rafmacalaba/datause-encoder-data huggingface dataset:
used. In Japan, on the other hand, the number of migrants comes out higher under the foreign citizenship criteria.

Where data are available for both definitions, priority is given to data by country of birth, for several reasons. First, country of birth is more appropriate in analyzing physical movements and handling the cases of former colonies and dependencies.<sup>4</sup> Second, while nationality can change, place of birth cannot.<sup>5</sup> Third, naturalization rates vary enormously across destination countries. Differences in laws on citizenship criteria (for both migrants and their children born in the destination country) do not affect data based on place of birth. Fourth, when migrants cannot be assigned to a specific origin, they are often recorded under an aggregated umbrella heading. These categories embody ambiguity about a migrant‘s origin, and since migrants are assigned to aggregated headings more frequently when the citizenship definition is used, the foreign born concept is again favored. Last, for migrants living in disputed territories, such as Kashmir and Western Sahara, an individual‘s status or official citizenship may be unclear, while country of birth is usually more certain.

# _How Are Migrants Recorded?_

Destination countries employ a wide range of tools to enumerate migrants, including population censuses, population registers and registers of foreigners, border statistics, and worker and residence permits.<sup>6</sup> This article focuses on census and population register records, which are widely available, have the broadest geographic coverage, and include similar questions, thereby yielding more standardized responses. For these reasons, they are the primary sources for most data in the Global Migration 4 This discussion of definitions highlights the somewhat paradoxical possibility of individuals being classified as migrants without ever having moved across an international border. As mentioned, this is generally possible only in the case of people born in one country but who are citizens only of another country. A similar situation arises with dependencies and former colonies. Residents of Martinique, a French dependency, are automatically granted French citizenship. The statistics for Martinique show all the domestic population as French, possibly leading one to think that Martinique is part of metropolitan France or that most of the population moved to France. In such cases, having data categorized by both foreign born and foreign nationality would enable differentiating between the number of locally born inhabitants of Martinique who are French (referred to as Martiniquais), those born in metropolitan France who moved to Martinique, and people from other countries.

5 Of course the country of birth may be redefined, as elaborated in the next section.

6 This article deals exclusively with migrant stocks. Nothing can be gleaned therefore about when a migration took place, save for inferences that can be made by comparing differences in stocks over time. Nor is anything known about the circumstances (such as visa type) under which an individual entered a particular destination country.

6
**Table 1.C. Literature review of forecasting methods crude oil and metal prices volatility (continued)**

|**Authors and**<br>**year**|**Metals**|**Models**|**Main findings**|**Real,**<br>**nominal or**<br>**both prices**|**In-sample, out-**<br>**of-sample or**<br>**both types of**<br>**forecasts**|
|---|---|---|---|---|---|
|Vo (2009)|WTI|Markov switching<br>stochastic volatility<br>(MSSV) model;<br>stochastic volatility<br>(SV) model, GARCH<br>model and Markov<br>switching (MS)<br>model|Out-of-sample<br>forecasts suggest<br>that the MSSV<br>outperforms other<br>models.|Nominal<br>prices|Both types of<br>forecasts|
|Wang, Wu<br>and Yang<br>(2016)|WTI and<br>Brent|Markov switching<br>multifractal (MSM)<br>volatility model,<br>GARCH models,<br>historical volatility<br>(HV) model|MSM models have<br>greater forecasting<br>abilities than the<br>GARCH or HV<br>models.|Nominal<br>prices|Both types of<br>forecasts|
|Wen, Gong<br>and Cai<br>(2016)|WTI|Heterogeneous<br>autoregressive models|Different models<br>exhibit different<br>predictive power in<br>forecasting the 1-<br>day, 1-week and 1-<br>month volatility of<br>crude oil futures.|Nominal<br>prices|Out-of-sample<br>forecasts|

_Note_ : APARCH (Asymmetric Power ARCH); EGARCH (Exponential GARCH); FIGARCH (Fractionally Integrated Generalized Autoregressive Conditionally Heteroskedasticity); GARCH (Generalized Autoregressive Conditional Heteroskedasticity); Gene expression programming (GEP); HAR (Heterogeneous Autoregressivetype volatility models); TGARCH (Threshold Generalized Autoregressive Conditional Heteroskedasticity).

