rafmacalaba/datause-encoder-data huggingface dataset:
4 the 20 countries for which data exist, international migration takes less than 10 percent of the population with a tertiary education. Migration to the OECD results in a slightly higher degree of braip drain, but even here international migration takes more than 10 percent of the tertiary-educated population from only 5 of 13 labor-exporting countries.

The balance of the paper is organized as follows. To provide perspective on the growing importance of international migration, Section II presents data on changes in the level of worker remittances received in the 24 countries over the last twenty years. Section III explains the methodology used for estimating the level of migration to the United States and the OECD. Section IV applies this methodology to estimate the extent of brain drain to the United States by analyzing migration rates and the educational composition of U.S. migrants. Estimates of the level of brain drain to the OECD are presented using similar methods in Section V. Section VI concludes.

# II. Official Remittances from International Migration

To gain a proper perspective on the importance of international migration, it is best to begin with the most visible product of international migration: remittances received. Not only are remittances critical to the foreign exchange position of many labor-exporting countries, but they are also vital to the consumption and investment behavior of migrant households themselves. Perhaps because of their importance to both labor-exporting countries and households, remittances tend to be the best measured and recorded aspect of the migration experience. For instance, the International Monetary Fund (IMF) keeps annual records of the amount of worker remittances received by each
banks were also cut substantially. On 7-year CCTs these were gradually but progressively curtailed from 1 to 1.25 percent in the 1980s to 0.30 percent in 2001 (Table 2.7).

**Table 2.7: Commissions on Government Securities**

||Until April<br>1991|April 1991 to<br>December<br>1992|January 1993<br>to December<br>1994|From January<br>1994|From January<br>1997|From January<br>2001|
|---|---|---|---|---|---|---|
|BTP 3 y|--|0,50|0,50|0,35|0,25|0,20|
|BTP 5 y|1,00|0,75|0,75|0,60|0,40|0,30|
|BTP 10y|1,25|1,00|0,75|0,60|0,40|0,40|
|BTP 15 and 30y|--|--|0,75|0,60|0,40|0,40|
|CCT 7y|1,25/1,00|1,00|0,75|0,60|0,40|0,30|
|CTE|1,00|0,75/0,50|0,50|0,35|0,25|--|
|CTZ|--|--|--|0,25|0,15|0,20|

Source: Banks of Italy The results of the new issuing strategy were a drastic change in the composition of debt by type of instrument. Fixed rate bonds rose from 16 percent of marketable debt in 1990 to 54 percent in 2000 (Table 2.8). Treasury bills accounted for only 9 percent, and various issues of Treasury certificates for 27 percent.

**Table 2.8:  Composition of Central Government Marketable Debt, 1990-2000**

||1990|1995|2000|
|---|---|---|---|
|Treasury Bills (BOTs)|31.6|21.6|9.2|
|Treasury Bonds (BTPs)|15.6|33.6|53.5|
|CCTs|40.6|27.7|21.4|
|CTEs|3.8|2.6|0.1|
|CTOs|4.2|2.6||
|CTZs||2.2|5.6|
|Other|4.2|9.7|10.2|
|Total|100.0|100.0|100.0|

Source: Scarpelli 2001:30.

During this period, the level of non-marketable debt continued to decline, reaching 8 percent of total debt in 2000 (Table 2.9). Marketable debt represented 87 percent and debt issued by other public entities 5 percent.

**Table 2.9:  Composition of General Government Debt, 1990-2000**

||1990|1995|2000|
|---|---|---|---|
|Marketable Central Government Debt|81.2|86.6|86.5|
|Non-Marketable Central Gvt Debt|16.9|7.6|8.2|
|Other General Government Debt|1.9|5.8|5.3|
|Total|100.0|100.0|100.0|
|Total in ITL trillion (* billion euro)|1284.6|2201.9|1285.1*|
|Total(% of GDP)|98.0|124.3|110.5|

Source: Scarpelli 2001 and OECD .

A clear shift also took place in the relative importance of different types of investors. The combined holdings of the Bank of Italy and banks declined from 27 percent of the total in 1992 to 21 percent in 2000 (Table 2.10). A much bigger fall was experienced by the share held by 21
consensus building. The more than one hundred members of Brazil’s CNDES may present challenges in this regard<sup>30</sup> .

