rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5394

# **Abstract**

Brain drain has long been a common concern for migrant-sending countries, particularly for small countries where high-skilled emigration rates are highest. However, while economic theory suggests a number of possible benefits, in addition to costs, from skilled emigration, the evidence base on many of these is very limited. Moreover, the lessons from case studies of benefits to China and India from skilled emigration may not be relevant to much smaller countries. This paper presents the results of innovative surveys which tracked academic high-achievers from five countries to wherever they moved in the world in order to directly measure at the micro level the channels through which high-skilled emigration affects the sending country. The results show that there are very high levels of emigration and of return migration among the very highly skilled; the income gains to the best and brightest from migrating are very large, and an order of magnitude or more greater than any other effect; there are large benefits from migration in terms of postgraduate education; most high-skilled migrants from poorer countries send remittances; but that involvement in trade and foreign direct investment is a rare occurrence. There is considerable knowledge flow from both current and return migrants about job and study opportunities abroad, but little net knowledge sharing from current migrants to home country governments or businesses. Finally, the fiscal costs vary considerably across countries, and depend on the extent to which governments rely on progressive income taxation.

This paper—a product of the Finance and Private Sector Development Team, Development Research Group—is part of a larger effort in the department to study the determinants and consequences of migration and remittances. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmckenzie@ 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
relative definitions. In particular, we build on World Bank’s definition of shared prosperity (as growth in mean consumption for the bottom 40 percent of the income distribution; see, e.g., Basu, 2013; Jolliffe et al., 2015) and define the two lower income groups respectively as the bottom 40 percent and the middle 40 percent. The remaining 20 percent of the income distribution thus forms the top 20 percent or the affluent.<sup>8</sup> But given our focus on dynamics analysis, after dividing the income distribution into three groups in the first period, we then keep these income thresholds fixed for the second period.<sup>9</sup> The second approach follows Dang and Lanjouw (forthcoming) and employs the existing (national or international) poverty line to define the category of the poor. It then further disaggregates the non-poor group into two subcategories: one group is the vulnerable, who are defined as those currently non-poor but facing a significant risk of falling into poverty in the next period (i.e., these are mostly people who belong to the low-middle-income group), and the remaining group of people who belong to the upper-middle class and the affluent. This approach derives the vulnerability line from a specified vulnerability index _P_ , defined as the percentage of the non-poor population in the first period that fall into poverty in the second period.<sup>10</sup> These two approaches are complementary along several dimensions. The first approach applies a fixed and static threshold to the income distribution, while the second approach takes into account

> 8 In a slight abuse of notation, we use the pairs of terms “income” and “consumption”, and “(un)happiness” and “(dis)satisfaction” interchangeably in this paper. A similar note applies for the terms “top 20 percent”, and “affluent”. We also refer to the poor or the bottom 40 percent as the lowest income group, the middle 40 percent as the middle income group, and the top 20 percent as the top income group. The term ‘pro-unhappy’ in the discussion of subjective welfare is equivalent to the term ‘pro-poor’ in the discussion of monetary welfare.

> 9 Put differently, the World Bank’s standard definition is most relevant for anonymous growth analysis, where the consumption level for the bottom 40 percent in each period is tracked. Our focus is on non-anonymous growth analysis, where we track welfare of the same households (individual) over time.

> 10 In particular, given a specified vulnerability index _P,_ the vulnerability line _V1_ can be empirically obtained from this equality _P_ ����� ���|�� ��� ���� _._ In other words, the vulnerability line is the highest income level among the currently nonpoor who have a specified probability of falling into poverty in the next period. See Dang and Lanjouw (forthcoming) for more details.

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Table A6: Heterogeneity - between groups transfers

|||Amoun|t transferred|(std)||
|---|---|---|---|---|---|
||1|2|3|4|5|
|C|0.065|-0.135|-0.222|-0.086|-0.336<sup>_∗_</sup>|
||(0.114)|(0.137)|(0.141)|(0.120)|(0.187)|
|Cooperation*Republican|-0.294||||-0.335|
||(0.214)||||(0.218)|
|Cooperation*High age||0.242<br>(0.193)|||0.288<br>(0.195)|
|Cooperation*Female|||0.395<sup>_∗∗_</sup>||0.385<sup>_∗∗_</sup>|
||||(0.194)||(0.194)|
|Cooperation*High income||||0.187<br>(0.202)|0.218<br>(0.202)|
|Republican|0.083|-0.061|-0.058|-0.062|0.083|
||(0.156)|(0.110)|(0.110)|(0.110)|(0.157)|
|High age|-0.270<sup>_∗∗∗_</sup>|-0.397<sup>_∗∗∗_</sup>|-0.286<sup>_∗∗∗_</sup><br>|-0.275<sup>_∗∗∗_</sup>|-0.418<sup>_∗∗∗_</sup>|
||(0.098)|(0.139)|(0.098)|(0.098)|(0.139)|
|Female|-0.063|-0.070|-0.262<sup>_∗_</sup>|-0.060|-0.257<sup>_∗_</sup>|
||(0.097)|(0.097)|(0.139)|(0.097)|(0.139)|
|High income|0.081|0.073|0.084|-0.022|-0.010|
||(0.101)|(0.101)|(0.101)|(0.143)|(0.142)|
|Constant|1.092<sup>_∗∗∗_</sup>|1.206<sup>_∗∗∗_</sup>|1.246<sup>_∗∗∗_</sup>|1.174<sup>_∗∗∗_</sup>|1.297<sup>_∗∗∗_</sup>|
||(0.114)|(0.119)|(0.123)|(0.114)|(0.137)|
|Observations|507|507|507|507|507|
|_R_<sup>2</sup>|0.022|0.022|0.027|0.020|0.036|

