rafmacalaba/datause-encoder-data huggingface dataset:
take between 1 and 5 weeks. Along the Northern Corridor, depending on the departure date from Mombasa and the size of the convoy, difference in transit time can be of more than 3 days to reach the Ugandan border.

**Figure 6.** Distributions of Transit Time Between Mombasa and Kampala

<!-- Start of picture text --> 800<br>600<br>Units 400<br>200<br>0<br>2 3 4 5 6 7 8 9 10 11 12 13 14 15+<br>Time Taken in Days<br><!-- End of picture text -->

**Trip in Kenya Entire trip including border crossing and Uganda**

Data on border-crossing delays from other corridors in Africa, Eastern Europe, and Latin America show that the dispersion of lead time is a universal phenomenon (Appendix 3 and 4). The stylized facts are:

- The lead-time is an asymmetric broad tailed distribution, which for practical purposes can be described by a log-normal distribution for lead time in excess of a baseline representing the minimum feasible time of transit considering current infrastructure, procedures, and services;

- The coefficient of variation for the excess over baseline lead time is from 0.5 to 1.5; and

- The shape of the curve allows for the not so rare occurrence of lead time largely in excess of the median or even the mean.

The broad tail paradigm is the reason why uncertainty has a huge impact on costs.

Appendix 4 proposes a quantitative analysis based on the log-normal distribution, which will be used in applications.

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the negative effects of shocks on households drawn from a large micro economic literature which emphasizes the direct effects of instability on poverty. Micro studies evidence that low income groups are more exposed than others because their incomes are less diversified; their lower human capital makes mobility across occupations and regions more difficult; their access to capital and financial markets is limited; their dependence on public services (especially health and education) exposes them to fiscal cuts.

# _The success of projects conditioned on the stabilizing impact of aid_

Since the effect of instability on growth and poverty seems to be unambiguous, it can be expected that if aid has a macro economic stabilizing impact, its effectiveness would be higher in a vulnerable country. In a recent paper Chauvet and Guillaumont (2006) evidence that their previous finding of higher aid effectiveness in more vulnerable countries can be explained by the stabilizing impact of aid. They define “the stabilizing impact of aid” as the difference between the volatility of exports and the volatility of aid plus exports aggregate flow, and show that in most cases aid has a stabilizing impact with regard to exports. This holds even when aid is pro cyclical with regard to exports (positive correlation between the cycle of aid and the cycle of exports) but less volatile than exports (aid is finally stabilizing in about 90% of cases). As far as the result depends on the relative levels of the volatility of aid and the volatility of exports, when the volatility of exports is high, aid is more likely to be stabilizing.

If aid effectiveness is higher in vulnerable countries then why do these countries have a lower success in development projects? The main reason is that the project evaluations do not include the total impact of the projects and particularly not the stabilizing impact. The latter is not always perceived because the main task of an evaluator is generally confined to appraise whether or not the objectives have been reached at a reasonable cost. To illustrate this, let us imagine the case of an irrigation project targeted to increase agricultural production. A negative shock (climatic, price shock...) can reduce production or its returns and thus harm the project. However the salaries given for the realization of the project might have been particularly beneficial in this period of shortfall, preventing malnutrition or children interrupting their education. These side effects would not be included in the rating if they were not initially part of the objectives.

We can thus divide the total impact of a project into a direct impact and an indirect stabilizing impact. The direct impact, observed by the evaluator, is considered satisfactory when 6
One of the contributions of this paper is to investigate explicitly the interaction between race and immigration and to isolate the fact that foreign born Blacks have better labor market outcomes than native Blacks, while the opposite tends to be true for the three other racial groups.

While this result survives several robustness checks, interpreting it is more difficult. It could be due to unobservable characteristics among Black immigrants or among Black natives. It is also interesting to consider how it could be driven by discrimination or by a negative impact of immigration on labor market outcomes among Blacks, as suggested by Borjas, Grogger and Hanson (2006).

An example of unobservable characteristic among Black immigrants would be that they are especially motivated and selected since immigration is a selection process or that they can rely on strong support networks. Table 3 indicates the main countries of origin of Black immigrants. More than 70% of them come from the Caribbean and Latin America, with the largest groups coming from Jamaica (26%), Haiti (19.3%), Trinidad (7.2%) and Guyana (4.7%). More than 25% migrated from Africa with Nigeria (6.8%), Ghana (3.4%) and Ethiopia (3%) being the countries sending the largest share of migrants. It might be the case that migrants from those countries benefit from particularly strong support networks. The census data do not allow me to test that hypothesis; however it seems that immigrants from other races, like Asians and Hispanic also have strong networks. Similarly, if there is an unobservable trait like the “immigrant drive”, that effect should also be present for immigrants from other races.