- Lack of engagement by the minister of finance can dilute the council’s link to the national budget. The ministers of finance in Brazil and Colombia are active in those countries’ councils, which is an asset.  Brazil has the added heft of the active participation of the BNDES.<sup>31</sup>

- The Councils (including their technical commissions) can be relatively inactive, which erodes credibility and the interest of participation by high level civil representation. Brazil’s CNDI apparently had not been meeting (although the Plano Brasil Maior apparently is trying to revive it)<sup>32</sup> . Peru’s National Accord has had extended periods of inactivity (with a recent attempt of the current government to revive it). The CNC of the Dominican Republic has been inactive, although its executive arm has been very effective serving as a liaison between government and business (Moguillansky, 2012).

- Major players are not discouraged by the government in their efforts to bypass the council for the tradition of intensive bilateral lobbying. This bypass of course is inevitable if the council does not meet regularly, or is not a credible interlocutor with the government. In Colombia the Executive apparently discourages private conversations with members of the National Competitiveness Commission (Moguillansky, 2012).

- The councils’ governance structure and method of dialogue do not serve to overcome distrust,  or indifference,  between government and business. This has been a problem in Chile. The National Competiveness Council of Peru also appears to have been ineffective in creating sustained real engagement between government and business. In Uruguay the ministries’ attention to, and priority for, the tripartite sectoral councils is quite variable.

- Generally the councils do not have well-financed independent and neutral technical secretariats that can facilitate problem-solving deliberations in the national interest and monitoring of the degree to which recommendations or agreements are really translated into policy and a budget. This raises the risk of capture of the government by private interests, or the capture of civil society representatives by the government.  The Concertation National of Panama has a financed Secretariat that provides these services and the additional support of UNDP. Peru’s resuscitated National Accord recently

> 30 Surveys show that the participants’ satisfaction with the CNDES was only moderate and some of the major players lost interest (Zebral, 2011).

> 31 The Competiveness Council in Peru is now housed in the Ministry of Finance, but as mentioned earlier, financing is largely provided by donors and multilateral agencies

> 32 This may explain why many business interests initially did not feel well informed about the PDP (Devlin and Moguillansky, 2011). The tripartite sectoral councils did involve significant private-public interaction.

24
# **Exhibit 2.10 – Statistical differences between the median Egyptian firm and the median benchmark group firm** _<u>Quantile regressions for period 2003-2007</u>_

||_Difference_<br>**Median diffe**<br>**countries, (pp**|_Statistically_<br>_different?_<br>**rence with LMI**<br>**)**|_Difference_<br>_Statistically_<br>_different?_<br>**Median difference with peer**<br>**group, (pp)**|
|---|---|---|---|
|**Total debt to Equity, %**|11.8|Yes|7.3<br>No|
|**Total liabilities to Equity, %**|29.8|Yes|30.3<br>Yes|
|**Current to Total Liabilities, %**|9.4|Yes|10.1<br>Yes|
|**ST debt to total liabilities, %**|3.1|No|4.0<br>Yes|
|**EBIT / Total Interest Expense Ratio**|2.9|Yes|2.2<br>Yes|

_Note: Differences are expressed in percentage points. Statistically different refers to a p-value less than 0.1._

# **2.2 Second Tier Firms vs. Top Tier Firms in Egypt**

After benchmarking Egypt’s Top Tier firms, we next contrast differences between Top and Second Tier firms.

# _2.2.1 Performance: Top Tier firms mostly outperform Second Tier firms, but Second Tier firms still likely outperform top companies in other LMI countries_

Across all profitability indicators, Top Tier firms clearly show superior performance relative to Second Tier firms. Looking at medians, Exhibit 2.11 shows a lower real yearly sales growth of 3.6 pp, a lower operating profit margin of 5.1 pp, a lower ROA of 2.9 pp, and a lower ROE of 5.6 pp. Unreported regression analysis shows this results is driven by lower COGS and total expenses for Top Tier firms which can fully be explained by the fact that Top Tier firms are larger and hence enjoy significant scale economies (-7.0 pp and 1.5 pp, respectively).

In fact, Second Tier firms even seem to outperform top firms in countries with a similar level of economic development: the LMI group. However, the Worldscope and Coface are not completely comparable, which is likely due to errors and differences in definitions. The Coface database slightly underestimates the operating margin, ROA and ROE of Top Tier firms. Therefore, assuming that differences between databases of Top Tier companies translate to the other companies, Second Tier firms also outperform the LMI group. Because the Coface values are on average between 80 to 95 percent of the Worldscope data, it is very unlikely that the Second Tier outperforms the peer group with all countries.

