rafmacalaba/datause-encoder-data huggingface dataset:
labor/immigration literature has noted that migrants often have a greater likelihood of being self-employed at destination than natives, and has sought to explain why this is the case (e.g. Portes and Yiu, 2013), and to estimate their contribution to the economy of the receiving region (e.g. Fairlie and Lofstrom, 2015). The development literature has, in contrast, focused on the role of remittances in funding self-employment activities for remaining family members (e.g. Woodruff and Zenteno, 2007), and on the tendency of return migrants to engage in self-employment activities (e.g. McCormick and Wahba, 2001; Mesnard, 2004). In both cases, the direction of interest has been from migration to self-employment outcomes, complementing our focus on the link from self-employment to migration decisions. The second is literature examining the impacts of social assistance programs like cash transfers, pensions, and job guarantees on migration. Adhikari and Gentilini (2018) provide a recent review of these studies, noting that unrestricted cash transfers to poor people have tended to increase migration, by overcoming credit constraints and providing some insurance against risk. In contrast, they note that place-based programs such as India’s rural public employment guarantee studied by Imbert and Papp (2019) have reduced (seasonal) rural to urban migration. We add to this literature by showing the causal impacts of a range of self-employment and entrepreneurship programs on migration.

The remainder of this is structured as follows: section 2 discusses what paper migration theory predicts for the relationship between self-employment and migration; section 3 uses different panel data surveys to answer the descriptive question of whether the self-employed are more or less likely to migrate than wage workers and the not employed; section 4 then analyzes different randomized experiments to causally examine whether interventions that increase self-employment in turn reduce migration; and section 5 concludes.

# **2 What does economic theory predict for the relationship between self-employment and migration?**

A economic model of has individuals or households the general migration compare difference between the discounted present value over _T_ periods of the utility gained from the income _W_<sup>_M_</sup> and amenities _A_<sup>_M_</sup> experienced with migration and the utility received from the income _W_<sup>_H_</sup> and amenities _A_<sup>_H_</sup> at home, to the cost of migrating _C_ (e.g. Gibson et al., 2019). Generalizing further to allow for the possibility of liquidity constraints which induce an additional cost term given by the Lagrange multiplier _λ_ , and for the possibility of time-inconsistency, represented by _β − δ_ preferences, we have 4
when is feasible, banks may have incentives to herd, thus causing a lack of diversity. This lack of diversity is potentially more costly for society as it implies that similar institutions will more likely face problems at the same time. On the other hand, recent work has pointed to risks from diversification, if all financial institutions diversify into the same portfolio (Wagner (2010)).

We gauge the relationship between lending specialization and bank performance and stability with two novel data sets. First, we rely on stock return-based indicators of sectoral factor exposures. Using an extended market model, we test whether banks are well diversified and are only exposed to the returns on a broad market index, or whether they additionally exhibit significant exposures to certain sector-specific portfolios. This method is similar in spirit to returns-based style analysis, which is a statistical technique mainly used to deconstruct mutual fund returns in exposures to investment strategies or asset classes (e.g. with respect to large versus small stocks or value versus growth stocks, (see e.g. Sharpe (1992), Brown and Goetzmann (1997) and ter Horst et al. (2004)). Second, we also construct a hand-collected database on the sectoral exposures of the largest banks based on the information they report in the notes to their financial statements. We limit this analysis to listed banks with total assets in excess of US$ 10 billion as these are more likely to publish a detailed report on their website, and find useful information for 317 banks over the period 2007-2011. Both the return-based and accounting-based sectoral exposures are then used to compute several lending specialization indicators, such as focus of the portfolio (standard deviation or HHI of the exposures) or herding with other banks (Euclidean distance measure).

