rafmacalaba/datause-encoder-data huggingface dataset:
-  52  -

- Kaminski, Bartlomiej, Zhen Kun Wang and L. Alan Winters. 1996. _Foreign Trade in the Transition. The International Environment and Domestic Policy_ , Studies of Economies in Transition, vol. 20., The World Bank, Washington, D.C.

- Landesmann, M. and R. Stehrer. 2003. “Structural Patterns of East-West European Integration: Strong and Weak Gershenkron Effects, in WIIW Structural Report 2003 on Central and Eastern Europe, Vol. 1, The Vienna Institute for International Economic Studies, Vienna.

- Markusen, James R. 1998. “Multinational Firms, Location and Trade,” _The World Economy_ . Vol. 21. No. 6. August (733-756).

- Mihov, Ilian. 1999. “The Economic Transition in Bulgaria 1989-1999,” paper presented at the Fifth Dubrovnik Conference on Transition Economies, Dubrovnik, Croatia, June.

- Szanyi, Miklós. 2004. “Competitiveness and Industrial Renewal.” Paper presented at the Seminar “Foreign Direct Investment and Multinational Corporations in Enlarged Europe,” Universidad Complutense de Madrid, Madrid, 22-23 November.

- UNCTAD. 2002. _World Investment Report: Transnational Corporations and Export Competitiveness_ . United Nations: New York and Geneva.

- Winters, L Alan. 1997. ‘The Economics of “Catching up” Revisited’, _The Vienna Institute for Comparative Economic Studies, Reprint Series, No. 168_ , June.

- World Bank. 1999. _Czech Republic: Toward EU Accession. A World Bank Country Study_ , The World Bank, Washington D.C.

- Wyzan, Michael. 1998. “The Political Economy of Bulgaria’s Peculiar Post-Communist Business Cycle.” _Comparative Economic Studies_ , 40(1), Spring (pp. 5 – 42).
unemployed. Indeed, self-employment in the EAP is quite extensive, ranging from 36 percent in the Philippines to 75 percent in PNG as a share of the labor force.<sup>24</sup> In middle-income countries such as those in the EAP, poorer workers are generally more likely to be selfemployed, while wealthier workers are more likely to work in salaried jobs (Fields 2019). Self-employment is relatively high in the agriculture sector and certain service sectors such as retail trade, which also had lower work stoppages (Figure 9, Panel A). Thus, looking solely at employment changes may understate the impact of the pandemic on inequality if poorer workers are less likely to report being out of work due to the nature of their employment.

Additionally, work stoppages only consider the extensive margin of employment impacts. Data from the World Bank’s Business Pulse Surveys suggest that firms have been more likely to reduce work hours, reduce wages, and/or grant paid or unpaid leave: Two-thirds of businesses in the sample made adjustments to their payroll, but only about one in five actually fired workers (Freund and Pesme, 2021). As a result, those who do not face job losses may still face reduced working hours and wages.<sup>25</sup> Income changes can thus provide a more accurate picture of the impact of the pandemic on inequality. Indeed, in all of the countries analyzed, the share of households that experienced labor income losses was larger than the share of respondents that experienced work stoppages. Moreover, in most countries, households were significantly more likely to experience reductions in business income than in wages (Figure 9, Panel B).

Figure 9. While the self-employed may be less likely to stop working, income losses from selfemployment were significantly larger than those from wage labor A. Self-employment and work stoppages B. Wage vs. Non-farm business income losses Note: Data from six countries are included: Indonesia, Lao PDR, Mongolia, Myanmar, the Philippines, and Vietnam. The share of workers who are self-employed come from the most recent available pre-pandemic LFS for each country. Share of households with incomes losses are the average across rounds by country. Source: ILOSTAT, HFPS Wealthier households engaged in wage or farm/non-farm business were less likely to report experiencing a labor income decline relative to the previous period. Across countries, households in the top 20 were on average about 5 percentage points less likely than those in the bottom 20 to experience losses in labor

> 24 Based on 2019 data from the World Development Indicators.

> 25 It is also plausible that losses in employment do not necessarily mean losses in income, particularly if work stoppages are temporary. However, as further elaborated in the Appendix, evidence suggests that while some work stoppages may not have resulted in reductions in income, the large majority has been associated with declines in wages or profits.