**Table 2. Sign restrictions on impulse responses**

||Supply shocks|Demand shocks|
|---|---|---|
|Oil or metal production|−|+|
|Global economic activity|−|+|
|Realprice of oil or metals|+|+|

32
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|(4)<br>(5)<br>(6)<br>(7)|**_Panel A: Second Stage_**|0.540***<br>1.142**<br>1.084**<br>0.801**<br>(0.160)<br>(0.530)<br>(0.500)<br>(0.370)<br>-5.695<br>-0.535<br>-6.062<br>(4.024)<br>(5.049)<br>(4.088)<br>35.386***<br>33.506***<br>(5.162)<br>(5.964)<br>**_Panel B: First Stage_**<br>0.843***<br>0.862***<br>0.825***<br>(0.255)<br>(0.253)<br>(0.261)<br>OLS<br>2SLS<br>2SLS<br>2SLS<br>529<br>547<br>529<br>529<br>96<br>96<br>96<br>96<br>0.9<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>nificant at 10%; ** significant at 5%; *** significant at 1%;<br>y banks and other financial institutions as a share of GDP;_EFNX_is<br>and imports as a share of GDP;_Log(Income)_is log of PPP-adjusted<br>ables regressions,_Predicted EFNX_is the predicted external finance<br>sions, and then generating predicted export shares for each country<br>... 1995-99. Variable definitions and sources described in detail in|
|---|---|---|
|(2)<br>(3)||0.739***<br>0.730***<br>(0.238)<br>(0.236)<br>0.443<br>(4.959)<br>OLS<br>OLS<br>547<br>529<br>96<br>96<br>0.87<br>0.88<br>yes<br>yes<br>yes<br>yes<br>vel are in parentheses; * sig<br>redit to the private sector b<br>_de/GDP)_is log of exports<br>es; In the Instrumental Vari<br>g sector-level gravity regres<br>verages 1970-74; 1975-97,|
|(1)|Dep. Var.: Private Credit/GDP|EFNX<br>Log(Trade/GDP)<br>2.459<br>(5.941)<br>Log(Income)<br>Dep. Var.: EFNX<br>Predicted EFNX<br>Estimation<br>OLS<br>Observations<br>529<br>Countries<br>96<br>R-Squared<br>0.87<br>Country FE<br>yes<br>Time FE<br>yes<br>Notes: Standard errors clustered at country le<br>Dependent variable,_Private Credit/GDP_is c<br>the external finance need of exports;_Log(Tra_<br>real per capita income from Penn World Tabl<br>need of exports constructed by first estimatin<br>and sector. All of the variables are five-year a<br>the text.|

31
20 widely for products and countries.2 This suggest that the EC was concerned mosdy with proecting domesdc markets and ignored dte interests of CEE-5 producers. Nonethdeless, CEE-5 coumtries have obtained significant concessions. As can be seen fom Table 5, a subsmandal proportion of imports of products bad duty-free access to EC markets. Its share in exports for this sample was between 34 and 47 percent. Furthermore, the margins of preference provided in the ITAs for some exporters are quite substantial. For instance, MFN tariffs on polyethylene and cars are relatively high (10 and 12.5 percent, respectively): since CEE-3 suppliers of these products had free access to EC makets for a very significant proportion of their exports (between 33 and 100 prcent), this has undoubtedly given them a subsantial compettive edge over MN suppliers.

Finally, in terms of 1992 exports to the EC, tff and ceilings/quotas seem to be on average less 'restraining" on cars than on other products. This conclusion can be drawn from comparing shares of passenger car quotas in total quotas with the shares of these exports in eos of tis group. Tariff ceilings and quotas applied against exports of this group focus on passenger motor vehicles, accountig for 47 percent of the FCSK's value of quotas for this group, for 79 percent of Hungarian quotas, and for _55_ percent of Polish quotas. Their shares in exports of this group are 16 percent for the FCSK, 3 percent for Hungary and 14 percent for Poland, suggesting that quotasfceilings free trade component of their exports is considerably larger in products which are not listed in Table 6.

# C. The MPA Grou,

The ITAs contain a special protocol addressing market access for texiles and clothing (CN 50 thugh 63 exchluing 520100, 5301, 530?') subject to the MFA restricting "... the volumes of most imported textiles and clotung products into North America and Westn Europe from developing countries"

**25** The "duty -free component is equal to the sum of quotas (if the value of imports exceed the tariff quota) or the value of exports (if it is lower than quota). Excluding cars, the duty free shar raises for the FCSK from 40 to 48 pern, and faIls for Hungy from 34 to 22 pect and for Polamd fm 47 to 40 paen.

