rafmacalaba/datause-encoder-data huggingface dataset:
What are some of the product groups which manufacturers in Penang could upgrade? These include some electronic products, other high-tech products and engineering products in which Penang does not have revealed comparative advantage, yet they are more sophisticated than the current export basket (see Figure 12). They could be the focus of upgrading efforts in Penang, assuming that Malaysian firms perceive them as being profitable opportunities.

**Figure 12: Product Space for Penang Where Penang Does Not Have Revealed Comparative Advantage Differentiated by Product Groups, 2007**

<!-- Start of picture text --> 5 10 15 20 25<br>invdensity<br>Electronics and Electricals Other, High-Technology<br>Automotive Process<br>Engineering Textile, garment & footwear<br>Other, Low-Technology Agro-Based<br>Other, Resource-Based Primary Products<br>20000<br>10000<br>0<br>-10000<br>-20000<br><!-- End of picture text --> Source: authors’ calculation Table 17, Table 18, and Table 19 list the highest density products in which Penang does not have comparative advantage (this includes those products exported by Penang and those that are not exported by Penang). In 1995 there were 5 products in high-tech categories. By 2000, this had increased to 9 commodities, but in 2007, more medium-tech and resource-based products had entered the dense category while those in the high-tech category had slipped to 8.

What is troubling is that the densest item in each year is progressively moving away from the origin. Thus, it is becoming harder for Penang to upgrade into these products. Take for instance, 7762 “Other electronic valves and tubes”. The density 18
provided by IMF-WEO and IMF-WCE, respectively.<sup>28</sup> Likewise, non-resource GDP is computed as the difference between GDP and resource GDP. The baseline world annual real interest rate, , is set to two percent, which is in line with the 10-year inflation-indexed US Treasury bond yields averaged over 2000-2019 from WW ff the St Louis Federal Reserve Bank Economic Data (Series: WLTIIT). The baseline debt-elasticity of the interest spread is set to , which implies that a ten percent of GDI increase in the external debt leads to a one ψψ= 0.1 percentage point increase in the country’s interest rate.<sup>29</sup> Finally, we set , which is sufficient to prevent any explosive paths for public debt as . φφ= 0.05 **Initial conditions.** GDP for 2020 is taken from World Bank’s World WW φφ> ff

**Initial conditions.** Development Indicators (WB-WDI), in constant 2010 U.S. Dollars.<sup>30</sup> In the absence of a data set containing comprehensive information on GDP at the industry level for several commodity-exporting countries, we proxy GDP in resource industry by exports of the resource good . More specifically, GDP in industry is set to match the average value of exports as a share of GDP.<sup>31</sup> The export data is ii ii ii taken from the UN-Comtrade Database (UN-CT), which provides information on export value for all 11 commodities and all 56 countries pre-loaded in the LTGMNR, with a time series that usually starts in 2002.<sup>32</sup>

28 As a complementary data set for government revenues (total, resource and non-resource), we use ICTD/UN-WIDER Government Revenue Dataset (UN-GRD). 29 The range of estimates for in literature varies widely across countries and papers. For example, while Schmitt-Grohe and Uribe (2003) set to match the volatility of the observed current-account-to-GDP ratio for Canada, Schmitt-Grohe and Uribe (2016) estimate ψψ for Argentina. We adopt as a compromise between these two seemingly extreme estimates. ψψ= 0.001 30 LTGM-NR spreadsheet since updated to constant 2015 USD. ψψ= 1 31 Initial GDP in industry ψψ= 0.1is computed as a share of total 2020 GDP. The default method is to use average value of exports in industry as a share of GDP since 2000. The following expression describes how the default initial real GDP in industry ii is computed: ii <u>�</u> 2010⁄ <u>� ,</u> 2020 � ii ii ii UUNNUUPP 2020 ii = ����������������������������<sup>1</sup> tt ii tt ~~�~~ WWDDWW pp pp x EEeeppf ttss P P i WWDDWW RRDDPP tt≥2000 tt x RRDDPP where is the real price (2010 U.S. dollars) of resource good NN RRDDPP in period taken from the World PPggPP iihP PP ssdd GGDDPP i ddi ttPPii ii i iissPP 2000 Bank Commodity Markets Outlook.ii is exports value (current U.S. dollars) of resource tt good in period pp , and is real GDP in period UUNNUUPP As ii tt is measured in current ii,tt U.S. dollars, it is deflated by WWDDWW 2010EEeeppf. ttss UUNNUUPP 32 Alternatively, we provide a measure of GDP in industry ii tt RRDDPPtt ii ii tt. derived directly from production data, EEeeppf ttssii,tt tt pp /pp 21 ii
# **5. Do choices in the lab-in-the-field experiment predict product take-up?**

