rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Cohort Size of Youth by Regions<br>g<br>=, ¢ ooo oe aneeed<br>Eo<br>3 3see<br>3 2 aan *<br>ent a= nn<br>1990 1995 2000 2005 2010<br>Vear<br>~*~ Europeand Central Asia ——~®~ South Asia<br>> LatinAmericaand Carribeans =—®— EastAsia Pacific<br>——*— Middle East and NorthernAfrica. —-TM— Sub-Saharan Africa<br><!-- End of picture text --> <!-- Start of picture text --> . oo ;<br>Population Growth Projection of Youth by Region<br>zs 8g =<br>a3<br>3 ~~<br>§ Es =<br>E °<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br>Year<br>—#— Eastern Asia ——4—— Sub-Saharan Africa<br>~*~ Europe ~*~ Souther Asia<br>—+— Central Asia © —®— Latin America and the Caribbean<br><!-- End of picture text -->
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis Table 10: Countries included in Resource-rich Sub-group, classified by Income Groups

||**Absolute**|**Relative**|
|---|---|---|
|High income|5|16.7%|
|Upper middle income|11|36.7%|
|Lower middle income|10|33.3%|
|Low income|4|13.3%|
|Total|30|100%|

According to the IMF report, “nations [rich in nonrenewable natural resources] produce 85% of the world’s petroleum, 90% of its diamonds and 80% of its copper, generating trillions of dollars in annual profits.” Table 11 summarizes the empirical results of the recursive model estimated for the subsample of resource-rich countries. These figures confirm the results of the full-sample model. The magnitude of the coefficient, as expected, slightly increases relative to the full-sample model, but the sign of the associations and their economic implications remain unchanged.

Table 11. Estimated Results from the Recursive Model Specification (resource-rich subsample)

||**Fixed**<br>|**Effect**<br>|**SU**<br>|**RE**<br>|
|---|---|---|---|---|
||**Gini index**|**GDP**|**Gini index**|**GDP**|
|Nonrenewable resource dependence|-0.067**|0.008|-0.177***|0.122|
|Lower Income<br>|dropped|dropped|6.090**|57.336***|
|Lower income * Nonrenewable resource<br>Dependence|0.078|-0.362***|0.129**|-0.756***|
|High-Low education ratio|-0.418***|-|-0.159|-|
|High-Low education ratio square|0.015***|-|0.008*|-|
|Consumer Price Index (2005)|-0.010*|-|-0.003|-|
|Services value added share|0.130***|-|0.117**|-|
|Manufacturing value added share|-0.006|-|0.064|-|
|Agriculture value added share|-0.175***|-|-0.154**|-|
|Log Labour Force|-|-8.651***|-|-26.131***|
|Log Gross fixed capital formation|-|0.975|-|5.280***|
|Log Exports of goods and services|-|2.663**|-|6.042***|
|Log REER|-|5.057***|-|3.325|
|World Oil price index|-|0.081***|-|0.091***|
|WorldMetalpriceindex|-|0.018|-|-0.001|
|Observations|667|707|580|580|
|R2|0.096|0.311|0.911|0.864|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

These findings are further confirmed and indeed reinforced in the non-recursive model results summarized in Table 12. In this case, resource dependence among higher-income countries is _positively_ associated with GDP per capita, suggesting that in wealthier countries natural resources are a blessing rather than a curse. In fact, greater resource dependence in higher-income countries is associated with lower levels of income inequality and greater GDP per capita. However, the opposite is true for lower-income 14
how men and women allocate money across these different scenarios. In an interesting recent study, <mark>Almås et al. (2015) use a novel identification strategy to capture decision-making power: willingness to pay. Specifically,</mark> the women selected to participate in the experiment are subject to a sequence of choices where they can either choose an amount X for themselves or an amount Y for their spouse (where X is usually smaller than Y). The experiment identifies the values that makes the participants indifferent between receiving X or letting their spouse receive Y, which gives us the respondent’s willingness to pay for receiving a transfer instead of having their partner receive it. The weaker the position of the woman in the household (the lower her control of resources), the more she should be willing to pay to obtain control of that transfer.

