rafmacalaba/datause-encoder-data huggingface dataset:
# **3.3 Descriptive Statistics**

Table 1 reports the summary statistics of the BRMS sample of return migrants.<sup>16</sup> A clear majority of migrants are men. Women represent only 4 percent of returnees in the survey sample; this level is consistent with statistics from administrative data from the Bureau of Manpower, Employment and Training (BMET). The low level of female migration can be explained by the low labor-force participation of women in Bangladesh, and by difficult work conditions in the destination countries (International Labour Organization 2015; Farole, Cho, Bossavie, and Aterido 2017). In addition, there is social pressure on women to stay behind because they bear household responsibilities, and because low-skilled temporary migrants are not allowed to take their families with them. Given the small share of female migrants in the sample, combined with the fact that post-migration patterns are likely to differ between genders, we restrict our working sample to men.

Most temporary migrants in the sample have some secondary schooling, while 17 percent have never attended school and only 2 percent have some tertiary education. The average years of schooling in the male sample of return migrants is 6.5 years, which is low compared to average educational attainment across the world (Barro and Lee 2013). Returning migrants have a higher level of schooling than the non-migrant, male, working-age population (5.8 years), but their schooling levels are lower than those of migrants who are currently overseas (7.6 years) according to the HIES 2016. These differences are largely explained by the older age of returnees compared to current migrants and increasing education levels across cohorts.

About 75 percent of temporary migrants in the sample have returned from GCC countries. Saudi Arabia, with about 25 percent of migrants, is the largest destination and closely followed by the United Arab Emirates. Malaysia represents 13 percent of temporary migrants, and is the largest destination in Southeast Asia. Only a small minority of migrants in our sample were employed in other high-income countries with Singapore as the leading destination.

Return migrants in the sample have been back, on average, for 2.7 years; with a median time since return of two years. The mean current age in the sample is 38, with average departure and return ages of 29 and 35, respectively. Therefore, most workers do not migrate overseas immediately upon entering the labor force, but often work for more than a decade before going abroad.

We observe substantial variation in the duration of migration episodes in our sample (Table 1). The median and mean duration of stay in all destinations are 4.7 years and 6.5 years, respectively. Close to a quarter of migrants stayed abroad for less than two years,

> 16For a detailed description of the BRMS and its sample characteristics, see Ahmed, Ahmed, Bossavie,  ̈Ozden, and Wang (2020).

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180km; on Vava’u they range from 260-280km; and on the distant Niuas, remoteness levels are approaching 600km.

Figure 2 presents estimates of population sparsity for the eight case study PICs. These estimates reflect both population size and the geographical scale of dispersion and provide an indicator of the lived experience of remoteness in the context of the geographical spread of a country’s population. The population is extremely sparsely settled in FSM (at a scale of 19.0km<sup>2</sup> per person), and to a lesser extent Kiribati (with 15.8km<sup>2</sup> per person). Sparsity is also notable in Tuvalu (8.4 km<sup>2</sup> per person), due to its small population (its geographical scale of dispersion is relatively moderate), and in RMI (4.8km<sup>2</sup> per person). The populations of Vanuatu (0.8 km<sup>2</sup> per person), Solomon Islands (0.5 km<sup>2</sup> per person), and Tonga (0.5 km<sup>2</sup> per person) are much less sparsely dispersed. Lastly, given its relatively large population size, coupled with the relatively modest geographical scale of dispersion, the Fijian population is more concentrated (with a sparsity measure of less than 0.1 km<sup>2</sup> per person).

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14 the same in both Porto Alegre and Sao Paulo, heads of female-headed households tend to have a slightly higher income in Porto Alegre.