20
# Table 2a: School Level Characteristics (N=306)

|Variable|Mean|Standard<br>Deviation|
|---|---|---|
|School's Location<br>(=1 if Urban/Semi-Urban;=0 if Rural)|0.209|0.407|
|Pupil Teacher Ratio|62.586|23.427|
|Amount of Money that is entitled to school as Annual<br>Entitlement Grant? (in Tanzanian Shillings)|7120.916|2989.730|
|Does the School have a School Management Team?<br>(=1 if Yes,=0 if No)|0.778|0.416|
|Does the School have a Whole School Development Plan?|0.869|0.338|

## Table 2b: Student Level Characteristics

||Math|(N=346)|Kiswah|ili(N=336)|
|---|---|---|---|---|
|||Stdd||Stdd|
|Variable|Mean|anar<br>Deviation|Mean|anar<br>Deviation|
|Student Age|8.820|0.907|8.836|0.893|
|Percentage of students who attend<br>private tuitions in respective<br>subject|4.741|11.260|3.712|10.375|
|Percentage of students who belong<br>to lowest asset quartile?|30.838|24.437|29.368|24.267|
|Baseline Test Score (%) in<br>respective subject|36.926|12.111|45.088|25.981|

25
# **3.2 Reading**

**Table 11:** The estimated impact of ‘Vietnam’ on Reading PISA test scores

||||||Re|ading|||||
|---|---|---|---|---|---|---|---|---|---|---|
|Variables|(|1)|(2|)|(|3)|(|4)|(|5)|
|VIETNAM|105.16|(5.03)|85.03|(4.33)|75.49|(4.69)|74.13|(4.59)|60.31|(6.06)|
|FEMALE|-|-|25.84|(1.69)|25.24|(1.74)|26.65|(1.87)|23.02|(1.60)|
|PRESCHOOL|-|-|41.47|(3.67)|37.82|(3.78)|34.86|(3.83)|23.68|(3.40)|
|REPEAT|-|-|-58.46|(2.79)|-55.65|(3.03)|-52.78|(3.50)|-43.16|(3.24)|
|ST08Q01|-|-|-8.6|(1.29)|-8.31|(1.25)|-8.03|(1.38)|-7.67|(1.43)|
|ST115Q01|-|-|-7.9|(1.66)|-7.82|(1.68)|-9.63|(1.78)|-8.37|(1.80)|
|BOOK<br>~~N~~|-|-|-|-|0.08|(0.01)|0.06|(0.01)|0.05|(0.01)|
|PARPRESSURE|-|-|-|-|10.72|(4.32)|11.21|(4.24)|3.69|(4.18)|
|VOLUMOM|-|-|-|-|-0.05|(0.06)|-0.06|(0.05)|-0.03|(0.06)|
|FUNDMOM|-|-|-|-|0.2|(0.06)|0.17|(0.05)|0.1|(0.06)|
|COUNCILMOM|-|-|-|-|-0.12|(0.06)|-0.16|(0.06)|-0.09|(0.07)|
|DUTYMOM|-|-|-|-|-0.05|(0.06)|-0.08|(0.06)|-0.1|(0.07)|
|PROPCERT|-|-|-|-|-|-|9.14|(4.84)|3.14|(5.59)|
|TCSHORT|-|-|-|-|-|-|-3.93|(1.84)|0.47|(1.91)|
|TCM<br>~~S~~TUASS|-|-|-|-|-|-|15.91|(7.67)|7.35|(8.22)|
|ASS<br>~~P~~ROG|-|-|-|-|-|-|4.31|(16.68)|-15.85|(10.44)|
|ASS<br>~~P~~ROM|-|-|-|-|-|-|6.25|(5.45)|13.94|(5.92)|
|ASS<br>~~N~~AT|-|-|-|-|-|-|5.71|(5.41)|1.44|(5.91)|
|ASS<br>~~C~~UR|-|-|-|-|-|-|-2.15|(7.50)|-3.81|(9.14)|
|STU<br>~~F~~EEDB|-|-|-|-|-|-|4.95|(4.67)|6.12|(4.28)|
|PCGIRLS|-|-|-|-|-|-|-|-|23.11|(10.88)|
|TOWN|-|-|-|-|-|-|-|-|-6.88|(3.71)|
|CLSIZE|-|-|-|-|-|-|-|-|0.95|(0.23)|
|COMPWEB|-|-|-|-|-|-|-|-|16.08|(5.56)|
|SCMATEDU|-|-|-|-|-|-|-|-|5.16|(2.52)|
|SCMATBUI|-|-|-|-|-|-|-|-|0.98|(2.35)|
|EXC2<br>~~P~~LAY|-|-|-|-|-|-|-|-|13.05|(3.92)|
|EXC6<br>~~M~~ATHCOMP|-|-|-|-|-|-|-|-|7.65|(5.02)|
|EXC10<br>~~S~~PORT|-|-|-|-|-|-|-|-|-4.43|(10.86)|
|EXC11<br>~~U~~NICORN|-|-|-|-|-|-|-|-|10.61|(5.11)|
|SCORE<br>~~P~~UBLIC|-|-|-|-|-|-|-|-|5.24|(4.22)|
|LEADINST|-|-|-|-|-|-|-|-|1.54|(2.03)|
|QUAL<br>~~R~~ECORD|-|-|-|-|-|-|-|-|-6.06|(7.10)|
|SCHSEL|-|-|-|-|-|-|-|-|1.39|(3.05)|
|TEACCLIM|-|-|-|-|-|-|-|-|-1.3|(2.63)|
|R2|19|.61|34|.5|36|.45|39|.63|41|.84|
|N|48|483|462|67|44|046|35|442|27|331|