The above studies suggest that in lab-in-the-field experiments offer valuable opportunities for researchers to directly observe the intra-household bargaining process. However, aside from the standard criticisms of randomized experiments - such as whether one can extrapolate from the experimental setting to predict real world outcomes - their usefulness in understanding agency also depends on whether the policy focus is narrow (specific to one domain, for example, such as savings) or a broader understanding of agency is important (spanning multiple domains).

# _Direct observations_

Respondents’ ability to act can also be measured through direct observation. At a basic level, this could include measuring individual outcomes in the household that are directly verifiable, such as women working outside the household, family planning, or involvement in children’s education. Moreover, recent observational studies on decision-making have also linked up with community-driven development projects, where individuals’ actions are more easily observable through village committees and discussions organized by these projects. Rao et al. (2015), for example, conducted qualitative interviews women in treatment and control villages covered by the large-scale _Jeevika_ poverty alleviation project in rural Bihar; which involved observing their participation in problem solving, arbitration and creating alternatives to the rule of existing village leaders through self-help groups created by the project. Another example is the study of the GoBifo project in Sierra Leone mentioned above, which provided grants to communities across 236 villages and assistance in setting up Village Development Committees (VDCs) to plan how these funds would be used. Women’s participation in these VDCs was integral to the project, and was measured through direct observation of respondents’ participation in different types of groups, attendance at community meetings, ability to speak up in meetings, as recorded by agents/enumerators attending the meeting. Such direct observation can shed light on respondents’ actions, although again the dynamics of the decision-making process (particularly within the household) may be more difficult to assess with this approach.

49
The sectoral growth performance is closely related to the development of the three economies since 1965. All three countries have been through major economic crises followed by structural adjustment programs aimed at restoring macroeconomic stability and increasing overall growth of the economy. In the following, we will briefly sketch the economic development of the three countries.

# _3.1._ **_Cote d'Ivoire_**

C6te d'Ivoire has an income per capita of $660 (in 1995). The agricultural sector accounts for 31 percent of GDP, and generates 70 percent of export revenues (table 1). Its main exports are cocoa, coffee, and timber. Starting in the early 1980s, C6te d'Ivoire experienced a severe economic and social crisis. The crisis began with macroeconomic imbalances that had grown to unsustainable levels: a budget deficit of about 10 percent of GDP and a current account deficit of about 17 percent of GDP. Public expenditures had increased markedly following the cocoa and coffee price booms of 1975-77, but then were not reduced as the booms ended. With the rapid build-up of external debt in the 1970s, Cote d'Ivoire started to encounter serious debt servicing problems. Tightly controlled and restrictive policies, including trade barriers and price interventions, began to create serious distortions in domestic markets in the 1980s. Most notably, these distortions resulted in inefficient resource allocation.

**Table 1. Distribution of Gross Domestic Product in COte d'Ivoire, Ghana, and Zimbabwe (percent)**

||Agricu|lture|Industr|y|Servic|es|Manuf|acturing|
|---|---|---|---|---|---|---|---|---|
|Country|1980|1995|1980|_1995_|1980|1995|1980|1995|
|C6te d'Ivoire|27|31|20|20|53|50|13|18|
|Zimbabwe|14|15|34|36|52|48|25|30|
|Ghana|58|46|12|16|30|38|8|6|

_Source:_ World Bank 1997.

Adjustment policies introduced before 1987 were only partially successful in reducing the main internal and external imbalances. During the 1990s, the government initiated more widespread structural reforms, supported by the World Bank and the IMF.

7
**Table 11. Descriptive** **<u>statistics</u>**

||Mean|SE|
|---|---|---|
|_Household variables_|||
|Log of consumption|7.30|1.06|
|Log of income<br>|7.99|1.28|
|Agricultural index|0.24|0.13|
|Income diversity|0.17|0.13|
|Household size|5.2|2.3|
|Number of children|1.9|1.7|
|Age of head|49.8|16.4|
|Male head|0.71|0.45|
|Years of schooling (head)|2.6|3.8|
|Activity of head|||
|No job|0.23|0.42|
|Agroalimentary|0.54|0.50|
|Industry|0.03|0.18|
|Construction|0.00|0.05|
|Trade|0.12|0.33|
|Services|0.05|0.21|
|Other activity|0.03|0.17|
|_Community variables_|||
|Average years of schooling|4.0|1.6|
|Land owners|0.76|0.24|
|Communityindex|0.38|0.31|

Source: Own computations using _Haitian Vulnerability and Food Security Survey_ , 2007.