# Measuring Income in Female-Headed Households

In this part of the analysis, we rank households according to a slight modification of the traditional concept of household per capita income. Specifically, for the rest of the paper, household income is defined as the income from all sources of all adult members (over age 18) of the household divided by the total number of members in the household. We do this because we seek to assess how outcomes for children (such as their labor-force participation and school attendance) depend on the rank of their household in the income distribution. Hence, it is useful to be able to measure the household resources without the contribution of non-adult members, in order to prevent households from upgrading their **14** ranking by using their children in the labor market.

To what extent does the exclusion of the income of non-adult members really have an impact on the ranking of households? In other words, to what extent do poor households in fact upgrade their rank by allocating children and teens (under age 18) to the labor force? To answer this question, we performed a simple experiment using data from the Sao Paulo metropolitan area, classifying households by their per capital adult income and by their per capita total income (Table 9). Ninety-four percent of all households are classified in the same relative income group whether we rank them by per capita adult income or per capita total income, and only 3 percent move up to a higher group. The proportion of households that remain in the same class, however, varies considerably from income class to income class. Poorer households rely much more heavily on the income of minors. Among those 14 Ideally, we would like to rank households based on the resources that they would have in the case that all children were attending school and out of the labor force. The income measure we use -- the adult total income per capita -- would equal this ideal measure whenever the labor supply of the household adult members was not influenced by the way in which the non-adult members allocate their time. We consider our measure preferable to one that would confound the analysis of children's labor-force participation because children's income is included. For a further discussion of this issue, see Barros and Mendonca (1990).
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components. The real estate market in Korea had becomw a substitute for the equity market and the holding of financial assets.19. It was pointed out that macro-economic policies may ave served to raise land values above what they would have been in the absence of such distortions. However, because of the almost total separation of policy formulation into "macro-economic policies" and "physical planning policies" there was little receptivity to such analyses. Policy makers continued to take a "partial equilibrium" approach to what is a "general equilibrium" prublem. Economic planners became even more determined not to fuel what they saw as "socially wasteful speculation" by not allowing the expansion of the housing finance system. Physical planners stiffened their resolve to prevent the "destruction" of prime agricultural land or open-space and tightened the land-use laws.

20. Analyses of national accounts for the late 1960s and early 1960s showed that the total domestic marginal savings rate was dominated by the margir.al public savings ratt. which was about 24 percent, while the marginal private savings rate was only 14 percent (Williamson, 1976). But it is well known that conventional national accounts ignore capital gains and only measure the accumulation of assets. In other words, national accounts measured only the cash-flow effects of policies and not their impact on the balance-sheet of various sectors, in this case households. From the household viewpoint, the reality is that the saving objective is to increase the stock of private assets to the desired relationship to current income. When income rises very rapidly as was the case in Korea, the desired level of wealth can be raised through the accumulation of new assets, the increase in the price<sup>of</sup> existing assets, or both.

21. The stock of assets has two components: land or real estate R of price Pr, and conventional capital K of current price Pk. The stock of land R being fixed (dR = 0), current saving which is simply an increase in wealth can be described in term of current prices as: _S_ = dW = (R.dPr + K.dPk] + Pk.dK

# [Savings]= (Capital Gains] + ["conventional" capital formation]

Dividing both sides of the equation by Y the income in current prices will yield an "augmented" savings ratio S/Y that more correctly reflects the behavior of private savings. When the appreciation of land<sup>and that</sup>

- 8/ Opposite portfolio shifts took place following the structural adjustment policies of the early 1980s and a clear reverse substitution between housing and the stock market took place. There is no empirical evidence at present, but one could expect that the early 1980 shifts were dominated by upper income groups but included also the emerging middle-class unable to finance housing for lack of mortgage finance.
**Figure A4 – Unwillingness to get vaccinated disaggregated by different demographic characteristics of respondents (Online experiment)**

<!-- Start of picture text --> a) Sex of respondents b) Location of respondents<br>.5<br>.35<br>.4<br>.3<br>.3<br>.25<br>.2<br>.2 .1<br>Female Male Highlands Islands Momase Southern<br>c) Age of respondents d) Education level of respondents<br>.3<br>.35<br>.3<br>.25<br>.25<br>.2<br>.2<br>18-39 years old 40 years old or more Tertiary education Secondary education or less<br>Share of respondents unwilling to get vaccinated Share of respondents unwilling to get vaccinated<br>Share of respondents unwilling to get vaccinated Share of respondents unwilling to get vaccinated<br><!-- End of picture text --> Note: This chart is based on answers to question 34 (control group only) in Appendix B.

