rafmacalaba/datause-encoder-data huggingface dataset:
Efforts to address e-security should consider two tradeoffs. The first is between convenience and risk: as a system becomes more convenient to use, it may become more vulnerable to risks. The second tradeoff is between costs and risks: e-security measures using the latest technology may be extremely costly, especially for emerging markets. Once a system’s level of risk has been determined, sustainable resources must be provided to cover it. Among the most crucial is sufficient expertise in e- security software and hardware. E-security is advancing quickly—both in terms of how systems can be attacked and how they can be protected from such attacks. External experts should be hired to assess a system’s vulnerabilities, as should in-house security officers at the same level as chief accountants and comptrollers.

# **How Does the Expanded Program Work?**

To purchase securities through the Expanded Small Investors Program, an investor must have a checking account with a participating bank. (Because the Philippines does not have an automated clearing house system, these banks settle transactions under the program.) To open an account, an investor must visit a bank branch and physically and electronically sign the account opening statement. For each account a securities account is created in the Registry of Scripless securities in the investor’s name.

The investor can then buy securities over the Internet. Purchases are simultaneously debited from the investor’s bank account and credited to the account in the Registry of Scripless Securities. Interest and principal payments are credited to the bank account when due. The system does not allow investors to sell securities before they mature.

This system has different implications for different parties. The Department of Finance, given its difficult fiscal position and heavy dependence on domestic borrowing, would be predisposed to favor initiatives that electronically issue securities directly to investors. This approach could eliminate layers in the workflow and so cut borrowing costs.

For investors, the program is at least as convenient as other online financial services, such as banking or stock trading. Online banking, however, involves the integration of multiple electronic systems that are all owned and controlled by a single legal entity. By contrast, the Expanded Small Investors Program involves shared services. The economic logic of shared services is based primarily on economies of scale. But the program also poses a significant challenge for the Treasury, requiring it to get multiple legal entities to integrate their proprietary electronic systems to deliver services. Thus it is important to understand the entities involved in the program, the roles of their systems, and the electronic links between the systems.

# **Entities involved**

The main entities involved in the Expanded Small Investors Program are:

- Smetrix—the developer and vendor of Document Hub, Gatekeeper, and the program application (e-SIP). Smetrix does not operate these software platforms; it licenses them to the operators of shared services (see below).

31
7 credit position with the CBE was the main alternative use of funds until 2009 when real credit to the government began taking over as the main use of banking sector funds.

In 2011, against a background of political unrest, real credit to the private sector decreased by 8.5 percent, as a result of contraction of sources by 17 percent and of alternative uses by 8.5 percent. The banking sector’s adjustment to capital outflow consisted of compensating the contraction of net foreign liabilities with a reduction in the net credit position with the CBE by a similar magnitude.

**Table 3: Change in sources and uses of funds of the banking sector**

**(for fiscal years ending in June) as a ratio to real credit to the private sector**

||**2005**|**2006**|**2007**|**2008**|**2009**|**2010**|**2011**|
|---|---|---|---|---|---|---|---|
|**_Change in credit to the private sector_**|**_-0.044_**|**_0.009_**|**_-0.015_**|**_0.017_**|**_-0.078_**|**_-0.029_**|**_-0.085_**|
|**Change in sources**|0.062|0.035|-0.026|0.116|-0.038|0.024|-0.170|
|Change in deposits|0.066|0.086|0.115|0.047|-0.171|-0.031|-0.044|
|Local currency|0.055|0.098|0.023|0.083|-0.072|0.030|-0.048|
|Foreign currency|-0.002|-0.013|0.030|-0.028|-0.053|-0.054|-0.020|
|Other|0.013|0.000|0.061|-0.008|-0.045|-0.008|0.024|
|Change in foreign liabilities|-0.010|-0.072|-0.121|0.046|0.146|0.060|-0.077|
|Change in capital|0.006|0.022|-0.019|0.023|-0.014|-0.005|-0.049|
|**Change in alternative uses**|0.106|0.026|-0.010|0.100|0.040|0.052|-0.085|
|Change in credit government|0.065|0.050|-0.108|0.013|0.210|0.072|0.044|
|Change in net credit to CBE|0.026|-0.020|0.066|0.074|-0.142|-0.025|-0.144|
|Change in other assets & cash|0.015|-0.004|0.033|0.013|-0.028|0.006|0.014|

Source: Central Bank of Egypt Note: Real figures of all aggregates were obtained by deflating nominal figures by the CPI index from CAPMAS.