# **4.3.Results**

# **_Regression Results_**

We use self-reported shocks in order to estimate their impact on consumption and income. Table 12 presents OLS estimates and GLLAMM estimates. Both models are estimated with log consumption and log income. Our preferred specification regroups a large set of explanatory variables such as household characteristics, regional dummies, community characteristics, interaction between household characteristics and community characteristics, shocks variables, interaction between shocks variables and household characteristics, interaction between shocks variables and community characteristics. Estimating the two-level linear random coefficient model (GLLAMM) allows us to decompose the variance of the residuals into an idiosyncratic variance and a covariate variance.

29
# **Figure 5. Differences in Equity Issuance Behavior: Connected Firms of Different Sizes**

This figure shows differences in equity issuances among connected firms of different sizes in the propensity score matched (PSM) sample. Firm size is measured as the average total assets in 2010-12. Panel A compares connected firms of different sizes with unconnected firms. Connected firms are divided into four groups according to their size: firms with assets below the 25th percentile, firms with assets between the 25th and 50th percentiles, firms with assets between the 50th and 75th percentiles, and firms with assets above the 75th percentile of the firm size distribution of connected firms. Panel B compares connected firms with sizes below the median (50th percentile) with those with sizes above the median. Both panels plot the difference-in-differences (DiD) coefficients (and their 90% confidence intervals) obtained by estimating Equation (1), using the cumulative amount of equity raised over 2012 assets as the dependent variable. The 2012 coefficients show the differences across groups that year. The shaded areas capture the implementation of the Stock Connect program and the incorporation of domestic listed stocks into the MSCI Emerging Markets Index. Table 5 and 6 report the coefficients shown in this figure.

## **A. Connected Firms of Different Sizes versus Unconnected Firms**

<!-- Start of picture text -->
1.20<br>1.00<br>Stock<br>MSCI<br>Connect<br>0.80 Incorporation<br>0.60<br>0.40<br>0.20<br>0.00<br>-0.20<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>Connected with Size < P25 Connected with Size between P25 and P50<br>Connected with Size between P50 and P75 Connected with Size > P75<br>B. Small versus Large Connected Firms<br>1.20<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>-0.20<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>Connected below Median Size versus Connected above Median Size<br>Confidence Interval (90% CI)<br>DiD Coefficient<br>DiD Coefficient<br><!-- End of picture text -->
- 40 -

# Table 3: DYNAMIC EQUATIONS FOR INVESTMENT (DIFFERENCE REGRESSIONS) (Dependent variable is the difference of the logarithm of real private investment)

