rafmacalaba/datause-encoder-data huggingface dataset:
not increase very much, although Bahrain suffered significant turmoil. This variation in political instability over time and space helps identification.

We also examine the impact of the Arab Spring on investment by defining a dummy variable that takes the value 1 if countries experienced violence and sustained unrest as a result of the Arab Spring and 0, otherwise. More specifically, the Arab Spring dummy is defined at the country-quarter level and takes value 1 if a country experienced a civil war, revolution or sustained civil disorder during that particular quarter in the period Q4 2010 – Q4 2012. Only Bahrain, Egypt, Libya, Syria, Tunisia, and Yemen experienced such events over the period Q4 2010 – Q4 2012 and countries that experienced only protests are not considered to have been affected by the Arab Spring using this definition.

# _2.2 Measuring Greenfield FDI Flows_

Data on greenfield FDI by sector, destination, and origin are obtained from the fDi Markets database, a detailed register of cross-border greenfield investments across the world.<sup>6</sup> While we would ideally have examined total FDI flows, comparable data on the value of investments through mergers and acquisitions are not available.<sup>7</sup> Out of necessity our analysis is thus restricted to greenfield FDI flows, which however represent the majority of FDI flows to the region (Burger and Ianchovichina, 2013). The database covers both new greenfield investment projects and expansions in 17 MENA countries for the period January 2003 – December 2012.<sup>8</sup> A major advantage of these data is that they enable us to classify investment flows both by sector

> 6 The data are recorded on the basis of formal announcements by the media, financial information providers, industry organizations, and market and publication companies and represent 78.6% of global FDI (fDi Markets, 2013).

> 7 Although the Thompson ONE database includes information on mergers and acquisitions in MENA, data on the value of these investments is typically limited or missing altogether.

> 8 West Bank and Gaza and Djibouti are excluded due to data sparseness.

11
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# **Appendix 2: Final vs. Intermediate Goods**

The broad economic category (BEC) classification, as defined by the UN, consists of 19 basic categories which are assigned to the final use of the good, namely capital good, consumption good, and intermediate good (see Table below). Two categories (motor spirit, passenger motor cars, and goods not elsewhere specified) are not assigned to these categories. We would suggest to classify motor spirit as intermediate goods and passenger motor cars as consumption goods, while the assignment of goods that are not specified elsewhere cannot be done.

**<u>Table A1: Classification of Sectors by Broad Economic Category</u>**

||**Broad Economic Category**|**Final Use**|
|---|---|---|
|1|Food and beverages||
|11|Primary||
|111|Mainly for industry|Intermediate goods|
|112|Mainly for household consumption|Consumption goods|
|12|Processed||
|121|Mainly for industry|Intermediate goods|
|122|Mainly for household consumption|Consumption goods|
|2|Industrial supplies not elsewhere s||
|21|Primary|Intermediate goods|
|22|Processed|Intermediate goods|
|3|Fuels and lubricants||
|31|Primary|Intermediate goods|
|32|Processed||
|321|Motor spirit|Intermediate and consumption goods|
|322|Other|Intermediate goods|
|4|Capital goods (exc. transp. equip.)||
|41|Capital goods (exc. transp. equip.)|Capital goods|
|42|Parts and accessories|Intermediate goods|
|5|Transport equipment||
|51|Passenger motor cars|Intermediate and consumption goods|
|52|Other||
|521|Industrial|Capital goods|
|522|Non-industrial|Consumption goods|
|53|Parts and accessories|Intermediate goods|
|6|Consumer goods not elsewhere spec.||
|61|Durable|Consumption goods|
|62|Semi-durable|Consumption goods|
|63|Non-durable|Consumption goods|
|7|Goods not elsewhere specified|Intermediate,consumption,and capitalgoods|

