rafmacalaba/datause-encoder-data huggingface dataset:
small developing island nations. Docquier et al. (2007) report these numbers and examine the patterns of brain drain across countries. The strongest association is with country population size—that is, countries with less population have a higher proportion of brain drain. In addition, brain drain rates are higher in countries with fractionalization and political instability, and with low levels of human capital. Skilled emigrant stocks are similar for males and females globally, which translates into higher brain drain rates for females, given that home-country education rates for women lag those for men in many developing countries (Docquier and Rapoport, 2011).

There is a large related literature which looks at what determines the share of a country‘s migrants who are highly skilled. Borjas (1987) noted the importance of inequality in the sending country relative to destination countries, predicting negative selection (i.e. migrants are more likely to be low-skilled) when inequality in the home country is higher than that abroad, and positive selection when the opposite holds. However, in practice, emigration levels are almost universally higher among tertiary-educated individuals than among those with less education. Grogger and Hanson (2011) suggest that one explanation is that income gaps are so large that it is the absolute gap in earnings rather than relative returns to skill that drives emigration. In the context of Mexican emigration to the U.S., McKenzie and Rapoport (2010) show that migrants become progressively less educated on average as the networks on which they can draw upon grow larger, showing the importance of migration costs in determining the pattern of selection.

Clemens (2009) compares the rate of brain drain across countries by population size to the rate of brain drain from internal migration across provinces or states of the United States, Brazil, Kenya and the Philippines. He finds that the rate of international brain drain from countries with small populations is similar to the rate of internal brain drain from areas of small population – both point to the desire for skilled workers to agglomerate in highly populated areas.

At the microeconomic level, our recent detailed surveys of the highest-scoring high school graduates from New Zealand, Papua New Guinea and Tonga also underscore the role of factors other than income gaps in determining the decision to migrate, and the decision of whether to stay abroad or return (Gibson and McKenzie, 2011). We find emigration decisions to be driven more by broader career concerns such as the quality of opportunities to conduct research, to work with leaders in the profession, and to learn from the best, as well as lifestyle and family reasons, than by how much more people could earn abroad. Clemens (2009) 11
**[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]** C16c_maternityactualpaid3 How many weeks of those weeks were paid?

**[If don’t know then put “Don’t Know”] [If refused then put “Refused”]** à **Go to C18 [ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]** C17a_maternity3 Would your employer have allowed you to take maternity leave from this previous MAIN job if you needed it?

1. Yes

2. No à **Go to C18** 888. Don’t know à **Go to C18 [ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]** C17b_maternitylength3 How many weeks of maternity leave (paid or unpaid) would your employer have allowed you to take if you needed it?

**[If as needed then put “As needed”] [If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

**[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]** C17c_maternitypaid3 For how many of those weeks would your employer have paid you?

**[If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

C18_overtime3 Did your employer give you extra pay for overtime work in this previous MAIN job?

1. Yes 2. No 3. Do not work overtime 888. Don’t know 999. Refused C19_bonus3 Did your employer offer any bonus payments in this previous MAIN job?

1. Yes 2. No 888. Don’t know 999. Refused

C20a_providentfund3 Did your employer offer a Provident Fund in this previous MAIN job?

1. Yes 2. No 888. Don’t know 999. Refused

5
# **Figure 5. Infrastructure Investment in Egypt (2003-2007) (Percentage of GDP)**

## **(a)Transportation**

<!-- Start of picture text --> 3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>2003 2004 2005 2006 2007<br><!-- End of picture text -->

**(b) Communications**

<!-- Start of picture text -->
1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>2003 2004 2005 2006 2007<br><!-- End of picture text -->

**(c) Electricity**

<!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>2003 2004 2005 2006 2007<br>Public Private<br><!-- End of picture text --> 34
dependence, persistent adverse shocks, or slowly-changing country characteristics. Our evidence suggests that persistent country characteristics are the most plausible explanation for the persistence of debt relief. This in turn underscores the point made by Easterly (2002) that countries are unlikely to "exit" from repeated cycles of debt relief and debt reaccumulation unless debt relief also alters these country characteristics.

