rafmacalaba/datause-encoder-data huggingface dataset:
At the household level, we again estimate that virtually all households should gain from WTO accession.

There are, however, differences in the distribution of gains among households, the standard deviation of the estimated gains is somewhat larger and the Gini coefficient slightly increases, rather than slightly decreases. As a result of the national accounts based reconciliation of the factor shares, several sectors are considerably more capital intensive, and among them are three key expanding export intensive sectors--ferrous metals, non-ferrous metals and chemicals. Consequently the return to capital expands considerably more than in our central model based on the household budget survey data. The percentage change in skilled wages, unskilled wages and return on capital in the **national accounts based** (central model) is **4.7%** (5.3%), **2.5%** (3.7%) and **4.4%** (1.8%), respectively. Then, since rich households depend more on capital income, we estimate slightly regressive results with the national accounts based model, as opposed to very slightly progressive results with our central model based on the household survey based data reconciliation.<sup>54</sup> We conclude that the aggregate results and the shape of the distribution of gains across all households are not significantly affected by the principal choice of data reconciliation. Although virtually all households gain due to the Dixit-Stiglitz variety effect, which households gain more or less, however, is affected by the choice of how we reconcile the data.

# **Piecemeal Sensitivity Analysis**

In table 5 of Rutherford and Tarr (2008) we present the impact on welfare of varying the value of key parameters. The results are extremely close to the results presented in table 3.4 so are not reproduced here. We have verified that in the household model as well, the same three parameters

> 54 Results, based on the national accounts model, for EV as a percent of consumption by decile are: decile 1, 5.7; decile 2, 5.7; decile 3, 6.1; decile 4, 6.2; decile 5, 6.5; decile 6, 6.6; decile 7, 7.0; decile 8, 7.3; decile 9, 7.5; decile 10, 8.0. With the data reconciled based on the national accounts data, the Gini coefficient is .403 in this benchmark equilibrium, and it increases to .405 after our WTO accession scenario.

