rafmacalaba/datause-encoder-data huggingface dataset:
environmental preferences than the overall national populations. On the other hand, the discrepancy could be due to the fact that experts in our survey were asked to explicitly ignore the carbon values in assessing the protection value of rainforests; such values represented a major component of value in the Horton et al study.

Another, very different and perhaps more speculative, “calibration” of our results can be obtained from comparisons with the Norwegian government’s funding for its forest protection program with Brazil, the most comprehensive such plan to date based on external funding. This program aims to provide $1 billion to Brazil over a 10-year period for “delivered” forest protection in that country (verified reductions in forest loss rates resulting from the funds being made available). With about 2 million households in Norway, this represents a total WTP per Norwegian household, reflected by this program, of about $500 to protect the Amazon rainforest over a 10-year period; thus $50 per household per year (or somewhat higher when applying the Norwegian government’s risk-adjusted project discount rate of 4%). Expert evaluation by the Norwegian experts in our survey is closer to $100 per household per year; thus higher than values directly embedded in the Norwegian program, but the difference is moderate. We here need to keep in mind that our survey attempted to keep carbon values out, while in the Norwegian Brazil program carbon values play a major role. On the other hand, this Norwegian program is hardly likely to represent the entire WTP within the Norwegian population.

A more open question is what such expert answers really can tell us. It is not clear whether all experts are “consistent” in the sense of answering to the same set of underlying questions. Experts’ answers have at least two alternative interpretations: (A) as their predictions of what would be the actual outcome of SP-CV surveys conducted in their respective countries, or (B) as their assessments of “true” household WTP in their respective countries. While we asked for (A), some of our experts may have been thinking in terms of (B). The two are not necessarily identical. Practical problems, such as acceptance of the payment vehicle and assurances that the good will be provided, can drive differences between the values associated with these two interpretations. Little was done formally in our survey to investigate this issue, so it will not be further pursued here.<sup>27</sup>

> 27 It seems clear that some confusion over this issue was present with some expert respondents, from their comments made at Round 2.

31
67

# <u>Institutional Quality Only</u>

Lastly, we turn to examine the effects of the quality of institutions, in addition to the rest of the knowledge variables, on economic growth. Tables A5 and A5a present the results of regressions where we included as an additional regressor the institutional quality index from Bosworth and Collins (2003), instead of the Sachs and Warner economic openness variable.

Firstly, we note that all of our ‘new’ regressions exhibit R-squared values that are around 20 percentage points higher than our control regressions. This does strongly suggest that institutional quality does play a large role in determining rates economic growth. Next, we notice that the institutions variable consistently produced estimated coefficients that are positive, which is theoretically consistent, and highly statistically significant. The magnitude of the effect of institutions on economic growth is also considerably large. Take for example Reg A5.2 and A5a.2, which includes patents and phones representing the innovation and ICT pillars, respectively. With the estimated coefficient value of 3.9258 and the regression sample mean of 0.6527 (Table A5b), it can be shown that a 20 percent increase in the quality of institutions tends to increase annual economic growth and output per worker by around 0.5 percentage point.

The next striking observation is that upon the inclusion of the institutional quality variable as a regressor, many of knowledge indicators, which had produce highly statistically significant estimated coefficients before, are now statistically not significant. More specifically, we see that our human capital stock variables, while maintaining their theoretically expected signs, are generally not statistically significant. The situation is similar with our ICT variables, with the exception that some of the ICT coefficients actually return coefficients with negative signs (see Regs A5.2, A5.4, A5.6, A5.7, A5.9, A5.11, A5a.6 and A5a.11). This result, where controlling for institutional quality eliminates prior present independent roles of other regressors of interest, is consistent with other studies that have included institutional quality in cross-country growth regressions<sup>53</sup> . However, there are a few variables that have coefficients that managed to maintain their statistical significance. In particular, the innovation variables, ‘journals’ (Regs A5.3 and A5a.3) and ‘royalties per capita’ (Regs A5.4 and A5a.4) exhibit positive and statistically significant coefficients. This again suggests that positive effects of innovation variables on economic growth tend to be relatively more robust of the knowledge variables.

