rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2. Comparison of Poverty Reduction Trend using Distribution-Neutral Projection and Linear Projection**

<!-- Start of picture text --> 25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Distribution-neutral Simulation Linear projection<br>Global Poverty Rate (%)<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027<br><!-- End of picture text -->

**_Source_** : Authors’ re-estimation using the 2008 global distribution from PovcalNet (as of October, 2013).

**Figure 3: Projection of Population Growth Rate by Region (%, annual)**

<!-- Start of picture text --> 2.5<br>2.3<br>2.0<br>1.5 1.4<br>1.2<br>1.0 0.8<br>0.4<br>0.5<br>0.1<br>0.0<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> Source: Population Projections by World Bank Data Group (as of October 9, 2013).

First, the assumption of uniform population growth rate does not seem consistent with the World Bank Data department’s latest population projections where population growth rates differ significantly, and more importantly, the population of the poorest region, Sub-Saharan Africa (SSA), is expected to grow the fastest (see Figure 3). The violation of this assumption has implications for the global projection of poverty. To see the impact, we estimate the poverty rate of the developing world in 2027 assuming that the populations of all regions grow as the World Bank Data Group’s projections predict with no difference in the population growth rate within a region. This assumption of no change in the population growth rate within a region is a strong one, but it is made to reduce the computational burden.

8
**Welfare Dynamics with Synthetic Panels: The Case of the Arab World in Transition**

Hai-Anh H. Dang and Elena Ianchovichina<sup>*</sup> World Bank JEL: C15, D31, I31, O10, O57 Key words: welfare dynamics, poverty, vulnerability, middle class, subjective well-being, synthetic panel, Arab countries, Middle East and North Africa, Arab Spring

> * Dang <u>(hdang@worldbank.org; corresponding author) and Ianchovichina (eianchovichina@worldbank.org) are</u> respectively Economist with the Poverty and Inequality Unit, Development Research Group, and Lead Economist, the MENA Chief Economist’s Office, the World Bank. We would like to thank Ragui Assaad, Martijn Burger, Sami Bibi, Hadi Salehi Esfahani, Francisco Ferreira, Ravi Kanbur, Peter Lanjouw, Norman Loayza, and participants at a workshop for the MENA Inequality Report (Washington, DC), the IARIW-CAPMAS conference (Cairo, Egypt), and DECRG Hub seminar (Kuala Lumpur, Malaysia) for helpful discussions on earlier versions. We would like to thank the UK Department of International Development for funding assistance through its Strategic Research Programs.
as a measure of absolute mobility of these disadvantaged children.<sup>31</sup> We report evidence consistent with the parallel trends assumption for the linear mobility model in equation (2). Event study graphs for the two parameters of interest _β_ 4 (intercept eect) and _β_ 7 (slope eect) are in Figure 1 (sons) and Figure 2 (daughters). The event study graph for sons show that the placebo eect of a ctitious school construction on the probability of primary completion of children at various years before the actual construction of schools is zero for both parameters of interest. In contrast, there is an appreciable shift in the estimated eects for the post school construction years for the fully exposed birth cohorts (1968-1972). The evidence is similar for girls.<sup>32</sup> In the online appendix, we report the event study graphs including the partially exposed cohorts (see Figures A.1 (sons) and A.2 (daughters)).

# (4.2) Estimating Equation: Quadratic Model

Many existing studies on intergenerational educational mobility, both in economics and sociology, rely on the linear model discussed above (see, for example, Hertz et al. (2008), Azam and Bhatt (2015), Emran and Shilpi (2015)). For a survey, see Torche (2019)). However, recent theoretical and empirical analysis suggest that the linear model may be inadequate for understanding intergenerational educational mobility (Becker et al. (2015), Emran et al. (2021)). For recent contributions where the mobility curve is nonlinear, and relative mobility and the eects of policy interventions vary across the distribution of father's education, see Card et al. (2022), Asher et al. (2023), Emran et al. (2021), and Ahsan et al. (2021).

Many authors use the estimate of relative mobility from a linear model as a summary measure because it can be interpreted as a weighted average of underlying heterogeneous relative mobility of dierent subgroups across the distribution. However, recent work by Maasoumi et al. (2022) shows that the weights implied by the OLS estimate of the linear mobility equation have no plausible economic interpretation. Our objective here is not to provide a summary measure, but to understand potentially very dierent eects of policies on dierent sub groups dened by father's schooling level.

> 31Measuring absolute mobility by the expected outcomes of children based on a conditional expectation function (CEF) has been popularized by the recent work of Chetty et al. (2014) on intergenerational income mobility in the United States. For details, see the discussion in section (5.3) below.

> 32The event study graphs for years of schooling also show that the placebo treatment eects for the preintervention birth cohorts are not signicantly dierent from zero at the 10 percent level. We also checked the placebo eects for the earlier birth cohorts in the pre-Inpres period (1950-1956), and the results support the DiD design. The details are available from the authors.