75
|(12)<br>arket<br> _GDP_|0.004*<br>(0.002)<br>0.005***<br>(0.001)<br>-0.013**<br>(0.005)<br>0.007<br>(0.009)<br>44<br>0.526<br>Private Credit by<br>pitalization/GDP.<br> This table reports<br>products exported<br> Rica, Dominican|
|---|---|
|(11)<br> D: Stock M<br>_talization / _|0.001<br>(0.002)<br>0.005***<br>(0.001)<br>0.003<br>(0.009)<br>44<br>0.443<br>Panel B:<br>arket Ca<br>0% level. <br>least 50 <br>re, Costa|
|(10)<br>Panel <br>_Capi_|0.005**<br>(0.002)<br>0.045***<br>(0.002)<br>44<br>0.136<br>ks/GDP;<br>: Stock M<br>at the 1<br> with at <br>, Singapo|
|(9)<br>t Money<br> _GDP_|0.014***<br>(0.004)<br>0.004***<br>(0.001)<br>-0.016***<br>(0.003)<br>0.004<br>(0.009)<br>46<br>0.67<br>Money Ban<br>P; Panel D<br> signifcant<br> countries <br> are Macao|
|(8)<br>C: Deposi<br> _Assets /_|0.009*<br>(0.005)<br>0.004***<br>(0.002)<br>0.001<br>(0.011)<br>46<br>0.467<br>eposit<br>ets/GD<br>level; *: <br>ample is<br>y==1|
|(7)<br>Panel <br>_Bank_|0.019***<br>(0.004)<br><br>0.033***<br>(0.003)<br>46<br>0.365<br>redit by D<br>y Bank Ass<br>at the 5%<br>nt. The s<br>hore dumm|
|(6)<br>redit by<br> _GDP_|0.009**<br>(0.004)<br>0.005***<br>(0.001)<br>-0.016***<br>(0.004)<br>-0.003<br>(0.010)<br>46<br>0.609<br>Private C<br>sit Mone<br>gnifcant<br>evelopme<br>d by of-s|
|(5)<br>: Private C<br> _and OFI/_|0.003<br>(0.005)<br>0.006***<br>(0.002)<br>-0.007<br>(0.012)<br>46<br>0.425<br>Panel A:<br>l C: Depo<br>evel; *: si<br>nancial d<br> capture<br> Africa.|
|(4)<br>Panel B<br>_DMB_|0.015***<br>(0.004)<br>0.037***<br>(0.004)<br>46<br>0.224<br>l depth. <br>DP; Pane<br>the 1% l<br>ures of f<br>. Locales<br>d, South|
|(3)<br>dit by<br> _/ GDP_|0.015***<br>(0.004)<br>0.004***<br>(0.001)<br>0.016***<br>(0.003)<br>0.004<br>(0.010)<br>46<br>0.657<br>fnancia<br>tions/G<br>fcant at<br>nt meas<br>ropped<br>itzerlan|
|re<br>_ks_|-<br>r<br>u<br>i<br>e<br> d<br>w|
|(2)<br>: Private C<br>_Money Ban_|0.009*<br>(0.005)<br>0.005***<br>(0.002)<br>0<br>(0.012)<br>46<br>0.457<br>proxies fo<br>cial Instit<br> ***: sign<br> for difer<br>200% are<br>mbourg, S|
|(1)<br>Panel A<br>_Deposit _|0.020***<br>(0.004)<br>0.034***<br>(0.003)<br>46<br>0.345<br>owth on<br>er Finan<br>entheses. <br>tion (5) <br>ter than <br>ng, Luxe|
|ariable:<br>_owth_|able<br> capita)<br>ality gr<br>and Oth<br>s in par<br>ng equa<br>es grea<br>ong Ko|
|t v<br> _gr_|vari<br> per <br>ons<br><br>qu<br>ks<br>ror<br>ati<br> rat<br> H|
|en<br>_ity_|l <br>P <br>e<br>t<br>ti<br>ed<br>of<br>n<br>r<br>m<br>h <br>a,|
|Depend<br>_Qual_|Financia<br>Ln (GD<br>Of-shor<br>Constan<br>Observa<br>R-squar<br>_Note:_ Regression<br>Deposit Money Ba<br>Robust standard e<br>the results of esti<br>to the US. Growt<br>Republic, Malaysi|

14
health outcomes. Compared to Meltzer _et al._ , the study is static since the parameters for health outcomes are all point estimates from prior Dutch influenza epidemics.

# **MODELING THE EFFECTIVENESS OF DIFFERENT POLICIES FOR CONTAINING PANDEMIC INFLUENZA**

As abstractions of reality, models of future pandemic transmission and its control are selective over the range of included policies and the efficacy for each policy. This is important for model parsimony, but the choices for inclusion and exclusion affect the conclusions. The choice set of policies to include for testing in simulations are those suggested by the modeling of potential bio‐terror attacks in the US, the experience with SARS, and public health practice over the latter half of the 20<sup>th</sup> century. The parameter ranges for the efficacy of vaccination and prophylaxis are either assumed in such modeling, or derived from studies of closely linked disease and epidemic situations. Table 5 presents a categorized menu of control policy choices available for a potential pandemic of influenza. Italicized prevention policies have received more research attention, or have had their effectiveness modeled. These are the only policies considered in detail in the section below.

