rafmacalaba/datause-encoder-data huggingface dataset:
|Figure 6|Projected Population (billions) in Less Developed Countries,<br>1985-2025, Cofnpared with U.N. and Previous World Bank Projections ....|x..<br>xiii|
|---|---|---|
|Figure 7|Projected Population (billions) in More Developed Countries;<br>1985-2025, Compared with U.N. and Previous World Bank Projections ....|x..<br>xiii|
|Figure 8|Percentage Distribution of World Population by Continent, 1985 ....<br>..|....<br>xv|
|Figure 9|I'ercentage Distribution of World Population by Continent, 2000 ....<br>..|....<br>xxiv|
|Figure 10|Percentage Distribution of World Population by Continent, 2025 .|xxiv|
|Figure 11|Population in Less Developed Countries by Age and Sex, 1985 ..|xxvii|
|Figure 12|Population in Less Developed Countries by Age and Sex, 2025 ..|xxvii|
|Figure 13|Population in More Developed Countries by Age and Sex, 1985 ..|xxvii|
|Figure 14|Population in More Developed Countries by Age and Sex, 2025 .<br>.|vii|
|Figure 15|Assumed Total Fertility Trend, Less Developed Countries, and U.N.<br>and Previous World Bank Trends .|xxx|
|Figure 16|Assumed Total Fertility Trend, More Developed Countries, and U.N.<br>and Previous World Bank Trends .xxx||
|Figure 17|Assumed lUfe Expectancy Trend, Less Developed Countries, and U.N.<br>and Previous World Bank Trends ..................................|xxx|
|Figure 18|Assumed Life Expectancy Trend, More Developed Countries, and U.N.<br>and Previous World Bank Trends ...<br>a||
|Figure 19|Percentage Contribution to World Population Growth from World Bank<br>Regions and Nonborrowers, 1985-2100..<br>i|xxi|
|Exhibit 1|Countries and Economies Classified by Income Group (based on GNP<br>per capita in 1987 U.S. dollars) .Xxxii||
|Exhibit 2|Countries and Economies Classified by World Bar, ReE jn and Country<br>Department.........................<br>................|v<br>. x.v|
|P086865|2006|2008|12/17/2007|4 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P086865|2006|2008|1/3/2008|5 Closed|Yemen,Republic of|
|P086865|2006|2009|7/12/2008|6 Closed|Yemen,Republic of|
|P086865|2006|2009|1/21/2009|7 Closed|Yemen,Republic of|
|P086865|2006|2010|12/3/2009|8 Closed|Yemen,Republic of|
|P086865|2006|2010|6/21/2010|9 Closed|Yemen,Republic of|
|P086865|2006|2011|3/8/2011|10 Closed|Yemen,Republic of|
|P086865|2006|2012|11/21/2011|11 Closed|Yemen,Republic of|
|P086865|2006|2012|6/19/2012|12 Closed|Yemen,Republic of|
|P086865|2006|2013|12/26/2012|13 Closed|Yemen,Republic of|
|P086865|2006|2014|9/21/2013|14 Closed|Yemen,Republic of|
|P086865|2006|2014|4/28/2014|15 Closed|Yemen,Republic of|
|P086865|2006|2015|12/12/2014|16 Closed|Yemen,Republic of|
|P086865|2006|2015|6/19/2015|17 Closed|Yemen,Republic of|
|P086874|2005|2005|3/2/2005|2 Closed|Congo,Democratic Re|
|P086874|2005|2005|6/7/2005|3 Closed|Congo,Democratic Re|
|P086874|2005|2006|12/19/2005|4 Closed|Congo,Democratic Re|
|P086874|2005|2006|3/13/2006|5 Closed|Congo,Democratic Re|
|P086874|2005|2006|6/6/2006|6 Closed|Congo,Democratic Re|
|P086874|2005|2007|2/20/2007|7 Closed|Congo,Democratic Re|
|P086874|2005|2007|6/13/2007|8 Closed|Congo,Democratic Re|
|P086874|2005|2008|12/12/2007<br>|9 Closed|Congo,Democratic Re|
|P086874|2005|2008|6/27/2008|10 Closed|Congo,Democratic Re|
|P086874|2005|2009|8/25/2008|11 Closed|Congo,Democratic Re|
|P086874|2005|2009|12/23/2008|12 Closed|Congo,Democratic Re|
|P086874|2005|2009|6/25/2009|13 Closed|Congo,Democratic Re|
|P086874|2005|2010|12/29/2009|14 Closed|Congo,Democratic Re|
|P086874|2005|2010|6/30/2010|15 Closed|Congo,Democratic Re|
|P086874|2005|2011|4/12/2011|16 Closed|Congo,Democratic Re|
|P086874|2005|2012|12/4/2011|17 Closed|Congo,Democratic Re|
|P086874|2005|2012|6/26/2012|18 Closed|Congo,Democratic Re|
|P086874|2005|2013|1/21/2013|19 Closed|Congo,Democratic Re|
|P086875|2007|2007||0 Closed|Namibia|
|P086875|2007|2008|1/28/2008|1 Closed|Namibia|
|P086875|2007|2009|1/21/2009|2 Closed|Namibia|
|P086875|2007|2019|5/9/2019|3 Closed|Namibia|
|P086877|2006|2006|12/30/2005|1 Closed|Morocco|
|P086877|2006|2006|6/22/2006|2 Closed|Morocco|
|P086877|2006|2007|12/21/2006|3 Closed|Morocco|
|P086877|2006|2007|4/19/2007|4 Closed|Morocco|
|P086877|2006|2008|7/31/2007|5 Closed|Morocco|
|P086877|2006|2008|2/7/2008|6 Closed|Morocco|
|P086877|2006|2009|7/31/2008|7 Closed|Morocco|
|P086877|2006|2009|5/21/2009|8 Closed|Morocco|
|P086877|2006|2010|12/30/2009|9 Closed|Morocco|
|P086877|2006|2010|6/29/2010|10 Closed|Morocco|
|P086877|2006|2011|1/4/2011|11 Closed|Morocco|
36 Table 8

