rafmacalaba/datause-encoder-data huggingface dataset:
1 % level for all sub-periods between 1975 and 1995. This provides evidence of clustering of similar growth rates across countries in the same RTA, consistent with the general presence of localized growth spillover effects.

# [table 1 about here]

Although Moran's I provides an indication of the general presence of clustering, it yields no insight into the possible existence of spatial heterogeneity in this process across major world regions. To explore the presence of geographically defined subgroups, we first construct a scatter plot of **W3** _y_ against _y_ where _y_ is an _n_  1 vector of observations on country growth rates expressed in deviations from the sample mean. Such a scatter plot divides countries into four categories. These categories correspond to fast growing countries with fast-growing neighbors (HH); slow growing countries with slow-growing neighbors (LL); fast-growing countries with slow-growing neighbors (HL); and slowgrowing countries with fast-growing neighbors (LH). Mapping these categories then provides a visual impression of the possible spatial heterogeneity in growth clustering.<sup>23</sup> Figure 2, which relates to 1990-1995, is typical of the results obtained. It shows several clusters of countries which shared fast growth relative to the sample mean. Notably, these clusters seem to be associated with regions of the world with higher levels of formal and informal integration (in particular, the USA-Canada, Europe, South Asia, and East Asia and Pacific). By contrast, SSA has more of a patchwork appearance with notable incidences of fast growing countries sharing RTAs with slow-growing countries. This

> 23 Such a map is referred to as a Moran scatter plot map (Anselin, 1996).

16
**Table 7.1 Summary of Total Benefits of a Reduction in Air Pollution in Four Scenarios for Ozone and PM10**

(in million US$ per year, 2010 value in 1999 prices, 3 percent discount rate)

|||||_Sc_|_enario_||||
|---|---|---|---|---|---|---|---|---|
||_1_|_0%_|_2_|_0%_|_A_|_QSI_|_A_|_QS2_|
|_Incomeelasticity_|<br>_1.0_|_0.4_|_1.0_|_0.4_|_1.0_|_0.4_|_1.0_|_0.4_|
|Ozone|||||||||
|Healthbenefit estimate1including:<br>Morbidity(Prod.loss+COI+WTP)<br>andWTPformortality|116|183|232|365|717|1129|794|1250|
|Healthbenefitestimate2including:|||||||||
|Morbidity(Prod. loss+_COI_+WTP)<br>andhumancapital lossesformortality|75|114|151|228|465|706|515|782|
|Healthbenefitestimate3including:<br>Morbidity(Prod.loss+COI)<br>and humancapitallosses formortality|18|18|35|35|109|109|_121_|121|
|Environmentalcontingenciesbenefits|36|36|45|45|45|45|45|45|
|**PM10**|||||||||
|Healthestimate1including:|||||||||
|Morbidity(Prod.loss+COI+WTP)<br>andWTPformortality|1451|2549|2903|5098|3186|5595|6793|11931|
|Healthbenefitestimate2including:|||||||||
|Morbidity(Prod.loss+COI+WTP)<br>andhumancapitallossesformortality|644|1184|1289|2367|1414|2598|3016|5540|
|Healthbenefitestimate3including:|||||||||
|Morbidity(Prod.loss+COI)<br>andhumancapitallossesformortality|96|96|191|191|210|210|448|448|
|Environmentalcontingencies|4|4|4|4|4|4|4|4|

Prod. loss = Productivity losses; COI = cost of illness; WTP = willingness to Pay.

|**Table7.2SummaryofBenefitsFromEachScenarioUs**<br>(inmillionUS$peryear,2010 valuesin1999prices)|**ingIncomeElasti**|**cityof1.0**|
|---|---|---|
|_Estimates_<br>_10%_<br>_20%_|_AQSI_|_AQS2_|
|High<br>1607<br>3184|3952|7636|
|Central<br>759<br>1489|1928|3580|
|Low<br>154<br>275|368|618|

The estimates in tables 7.1 and 7.2 clearly show that the calculated benefits associated with air pollution reduction give an economic basis for spending to further reduce polluting emissions. Exactly how much is open to debate. Ideally, this study on economic benefits should be combined with estimates of emission abatement costs to determine an economically justifiable level of abatement. Hence, conducting a cost-benefit analysis is the logical next step. This would also necessitate further advances in atmospheric chemistry modeling for Mexico City, which is needed to compare costs from emissions reductions with benefits of lower concentrations of pollutants.