Following the lab-in-the-field experiment, our field team explained the new _EduPay_ product developed with BPI with the help of some marketing material. Migrants who were interested in using the product signed a request letter to the school, asking them to release the students’ identification number, make available an invoice for payment to the school, provide details of the bank account of the school, and also release the grades of the student. These forms were then sent to our project coordinator in the Philippines, who worked with PAPSCU to contact the schools and arrange the logistics. Overall 132 individuals out of 487 offered the product signed this letter of intent (27.1 percent).<sup>5</sup> We use this as our take-up measure, since in practice during the pilot phase, many of the schools did not provide the needed information in a timely fashion, so that the majority of these intended transactions were not executed.<sup>6</sup> We examine the determinants of take-up via probit regressions of the form: Pr(Use EduPay) = Probit(δ<sup>′</sup> GameBehavior+ ζ<sup>′</sup> X) Where _X_ are additional control variables that may be likely to determine the desire of the migrant to remit for education. We include here gender, the number of children in the Philippines, whether the individual has their spouse in the Philippines, whether they have nephews and nieces of school-age in the Philippines, their own education level, whether they have been in Italy longer than the median of 7 years, whether they earn less than the median of 800 euros per month, and the amount they report having sent to the Philippines for educational purposes in the past 12 months.

We consider several measures of behavior in the lab-in-the-field games. The first measure is simply the amount of money they decide to remit explicitly in the form of a direct payment to the school under the direct payment choice option (choice iii). The mean is 316 € with a standard deviation of 364 €. For ease of interpretation, we divide by 100€, so the coefficient represents the marginal effect of allocating 100€more to direct payment in the choice experiment.

The second measure is the difference in the total amount they choose to remit to the Philippines when given the option to label money for education compared to the basic, no-label,

> 5 We had to drop 14 individuals because of lack of information on the schools, or because the schools were public, or because the school could not be managed by PAPSCU.

> 6 There were several reasons for this. First, a number of schools did not have bank accounts set up, and were not prepared to do so for only a small number of transactions. Second, schools typically took two to three weeks to provide the invoice and bank account information, and so migrants, being nervous about meeting tuition deadlines, opted to send money through other means. Ultimately this resulted in only 21 individuals making an EduPay transaction, 11 in the first pilot phase and 10 in an extension phase.

15
Fabrizio, S. and A. Mody. 2006. "Can Budget Institutions Counteract Political Indiscipline?" _Economic Policy_ 21(48): 689-739 Fatás, A. and I. Mihov. 2001. “The Effects of Fiscal Policy on Consumption and Employment: Theory and Evidence.'' INSEAD Working Paper.

Fatás, A., J. Von Hagen, A. H. Hallett, A. Sibert, and R. Strauch. 2003. _Stability and Growth in Europe: Towards a Better Pact_ . Vol. 13. Centre for Economic Policy Research.

Fatás, A., and I. Mihov. 2003. “The Case for Restricting Fiscal Policy Discretion.” _The Quarterly Journal of Economics_ 118(4): 1419-1447.

Fatás, A., and I. Mihov. 2007. “Fiscal Discipline, Volatility and Growth,” in _Fiscal policy, stabilization, and growth: Prudence or abstinence_ ? Edited by L. Serven, G. Perry and R. Suescun. World Bank, Washington, DC, USA Fatás, A., and I. Mihov. 2013. "Policy Volatility, Institutions, and Economic Growth." _The Review of Economics and Statistics_ 95(2): 362-376.

Jesús Fernández-Villaverde, J., P. Guerrón-Quintana, K. Kuester, and J. Rubio-Ramírez. “Fiscal Volatility Shocks and Economic Activity.” American Economic Review 105(11): 3352-84.

Frankel, J. A. 2011. “A Solution to Fiscal Procyclicality: The Structural Budget Institutions Pioneered by Chile.” NBER Working Paper 16945, National Bureau of Economic Research, Cambridge, MA.