15
# **Table A34: OLS; Effects of refugee presence on household total Cereals production by change in main source of income, with analysis at cluster level and the distance weighted refugee index at -** **<u>50km from the clusters (Panel 2009 2012)</u>**

||Log of T|otal Cereals Produc|tion (UGX)|
|---|---|---|---|
||(1)<br>|(2)<br>|(3)<br>|
|Log of refugees (50km)|-0.0394|-0.0329|-0.0226|
||(0.0392)|(0.0395)|(0.0464)|
|Log of refugees(50km)#To Commercial farming|0.0073|-0.0082|0.0221|
||(0.0887)|(0.0863)|(0.0644)|
|Log of refugees(50km)#To Wage employment|-0.0042|-0.0043|0.0108|
||(0.0462)|(0.0463)|(0.0471)|
|Log of refugees(50km)#To Non-Agric self-employment|0.0119|0.0073|0.0522|
||(0.0534)|(0.0525)|(0.0553)|
|HH head age||0.0577***|0.0306*|
|||(0.0136)|(0.0178)|
|Age squared||-0.0005***|-0.0003*|
|||(0.0001)|(0.0002)|
|HH head sex (male)||0.4698***|0.2568**|
|||(0.0844)|(0.1061)|
|Annual mean temperature||0.1572|0.1050|
|||(0.1143)|(0.1141)|
|Average 12 months total rainfall||0.0031|0.0064**|
|||(0.0021)|(0.0026)|
|Household size|||0.0380**|
||||(0.0150)|
|Experienced shocks|||-0.0530|
||||(0.0987)|
|**_HH head marital status_**||||
|Married monogamously|||0.1286|
||||(0.3281)|
|Married polygamously|||0.0653|
||||(0.3493)|
|Separated (divorced/widowed)|||0.0660|
||||(0.3368)|
|**_HH head education level_**||||
|Completed primary sch|||0.2547***|
||||(0.0843)|
|**_HH main source of income_**||||
|Change to Commercial farming|0.4463*|0.4674*|0.4492|
||(0.2619)|(0.2548)|(0.2955)|
|Change to Wage employment|-0.3282**|-0.3324**|-0.3595**|
||(0.1637)|(0.1423)|(0.1448)|
|Change to Non-agricultural self-employment|0.0900|0.0747|-0.0344|
||(0.1475)|(0.1412)|(0.1382)|
|**_Other source of HH income_**||||
|Property income and investments|||0.2074|
||||(0.2160)|
|Transfers and other benefits|||0.1101|
||||(0.1087)|
|**_HH Asset_**||||
|Own > median size of land (2.5 acres)|||0.3492***|
||||(0.0921)|
|Observations|2,471|2,470|2,110|
|R-squared|0.2808|0.3098|0.3653|

54
**TABLES**

# <u>Table 1: Estimation results</u>

|_Type of regression: Co_<br>|_mmon shocks acr_<br>Service em<br>(correc<br>|_oss countries a_<br>ployment<br>ted)<br>|_ssumed_<br>Unemplo<br>|yment<br>|
|---|---|---|---|---|
|Variable|Coefficients|t-statistics|Coefficients|t-statistics|
|Shocks<br>(given<br>average institutions)|0.8%||0.85%||
|Start-upcost index|-0.29*|(-1.45)|0.09|(0.26)|
|Exit cost index|-0.003|(-0.28)|-0.006|(-0.24)|
|Finance index|**0.41****|(2.95)|-0.18|(-0.42)|
|Legal system index|-0.04|(-1.13)|-0.05|(-0.86)|
|Mk.<br>Regulation<br>index|**-0.64****|(-2.45)|**1.73***|(1.53)|
|Corruption index|0.06|(0.33)|**1.57****|(2.37)|
|Tax burden|-0.08|(-1.11)|**0.43****|(2.35)|
|Observations|106||104||
|Adjusted R-Squared|0.9|5|0.7|7|
|Non-linear least square<br>*significant at 15%<br>**significant at 1%|estimation||||