|Spending on fam<br>transiti<br>|ily benefits in s<br>on economies<br>|elected<br>|
|---|---|---|
|o<br>GDP<br>||c<br>in 1992-93|
|eec|e||
|Lithuania<br><br>|||
|prose<br>tt<br><br>|fos<br>||
|<br>jstovakRepaoie<br>tae?|faa|||
|<br>purine<br>foe|<br>fst|<br>w|
|<br>e<br>|||
|e|e||
|CentralAsia||146|

Note: Regional means are unweighted averages. Source: World Bank PRDTE data files.
# Addressing the Education Puzzle

# The Distribution of Education and Economic Reforms

## Ram6n Lopez, Vinod Thomas, and Yan Wang

## Summarv

No country has achieved sustained economic development without substantial investment in human capital. Previous studies have shown the handsome returns to various forms of human capital accumulation: basic education, research, training, learning-by-doing and capacity-building. Yet history also tells us that education by itself does not guarantee successful development. Examples include the former Soviet Bloc, Sri Lanka, the Philippines, and the Indian states of Kerala and West Bengal. The real question, then, is when and how education can bring high payoffs.

While theories suggested a strong causal link from education to growth, the empirical evidence has not been unanimous and conclusive. This paper focuses on two explanatory factors. First, who gets educated matters a great deal. The distribution of education is a complex issue and not much has been written about it. In this paper we construct an asset allocation model that elucidates the importance of the distribution of education for economic development.

Second, the impact of education on growth is greatly affected by the economic policy environment. Policies determine what people can do with their education. Reforms of trade, investment, labor policies can increase the returns from education. Using panel data from 12 Asian and Latin American countries for 1970 to 1994, the paper investigates the relationship of education, policy reforms, and economic growth. Our empirical results are promising.

First, the distribution of education matters. Unequal distribution of education tends to have a negative impact on per capita income in most countries. Moreover, controlling for human capital distribution and the use of appropriate functional form specifications consistent with the asset allocation model makes a difference for the effect of mean education on per capita income. Controlling for education distribution leads to positive and significant effects of average education on per capita income, while failure to do so leads to insignificant and even negative effects of average education.

Second, the policy environment matters a great deal. Our results indicate that economic policies that suppress market forces tend to dramatically reduce the impact of human capital on economic growth. Moreover, the economies in the sample are in a phase of diminishing growth as the stock of physical capital increases relative to that of human capital.

2
**Figure 7** : Availability of personal protective equipment in facilities in Sierra Leone by facility type (hospital vs. non-hospital)

<!-- Start of picture text --> Hospitals Non-hospitals<br>Apron<br>Boots<br>Facemask<br>Gloves<br>PPE all available<br>0 20 40 60 80 100<br>Facilities with item available (%)<br><!-- End of picture text --> Importantly, the SDI (like other large-scale health facility surveys) does not collect information on the depth of stocks of equipment and supplies. A facility will be recorded as having disposable gloves even if only one pair is available. Given the breadth of the survey, counting stocks of supplies is infeasible; however, it also means that the estimates of availability described here do not necessarily reflect adequate supply. In addition, stocks to address surges in demand for equipment and supplies may not be stored in the facilities, so even if gaps in availability at the facility-level were measured comprehensively, this would not necessarily imply a shortage at the national level.