_45_
Figure 35: State Level Water Deprivation Shelter Deprivation - by State - MICS 80% 70% 60% 50% 40% 30% 20% 10% 0% Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. Estimates are for children aged 0- 17.

Figure 36: State Level Shelter Deprivation a) b) <!-- Start of picture text --> a) b) c)<br>Shelter Deprivation - by State in Shelter Deprivation - by State in Shelter Deprivation - by State in<br>North Central - MICS North East - MICS North West- MICS<br>80%<br>80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10%<br>10%<br>0% 0%<br>0% Children 0-17 Children 0-17<br>Children 0-17<br>Adamawa Bauchi Borno Jigawa Kaduna Kano Katsina<br>Benue FCT Abuja Kogi Gombe Taraba Yobe Kebbi Sokoto Zamfara<br>Kwara Nasarawa Niger<br>d) e) f)<br>Shelter Deprivation - by State in Shelter Deprivation - by State in Shelter Deprivation - by State in<br>South East - MICS South South MICS South West- MICS<br>80% 80%<br>80%<br>70% 70%<br>70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 0-17 Children 0-17 Children 0-17<br>Akwa Ibom Bayelsa Cross River Ekiti Lagos Ogun Ondo Osun Oyo<br>Abia Anambra Ebonyi Enugu Imo Delta Edo Rivers<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis 46
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|||(0.078)***||(0.079)***||(0.117)***||||
|---|---|---|---|---|---|---|---|---|---|
|pctel_mena|||-0.026|||||||
||||(0.013)**|||||||
|pctel_grmena||||-0.008||||||
|||||(0.006)||||||
|pcmob|||||||0.082|0.117|0.132|
||||||||(0.031)**|(0.026)***|(0.082)|
|pcmob_mena||||||||-0.063||
|||||||||(0.024)**||
|Observations|66|57|66|57|48|46|72|72|60|
|R-squared|0.47|0.54|0.48|0.56|0.30|0.59|0.50|0.52|0.53|
|Wu-Hausman F<br>test, p-value|||||0.09|0.28|||0.64|
|Robust standard<br>are beginning of<br>per capita terms|errors in<br>the perio<br>except wh|parentheses.<br>d indicators f<br>ere variable|* significant<br>or: infrastru<br>name does n|at 10%; **<br>cture, agri/g<br>ot begin wit|significant a<br>dp, populati<br>h ‘pc’|t 5%; *** s<br>on density, p|ignificant at<br>opulation.|1%. Instrum<br>Variables ar|ents<br>e in|

Table 7 reports the regressions relative to railroads. Overall, no results are significant, except the effect of the growth rate on the MENA subgroup of countries, indicating a lower overall effect for that group (although the overall effect is not significant).

**Table 7: Railroads**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||OLS|OLS|OLS|OLS|2SLS|2SLS|
||Pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|
|Constant|-0.026|-0.407|-0.029|-0.361|-0.025|-0.527|
||(0.022)|(0.268)|(0.022)|(0.223)|(0.019)|(0.659)|
|pcgdp80|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|
||(0.000)**|(0.000)***|(0.000)|(0.000)*|(0.000)|(0.000)|
|school_enrol_secondary|0.000|0.000|0.000|0.000|0.000|0.000|
||(0.000)*|(0.000)|(0.000)*|(0.000)|(0.000)*|(0.000)|
|school_enrollment_primary|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|
|inv_gdp|0.155|0.161|0.182|0.245|0.138|0.169|
||(0.061)**|(0.074)**|(0.060)***|(0.059)***|(0.054)**|(0.059)**|
|pcrail|3.968||3.131||-2.587||
||(7.129)||(7.762)||(18.910)||
|pcrail_gr||0.413||0.365||0.541|
|||(0.283)||(0.235)||(0.716)|
|pcrail_mena|||-92.189||||
||||(58.814)||||
|pcrail_grmena||||-0.027|||
|||||(0.008)***|||
|Observations|39|26|39|26|32|24|
|R-squared|0.46|0.51|0.51|0.70|0.46|0.51|
|Wu-Hausman F<br>test, p-value|||||0.46|0.83|

Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Instruments are beginning of the period indicators for: infrastructure, agri/gdp, population density, population. Variables are in per capita terms except where variable name does not begin with ‘pc’.