Notes: Figures in parentheses are t-values (95% significance level). For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix.

For scores on Reading (table 11), the gap is lower to begin with: 105.16 compared to 128.05 for mathematics. The pattern revealed by the regession is quite similar: the student specific variables bring down the Vietnam dummy coeffcient to 85; the parent related variables accounts for a further 9 points, with only PARPRESSURE being statistically sig24
# **References**

- Alegana, V. A., Atkinson, P. M., Pezzulo, C., Sorichetta, A., Weiss, D., Bird, T., ErbachSchoenberg, E., and Tatem, A. J. (2015). Fine resolution mapping of population agestructures for health and development applications. _Journal of The Royal Society Interface_ , 12(105):20150073.

- Anderson, W., Guikema, S., Zaitchik, B., and Pan, W. (2014). Methods for Estimating Population Density in Data-Limited Areas: Evaluating Regression and Tree-Based Models in Peru. _PloS one_ , 9(7):e100037.

- Athey, S. (2017). Beyond prediction: Using big data for policy problems. _Science_ , 355(6324):483–485.

- Biljecki, F., Ohori, K. A., Ledoux, H., Peters, R., and Stoter, J. (2016). Population estimation using a 3d city model: A multi-scale country-wide study in the netherlands. _PloS one_ , 11(6):e0156808.

- Centre for International Earth Science Information Network (2018). High resolution settlement layer. Retrieved from https://www.ciesin.columbia.edu/data/hrsl/ on August 4th 2018.

- Department of Census and Statistics (2015). Household income and expenditure survey 2012/13. final report. Department of Census and Statistics, Ministry of Policy Planning Economic Affairs, Child Youth and Cultural Affairs, Sri Lanka.

- Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F. R., Gaughan, A. E., Blondel, V. D., and Tatem, A. J. (2014). Dynamic population mapping using mobile phone data. _Proceedings of the National Academy of Sciences_ , 111(45):15888–15893.

- Engstrom, R., Hersh, J., and Newhouse, D. (2017). Poverty from space: Using high-resolution satellite imagery for estimating economic well-being. World Bank Working Paper No. 8284.

- European Commission (2018). Global Human Settlement (GHSL). Retrieved from https://ghsl.jrc.ec.europa.eu/data.php on November 19th 2018.

- Guiteras, R., Levinsohn, J., and Mobarak, M. (2018). Demand estimation with strategic complementarities: Sanitation in bangladesh.

- Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S., Tyukavina, A., Thau, D., Stehman, S., Goetz, S., Loveland, T., et al. (2013). High-resolution global maps of 21st-century forest cover change. _science_ , 342(6160):850–853.

- Harvey, J. (2002). Estimating census district populations from satellite imagery: some approaches and limitations. _International Journal of Remote Sensing_ , 23(10):2071–2095.

- Hastie, T. and Qian, J. (2018). Glmnet vignette. Retrieved from https://web.stanford.edu/ hastie/glmnet/glmnet _alpha.htmlpoi_ on November 19th 2018.