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 _Note:_ The table reports OLS regressions on transfers when the two workers belong to different groups. The dependent variable is the standardized amount transferred to the unlucky worker. “Cooperation” is an indicator for the spectator being assigned to a treatment where the workers cooperate within group. “Republican” is an indicator for the spectator self-reporting voting Republican. “High age” is an indicator for the spectator being above median age in the sample. “Female” is an indicator variable for the spectator being female. “High income” is an indicator for the spectator having an income above the median income in the sample. Standard errors in parentheses, where * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

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**Table 1: Evolution in Inequality – Key Indicators, Age 25 years or more**

|||**Ci**|**rca 20**|**01**|||**C**|**irca 2**|**009**||
|---|---|---|---|---|---|---|---|---|---|---|
||**Mean**|**Median**|**SD**|**Dif 75 25**|**Gini**|**Mean**|**Median**|**SD**|**Dif 75 25**|**Gini**|
|**Costa Rica**|7.3|6|6|4.3|0.32|7.9|6|5|4.3|0.30|
|**Panama**|9.1|9|6|5.0|0.31|9.4|9|6|5.0|0.30|
|**Guatemala**|3.7|2|6|4.6|0.63|4.4|3|6|4.9|0.58|
|**El Salvador**|5.8|5|8|5.2|0.50|6.4|6|9|5.3|0.47|
|**Honduras**|5.1|4|5|4.6|0.49|5.4|6|5|4.4|0.46|
|**Nicaragua**|5.0|4|8|4.7|0.51|5.9|6|7|5.0|0.48|

Note: Dif 75 25 refers to the difference in mean years of education between the 75th and 25th percentile. Source: Authors calculations based on Costa Rica EHPM 2001 and 2009, Guatemala ENCOVI 2000 and 2006, Honduras EPHPM 2001 and 2009, Nicaragua EMNV 2001 and 2009, Panama ECH 2000 and 2009, El Salvador EHPM 2001 and 2009

# **Inter and Intra Generation Evolution of Inequality**

The average gain in years of education is mainly achieved by marginal gains as new generations accumulate more years of education, which raises two important questions. First, is the education achievement of more educated new generations more equal? And second, what is the current level of intergenerational inequality?

Clearly, new generations are more educated, and currently the education achievement reaches its peak for the age group 25-29 years old in Panama and for the age group 20-24 years old in other countries in the region (Figure 5). Surprisingly, no country in the region has a cohort that has finished on average upper secondary; Panama is the closest one to achieving that level (approximately 11 years of education); in Costa Rica, the highest level achieved is lower secondary education (9 years) whilst in Guatemala the youngest cohort finishes only primary education (approximately 6 years of education). In comparison, in the OECD countries, an average of 81% of 25-34 years olds have achieved upper secondary education<sup>9</sup> and the average number of years of schooling for this cohort is just over 12.5 years.<sup>10</sup> Analyzing the education attainment across cohorts enables us to compare the speed at which accumulation of human capital took place over time. Of particular interest is the fact that Costa Rica, Panama and Nicaragua had “plateaus” in the accumulation of human capital, the first two during the period 1960 to 1970 (ie for the 40-49 year age group), and Nicaragua in the period 1970 to 80 (30-39 year age group). For El Salvador and Guatemala, successive cohorts have steadily increased their investment in education, albeit starting from a low level.

> 9 OECD Education at a Glance 2011.  Population with at least upper secondary education (2009)- Year of reference is 2002.

> 10 OECD Family database.   Educational Achievement expressed in average number of years in formal education (2004), Year of reference is 2003. Available online: <u>http://www.oecd.org/document/4/0,3746,en_2649_34819_37836996_1_1_1_1,00.html.  Data accessed: June</u> 15, 2012.