|VARIABLE|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|C|0.08<br>(1.41)|0.09<br>(1.89)*|0.08<br>(1.42)|0.04<br>(0.61)|0.05<br>(0.49)|0.06<br>(1.16)|0.01<br>(0.08)|-0.05<br>(0.86)|
|DGDP|1.97<br>(2.44)*"<br>|1.39<br>(2.19)**<br>|1.34<br>(2.03)**|1.55<br>(1.77)*|1.90<br>(1.37)<br>|1.61<br>(2.14)**|1.88<br>(2.64)**|2.65<br>(3.26)**|
|DGDP(-1)|-0.72<br>(0.88)||||-0.84<br>(0.96)||||
|DXCOSTS|-2.00<br>(2.44)**|-1.45<br>(1.96)*|-'.44<br>(1.92)*<br>|-1.88<br>(2.08)**|-1.75<br>(1.29)<br>|-2.11<br>(2.65)**|-2.20<br>(2.97)**|-2.68<br>(3.07)**|
|DICOSTS(-1)|0.79<br>(1.10)||||1.32<br>(1.58)|1.17<br>(1.79)*|0.87<br>(1.39)||
|DMARXUP|0.64<br>(1.12)||1.30<br>(2.69)**|0.9<br>(1.64)|0.25<br>(0.32)|||1.01<br>(0.61)|
|DMARKUP(-1)|0.86<br>(1.59)|1.36<br>(2.93)**|||0.91<br>(1.44)|1.11<br>(2.37)**|1.05<br>(2.41)**||
|DGVIINF|||||0.21<br>(0.89)|||0.11<br>(0.61)|
|r1GVIINF(-1)|||||0.18<br>(0.47)|0.43<br>(1.82)*|0. 20<br>(0.77)||
|DPVCRDY|1.15<br>(3.78)**|1.16<br>(3.95)**|1.11<br>(3.84)**|0.80<br>(2.78)**|1.35<br>(4.05)**|1.10<br>(3.62)**|1.27<br>(4.26)**|1.27<br>(4.81)**|
|DPVCRDY(-1)|0.44<br>(1.42)|0.62<br>(2.54)**|0.56<br>(2.00)**||0.27<br>(0.73)||||
|DPRIVI(-l)|0.19<br>(0.86)||0.11<br>(0.53)|0.37<br>(1.76)*|0.22<br>(0.66)||0.33<br>(1.72)*|0.50<br>(3.11)**|
|RES(-1)|-0.61<br>(2.58)**|-0.39<br>(2.23)**|-0.43<br>(2.19)**|-0.63<br>(2.60)**|-0.72<br>(1.09)|-0.52<br>(1.81)*|-0.85<br>(2.60)*"|-1.33<br>(4.82)**|
|R2|0.64<br>|0.56<br>|0.56<br>|0.42<br>|0.86<br>|0.73<br>|0.78<br>|0.76<br>|
|R2(ADJ)<br>|0.39<br>|0.41<br>|0.38<br>|0.22<br>|0.62<br>|0.57<br>|0.63<br>|0.62<br>|
|SE<br>|0.17<br>|0.17<br>|0.17<br>|0.19<br>|0.14<br>|0.15<br>|0.14<br>|0.15<br>|
|DW|1.73<br>|1.74<br>|1.86<br>|1.89<br>|2.10<br>|1.56<br>|1.91<br>|1.66<br>|
|F<br>|2.53<br>|3.77<br>|3.14<br>|2.16<br>|3.61<br>|4.54<br>|5.00<br>|5.43<br>|
|DF<br>|-5.14<br>|-4.65<br>|-5.03<br>|-4.65<br>|-4.78<br>|-3.46<br>|-4.22<br>|-3.63<br>|
|ADF(2)<br>CHOW|-4.21|-3.88<br>0.31|-4.19|-4.30<br>1.21|-4.06|-2.43<br>5.10|-3.39<br>10.78|-3.16|

NOTES: -NUMBERS IN PARENTHESES ARE T STATISTICS. ONE ASTERISK IMPLIES SIGNIFICANCE AT THE 10% LEVEL. TWO ASTERISKS IMPLY SIGNIFICANCE AT THE 5% LEVEL.
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 7. Optimal and existing fuel levy in selected countriesof Sub-Saharan Africa**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Existing Optimal for maintenance<br>$ cents per liter<br><!-- End of picture text --> Source: Gwilliam and others 2008.

Compared to other African peers, Cameroon does not appear to be underspending on road maintenance. This analysis is based on the assumption that road maintenance services can be purchased at an efficient standardized unit cost. To the extent that the unit costs of road maintenance in Cameroon are above this benchmark level, maintenance _effort_ could remain inadequate despite the apparent adequacy of _spending_ levels. Current levels of capital spending in Cameroon fall well below what is needed to clear rehabilitation backlogs within a reasonable five-year period (figure 8).<sup>5</sup>

> 5 Using the RONET model, it is possible to produce detailed estimates of the rehabilitation requirements for each country’s road network taking into account the current distribution of network condition and working toward a target of clearing the current rehabilitation backlog within a five-year period. On that basis, the rehabilitation requirements can be compared with the current levels of capital expenditure to determine whether these are high enough to eliminate the rehabilitation backlog within a reasonable period of time. The calculation is helpful in illustrating whether current levels of capital expenditure would be high enough to address the rehabilitation problem if they were fully allocated to rehabilitation works (Gwilliam and others 2008).