Our main are that sectoral specialization ia positively related to bank risk, while not being associated with higher returns. Moreover, it is associated with higher systemic risk exposures as well as increases in total volatility. Hence, it seems that the portfolio diversification gains outweigh the potential benefits of screening and monitoring efficiency in specialized lending. Furthermore, we find that dissimilarity with respect to other banks is also associated with higher risk along all dimensions, in contrast with the theoretical predictions. The results are similar using either the return-based or hand-collected sectoral 2
Table 1: Summary Statistics: Annual Percent Growth, 2005-2020.

|||||A. Overall|.|||
|---|---|---|---|---|---|---|---|
|Variable|Countries|Years|Mean|Std. Dev.|Minimum|Maximum|Lat. _×_ Lon.|
|%∆GDP|178|05-20|3.06|5.02|-36.39|59.53||
|%∆NO2|178|05-20|0.37|8.71|-32.60|101.17|0.1° _×_ 0.1°|
|%∆Lights|178|05-20|9.44|42.22|-53.24|545.27|_<_ 0.01° _×_ 0.01°|

B. Means, By-Data Quality Grade.

|Grade|Countries|%∆GDP|%∆NO2|%∆Lights|
|---|---|---|---|---|
|A|16|1.10|-2.14|2.70|
|B|13|1.63|-1.28|3.33|
|C|109|3.12|0.67|8.27|
|D|27|4.50|1.15|16.81|
|E|10|4.33|1.75|24.79|
|All|175*|3.06|0.37|9.44|

_Notes._ Henceforth, “NO2” or “Lights” means per area. “GDP” is real GDP at constant 2017 prices. *3 countries out of the full sample of 178 appearing in Panel A are ungraded, and therefore do not appear in Panel B: Cura ̧cao, Palestinian Territory, and Sint Maarten. Country lists by-grade are reported later in Table 7.

fine-grained analysis is of interest.<sup>7</sup> The lesser average growth rate of NO2 is at least in part due to heterogeneity in decarbonization efforts across countries, as we now discuss.

**Data Quality Grades.** Table 1 Panel B breaks down the mean growth rates in Panel A by-country data quality grade. These grades are provided in the Penn World Tables on the basis of countries’ capacity for compiling accurate statistics. For consistency, we use Chen and Nordhaus (2011)’s expanded version with grade “E” meaning those countries having basically no statistical organizations.<sup>8</sup> In general, higher grades are associated with more developed economies. It is no mistake that grade A and B countries have trend NO2 declining over time, which is primarily due to decarbonization efforts, akin to the theory of an environmental Kuznets curve. This is unlike lights, which are associated with upwards trends over time across all grades, and with conditional convergence. So, when considering economic growth i.e. trend changes in output, night lights are most useful. Critically, this conclusion flips when we consider

> 7The resolution of lights is 30 arc-seconds for the DMSP-OLS database and less for VIIRS. An arc-second is one sixtieth of an arc-minute, which is one-sixtieth of a degree of latitude or longitude. Thus, OMI’s resolution of 0.1° _×_ 0.1 is about 360 arc-seconds, or 12 times as coarse as night lights. An alternative European Space Agency NO2 instrument, TROPOMI, with data beginning in 2017, is about 3 times as coarse as night lights. The upcoming TEMPO instrument for NO2 has resolution near lights. See Online Appendix A.1 for more information.

> 8An alternative ranking from the World Bank favored by Henderson et al. (2012) is more careful to rank countries regarding GDP accuracy specifically, but is only available for 114 countries in the sample and importantly contains no Penn World Tables “A” graded countries. These two rankings are positively correlated, but there are exceptions. Later when we report substantive results regarding data quality grades in Section 6, we also show they are robust to this alternative.

7
This contradiction is significantly compounded by aid that seeks to reduce inequality. For any aid intervention to succeed in a federation, downward ties of accountability must be replaced with upward accountability to donors, who specify goals, finance specific actions, and measure results; this is all the more so for anti-inequality aid programs. Can aid-funded programs to decrease inequality succeed in a federation? Will upward accountability succeed? Or will the internal logic of federalism be distorted by external demands and extraneous norms insufficient to constrain officials’ behavior, undermining the institutional integrity of government and reducing accountability overall?