15
Notice that now the fixed effects that are included in the regression are at the country level instead of country‐product level.

Results for the product‐level estimations aggregated at the sectoral level are presented in Table 4. The negative impact of time delays on exports ranges between ‐0.002 and ‐0.06. For sectors such as wood/wood products (e.g. chemical wood pulp and wood wool) and vegetable products (e.g. ground nut oil and its fractions, mate, and cucumbers and gherkins), the GIS time coefficient is the highest, suggesting that these sectors are more sensitive to time variations. Specifically, a one‐day increase in export delays decreases exports in these industries by more than 25 percent on average. For other sectors such as footwear/headwear, miscellaneous products (works of art and clocks/watches) and transportation equipment, the impact of a one‐day increase on exports is much lower and equal to 3.9 percent on average.

_Table 4: Average effect of GIS Time to trade – At the 4‐digit level aggregated by sector_

|**Sector**|**Coefficient**|
|---|---|
|_Wood & Wood Products_|‐0.058|
|_Vegetable Products_|‐0.051|
|_Stone / Glass_|‐0.011|
|_Mineral Products_|‐0.010|
|_Raw Hides, Skins, Leather, & Furs_|‐0.007|
|_Animal & Animal Products_|‐0.005|
|_Foodstuffs_|‐0.003|
|_Plastics / Rubbers_|‐0.003|
|_Chemicals & Allied Industries_|‐0.003|
|_Metals_|‐0.003|
|_Textiles_|‐0.003|
|_Machinery / Electrical_|‐0.003|
|_Transportation equipment_|‐0.003|
|_Miscellaneous (Hs 90‐97)_|‐0.002|
|_Footwear / Headgear_|‐0.002|

11
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democratic country. _Culture_ contains two variables: individualistic culture and out-of-group trust. As discussed earlier, we use the imputation-augmented cultural variables to maintain sample size and its consistency across specifications.<sup>11</sup> In all specification, we also control for the continental dummies using the World Bank classification.<sup>12</sup> These continental dummies serve to hold constant factors related to geography, and cultural and institutional clusters. We include longitude and latitude in some specifications as the additional geography control variables.

An issue in modeling the pandemic spread is that there are strong spatial interactions. Economists have increasingly recognized the importance of Tobler’s law of geography—adjacent locations are more likely to share common characteristics than distant ones (Tobler 1970), which suggests that omitting spatial interactions could result in spurious correlation of location-specific variables (Kelly 2019). In the case of pandemic spread, spatial interactions and spillover could be especially relevant: countries near each other have more trade and social interactions, all facilitating pandemic spread. To accommodate general spatial interactions, we estimate the following spatial autoregressive model (SAC) to allow for cross-country interactions of both the dependent variable and the error term:<sup>13</sup> + α α θ (2) ∂∂ ∂∂ ∂∂ ∂∂ ∂∂ ∂∂ ∂∂ YY = ρρWW YY+ ββXX ααyy + δδ V ααVVααVVααV ααV yy + θθα θθVVααθθyy+ η VVααV VVαα + vv ∂∂ ∂∂ ∂∂ Here _Y_ is a vector of the outcome for all country. vv = λλWW VV+ εε is the _i_ th row of W, which is the spatial weights matrix, for which we use the inverse-distance matrix—the weights are inversely related ∂∂ WW to the distances between the capital cities of countries. is the error term, and the _i_ th element of the vector . is the error term in the equation. We allow a country’s pandemic outcome to be ∂∂ vv affected by those of other countries; we also allow for the error term to have spatial autocorrelation ∂∂ ∂∂ VV εε vv across countries as well. We report the estimates for and , which indicates strength of spatial interactions. ρρ λλ Before proceeding, we need to underline the limitations of descriptive studies like this, as emphasized by Knittel and Ozaltun (2020). First, we have not seen the end of the pandemic tunnel, and we could only rely on data up to this point. Nevertheless, it is a good cutoff time to examine

> 11 We also control for the indicators for missing Trust and Individualistic Culture. 12 That is, East Asia and Pacific, Europe and Central Asia, Latin America and Caribbean, Middle East and North Africa, South Asia, and Sub-Saharan Africa. Since the U.S. and Canada are just two countries, we combine them with Europe and Central Asia.

