rafmacalaba/datause-encoder-data huggingface dataset:
percent. Those with an average bound tariff between 30 and 50 percent must bind at 27 percent or less. Those with average bound tariffs between 20 and 30 percent are to bind at an average of 18 percent or less. Those with an average bound tariff below 20 percent must bind at an average rate equal to that arising from a 5 percent cut in 95 percent of tariff lines.

RAMs receive a grace period of 3 years and an extended implementation period of 3 years. In contrast with the case of agriculture, they do not receive smaller cuts in tariffs. However, very recent acceded members benefit from tariff reduction exemption. The NAMA proposal includes provision for sectoral initiatives, for which participation is not mandatory, but agreement is to be reached when 90 percent of world trade is included. In most cases, it is proposed to move to zero tariffs on these products.

# **Specifying Cuts in Tariffs**

To provide a preliminary assessment of the implications of the modalities for the applied protection, we use the MAcMapHS6 version 2.1 database (Boumellassa, Laborde and Mitaritonna, 2009) for 2004 together with a set of bound tariff rates for which _ad valorem_ equivalents have been calculated on the same basis. We first cut the bound tariff rates using the approaches considered in the modalities, and then assess their implications for applied rates. Where the draft agreements involve a range, we generally use the mid-point. The specific choices of parameters used are set out in Table 2. In this analysis, we use the conventional assumption that applied rates are not reduced unless the new bound rate falls below the baseline applied rate<sup>5</sup> , assumed to be the applied rate in the tariff baseline which is which is for 2004 - MAcMapHS6 v2.1 dataset – with several key updates. We take into account for some internationally-binding commitments to reform that will affect the tariffs that would have applied in 2025 in the absence of an agreement. The adjustments to the baseline tariff include WTO commitments taken in the accession process by a range of countries, including China and Ukraine. In addition, the Japanese GSP for LDCs has been updated based on 2007 improvements in terms of product

> 5 This assumption neglects the important value that can arise from bindings above current applied rates, but ruling out incidents of higher tariffs in the future (Francois and Martin 2004).

9
geographical controls and an expanded list of controls that includes a large number of other proximate correlates of child nutrition. In each case, we also present an aggregate rank which is a simple average of the rank of each food security indicator across the five different countries. This is admittedly a very crude procedure and is intended only to come up with an aggregate ranking for the food security indicators across the five countries.

**Table 11: Ranking of Food Security Indicators (Height-for-age)**

**<u>Model 1: Based on usefulness for nutritional targeting over and beyond the geographical</u>**

|||Rank w|ithin each c|ountry|||
|---|---|---|---|---|---|---|
|Food Security||||||**Overall**|
|<br>Measures|Bangladesh|Nepal|Pakistan|Tanzania|Uganda|**rank**|
|Per capita kcal||7|5|5||5.7|
|<br>SSR||6|2|3||3.7|
|SSEXR||5|3|4|4|4.0|
|FCS|1|3|||1|1.7|
|HDDS||4|1|1|3|2.3|
|MDDS|3|||||3.0|
|CDDS|4|||||4.0|
|HFIAS|2|||||2.0|
|Per capita|||||||
|<br>expenditure||1|4|6|2|3.3|
|Food share||2|6|2|5|3.8|

**Model 2: Based on usefulness for nutritional monitoring**

|||Rankw|ithin each c|ountry|||
|---|---|---|---|---|---|---|
|Food Security||||||**Overall**|
|<br>Measures|Bangladesh|Nepal|Pakistan|Tanzania|Uganda|**rank**|
|Per capita kcal||3|6|6||5.0|
|<br>SSR||7|3|3||4.3|
|SSEXR||6|4|5|5|5.0|
|FCS|1|1|||1|1.0|
|HDDS||2|1|1|3|1.8|
|MDDS|2|||||2.0|
|CDDS|3|||||3.0|
|HFIAS|4|||||4.0|
|Per capita|||||||
|<br>expenditure||4|2|4|2|3.0|
|Food share||5|5|2|4|4.0|

At the outset, it is important to point out that it is difficult to interpret the aggregate ranks of MDDS, CDDS, and HFIAS, because these indicators are available only for Bangladesh. However, given that these are indicators based on actual individual-level dietary intake and psychological perceptions 30
forms, while also tracking the case’s development.<sup>172</sup> Complaints may also be lodged by the DPA itself pursuant to the investigative and supervisory powers granted by the GDPR.