11
|**Table A6: Implementation costs**|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||**TOTAL C**|**OSTS**|**Share**|**Share**|**Share**|
||**In CFAF**|**In USD**|**Group 1**|**Group 2**|**Group 3**|
|**Costs associates to GUFE (one‐stop‐shop for formalization)**||||||
|_Set up costs:_||||||
|‐Set up costs of Hardware and Sofware:3 computers|4 500 000|7 550|12,5%|37,5%|50%|
|including softwares and buying and setting up the serveur||||||
|‐Investments required to edit Entreprenant cards:printer<br>and software|12 500 000|20 973|12,5%|37,5%|50,0%|
|_Variable costs:_||||||
|‐Salary of GUFE entreprenant staffs :Two full‐time staffs<br>for 18 months, and one for 12 months|21 600 000|36 242|12,5%|37,5%|50%|
|‐Hardware and sofwares:Maintenance for 2 years|6 812 344|11 430|12,5%|37,5%|50%|
|‐Office supplies|5 190 000|8 708|12,5%|37,5%|50%|
|**Costs associates to the CGA (implementing agency)**||||||
|_Set up costs:_||||||
|‐ Initial training of CGA advisors|13 450 000|22 567|5,1%|42,0%|52,8%|
|‐ Motobikes for CGA advisors (including insurance)|19 200 000|32 215|5,1%|42,0%|52,8%|
|‐ Office and mobile phones (one phone for each advisor)|1 250 000|2 097|5,1%|42,0%|52,8%|
|‐ Hardware and sofware:computers, serveurs and|35 553 000|59 653|5,1%|42,0%|52,8%|
|‐ Set up costs of upgrading CGA office|1 010 000|1 695|5,1%|42,0%|52,8%|
|_Variable costs:_||||||
|‐ Salary of CGA staffs for 2 years: CGA supervisor, 24|175 144 000|293 866|5,1%|42,0%|52,8%|
|advisors (10 of them only for 14 months) and one hotline||||||
|‐ CGA overheads:printing, administrative fees, office|12 979 560|21 778|5,1%|42,0%|52,8%|
|supplies, water and electricity||||||
|‐ Transportation costs: gazoline|18 600 000|31 208|5,1%|42,0%|52,8%|
|‐ Communication|11 160 000|18 725|0,0%|44,3%|55,7%|
|‐ Maintenance of hardware and sofware:|6 147 000|10 314|5,1%|42,0%|52,8%|
|‐ Office rent|9 600 000|16 107|5,1%|42,0%|52,8%|
|‐ Grouptrainings organization:office supplies and coffee|16 500 000|27 685|0,0%|44,3%|55,7%|
|Total Set up costs|87 463 000|146 750|6,5%|41,2%|52,3%|
|Total Variable costs|283 732 904|476 062|5,5%|41,7%|52,8%|
|**Total costs ofprogram implementation**<br>Notes: Data on costs and group allocation from CGA and GU<br>2016)|371 195 904 <br>FE. 1 USD = 59|622 812<br>6 CFAF (exc|5,7%<br>hange rat|41,6%<br>e on June|52,7%<br>1st,|

47
**Table A6.** Decomposition results of the global emissions changes and contributions of various driving forces in the **UMI countries** over the early period (1980-1990), late <u>period (2005-2015), and entire period (1980-2015).</u>

|**Period**|**1980-1990**|**2005-2015**|**1980-2015**|
|---|---|---|---|
|**Emissions change (Mt)**||||
|Overall change|1,095.4|4,770.7|9,886.4|
|_EC_|-122.8|453.7|391.7|
|_ECS_|131.0|61.9|1,038.6|
|_EI_|-719.9|-2,948.2|-6,756.7|
|_PCG_|1,247.9|6,457.0|12,683.9|
|_P_|559.3|746.4|2,528.9|
|**Contribution (%)**||||
|Growth rate|39.1|60.2|352.5|
|_EC_|-4.4|5.7|14.0|
|_ECS_|4.7|0.8|37.0|
|_EI_|-25.7|-37.2|-240.9|
|_PCG_|44.5|81.5|452.3|
|_P_|19.9|9.4|90.2|

**_Note:_** _EC_ , _ECS_ , _EI_ , _PCG_ , and _P_ refer to emission coefficient, energy mix, energy intensity, income, and population, respectively.

