jones, and P. J. Klenow. 2019. “The Allocation of Talent and U.S. Economic Growth.” Econometrica 87 (5): 1439–74.
International Monetary Fund (IMF). 2016. “Corruption: Costs and Mitigating Strategies.” IMF Staff Discussion Note 16/05, International Monetary Fund, Washington, DC.
International Monetary Fund (IMF). 2019. “Curbing Corruption.” In Fiscal Monitor (2019), Chapter 2. Washington, DC: International Monetary Fund.
International Monetary Fund (IMF). 2021. “A Fair Shot.” In Fiscal Monitor (April 2021), Chapter 2. Washington, DC: International Monetary Fund.
Ivanyna, M., and A. Salerno. 2021. “Governance for Inclusive Growth.” IMF Working Paper 21/98. International Monetary Fund, Washington, DC.
Jaimovich, N., and S. Rebelo. 2017. “Nonlinear Effects of Taxation on Growth.” Journal of Political Economy 125 (1): 265–91.
Kaitz, H. B. 1970. “Analyzing the Length of Spells of Unemployment.” Monthly Lab. Rev. 93: 11.
Kaplan, G., B. Moll, and G. L. Violante. 2018. “Monetary Policy According to HANK.” American Economic Review 108 (3): 697–743.
Kazandjian, R., L. Kolovich, K. Kochhar, and M. Newiak. 2019. “Gender Equality and Economic Diversification.” Social Sciences 8 (4): 118.
Kiyotaki, N., and J. Moore. 1997. “Credit Cycles.” Journal of Political Economy 105 (2): 211–48.
Kornai, J., E. Maskin, and G. Roland. 2003. “Understanding the Soft Budget Constraint.” Journal of Economic Literature 41 (4): 1095–136.
Krueger, D., and A. Ludwig. 2016. “On the Optimal Provision of Social Insurance: Progressive Taxation Versus Education Subsidies in General Equilibrium.” Journal of Monetary Economics 77: 72–98.
Kumhof, M., R. Ranciere, and P. Winant. 2015. “Inequality, Leverage, and Crises.” American Economic Review 105 (3): 1217–45.
La Porta, R., and A. Shleifer. 2014. “Informality and Development.” Journal of Economic Perspectives 28 (3): 109–26.
Lagarde, C. 2019. “A Global Imperative.” Finance & Development 56 (1): 5.
Lambert, F., A. Pescatori, and F. Toscani. 2020. “Labor Market Informality and the Business Cycle.” IMF Working Paper 20/256, International Monetary Fund, Washington, DC.
Lucas, R. E. Jr. 1978. “On the Size Distribution of Business Firms .” The Bell Journal of Economics 9 (2): 508–23.
Matsuyama, K. 2002. “The Rise of Mass Consumption Societies.” Journal of Political Economy 110 (5): 1035–70.
Midrigan, V., and D. Yi Xu. 2014. “Finance and Misallocation: Evidence from Plant-Level Data.” American Economic Review 104 (2): 422–58.
Mincer, J., and H. Ofek. 1982. “Interrupted Work Careers: Depreciation and Restoration of Human Capital.” Journal of Human Resources 17 (1): 3–24.
Moll, B. 2014. “Productivity Losses from Financial Frictions: Can Self-Financing Undo Capital Misallocation?” American Economic Review 104 (10): 3186–221.
Mroz, T. A., and T. H. Savage. 2006. “The Long-Term Effects of Youth Unemployment.” Journal of Human Resources 41 (2): 259–93.
Murphy, K. M., A. Shleifer, and R. W. Vishny. 1989. “Income Distribution, Market Size and Industrialization.” Quarterly Journal of Economics 104 (3): 537–64.
Murphy, K. M., A. Shleifer, and R. W. Vishny. 1991. “The Allocation of Talent: Implications for Growth.” Quarterly Journal of Economics 106 (2): 503–30.
North, D., D. Acemoglu, F. Fukuyama, and D. Rodrik. 2008. “Governance, Growth, and Development Decision-Making.” Washington, DC: World Bank Group.
Ortigueira, S., and N. Siassi. 2013. “How Important Is Intra-Household Risk Sharing for Savings and Labor Supply?” Journal of Monetary Economics 60 (6): 650–66.