The second year of GDPR implementation has seen a dramatic increase in the quantity of complaints received by Member States. Since May 25, 2018, the United Kingdom alone has received 64,667 complaints and the German authorities 66,965.<sup>173</sup> Each complaint requires processing by DPA employees, and if appropriate, an investigation to determine the complaint’s validity. As awareness of data protection rights increases through media reports and DPA sponsored podcasts and social media accounts, several Member States have turned to helpdesk services and online live chats in an effort to respond to the influx of complaints received by overworked complaint handlers.<sup>174</sup> These approaches seek to offer early-stage assessments of data privacy queries by answering questions and suggesting when potential complaints should be lodged.<sup>175</sup> In 2019, Ireland’s Department of Information and Assessment received 48,500 contacts related to data privacy: 22,300 emails, 22,200 phone calls, and 4,000 letters through post.<sup>176</sup> Ireland relies on the early-stage assessment tool as their DPA reportedly receives 150 new complaints every week – with a growing number of data subjects finding “novel ways” to apply the GDPR, according to Data Protection Commissioner Helen Dixon.<sup>177</sup>

> 172 EUROPEAN COMM'N, SINGLE MARKET SCOREBOARD 2019 (European Comm'n 2019).

> 173 _Id._ , _Individual replies from the data protection supervisory authorities_ (2020).

> 174 Info. Commissioner’s Office, GDPR: One Year On (Info. Commissioner’s Office 2019).

> 175 The Commissioner for Data Protection, Annual Report (Data Prot. Comm'n 2020).

> 176 _Id_ . at 17.

> 177 Simon Carswell, _Big Tech ‘procedural Queries’ Delay Decision on First Data Fines – Watchdog,_ 2020 THE IRISH TIMES, Feb. 20, 2020 at (2020), https://www.irishtimes.com/business/technology/big-tech-procedural-queriesdelay-decision-on-first-data-fines-watchdog-1.4178751.

30
_Brazilian spatial labor dynamics Page 14_ FERREIRA, A. H. B., DINIZ, C. C. _Convergência entre as rendas per capita estaduais no Brasil_ . Belo Horizonte: UFMG/CEDEPLAR, 1994. 27p. (Texto para Discussão, n.79) FERREIRA, P. C. G., ELLERY JR., R. G. Convergência entre a renda per capita dos estados brasileiros. _Revista de Econometria_ , v.16, n.1, p.83-103, abr. 1996.

FIESS, N.M. and D. WERNER. .2004. Migration and human capital in Brazil during the 1990s. World Bank Policy Working Paper no. 3093.

GLAESER, E. L. _et al_ . Growth in cities. _Journal of Political Economy,_ v.100, n.6, p.11261152, Dec.1992.

GLAESER, E. L., SCHEINKMAN, J. A., SHLEIFER, A. _Economic growth in a cross-section of cities_ . Cambridge, Mass.: NBER, 1995. 19p. (Working Papers, n.5013) GOVERNO DO ESTADO DA BAHIA (2003) _O Futuro a Gente Faz: Plano Estratégico da Bahia._ Salvador: SEPLAN.

LALL, S., FUNDERBERG, R., YEPES, T. (2005). Location, concentration, and performance of economic activity in Brazil. World Bank Policy Research Working Paper 3268.

LUCAS, Robert. On the mechanics of economic development. _Journal of Monetary Economics_ , v.22, n.1, p.3-42, Jul. 1988.

MAGALHÃES, A., HEWINGS, G., AZZONI, C. _Spatial dependence and regional convergence in Brazil_ . Chicago: University of Illinois, 2000. 20p. (Working Papers REAL 00-T-11, Urbana) Disponível em: <http://www2.uiuc.edu/unit/real/d-paper/00-t-11.pdf>.

MAIA GOMES, G. 2001. _Velhas secas em novos sertões: continuidade e mudanças na economica do semi-arido e dos cerrados nordestinos._ Brasilia: IPEA.

PRITCHETT, L. (2004) Boom towns and ghost countries: geography, agglomeration, and population mobility. Center for Global Development, Working Paper no. 36.

RIVKIN, S., HANUSHEK, E., KAIN, J. (2005) Teachers, schools and academic achievement, _Econometrica_ , v.73, p.417-458.

ROMER, P. M. Endogenous technological change. _Journal of Political Economy,_ v.98, n.5 (part 2), p.71-102, Oct. 1990.

ROMER, P. M. Increasing returns and long-run growth. _Journal of Political Economy_ , v.94, n.5, p.1002-1037, Oct. 1986.