53 For example, Bosworth and Collins (2003) found that with the inclusion of an institution quality variable, their human capital stock / education variables became statistically insignificant.
# **How to Cope with a Refugee Shock? Evidence from Uganda**

**Authors** : Mark Marvin Kadigo<sup>1</sup> , Nene Oumou Diallo<sup>2,3</sup> , Jean-Francois Maystadt<sup>4,5,6</sup>

## **Affiliations** :

1Institute of Development Policy (IOB), University of Antwerp, Antwerp, Belgium.

2 Université de Sherbrooke (Canada), World Bank Group Africa Fellowship Program 3 KIT Royal Tropical Institute, Amsterdam, Netherlands 4Fonds de la Recherche Scientifique – FNRS 5LIDAM/IRES-UC Louvain. Google scholar: <u>https://scholar.google.fr/citations?user=xKIa5eEAAAAJ&hl=fr</u> 6Lancaster University Management School, Lancaster, LA1 4YX, UK.

Note: a) Unit: Jobs Group, (b) Project: Jobs and Forced Displacement Research (P167671), (c)TTL: Jan von der Goltz and Kirsten Schuettler.

JEL Codes: J6, J11, J21, J24, D13, D6

**Key words** : Refugees, Welfare, Labor Markets, Coping Strategies, Uganda.

## **TF acknowledgments** :

This work is part of the program "Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership''. The program is funded by UK aid from the United Kingdom's Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.

## **Other acknowledgments** :

We thank Jan von der Goltz and Kirsten Schuettler (Work Bank Group) for the valued feedback and support throughout the process of this research. We thank Marijke Verpoorten, Nik Stoop (Institute of Development Policy, IOB) and Ben Reese (Work Bank Group) for the insightful feedback. We also thank all the colleagues at the Institute of Development Policy (IOB) for the prized feedback offered, during the Fruit for Thought Workshop, towards improvement of the paper.
10 In addition, the PHL estimates of both survey rounds in Tanzania are of a similar order of magnitude (4.4 and 2.9 percent), with the decline in national annual PHL between both survey rounds consistent with an increase in the share of marketed harvest (from 19.4 to 26 percent)<sup>16</sup> as well as the increased share of the maize harvest stored in improved storage facilities (from 5.9 to 11.5 percent) and treated with crop protection (smoking or chemical spraying) (from 37.2 to 49 percent) (Table 2). Improved modern storage facilities include the use of modern stores, improved local structures, and air-tight drums. Traditional storage structures include granaries. Plastic bags, inhouse storage, and unprotected piles are considered poor storage techniques.

A more detailed multivariate analysis of the drivers of post-harvest loss is pursued for Tanzania below. Note however that the adoption of improved storage technologies is in general low, also in the other LSMS-ISA countries studied here, with only 0.6 percent of the maize production in Uganda and 2 percent in Malawi subject to improved storage techniques, while the use of crop protection (spraying or smoking) is much more prevalent (63.1 percent of the maize harvest treated in Uganda and 10.8 percent in Malawi) (Table 2).

Finally, respondents in all three countries indicate insects and rodents/pests (biotic factors) as the most important reasons for their reported loss of maize post-harvest (Table 3). Insects such as the maize larger grain borer (LGB, Prostephanus truncatus) and pests such as fungal rotting and mycotoxin contamination, as well as rodents are also the most widely discussed factors of on-farm PHL in the scientific literature (World Bank, 2011b). The prevalence of these reasons as reported causes of PHL, is also consistent with the interpretation advanced above that the self-reported PHL estimates in the LSMS-ISA surveys mainly capture post-harvest on-farm handling and storage losses. In Uganda, “other” reasons are also frequently mentioned to explain PHL, further increasing Uganda’s PHL incidence to the highest level among the three countries.