Source: UN, 2002 34
|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|Calculating Profits<br>(Standardized)|S3|Core<br>Respondent|No|4|Standardized index of ability to calculate<br>profits from hypothetical scenarios.|
|Total Time Spent on<br>Competencies<br>(Standardized)|S3|Core<br>Respondent|No|4|Standardized index of average time taken<br>across the five competencies|
||||||=1 if core respondent ever married at the time|
|Ever Married|II|Core<br>Respondent|Yes|2,3,4|of the household survey. Variable is corrected<br>for inconsistences across rounds.|
|Age at First Marriage|II|Core<br>Respondent|Yes|4|Age of core respondent at first marriage.|
||||||=1 if core respondent ever pregnant at time of|
|Ever Pregnant|II|Core<br>Respondent|Yes|2,3,4|the household survey. Variable is corrected for<br>inconsistences across rounds.|
|Number of Live Births|II|Core<br>Respondent|Yes|2,3,4|Number of live births reported by core<br>respondent at time of the household survey.|
|Age of First Birth|II|Core<br>Respondent|Yes|4|Age of core respondent at first live birth.|
|Desired Fertility|II|Core<br>Respondent|No|4|Reported number of total children desired by<br>the core respondent (including any they<br>already have).<br>=1 if core respondent is HIV positive.|
|HIV Positive<br>Anemic|III<br>III|Core<br>Respondent<br>Core<br>Respondent|Yes<br>Yes|2,3,4<br>4|Biomarker data for HIV were collected<br>through home based voluntary counseling and<br>testing (HCT).<br>=1 if core respondent is anemic (biomarker<br>data). An individual is considered anemic if<br>her hemoglobin count is less than or equal to<br>11g/dL if pregnant and less than or equal to<br>12d/dL if non-pregnant based on WHO<br>guidelines to define mild anemia.|

63
Table 2. The Thirty Largest Non-Energy Three-Digit SITC Products Exported by Sub-Saharan Africa to OECD Markets in **1970, 1980** and<sup>**1990.**</sup>

||Share o|f Total Export|s (%)|Value of|Exports (S-illon|)|
|---|---|---|---|---|---|---|
|Product Group (SITC)|1970|1980|1990|1970|1980|1990|
|Pearls and precious stones (667)|1.9|3.0|9.6|112|529|1,739|
|<br>Copper (682)|24.8|10.8|9.4|1,496|1,904|1,703|
|<br>Cocoa (072)|10.0|12.2|8.6|606|2,144|1.572|
|Coffee (071)|13.6|16.5|7.9|819|2,903|1.433|
|<br>Ships and boats (735)|0.2|1.1|6.0|13|185|1.095|
|<br>Wood in the rough (24L.)|5.2|6.9|4.9|315|1,213|896|
|<br>Ores of non-ferrous metals (283)|2.5|3.4|4.6|149|589|836|
|<br>Fresh fish (031)|0.5|1.1|4.3|30|.93|785|
|<br>Clothing not of fur (841)|.|0.6|4.0|-|102|720|
|<br>Cotton(263)|4.1|2.7|3.2|250|466|574|
|<br>Sugar and honey (061)|1.8|3.4|2.9|106|595|535|
|Wood shaped (243)|1.0|1.3|2.9|b2|238|525|
|<br>Unmanufactured tobacco (121)|0.8|1.4|2.4|48|254|448|
|Iron ore and concentrates (281)|6.8|3.6|2.4|414|636|429|
|<br>Fresh fruit and nuts (051)|1.3|1.7|2.1|80|291|392|
|Aluminum (684)|1.2|1.6|**2"**|75|285|356|
|<br>Radioactive materials (515)|0.0|3.1|1.5|0|549|280|
|Tea and mate (074)|1.5|1.5|**1.5**|92|266|278|
|<br>Crude rubber (231)|1.4|1.1|1.2|83|203|227|
|Crude vegetable materials, nes (292)|0.9|0.9|1.2|52|163|220|
|<br>Fish in airtight containers (032)|0.2|0.6|1.1|12|102|200|
|<br>Leather (611)|0.1|0.4|1.1|8|65|191|
|<br>Pig iron (671)|0.0|0.7|0.9|3|122|163|
|Plywood and veneers (631)|0.5|0.7|0.9|31|126|163|
|<br>Misc. non-ferrous metals (689)|1.7|1.9|0.9|106|329|158|
|Special transactions (931)|0.2|4.9|**0.8**|10|868|150|
|<br>Fixed vegetable oils (421)|1.6|0.7|0.8|98|117|140|
|Hides and skins (211)|0.9|0.8|0.8|53|147|140|
|<br>Nickel (683)|-|0.3|0.7|2|44|121|
|Crude fertilizers (271)|0.7|1.2|0.7|42|215|121|
|MANUFACTURES (5 to 8 less 68)|3.8|5.0|13.4|265|2.205|5,087|
|<br>FOODS (0 +1+22 +4)|36.7|17.4|16.7|2,494|7,729|6,390|
|<br>AGRICULTURAL MATERIALS (2-22-27-28'|12.9|5.7|6.9|877|2,529|2,619|
|<br>ORES AND NONFERROUS METAL (27+28+68)|35.1|9.8|10.5|2,391|4.376|3,960|
|<br>FUELS**(3'**|11.1|60.1|51.9|759|2' 705|I,|