Table 2: _contribution of investment climate institutions to service employment change_ _<u>over the last decade in new EU countries</u>_

||Hungary<br>(92-02)|Czech Rep.<br>(92-02)|Slovakia<br>(94-02)|Poland<br>(94-02)|Latvia<br>(96-02)|Lithuania<br>(97-02)|Slovenia<br>(93-02)|Average<br>new EU<br>countries|
|---|---|---|---|---|---|---|---|---|
|Actual change in<br>service employment,<br>in%|1.11|3.78|0.66|1.33|1.89|-1.83|0.61|1.08|
|Predicted change by<br>the model in service<br>employment, in %<br>Of which:|1.92|1.41|0.42|1.51|1.26|0.13|0.98|1.09|
|Time shock|1.42|1.33|1.08|1.08|0.44|0.10|1.33|0.97|
|Start-up costs|-0.57|-0.15|-0.16|-0.26|0.17|0.03|0.02|-0.13|
|Access to finance|0.70|0.43|0.02|0.52|0.39|-0.02|1.03|0.44|
|Market regulation|1.00|0.24|-0.36|0.06|0.14|-0.01|-0.53|0.08|
|Tax burden|-0.63|-0.44|-0.16|0.11|0.12|0.04|-0.87|-0.26|

_Note that only the significant variables from table 1 have been included in table 2_ 28
both give similar results. In the presence of over-dispersion<sup>4</sup> (variance>mean), the participation decision can be similarly decomposed in a zero-inflated negative binomial model as: Pr( _yi_ | **x** _i_ ) =

where E( _yi_ ) = (1 - _Pi_ )λ _i_ and Var( _yi_ ) = (1 - _Pi_ )[1 + λ _i_ (α + _Pi_ )]λ _i_ . The presence of the α parameter in the calculation of the conditional variance of _y_ (if greater than 0), guarantees that the variance is greater than the mean. As α → 0, the moments of the distribution converge to a Poisson distribution and so testing for α =0 provides a case for selecting the negative binomial over the Poisson, and indirectly for the presence of over-dispersion.

The flexibility of modeling the participation decision in this manner has lead to a number of interesting applications in recreational demand analysis, including beach trips (Shonkwiler and Shaw, 1996; Haab and McConnell, 1996), rock climbing (Shaw and Jakus, 1996), lake recreation, (Gurmu and Trivedi, 1996), water-based recreation (Curtis, 2003), and angling site choice (Scrogin _et al_ ., 2004).

# _(ii) On-site sampling_

Interview surveys conducted on-site obviously avoid the non-participation issue, but as the dependent variable _yi_ is strictly non-zero, the truncated demand relationship

> 4 An undesirable feature of Poisson count models is the assumption that the conditional mean and variance are equal (Yen and Adamowicz, 1993). This is especially problematic in empirical research because conditional variances are typically greater than conditional means in socio-economic data (also known as over-dispersion, a form of heteroskedasticity).  The presence of over-dispersion still allows for consistently estimated means of parameter estimates (Gourieroux _et al_ . 1984), but causes the standard errors of these estimates to be biased downward, resulting in erroneous tests of their statistical significance (Cameron and Trivedi, 1986).  The equality of the mean and the variance property of Poisson count models led to the development of negative binomial models (Hausman _et al_ ., 1984). This model allows for over-dispersion by combining the Poisson distribution with a gamma distribution and hence allowing for heterogeneity to be gamma distributed.

5
-7-

distinct ways, depending upon the dominant regime in th" cconomy.

The model first identifies which are the maisi macroeconomic constraints to growth following the traditio.aal distinction between the external constrpkint (or foreign gap), che savings constLaint (or internal gap) and the novel fiscal constraint whose meaning will be discussed later.

The next step is to provile a behavioral content to the main equations -if the model. Then, we specify which variables endogenously adjust to bring macro equilibrium and which variables are taken as exogenously determined The closure rule adopted is what defines the growth-regime.

Let us start, first, with the national accounts identity between investment and savings.

where: Y is output (GDP), C is private consumption, T is cuirrent revenues of the government, G is government consumption, M represents total imports, X are total exports and I is gross investment.