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# **I. Introduction**

To effectively reduce poverty, we want to understand the factors that help households escape poverty as well as those that induce them to remain in, or fall back into, poverty. It is commonly claimed that we need panel data to answer these questions, especially at the household or individual level. However, for a variety of reasons, cross-sectional data are far more common, in most developing countries, than panel data. Panel data collection can be very costly, for example, and can also pose a number of logistical and capacity-related challenges. For whatever reason, the scarcity of panel data has rendered the analysis of welfare dynamics difficult, if not impossible, in many developing country settings.

To overcome the non-availability of panel data, there have been a number of studies that develop pseudo-panels (or synthetic panels) out of multiple rounds of cross-sectional data. Following the seminal contributions of Deaton (1985), synthetic panels based on cohorts have been widely used to track income and consumption outcomes over time (e.g., Deaton and Paxson, 1994; Banks, Blundell, and Brugiavini, 2001; Pencavel, 2007). Other outcomes have also been analyzed with synthetic panels; these include, for example, labor responses to tax reforms (Blundell, Duncan, and Meghir, 1988), the returns to academic and vocational qualifications (Mcintosh, 2006), and household demand for private medical insurance (Propper, Rees, and Green, 2001). Notably, since cross-section samples are typically refreshed each time that the surveys are fielded, synthetic panels are possibly less exposed to the concerns surrounding attrition and measurement error that are often leveled at actual panel 2
million and US$1,106 million, respectively (Ziegler, 2021). Narrowing down the online alternative finance to the four sub-regions in Sub-Saharan Africa (Central Africa, West Africa, Southern Africa, and East Africa), the East Africa sub-region took the lead recording a proportion of 50 percent of all online alternative finance in 2020. It was followed by West Africa with 45 percent, and Southern Africa with 4 percent. In terms of countries, in 2020 the region’s largest online alternative finance markets included Ghana ($528 million), Zambia ($297 million), and Uganda ($115 million). They were followed by Tanzania ($103 million), Kenya ($82 million), South Africa ($23 million), and Botswana ($21 million).

By model, P2P/Marketplace Consumer Lending and Balance Sheet Consumer Lending models dominate the online alternative finance landscape in the region. In 2020, they represented 63 percent and 29 percent of the total online alternative finance activity, respectively. As highlighted in the share of the market by model, it is important to emphasize that most alternative finance volumes in the region have been associated with consumer lending rather than business lending. Thus, online alternative finance platforms have played an important role by promoting financial inclusion among households. According to the information from 74 online alternative finance platforms surveyed by the Cambridge Centre for Alternative Finance, 49 percent of the platforms’ clients were considered unbanked, 48 percent underbanked, and only 3 percent banked. Figure 9 shows the clients’ banking status by sub-region.

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3.4 Floor area adjusted population densities can sort between crowded and livable cities Population density is the most common measure of urban concentration. Most measures of population density take the form of people/urban built-up area or people/total urban area. However, population density is an imperfect measure of urban proximity as it does not account for the quality of built structures.

Figure **8** a shows a scatter plot of population density and city incomes. Population density is measured as people per built-up area of the city, for 400 cities worldwide. Two striking features emerge. First, population densities decline with incomes, as countries move across stages of economic development. Kinshasa, Mumbai and Karachi are four to five time more densely populated than Shanghai, Tokyo or London. Second, naïve measures of population density can conflate crowding with livable densities. Kinshasa, Mumbai and Hong Kong are three cities with similar population density but at very different levels of income.

While Hong Kong’s residents can live in tall buildings with adequate floor space, over 50 percent of Mumbai’s population lives in slums. Most of Kinshasa’s population lives in squalid conditions. In low- and lower-middle-income countries, population densities in the range of 50,000 people per km2 are reflections of squalor and crowding, not livable densities. Such crowding has been associated with high degree of contagion risk during the COVID-19 pandemic, where residents do not have physical space to be socially distant and need to access shared toilets and water kiosks in their neighborhood (Lall and Wahba 2020).

To untangle crowding from livable densities, we introduce a new measure called the Floor-area adjusted population density (FAPD), which we measure using the heights data available from DLRs WSF 3D database. We first compute total floor area using the heights data as follows: Where TFA is the total floor area. The average floor height is assumed to be 3 meters. This measure can be compared across pixels in a city, or aggregates compared across cities. Using the TFA measure, we can compute FAPD as follows: 13
Table 1: Summary statistics of village characteristics ( _m_ = 30)

|Characteristic|mean|s.d.|min.|max.|
|---|---|---|---|---|
|Forest area (percent of total)|8.10|14.0|0|19.0|
|Irrigated area (percent of total)|3.67|5.9|0|60.0|
|Pop. density (persons per ha., total area)<sup>_a_</sup>|2.36|1.64|0.34|6.83|
|Pop. density (persons per ha., cultivable)<sup>_a_</sup>|2.63|1.69|0.69|6.84|
|Road jeepable (months per year)|10.6|1.9|6.0|12.0|
|Distance to bus stop (km)|4.4|2.9|0|13.0|
|Distance to train station (km)|13.3|8.1|2.0|35.0|

> _a_ Refers only to the four blocks selected in Bolangir district. Source: van Dillen (2008).

and an important extension of, these newly canvassed data, especially in the domains of education and health. In this connection, it was decided to collect certain other data pertaining to earlier seasons only from households in the said nine villages (see below). In the event, no fewer than 236 of the original 240 households could be traced and re-surveyed, though births, deaths, marriage and migration had combined to alter the character of many families in the interim. The associated summary statistics are reported in Table 2.