# Availability of basic medical equipment

Availability of key medical equipment is imperative to ensure that adequate triage, diagnosis, and treatment can be provided during a pandemic. Three pieces of basic equipment (thermometer, stethoscope, and sphygmomanometer) are crucial to, among other activities relevant to COVID-19, diagnose fever, examine for respiratory symptoms, and identify the risk factor hypertension, respectively. Any health facility, regardless of level, should have these three pieces of basic equipment available.

In general, availability of basic equipment is high but imperfect. Specifically, in Kenya and Tanzania, over 90% of facilities have a functioning thermometer, while only 80% in Sierra Leone do. Across all three countries, over 90% have a functioning stethoscope. Only 77% of facilities in Sierra Leone have a functioning sphygmomanometer, but over 90% of facilities in Kenya and Tanzania do. Taken together, 89% of facilities in Tanzania have all three items of basic equipment; 87% do in Kenya, and only 63% do in Sierra Leone. However, Figure 8 shows that, perhaps as expected, the probability that all three of these basic items is available depends on facility level (hospital or lower level) and urban or rural location 16
branches in 2006, reaching 750,000 depositors.<sup>31</sup> The privatization of the original National Bank of Commerce therefore provides an example of how the privatization of a large state-owned bank can contribute to sustained improvement of access to financial services in rural areas.

Although it is difficult to assign a precise weight, the enabling environment no doubt played a role in the success of the privatization. As noted above, macroeconomic stabilization was achieved, with inflation rates falling throughout the late 1990s and remaining low thereafter. The privatization also occurred within the context of broad parastatal reform. Finally, and as noted above, the Bank of Tanzania permitted substantial new entry in the banking sector, which fueled recent growth in credit and other services. The influx of private capital into the banking system and rapid growth in the balance sheets of the new entrants also speaks to the bankers’ trust in Bank of Tanzania’s management of the system.

Although they have become less important in terms of size (asset share), NBC and NMB show additional signs that they are thriving in their new niches. NBC has been expanding its branch network, also after the ABSA Group was bought by Barclays in 2005. At NMB, there appears to be strong commitment on the part of the new management (Rabobank) to the original mission, even as products are added for enterprises and individuals who demand more sophisticated financial services. At the same time, new approaches are being pursued to expand access beyond NMB branches using Savings and Credit Cooperatives (SACCOs) as intermediary customers. For example, NMB has begun offering free training courses to SACCOs in order to improve their capacity and thus their ability to do business with NMB. A continued focus on expanding access and improving service quality has secured the extension of the contract with the government for payments transactions. In 2007, NMB added more than 100 ATMs country-wide to its services and ended the year as the most profitable bank in Tanzania.<sup>32</sup> Though the last chapter has yet to be written on the privatization of the original National Bank of Commerce, some ten years into the process, and despite numerous hiccups, much has been achieved.

> 31 Source: Bank of Tanzania.

> 32 Source: NMB website: <u>www.nmbtz.com</u> as of March 14, 2008

23
on the dependent variable. The follow section explains the construction of the five business environment indicators that are used in the paper.

# **_3.3. Business Environment Indicators: Principal Component Analysis (PCA)_**

Synthetic indicators are constructed using PCA on the BEEPS manufacturing dataset for the following five distinct aspects of the business environment: (a) infrastructure quality, (b) financial development, (c) governance, (d) labor market flexibility, and (e) labor quality. Intuitively, the method of principal components is used to describe a set of variables with a set of variables of lower dimensionality; for this paper, the objective of PCA is to construct one series that summarizes the behavior of a group of three or more underlying BEEPS variables that describe a particular aspect of the business environment. Statistically, PCA reduces the number of variables in the analysis by specifying linear combinations (“principal components”) of the underlying BEEPS variables such that the resulting series contains most of the information, i.e. has maximum variance.<sup>12</sup> Specifically, BEEPS variables are first mapped into one of five distinct aspects of the business environment, and then the main variation commanded by each aspect is extracted through the use of their respective principal components. Before applying PCA, the underlying variables are rescaled so that higher values indicate improvements in the business environment and then standardized to having mean zero and standard deviation one in order to abstract from units of measurements.