13
**<u>Table 11: Probability of upward mobility upon remarriage</u>**

||Prob(upward<br>mobility)|Prob(upward<br>mobility)|
|---|---|---|
|Married widow|0.012<br>(0.053)|0.024<br>(0.055)|
|Last dissolution: rural resident|-0.199<sup>***</sup><br>(0.047)|-0.140<sup>***</sup><br>(0.051)|
|Dissolution before age 40||-0.115<sup>*</sup><br>(0.064)|
|Ever been to a French school||0.208<sup>***</sup><br>(0.058)|
|Number of marital dissolutions||-0.020<br>(0.049)|
|Mean of dep. var|0.28|0.28|
|N|289|286|
|Pseudo- R2|0.045|0.087|

Note: Logit model, marginal effects shown.<sup>***</sup> _p_ < 0.01,<sup>**</sup> _p_ < 0.05,<sup>*</sup> _p_ < 0.1,<sup>+</sup> _p_ < 0.12. Sample: Remarried widows and remarried divorcees Source: Authors’ estimations using PSF1.

42
**r** 'be emainder of tlic ri:.,>mi;',d ,i'; fo11ws Ill Section 2 the e.npirical method and th'ti **e** .:re !est **ib-d,** Sect ion 3 contains the results Sectiou, .S **a** a discussion t :he resulls and Sect ioni _5_ contains concluding remarks.

# 2. **Ezoirical Issues**

# 2.1. Methodology

The impact of a borrower's repavment history on the credit **terms it** later faces in **the** Eurocurrency r..arket is examined. In the Eurocurrency credit market the rate of interest has two components: the interbank interest rate, which represents the cost of capital to banks, and the spread above the interbank rate. The interbank interest rate is excgenous to the lending decision to specific borrowers. Hence, the determination **of** spreads will be investigated, with particular regard to the relevance of repayment histories.

The relationship between the spread, s, and the probability of default, p, can be posited (see Feder and Just (1977), Edwards (1984)) as: where 0 represents other variables, such as the discount rate that affect the spread ( For notational convenience the subscripts that would indicate country- and time-periods are not employed.). This equation is easily justified if perfect competition and risk neutral banks are assumed.<sup>1 0</sup> In implementing this model empirically, consistent with the convention, we assume that the functional form of p is logistic. l Geographic region dummv variables and time-specific dummv variables are also employed. Timespecific dumm- variables are incorporated to capture the variations of
to traffic congestion is likely to harm less the agglomeration benefits of firms in the tradable sector relative to the non-tradable sector and formulate the following two related hypotheses.

_Hypothesis 2A_ : The effect of urban density on firm productivity in developing economies depends on mobility within metropolitan areas, so that firms in lower mobility cities benefit less from agglomeration economies than firms in higher mobility cities.

_Hypothesis 2B_ : The moderating effect of congestion on the relationship between urban density and labor productivity is stronger for firms in the non-tradable sector than for firms in the tradable sector.

# **3. Data and methodology**

In our empirical investigation, we would like to examine the “pure” agglomeration effects, which capture both the supply-side learning externalities and the demand-side market-size externalities, _net_ of any geographical, agglomeration costs, quality effects due to sorting, and any potential effects of productivity on density (reverse causality). We start with a baseline model of the relationship between urban density and labor productivity, in which none of the controls for agglomeration costs are included and in which we do not address reverse causality and sorting. In each subsequent model, we address each of these issues by controlling for geographical, agglomeration costs, quality effects due to sorting, and any potential effects of productivity on density.

# _3.1. Data sources_

We use a harmonized and geo-coded data set based on the World Bank Enterprise Surveys for 98 low- and middle-income developing economies for the period from 2009 to 2020. See Appendix A1 for information on the economies included in the analysis. These firm-level surveys are representative of a country’s private sector, but exclude the resource-based sector, the financial sector, and firms with less than five employees. The enterprise surveys are produced using a stratified random sampling strategy and cover in a comparable way a range of businessenvironment topics including sales, number of employees, age of firm, manager experience, ownership characteristics, employees’ education, the degree to which firms see crime as an obstacle, and the location of the establishments, among others.<sup>7</sup> Appendix B displays the list of all firm-level variables used in the analysis.