SECRETARIA DE DESENVOLVIMENTO LOCAL E REGIONAL, n.d. (2004?). _Planos de Desenvolvimento Regional._ (various volumes: _Baixo Jaguaribe, Maçico de Baturite, Vale deo Coreaú e Ibiapaba_ ) Fortaleza: Governo do Estado do Ceará, Secretaria De Desenvolvimento Local e Regional.
Figure 4: Remittances, economic and financial development across Italian provinces <!-- Start of picture text --> Quintiles Quintiles<br>[12718,15712] [.841,2.52]<br>(15712,20732] (2.52,3.07]<br>(20732,24357] (3.07,3.6]<br>(24357,26732] (3.6,4.46]<br>(26732,35061] (4.46,5.92]<br>(a) Per capita GDP (aver. 2005-10) (b) Functional distance (aver. 2005-10)<br>Quintiles<br>[-.0678,-.0215] Quintiles<br>(-.0215,-.0102] [.000742,.00127]<br>(-.0102,-.0000343] (.00127,.00165]<br>(-.0000343,.0116] (.00165,.00203]<br>(.0116,.0833]No data (.00203,.00242](.00242,.0394]<br>(c) GDP growth 2008-2010 (d) Remittances as a share of GDP (aver.<br>2005-10)<br><!-- End of picture text --> Source: Bank of Italy, ISTAT and Istituto Tagliacarne. See Table 1 for definitions.

# **5 Results**

## **5.1 Remittances as a countercyclical financial flow**

Remittances increase in response to cyclical output declines in the recipient country, _CY CLEj_ . The response is statistically significant across all specifications in Table 3. The elasticities range between 1.8 and 2.5. This suggests that remittances can indeed play a significant role in stabilizing output during downturns, smoothing consumption, and mitigating the effects of macroeconomic fluctuations in developing countries.

Even after controlling for output per capita, remittances increase significantly in response to different factors of vulnerability in recipient countries. Remittances are approximately 3 percent larger when recipient countries experienced natural disasters (Yang, 2008; Mohapatra _et al._ , 2012; Ebeke and Combes, 2013), and 7.6 percent larger in the case of a significant terms-of-trade deterioration (see Table 3, column 6 and 7, respectively).<sup>19</sup> The outbreak of armed conflicts is not associated with

> 19 Such effects are computed by means of the following formula: (exp( _βi_ ) _−_ 1) _∗_ 100, where _βi_ is the estimated

12
Table 5: Within-firm Variance in Technology Sophistication and Firm Characteristics

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|EXT<br>Var(ABF)|MOST<br>Var(ABF)|GAP<br>Var(ABF)|
|ABF MOST|1.44***|1.49***|0.50***|
||(0.09)|(0.07)|(0.11)|
|ABF MOST<sup>2</sup>|-0.29***|-0.26***|-0.09***|
||(0.02)|(0.02)|(0.02)|
|Vietnam|-0.26***|-0.35***|-0.07***|
||(0.02)|(0.02)|(0.02)|
|Senegal|0.12***|-0.17***|-0.05*|
||(0.03)|(0.02)|(0.03)|
|Manuf|0.24***|0.03|0.17***|
||(0.04)|(0.04)|(0.04)|
|SVC|0.26***|0.03|0.13***|
||(0.04)|(0.03)|(0.04)|
|Medium|0.07***|-0.02|0.01|
||(0.02)|(0.02)|(0.02)|
|Large|0.13***|0.04|0.14***|
||(0.04)|(0.03)|(0.04)|
|Age 6 to 10|-0.09***|0.04**|-0.08***|
||(0.02)|(0.02)|(0.03)|
|Age 11 to 15|-0.11***|-0.01|-0.10***|
||(0.02)|(0.02)|(0.03)|
|Age 16+|-0.10***|0.02|-0.08***|
||(0.02)|(0.02)|(0.02)|
|MNCs|0.12***|0.03|-0.02|
||(0.03)|(0.03)|(0.04)|
|Exporter|0.05**|0.04**|0.13***|
||(0.02)|(0.02)|(0.02)|
|Constant|-0.92***|-1.09***|0.01|
||(0.11)|(0.09)|(0.12)|
|Observations|3,888|3,893|3,135|
|R-squared|0.17|0.44|0.09|

Note: Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Reference group for country, sector, size, and age categories is Brazil, Agriculture, Small, and Age 0 to 5, respectively.

in technology. In particular, it is consistent with the presence of non-homotheticities in production.<sup>33</sup> Non-homotheticies affect the relative value of having more sophisticated technologies across business functions. As average technology in the firm increases, the value of increasing the sophistication may change at different rates across business functions. This will lead to business functions whose sophistication of technology increases more steeply with the average sophistication in the firm (i.e., technology elastic), and others where the

> 33See Hanoch (1975) and Comin, Lashkari and Mestieri (2020) for non-homotheticities in demand and Comin, Dmitriev and Rossi-Hansberg (2020) for non-homotheticities in production.