Ostry, J. D., J. Alvarez, R. A. Espinoza, and C. Papageorgiou. 2018. “Economic Gains from Gender Inclusion: New Mechanisms, New Evidence.” IMF Staff Discussion Notes 18/06, International Monetary Fund, Washington, DC.
Pande, R., and P. Topalova. 2013. “Women in Charge.” Finance & Development 50 (2): 12–15.
Parker, S. W., and E. Skoufas. 2004. “The Added Worker Effect over the Business Cycle: Evidence from Urban Mexico.” Applied Economics Letters 11 (10): 625–30.
Perotti, R. 1996. “Growth, Income Distribution, and Democracy: What the Data Say.” Journal of Economic Growth 1 (2): 149 – 87.
Porzio, T. 2017. “ Cross-Country Differences in the Optimal Allocation of Talent and Technology.” Manuscript.
Prado, M. 2011. “Government Policy in the Formal and Informal Sectors.” European Economic Review 55 (8): 1120–36.
Purfeld, C., H. Finger, K. Ongley, B. Baduel, C. Castellanos, G. Pierre, V. Stepanyan, and E. Roos. 2018. “Opportunity for All: Promoting Growth and Inclusiveness in the Middle East and North Africa.” IMF Departmental Paper 18/11, International Monetary Fund, Washington, DC.
Qi, J., X. Tang, and X. Xi. 2021. “The Size Distribution of Firms and Industrial Water Pollution: A Quantitative Analysis of China.” American Economic Journal: Macroeconomics 13 (1): 151–83.
Restuccia, D., and R. Rogerson. 2008. “Policy Distortions and Aggregate Productivity with Heterogeneous Establishments.” Review of Economic Dynamics 11 (4): 707–20.
Sahay, R., and M. Cihak. 2018. “Women in Finance: A Case for Closing Gaps.” IMF Staff Discussion Note 18/05, International Monetary Fund, Washington, DC.
Shore, S. H. 2010. “For Better, for Worse: Intrahousehold Risk-Sharing over the Business Cycle.” Review of Economics and Statistics 92 (3): 536–48.
Song, Z., K. Storesletten, and F. Zilibotti. 2011. “Growing Like China.” American Economic Review 101 (1): 196–233.
Stiglitz, J. 2012. The Price of Inequality: How Today’s Divided Society Endangers Our Future. New York: W. W. Norton & Company.
Tombe, T., and X. Zhu. 2019. “Trade, Migration and Productivity: A Quantitative Analysis of China.” American Economic Review 109 (5): 1843–72.
Shant Arzoumanian has provided outstanding research assistance.
For the same reason, Aghion and others (2009) find that in US states at the technological frontier, each thousand dollars of spending in research and development raises the number of patents by 6 per 100,000 people, while an exogenous increase in two-year college education has no discernable effect.
The collateral requirement is, of course, only one aspect of potential financial frictions. In the terminology of Dabla-Norris and others (2021), it captures the depth of the financial system. Two other dimensions that they consider are the width and efficiency of the financial system, proxied by the fraction of firms with access to the financial system and the interest rate spread, respectively.
Most parameters describing the frictionless benchmark are taken from Buera and Shin (2013).
The average MENA economy is built as a weighted average of Afghanistan, Djibouti, Egypt, Iraq, Jordan, Lebanon, Mauritania, Morocco, Sudan, Tunisia, and Yemen, using purchasing power parity– adjusted GDP (average from 2003 to 2019) as weights.
The model is set (1) to match the share of workers employed by the largest quintile of firm distribution, estimated for the MENA region from the latest vintage of the World Bank Enterprise Survey data, and (2) to match the external finance-to-GDP ratio to the MENA region’s average over 2006–17, obtained from the World Bank’s Financial Development and Structure Dataset.
Here we increase λ to a level at which further relaxing financial constraints has no impact on the economy
Tough it appears that the costs from governance and product market distortions are smaller among several dimensions (for instance, wage loss, labor demand per firm), these results reflect the fact that a higher turnover rate reduces the average firm size, and hence the total amount of distortions imposed on the economy.