34
Bureau of Economic Analysis (BEA) of the US, and Eurostat.<sup>27</sup> The unit of observation is at the host/ultimate parent/year level, reporting the sum, over all multinationals with ultimate parents in a particular country, of their gross investment into foreign affiliates in each of the host countries covered by these sources (together with their turnover, employment, exports, and other features). More precisely, these data are for foreign affiliates’<sup>28</sup> gross investment in tangible assets, both new and existing (Eurostat, 2012).

The sample covers inward FAI into 32 host countries from 187 parent countries over the period 1998-2016 for the United States and 2003-2016 for all other countries: this makes a total of 1,630 observations in the main sample. The sample of host countries is mainly limited to advanced economies, and all but four are EU members.<sup>29</sup> Collectively, they account for more than half of the global FDI stocks for each year in our sample.

Figure 2 compares levels of inflows of FAI and FDI over the period 2003-12, both for all host countries in the sample and for the subset of EU members.<sup>30</sup> What emerges—beyond an impression of the importance of EU hosts in our dataset—is that, both in general and for EU hosts in particular, FAI is far lower than FDI, suggesting that double counting is indeed extensive in the latter, and is also less volatile. Notable too is that the gaps between the FDI and FAI have widened substantially. In our sample of host countries, over 10 years in which FAI and FDI data overlap, aggregate FAI was about 50 percent of FDI, broadly consistent with recent findings: as mentioned above, ‘phantom’ investments seem to account for around 40 percent of FDI.

> 27These can be accessed at, respectively: https://www150.statcan.gc.ca/n1/pub/13-607-x/2016001/ 145-eng.htm, https://www.bea.gov/data/intl-trade-investment/activities-us-affiliates-foreign-mnes, and https://ec.europa.eu/eurostat/web/structural-business-statistics/data/database.

> 28Meaning entities that are majority owned by ultimate parents that reside in a different country and can determine those affiliates’ general policies.

> 29The host countries covered (with the * indicating non-EU members) are: Austria, Belgium, Bosnia and Herzegovina (*), Bulgaria, Canada (*), Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway(*), Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, the United Kingdom and the United States (*). All those listed as EU members were members throughout the sample period used for estimation.

> 30Here and in results reported below we combine FDI data from the IMF Coordinated Direct Investment Survey (CDIS) and UNCTAD. Bilateral CDIS data are available starting in 2009; these are stock data, from which we back-out flows using the perpetual inventory method, as _It_ = _Kt −δKt−_ 1, with _δ_ = 0 _._ 195. Bilateral UNCTAD data are publicly available only to 2012.

15
together with complementary interventions (Blattman and Ralston, 2015). In contrast, it is unclear that interventions targeting one mechanism can produce similar impacts on poverty. For TUP programs, Banerjee et al. (2018) study whether providing only a transfer of a productive asset or access to savings are each sufficient on their own to replicate the generated impact of the multi-faceted TUP program in Ghana. None of the interventions were able to replicate these results.

In more stable contexts, cash transfer programs have been successful in reducing poverty and increasing investments in education and health (Fiszbein and Schady, 2009; Macours and Vakis, 2019; Araujo, Bosch, Schady 2019; Baird et al., 2011, 2013; Haushofer & Shapiro, 2016). However, existing evidence suggests they may only have a modest ability to increase incomes of the recipients and their standard of living after the cash transfer stops (Ikegami, Carter, Barrett, Janzen, 2019; Araujo, Bosch, and Schady, 2019). Similarly, conditional cash transfer programs can be an effective tool to increase investments in education and health while reducing current poverty, but evidence is mixed on whether their impacts persist (Fiszbein and Schady, 2009; Macours and Vakis, 2019; Araujo, Bosch, Schady 2019). They seem unlikely to move recipients from one equilibrium to another one (Kraay and McKenzie, 2014). Relatedly, microfinance interventions have not produced permanent increases in consumption or income that can support long-term reductions in poverty (Buera, Kaboski, Shim, 2019; Banerjee et al., 2014). Taken as a whole, the existing evidence is consistent with our findings that a big-push approach may be appropriate to generate meaningful changes for ultra-poor households in conflict settings.

This paper proceeds as follows. Section 2 describes the economic constraints facing ultrapoor households in our sample. Section 3 describes the TUP program. Section 4 lays out the Design and Methods. Section 5 presents the results. Section 6 presents a cost-benefit analysis of the program, and Section 7 describes the study limitations. Section 8 concludes.