Figures 15 and 16 investigate whether the region of origin of the foreign born Blacks makes a difference. I have divided them in three subgroups according to their birthplace: those born in Latin America or the Caribbean (LAC), those born in Africa and those born elsewhere, a smaller group essentially made of individuals born in Canada or the United Kingdom. The results, for both males and females, indicate that the advantage in labor force participation of foreign born Blacks compared to native Blacks is strongest among those born in Latin America or the Caribbean. The point estimates for Blacks born in 14
a straightforward estimation of _λ_ . A key disadvantage of tax record data is that it is often difficult to obtain access to them. Moreover, they are more likely to be available in developed countries with good quality data systems in place, and less likely to be available in developing countries.

This paper explores the feasibility of using an alternative to tax record data that are more readily available. The empirical application presented in Section 4 considers data on house prices compiled from publicly available real estate property listings as the alternative.<sup>7</sup> The advantage of these data is that their availability extends to developing countries. The flip-side is that they also introduce a number of key methodological challenges due to the fact that the alternative database (a) observes predictors of income, not actual incomes, and (b) need not constitute a proper sample, so that it is unclear what population is being represented by the data.

The following two subsections aim to provide workable solutions to these two challenges that will hopefully contribute to a wider application of this approach.

# **2.2.1 A database of predictors of top incomes**

Let us first focus on the challenge posed by observing a predictor of household income rather than actual income. Consider the following assumption.

**Assumption 1** _Suppose that household income per capita can be described by:_

_where xh denotes the predictor of household income, εh denotes a zero expectation error term, subscript h indicates the household, and where β denotes a vector of model parameters._ The assumption of a log-linear model is motivated by ease of exposition and by the fact that it fits our empirical data remarkably well. This assumption can be relaxed however by accommodating flexible functional forms for _m_ ( _xh_ ; _β_ ) if the data call for it. In our application the value of the household’s house (or rental value) will serve as the predictor _xh_ .

To obtain some intuition for the implications of Assumption 1 it may be helpful to verify what it implies for the relationship between the expenditure share on housing and household income. Note however that we are concerned with predicting household income per capita rather than household income. (In

> 7 Alternatively one could for example also look to data on mortgages or credit card statements. However, this approach may not be feasible in countries with underdeveloped or non-existing mortgage markets.

7
these two variables. Further work is needed to understand the channels through which pollution may have an impact on competitiveness, understand what policies at the city level can help curb pollution while fostering competitiveness, and identify successful examples of cities that have managed to achieve the two objectives in tandem. Interesting questions arise, but a deeper analysis is needed in order to answer them rigorously.

# References

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Apergis, N., & Ozturk, I. (2015). Testing environmental Kuznets curve hypothesis in Asian countries. _Ecological Indicators, 52_ , 16‐22.

- Asnap, F. (2012). _'What a Waste' Report Shows Alarming Rise in Amount, Costs of Garbage_ . http://www.worldbank.org/en/news/feature/2012/06/06/report‐shows‐alarming‐rise‐ in‐amount‐costs‐of‐garbage

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Brajer, V., Mead, R. W., & Xiao, F. (2011). Searching for an Environmental Kuznets Curve in China's air pollution. _China Economic Review, 22_ (3), 383‐397 .

Carmichael, L., Racioppi, F., Calvert, T., & Sinnett, D. (2017). _Environment and health for European cities in the 21st century: making a difference._ World Health Organization.

Chang, T., Graff Zivin, J., Gross, T., & Neidell, M. (2016). "Particulate Pollution and the Productivity of Pear Packers". _American Economic Journal: Economic Policy, 8_ (3), 141‐169.

Charbit, C. (2011). _“Governance of Public Policies in Decentralised Contexts: The Multi‐level Approach”._ OECD Regional Development Working Papers. OECD Publishing. Obtenido de http://dx.doi.org/10.1787/5kg883pkxkhc‐en Ciccone, A., & Hall, R. E. (1993). Productivity and the density of economic activity. _NBER Working Papers Series_ (w4313).

City of Copenhagen. (2014). _Better Mobility in Copenhagen. ITS Action Plan 2015‐2016._ 31
This includes the cost of supporting community leaders, training and mentoring but excludes the time of volunteers and any support provided to the CPBA by community members.

# _Reading instruction intervention_

The reading instruction intervention, entitled “Come Let’s Read and Write” (CLRW), provided teachers in treatment schools with training and materials to implement a new pedagogic approach for reading instruction. The training and materials were coupled with monitoring and coaching, and offered to 1<sup>st</sup> and 2<sup>nd</sup> grade teachers. The approach aligns key learning competencies for basic reading and writing stipulated in the official Tongan curriculum with a greater degree of clarity on the sequence in which these skills should be taught.