**Figure A5 – People’s most preferred way of receiving information about the vaccine**

Single most preferred way information is communicated <!-- Start of picture text --> 4.2<br>Videos<br>7.5<br>F2F Prominent leaders<br>7.5<br>F2F Church and community members<br>80.1<br>F2F Health workers<br>0.7<br>Other<br>0 20 40 60 80<br>Share of respondents that selected this answer<br><!-- End of picture text --> Note: This chart is based on answers to question 7 in Appendix B.

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Figure VII: Human Capital Utilization and Development <!-- Start of picture text --> Capital Utilization<br><!-- End of picture text --> <!-- Start of picture text --> (a)<br>(b)<br>(c)<br>Human Capital<br>Accumulation (d)<br>(e)<br>(f)<br><!-- End of picture text --> <!-- Start of picture text --> (f)<br>(g)<br><!-- End of picture text --> <!-- Start of picture text --> (h)<br><!-- End of picture text --> <!-- Start of picture text --> Transformation<br><!-- End of picture text --> <!-- Start of picture text --> Adoption<br><!-- End of picture text --> <!-- Start of picture text --> Firms<br><!-- End of picture text --> <!-- Start of picture text --> Misallocation<br><!-- End of picture text --> <!-- Start of picture text --> (i)<br><!-- End of picture text --> <!-- Start of picture text --> Growth and<br>Development<br><!-- End of picture text --> <!-- Start of picture text --> (j)<br><!-- End of picture text --> <!-- Start of picture text --> (k)<br><!-- End of picture text --> <!-- Start of picture text --> (l)<br><!-- End of picture text -->

# **3.2 Structural transformation**

**Human capital is not equally distributed across sectors and space** . Across multiple countries, highly educated workers are less likely to work in agriculture and live in rural areas (Gollin et al., 2014; Young, 2013), and more likely to work in skill-intensive services such as education, finance, public administration and health (Herrendorf and Schoellman, 2017).<sup>4</sup> Figure VIII visualizes this pattern by plotting average years of schooling across four broadly defined sectors against GDP per capita. High-skill services is the most schooling intensive sectors, followed by low-skill services and manufacturing, and then agriculture. Interestingly, these cross-sectors differences in schooling are largest in poor countries and tend to diminish with development.

**Returns to education are generally larger in sectors with many highly educated workers and urban areas** . Herrendorf and Schoellman (2017) document across 13 countries that an additional year of schooling is associated with a significantly larger increase in wages in outside of agriculture compared to agriculture. Figure IX makes use of the World Bank I2D2/GLD database to generalize this finding to a larger sample of country - on average, the return to schooling is 9% in non-agriculture and 7% in agriculture. One caveat for these comparisons is that many agricultural workers in poor countries are self-employed, and as such are not in-

4Moreover, when focusing on workers moving across sectors, the more educated agricultural workers are more likely to leave agriculture, while the less educated non-agricultural workers are more likely to move to agriculture (Young, 2013; Hamory et al., 2020b).

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were able to attract and retain customers and earn enough income to keep them afloat (Octavia, 2021).