19
23 Nevertheless, some of the empirical regularities arising from the studies of Latin America and East Asia in Bourguignon et al. (2005) are quite interesting. First, the increase in the returns to schooling that accompanied rapid growth in countries like Taiwan (China) or Indonesia tended to contribute to increases in inequality. This effect was also present in countries that grew less rapidly, like Mexico, and is reminiscent of the so-called “Tinbergen Race” between increases in the demand for schooling (arising from technological progress) and the rising supply of skilled workers (brought about by expansions in the educational system). In most countries in the sample, the demand-side dominated, leading to increased earnings inequality; the only exceptions were Brazil and Colombia.

Greater earnings inequality often led to higher inequality in household incomes, but not always. An interesting example is provided by Taiwan, where a marked increase in labor force participation by women led to a divergence between the earnings and income distributions. While the entry of relatively skilled women into the labor force reduced earnings inequality (as they entered roughly in the middle of the distribution), it contributed to an increase in the dispersion of household incomes: most of these new workers were married to skilled men, and lived in households that were already relatively well-off. The importance of changes in labor force participation and occupational structure is not an isolated characteristic of the Taiwanese experience. In Brazil, too, between 1976 and 1996, a substantial increase in extreme poverty was associated primarily with an increase in unemployment, informality, and underemployment. In Indonesia, a large share of the overall increase in inequality was associated with large movements of labor away from wage employment (in agriculture) towards (predominantly urban) self-employment.

This approach also illustrates the ambiguous effect of rising levels of education on inequality. In Colombia, Indonesia and Mexico, substantial increases in the average level of schooling of the population did not lead to lower inequality. On the contrary, when one controls for the changes in returns, it seemed to be associated with higher inequality levels. This result was due to two effects: increases in the education stock that raised inequality in educational attainment itself (i.e. where most of the increase is accounted for by rises among the bettereducated), but also the fact that when returns to education are convex, even a distribution-neutral increase in schooling can lead to higher earnings inequality. Of course, educational expansions
|**Table 5**|**OE**|**CD questi**|**onnaire for entry regulation in the professions**<sup>**a**</sup>|
|---|---|---|---|
|Weights<br>by theme|Question<br>weights|Score|Question|
|2/5|||**_Licensing_**|
||1|6|**How many services does the profession have an exclusive or**<br>**shared exclusive right to provide?**<br>More than 3|
|||4.5|3|
|||3|2|
|||1.5|1|
|||0|0|
|2/5|||**_Education requirements (only applies if licensing not 0)_**|
||1/3|Equals nu|**What is the duration of special education/university/or other**<br>**higher degree?**<br>mber of years of education (max of 6)|
||1/3|Equals nu|**What is the duration of compulsory practice necessary to become**<br>**a full member of the profession?**<br>mber of years of compulsory practice (max of 6)|
||1/3||**Are there professional exams that must be passed to become a full**<br>**member of the profession?**|
|||6|Yes|
|||0|No|
|1/5|||**_Quotas and economic needs tests_**|
||1||**Is the number of foreign professionals/firms permitted to practice**<br>**restricted by quotas or economic needs tests?**|
|||6|Yes|
|||0|No|

> **a** The indicator for each profession is calculated as the simple average of the indicators of entry (Table 4) and conduct (Table 5) regulation.

Source: OECD (2005a).

