rafmacalaba/datause-encoder-data huggingface dataset:
Cluster locations for the DHS surveys are identified in Figure 1 by circles for 2004 and stars for 2007.<sup>9</sup> They are sited at widely-varying distances from salinity monitors, with some clusters close to several monitors. Using the information illustrated by Figure 1, we estimate salinities for DHS clusters using two basic assumptions. First, we assume that the soil salinity measure for an area provides a useful proxy indicator for drinking water salinity. This seems reasonable on two grounds. First, runoff from fields and subsurface diffusion should produce higher water source salinity in areas with more saline soils. As Yu (2010) shows, soil composition in coastal Bangladesh is particularly conducive to vertical diffusion of salinity from the surface to groundwater. Second, in cases where soil monitors are near more scattered river monitors, Dasgupta, et al. (2011a) show that neighboring soil and water salinity readings are highly correlated.<sup>10</sup> This provides further evidence of diffusion from runoff. Although we believe that local soil salinity provides a useful proxy indicator for drinking water salinity, we recognize that this assumption introduces random measurement error of unknown magnitude.

Our second assumption is that we can use spatial interpolation to compensate for the locational mismatch between monitors and DHS clusters in our database. We apply spatial interpolation in two steps. First, we select monitors within a constant distance from each DHS cluster. Then, for each month and year, we estimate cluster salinity as the weighted average of monitor salinity readings, where the weights are inversely proportional to squared distances. To test for robustness, we create three sets of cluster estimates for bounding distances of 20, 30 and 40 km. We face an unavoidable tradeoff in setting the bound: Greater distance permits salinity estimation for more DHS clusters, but it may also

> 9 The indicated locations are cluster centroids supplied by the DHS, which randomly displaces GPS latitude/longitude positions to ensure confidentiality for respondents.  DHS-supplied urban centroids are within 2 km of actual centroids; 99% of rural cluster centroids are within 5 kilometers of actual centroids and 1% are within 10 km.

> 10 This study uses the soil salinity monitors because they provide many more observations for estimation.  Soil monitors substantially outnumber river monitors in the databases available for this study.  The soil monitors are also more widely scattered geographically, so they are relatively close to many more DHS clusters than the river monitors.

8
<mark>our sample, labor informality is a pervasive phenomenon that may operate as a buffer mechanism</mark> during a crisis.<sup>7</sup> <mark>Having estimated the sectoral elasticities, we project the 2020 employment level for each country and sector. To obtain these projections, we use ILO data for 2019 sectoral employment levels and estimated sectoral GDP changes from the World Bank's Macro Poverty Outlooks. We then add the sectoral projections within each country and obtain an estimation of the total employment level for 2020 that we compare with the observed employment level reported by National Statistical Offices.</mark><sup>8</sup> Figure 1 presents, for each country and method, the percentage difference between our employment level projection for 2020 and the observed employment level.

**Figure 1. Percentage difference between 2020 employment projections and actual 2020 employment levels**

<!-- Start of picture text --> 30%<br>Average Average without outliers<br>25% Mean Median<br>Mean + GDP Mean + GDP*Informality<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>-15%<br>-20%<br>ALB VNM MKD IDN TUR BGR ROU ECU COL MEX PHL BRA ZAF CRI PER<br><!-- End of picture text --> Notes: The mean regression method controlling for the logarithm of GDP interacted with the informality rate is not available in Bulgaria, Mexico, the Philippines, Romania, and <mark>Türkiye.</mark> Actual 2020 employments levels come from National Statistical Offices. The exact data of actual 2020 employment data is shown in Table A1 in the Appendix.

> 7 Bulgaria, Mexico, the Philippines, Romania, and <mark>Türkiye</mark> are excluded from this sixth method due to lack of data on labor informality.

> 8 The date of the observed 2020 employment level differs between countries depending on the information available in National Statistical Offices. Table A1 in the Appendix contains information on the exact date considered in each country.

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associated with this is shifting steadily away from the origin.<sup>5</sup> Of course, there are many products showing an increase in density consistently since 1995.<sup>6</sup> However, these items tend to be located farther away from the origin. Among more than 300 commodities in which the density is increasing, the closest to the origin was “7761: traditional television picture tubes,” hardly a cutting-edge product (see Table 8).