14
of these data sources are assigned to the six governance dimensions. Annex 1 provides an annotated summary of the WGI data sources with links to their methodology and data, and Annex 2 provides a complete listing of the assignment of individual indicators to the six aggregate indicators. Section 3 reviews the aggregation methodology for the WGI, and Annex 3 provides further technical details. Section 4 provides a brief tour of the most recent aggregate indicators, with a particular emphasis on how to use margins of error when comparing governance across countries and over time. Section 5 updates and expands on earlier analysis of three key methodological issues in the WGI, and Section 6 offers brief concluding remarks.

# 2. Data

In this section of the paper, we first define the six dimensions of governance corresponding to the six aggregate indicators reported in the WGI. We then discuss the data sources themselves, how they are selected, and how the individual indicators from these many data sources are assigned to the six aggregate indicators.

## 2.1 Six Governance Dimensions

We organize the many individual indicators of governance described below into six governance dimensions, based on a definition of governance as _“the traditions and institutions by which authority in a country is exercised. This includes (a) the process by which governments are selected, monitored, and replaced; (b) the capacity of the government to effectively formulate and implement sound policies; and (c) the respect of citizens and the state for the institutions that govern economic and social interactions among them”_ . We consider two governance dimensions in each of these three areas for a total of six dimensions.

### **(a) The process by which governments are selected, monitored, and replaced**

**1. Voice and Accountability (VA)** – capturing perceptions and views of the extent to which a country's citizens can participate in selecting their government, as well as freedom of expression, freedom of association, and a free media.

**2. Political Stability and Absence of Violence/Terrorism (PV)** – capturing perceptions and views of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically-motivated violence and terrorism.

### **(b) The capacity of the government to effectively formulate and implement sound policies**

4
There is evidence, mostly from high-income countries, showing that learning outcomes can improve as a result of an intervention increasing transparency and accountability through the use of test scores. For instance, in the Netherlands, both average grades and the number of diplomas awarded increased after schools received a negative report card. For schools that received the lowest ranking, the one-year effects on final exam grades amounted to 10 to 30 percent of a standard deviation (Koning and van der Wiel, 2013). Evidence shows that schools in the United States respond to accountability pressures with improved test scores (Carnoy and Loeb, 2003; Hanushek and Raymond, 2005). Students in high-accountability states averaged significantly greater gains on 8th grade math tests than did students in states with little or no state measures to formally track student performance. One of the most prominent examples of accountability interventions is the state-level ranking system of No Child Left Behind (NCLB) in the United States. Rockoff and Turner (2010) found that the introduction of student tests and other measures to assign each school a grade was enough to increase student achievement in New York City. Rouse et al. (2013) show that schools facing accountability pressures in Florida changed instructional practices and this, in turn, partly accounted for increases in test scores. Using administrative data for the entire country, Ahn and Vigdor (2014) find evidence of a short term positive impact on school performance, and among low performing students in the medium term.

In most of the documented cases of education _accountability interventions_ in high-income countries, school actors respond to this type of intervention when they are followed by rewards and/or sanctions. However, in low- and middle-income developing countries with a combination of relatively weak institutions, high poverty and inequality levels, and the presence of powerful teacher unions, such high stakes accountability interventions might not be feasible nor desirable. Instead, a shared responsibility approach characterized by a supportive and collaborative environment rather than a punitive one might prove to be a more effective accountability intervention. Although low-stakes accountability interventions like the one describe here could work through implicit mechanisms, such as stigmatization or reputational damage, they could also have an impact through coordinated collaboration among school actors and pedagogical tools to sustain a shared responsibility intervention. Identifying the schools as low performing, meeting with them, developing a detailed diagnosis to identify their main challenges, and offering them advice to design a school-specific improvement plan, could be enough to improve service delivery.

The evidence on the effects of low-stakes _accountability interventions_ within a supportive and collaborative environment in developing countries is mixed. The experimental evidence for India presented in Muralidharan and Sundararaman (2010) shows that a program that provided low-stakes diagnostic tests and feedback to teachers had no effect 2
The final characteristic in Table 6 is a measure of the asset wealth of the household. We see that consumption reporting with respect to the benchmark diverges dramatically depending on whether we look at the recall or diary format. All recall formats have a significant positive interaction term with the household asset index, implying that asset-poor households significantly under-report recall consumption vis-à-vis asset-poor households with the benchmark module, while asset-rich households report roughly equal or even greater amounts of consumption than asset-rich benchmark households. The asset index is a normalized mean-zero standard deviation-one random variable, which implies that rich households with an index score of 1 to 2 achieve consumption parity with rich benchmark households, depending on the particular module administered. This finding suggests that net recall error may be particularly acute among poorer households. If this is the case, then recall modules may actually overstate the degree of consumption inequality in a population relative to actual consumption. This possibility is explored further below.