Frankel, J., C. A. Vegh, and G. Vuletin. 2013. “On Graduation from Fiscal Procyclicality.” _Journal of Development Economics_ 100(1): 32-47.

Gavin, M., and R. Perotti. 1997. “Fiscal Policy in Latin America.” In _NBER Macroeconomics Annual 1997, Vol. 12_ , edited by B. Bernanke and J. Rotemberg, 11-72. Cambridge, MA: MIT Press.

Ghosh, A. R., and J. D. Ostry. 1994. “Export Instability and the External Balance in Developing Countries.” _IMF Staff Papers_ 41 (2): 214-235.

Henisz, W. 2000. “The Institutional Environment for Economic Growth.” _Economics and Politics_ 12 (1): 1-31.

Hesse. H. 2008. “Export Diversification and Economic Growth.” Commission on Growth and Development Working Paper 21, World Bank, Washington, DC.

Husain, A., K. Tazhibayeva, and A. Ter-Martirosyan. 2008. "Fiscal Policy and Economic Cycles in OilExporting Countries." IMF Working Paper 08/253, International Monetary Fund, Washington, DC _._ 21
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

**Figure 30: Price indexation lowers Government expenditure for civil servants more than wage indexation does****

<!-- Start of picture text -->
1.0%<br>0.9%<br>0.8%<br>0.7%<br>0.6%<br>0.5%<br>0.4%<br>0.3%<br>0.2%<br>0.1%<br>0.0%<br>ref I.2 ref I.3<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br><!-- End of picture text -->

**Notes** : ** Comparison of Government expenditure for civil servants – Reform I.2 with I.3 (% of GDP). **Source** : WB calculations based on PROST.

**Figure 31: Price indexation lowers Government expenditure for teachers more than wage indexation does**

<!-- Start of picture text -->
1.0%<br>0.9%<br>0.8%<br>0.7%<br>0.6%<br>0.5%<br>0.4%<br>0.3%<br>0.2%<br>0.1%<br>0.0%<br>ref I.2 ref I.3<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br><!-- End of picture text -->

**Notes** : ** Comparison of Government expenditure for teachers – Reform I.2 with I.3 (% of GDP). **Source** : WB calculations based on PROST.

- 63 -
**Table 1: Parameter values used**

|Parameter|Value|Description|Source|
|---|---|---|---|
|_δ_|1|Lower bound in DNWR|Normalization|
|_γ_|4%|Growth rate of world nominal GDP in $|Suggestive|
|_ν_|0.55|Inverse elasticity of moving across sectors|RUV|
|_σ_|6|Trade elasticity|Trade Literature|

**Notes:** This table contains the parameter values used in the baseline specification, together with their description and the source where they are taken from.

wages in dollars cannot fall, and putting the burden of the nominal adjustment on _γ_ .

We set _γ_ = 4%, due to the high nominal growth rate in the post-pandemic period. We discuss the implications of different _γ_ ’s (between 2% and 6%) in Section 6. The implications go in the direction expected, the higher the _γ_ , the less binding the DNWR is, and the less unemployment is generated in the model. For a high _γ_ of 6% or higher, the model has essentially the same behavior as the model without DNWR. The outcomes of the model unrelated to unemployment are only slightly affected by the choice of _γ_ .

Parameter _ν_ for the inverse elasticity of moving across sectors is taken directly from RUV and set to 0.55. In that paper, the inverse elasticity of moving across sectors is set to match the evidence shown in Autor et al. (2013) on how more exposure to the China shock across U.S. commuting zones affects labor force participation. As we will discuss in Section 6, our results do not change substantially when using other reasonable values of _ν_ . Finally, we set _σ_ to 6 in all sectors, which implies a trade elasticity of -5, consistent with the trade literature (see, e.g., Costinot and Rodriguez-Clare, 2014).

# **3.3 Trade Shock**

As indicated in the introduction, the baseline exercise examines a 12% increase in the iceberg trade costs of sending products across countries that reverts after three years. We also explore, in alternative exercises described in Section 6, how the effects of the shock depend on its size (i.e., 6%, 18%, or 24% instead of 12%) or persistence (i.e., 2, 4, 5, or 6 years instead of 3).