Tntroducing net factor payments abroad, R* , (which includes interest payments on foreign debt) and assuming that a share q of the debt is owed by the private sector and a share l-q by the government, 0 < q < 1, equation (1) can be written as :

- Redefining terms, Sp is national savings equal to Y -C qR*. Sg is public savings equals to T -G -(l-q)R* and Sf is foreign savings, namely the deficit in the current account, H - X + R*. Therefore,
where _Eis_ is the total lifetime earnings differential for an individual _i_ with education level _s_ who dropped out before achieving a higher educational level s’ = _s+1_ for _s_ =0, 1, 2 where _s_ =0 corresponds to incomplete primary school, _s_ =1 corresponds to primary school completed only, _s_ =2 corresponds to secondary school completed only, and s’=3 corresponds to tertiary education; _e_ are the annual earnings at time _t_ of an individual with _s_ (or _s’_ ) educational level, _t_ is the age at which this individual begins work and _T_ is the age at which this individual will retire; and _r_ is the discount rate. For simplicity, we assume that _T-t_ for those with a level of education _s_ is equivalent to _T-t_ for those with _s’_ level of education. Finally, to calculate the total lifetime loss in earnings for all girls who were not in school in the observation year, we multiply _Eis_ by the number of students in the population who did not go on to continue their education (Es). We repeat this exercise for each s and sum together to generate the total foregone earnings of the most recent cohort of school leavers (E): As in Cunningham et al. (2008), we use 45 years as the length of the working life, and a 6 percent discount rate.

Assuming that if girls had just completed the next level of education, they would earn the same average wage as those who actually did complete that level of education would over-estimate the productivity gains to education since part of that gap is likely due to a difference in abilities that cannot be attributed to differential education levels.<sup>8</sup> To account for ability bias, we introduce an adjustment to average wages. We use two adjustment factors. First, based on Card (1999), we assume that girls with only _s_ level of education, if they completed _s+1_ years of education, they would earn wages that are 10%

> 8 Numerous studies have shown that those who complete _s_ level of education are less ―able‖ than those who complete _s+1_ level of education due to genetic or social factors that are unobservable to the researcher, but observed and acted upon by the girl, her parents, the school, or others (Card 1999).  Thus, we cannot assume that if a girl only had completed one additional level of education, she would be earning the average wage equivalent to the observed wages of girls who actually did complete that level of education. Instead, we need to account for ability bias when assigning average wages of girls with _s+1_ level of education to girls with only _s_ level of education.

7
**_<u>________________________________________________________</u>_**

———. 2002b. _Economic Consequences of Terrorism_ . OECD Economic Outlook 71: 117–40. Paris.

———. 2003. _The Role of Automation in Trade Facilitation_ . Document TD/TC/WP(2003)21, Paris. ———.. _Doha Development Agenda: Welfare Gains from Further Multilateral Trade Liberalisation with Respect to Tariffs_ . Document TD/TC/WP(2003)10/FINAL, Paris.

Poirson. 1998. _Economic Security, Private Investment and Growth in Developing Countries._ IMF Working Paper WP/98/04, The International Monetary Fund, Washington, D.C.

Radelet, Steven and Jeffrey Sachs. 1998. _Shipping Costs, Manufactured Exports, and Economic Growth._ Mimeo. Harvard Institute for International Development.

Raven, John. 2001. _<u>Trade and Transport Facilitation: A Toolkit for Audit, Analysis and Remedial Action</u>_ <u>.</u> The World Bank, Washington, D.C.

Regibeau, Pierre and Katharine Rockett. 2001. October. _<u>Administrative Delays as Barriers to Trade</u>_ <u>.</u> Center for Economic Policy Research. Schware, Robert and Paul Kimberley. 1995. _<u>Information Technology and National Trade Facilitation - Making the Most of Global Trade</u>_ <u>. World Bank Technical</u> Paper number 316, The World Bank, Washington, D.C.