This paper contributes to a small existing literature on determinants of debt relief. Alesina and Weder (2001) study the relationship between various forms of aid and corruption, and document that less corrupt countries are not more likely to get either aid or debt relief. Neumayer (2002) revisits the same question, but looks at a broader set of measures of institutional quality as determinants of debt relief, finding mixed evidence -- some measures of governance are associated with greater debt relief but others are not. The main drawback of both of these studies is that they rely on data on debt forgiveness as reported in the World Bank's Global Development Finance publication. As we discuss further below these data have serious shortcomings that we try to remedy with our new estimates of debt relief. This paper also differs from the two mentioned above in that it focuses on determinants of debt relief in low-income countries alone. Since these countries borrow primarily from official rather than private creditors, it seems plausible a priori that the considerations driving debt relief are different in this set of countries. One previous paper shares our particular focus on low-income countries, but only in passing. Cohen et. al. (2004) report a single OLS cross-sectional regression for HIPC debt relief recipients showing a significant positive partial correlation between debt relief and (a) initial indebtedness, (b) poor policy performance, and (c) worse values on the UN Human Development Index. While they use more reliable estimates of HIPC debt relief as reported by the World Bank, they consider only countries receiving HIPC debt relief since 2000, and thus do not address the question of which countries get debt relief and which do not, nor do they study earlier rounds of debt relief.

# **2.  Measuring Debt Relief**

Despite its prominence as a form of aid to developing countries in recent years, reliable and comprehensive data on debt relief do not exist. Here we briefly describe the limitations of published data on debt relief and report on an ongoing project to develop more comprehensive measures of debt relief based on creditor-reported and debtor3
# **4 Discussion and Conclusion**

Teacher incentives have the potential to encourage teachers to exert more effort and ultimately lead to increase in student learning, but they could also lead to distortionary behaviors which could be detrimental for learning. The details of program design have been shown to matter for impacts, especially if they interact with teachers’ intrinsic motivations differently. In the context of primary schools in Guinea, this impact evaluation shows that teacher incentives led to improvements in student learning outcomes after one year of program implementation —with in-kind rewards having impacts that are roughly twice as large as those of recognition rewards. After two years of the program, the impacts are substantially muted —they roughly halved in size— and no longer generally statistically significantly different from zero. This reduction in effect sizes was likely driven by the Ebola epidemic that was emerging close to the two-year mark of the program. Analysis of enrollment patterns across years and across cohorts does not suggest that there were any perverse enrollment impacts of the program. Data from the school inspections suggest an improvement in teaching preparation and practice: teachers made better use of resources in the classroom, were better prepared for the class, and implemented better teaching practices. Moreover, the results from lesson observations mirror the results on student learning.

Male and female teachers respond differently to the different types of incentives. While there are many potential explanations for this result, we have suggestive evidence that the different types of incentives may be activating different underlying sources of motivation. In particular, the degree to which a teacher identifies themselves with the profession—potentially signaling a greater degree of intrinsic motivation—might be related to their responsiveness to the recognition incentive.

These results have potential implications for policy. They suggest that teachers do indeed respond to performance incentives, and can do so in ways that improve student learning outcomes, even in a low-capacity environment. However, non-pecuniary rewards—which are often preferred by policy makers—might only be effective for some types of teachers.<sup>16</sup> This means that the underlying motivations of teachers will be important in determining the effectiveness of such interventions, and that policy makers should invest in understanding those motivations prior to implementation.

> 16This result complements Leaver et al. (2020) who show in Rwanda that there is a range in the degree to which teachers are intrinsically motivated, and that the less intrinsically motivated teachers are more responsive to pecuniary performance-related rewards.