Source: United Nations COMTRADE records. Import statistics as reported by OECD Countries to the United Nations.
the individual, we endeavored to identify their current location, either directly from them, or from friends and family. Current location is known for the majority of the sample frame from Tonga, Micronesia and New Zealand, and for 47 percent of the full sample.

We view these response rates as incredibly high, given the logistics of tracking individuals over multiple countries based only on a name (which may have changed for some females upon marriage) and the high school they attended. This is particularly the case given the sample of interest are individuals with very high opportunity costs of time, who typically have lower survey response rates. Nevertheless, we are sensitive to the possibility of potential bias caused by incomplete tracking. In particular, we can examine how sensitive the measured migration rates are to survey non-response, using both the known characteristics (age and gender) of the individuals not surveyed, as well as through comparison of the individuals who it took more effort to locate to those located more easily. The results suggest relatively little bias from non-response, at least with regard to migration status.<sup>7</sup> In terms of looking at the consequences of migration, we believe that if there is any bias, it will be towards not being able to locate the less successful individuals. To the extent this is the case, it should mean we are obtaining upper bounds on the extent to which migrants are engaging in certain activities such as trade and investment. Section 4.7 discusses robustness to sample non-response in more detail.

# **2.4 Migration Rates**

Table 1 demonstrates that the incidence of migration is very high among this highly skilled population. In our sample, 65 percent of the best and brightest aged 22 and over have ever migrated overseas since graduating high school, and 36 percent are current migrants. Comparing these numbers also indicates a high rate of return migration. The highest rates of ever migrating are in Tonga and Micronesia, the two smallest countries. Both countries have very limited tertiary education options at home, and so migration is needed for education. The lowest current migration rate is seen in Papua New Guinea, whose citizens have rather limited options for migration. Educational scholarships which bond individuals to return are one additional factor limiting the extent to which individuals who go abroad to study can stay on and work afterwards in this case. Overall, the sample gives us a good sized sample of migrants with which to examine

> 7 See Gibson and McKenzie (2010) for details of this for New Zealand, Papua New Guinea and Tonga.

8
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# **_Study Limitations_**

Most of the studies covered in this review employ quasi-experimental designs that rely on assumptions that may fail at controlling for all sources of confounders. The process of elaboration of this review confirmed a point made by Baird et al. (2013) that very few economic papers report the exact information necessary to perform ES calculations. So assumptions had to be made. In addition, to synthesize the ES across different studies, we made a considerable simplification in averaging SMD obtained through estimation of different parameters – such as intention to treat (ITT) often reported in RCTs, average treatment on the treated (ATT) reported in DID and PSM, and the local average treatment effect (LATE) reported in RDD and IV. Our review also gathered evidence from 18 countries, four regions, – Asia, Africa, Latin America, and East Europe – various contexts, and with differences in program scale, intensity, and period, which considerably complicated study comparability and the drawing of general conclusions.<sup>15</sup> We tried to account for heterogeneity within and between studies by estimating random effects models and using moderator variables in the meta-regressions. However, the I-squared and tau-squared statistics showed a high degree of variability in the main findings.

Additional limitations of this review are worth noting. We searched for and included evidence published or made available after 2000. However, judging by other systematic reviews conducted in this field and by the publication dates of included studies, it is unlikely that more studies would be included in the review in case searches were defined with an earlier starting date.<sup>16</sup> We did not conduct a specific search in French, but we searched several databases that include studies written in other languages and we screened French language studies for inclusion in the review. We did not conduct specific searches in the RePec database, nevertheless, it is worth mentioning that we did conduct electronic searches in the Econlit database that encompasses all RePec working papers. We did not conduct moderator analysis by all types of global region, only for those regions where we had sufficient observations to undertake

> 15 Studies were done in different countries, different years, and scale as some used administrative data and other small-scale RCTs.