The re-survey of households was supplemented by so-called focus group interviews. Dr. Behera personally assembled a diverse group of villagers in each community and put to them a set of structured questions aimed at cross-checking and extending the answers to those in the household questionnaire. Important disagreements among the participants were duly noted. As time had become very short in relation to the preparation of the loan proposal, such interviews could be conducted in only 12 of the 30 villages. Included in these 12 were the 9 villages that had received a new road no earlier than 2007. Of the remaining three, two had had an all-weather road at the time of the original survey; the other still awaited such a connection.

Before embarking on an analysis of the survey data, it should be noted that this task is somewhat complicated by a feature common to many surveys, namely, that households were sampled in clusters (villages). All members of the same village share a common socio-economic and natural environment, and the great majority have some familial and other social ties; so that the observations in the whole sample are unlikely 5
Policy Research Working Paper 5418

# **Abstract**

This paper investigates the effectiveness of post-conflict aid at the project level and aims to identify post-conflict situations as a window of opportunity for project success. The Independent Evaluation Group dataset provides extensive information on the characteristics of World Bank projects including an independent rating of their success, supervision and evaluation quality. The paper estimates the probability of success of aid projects depending on the characteristics of the intervention and looks for possible special patterns in post civil war situations. The results suggest that the probability of success of World Bank projects increases as peace lasts. Supervision appears to be a crucial determinant of the success of projects, especially during the first years of peace. Although the results of the sector-level analysis need to be taken with caution, the authors find that projects in the transport sector and in the urban development sector appear more successful in post-conflict environments. On the contrary, education projects seem less successful and therefore need to be highly supervised. Projects in the private sector should wait as they face a higher probability of failure in the first years of peace.

This paper—a product of the Agriculture and Rural Development Team, Development Research Group—is part of a larger effort in the department to understand how interventions can best contribute to poverty reduction. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mduponchel@ 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
trade with a program to raise capacity in trade facilitation across the region. The trade gains to a country from unilateral action correspond to gains from improvement by that country alone in a given category of trade facilitation. The gains to a country from collective capacity building in a given category of trade facilitation are equal to the gains to that country from the improvement by its trading partners _plus_ those from unilateral capacity building.

WMO (2004) include estimated elasticities for the value of bilateral manufacturing trade with respect to each trade facilitation indicator. The analysis here draws on the data and elasticities estimated in WMO (2004). The performance of the East Asia region is set as a goal for the South Asia region. This is both due to the geographic proximity of the regions and the fact that East Asia is more advanced in trade facilitation. We will set the target level at halfway to the East Asia average.<sup>29</sup> We calibrated the Pakistan’s values of regulatory environment and service-sector infrastructure by using predicted values based on GDP per capita in order to avoid a significant underestimation of trade gains in Pakistan, hence, those in the South Asia region.<sup>30</sup> In this analysis we ask the question: “How would intra-regional trade change if South Asia would act collectively to raise capacity in trade facilitation?” We assume that only countries in the region improve in these measures. We find that such action taken together by the South Asian countries is estimated to create considerable gains to trade within the region. Table 2 indicates that the total estimated gain from capacity building in all four categories of trade facilitation is approximately $2.6 billion. This is almost a 60 percent rise in total intra-regional trade in South Asia. The projected gains from both unilateral and partners’ capacity building are significant. The country with the largest projected gains in South Asia is India. Trade flows for India is expected to increase by $1.1 billion as shown in Table 2. Capacity building in service-sector infrastructure contributes the most to those gains. Sri Lanka gains the most from other South Asian countries’ capacity building relative to gains from its own action. This is because Sri Lanka has relatively high scores in the indicators, which suggests more limited improvement needed to reach East Asia levels. Figure10 also illustrates trade gains in percentage. It is estimated that India and Bangladesh, in particular, could significantly increase trade across all the areas examined.

> 29 Clearly given financial and others resources are limited, achieving 100 percent of the East Asian level may not be feasible in a short term.

> 30 We used the predicted value for each of regulatory environment and service-sector infrastructure indicators once it is fitted on log of GDP per capita using the 75 country sample in the WMO database.

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