To test our hypotheses, we link the enterprise survey data to metropolitan area–level data by obtaining GPS coordinates or detailed location names for all establishments in the Enterprise

> 7 We looked at the country classification at the time of the survey.

8
Table 8: Urban and poverty status changes in 16 SSA countries

|**(A) Original defn**|**itions to DOU**|**Non**|**-spatia**|**lly defa**|**ted**|**S**|**patially **|**defate**|**d**|
|---|---|---|---|---|---|---|---|---|---|
|**Non-poor status:**||Non-|poor|Po|or|Non-|poor|Po|or|
|**DOU:**||Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|
|**Ofcial urban def**|**nition:**|||||||||
|Non-poor|Urban|87.4|12.6|0.0|0.0|86.0|12.7|1.3|0.1|
||Rural|21.0|79.0|0.0|0.0|19.0|77.4|1.4|2.2|
|Poor|Urban|0.0|0.0|77.5|22.5|4.8|2.5|75.1|17.6|
||Rural|0.0|0.0|17.1|82.9|0.1|3.0|17.6|79.3|
|**(B) Original defn**|**itions to DB**|**Non**|**-spatia**|**lly defa**|**ted**|**S**|**patially **|**defate**|**d**|
|**Non-poor status:**||Non-|poor|Po|or|Non-|poor|Po|or|
|**DOU:**||Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|
|**Ofcial urban def**|**nition:**|||||||||
|Non-poor|Urban|90.1|3.4|0.0|6.5|92.9|6.5|0.5|0.0|
||Rural|46.5|27.2|0.0|26.3|70.2|25.3|3.4|1.0|
|Poor|Urban|0.0|0.0|91.8|8.2|6.3|1.0|86.2|6.5|
||Rural|0.0|0.0|55.5|44.5|1.4|2.2|64.6|31.8|
|**(C) DOU to DB**||**Non**|**-spatia**|**lly defa**|**ted**|**S**|**patially **|**defate**|**d**|
|**Non-poor status:**||Non-|poor|Po|or|Non-|poor|Po|or|
|**DB:**||Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|
|**DOU:**||||||||||
|Non-poor|Urban|96.3|1.8|0.0|1.9|97.6|1.9|0.5|0.0|
||Rural|34.6|32.0|0.0|33.3|64.7|32.3|2.3|0.7|
|Poor|Urban|0.0|0.0|96.9|3.1|7.0|0.3|90.3|2.4|
||Rural|0.0|0.0|44.7|55.3|1.1|2.2|58.9|37.9|

Source: International Urban Poverty Database. Note: WorldPop 250m is used for the DOU and DB methods. Poverty is measured using the $2.15 poverty line. In Panel (A), welfare is deflated using official spatial deflators. In Panel (B) and (C), welfare is deflated using our updated spatial deflators.

The Theil indexes range from 0.13 to 1.06 nationally among the countries analyzed in this study (Figure A14 and Figure A15). In most countries, urban Theil indexes are higher than rural Theil indexes, with a few exceptions such as Lesotho, Gabon, and Mauritania in the DOU definition, as well as Ghana and Gabon in the DB definition. The higher urban inequality is not surprising, as urban areas tend to accommodate wealthier populations. However, we did not find a clear pattern between Theil indexes, whether at the national or subnational level, and a country’s GDP per capita.<sup>29</sup> To further explore inequality, we examine two factors: 1) inequality within urban and rural areas, respectively, and 2) inequality between urban and rural groups. The decomposition of Theil indexes into between-group and within-group factors, as shown in Figure A16 and Figure A17 in Appendix A, reveals that within-group inequality is higher in all countries for both the DOU and the DB approaches.

> 29This lack of pattern may be attributed to the focus of this paper on low-income countries. In general, a Kuznets curve (Kuznets, 1955) is observed for countries worldwide (see Panel D in Figure A1), indicating that inequality widens as the economy further develops and then declines at the high-income stage.

22