> 16 For instance, a paper by Grimm and Paffhausen (2015) studies a similar issue but focuses only on employment outcomes. Their search was done after 1990 and only one paper from prior to the year 2000 (Fretwell et al, 1999) was found. This paper would not qualify to enter this review as it is designed to assess active labor policy in general (not SMEs specifically) and also includes assessment of self-employment, which is not covered by this review.

28
11 It should also be noted that the centrally planned economy and Council for Mutual Economic Assistance (CPE-COMECON) countries of Eastern Europe are source countries of FDI in developing countries--although at relatively modest levels (McMillan 1987, pp.33-46). In 1983 there were at least 213 COMECON FDI projects in 75 developing countries, including Mexico, India, and Nigeria, with a disproportionate number in Africa. The total stock of these investments, however, has been estimated at only $1 billion to $2 billion (out of a worldwide total COMECON FDI stock of approximately $4 billion to $6 billion in 1983) (McMillan 1987, p.40). This compares with a total FDI stock in all developing countries from all sources of approximately $200 billion.11 In addition to the FDI from the state enterprises of the COMECON countries, there is also outward FDI by the state economic enterprises (SEEs) of developed and developina countries. Unfortunately, the readily available data on FDI flows and stocks do not separate the portion of FDI from SEEs in the developed or developing countries, but the share of FDI in developing countries controlled by home country SEEs is low.

# Sectoral Shares

A sectoral analysis of the shares of FDI must be qualified by the data's limitations. Nevertheless, some patterns and trends can be observed in the data presented in tables 2-7 and 2-8. Although these data do not extend back far into the past, it is well known that FDI in manufacturing began to increase in importance compared with the earlier emphasis on FDI in the primary sector before the 1970s (table 2-8)." Table 2-8. Sectoral Shares of FDI Stock in All Developing Countries, Selected Years (percent)

|Sector|1971|1975|1978|1982|
|---|---|---|---|---|
|Extractive|22.9|26.7|12.8|22.6|
|Manufacturing|59.0|53.2|64.5|54.1|
|Services|18.1|20.1|22.7|23.3|

Source: Dunning and Cantwell, (1987), table B2, p. 793, and table B3, p.795 for 1975 and 1982 respectively; Stopford and Dunning (1983), as reported in OECD, International Investment and Multinational EnterRrises (1987) table 3-12 on p. 197, for 1971 and 1978.
**Table 2: Number of Pensioners in the Old and New Pension Systems, 1990-2008**

|Year|Pensioners in<br>Old System|Pensioners in<br>New System|New Pensioners/<br>AFP Contributors|New Pensioners/<br>Total Pensioners|
|---|---|---|---|---|
|1990|894,359|57,119|2.2%|6.0%|
|1995|872,946|190,400|6.4%|17.9%|
|2000|878,297|343,965|10.8%|28.6%|
|2005|882,175|574,011|15.2%|39.4%|
|2008|795,305|693,929|15.2%|46.6%|

Sources: SP, INP Contributions and total assets in the AFP system have grown rapidly. Annual contributions have amounted to nearly 4 percent of GDP, reflecting the labor income that is covered by the pension system. Total assets reached 26 percent of GDP in 1990 and 65 and 53 percent in 2007 and 2008 respectively. AFPs have achieved on average high investment returns but have also suffered from relatively high operating fees.

# **2.2 The Insurance Sector**

The increase in the number of pensioners from the AFP system has led to a strong demand for both annuities and PWs and fast growth of the Chilean insurance sector in the past decade. As shown in Figure 1, total insurance premiums increased from 2.7 percent of GDP in 1990 to about 4 percent of GDP in 2001-2003. This was followed by a decrease to levels of about 3.2 percent in 2006 and a subsequent recovery to levels of 4.0 percent. The evolution of insurance premiums has been driven primarily by the life sector and fluctuations in life business have been driven in turn by the expansion of the annuity business. The tightening of the early retirement conditions in 2004 explains the reductions in premiums written by life insurance companies between 2004 and 2006. As the old age annuity market matures, the total premium is expected to recover in the future.