The choice of a 12% magnitude for the increase in trade costs in our baseline specifi11
**Polkyanning, and Rawsrch**

**World Dowaloprnent Report|**

The growth of government in the developing economies is compared with the experience of the industrial countries. Relying on measures of government expenditure as proxies for govemment size the following is observed:

* Transfer payments in developing nations are still at low levels when compared with advanced economies but appear to be growing quickly. Government consumption expenditures tend to be growing faster than GDP.

* In the developing nations, central government expenditures as a share of GNP range from 10.8 percetit to 62.1 percent and exhibit greater variance than is found in the industr;al countries.

. Developing economies, especially the lowincome nations, devote, on average, smaller percentages of GDP to government spending than do OECD countries. But compared with the historical experience of the industrial nations, low and middle income nations already consume much higher fractions of GDP.

* For the last 20 to 30 years, expansion in the share of government spending as a percentage of GDP appears to have been the norm in both developing and developed countries.

* Available data make it difficult to draw firm conclusions about what increases in government input costs versus increases in the level of pu9bic output c -ntribute to the growth in public spending.

Numerous arguments can be raised to explain why the size of government relative to GDP has grown in most developing nations. Demographic factors, preferences for public provision of goods and services, and increasing unit costs of government production are all likely to have been influential. Development theorizing itself as well as the "demonstration effect" of advanced capitalist and socialist economies may also have played a role.

This is a background paper for the 1988 World Development Report. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Rhoda Blade-Charest, room S 13-060, extension 33754.

> The PPR Working Paper Series dissemninates the findings of work under way hi the Bank's Policy, Planning, and Research 7 Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished.I Thc findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Copyright 0 1988 by the Intemational Bank for Reconstruction and Development/The World Bank
# **1 Introduction**

The recent wave of ‘new’ trade models has rekindled interest in the gains from trade. The results and theorems on the aggregate gains from trade in Dixit and Norman (1980, 1986) have been extended by Arkolakis, Costinot and Rodriguez-Clare (2012), Costinot, Donaldson and Komunjer (2012) and Costinot and Rodriguez-Clare (2014).<sup>1</sup> Concurrently, there has also been a renewed interest in the distribution of those gains. These are the focus of Porto (2006), Fajgelbaum and Khandelwal (2015), Atkin and Donaldson (2015), and Atkin, Faber and Gonzalez-Navarro (2018).<sup>2</sup> In this paper, we combine these two questions and assess the income gains relative to the inequality costs of trade policy. Using survey data for 54 developing countries, we explore the potential trade-off between the gains from trade and the distribution of those gains and we provide a quantification of the inequality-adjusted welfare gains from trade. The evaluation of this trade-off is important, especially because free trade is often opposed on inequality grounds.

We develop a comprehensive model that describes how trade policy affects the real income of different households. Tariffs determine domestic prices which affect households both as consumers and as income earners. As consumers, households are affected through the cost of the entire bundle of traded consumption goods. Similarly, household income is affected through changes in the returns to household production activities, crop growing, family businesses, labor earnings, and government transfers. Our model encompasses all these mechanisms. Following Deaton (1989), we use a first-order approximation to measure how changes in tariffs impact real income.

We then combine tariff data on various goods with household survey data on detailed income and expenditure patterns to estimate these first order welfare effects for 54 low and middle income countries. With estimates of the welfare effects of import tariff liberalization for each household, we study the aggregate gains from trade (as in Arkolakis, Costinot and Rodriguez-Clare, 2012) and the distribution of the gains from trade (as in Porto, 2006). Using

> 1See also Artuc, Lederman and Porto (2015), Caliendo and Parro (2015), Melitz and Redding (2015), Arkolakis, Costinot, Donaldson and Rodriguez-Clare (2015), and Caliendo, Dvorkin and Parro (2018).

> 2See also Nicita, Olarreaga and Porto (2014), Faber (2014), Goldberg and Pavcnic (2007), Topalova (2010), Kovak (2013), Autor, Dorn and Hanson (2013), Dix-Carneiro and Kovak (2017).

1
# 10% level) are all negative, but very small in magnitude.