**2'** This is the equivalt of SITC. Rev.3. (26-2632)+(65-65911)+(84848144812-8813).
Table 7: Effects of mining for a sub-sample of women who have never moved.

|VARIABLES|(1)<br>Working|(2)<br>Service|(3)<br>Sales|(4)<br>Agriculture|
|---|---|---|---|---|
|active (20 km)|0.018|0.026***|0.019|-0.029*|
||(0.014)|(0.008)|(0.012)|(0.017)|
|inactive (20 km)|0.116***|0.007|-0.000|0.071***|
||(0.026)|(0.007)|(0.018)|(0.026)|
|Observations|194,103|194,103|194,103|194,103|
|R-squared|0.218|0.091|0.143|0.341|
|F test: suspended-active=0|11.22|3.164|0.797|10.44|
|p value|0.000811|0.0753|0.372|0.00124|

Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and region fixed effects, regional time trends, living in urban area, age, years of education, and religious beliefs. Please see Table 3 for more information about coefficients of interest.*** p<0.01, ** p<0.05, * p<0.1

# **5 Heterogeneous impacts**

## **5.1 Heterogenous effects by world prices**

Labor market effects are likely to be stronger in years with high mineral prices, due to higher production and higher wages. We therefore interact our variables of interest, active and inactive, with yearly world prices. The world price data comes from RMG and is available for ten of the most important minerals (gold, silver, platinum, aluminum, copper, lead, nickel, tin, zinc and palladium) for the years 1992 to 2011.<sup>12</sup> Prices are normalized with 1992 as baseline price, set to zero. The variables active and inactive are interacted with the yearly normalized prices, and test statistics for the difference between these interaction terms are presented. This allows comparisons of the difference in the price effects for those having an active mine nearby and those having an inactive mine nearby.

Table 8, Panel A presents the results for women’s employment. The baseline coefficients for active (20 km) and inactive (20 km) are now interpreted as the relation between closeness to a mine and employment when the normalized prices are zero, i.e. when the prices are at their 1992 level. We see that while the difference between active and inactive points in the same direction as in the baseline regressions, the effects are generally not as large. The price interaction terms indicate that higher prices lead to stronger labor market effects. In particular, higher prices lead to larger negative effects of mine openings on working, driven by less self-employment in agriculture but partly offset by a larger increase in service work. Higher prices also intensify the effects for men (Table 8, Panel B). High prices lead to a larger decrease in self-employment in

> 12 This restricts the sample to 266,020 women and 138,483 husbands. The sample is nonetheless generalizable to the wider sample as indicated by similar descriptive statistics and baseline results (results are available upon request).

19
employment could generate strategic behavior by some individuals and create distortions in the labor market. As is common in empirical work, especially work on developing countries, the ideal dataset needed to conduct this analysis does not exist. Thus, to strengthen our findings, we use two different datasets that, though not perfect, provide consistent results for the question we address.

Using the onset of the SISBEN interviews and household survey data, we estimate that informal employment was approximately 4 percentage points higher than it would have been in the absence of the reform, or roughly 8% of the labor force not entering the formal sector.

Using an alternative dataset that we constructed using matched individuals in the first and second SISBEN and individual fixed effect regressions, we estimate the effect of the SR on informality to be approximately 3 to 4 percentage points when considering people 10 points around the eligibility threshold. These results are similar in magnitude to those obtained with the survey data. The sign of the coefficient suggests that some marginal individuals were optimizing when deciding whether to participate in the formal sector. Our empirical results are consistent with an informality model developed by Galiani and Weinschelbaum (2012).

It is important to consider that our estimates could combine both the effect of the implementation of the SR and those of a change in the enforcement of laws that prohibit informal employment (see Almeida and Carneiro 2012 and Ronconi 2010 for papers that discuss enforcement for labor and social security regulations in Latin America).<sup>22</sup> If enforcement changed with the passing of the law and not with the implementation of the SISBEN, then the variation across municipalities and the year fixed effects, as well as the discontinuous change in the eligibility threshold, should allow us to disentangle the effects of the SR from those of enforcement. If enforcement changed with the implementation of the SISBEN and at the eligibility threshold, then our estimates combine both effects. Looking at newspaper article counts of labor inspections

> 22We thank a referee for bringing the issue of enforcement to our attention.