Household diary performance interacted with the household asset index yields qualitatively different findings from that for recall, although this conclusion is more tentative given the relative lack of estimate precision. For both household diary types, the interaction terms are negative, and statistically significant for the infrequently supervised diary for select consumption subcomponents. Household diaries diverge from actual consumption as the wealth of the household increases, likely due to the increasing importance of private information about consumption as households grow wealthier. Where recall surveys may overstate the degree of inequality in the population, household diaries may actually understate it.

# _Distributional Measurement_

The results so far have focused on mean differences in measured consumption but it is possible that module type also affects distributional measures, as suggested in the sub-section above. Table 7 reports the Gini coefficient and its jackknife standard error for each module over the four consumption categories. When looking at total per capita consumption, inequality for the diary-based modules is lower than for the recall modules, although the differences are not always statistically significant. The same general pattern persists when looking at food consumption and the differences between recall and diaries are especially large for frequent non-food consumption. Inequality differences for non-frequent non-food consumption items are relatively slight as we
# CONTENTS

|Detaid Popultion Projwons<br>............<br>e.<br>1|
|---|
|World Regions,ndIncome Groups.........<br>1|
|World ..........................<br>2|
|LDC<br>..........................<br>4|
|MDC<br>.<br>..<br>6|

|Foreword..........................iii|
|---|
|Acknowledgements .......................<br>v|
|Introduction .........................<br>vi|
|Overview of Trends and Projections .......<br>viii|
|World and Regions ..................<br>viii|
|Population Growth .......<br>....<br>viii|
|Fertility ............<br>xii|
|Mortality .........<br>ii<br>|
|Migration ........<br>xiv|
|Age Structure ........<br>xv|
|World Bank Regions and Income Groups..xv|
|Some Country Results .......<br>x........<br>xiii|

## Geographic Regions

|i<br>Africa .........................<br>8|
|---|
|East Africa ........................<br>10|
|WestAfrica .......................<br>12|
|North Africa .14<br>|
|<br>America ........................<br>16|
|Latin America and the Caribbean .......<br>18|
|Northern America ...................<br>20|
|Asia........................<br>22|
|East and Southeast Asia ..............<br>24|
|South Asi ......<br>South.As<br>i<br>a.26|
|...<br>Southwest Asia .....................<br>28|
|Europe and Former Soviet Republics .....<br>30|
|Oceania..............bn..<br>32|

Summary Tables ..................... xxv

|Special Topic I: Projecting AIDS Mortality.v.v|
|---|
|Epidemiological Model ................<br>Iv|
|Simulations ........<br>iii|
|Application to Sub-Saharan Countries ..... .|
|Conclusions ...............<br>x.........<br>k|
|References ........................<br>bx|

|Special Topic II: Demographic Trends in the|
|---|
|Republics of the Former U.S.S.R....b|
|References ..........<br>_bmv_|

## Income Groups

Data and Methods ...... ...... _bvi_ Sources of Population Data .......... .xxiv References ...... dv Addendum ..... _cv_

|Low-income Economies ......<br>.<br>34|
|---|
|Loer-middle-income Economies .36|
|Upper-middle-income Economies ........<br>38|
|High-income economies .40|
|Nonreporting Nonmember Economies .....<br>42|

i
of affordable rental housing is relatively uncommon; however, successful examples include Singapore; Hong Kong SAR, China; and Mainland China.

- Security of land tenure is essential for market-driven housing construction, as tenure insecurity strongly reduces investment De Soto (2000).

- Targeted subsidies for both the demand and supply side of the market for low-income housing are common in high- and middle-oncome countries. The track record of such interventions is mixed, though in areas with highly unaffordable housing, no country has resolved the slum issue without some form of public intervention on the housing supply side (Hammam, 2014).

- Expanding housing finance has improved housing affordability in a number of countries, particularly for middle-income groups. The Chilean case and similar programs in Costa Rica highlight the importance of finance in stimulating private housing supply for middle- and lower middle-income groups (Hammam, 2014). Bertaud (2010) credits Thailand’s housing finance system with facilitating a rapid increase in affordable housing production by the private sector in the 1980s.

- Increasing the supply of serviced land is commonly needed in the case of rapidly growing cities, as in-fill development is often limited in scope and costly.

Complementary policy is also an important consideration in avoiding the creation of slum conditions. Absent migration restrictions, expanding public services would be required in Uzbekistan to accommodate rapid urbanization. Without careful urban planning, schools, hospitals, and other important services may become overburdened. However, it is important to consider that the UN projects Uzbekistan’s population will continue growing for the next 30 years regardless of potential domestic migration flows. In such a context, it is likely that most places, both rural and urban, will continue growing in terms of population for some time, and the key question for policy makers is how to balance these long-term investments to match the future patterns of population movement and economic activity. Another important note is that providing quality public services is much less costly per capita in urban areas than in rural areas.