# 2. SOCIOECONOMIC CONDITIONS OF THE ULTRA POOR

We study ultra-poor households in four districts of Afghanistan’s Balkh province. To identify these households, the poorest villages in the province were initially chosen through a qualitative assessment by the program implementer, a government-owned entity. After this selection, a Participatory Rural Appraisal (PRA) was conducted, including a community poverty wealth ranking and physical verification of the program’s eligibility criteria, resulting in 1,219 ultra-poor households in 80 study villages (details of this process are reported in Section 4). In addition to the main TUP sample, we selected approximately 20 households from each study village, randomly drawn from the PRA population census list (excluding TUPeligible households), to provide a representative benchmark for the TUP sample. Baseline and follow-up data were collected from 1,680 households using this approach, which we 6
instruments such as twins and sibling sex composition is that the family-planning instrument allows us to analyze the impacts of family size on all the children in the household (or the single child, if there is only one), while using either twins or children sex composition restricts analysis to a subset of these children.<sup>12</sup> Turning now to the second strand of literature, on private tutoring, few papers have investigated the correlation between household size and household educational investment in their children through private tutoring. To our knowledge, the exceptions are the two papers on Korea by Lee (2008) and Kang (2011), and the former touches only briefly on tutoring. Both these papers share the same identification strategy, in that they use the sex of the first-born child as an instrument for family size,<sup>13</sup> but the former implements this analysis at the household level, while the latter does so at the level of the child. Lee (2008) finds a negative impact of larger family size on household investment in education in general and tutoring in particular, but Kang (2011) finds these negative impacts to be significant only for girls.

> Gibson and McKenzie (2007) provide a related review of household surveys’ use of distances measured via global positioning systems (GPS).

> 12 Using twins as the instrument also requires a much larger estimation sample size; as a result, most previous studies that took this strategy have had to rely on population censuses.

> 13 The use of the sex of the first-born child as an IV has some limitations.  First, it requires the assumption of son preference—which appears to be a weak IV, so that Kang (2011) has to rely on bound analysis to identify bounds of impacts of family size in the case of boys. Second, the assumption of son preference in turn requires the assumption that parents do not abort girls at their first childbearing; if they do, the sex of the first-born child is clearly not valid as an exogenous instrument. This concern is especially relevant to Vietnam, which has one of the highest abortion rates in the world (Henshaw, Singh, and Haas, 1999). And finally, this identification approach may only work for families with more than one child; our study makes no such restriction on family size, investigating families with between one and seven children.

8
These same market failures that can limit the ability of households to recover quickly from disasters are also likely to inhibit the recovery of microenterprises and small businesses. Yet the existing literature has not looked at the process of enterprise recovery in developing countries, and even in developed countries there are only a handful of qualitative case studies. Until recent disasters such as Hurricane Katrina and the Asian tsunami, the conventional wisdom, reflected in the quote from Dacy and Kunreuther’s book, is that the economy recovers surprising quickly.<sup>2</sup> Based in qualitative interviews with small business owners shortly after Hurricane Katrina, Runyan (2006) concludes that insurance is key: firms with insurance quickly replace destroyed assets, but that those without insurance could not, often because business records lost in the flooding and destruction were required to access federal aid. Large government and non-profit aid flows are also common in developing countries after natural disasters. The literature has examined the determinants of how international financial flows such as aid respond to disasters (e.g. Eisensee and Strömberg, 2007; Yang, 2009), but there is little about how and whether this aid reaches enterprise owners.

This paper provides the first microeconomic study of the recovery of the private sector in a developing country following a major natural disaster. We use firm-level panel data gathered from micro enterprises in southern Sri Lanka following the December 2004 Asian tsunami. In addition to surveys, the post-tsunami project involved a field experiment providing grants to randomly selected enterprises. The grants allow us to assess the importance of capital in the recovery process and measure the return to capital immediately following a disaster.

> 2  Most of the research by economists examining the aftermath of disasters focuses on short-term recovery process, rather than the longer-term rebuilding process (Okuyama 2003). Analysis of longer term recovery generally uses aggregate data such as building permits (Dacy and Kunreuther 1969). Two exceptions using micro-level data are Smith and McCarty (1996), who analyze the demographic changes in southern Dade Country, Florida following Hurricane Andrew in 1992, and Dolfman et al (2007) who estimate losses in employment and wages in New Orleans following Katrina.

3