> 16 The higher the marketed share, the lower the storage duration and opportunities for incurring on-farm losses—most commercialization happens within 3 months after harvest.
are not overrepresented in the set of statistically significant leaders, even though they matter more for growth in general, because autocratic countries also have noisier growth processes that make it difficult to isolate true leader effects. We also show that a leader’s growth rate in office is virtually useless by itself to identify good- and bad-for-growth leaders.<sup>45</sup>

# **References:**

Aruoba, S.B., A. Drazen, and R. Vlaicu. 2019. “A Structural Model of Electoral Accountability.” _International Economic Review_ (forthcoming).

- Ashworth, S., E. Bueno de Mesquita, and A. Friedenberg. 2018. “Learning about Voter

- Rationality.” _American Journal of Political Science,_ 62 (1): 37-54.

- Acemoglu, D., S. Johnson, and J. Robinson. 2003. “An African Success Story: Botswana” in _In Search of Prosperity: Analytic Narratives on Economic Growth_ , edited by Dani Rodrik, 80-119. Princeton: Princeton University Press.

- Besley, T., J. Montalvo, and M. Reynal-Querol. 2011. “Do Educated Leaders Matter?” _Economic Journal_ , 121, F205–F227.

- Blinder, Alan S. and Mark W. Watson. 2016. "Presidents and the US Economy: An Econometric Exploration." _American Economic Review_ , 106 (4): 1015-45.

- Berry, C. and A. Fowler. 2018. “Leadership or Luck? Randomization Inference for Leader Effects (RIFLE).” _Mimeo_ , University of Chicago.

Brown, C. 2019. “Economic Leadership and Growth.” Working Paper Chetty, R, J. Friedman, and J. Rockoff. 2014. "Measuring the Impacts of Teachers I: Evaluating

- Bias in Teacher Value-Added Estimates." _American Economic Review_ , 104 (9): 2593632.

- de Luca, G., A. Litina, and P. Sekeris. 2015. “Growth-friendly Dictatorships.” _Journal of Comparative Economics_ 43: 98–111.

- 45 Interesting areas for future research include investigating whether some leaders permanently shift growth, the extent to which leader effects can be extracted in real time (before they leave office), and leaders’ broader contributions in other realms (such monetary policy, fiscal policy, inflation, conflict or democratization).

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Appendix III – Simplified system of education in Vietnam

# **<u>LFS</u>**

|**Classification of education levels in LFS**|**Simplified system used in the analysis**|
|---|---|
|NEVER ATTENDED<br>SOME PRIMARY<br>PRIMARY EDUCATION|ICSED 1: Primary Education or less|
|LOWER SECONDARY|ICSED 2: Lower Secondary|
|SHORT ‐TERM TRAINING|Short‐term vocational traningcourse|
|HIGHER SECONDARY<br>TRADE VOCATIONAL SCHOOL<br>VOCATIONAL SCHOOL|ICSED 3: Upper Secondary|
|TRADE COLLEGE<br>COLLEGE|ICSED 4: Post‐secondary Non‐tertiary|
|UNIVERSITY AND OVER|ICSED 5: Tertiaryor higher|

# **<u>STEP</u>**

|**Classification of education levels in LFS**|**Simplified system used in the analysis**|
|---|---|
|PRIMARY SCHOOL|ICSED 1: PrimaryEducation|
|LOWER SECONDARY SCHOOL|ICSED 2: Lower Secondary|
|ELEMENTARY VOCATIONAL|Short‐term vocational trainingcourse|
|UPPER SECONDARY SCHOOL<br>UPPER SECONDARY VOCATIONAL<br>PROFESSIONAL SCHOOL|ICSED 3: Upper Secondary|
|VOCATIONAL COLLEGE<br>COLLEGE(NON‐VOCATIONAL)|ICSED 4: Post‐secondary Non‐tertiary|
|BACHELOR<br>MASTER'S<br>DOCTORATE/PHD|ICSED 5: Tertiary or higher|