# _The Ethiopian Institutional Context_

We argued above that Ethiopia is a particularly interesting empirical context in which to explore such issues. Its modern political and institutional history underline this further. Under the Ethiopian Empire (1941-1974), government was highly centralized, with all important decisions originating in Addis Ababa, little citizen participation, and a highly unbalanced pattern of regional development. Provincial governments were run by centrally appointed governors whose central priorities were collecting tax and maintaining law and order, and who themselves held little power. Public service provision was minimal, and the notion of downward accountability to citizens did not exist. The empire was succeeded by the Dergue, a military regime (1975-1991) which retained subnational governments as appendages of the center (Word Bank 2013a). Ethiopia’s modern history of autocracy and repression reached its nadir under the Dergue during the Red Terror of 1977-78, when civil repression descended into full-scale war, involving the manipulation of food aid and enforced relocation or villagization, wielded against a starving but recalcitrant population, in the name of counter-insurgency, (Africa Watch 1991, Vaughan and Tronvoll 2003).

The overthrow of the Dergue in 1991 by the Ethiopian People’s Revolutionary Democratic Front (EPRDF) saw the reversal of over a hundred years of ethnic homogenization. More than the collapse of a particular regime, this marked the end of a project, dating back to 1889, of creating a ‘modern’, centralized Ethiopian state around a highland ethnic core (Clapham 1994). The EPRDF began as a federation of regional, ethnically based guerrilla movements that came together to depose the Dergue. Some were stronger than others, but none was predominant, so rebel leaders quickly opted for a federal structure to their new government, 8
# II. DATA

The migration data come from Artuc, Docquier, Özden, and Parsons (2015) data set, the last extension of the Docquier and Marfouk (2006) dataset, which includes bilateral data on migration by country of birth,<sup>5</sup> skill category (skilled versus unskilled, the former having college education) and gender for 195 sending/receiving countries in 1990 and 2000. The main additional novelty is that the dataset now captures South-South migration based mainly on observations (taken from Özden et al. 2011) and occasionally on estimated data points (for the skill structure).<sup>6</sup> Our financial data come from two commonly used sources. Data on international bank loans are from the Consolidated Banking Statistics published by the Bank of International Settlements (BIS). These data capture the worldwide consolidated positions of internationally active banking groups headquartered in the reporting countries. The data include the claims of reporting banks’ foreign affiliates but exclude intragroup positions. Nationality of the borrower is determined by the immediate borrower with whom the bank contracts to lend (see Bank of International Settlements [2015] for more details). Our main specification refers to the year 2000, for which we have 17 lending countries, 175 borrowing countries, and a total of 1,628 country pairs (observations) with positive loan values, given missing data. In addition, we take data on investment in short-term bonds and long-term bonds from the International Monetary Fund (IMF) in its Coordinated Portfolio Investment Survey (CPIS).

Further details on the sample and summary statistics are reported in the supplemental appendix (tables S.1-S.4).

Our migration data include two years, 1990 and 2000. Ideally, we would like to estimate a panel equation, but since we only have two time periods, this is not feasible. Alternatively, we could run a difference equation. However, this would introduce a serious endogeneity problem. Specifically, the sample period is characterized by the formation of many new countries, mainly in Eastern Europe. These countries experienced strong outflows of migrants, and strong inflows of foreign investments, to build the new economies. This phenomenon introduces endogeneity to the sample, which is difficult to control for. In addition, both our main

> 5. For some countries for which information on immigrants’ country of birth is missing, immigrants are defined by their citizenship.

> 6. See http://perso.uclouvain.be/frederic.docquier/filePDF/DMOP-ERF.pdf for further details.

8
those focusing on its relationship with FDI, are considerably less abundant. Moreover, those that consider developing nations show conflicting results (Abadie & Gardeazabal, 2008; Enders et al., 2006; Li, 2006; Oetzel & Oh, 2014; Powers & Choi, 2012). Again, several of these authors ignore other forms of political violence and risk in their empirical strategies, which could largely explain the mixed results.