14
- 10 -

Table.it Mean Earnings, Years of Schooling and Mlncerian Returns to Education: Entlre Sample circa 1989

|Country|Survey<br>Year|Earnings<br>(in local<br>currency)|Mean<br>Years of<br>Schooling|Rate of<br>Return (%)y|
|---|---|---|---|---|
|Argentina|1989|7,456|9.1|10.3|
|Bolivia|1989|364|10.1|7.1|
|Brazil|1989|6,969|5.3|14.7|
|Chile|1989|40,275|8.5|12.0|
|Colombia|1989|53,643|8.2|14.0|
|Costa Rica|1989|16,346|6.9|10.9|
|Dominican Republic|1989|652|8.8|9.4|
|Ecuador|1987|27,313|9.6|10.8|
|El Salvador|1990|899|6.9|9.7|
|Guatemala|1989|242|4.3|14.9|
|Honduras|1989|453|6.5|17.6|
|Jamaica|1989|5,886|7.2|28.8|
|Mexico|1984|81,029|6.6|14.1|
|Panama|1989|292|9.2|13.7|
|Paraguay|1990|239,861|9.1|11.5|
|Peru|1990|9,912|10.1|8.1|
|Uruguay|1989|145,840|9.0|9.7|
|Venezuela|1989|6,894|9.1|8.4|

Source: Annex 3. Notes: I' Coefficient of the years-of-schooling variable in the basic earnings function, times 100.
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE importing to or exporting from Togo and, to a lesser extent, Ghana. Beninese costs are about 60 percent of the average costs in Sub-Saharan Africa (table 2).

**Table 2. Trading across borders in West African countries**

|Country|**C/ L**|**Documents**<br>**to export**<br>**(number)**|**Time to**<br>**export (days)**|**Cost to export**<br>**($ per**<br>**container)**|**Documents to**<br>**import (number)**|**Time to import**<br>**(days)**|**Cost to**<br>**import ($**<br>**per**<br>**container)**|
|---|---|---|---|---|---|---|---|
|Benin|C|7|30|1,251|7|32|1,400|
|Cote d'Ivoire|C|10|25|1,969|9|36|2,577|
|Gambia, the|C|6|24|831|8|23|922|
|Ghana|C|6|19|1,013|7|29|1,203|
|Guinea|C|7|33|855|9|32|1,391|
|Guinea-<br>Bissau|C|6|23|1,545|6|22|2,349|
|Liberia|C|10|17|1,232|9|15|1,212|
|Mauritania|C|11|39|1,520|11|42|1,523|
|Nigeria|C|10|25|1,263|9|41|1,440|
|Senegal|C|6|11|1,098|5|14|1,940|
|Sierra Leone|C|7|26|1,573|7|31|1,639|
|Togo|C|6|24|940|8|29|963|
|West Africa, la|ndlocked|9|44|2,627|10|50|3,443|
|WestAfrica, co|astal|7|24|1,263|8|28|1,514|
|Sub-Saharan A|frica|8|34|1,942|9|39|2,365|

_Source_ : World Bank, Doing Business 2011.

_Note_ : C= coastal; L= Landlocked. Documents to export (import): The total number of documents required per shipment to export (import) goods. Documents required for clearance by government ministries, customs authorities, port and container terminal authorities, health and technical control agencies and banks are taken into account Time to export (import): The time necessary to comply with all procedures required to export (import) goods. If a procedure can be accelerated for an additional cost, the fastest legal procedure is chosen Cost to export (import) The cost associated with all procedures required to export (import) goods. Includes the costs for documents, administrative fees for customs clearance and technical control, customs broker fees, terminal handling charges and inland transport.

The time and costs associated with trading across borders reflect the need to do more to improve the quality of Benin’s transport infrastructure, in particular in the port sector. High costs in the Port of Cotonou account for the bulk of the cost of importing goods through the Cotonou–Niamey corridor (figure 6a), the highest share among alternative gateways in West Africa. In alternative corridors, which compete directly with the Cotonou–Niamey corridor, the port share is much lower. For instance, in the Lomé–Niamey corridor, ports account for only 39 percent of total costs; in the Abidjan–Ouagadougou corridor, 37 percent. On top of port costs, surface transport (roads and railways) accounts for 35 percent of the cost of moving goods along the Cotonou–Niamey corridor.

9
for a society. This is evidently a gross simplification of Runciman’s relative deprivation theory but one that provided appealing empirical applications. Not only relative deprivation could be measured by simply summing up income distances but also, as shown by Yitzhaki, the relative deprivation measure at the societal level was equivalent to the absolute Gini index (the Gini multiplied by the mean).