During the capital inflow period 2006 to 2008, the Egyptian banking sector did not fund credit operations expanding its net foreign liabilities. In fact, during the capital inflow years the banking sector’s liabilities with the rest of the world became instead a contractive factor. In 2009, when capital flowed out in response to the global crisis, banks used their external liabilities as a funding source, playing a counter-cyclical role. Only in 2011, during the capital outflow related to the January uprising, were the net foreign liabilities pro-cyclical.

The role of Egypt’s central bank (CBE) helps explain this puzzling behavior. During the capital inflow period, the CBE intervened to prevent the domestic currency’s appreciation. CBE’s foreign currency assets increased from USD 23 billion in June 2006 to USD 48 billion in June 2008. To compensate for this expansion, the banks had to increase their net credit
such as, payments of bribes for obtaining a tax clearance certificate or a tax refund, or for preventing a tax audit from taking place.

In order to eliminate possible negative impacts of such cross-country differences, fixed country effects are introduced in regression analyses. In addition to this measure, some countries are eliminated if their tax corruption ratio is unexpectedly high or low. For this purpose, two country rankings are compared to each other: the ranking based on the tax corruption ratio calculated from the Enterprise Survey database as defined above and the ranking based on the bribery index from the Global Competitiveness Index Database. Because the series from the Enterprise Survey Database include subjective elements, it is helpful to compare country rankings by using the two variables on corruption to identify countries with “unexpected” data. The tax corruption ratio is between 0 and 100 where higher numbers indicate higher corruption. The bribery index, which is defined as irregular payments and bribes, is an index between 1 and 7, where lower numbers indicate higher corruption.<sup>6</sup> Each of the 138 countries from our initial dataset is ranked based on these two measures, and then these two rankings are compared to each other for each country. If the absolute value of the

> 6 The definition in World Economic Forum (2013) is “average score across the five components. The question is: In your country, how common is it for firms to make undocumented extra payments or bribes connected with (a) imports and exports; (b) public utilities; (c) annual tax payments; (d) awarding of public contracts and licenses; (e) obtaining favorable judicial decisions.” In each case, the answer ranges from 1 (very common) to 7 (never occurs).

12
24 premium over this period in Brazil.<sup>31</sup> Nevertheless, the estimated size of the impact is small: using the ERP coefficient from specification 8, the fall in average ERP from 51.4% in 1988 to 20.0% in 1995 (with all other variables held at their mean values) would result in a 1.6% decrease in average industry wage premium.

Although theory suggests that the pass-through of tariffs to product prices, and thus to wages, is mediated by the sector’s import penetration (see Gonzaga et al., 2006), importpenetration does not appear to be important in mediating the effect of tariffs on industryspecific wage premia, as shown by the insignificant coefficients on the interaction terms. The same is not true of (import-weighted) exchange rate effects, which have the predicted (negative) sign<sup>32</sup> when interacted with lagged import penetration: as the currency appreciates and imports become more competitive for a particular industry, wage premia in that industry decline. When the RER is export-weighted and interacted with lagged export-share, the effect is positive and significant.<sup>33</sup> Table 6 presents the second-stage results for the regression of industry-specific skill premia on the same set of trade-related variables. For industry-specific skill premia, once the economy-wide returns to skill are controlled for in the first stage, there are no particular theoretical predictions, and we find that the coefficients on ERPs are insignificant across all specifications. Interestingly, however, we find a fairly robust pattern of negative and significant coefficients on lagged import penetration, suggesting that skill premia were falling for those industries where the growth in import penetration was largest. We know that these were largely skill-intensive industries that were most highly protected prior to 1988, as seen in Table 1. This movement in industry-specific skill premia is therefore consistent with the decline in the economy-wide skill premium

> 31 Although Pavcnik et al. (2004) expected these results (“The models predict a  positive association between industry tariffs and wages, so that declines in industry tariffs lead to proportional declines in industry wages”, p.321), they did not find them. This may have been due to their use of the less representative PME data, or to specifications that did not include lagged ERPs. Lagging ERPs accounts for the fact that the effects of reduced protection on industry premia may take time to flow through.  Our protection results are, however, consistent with those for Colombia (Attanasio, Goldberg and Pavcnik, 2004), and Mexico (Revenga, 1997).