Like the school readiness intervention, CLRW was expected to affect student reading outcomes in two stages. First, the intervention provided teachers with training, materials and coaching in order to induce them to adopt the new pedagogic approach. Second, students exposed to the new pedagogic approach were expected to have acquired better reading skills. For this paper, we refer to the former as the CLRW intervention and the latter as the CLRW pedagogic approach.

Evidence that the pedagogic approach can improve students’ reading ability stems from research on what skills children need in order to learn alphabetic languages (Wolf 2007; Linan-Thompson and Vaughn 2007; Sprenger and Charolles 2004; Chiappe & Wade-Woolley 2002; Gove & Cvelich 2011 and National Reading Panel 2000). These include, among others, an understanding of the relationship between printed letters and sounds (Scarborough 2002) and the speed at which a child can read (Fuchs et al. 2001; Abadzi 2006). CLRW is designed around the sequencing of skills and pedagogic methods based on this research (e.g.: August & Shanahan 2006; National Institute for Child Health and Human Development 2000; Pressley 1998; Snow, Burns & Griffin 1998).

In order to induce teachers to implement this structured pedagogic approach, the CLRW intervention relied on highly scripted lesson plans and periodic monitoring and coaching. Previous evaluations of similar interventions have shown positive effects on reading skills (Piper 8
Kim, Jong-il and Lawrence J. Lau (1994) ‘The Sources of Economic Growth of the East Asian Newly Industrialized Countries’, _Journal of Japanese and International Economies_ 8:3:235-271.

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Mankiw, Gregory N. (1995) ‘The Growth of Nations’ _Brookings Papers on Economic Activity_ , 275-310.

Lewis, W. Arthur. 1954. "Economic Development with Unlimited Supplies of Labor." _Manchester School_ 22 (2): 139–91.

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Okun, Arthur, M. 1962. “Potential GNP: its measurement and significance.” American _Statistical Association, Proceedings of the Business and Economic Statistics Section_ , 98-104.

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Schumpeter, Joseph Alois. 1942. _Capitalism, Socialism, and Democracy_ . New York: Harper & Brothers.

World Bank (2010) _Indonesia Jobs Report: Towards Better Jobs and Security for All_ The World Bank.

World Bank (2011) ‘Navigating Turbulence, Sustaining Growth’ _, World Bank East Asia and Pacific Economic Update 2011_ , Volume 2, The World Bank.

Young, Alwyn (1992) ‘A Tale of Two Cities: Factor Accumulation and Technical Change in Hong Kong and Singapore’, in Olivier J. Blanchard and Stanley Fisher (eds) _NBER Macroeconomics Annual 1992_ Cambridge, MA: MIT Press, 13-54.

Young, Alwyn (1994) ‘The Tyranny of Number: Confronting the Statistical Realities of the East Asian Growth Experience’, _Quarterly Journal of Economics_ 110:3:641-680.

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of 1.5 ESAL per vehicle. In either case, the heavier axle loading in Ghana (compared with Tunisia) contributes to a lower estimate of the average RDE, the estimate being in the range of 0.5-0.6 per EsAL-km (compared with Newbery's estimate of 0.8).

Table 9. The Estimation of Road Damage Externality (C/esai-km)

|Annual|RDE|Perc|ent of traffic volume|
|---|---|---|---|
|ESAL<br>('000)|(¢/ESAL-km)|||
|||Tunisia|Ghana|
||||(a)<br>(b)|
|(1)|(2)|(3)|(4)<br>(5)|
|Under 40|1.76|3.1|1.1<br>0.3|
|40-100|1.44|22.1|8.2<br>2.1|
|100-200|0.87|21.4|15.0<br>9.4|
|200-400|0.50|24.1|19.4<br>16.0|
|400-850|0.39|14.4|33.2<br>21.1|
|850+|0.36|14.9|23.1<br>51.3|
|Average RDE (¢|/ESAL-km) 0.79|0.58|0.46<br>0.46|

- Sources: Columns (1), (2) (3) Newbery (1986b), assuming ESAL/veh. in Tunisia is 0.38. - Columns (4), (5) Ghana 1988 traffic count, assuming ESAIJveh. is (a) 0.9 and kb) _1.5._

An alternative crude estimate is based on the estimate of long-run maintenance costs of paved roads. Abbey-Sam and Bhandari (1988) estimate that a 7-year rehabilitation program that will increase the percentage of paved trunk roads in good condition from 30 to 70 percent, keep 20 perccnt of the roads in fair condition, and cut the fraction of poor roads from 40 to 10 percent may cost about $180 million for periodic maintenance plus $365 million for major rehabilitation. (There is, of course, no merit in imposing these large, nonrecurrent costs on present and future travelers.) However, once this goal is achieved, the
level. The total effect was positive up to about 30 years, though not significant even when the father died recently. For agricultural self-employment the interaction effect was often positive though generally not significant, except for urban men; for non-farm employment the pattern switched, with a negative interaction effect, though again not strong. However, allowing for such an interaction effect does not change our main findings reported above.