37
effort of these two individuals will result in two different income levels, y2, for the individual living in a rich country, and y1, for the one living in a poor country.<sup>13</sup> If we adjust for the advantage conferred by country’s higher mean income, and still obtain incomes such that y2>y1 (even if the gap between the two is smaller), we may conclude that there are other circumstances for which we have failed to adjust. They could be country-specific inequality or institutions and norms that limit social mobility; or they could be individual-specific circumstances that are independent of country. These additional factors also confer “advantages” and have to be included under the rubric of “circumstance”. Eventually, after we have adjusted for all (reasonable) circumstances that may give advantage to one or the other individual, the same relative efforts should be rewarded equally in the income space: y1 and y2 will be equalized (see the two arrows in Figure 1). Our objective will be to find out how much of the original difference y2-y1 can be explained by a relatively few circumstances that we can quantify empirically.

The questions we want to ask are the following: How much of one’s life chances will be determined by his assignment to a given country vs. given income class? Does this “trade-off” systematically vary with income class? How much can one improve one’s position in the world income distribution through his own effort (that is, by climbing income ladders in his country)? What does all of this tell us about the equality of opportunity across all individuals in the world?

We shall first (section 2) describe the source of global income distribution data that help us address these questions empirically and review our definitions of country and class. In Section 3, we present some broad regularities regarding the way global income is distributed between countries and income classes. Sections 4 and 5 are the core parts of the paper: they present the analysis that attempts the answers the questions posed above. The last part gives the conclusions.

> 13 The same would, of course, be true for all pairs of individuals who are at the same percentile of their type’s effort distributions. The income distribution curve of  the richer country, 2, will extend over the higher values of income than the income distribution curve of  the poorer country, 1.

6
Table 1 Evaluation Format

|Sector/<br>Activity|Air|Water|Land|Living<br>Resources|
|---|---|---|---|---|
|Agriculture|||||
|Industry|||||
|Energy<br>__|__<br>____||||
|Transport||||_|
|Urban<br>_|-||_||

Using the four dimensional indices and a composite index, we summarize our results as country rankings in Table 2A. Actual values are displayed in Table 2B. Table 2A also ranks countries on the basis of per capita C-NP (PCGNP) and per capita GDP estimates compiled by the UN International Comparisons Program (ICPGDP) . The ICPGDP computation explicitly adjusts the standard income data to take account of purchasing power parity. Where countries in our sample are not covered in the most recent International Comparisons Program Study (Phase V, 1985), we have adopted a World Bank estimate. The 1985 figures have been extrapolated to 1990 using World Bank estimates of real per capita GDP growth.

Table 3 presents summary statistics for the four dimensional performance indices, whose possible maximum values are all 250. The results suggest fairly similar distributions with the exception of Air, which has a significantly lower mean and greater variance. Our statistical results suggest that air pollution gets relatively low priority in poor countries but 5
OZIER - REPLICATION REDUX – p. 41 of 41 The coefficients do not change appreciably in magnitude, but the direct effect is slightly more precisely estimated (and the spillover effect slightly less precisely estimated) in the revised calculations. Columns 1 and 3 provide the original and revised estimates when the specification also includes the externality effect of deworming each additional thousand children between 3 km and 6 km away. The revised calculations include an estimated effect of more distant (3km‐6km) deworming that is negative and twice as large as before. It remains statistically indistinguishable from zero, consistent with the lack of any known mechanism by which deworming children far away would harm the health of those nearby. But its change in size, from ‐0.01268 in column 1 to ‐0.02429 in column 3, is important in what follows.