**Table 8: Top 20 commodities whose density is increasing**

|SITC4|Short description|invdensity|Lall<br>Class|exporting|
|---|---|---|---|---|
|7761|Television picture tubes, cathode ray|6.5|HT1|1|
|8710|Optical instruments and apparatus|6.9|HT2|1|
|8841|Lenses and other optical elements of any material|7.2|MT3|1|
|5826|Epoxide resins|7.5|MT2|1|
|7643|Television, radio-broadcasting; transmitters, etc|7.7|HT1|1|
|5112|Cyclic hydrocarbons|8.3|RB2|1|
|7523|Complete digital central processing units; digital processors|8.5|HT1|1|
|5827|Silicones|8.7|MT2|1|
|4233|Cotton seed oil|8.7|RB1|0|
||Internal combustion piston engines, for aircraft, and parts,||||
|7131|nes|8.8|MT3|1|
|3415|Coal gas, water gas and similar gases<br>Sewing machines, furniture, needles etc, and parts thereof,|8.8|PP|0|
|7243|nes|8.8|MT3|1|
|5137|Monocarboxylic acids and their derivatives<br>Machinery, accessories for type-setting, for printing blocks,|9.0|MT2|1|
|7263|etc<br>Domestic electro-mechanical appliances; and parts thereof,|9.1|MT3|1|
|7757|nes|9.2|MT3|1|
|1221|Cigars, cheroots: cigarillos<br>Under garments of textile fabrics, not knitted or crocheted;|9.2|RB1|1|
|8442|mens, boys under garments; other than shirts|9.3|LT1|1|
|5233|Salts of metallic acids; compounds of precious metals|9.4|RB2|1|
|8952|Pens, pencils and, fountain pens|9.6|LT2|1|
||Yarn 85% of synthetic fibres, not for retail; monofil, strip,||||
|6514|etc|9.6|LT1|1|
|5154|Organo-sulphur compounds|9.7|RB2|1|
|2671|Regenerated fibre suitable for spinning|9.7|MT2|0|

_Note:_ PP: Primary Products, RB1: Agro-Based, RB2: Other Resource-Based, LT1: Textile, garment & footwear, LT2: Other, Low-Technology, MT1: Automotive, MT2: Process, MT3: Engineering, HT1: Electronic & Electrical, and HT2: Other, High-Technology. Source: authors’ calculation

> 5 It was also the case for the commodity “8811”, but Penang had revealed comparative advantage in this product in 2007.

> 6 That is, the density increases from 1995 to 2000, and from 2000 to 2007.

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<!-- Start of picture text --> Authorization = Apportionment _ Reservation > Commitment<br>‘© Legal authoriza- * Spending authority © Fundsare blocked * A future obligation<br>thetionauthority/ Spendingthat sets isSpecificapportionedperiodsfor expensefora specificbefore tothepayfulfilmentsubject oftoa<br>and limit,purpose horizonof units.and/or spending thecommitment actual legalor coniractdelivery of service<br>* expenditure * Example: Quarterly’ contract * Example: Supplier<br>Example: Budget monthly allotment * Example: The Span- contract<br>appropriation ish system<br>Verification | Payment Order «=f Payment<br>* Confirmation * An order to pay * Actual payment (by<br>of goods or against a bill or cash, check, bank<br>services received invoice is issued transfer, etc.) is<br>and recognition of by a designated Made<br>aliability based on Officialto clear the liabilityto discharge<br>due date: liability. © Example: Check<br>* Example: Certifica- * Example: In the issued in favor ofa<br>fion and recording French system, an supplier<br>of stock by an ordonnateur autho-<br>authorized staff rizes payment<br><!-- End of picture text -->
# **Determinants of Remittances:**

# **Recent Evidence Using Data on Internal Migrants in Vietnam***

## **Niimi, Y., T. H. Pham, and B. Reilly**

#### **_Affiliation_**

Yoko Niimi: World Bank, Mailing address: DECRG, World Bank Mailstop MC3-303. 1818 H Street, NW. Washington, DC 20433, USA. E-mail: yniimi@worldbank.org.

Thai Hung Pham: Department of Economics, University of Sussex. Mailing address: Department of Economics, University of Sussex, Falmer, Brighton, BN1 9SN, UK. E-mail: <u>T.Pham@sussx.ac.uk.</u> Barry Reilly: Department of Economics, University of Sussex. Mailing address: Department of Economics, University of Sussex, Falmer, Brighton, BN1 9SN, UK. E-mail: b.m.reilly@sussex.ac.uk.

### **JEL codes:** J61.

**Key words:** Internal migration, remittances, Vietnam.

* The authors thank the Population and Labour Statistics Department of the General Statistical Office (GSO) in Vietnam for allowing access to the 2004 Migration Survey. In addition, the authors gratefully acknowledge the constructive and detailed comments provided on an earlier draft of this paper by an anonymous referee of this journal, and the suggestions of an Associate Editor of the journal. However, the usual disclaimer applies. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank, its executive directors, or the countries they represent.
depicts countries with which Pakistan under-performs above the 45-degree line below. In South Asia, Pakistan’s exports to Sri Lanka have grown in response to a bilateral trade agreement in place. Pakistan trades heavily with members of the Gulf Cooperation Council (GCC) which is explained by proximity and trade complementarities as well as from the fact that these serve as re-export bases for Pakistan’s trade with a number of third countries.