> 32 An increase in our exchange rates means an appreciation in the currency.

> 33 This likely reflects the fact that an increase in an industry’s export share would be expected to increase that industry’s wage premium.
1... 3All of these life tables are based on recorded age and sex patterns of mortality, as derived either from vital registration, or from vital statistics and population data collected through sample surveys. In some case;,, recorded mortality levels have been adjusted, based on the application of a variety of analytical techniques, because of the incompleteness of the collected death data (relative to that of recorded births or population).

This paper relies on interpolated data produced by the World Bank ANDREX database, as the UN life expectancy data was decidedly too scant. Although the data are adequate for assessing time trends and comparing among regions, country and year specific numbers should be taken with considerable caution. As these data can be derived directly from ANDREX, they have not been reproduced as part of the annex tables in this report.

Infant mortality covers the death of children eunder one year of age and is expressed per thousand. Child mortality is most frequently defined as referring to the death rate, also per thousand, of children between age one and are 5. Under five mortality, also occasionally referred to as child mortality, encompasses both earlier measures. This paper, uses under five mortality rather than infant and child mortality. The main reason for this is that under five mortality gives a broader picture of the health of children under five (both children and infants) and is less affected by cultural factors such as the time of weaning which may differ among countries and consequently affect the infant mortality picture.
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# **Table 3:** Are some B40 individuals more likely to have savings?

|**Mo**|**del 1**|**Mod**|**el 2**|**M**|**odel 3**|**M**|**odel 4**|**M**|**odel 5**|**M**|**odel 6**|**M**|**odel 7**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|B40|-0.097***|B40|-0.102***|B40|-0.096***|B40|-0.116***|B40|-0.086***|B40|-0.066**|B40|-0.083***|
||(0.013)||(0.017)||(0.013)||(0.017)||(0.014)||(0.031)||(0.013)|
|tertiary|0.143***|secondary|0.048***|self-employ|ed 0.063***|employed|0.060***|retired|0.060***|house|0.047**|other real|0.083***|
|education<br>B40*tertiary|(0.016)<br>-0.011|education<br>B40*secondary|(0.013)<br>0.008|B40*self-|(0.019)<br>-0.035|B40*employe|(0.017)<br>d 0.034*|B40*retired|(0.021)<br>-0.052**|B40*house|(0.02)<br>-0.037|estate<br>B40*other|(0.013)<br>-0.075***|
|education|(0.033)|education|(0.021)|employed|(0.038)||(0.021)||(0.026)||(0.031)|real estate|(0.027)|
|Log-L|-5295|Log‐L|-5295|Log-L|-5294.7|Log-L|-5293.7|Log-L|-5292.8|Log-L|-5294.4|Log-L|-5290.2|
|N|9893|N|9893|N|9893|N|9893|N|9893|N|9893|N|9893|
|Further<br>controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|P(DepVar=1)|0.43|P(DepVar=1)|0.43|P(DepVar=1|) 0.43|P(DepVar=1|) 0.43|P(DepVar=1|) 0.43|P(DepVar=1|) 0.43|P(DepVar=1|) 0.43|

Note: Cluster standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.01. The dependent variable for all models is any savings. All models include country-time fixed effects and further individual level controls.