25
# Disquiet on the Weather Front: The Welfare Impacts of Climatic Variability in the Rural Phili ines <u>pp</u>

Abla Safir, The World Bank Sharon Faye Piza, Asia Pacific Policy Center, Diliman, Philippines Emmanuel Skoufias, The World Bank _Corresponding author:_ Emmanuel Skoufias, The World Bank (Mail Stop: MC4-415), 1818 H Street NW, Washington DC 20433-USA. tel: (202)458-7539. fax: (202) 5223134. e-mail: <u>eskoufias@worldbank.org.</u> _Acknowledgements_ : The authors are grateful to PAGASA for making available the historic rainfall station data used in this study and to Dr. Arsenio Balisacan for his strong encouragement and continuous guidance during this study. The findings, interpretations, and conclusions in this paper are entirely those of the authors and they do not necessarily reflect the view of the World Bank.
assumption that block groups in a sufficiently narrow window around the 20 percent poverty rate threshold are similar along observable and unobservable dimensions. Using rich administrative data derived from unemployment insurance records, the study finds that EZ designation increases resident employment in block groups with poverty rates near 20 percent by 1–2 percent per year. The employment effects seem to be concentrated in jobs paying less than $40,000 annually and are largely in the construction, manufacturing, retail and wholesale trade industries.

This RD design overcomes the problem of program spill-over effects as well as the issue that treatment and control locations can differ in unobservable ways<sup>13</sup> and that differences can vary over time. However, this RD design enables the detection of only a LATE effect; the results are only valid for a narrow group of census block groups with poverty rates near 20 percent. Another critical assumption is that the selection criteria, namely, the poverty rate of the location, is exogenous to the EZ designation. In other words, there is no sorting of areas around EZ cut-off points and no manipulation of local poverty data in the anticipation of being designated as an EZ area. While it is difficult to rule out all these selection issues, the study does not find any apparent indication of such selection issues in the data.

# _<u>Investment Promotion</u>_

Besides incentives, a number of countries around the world compete to attract FDI through investment promotion activities (IPAs). While the accurate number of total IPAs in the world is unknown, there are about 189 IPAs who are the members of World Association of Investment Promotion Agency (WIPA). A number of years ago, UNCTAD estimated the total number of IPAs to be over 1,000. China alone probably has 500 plus IPAs at national, provincial, municipal, and zones levels to attract FDI. How effective are these IPAs in attracting FDI and what is the impact of that FDI in a host country in terms of job creation? Harding and Javorcik (2011) attempt to answer this question using the data on IPAs from the Census of Investment Promotion Agencies conducted under the aegis of the World Bank and the data on U.S. outward FDI disaggregated by host country and sector for 1990-2004. As the majority of the IPAs target

> 13 As typically a location needs to apply for an EZ status in different states, the locations those apply could be different in unmeasurable ways than localities which do not apply (i.e., the surrounding areas). Similarly, the decision of a business to invest in or hire from a particular location could be also influenced by unobserved local characteristics and expectations about the future prospects of the area.

29
<!-- Start of picture text --> Annex Figure 1‐ GDP per capita<br>(constant 2000 US$)<br>30000<br>25000<br>20000<br>15000<br>10000<br>5000<br>0<br>SSA (resource rich countries) GULF SSA<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br><!-- End of picture text --> Source: World Bank World Development Indicators.

28
20 Five points are worth making. First, as the poverty line rises, we see the share of households falling into the immiserizing spending category increase, the shares falling into the impoverishing and catastrophic spending categories fall, and the financial protection index value become smaller. Second, whatever the poverty line and consistent with Figure 3, the incidence of catastrophic spending is higher (and the financial protection index value is therefore lower) when catastrophic payments are defined as payments that leave nonmedical consumption below 120 percent of the poverty line. Third, even under the second interpretation of catastrophic payments, we see quite small shares of households falling into the impoverishment and catastrophic spending categories; these are dwarfed by the number falling into the immiserizing spending category. In fact, under the more traditional definition of catastrophic spending, the number of households who experience immiserizing spending is larger than the combined number of households who experience impoverishing or catastrophic spending. Fourth, we see a higher fraction of the sample reporting no spending when the variable is constructed from the individual questionnaire. This reflects the fact that it captures spending from just one randomly-selected household member and misses the spending of other household members.<sup>12</sup> Fifth, based on the data from the individual questionnaire, nonspenders divide roughly equally into those who have received inpatient or outpatient care but have not paid for them at the point of use, and those who did not receive care.