_-_ Schware, Robert and Paul Kimberley. 1995. _<u>Information Technology and National Trade Facilitation Guide to Best Practice</u>_ <u>, World Bank Technical Paper number 317, The World Bank, Washington, D.C.</u> Subramanian, Uma. 2001. _Transport, Logistics, and Trade Facilitation in the South Asia Subregion_ . In T.R. Lakshmanan, Uma Subramanian, William P. Anderson and Frannie A. Léautier (eds), _Integration of Transport and Trade Facilitation_ . The World Bank, Washington, D.C.

Subramanian U., and J. Arnold. 2001. _<u>Forging Subregional Links in Transportation and Logistics in South Asia.</u> The_ World Bank, Washington, D.C.

UNCTAD (United Nations Commission on Trade and Development). 2001. E-Commerce and Development Report.

Walkenhorst, Peter, and Nora Dihel. 2002. _Trade Impacts of the Terrorist Attacks of 11 September 2001: A Quantitative Assessment_ . Paper prepared for the Workshop on The Economic Consequences of Global Terrorism. Berlin, June 12–13.

Walkenhorst, Peter and Tadashi Yasui. 2003. _Quantitative Assessment of the Benefits of Trade Facilitation._ OECD. TD/TC/WP(2003)31. Paris.

Wilson, John S., and Tsunehiro Otsuki. 2003. _Food Safety and Trade: Winners and Losers in a Nonharmonized World_ . Journal of Economic Integration 18 (2): 266–87.

———. 2003. April. _Standards and Technical Regulations and Firm’s Ability to Export New Evidence from World Bank Standards and Trade Survey_ , (Draft), The World Bank, Washington, D.C.

Wilson, John S., Catherine L. Mann, and Tsunehiro Otsuki. 2003a. _Trade Facilitation and Economic Development: Measuring the Impact_ . World Bank Working Paper 2933, The World Bank, Washington, D.C.

68 __________________________________________________________________________________________________________
**_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

firms report expanding outsourcing relationships that proved successful during the recession because there is insufficient time to install new capacity to meet rapidly growing demand.

**Figure 1** World Imports of Intermediate, Capital, and Consumption Goods, 1962–2006

<!-- Start of picture text --> 4,500,000,000,000<br>Imports Intermediate Goods<br>4,000,000,000,000<br>Imports Consumption Goods<br>Imports Capital Goods<br>3,500,000,000,000<br>Imports Final Goods (Capital + Dot-com bubble<br>Consumption)<br>3,000,000,000,000<br>Asian financial crisis<br>2,500,000,000,000<br>1991 US<br>2,000,000,000,000 1982 US Recession<br>Recession<br>1,500,000,000,000 Second oil<br>shock 1985 PC<br>bubble<br>1,000,000,000,000 First oil<br>shock<br>500,000,000,000<br>0<br>19621964196619681970197219741976197819801982198419861988199019921994199619982000200220042006<br><!-- End of picture text -->

**_Source:_** Sturgeon and Memedovic (forthcoming) from UN Comtrade Standard International Trade Classification (SITC) Rev. 1 data. To identify commodities as Consumption, Capital, and Intermediate goods, the conversion table Broad Economic Category (BEC) to SITC Rev. 1 from World Integrated Trade Solution (WITS) was used. In order to calculate constant price data, National Accounts data from United Nations Industrial Development Organization (UNIDO) Statistics Unit and a GDP deflator were applied.

In the case of the United States, where up-to-date trade statistics are available (Table 3), the value of electronic component imports decreased at an average rate of 11 percent per year during the crisis period 2008–09 after being relatively stable during the period 1996–2007 at about $70 billion per year. Remarkably, imports of final products decreased much less. While these declines are significant, declines in imports of automobiles (–23.0 percent) and auto parts (– 20.2 percent) were more dramatic. The value of electronic component exports also decreased during the crisis period, by 9 percent per year, which is even more remarkable since component exports increased at an average annual rate of nearly 6.7 percent per year during the 1996–2007 period, regaining in 2007 the peak of $50 billion reached in 2001, the height of the technology bubble (see Table 3).

8