16
# **-** **<u>Table 1 Descriptive statistics of the cross country panel</u>**

||Mean|SD|Min|Max|Obs|
|---|---|---|---|---|---|
|_Mobility indicators (with respect to mothers)_||||||
|Intergenerational persistence (cohort weighted)|0.408|0.134|0.162|0.894|1,244|
|Intergenerational Correlation (cohort weighted)|0.418|0.096|0.212|0.645|1,244|
|Upward mobility gap (cohort weighted)|3.909|1.343|1.323|8.512|1,244|
|Upward mobility in higher education (cohort weighted)|0.233|0.120|0.023|0.633|1,244|
|_Mobility indicators (with respect to fathers)_||||||
|Intergenerational persistence (cohort weighted)|0.400|0.125|0.189|0.807|1,244|
|Intergenerational Correlation (cohort weighted)|0.440|0.094|0.259|0.649|1,244|
|Upward mobility gap (cohort weighted)|3.473|1.225|1.220|7.795|1,244|
|Upward mobility in higher education (cohort weighted)|0.224|0.116|0.023|0.613|1,244|
|_Socio-economic variables_||||||
|Log GDP per capita|10.194|0.699|8.348|11.514|1,244|
|Log population|16.499|1.513|12.547|19.619|1,244|
|Average years of schooling (cohort weighted)|11.191|2.358|4.589|15.618|1,244|
|Standard deviation of schooling (cohort weighted)|3.599|0.713|1.931|5.454|1,244|
|Population at birth (cohort weighted)|24.799|37.003|0.148|207.414|1,244|
|Log GDP per capita at birth (cohort weighted)|8.877|0.729|4.822|10.138|1,244|
|Infant mortality rate at birth (cohort weighted)|49.814|33.653|9.995|148.961|1,244|

Note: the sample includes all the countries for which there are at least 10 non-missing observations in the period 20002020. This amounts to a total of 68 countries.

24
# **5. CONCLUSIONS**

This paper investigates how patterns in occupational placement of skilled male immigrants on the US labor market vary over time and by country of origin when education and other human capital characteristics are accounted for. By focusing on occupational placement rather than earnings, the analysis reaps the former’s benefit of better comparability across geographic regions within the US and over time.

Employing three US Census samples, 1980, 1990, and 2000, to follow the progress of cohorts who arrived during the 1970s and 1980s, our first finding is that (i) the initial occupational placements by country and (ii) their improvements within the first 10 or 20 years since arrival are highly heterogeneous. Second, immigrants from countries with lower initial occupational placement are shown to improve their performance faster than those from countries with better initial placement, thus there is convergence among migrants. However, our third main result shows that, the magnitude of the convergence factor is not large enough to ensure full catch-up to the levels of those countries which had high initial performance.

Country specific attributes have both qualitative and selection effects on the predicted initial occupational placement. However, their impact on predicted occupational placement improvement over a decade seems to be indirect, in the sense that it is channeled through the initial levels of performance which are shown to be negatively correlated with the growth rate. Once initial occupational placement is introduced in the estimation explaining the predicted improvement in occupational placement, most country specific variables change signs and lose significance.

The findings in this paper may have several policy implications. The first relates to the heterogeneity on the initial occupational performance levels across different countries. It is possible that the variance arises from the fact some migrants are unable to practice their professions due to regulatory restrictions (such as absence of mutual recognition agreements between countries) and migrants struggle to obtain necessary licenses. Then, it is economically and socially efficient to remove those barriers. Another potential constraint are language barriers which means appropriate linguistic training for new migrants can have long term positive benefits. On the other hand, if the migrants perform well because they have the nominal diploma but lack the necessary human capital and skills expected, then origin countries might need to focus on their education systems, especially if those professions are global in nature. As another practical implication, information about migrants’ professional achievement abroad could benefit policy makers in origin countries by helping them allocate education resources in a more efficient 21
_Figure 2: Assets, 1998 to 2006 (1_<sup>_st_</sup> _half)_ <!-- Start of picture text --> DKK bn<br>1,400<br>1,200<br>1,000<br>800<br>600<br>400<br>200<br>0<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 (1st<br>half)<br><!-- End of picture text --> _Table 6: Return on Investments, percent, 1998 – 2004 (old accounting standards)_

||1998|1999|2000|2001|2002|2003|2004|
|---|---|---|---|---|---|---|---|
|Real Estate|9.8|11.0|10.9|17.3|11.8|7.2|12.0|
|Subsidiaries|3.3|14.5|9.7|-0.8|-2.3|1.0|5.2|
|Equity, domestic/1|-4.8|24.3|23.8|-15.9|-20.5|30.5|25.8|
|<br>Equity, foreign|15.7|71.4|-5.9|-19.9|-30.1|16.3|9.3|
|<br>Bonds, domestic/2|10.2|1.5|10.2|9.5|11.5|4.9|7.7|
|<br>Bonds,foreign|9.7|0.8|12.5|7.4|10.1|5.6|10.4|
|Total|8.1|15.4|6.5|-1.8|1.5|7.8|10.6|

Notes and Source: Danish FSA /1: Excluding private equity. /2: Excluding index-linked bonds.