In Table 3, we include measures for _terrorism_ , _political terror_ and _assassinations_ in our regression models to test our assumption that political violence must be both relatively continuous and detrimental to returns on economic activity. We measure terrorism using the number of deaths during terrorist attacks from the Global Terrorism Database (GTD). This database, developed by the National Consortium for the Study of Terrorism and Responses to Terrorism, is based on reports from a variety of open media sources (LaFree & Dugan, 2007). We prefer this proxy to the number of terrorist attacks because it measures not only the prevalence of attacks, but also their intensity. The number of terrorist fatalities is highly skewed; hence, we transform the measure using the natural log of the inverse hyperbolic sine function. We measure political terror with the widely used Political Terror Scale (Wood & Gibney, 2010), an index constructed based on information from three sources: Amnesty International Yearly Country Reports, the U.S. State Department Country Reports on Human Rights Practices, and the World Reports of Human Rights Watch. The index ranges from 0 in the case of a strong rule of law to 5 in the case of widespread political terror. We measure the numbers of assassinations using the Cross-National Time Series data developed by Banks (2015).

The results confirm that terrorism, political terror, and assassinations in a country do not affect its total greenfield FDI inflows (Figure 2, columns 1-3). The coefficient of the conflict variable BRD is negative and significant at the 10% level in two of the three regression models (columns 1-2). Although terrorism and assassinations do not significantly affect greenfield FDI in the resource sector, political terror has a positive effect on investment in this industry (columns 4-6), indicating that MNEs in the resource sector benefit from a certain level of oppression. This benefit could be explained by the fact that a high level of oppression might be necessary to keep certain governments in place. MNEs active in the resource sector might benefit in such an environment because they depend upon government contracts that might be reneged upon by a new government; hence, these MNEs have the most to lose if an incumbent ruler is removed from office. The effect of conflict on resource greenfield FDI remains 27
# **_5.2 Institutional Features_**

Recent theoretical contributions have emphasized the important effects that institutional features of the school system can play in determining educational performance, because they determine the incentives that the involved actors have to further student learning (cf. Bishop and Wößmann 2004 and the references therein). Furthermore, evidence based on the crosscountry variation in student performance shows that institutional effects matter empirically (e.g., Wößmann 2002, 2003; Fuchs and Wößmann 2004a, 2004b). However, most of the institutional features generally do not vary substantially within school systems, but only between countries. Empirically, institutional effects should therefore mainly be an issue in cross-country rather than within-country research. That is, altered institutional set-ups of their school systems could potentially shift countries onto production functions that are substantially different in terms of overall efficiency.

As is apparent from Table 2, the PIRLS background data reveal that a few institutional features do also vary within some of the considered school systems. Unfortunately, PIRLS does not provide data on such institutional features as the private or public operation or financing of individual schools, nor on decentralization and school autonomy on budgets, personnel and process decisions.<sup>15</sup> But there is data on testing policies and curriculum determination, as well as on ability-based class formation and on how long primary students stay with the same teacher. While these institutional features seem much more important on a cross-country perspective (cf. Fuchs and Wößmann 2004b for the importance of institutional effects in cross-country PIRLS evidence), it seem worth including them also in the withincountry analyses presented here.

Teachers’ method to monitor students’ progress, in terms of use of classroom tests or centralized examinations, are not statistically significant related to reading performance in Argentina and Colombia, as well as within most other considered countries.<sup>16</sup> In Argentina, as well as in Turkey, students in schools whose curriculum is strongly influenced by the national or regional curriculum perform statistically significantly better than otherwise. This might suggest that external standards are conducive to student performance in these countries. At 36 AP, the effect size is substantial in Argentina.

> 15 Galiani and Schargrodsky (2002) present important results on the effects of decentralization on student performance in Argentina.

> 16 The positive relationship between use of national or regional examinations and student performance in Argentina is statistically significant only at the 20 percent level.