34
# _Is Africa’s Economy At A Turning Point?_<sup>_1_</sup>

Jorge Arbache, Delfin S. Go, and John Page

## **I. Introduction**

There is something decidedly different and new about the economic landscape of SubSaharan Africa (Africa or the region hereafter). After stagnating for much of 45 years, economic performance in Africa is markedly improving. In recent years, for example, GDP growth in SSA is accelerating to its strongest point at about 6 percent a year while inflation registered below two-digit level, its lowest point. The much improved economic performance is confirmed by several recent assessments.<sup>2</sup> Although the current economic growth is still short-lived relative to Africa’s long history of growth crisis and is certainly nowhere close to the standard of East Asia, it is nonetheless noteworthy and a cause for guarded optimism. The performance at least calls for a closer examination. Several interesting questions can immediately be raised: Is Africa finally overcoming the challenges of growth and poverty to claim the 21st century, as posed, for example, by Gelb et al. (2000), Collier (2007), and Ndulu et al. (2007)? In particular, is there indeed a turning point? How widespread are the recent gains among the 47 countries in the region? What are the key factors underlying the recent improvements in Africa’s economic performance? Why have the recent oil price increases not dampened its growth performance? Is the growth robust and supported by improvements in the economic fundamentals? What major risks and challenges remain? Will Africa attain the Millennium Development Goals by 2015?

The rest of the paper looks at these questions and is organized as follows. The recent acceleration of growth in Africa is discussed in the next section. Then, the growth diagnostics in the following section carefully examine the differences since 1995, the likely causes and robustness of the recent growth. The paper then looks at the key actions

> 1 This paper will appear in a revised form in _Africa at a Turning Point? Growth, Aid and External Shocks, e_ dited by Delfin Go and John Page (forthcoming), Africa Development Essay Series, The World Bank.  We thank Betty P. Dow, Cristina Savescu, and Xiao Ye for excellent research assistance.

2 These assessments include AfDB and OECD (2007); AFRCE (various years); EIU (2007); IMF (2007a, 2007b); and World Bank (2007a, 2007b, 2007d). AFRCE briefs, from which much of this paper is derived, are biannual economic briefs prepared by the Chief Economist Office of the Africa Region at the World Bank. The briefs cover recent economic growth in Sub-Saharan Africa, issues about the scaling up of foreign aid, and the effect of external shocks. In addition, the papers by Arbache and Page cited in section 3 are key sources of the growth analysis.
Policy Research Working Paper 9256

# **Abstract**

After almost two decades of poverty maps produced by the World Bank and multiple advances in the literature, this paper presents a methodological update to the World Bank’s toolkit for small area estimation. The paper reviews the computational procedures of the current methods used by the World Bank: the traditional approach by Elbers, Lanjouw and Lanjouw (2003) and the Empirical Best/Bayes (EB) addition introduced by Van der Weide (2014). The addition extends the EB procedure of Molina and Rao (2010) by considering heteroscedasticity and includes survey weights, but uses a different bootstrap approach, here referred to as clustered bootstrap. Simulation experiments comparing these methods to the original EB approach of Molina and Rao (2010) provide empirical evidence of the shortcomings of the clustered bootstrap approach, which yields biased point estimates. The main contributions of this paper are then two: 1) to adapt the original Monte Carlo simulation procedure of Molina and Rao (2010) for the approximation of the extended EB estimators that include heteroscedasticity and survey weights as in Van der Weide (2014); and 2) to adapt the parametric bootstrap approach for mean squared error (MSE) estimation considered by Molina and Rao (2010), and proposed originally by González-Manteiga et al. (2008), to these extended EB estimators. Simulation experiments illustrate that the revised Monte Carlo simulation method yields estimators that are considerably less biased and more efficient in terms of MSE than those obtained from the clustered bootstrap approach, and that the parametric bootstrap MSE estimators are in line with the true MSEs under realistic scenarios.

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

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
received the communication letter. We find that spillover effects are stronger in blocks that exhibited a higher degree of tax compliance in the pre-treatment period. This application showcases the usefulness of our methodological framework for designing partial population experiments.

# **References**

- **Abadie, Alberto, Susan Athey, Guido W Imbens, and Jeffrey M Wooldridge.** 2022. “When Should You Adjust Standard Errors for Clustering?” _Quarterly Journal of Economics_ , 138(1): 1–35.

- **Angelucci, Manuela, and Giacomo De Giorgi.** 2009. “Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles’ Consumption?” _American Economic Review_ , 99(1): 486–508.

- **Antinyan, Armenak, and Zareh Asatryan.** 2024. “Nudging for Tax Compliance: a Meta-Analysis*.” _The Economic Journal_ , ueae088.