This last property of the Yitzhaki relative deprivation measure is particularly important for our review as it allows us to relate the debate on the aggregation of individual utilities with the debate on relative income and relative deprivation. By adopting income as a unit measure, the Yitzhaki measure is _de facto_ a relative income measure while the fact that this measure at the societal level is equal to the absolute Gini index implies that this measure can be used to measure the degree of equality of a society. In a nutshell, the more unequal a society is the more relatively deprived this society will be. Moreover, while the Gini index _per se_ is a cross-section measure of inequality, the absolute Gini also considers mean income. This implies that, the Gini index being equal, with increasing mean incomes over time relative deprivation increases. In longitudinal studies that try to explain individual and societal behavior with changing levels of incomes, this property could potentially help to explain the inconsistency between longitudinal and cross-section studies on happiness and incomes.

A few years before Yitzhaki’s constribution, Easterlin (1974; updated in 1995) noticed another inconsistency between longitudinal and cross-section data, a paradox that would eventually lead research on happiness for years to come and revive the relative income hypothesis. Easterlin noticed that the US and other Western countries had been growing in income per capita for several decades without any corresponding rise in reported happiness levels. For example, while income per capita in the US almost doubled between 1973 and 2004, happiness - as measured by the General Social Survey - showed no trend. This paradox acquired quickly popularity across the social sciences confirming the initial intuition and providing some possible explanations in disciplines as diverse as political science (Inglehart, 1990) and psychology (Diener et al., 1995). The search for a solution to the Easterlin paradox has also largely hovered around the idea that, beyond a minimum absolute income necessary for living, relative income becomes more important than absolute income for explaining happiness. This would explain why in richer societies average happiness and average absolute income do not covariate as expected while richer people tend to be happier than poorer people.

6
2. To what extent do you think [this country] should allow people of a _different race or_ _ethnic group_ from most people to come and live here?

Where the possible answers to each question are:

- a) Allow many to come and live here;

- b) Allow some;

- c) Allow a few;

- d) Allow none;

- e) Don’t know.

These questions may not be strictly comparable across countries, as the answers could be affected by a host of other factors such as the current stock and composition of immigrants in each country. However, while we are aware of this limitation, the ESS has been extensively used in papers studying the determinants of attitudes towards immigration across countries (see, for instance, Card et al (2012) and Facchini and Mayda (2009)). Moreover, Card et al (2012) mentions that these questions in the ESS eliminate ambiguities by referring to _people_ _who come to live in a country_ , rather than to _immigrants_ . In countries where citizenship is based on blood ancestry such as Germany, a translation of immigrants would include people who were born in the country but are not citizens.

13
settlements. The microregions can be grouped into 20 counties, including Budapest, which can be further aggregated into 7 regions, which represent the NUTS‐2 geographic aggregation.<sup>9</sup> The choice of the geographical unit to create the spillover variables is essential to reduce spurious results. A highly aggregated geographical unit may result in spurious correlations between the spillover and firm performance variables.<sup>10</sup> Counties are likely to involve many supplier‐buyer relationships as firms within a value‐chain tend to co‐locate near each other.<sup>11</sup> Microregions are the relevant unit of observation of local labor markets, and thus may be the appropriate unit for the analysis of knowledge spillovers via labor mobility. We show that spillovers from HGFs are the strongest when counties are the geographical unit of consideration, underlining the importance of buyer‐supplier relationships in HGF spillover effects.