_Table 7: Return on Investments, percent, 2003 – 2005 (new accounting standards)_

||2003|2004|2005|
|---|---|---|---|
|Real Estate|7.2|12|23|
|Subsidiaries|1|5.2|2.3|
|Equity, domestic|30.5|25.8|47.2|
|Private equity, domestic|9.1|5.3|26.7|
|Equity, foreign|16.3|9.3|24.2|
|Bonds|5.3|8.7|6.8|
|Mortgages|7.2|6.9|4.5|
|Other financial investment assets|-0.1|43.9|25.6|
|Total|7.8|10.6|13.9|

Source: Danish FSA 10
|Manufactured|0.119|0.007|
|---|---|---|
|Transport|0.052|0.005|
|Health|0.034|0.004|
|Trade|0.052|0.005|
|Domestic|0.220|0.009|
|Other|0.074|0.006|
|Age|35.379|0.203|
|Age-Squared|1341.918|15.492|
|Female|0.472|0.011|
|Household Size|2.115|0.027|
|Rural|0.330|0.010|
|Married|0.615|0.010|
|Children|0.241|0.009|
|No education|0.004|0.001|
|Prime education|0.008|0.002|
|General education|0.067|0.005|
|Vocational education|0.276|0.010|
|High School education|0.516|0.011|
|Higher education|0.131|0.007|
|South Europe|0.714|0.010|
|Northern Europe|0.203|0.009|
|Non-Rom South Europe|0.063|0.005|
|Eastern Europe|0.006|0.002|
|Non-European Anglophone|0.010|0.002|
|Other countries|0.004|0.001|
|Observations|2,163||

_Sources: IASCI; Soros Foundation; Author calculations._

# **V Results**

The regression results for planned return migration are presented in Table 2. Controls are included for socioeconomic characteristics, region of migration and current industry. In line with migration and return migration theory, we are mainly interested in push and pull factors as well as variables that we interpret as possible avenues for ‘brain gain’ effects. We present results as ranges as results differ slightly depending upon model specification.

Consistent with standard migration theory, we find that an increase in the earnings gap of 1 percent (the difference between actual earnings in host country and predicted earnings in Romania) reduces the likelihood of perceived returning to Romania by around 10 percent.<sup>10</sup> Interestingly, the probability of being employed in Romania is negative (though insignificant), suggesting that those who are more likely to find work in Romania are less likely to return. This

> 10 An avenue of future work would be to estimate other relative earnings measures, notably actual earnings relative to predicted earnings in the host country, or by current or previous occupational group.  The importance of relative earnings and relative position are known to be important in both mainstream economics, but also in other schools of thought notably within happiness economics and behavioural economics.

13
# Figure la: Education Projects w/Higher Education 1972-1981

<!-- Start of picture text --> Al rica<br>- 38%<br>ELAO<br>X~~~~w: 19%tI<br>EMENA%\ S. Asia<br>5%<br><!-- End of picture text --> Figure lb: Education Projects 164 w/Hlgher Educatlon 1982-1992 19% ~~ ~ ~ ~~~~~~8 <!-- Start of picture text --> LACO.Al<br>Sogure l: Edcalcltedn Projet ToalIvblen<br>Souroo: C :.eu d E.om Tablo t.Asia<br><!-- End of picture text --> <!-- Start of picture text --> Total Investment in Higher Education<br>1972-1981<br><!-- End of picture text --> <!-- Start of picture text --> EMENA<br>S1%<br>4% * % C91 LC<br>E Asia<br>54%<br><!-- End of picture text --> <!-- Start of picture text --> Total Investment in Higher Education<br>1982-1992<br><!-- End of picture text --> <!-- Start of picture text --> 9 %<br><!-- End of picture text --> <!-- Start of picture text --> MN<br>EMENA a c Africa<br>n1.geEuato<br><!-- End of picture text -->