Our assumption that past inheritance is exogenous to current living standards can be questioned. Choices about who inherits the land may be influenced by factors that are unobserved by us, but observed by the parents or other stakeholders—factors that are also correlated with the economic activity and economic welfare of the child on reaching adulthood. It may be decided by the family group that one of the sons is best suited to taking charge of the family farm on the father‘s death. This may reflect a latent interest or ability at farming, revealed while growing up. Or it may be that other sons show more aptitude for non-farm work. Parents may also have gender preferences in their choices about inheritance and schooling—choices that are influenced by both market and non-market parameters.<sup>18</sup> Another potential source of endogeneity is the fact that children could possibly decline the inheritance, in particular of land, if taking care of it is not compatible with their preferred activity or if, being themselves economically successful, they feel that their siblings have a greater need for it.

In testing the robustness of our results to treating inheritance as endogenous, the key identifying assumption we make is that the death of the father or mother only matters to an individual adult‘s current economic welfare via inheritance of land or other assets. It is hard to see why parental death sometime in the past would matter to current adult consumption except via inheritance and (hence) wealth. Possibly the shock of parental death will have an impact, but then we control for a father‘s recent death (within the last two years) in all our regressions.

We can only convincingly treat one inheritance variable as endogenous, solving out other endogenous variables.<sup>19</sup> We do so for any inheritance taken together, and separately for land, excluding other forms of inheritance. We also drop any variables that could be endogenous by the same logic, notably own education. (Parents may decide that one son gets the schooling while the other gets the land.) We also drop fostering and age at marriage for the same reason.

> 18 For example, using data from the rural Philippines, Estudillo et al. (2001) show how sons are preferred for land inheritance, while daughters are preferred for investments in schooling.

> 19 We tried using death of father and death of mother as two IVs for two inheritance variables (land and other) but these did not have sufficient power for credible identification.

19
The Changing Wealth of Nations 2021 report provides a measure of the stock of national wealth that will enable economies to maintain and increase their growth in the long run. In addition to GDP, the measure of change in wealth per capita over time is an important indicator to consider, because it provides an actionable way to track sustainability (World Bank 2021).

When we consider stocks rather than flows, wealth rather than income, the picture is even more stark. Consider, for example, the value of the bottom billion countries’ assets that generate income and support of well-being.

The report’s estimates show that although global total wealth per capita increased between 1995 and 2018, there are many countries where one or more of its main wealth components – natural, produced, or human capital – have declined. This has created an unsustainable development path that compromises these countries’ future growth. This decline has been driven by a lack of investment in their productive and human capital, often accompanied by rapid depletion of natural capital stocks such as oil and minerals. For example, in the past two decades, Burundi and Zimbabwe’s total wealth per capita has dropped by about 31 and 20 percent, respectively, mainly driven by the depletion of their natural assets. They have lost about 66 and 32 percent of their 1995 natural capital value, despite having more than doubled the value of their natural resource exports during the Golden Decade.

During the Old Normal, the bottom billion was losing on average 0.1% of their total wealth each year, while the OECD countries and emerging markets were growing at positive rates. But during the Golden Decade, the trend in the bottom billion shifted and its average wealth per capita grew even faster than the OECD group average. However, this was not enough to reach the average wealth accumulation rate that was occurring in the rest of the world (Figure B.1).

Some countries, especially resource-rich ones like Azerbaijan, saw an impressive average annual total wealth growth of 8 percent during this decade. For others the same acceleration failed to materialize. Despite the mineral and hydrocarbon abundance of Côte d’Ivoire, its total wealth grew each year on average less than 1 percent during the Golden Decade.

Unfortunately, when the Golden Decade came to an end, the bottom billion fell back to almost zero wealth per capita average growth during the New Normal, a minuscule growth rate compared to the OECD and emerging markets averages of 1.6 and 3.4 percent, respectively. And far from the rates needed to secure convergence in wealth. Lack of convergence is therefore seen clearly not just in the GDP rates but also in the collapse in wealth growth after the end of the Golden Decade.

# **Figure B.1. Rapid wealth accumulation during the Golden Decade ended in the New Normal, ceasing the catching up trend from previous years.**

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