**Figure 5**

<!-- Start of picture text --> Pakistan's Export Partners, 2007-09<br>PREDICTED VS. ACTUAL (GRAVITY)<br>16<br>USA<br>IND<br>JPN CHN<br>GBR<br>FRA DEU<br>RUS ITA<br>BRA AUS KOR<br>THA BGD SAU<br>12 MEX TUR<br>VNM M YS CAN ARE<br>EGY ZAF<br>HKG<br>SGP KWT LKA<br>NPL<br>QAT OMN<br>8<br>8 12 16<br>Log of Actual Exports, 2007-09<br>Log of Predicted Exports, 2007-09<br><!-- End of picture text --> Source: Authors’ calculations Furthermore, how a country’s industry is faring in international competition can be gauged by its share in strategic markets, such as those that are expanding (and importing) rapidly. Generally, there is no correlation between Pakistan’s exports and their growth in the world market. Ideally, in the figure below showing world growth (on the y-axis) and the importance of such high-growing products in a country’s export portfolio (x-axis), we seek a positive, upwardsloping relationship. The top exports of Pakistan -- cotton, apparel, leather -- are not growing as rapidly as other products like fruits/grain/oil seeds, cereals, chemicals, ship/boats, cement, plastics, etc. The growth orientation of products, in general, is flat. Likewise, Pakistan’s top export partners are not among those that have seen the highest rates of import growth between 1999-2009, with the exception of China, and to a lesser extent Turkey. The orientation is faintly weak. For Pakistan’s exports to be “pulled” by the world growth of products which it exports, this relationship would have to be stronger. In fact, Pakistan ranks really low when we compare exporters can be inferred from the export destinations reached. With these steps, the gravity results of whether a country over-trades or under-trades with particular partners are better grounded on trade theory.

9
Indeed, these estimates of poverty improve on those offered by proxy means testing method. This result is formalized in the following proposition.

**Proposition 3:** _Given assumptions IV.1 and IV.2, the imputed household consumption in equation (7) offers unbiased poverty estimates._ **Proof** : Appendix 1.

We can test for Assumption IV.2 if consumption data exist in both survey rounds. We can use a decomposition that is similar in spirit to the Oaxaca-Blinder framework (Oaxaca, 1973; Blinder, 1973), where the change in poverty between the survey rounds can be broken down into two components, one due to the changes in the estimated coefficients (the first term in square brackets in Equation (10) below) and the other the changes in the _x_ characteristics (the second term in square brackets in Equation (10) below). Assumption IV.2 would be satisfied if the poverty change is mostly explained by the latter component. This can be expressed as where  _j_ is defined as  _j_  _j_ , j= 1, 2, for less cluttered notation.

However, we want to predict poverty rate for the current period where consumption data are missing. Thus a practical use of Assumption IV.2 is model selection; in particular, we can test for this assumption in contexts where consumption data exist for earlier survey rounds (i.e., there are two earlier survey rounds with consumption data), and select the imputation model that offers estimation results that are most satisfactory for this assumption.

This imputation method is also related to a larger literature on missing data (or multiple) imputation (MI) in statistics (see, e.g., Little and Rubin, 2002; Carpenter and Kenward, 2013). Official agencies such as the U.S. Census Bureau routinely use imputation to fill in important missing data on various statistics for income (Census Bureau, 2014a) and labor (Census Bureau, 2014b). However, several differences exist between the two literatures. First, MI studies often 23
**Plc,Planning, and Research**

**Educ-atlon and Employment**

How does post-school training affcct the employment and wage opportunities of urban women? This study of 3,826 urban Peruvian women aged 15 to 65 - drawn from the Peru- vian Living Standards Survey produced several conclusions: Attendance in post-school training programs is extensive. About 28 percent of all women participate in occupational training outside the educational system. Most female trainees prepare for predominantly female occupations (clerical jobs) by attending proprietary institutes (academes) rather than job-based programs sponsored by the government through vocational training institutions.

The more schooling a women has, the more likely she is to receive job training. Women with less than secondary schooling have few opportunities for training. The typical trainee has completed secondary school, is in her carly twenties, and tends to have had over a year of work experience when she reccives training.

Post-school training generally increascs a woman's chances of entering the labor force.

Training increases the probability of private scctor wagc employment by 10 percent; public sector employment by 2 percent; nonwage sector employment by 5 percent.

Controlling for this employment effect and contrary to expectations, job training did not increase the hourly wage rates of women in the wage (private) and nonwage employment sectors.