# _Leisure_

Our measure of leisure time is constructed as a residual, that is as the difference between 24 hours and the time spent on all reported activities. In particular, we use the time use module (present only in the June 1999 round) with information on 18 activities carried out during the previous day; the previous day as reference period is not particularly ideal, as for some individuals, the survey may refer to a day which was not “typical” of normal activities. Additionally, many activities may be activities which are done infrequently (i.e. not daily). The survey was carried out this way as it was thought that a “previous day” reference period would reduce recall bias, given the large number of activities included in the questionnaire. The control variables included are the same of those in the labor force participation estimation (discussed above).

Table 3 presents the CSDIF estimates of the effect of PROGRESA on adult leisure (summarized by parameter β _T_ in equation (7)). Note that one hypothesis of the impact of PROGRESA on leisure is that, if the PROGRESA transfers are perceived as strict income transfers, and leisure is a normal good, then one might expect leisure to increase with PROGRESA. Nevertheless, the structure of the grants which reduce the price of schooling of children and thus may reduce the work of children may imply that overall hours dedicated to household production work (previously done by children) might increase. This would then imply that the program would have an ambiguous effect on the leisure time of adults and especially women. For example, it is conceivable that complying with the program’s requirements might decrease the leisure time of women, as they attempt to substitute for the 18
# Exploiting Externalities to Estimate the Long-Term Effects of Early Childhood Deworming<sup>_∗_</sup>

Owen Ozier<sup>_†_</sup> Development Research Group The World Bank

## JEL: I12, I20, I38, O1, O12, O15

> _∗_ This research was supported by USAID, the Partnership for Child Development, the Fernald Laboratory at the UC Berkeley School of Public Health, the UC Berkeley Center for Equitable Growth, the UC Berkeley Institute for Business and Economic Research, the UC Berkeley Center of Evaluation for Global Action, the John Carter Endowment, the Rocca Fellowship from the UC Berkeley Center for African Studies, the Bears Breaking Boundaries competition and the Henry Wheeler Center for Emerging and Neglected Diseases at UC Berkeley, and partnership with projects funded by the Center for Research in Economic Studies at the Olin Business School, Washington University at St. Louis. The project would not have been possible without the organizational support of Innovations for Poverty Action (Kenya). Jamie McCasland and John Ikoluot provided superb field research assistance and team leadership. Thanks to Harold Alderman, Prashant Bharadwaj, Simon Brooker, Donald Bundy, Eric Edmonds, Lia Fernald, Gerald Ipapa, Pamela Jakiela, Matthew Jukes, Carol Kemunto, Rose Kimani, Joseph Konde-Lule, Michael Kremer, Karen Levy, Edward Miguel, Rohini Pande, Adam Wagstaff, and Dean Yang, as well as MWIEDC, PACDEV, CSAE, CGD, IFPRI, and University of Virginia seminar participants for comments and suggestions as this project unfolded. First version of this paper: October 17, 2010. All errors are my own.

> _†_ Please direct correspondence to oozier@worldbank.org.
<!-- Start of picture text --> Figure 7b: Components of Adequate Health Services<br>Components of Adequate Health<br>95<br>80 80 80<br>76<br>72 72 72<br>67 66<br>64<br>61 60<br>58<br>53 55<br>47<br>30<br>28<br>26<br>23<br>17<br>15<br>10<br>BGD (HK, '11) BOL ('08) IDN (RKD, '10) NPL ('11) ZWE ('10)<br>BGD (IFPRI, '11) ETH ('11) KHM ('10) PER ('12)<br>Adequate vaccinations 4+ prenatal visits<br>Vitamin A supplementation<br>Source: Author estimates.<br>Note: For Bangladesh (HK) Vitamin A supplementation is only for children over 6 months.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text --> Applying stricter standards by requiring simultaneous access to adequate levels of each component of environment/infrastructure and health services, access to adequate infrastructure appears to have increased over time in most countries. For example, access to adequate infrastructure has increased in Bolivia, Nepal, Cambodia, and Peru (Figure 7c). Ethiopia shows very little progress primarily because of the low prevalence of improved sanitation at the community level (see also Figure 7a). Along similar lines, access to adequate health services have increased in Bolivia, Cambodia, and Zimbabwe, with little or very minor progress in Ethiopia (Figure 7d).<sup>16</sup>

> 16 In Peru the decline was caused by the decline in vitamin A supplemental dose coverage which fell from 22% to 10%.  All the other components of the measure improved over time.

30