Variable selection for PCA is guided by the preference for quantitative over qualitative indicators. First, quantitative responses link directly to objective, actionable policy actions, as opposed to firm perceptions. Second, there are numerous statistical problems associated with the use of perception-based data, such as Likert-scale survey responses. The most fundamental is whether responses along a semantic continuum can be treated as if they were interval data. Additionally, there are several potential sources of measurement error with perception-based data. For example, individual respondents may differ in their use of the Likert scale owning to

> 12 Algebraically, this method locates _n_ linear combinations of the _n_ columns of the _X'X_ matrix, all orthogonal to each other, with the following property: the first principal component _p_ l minimizes _tr_ ( _X_  _p_ 1 _a_ 1  )  ( _X_  _p_ 1 _a_ 1  ) , where _a_ 1 is the eigenvector of the _X_  _X_ matrix associated with the largest eigenvalue.  Intuitively, _p_ l summarizes the _n_ variables in _X_ by giving the best linear description of the columns of _X_ in a least squares sense.  The second principal component of _p_ 2 also describes what is not “captured” by the first component _p_ l by minimizing the sum of squared residuals after subtracting _p_ l, i.e. _p_ l minimizes _tr_ ( _X_  _p_ 1 _a_ 1   _p_ 2 _a_ 2  )  ( _X_  _p_ 1 _a_ 1   _p_ 2 _a_ 2  ) where _a_ 2 is now the eigenvector associated with the second largest eigenvalue, and so on.  See Alesina and Perotti (1996).

16
poor understanding of the choice environment and the trade-offs involved. We pursue this hypothesis further as we examine the revision behavior in stage two of the experiment.

# 3.2 **Stage Two: Undoing Commitment**

Having described some basic features of preferences under commitment, we now examine the relationship between revision behavior in stage two of the experiment, on the one hand, and preferences under commitment and other participant characteristics on the other. Our goal is to evaluate, quantitatively, the importance of time preferences relative to other motives for revision.

# 3.2.1 <u>Qualitative Features of Revision Behavior</u>

Because this is, to our knowledge, a first attempt to study revision behavior in a field experiment, we begin with a simple description of the choices upon revisiting. Recall that stage two of the experiment applies only to those households whose randomly selected choice was an allocation between 61 and 91 days from the baseline interview. The randomization was designed to favor (with two-thirds probability) the later time frame, so we aimed to revisit 722 respondents and present them with a revision opportunity. Of these we were successful in collecting revision choice data from 661 (91.6%).

A first observation about revisions is that they are common. While the experiment made the original choice clear and salient, 65% of participants (432) made some adjustment to their allocation decision.<sup>32</sup> It could be that implicit experimenter demands caused some participants to feel as though some change was expected of them. However, a large majority (87%) made a reallocation involving a shift of at least two tokens, and 64% made a reallocation involving a shift of at least 4 tokens. Figure 4 presents a histogram of changes in the participants’ allocations to sooner (t=61) upon revisiting, excluding those who made no change (who represent 35% of observations), and illustrates the frequency of relatively large revisions.

A second observation about the revisions distribution is that revision decisions shift the allocation of income forward and backward in time with nearly equal frequency. Of the 432 participants who made some adjustment to their allocation decision, 52% shifted income toward sooner and 48% shifted income toward later. As the histogram also indicates, the adjustments that moved allocations toward later tended to be more modest in size. Of these, approximately

> 32 When we simulate random choices, only 4.7% individuals (one in 21) would not adjust their allocation decision.

20
28

# **_2.5.1. Industry-level studies_**

In a very influential study, Rajan and Zingales (henceforth RZ, 1998) use industry-level data to study the mechanisms through financial development may influence economic growth and to deal rigorously with causality issues. They argue that better-developed financial systems ameliorate market frictions that make it difficult for firms to obtain external finance. Thus, industries that are naturally heavy users of external finance should benefit disproportionately more from greater financial development than industries that are not naturally heavy users of external finance. If researchers can identify those industries that rely heavily on external finance in an economy with few market frictions – i.e., “naturally heavy users” of external finance -- then this establishes a natural test: Do industries that are naturally heavy users of external finance grow faster in economies with better developed financial systems? If they do, then this supports the view that financial development spurs growth by facilitating the flow of external finance.