3
3D PRINTING AND TRADE 14 _Table 3: Effects of 3D printing on exports in hearing aids for countries at different levels of development_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Sector control|HS Ch. 90 or<br>High-tech|HS Ch. 90|High-tech|HS Ch. 90 and<br>High-tech|HS Ch. 90 or<br>High-tech|HS Ch. 90|High-tech|HS Ch. 90 and<br>High-tech|
|VARIABLES|log(exports)|log(exports)|log(exports)|log(exports)|log(exports)|log(exports)|log(exports)|log(exports)|
|(Hearing Aid 2007-)|0.584***|0.583***|0.560***|0.501***|||||
||(0.190)|(0.193)|(0.190)|(0.193)|||||
|(Hearing Aid 2007-)*(Dummy Developing)|-0.007|-0.061|0.002|-0.085|||||
||(0.310)|(0.309)|(0.311)|(0.311)|||||
|(Hearing Aid 2007-)*(Dummy Low income)|||||-0.744|-0.870*|-0.780|-1.111**|
||||||(0.481)|(0.477)|(0.478)|(0.462)|
|(Hearing Aid 2007-)*(Dummy Lower middle income)|||||0.503|0.501|0.488|0.435|
||||||(0.453)|(0.438)|(0.464)|(0.457)|
|(Hearing Aid 2007-)*(Dummy Upper middle income)|||||0.929***|0.843***|0.919***|0.728***|
||||||(0.290)|(0.294)|(0.285)|(0.279)|
|(Hearing Aid 2007-)*(Dummy High income)|||||0.584***|0.583***|0.560***|0.501***|
||||||(0.190)|(0.193)|(0.190)|(0.193)|
|Observations|476,248|234,932|360,007|118,678|476,248|234,932|360,007|118,678|
|R-squared|0.847|0.859|0.844|0.862|0.847|0.859|0.844|0.862|
|Sum of coefficients|.577**|.522**|.562**|.416*|1.272*|1.057|1.188|.553|
|Period|1995-2016|1995-2016|1995-2016|1995-2016|1995-2016|1995-2016|1995-2016|1995-2016|
|Country-Year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Country-Product FE|YES|YES|YES|YES|YES|YES|YES|YES|
|% Zeroes|.44|.4|.44|.37|.44|.4|.44|.37|

Note: Robust standard errors, clustered at the country-product level, are in parentheses. Countries with population over 5mln. *** p<0.01, ** p<0.05, * p<0.1
**PRE** Wowkna-Pane Serie

||33b<br>|~~~~~~A|Zho|Contact<br>for2a2er|
|---|---|---|---|---|
|WPS595|Hungary: Financia! Sector Reform<br>in a Socialist Economy|Mario I. Blejer<br>Silvia B. Sagari|February 1991|Z. Sequis<br>37665|
|WPS596|The Mexican Sugar Indus?ry:<br>Problems and Prospects|Brent Borrell|February 1991|P. Kokila<br>33716|
|WFS597|Rent Sharing in the Multi-Fibre<br>Arrangement: Theory and Evidence<br>from U.S. Apparel Imports from<br>Hong Kong|Refik Erzan<br>Kala Krishna|February 1991|G. Ilogon<br>33732|
|WPS598|Africa Region Population Projections<br>1990-91 Edition|: Patience W.<sup>Stephens</sup><br>Eduard Bos<br>My T. Vu<br>Rodolfo A. Bulatao|February 1991|0. Nadora<br>31091|
|WPSS99|Asia Region Population Projections:<br>1990-91 Edition|Eduard<sup>Bos</sup><br>Patience W. Stephens<br>My T. Vu<br>Rodolfo A. Bulatao|February 1991|0. Nadora<br>31091|
|WPS600|Latin America and the Caribbean<br>Region Population Projections:<br>1990-91 Edition|My T. Vu<br>Eduard Bos<br>Patience W. Stephens<br>Rodofo A. Bulatao|February 1991|0. Nadora<br>31091|
|WPS601|Europe, Middle East, and North<br>Africa Region Population Projections<br>1990-91 Edition|Educard Bos<br>: Patience W. Stephens<br>My T. Vu<br>Rodolfo A. Bulatao|February 1991|0. Nadora<br>31091|