T here are several possible explanations for this finding. First, since women, on average, tend to expect to work outside the home **i..** ver years than men, they have an incentive to train for occupations that require lower investments in human capital than those chosen by men. Typically, these occupations are characterized by flat wage profiles. Second, there are no standards for assessing the quality of the training in proprietary institutes where most women are trained. Finally, discrimination against women may prevent them from entering not only the most successful training programs but also the jobs that allow more wage and career advancement as well.

This paper is a product of the Education and Employmcnt Division, Population and Human Resources Department. Copies are available from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Cynthia Cristobal, room S6-00 1, extension 33640.

Thc PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get thr:se fiundings out quickly, even if prcsentations arc less than fully polished.i The findings, interprctations, and conclusions in thcse papers do not necessarily represent official policy of thc Bank.

Produced at the PPR Dissemination Center
wage differentials can be interpreted as direct evidence of wage discrimination. In so far as occupational skills are comparable across countries, the occupational gender wage gap can be compared across countries as well.

Table 3 presents the occupational gender wage gap by country for 1983-99, organized by the level of development. Here the occupational gender wage gap is measured as one minus the average ratio of the reported female and male wage for a given country and year across occupations. This definition implies that a gap of zero indicates no difference between female and male occupational wage, while a gap more than zero indicates that the female wage is lower than the comparable male wage.

In Table 3 for each country the year is selected for which most occupational gender wage gaps were reported (at least two occupational gaps). We report two occupational gender wage gap statistics, namely the unadjusted and the adjusted. The unadjusted occupational gender gap does not correct for differences in pay, averaging, and period concepts.<sup>13</sup> The adjusted occupational gender wage gap corrects for differences in pay, averaging and period concepts.<sup>14</sup> It indicates what the gender wage gap is for hourly average earnings.

First of all, there are relatively minor differences between the unadjusted and adjusted occupational gender wage gap, suggesting that the heterogeneity in reporting is not a major problem if one is looking at the gap between female and male wages. Secondly, the occupation gender wage gap is typically above zero, as female workers tend to earn less than male workers in the same occupations. The overall average occupational gender wage gap is 0.11 across all countries in the dataset. Looking across income groups, the richer countries actually appear to have a higher occupational gender wage gap. The average occupational gender wage gap is 0.13 (0.12 if adjusted) for the high income countries, and 0.04 (or 0.03 if adjusted) for the poor countries.

Recent empirical studies from 71 countries indicate that on average the gender raw wage gap is 0.23 in developed countries, and 0.27 in developing countries.

> 13 But dimensional analysis and the hours of work data have been used to calculate the gender gap on a hourly basis as much as possible.

> 14 More precisely, a regression has been estimated with dummies for the pay, averaging and period concept to adjust the occupational gender wage gap for different units. Lexicographic weighting has been used in case there are multiple standardized observations for a given country/year/occupation pair (see Freeman and Oostendorp 2000).

12
policymakers with useful guidance for how to actually apply a safeguard in a WTO-consistent manner.<sup>15</sup> In a trading system where governments have access to relatively substitutable instruments of import protection, discouraging the use of safeguards in isolation may not lead to less protection overall. While such rulings may help explain the decline in safeguard use, they may also help explain the steady increase in use of antidumping.

# **5. NEW APPROACHES**

Notwithstanding the additional questions raised by the burgeoning empirical literature on the determinants of use of different trade policy instruments over time, a number of new theoretical and methodological approaches are also worth introducing.

Ossa (2011) has provided an innovative theoretical approach that identifies an international costshifting effect of trade policy that is separate from the terms-of-trade externality found in Bagwell and Staiger (1999, 2002). The approach starts from a monopolistically competitive market of the ‘new trade theory’ models which feature shipping costs and two-way trade in similar (but differentiated) products. One important result is that a beggar-thy-neighbor motive for trade policy intervention can arise from a ‘firm-delocation’ (or ‘profit-shifting’) effect, as governments have incentives to attract more of the world’s firms to locate locally so as to save on transport costs.

Antràs and Staiger (2012) have introduced a new approach out of recognition for the global fragmentation of production, offshoring, and the increasing economic importance of trade in intermediate inputs.<sup>16</sup> They conclude that one change to a simple assumption maintained in most prior economic theory on trade agreements – i.e., prices being determined not from market clearing conditions but due to

> 15 Goldstein and Martin (2000) offer other examples of ways through which the increased legalization of the WTO system may have unintended consequences for domestic political economy forces and trade liberalization.

> 16 In their extensive work studying value-added trade, Johnson and Noguera (2012), for example, find that intermediate inputs may account for as much as two thirds of international trade.

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