As Indonesia recovers from the pandemic, the digital economy will play an increasingly important role for both informal enterprises and workers. While it has already benefited some groups, the key challenge now lies in ensuring that these benefits are shared more widely. Accomplishing this will require concerted effort on several fronts, including increasing internet penetration in rural areas where internet access is only half of that in urban areas (OECD, 2021). Moreover, Indonesians in the top 10 percent of the income distribution are 5 times more likely to be connected to the internet than those in the lowest 10 percent (Tiwari et. al 2021). There is also a large urban-rural divide in mobile phone usage for work: 33.6 percent in rural areas relative to 57.7 percent in urban areas (SAKERNAS, 2022).<sup>15</sup> In addition, many Indonesians lack the skills needed to navigate the digital economy effectively (OECD, 2021). Some informal workers, for example, may not know how to use certain platforms such as social media to promote their work (Octavia, 2021). Policy therefore has a critical role to play in bridging these digital inequalities.

Evidently, the pandemic not only exacerbated pre-existing inequalities between the formal and informal segments of the economy, but also within the informal economy itself. In other words, the pandemic contributed to the dualistic nature of informality in Indonesia – increasing both ‘bad’ informal jobs as well as ‘good’ informal jobs. Nonetheless, the pandemic has also reinforced some of the benefits that could be gained from formalization. For example, it is easier for the government to provide assistance to formal enterprises and workers who are registered in national databases. In contrast, individual-level data on informal workers is largely absent in Indonesia, which makes it extremely difficult for the government to assist informal workers in times of crisis (Wihardja & Cunningham, 2021).

> 15 SAKERNAS does not distinguish between mobile phones (texting and calling only) and smart phones, so we report the combined gap here.

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quantifies the effect of innovation outsourcing on firms’ output productivity, particularly in developing countries.

The insignificant coefficient of internal innovation might be explained by a number of factors. First, if innovation investment requires a certain threshold amount to successfully enhance firms’ productivity, it may be that current internal innovation has not yet reached the threshold. Second, Tunisian firms’ outsourced innovation might be relatively simple, so it does not require a high absorptive capacity, which can be measured by the internal innovation investment. Otherwise, internal innovation efforts might not be good proxies of absorptive capacity in Tunisia. As found in a previous study, if technology is difficult to transfer, firms must have a certain prerequisite capacity to learn and adopt the external source of innovation (absorptive capacity). Lastly, it may be that internal innovation is not used efficiently within the firms. In fact, the previous study found that Tunisia’s relatively large spending on R&D has not increased innovation outcomes at the national level, due to the inefficient innovation environment (World Bank 2010<sup>16</sup> ). In any case, it has been more efficient for Tunisian manufacturing firms to simply acquire technology created externally to enhance their output productivity during the period of data coverage.

The results are consistent with the previous study on Tunisia. Using the innovation survey, Ayadi et al. (2009) concluded that Tunisian firms must benefit from external knowledge sources to exhibit significant innovation propensities for either product or process innovation. Internal innovation, specifically investment in R&D, plays a limited role in having a positive effect on product innovation but no effect on process innovation. Harbi, Amamou, and Anderson (2009) conducted surveys on 60 Tunisian ICT firms and found that internal innovation, specifically R&D, is negatively associated with firms’ success. Later, Harbi, Amamou, and Anderson (2012) conducted surveys on 92 small Tunisian ICT companies and found that although some innovation cultural values were positively related to performance,

> 16 Tunisia spent an estimated 1.25 percent of its GDP on R&D in 2009. That and the number of researchers per million people are typical proxies showing innovation inputs; in Tunisia, they are above regional average. However, innovation outcomes are disappointing in Tunisia, which shows a low number of international patent applications and low utilization of the results of research by firms, with both indicators below the regional average (WB 2010).