20
# **1.  Introduction**

The number of bilateral and regional free trade agreements (FTAs) involving members of the Association of South East Asian Nations (ASEAN) is increasing at a mind-boggling pace. Table 1 offers an overview of agreements in which at least one ASEAN country participates. All of these agreements seek the liberalization of trade in services, at a faster pace than what is foreseen under the WTO’s General Agreement on Trade in Services (GATS).

An important question in the design of FTAs covering services is to what extent non-members benefit from the trade preferences that are negotiated among members. This question is resolved through so-called _rules of origin_ (sometimes also referred to as _denial of benefit_ ). The restrictiveness of rules of origin determines the degree of preferences entailed in market opening commitments, shaping the bargaining incentives of FTAs and their eventual economic effects.

Notwithstanding the conclusion of many new FTAs, hardly any research is available that can guide policymakers on the economic implications of different rules of origin. This paper summarizes the main findings of a research project that investigated some of these implications for five ASEAN countries: Laos, Malaysia, the Philippines, Thailand, and Vietnam. In particular, for selected service sub-sectors and a number of criteria for rules or origin, national research studies in these countries simulated which service providers would or would not be eligible for preferences negotiated under an FTA.

The paper is structured as follows. We first describe the rules of origin question in greater detail, explain why it is important, what different options for rules of origin exist, and what factors determine the relevance and restrictiveness of rules of origin. Based on this discussion, we then summarize the main findings of the national research studies. The final section offers concluding remarks.

# **2.  Rules of origin in services: conceptual issues**

**_What are rules of origin in services?_**

Rules of origin have long been a prominent issue in the case of goods trade. In the goods case, they seek to prevent the transshipment of goods for the sole purpose of obtaining preferential tariff treatment under an FTA. This case is illustrated in Figure 1. Suppose countries A and B sign a trade agreement that eliminates tariffs on all trade between the two countries. Suppose also that country A’s remaining tariff for imports from the rest of the world—its most favored nation (MFN) tariff—is lower than country B’s MFN tariff. Ignoring transport costs, exporters from the rest of the world would have an incentive to transship their exports to country B through country A and thereby avoid paying country B’s higher tariff. If this were possible, an FTA would de facto reduce member countries’ external tariffs to the lowest MFN rate in the FTA area. To allow varying MFN tariff rates among FTA members, FTAs need to establish rules of origin that define the level of transformation imported goods need to undergo, in order for the transformed product to be exported to the FTA partner at the preferential tariff.

2
# **<u>ANNEX 3.9: WORLD BANK GROUP INSTRUMENTS AND SERVICES</u>**

||||<br>|**WBG Instr**<br> <br>|**uments and**<br> <br>|**Services**<br>||
|---|---|---|---|---|---|---|---|
|**Mechanisms & Programs**||**Loans**|<br>**Grants**<br>|<br>**Guarante**|**es**<br>**Swaps**<br>**O**|**, Insurance,**<br>**ptions**|**CAT-**<br>**DDO**<br>**TA**|
|Public-private partnerships||√|√||||√|
|Output-Based Aid|||√||||√|
|Carbon Finance|||||||√|
|Mitigation of sovereign and|project risk|s||√|||√|
|Mitigation of currency, com<br>thltd ik|modity, and|||||√|√<br>√|
|weaer-reae rss|||||**FY1996**|||
|**Mechanisms & Programs**|**FY64–80**|**FY81–85**|**FY86–90**|**FY91–95**|**–**<br>**2000**|**FY2001–5**|**FY2006–9**|
|Public-private partnerships|√|||||||
|Output-Based Aid||||||√||
|Carbon Finance|||||||√|
|Mitigation of sovereign and<br>project risks||IFC PCG||MIGA PRG;<br>IBRD PRG &<br>PCG|IBRD<br>“enclave”<br>PRG;<br>IDA PRG|||
|Mitigation of currency,<br>commodity, and weather-<br>related risks|||||||Malawi weather<br>hedge, CAT<br>DDO & all<br>other cat<br>insurance<br>products|

49
also examine _what_ is said. More specifically, we draw on natural language processing methods that use _text as data_ to beter understand the content and character of speech. By treating our transcripts as textual data, we can estimate an unsupervised topic model, which is a computational tool to “discover” a set of a salient topics within a document collection.