> 12 Offsetting this effect (but apparently only partially) is the fact that the recall period for outpatient care is longer for the variable constructed from individual-level.
Policy Research Working Paper 9952

# **Abstract**

This paper examines the effect of foreign direct investment on local structural transformation and human capital accumulation in China, exploiting variations in foreign direct investment inflows across manufacturing sub-sectors caused by China’s foreign direct investment deregulation and initial sectoral composition patterns across China’s cities and provinces. Using a panel of city-level data from 1990 to 2005, the paper shows that manufacturing foreign direct investment inflows greatly accelerated city-level structural transformation and human capital accumulation. By expanding access to the global market, foreign direct investment created a huge pull factor that drew excess labor away from farms into factories and services. Foreign direct investment has promoted high school and university enrollment by paying a higher wage premium for skilled workers and pushing up the skill premium. The positive effect on structural transformation is largely driven by export-oriented foreign direct investment, while market-seeking foreign direct investment has a much larger effect on college enrollment. High-skill foreign direct investment has a larger effect on college enrollment than low-skill foreign direct investment.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at yanliu@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
23 _Table 10:Results for components of unearned income_

|VARIABLE|Asset<br>rentals|Boarders<br>and lodgers|Retirement<br>pension|Poverty,<br>disability,<br>welfare<br>fund|Transfers<br>from family<br>and friends|Other<br>sources|Value of in-<br>kind gifts<br>from family<br>and friends|Gifts from<br>local<br>enterprises|
|---|---|---|---|---|---|---|---|---|
|SAH_CHANGE|0.031|0.010|0.039|**-0.023**|0.006|-0.076|-0.037|-0.020|
||1.230|1.140|1.260|**-1.760**|0.190|-2.240|-1.080|-1.350|
|POOR X SAH_CHANGE|-0.022|-0.014|-0.030|**0.037**|0.027|**0.117**|**0.107**|0.026|
||-0.550|-1.050|-0.630|**1.760**|0.550|**2.170**|**1.970**|1.100|
|**_LINEAR COMBINATIONS_**|0.009|-0.004|0.008|0.013|0.032|0.040|**0.070**|0.006|
|_SAH_CHANGE + POOR X SAH_CHANGE_|0.316|-0.449|0.239|0.895|0.933|1.048|**1.787**|0.338|
|N|7,486|7,486|7,486|7,486|7,486|7,486|7,486|7,486|

_Note: Results estimated with household-level fixed effects. Coefficients on controls (household mean age (level, squared, cubed), household size (level, squared, cubed), and wave dummies are not reported. t-statistics are reported under each coefficient. Coefficients in bold are significant at the 10% level (two-sided)._

# _Table 11: Components of unearned income_

|YEAR|Total<br>d|||Compo<br>|nents as % of<br>|total unearned in<br>|come<br>|||
|---|---|---|---|---|---|---|---|---|---|
||unearne<br>income|Asset<br>rentals|Boarders<br>and lodgers|Retirement<br>pension|Poverty,<br>disability,<br>welfare<br>fund|Transfers<br>from family<br>and friends|Other<br>sources|Value of in-<br>kind gifts<br>from family<br>and friends|Gifts from<br>local<br>enterprises|
|1991|157.2|9.84%|0.41%|42.19%|1.42%|6.11%|27.20%|9.80%|3.02%|
|1993|187.9|8.73%|0.47%|44.04%|0.74%|6.32%|28.15%|9.40%|2.16%|
|1997|220.5|15.83%|1.03%|43.56%|2.13%|9.76%|16.92%|10.15%|0.62%|
|2000|326.2|17.94%|0.56%|56.09%|2.36%|13.24%|0.00%|8.78%|1.03%|
|**Total**|**225.8**|**14.24%**|**0.65%**|**48.07%**|**1.82%**|**9.78%**|**14.55%**|**9.44%**|**1.45%**|

_n = 2,719 (1991), 2,715 (1993), 3,089 (1997), 2,798 (2000)_