Panel B of Table A1 provides the mean number of treated children in two areas: 0 km – 3 km, and 3 km – 6 km. Again, the 0‐3 km row hardly changes, but the 3‐6km row shows more than a doubling in the number of children in this area, from just over 700 to just over 1,600. This has consequences in Panel C of Table A1. In column 1, taking into account both 0‐3 km and 3‐6km spillovers, MK had originally found an imprecisely estimated 2.0 percentage point spillover total, leading to a 7.47 percentage point total effect – the “7.5 percentage point increase in school participation” that is well‐ known from this study. This is the more conservative of the two figures that might have been presented from that version of the data: column 2 shows that without the more distant spillovers, the total effect would have been estimated to be 8.14 percentage points. However, with the corrected data set, the imprecise 2.0 percentage point increase in attendance caused by spillovers at both 0‐3km and 3‐6km distances (standard error 1.35 percentage points) changes sign to become a much more imprecise 1.66 percentage point decrease in attendance (standard error 3.00 percentage points). Adding in the direct effect, the total effect estimate is an imprecisely estimated 3.87 percentage point increase in school attendance. With the corrected data set, column 4 shows that without the more distant (and now less precisely estimated) 3‐6km spillovers, the total effect is estimated to be an 8.48 percentage point increase in school attendance, statistically different from zero.
Policy Research Working Paper 7082

# **Abstract**

Researchers in many fields, such as demography, economics, and sociology, have established various data collection methodologies and principles to answer a range of academic and policy questions on migration. Although the progress has been impressive, some basic challenges remain. This paper addresses some basic, yet fundamental, questions on identification of international migrants and how their various demographic, personal, and human capital characteristics are captured via different data sources. The critical issues are the construction of proper sampling frames in censuses, registers, and surveys and the design of questionnaires in household, labor market, and other relevant surveys. The paper discusses how these data sources can be used to answer policy questions in areas such as labor markets, education, or poverty. The focus is on how some of the existing shortcomings in availability, quality, and relevance of migration data can be overcome via improvements in data collection methods.

This paper is a product of the Poverty and Inequality Team and Trade and Integration Team, Development Research Group. 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:// econ.worldbank.org. The authors may be contacted at gcarletto@worldbank.org and cozden@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
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE There is still significant scope for commercially viable expansion of global system for mobile communications (GSM) coverage into rural areas. Cape Verde has made substantial progress in expanding the percentage of the population living within range of a GSM signal, which rose from 62 percent in 2005 to 83 percent in 2008. Nevertheless, this value falls short of simulations, suggesting it would be feasible to provide a GSM signal to almost 100 percent of the population on a commercially viable basis (figure 8). Most islands would even be commercially viable for universal access to limitedperformance WIMAX<sup>**3**</sup> broadband via public telecenter facilities (figure 9).

**Figure 8. Almost all of Cape Verde’s population could be commercially served with a GSM signal**

<!-- Start of picture text --> Congo, Dem. Rep. of<br>Madagascar<br>Zambia<br>Congo, Rep. of<br>Niger<br>Mozambique<br>Chad<br>Botswana<br>Namibia<br>Lesotho<br>Tanzania<br>Ethiopia<br>Cameroon<br>Cape Verde<br>Burkina Faso<br>Malawi<br>Sudan<br>Kenya<br>Senegal<br>Côte d’Ivoire<br>Benin<br>Ghana<br>Uganda<br>Rwanda<br>Nigeria<br>South Africa<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>% of population<br>coverage gap efficient market gap existing access<br><!-- End of picture text --> _Source:_ Mayer and others 2009. _Note:_ GSM = global system for mobile communications. Bar segments in gray represent the percentage of population currently covered by voice infrastructure as of the third quarter of 2006.

Bar segments in white represent the efficient-market gap—the percentage of the population for whom voice telecommunications services are commercially viable given efficient and competitive markets. Bar segments in black represent the coverage gap—the percentage of the population for whom services are not viable without subsidy.

- 3 WIMAX = Worldwide interoperability for microwave access.

19