20
**Table 5: Relative Mobility and Absolute Mobility (Full Sample) Risk Adjusted vs. Standard Estimates**

|||**CHINA**|Li||**INDIA**|Li|**IN**|**DONESI**|**A**<br>Li|
|---|---|---|---|---|---|---|---|---|---|
||RIGRC|RES|near<br>ES|RIGRC|RES|near<br>ES|RIGRC|RES|near<br>ES|
|No Sch|0.525|4.359|5.943|1.013|1.872|4.244|0.637|5.134|6.437|
||(0.019)|(0.195)|(0.152)|(0.011)|(0.069)|(0.043)|(0.014)|(0.140)|(0.113)|
|Primary|0.457|7.272|8.239|0.752|6.109|7.350|0.562|8.691|9.418|
||(0.019)|(0.126)|(0.111)|(0.006)|(0.042)|(0.035)|(0.012)|(0.082)|(0.075)|
|Junior High|0.440|8.615|9.386|0.686|9.674|10.455|0.546|10.351|10.908|
||(0.015)|(0.122)|(0.111)|(0.005)|(0.047)|(0.043)|(0.012)|(0.077)|(0.072)|
|Senior High|0.428|9.915|10.534|0.673|11.031|11.698|0.535|11.971|12.399|
||(0.015)|(0.134)|(0.125)|(0.005)|(0.053)|(0.049)|(0.011)|(0.087)|(0.083)|
|College|0.417|11.604|12.064|0.660|13.028|13.561|0.525|14.089|14.386|
||(0.015)|(0.168)|(0.160)|(0.005)|(0.063)|(0.060)|(0.011)|(0.114)|(0.110)|
|**IGRC**|0.383|||0.621|||0.497|||
||(0.014)|||(0.005)|||(0.011)|||

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia. (2) The RIGRC refers to the Risk Adjusted IGRC. The RES refers to the Risk Adjusted Expected Schooling. The Linear ES refers to the Expected Schooling from a linear CEF mobility model. And the IGRC is the Intergenerational Regression Coefficient from a linear CEF mobility model.

29
To motivate the analysis, we first provide some background information on ASEAN economic integration. We then review the different rules of origin that have been adopted in FTAs negotiated by ASEAN countries. The final part of this section describes the simulation setup and summarizes the studies’ main findings.

# **_ASEAN trade and investment policy_**

ASEAN is one of the most successful regional groupings among developing countries to date. Its combined GDP totaled US$ 2.53 trillion, roughly 4.5 of the World GDP, while its exports totaled US$ 500 billion, approximately 6 percent of total world exports.<sup>7</sup> ASEAN has made a remarkable achievement in liberalizing trade in goods within the region under the ASEAN Free Trade Agreement (AFTA). In January 2003, tariffs between member countries were reduced to 0-5% for all products, except those on the general exception and sensitive product lists of each member country. By contrast, the region’s record on service sector and investment liberalization is much less impressive. Countries’ commitments under the ASEAN Free Trade Agreement in Services (AFAS) improve only marginally on those made in the GATS.<sup>8</sup> The ASEAN Investment Area (AIA), in principle, became effective in January 2003, but its liberalization content is substantially undermined by extensive lists of exemptions.<sup>9</sup> Starting in 2001, several of the more ambitious ASEAN members—particularly Singapore— have entered into a series of bilateral free trade agreements with countries outside the ASEAN region, including the United States and Japan (see Table 1). Thailand also signed FTAs with a number of developing countries and is negotiating an FTA with the United States and Japan as well. The trade agreements with the most far-reaching implications for ASEAN service sector liberalization have arguably been those with the United States. For example, the Bilateral Trade Agreements that Laos and Vietnam signed with the US require market access to telecommunications, financial, healthcare, tourism and distribution services. Similarly, the Singapore-US FTA requires across-the-board service market liberalization with only few reservations listed.

# **_What rules of origin have been adopted in existing services agreements?_**

As in trade in goods, the rule of origin for services trade in each bilateral or regional free trade agreement may differ, depending on the particular interests of the signatories or their relative bargaining power if interests are divergent. At the same time, in crafting rules of origin in an FTA, members of the WTO are bound by the disciplines of GATS Article V—as discussed above.

The narrowest rule of origin with respect to investment is that which defines origin of a service supplier by the **_nationality_** of the person(s) who own or control the service supplier. The Thailand-Australia Free Trade Agreement stipulates that either Party may deny the benefits of

> 7 CIA World Fact Book 2005.

> 8 See Stephenson and Nikomborirak (2002).

> 9 For Lao PDR, Vietnam, and Myanmar, the AIA will only become effective in 2010.  See Nikomborirak (2004).

10