> 13 This is implemented in Stata relying on Belotti et al. (2017).

14
# _Relation to Free Trade Agreements and Multilateral Process_

In East Asia, regional trade agreements, such as Free Trade Agreements (FTAs), are increasingly dominant trade liberalization tools. The APEC process appears to be influenced directly and indirectly by this trend. While an FTA will work as a building block to achieve free trade in the world, serious trade diversion and formation of closed trade blocks would be the undesirable bi-products. Trade facilitation measures, including the improvement in transparency, are inherently suitable for multilateral and open-regional initiatives with little trade diversion. In the regional trade liberalization initiatives, simultaneously promoting FTAs and trade facilitation may work as very effective policy mix while providing complementarities each other.

# **_Further Research_**

Several issues remain for the future research suggested by our results here. Our simulation assumes only a basic static specification of perfect competition with constant returns to scale. Other specifications, including increasing returns to scale, should be tested, particularly in the context of trade in the Asia Pacific region. Assessing the policy implications on the combination of FTAs and trade facilitation reform in the Asia Pacific region would be another issue of interest. There may be a possibility to offset the shortcomings associated with only one mode of reform at a time. Finally, there is room for further theoretical consideration and empirical research on the cost of reducing mechanisms of transparency improvement, including the magnitude of irregular payments, in particular. This is relevant in regard to reducing risks for the exporters that could be addressed in CGE modeling.

# (References)

Anderson, J. E., and Douglas Marcouiller, 2002, “Insecurity and the Pattern of Trade: An Empirical Investigation”, _Review of Economics and Statistics_ , 84(2), 342-352. Anderson and van Wincoop (2003) “Gravity with Gravitas: A Solution to Border Puzzle,” _American Economic Review_ , Vol. 93, No.1, pp. 170 – 192.

------------ (2004) “Trade Costs,” _NBER Working Paper_ , No, 10480.

APEC Economic Committee (1998) _The Impact of Trade Liberalization in APEC_ , 31
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**Table 1: National Legal Protections for Breastfeeding and Complementary Feeding in South Asia**

|Protection|Afghanistan|Bangladesh|Bhutan|India|Maldives|Nepal|Pakistan|Sri Lanka|
|---|---|---|---|---|---|---|---|---|
|International Code of Marketing<br>of Breastmilk Substitutes and<br>subsequent relevant World<br>Health Assembly provisions|Full provisions|Many provisions|No legal<br>measures|Full provisions|Full provisions|Full provisions|Full provisions|Full provisions|
|Regulations governing marketing<br>of complementary foods (up to<br>age in months)|Yes (30 months)|Yes (60 months)|No legal<br>measures|Yes (24 months)|Yes (12 months)|Yes (12 months)|Yes (12 months)|Yes (12 months)|
|Maternity protection|||||||||
| Duration of maternal leave|13 weeks|16 weeks|6 months for<br>public sector; 8<br>weeks for private<br>sector|26 weeks|60 days<sup>a</sup>|14 weeks (98<br>days)<sup>b</sup>|12 weeks|12 weeks|
| % of previous earnings for<br>maternity benefit|100%|100%|100%|100%|100%|100%|100%|86–100%|
| Source of maternity benefit|Employer|Employer|Employer|Social security<br>funds and<br>employer|Employer|Social security<br>funds|Employer|Employer|
| Entitlement to breastfeeding<br>breaks at work|Paid|No entitlement|Paid|Paid|Paid|Paid|No entitlement|Paid up to 12<br>months|

_Sources_ : WHO et al. 2016; ILO 2014; country laws and regulations accessed by UNICEF.

a. Law does not specify whether 60 working days or calendar days. It is interpreted by the public sector as 60 working days, and by the private sector as 60 calendar days.

b. Up to 60 days is paid, and the remainder is covered through accumulated leave or unpaid leave. The total leave days can be extended by a further month as unpaid leave upon recommendation from a certified medical practitioner.

19
decisions to enter the labor market as “added workers” during the pandemic—an aspect that could not be analyzed in this paper due to unavailability of household identifiers in the Sakernas datasets. Third, as more data points become available in the future, future studies could also look at the long-term effects of the pandemic on the employment outcomes of individuals. This is a policyrelevant question that is particularly important among the youth, who arguably stand to be the most negatively affected group in the long term via the “scarring” effect.

41