While the complexity of language will never be fully captured by an automated method such as ours, this sort of analysis can help to overcome meaningful challenges in hand-coded analyses of deliberation — including biases due to the researcher’s priors and inconsistencies in coding across various setings. Hand-coding begins with a pre-determined set of categories into which documents are classified — based on their content, tone, etc. By contrast, the unsupervised approach allows us to learn the underlying features of the text without imposing our own assumptions. Tough this is necessarily imperfect and requires ex-post validation, it can be useful for identifying previously understudied or theoretically new aspects of speech in these setings, as well as scaling up large volumes of textual data.

Prior to estimating the topic model, we pre-process the set of 1,736 documents such that infrequent words (those with fewer than 5 occurrences in the corpus) and certain proper nouns, as well as overly common “stopwords” are removed.<sup>13</sup> Infrequent and proper nouns are ofen names of beneficiaries, townships, or neighborhoods that are mentioned in meetings, but are not in common usage. Te remaining terms are then “stemmed” such that various forms of the same word are counted together.<sup>14</sup> We also exclude numbers. From the original set of citizen speeches, 1,700 documents remain afer processing.

Using this processed corpus, we adopt the approach of Roberts et al. (2016) to estimate a _Structural Topic Model_ (STM), which allows us to inductively discover topics, or clusters of words that commonly co-occur within the data. Te model outputs (1) a set of topics, which are defined as mixtures of words, where each word has a probability of belonging to each topic, and (2) for each document analyzed, the proportion of the document associated with each topic. As such,

> 13Stopwords are overly common words which are filtered out before the use of natural language processing methods to improve the estimation process. Tey ofen include functional words, including articles, prepositions, basic verbs such as “is,” and pronouns.

> 14For example “repair,” “repairs,” “repairing” and “repaired” all stem to “repair.”

18
- 7 -

Household income is assumed to depend on two types of road accessibility, _D_<sup>_RSDP_</sup> and _D_<sup>_URRAP_</sup> , as well as agricultural inputs used ( _X_ ) and other household-specific characteristics ( _Z_ ). _DitRSDP_ is set to be one if the main road closest to household _i_ was rehabilitated by the point of time _t_ . Similarly, _DitURRAP_ is set to be one if the closest feeder road was rehabilitated by URRAP. _β3_ is expected to capture the interaction effect between the two interventions. As our data are panel, _ci_ and _ct_ are the individual- and time-specific fixed-effects, respectively.

Our identification strategy is based on the phased nature of the two road programs. Even along the same road, some segments may have been completed earlier than others. Therefore, some groups benefit from the programs earlier than others. The current paper takes advantage of such time lags in project implementation and applies the difference-in-differences (DID) method with various covariates taken into account. Among the RSDP roads, for example, the Aposto-Wendo-Negele Corridor was divided into three contract lots ( **Table 1** ). While two lots were completed by 2013, the final lot was handed over in 2014. The household surveys were carried out in the almost same season every year. This creates an opportunity to distinguish beneficiaries and nonbeneficiaries at a certain point of time. The Kombolcha-Bati-Mille and Ankober-Awash Arba Corridors can be considered as a full comparison group because no road work had yet been completed when the last round of the survey was carried out.

**<u>Table 1. Timeline of project implementation of the main road works and household surveys</u>**

|<br>Road|<br>Lot|<br>2009|<br>2010|<br>2011<br>2012|<br>2013<br>2014|<br>2015<br>|2016<br>2017|
|---|---|---|---|---|---|---|---|
|Aposto-Wendo-Negele|Aposto-Irbamoda|||||||
||Irbamoda-Wadera|||||||
||Wadera-Negele|||||||
|Mekenajo-Dembidolo|Mekenajo-Ayira|||||||
||Aiyra-Chanka|||||||
||Chanka-Dembidolo|||||||
|Kombolcha-Bati-Mille|Kombolcha-Burka|||||||
||Burka-Mille|||||||
|Ankober-Awash Arba|Ankober to Dulesa|||||||
||Dulesa to Awash Arba|||||||