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# The Income Lever and the Allocation of Aid

Lidia Ceriani<sup>1</sup> and Paolo Verme<sup>2</sup>

> 1 _Universit`a Bocconi, Milano, Italy_

> 2 _The World Bank, Washington DC, USA_

## **Abstract**

The paper develops a concept and a measure of the monetary capacity of a country to reduce its own poverty and shows how these tools can be used to guide budget allocations or the allocation of aid. The authors call this concept the _income lever_ . Making use of tax and distributive theory, the paper shows how different redistributive criteria correspond to the different normative criteria of the income lever. It then constructs various income lever indexes based on these criteria and uses such indexes to rank countries according to their own capacity to reduce poverty. As shown in the empirical application, this methodology can provide an equitable tool to rank countries or regions when it comes to budget or aid allocations, whether it is the allocation of social funds within the European Union (North-North transfers) or the allocation of aid from rich to poor countries (North-South transfers). The findings indicate that the allocation of social funds in the European Union follows closely the rank that results from the income lever indexes proposed while the allocation of aid to Sub-Saharan African countries does not.

**JEL:** H2, H5, I3, O1, O2, F3

**Keywords:** Aid Distribution, Poverty Reduction, Redistribution Policies **Sector Board** : Poverty Reduction (POV)

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crop; this is the yield or output per unit of land. The yield has been the main criterion for the introduction of the modern varieties of cereals and other crops, termed as the green revolution, beginning around the middle of the last century. The higher is the yield of the modern varieties, or the larger is the area devoted to these varieties, the larger is the average yield. Extending this concept to aggregate output, we construct an aggregate peak yield variable. For each country and each commodity, the maximum of the past yields is computed, thereby reflecting the potential output from the implemented technology in any given year. Country-specific Paasche indices (1990=1) are constructed of these peak commodity yields, weighted by land area. A Paasche index is used since changing the composition of output changes the relevance of existing technologies.

The most common variable used in empirical studies as a carrier or representative of technology is some measure of human capital, mostly schooling. The basic idea is that higher levels of education are conducive to technological progress.<sup>16</sup> We include the average schooling years of the total labor force, taken from Barro and Lee (2000).<sup>17</sup> Empirical studies show the relevance of various public goods that are associated with productivity, such as infrastructure in transportation and communication, measures of public health, and research and extension.<sup>18</sup> In this study we do not attempt to determine the contribution of these variables individually, but rather allow for the overall effect of the group on the estimation. We do this by selecting the per capita output in the country as a comprehensive measure of capital and technology (Mundlak and Hellinghausen 1982). We measure it as the ratio of the country per capita output to that of the United States and refer to it as a development indicator. This variable replaces the need for introducing a dummy variable to differentiate between developed and developing countries as some studies do.

> 16 However, the causality could go in either direction in that economic progress generates a demand for schooling. Therefore, the interpretation of a schooling variable in empirical analysis is somewhat ambiguous.

> 17 Education data are reported for every five years through the World Bank website

> <u>(http://devdata.worldbank.org/edstats/). Data for other years are obtained through linear interpolations.</u>

> 18 See for instance Evenson and Kislev (1975), Antle (1983), Binswanger et al. (1987), Lau and Yotopoulos (1989), and Craig, Pardey, and Roseboom (1997).

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will be a function of other variables, including the<sup>amount of</sup> borrowing. If the model is run under the "availabilities"<sup>case,</sup> the Bs are not used. However, the user must enter the loan commitments that are expected to be made by each of<sup>the creditors.</sup>

# **III.3** Existing Debt

Broadly speaking, there are three steps to calculate<sup>existing</sup> debt. First, the user must enter the pipeline data. Second,<sup>the</sup> DM calculates the effects of possible debt-restructuring operations. Finally, existing debt is obtained by adding the previous calculations.

We turn now to the third step. It is assumed, for presentational purposes, that both pipeline debt<sup>and the effects</sup> of debt-restructuring have already been calculated. In the next subsection, we will come back to the debt-restructuring<sup>menu and</sup> we will detail how the effects of alternative debt-restructuring schemes are calculated.

# 3.3.1 Existina Debt

The DM calculates the values of existing gross disbursements and amortization payments for each creditor as follows: (3.5) GDIe = GDJp + AGDJrs + tGDJar (3.6) APie = APJ<sup>p</sup> + AAPJrs + &APJbb + tAAPar + 6APJwoff 44