**Table 1: Share of firms with HGF status (3‐year Birch employment growth definition)**

|**A) Birch**||||||||
|---|---|---|---|---|---|---|---|
|**Sector type **|**1998**|**2001**|**2004**|**2007**|**2009**|**2011**|**Total**|
|Low‐tech mfg.|6.2%|5.8%|4.4%|6.4%|6.8%|5.9%|5.9%|
|Medium‐low tech||||||||
|mfg.|7.2%|6.8%|6.9%|4.7%|9.5%|8.3%|7.1%|
|Medium‐high tech||||||||
|mfg.|8.0%|7.7%|9.4%|7.2%|12.6%|9.6%|9.1%|
|High‐tech mfg.|13.6%|12.2%|19.7%|19.4%|22.7%|12.6%|17.0%|
|KIS|3.2%|3.7%|4.2%|4.8%|4.7%|4.7%|4.4%|
|LKIS|4.7%|4.8%|3.5%|4.3%|3.2%|3.4%|3.9%|
|Construction|3.8%|3.7%|3.3%|3.6%|2.4%|3.4%|3.3%|
|Electricity, water<br>etc.|2.4%|5.1%|4.0%|9.6%|8.0%|8.4%|6.6%|
|**B) OECD**||||||||
|**Sector type **|**1998**|**2001**|**2004**|**2007**|**2009**|**2011**|**Total**|
|Low‐tech mfg.|8.5%|5.2%|5.1%|3.9%|5.5%|5.1%|5.5%|
|Medium‐low tech||||||||
|mfg.|8.8%|6.5%|6.6%|2.9%|7.0%|5.9%|6.1%|
|Medium‐high tech||||||||
|mfg.|8.6%|7.1%|6.5%|3.6%|4.9%|3.4%|5.7%|
|High‐tech mfg.|8.7%|8.1%|13.4%|10.1%|10.5%|4.2%|9.0%|
|KIS|5.5%|6.1%|7.4%|5.3%|6.2%|5.6%|6.0%|
|LKIS|9.7%|7.1%|5.9%|4.6%|4.5%|4.8%|5.8%|
|Construction|9.8%|6.7%|7.3%|4.1%|4.4%|6.5%|6.2%|
|Electricity, water||||||||
|etc.|1.2%|5.4%|4.7%|4.7%|6.4%|7.0%|5.1%|

9 The NUTS (Nomenclature of Territorial Units for Statistics) system of classifying geographical units is the standard statistical system used for all European Union member countries.

> 10 This issue has been identified as the <mark>Modifiable Areal Unit Problem (MAUP). See Briant et al (2010) for a discussion.</mark>

11 Hillberry and Hummels (2008) show spatial frictions reduce trade drastically and attribute this fall to the co‐ location of firms within a value‐chain.

6
be unique, average development is associated with an inverted _U_ -shape in deforestation, pollution and carbon intensities of production units. Per capita carbon missions follow a _J_ -curve as the increase in per person productivity is not sufficiently offset by efficiency improvements. Disregarding the level of per capita GDP, we observe that at least one form of natural capital degradation is high, conflicting with the belief that countries tend to ”clean up” as they develop. One could argue that the scope of the impacts of externalities to production increases with development, with the burden falling to increasingly distant households both in time and space. Although local air pollution may be more intrusive on daily life, the consequences of climate change will remain globally impactful for generations to come.

We extrapolated our descriptions forward in time to highlight the daunting implications of development under continuation of current practices without improving policies. Our results are generally in line with emission paths associated to the high radiative forcing scenario’s considered in IPCC’s 4.9<sup>_◦_</sup> world (RCP 8.5). Our projections did not indicate successes on the fronts of reducing deforestation. Air quality improves in some currently severely polluted places, but worsens in poor regions.

In our results, deforestation follows an inverted _U_ -shape across average development in the developing countries. This confirms and extends recent results from Crespo Cuaresma et al. (2017) that provide evidence for a partial EKC for forest cover at low income. However, we find that growth alone is not sufficient to halt forest loss, and we find evidence that within the bottom 60% income countries, deforestation shifts to the bottom 30%, and that countries within the top 40% income do not fully stop deforesting. Others have similarly detailed forest loss in high-income countries, for example in the United States (Sleeter et al., 2012). Future efforts should also aim to understand the regrowth dynamics across economic development, we have only used it as a control variable in our models. Generally, the temperate zones have much better regrowth rates. Taking this and projected increases in the bottom 20% into account, the African forests seem to be at increased risk as economic successes in these areas accelerate, while the amazon faces only marginal improvements in the immediate future in our modeled projections.

On the pollution side, our model projects rising PM2 _._ 5 levels in the lowest 30% income countries, with a general decrease in PM2 _._ 5 in middle income countries. PM2 _._ 5 remains far above WHO air quality guidelines in many countries, particularly in lower and middle income groups. Given population growth, these levels will expose greater and greater popu31