RZ work under three maintained assumptions: (1) financial markets in the U.S. are relatively frictionless, (2) in a frictionless financial system, technological factors influence the degree to which an industry uses external finance, and (3) the technological factors influencing external finance are reasonably constant across countries. RZ use the external financing of industries in the U.S. as a benchmark of the external financing needs of industries in a comparatively frictionless financial system. They, then assess whether industries that are naturally heavy users of external finance grow comparatively faster in countries that are more financially developed.

RZ develop methods to examine whether industries that are naturally heavy users of external finance grow faster in financially developed economies. Consider the equation.

_Growth(i,k) = aC + bI + cShare(i,k) + d[External(k)*FD(i)] + u(i,k)_
# Figure 7. Time-varying sensitivity of inflation expectations to shocks: Country experiences

A. Sensitivity of inflation expectations to inflation shocks, advanced economies (1) <!-- Start of picture text --> Percentage points<br>1.5<br>2005 2010 2015<br>1.0<br>0.5<br>0.0<br>-0.5<br>Canada Korea, Rep. New United<br>Zealand Kingdom<br><!-- End of picture text --> C. Sensitivity of inflation expectations to inflation shocks, Europe and Central Asia <!-- Start of picture text --> Percentage points<br>1.2<br>0.8<br>0.4<br>0.0<br>-0.4<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> E. Sensitivity of inflation expectations to inflation shocks, India <!-- Start of picture text --> Percentage points<br>1.5<br>1.0<br>0.5<br>0.0<br>2005 2010 2015<br><!-- End of picture text --> B. Sensitivity of inflation expectations to inflation shocks, advanced economies (2) <!-- Start of picture text --> Percentage points<br>1.5 2005 2010 2015<br>1.0<br>0.5<br>0.0<br>-0.5<br>Euro Area Japan United States<br><!-- End of picture text --> D. Sensitivity of inflation expectations to inflation shocks, Latin America <!-- Start of picture text --> Percentage points<br>1.5 2005 2010 2015<br>1.0<br>0.5<br>0.0<br>Brazil Chile Mexico<br><!-- End of picture text --> F. Sensitivity of inflation expectations to inflation shocks, South Africa <!-- Start of picture text --> Percentage points<br>1.5<br>1.0<br>0.5<br>0.0<br>2005 2010 2015<br><!-- End of picture text --> Source: World Bank.

A-F. Inflation expectations are five-year-ahead expectations of annual inflation. Time-varying sensitivity is estimated by regressing long-term inflation forecast revisions on inflation shocks. Vertical lines denote 68 percent confidence intervals. The model is described in Appendix 3.

B. The Euro Area here comprises Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, the Netherlands, Portugal, and Spain.

24
# **6 Conclusion**

In this paper we discussed and estimated a fat-tailed Spatial Vector Autoregressive Moving Average model that enables analyzing high-dimensional interactions between multiple crosssectional time series. This type of model is particularly useful to study Granger-causal interactions. The model requires specifying multiple matrices that define cross-sectional spillover channels. Networks may be part geographic, but also relate to economic distances. We introduced a framework to dynamically construct networks from the data. The smoothness of the network is controlled by a bandwidth parameter that can be integrated into the Likelihood framework.

We estimated the model parameters using remotely sensed pollution statistics of urban areas in Indonesia, together with subnational household expenditure data from surveys. We estimated two models with respectively 113 urban and 60 highly polluted urban areas over the period 1999-2014. Our networks are based on the similarities in sample moments and quantiles of local returns in the data, and the smoothness has been estimated within the likelihood framework. We contrasted the spatial model with a non-spatial counterpart and found that the spatial framework improves considerably in terms of various diagnostics, while using fewer ARMA parameters. Our approach to network modeling is not only favored by the data, it also provides additional insights. We find that cross-sectional dependencies in pollution are centered in smaller, but stronger, clusters than the economic variables. Expenditures of poor households, spill over more locally than those of average households.

Our economic findings are summarized in three main points: first, expenditure growth reduces pollution, particularly growth of poor households; second, pollution reduces growth in expenditures, particularly of poor households; third, growth is non-exclusive, there are significant spillovers from bottom-up growth but not from top-down growth. This imbalance in growth spillovers aligns with a body of literature debunking so-called “trickle-down” economics (see, for example, Quiggin (2009); Ranieri and Almeida Ramos (2013)), and suggests instead that investment in the poor is more effective than raising average incomes. Non-inclusive growth, lower resilience of the poor to pollution damages, and the importance of growth in bottom households to reduce pollution, together lay the basis for polluted poverty traps.

We find that damages from pollution in Indonesia are considerable, over 3 billion annually 30