# IV – Reform Preferences

A large majority of L2CU respondents support both removing propiska limits and increasing the pace urban housing construction. In the 2018 baseline when the question was first asked, about 86 percent of respondents said that they believed “Citizens should be allowed to move to wherever they want in Uzbekistan.” Support was similarly high in both rural and urban areas and across welfare quintiles (figure 14 - left). The question was worded slightly differently in the monthly panel component of L2CU. The later version read: “Do you strongly agree, agree, disagree or strongly disagree with the 36
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 42 between observations in different clusters within the same area. These two problems were eventually solved by the Empirical Best (EB) method and the bootstrap MSE estimator proposed by Molina and Rao (2010). The EB method is similar to ELL in that it combines survey data with auxiliary data, obtained typically (but not exclusively) from a census or administrative records, uses a unit-level model and is designed for general (and perhaps several) indicators that depend on a welfare measure, based on a single model. This original EB method of Molina and Rao (2010) assumes that a one-to-one transformation of the welfare variable, _ydi_ = _T_ ( _zdi_ ), follows the nested error model (5.1) with normality for the area effects _ud_ and the errors _edi_ . Under this model, the area vectors **y** _d_ = ( _yd_ 1 _, . . . , ydNd_ )<sup>_′_</sup> , _d_ = 1 _, . . . , D_ , are independent and satisfy _ind_ **y** _d ∼ N_ ( **_μ_** _d,_ **V** _d_ ), with mean vectors **_μ_** _d_ = **X** _d_ **_β_** , and covariance matrices **V** _d_ = _σu_<sup>2</sup><sup>**1**</sup><sup>_N_</sup> _d_<sup>**1**</sup> _N_<sup>_′_</sup> _d_<sup>+</sup><sup>_σ_</sup> _e_<sup>2</sup><sup>**A**</sup><sup>_d_,where</sup><sup>**A**</sup><sup>_d_= diag(</sup><sup>_k_</sup> _di_<sup>2;</sup><sup>_i_= 1</sup><sup>_, . . . , Nd_).</sup> The best predictor of a general indicator _δd_ = _hd_ ( **y** _d_ ) is defined as the function of the sample observations, _δ_<sup> ̃</sup> _d_ = _δ_<sup> ̃</sup> _d_ ( **y** _s_ ), which minimizes the MSE under the model, given by MSE( _δ_<sup> ̃</sup> _d_ ) = _E_ �( _δ_<sup> ̃</sup> _d − δd_ )<sup>2�</sup> . The minimizer is given by where the expectation is taken with respect to the distribution of the outof-sample vector **y** _dr_ given the in-sample values **y** _ds_ from area _d_ . Since under the nested error model (5.1), the area vector **y** _d_ follows a normal distribution, the distribution of **y** _dr|_ **y** _ds_ , required to calculate the best predictor (6.5), is also normal, given by

where the vector of conditional means and the corresponding covariance matrix take the form
**_<u>Figure 4: Trends in Key Growth Drivers</u>_**

<!-- Start of picture text -->
1. Infrastructure Trends 2. Public investment and consumption (% of GDP)<br>100 1.4 20<br>Telephone  18 Public<br>80 (Right Axis) 1.1 investment<br>16<br>60 Road  0.8 14<br>density<br>12<br>40 0.5<br>10<br>Public<br>20 Mobile 0.2 8 consumption<br>6<br>0 -0.1<br>4<br>2<br>Mobilesubscriptions (per 100 people)<br>0<br>Road density (km per 1,000 sq.km)<br>1987198919911993199519971999200120032005200720092011<br>Telephone lines (per 100 people, RHS)<br>3. Private sector credit (% of GDP) 4. Exports: Value and Volume, 2000-2011<br>25 600 VALUE VOLUME<br>SSA<br>Average<br>500<br>20<br>LIC Average 400<br>15 Ethiopia<br>(NBE<br>300<br>definition)<br>10<br>Ethiopia  200<br>(IMF<br>5 definition)<br>100<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>5. CPI Inflation (average, percent) 6. Real Effective Exchange Rate (REER)<br>50<br>130<br>Ethiopia<br>45<br>125<br>40<br>120<br>35 Ethiopia 115<br>30 110<br>25 105<br>20 100<br>LIC<br>15 95<br>10 90<br>SSA<br>5 85<br>0 80<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Export Growth<br>(Index for year 2000=100)<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text -->

Source: 5.1: World Bank (2013). 5.3: World Bank (WDI). 5.4: World Bank (2014a). 5.5-5.6: IMF (WEO).

15