- **Athey, Susan, Dean Eckles, and Guido W. Imbens.** 2018. “Exact P-values for Network Interference.” _Journal of the American Statistical Association_ , 113(521): 230–240.

- **Baird, Sarah, Aislinn Bohren, Craig McIntosh, and Berk Ozler.**<sup>** ̈**</sup> 2018. “Optimal Design of Experiments in the Presence of Interference.” _The Review of Economics and Statistics_ , 100(5): 844–860.

- **Bai, Yuehao.** 2022. “Optimality of Matched-Pair Designs in Randomized Controlled Trials.” _American Economic Review_ , 112(12): 3911–3940.

- **Barrera-Osorio, Felipe, Marianne Bertrand, Leigh L. Linden, and Francisco Perez-Calle.** 2011. “Improving the Design of Conditional Transfer Programs: Evidence from a Randomized Education Experiment in Colombia.” _American Economic Journal: Applied Economics_ , 3(2): 167–195.

- **Basse, Guillaume, and Avi Feller.** 2018. “Analyzing two-stage experiments in the presence of interference.” _Journal of the American Statistical Association_ , 113(521): 41–55.

- **Basse, G W, A Feller, and P Toulis.** 2019. “Randomization tests of causal effects under interference.” _Biometrika_ , 106(2): 487–494.

- **Berger, Martijn P.F., and Weng-Kee Wong.** 2009. _An Introduction to Optimal Designs for Social and Biomedical Research._ Wiley.

- **Bergeron, Augustin, Gabriel Tourek, and Jonathan Weigel.** 2024. “The State Capacity Ceiling on Tax Rates: Evidence from Randomized Tax Abatements in the DRC.” _NBER working paper_ .

## **Beuermann, Diether W., Julian Cristia, Santiago Cueto, Ofer Malamud, and Yyannu Cruz-Aguayo.**

2015. “One Laptop per Child at Home: Short-Term Impacts from a Randomized Experiment in Peru.” _American Economic Journal: Applied Economics_ , 7(2): 53–80.

27
implications for welfare. Specifically, we show that the auto-correlation coefficient of the AR(1) process (the catching-up parameter) measures “good mobility” in the sense that a reduction in this parameter increases both mobility and welfare. In contrast, social welfare is (almost) unaffected by measurement error or transitory income shocks even though mobility increases with the variance of the i.i.d. component of labor income. Finally, the variance of persistent income shocks (income risk) increases mobility, but decreases social welfare. This implies that two societies with the same initial distribution of income and the same level of measured income mobility and aggregate growth may experience quite different social welfare changes depending upon the different combinations of the underlying income parameters.

We present a quantitative implementation of our framework that underscores the importance of decomposing income dynamics into its components, as we have discussed. Specifically, an application using data on individual incomes from Mexico yields striking results. Most of measured income mobility is driven by measurement error or transitory income shocks and therefore (almost) welfare-neutral, and only a small part of measured income mobility is due to either welfare-reducing income risk or welfare-enhancing catching-up of low-income individuals with high-income individuals. However, despite the small mobility effects, (idiosyncratic) persistent income risk has significant negative effects on social welfare – eliminating or insuring it would generate welfare gains that are equivalent to an increase in lifetime consumption by about 10 percent even if workers are only moderately risk-averse (log-utility).<sup>9</sup> Eliminating the catch-up of low income individuals with high income individuals yields a loss in social welfare of similar magnitude. Decomposing mobility into its fundamental components is thus seen to be crucial from the standpoint of welfare evaluation.

# II. Income and Mobility

## II.1. Income Process

Consider a large number of workers indexed by i. For notational ease, we focus on one cohort of workers who enter the labor market for the first time in period t = 0 so that t = 0, 1, . . . stands for both calendar time and age (experience) of the worker. Let yit stand for the labor income of worker i in period t. Following a longstanding tradition in micro-econometrics,

> 9In comparison, for the same preference parameters, Lucas (2003) computes welfare cost of aggregate consumption fluctuations in the US that are two orders of magnitude smaller. Thus, even though our estimates of persistent income risk seem small when measured mobility is the yardstick, their welfare effect is large indeed.

4