rafmacalaba/datause-encoder-data huggingface dataset:
As we are concerned about the endogeneity of the migrant stock, we employ the instrumental variable approach. Our first instrument is the stock of migrants from country _c_ living in the European Union (EU) at time _t_ . Given that both the US and the EU are major destinations for migration flows, the stocks of migrants in the US and EU will be correlated. However, there is less reason to suspect that the stock of migrants in the EU is correlated with the error term in the regression.<sup>8</sup> The definition of this instrument corresponds to the definition of the variable for which it is instrumenting. An aggregate migrant stock in the US is instrumented using the aggregate migrant stock in the EU, and the stock of college educated migrants in the US is instrumented with the stock of college educated migrants in the EU.

Our second instrument is the cost of obtaining a national passport in the partner country _c_ , normalized by the country’s GDP per capita. High passport fees are likely to constitute a barrier to emigration, particularly for the poorer segment of the population. Indeed, McKenzie (2005) finds that high passport fees are associated with lower levels of outward migration.

As the third instrument, we use the log of population density in the partner country c. Earlier migration research has shown that population density is an important push factor, especially in countries with significant rural populations, as it reflects the absence of adequate land ownership.

We also experiment with two additional instruments capturing legal barriers to obtaining a passport and traveling out of the country faced by citizens of some countries. For instance, in some countries married women need to obtain permission from their husband and unmarried women need to obtain permission from their father or an adult relative in order to travel abroad. Such legal restrictions are often accompanied by a social disapproval of women migrating alone. As McKenzie (2005) shows, the presence of such restrictions is negatively correlated with the number of migrants relative to the population size. The second set of restrictions pertains to the need to obtain government permission or an exit visa to travel abroad and to travel restrictions imposed on citizens of national service age. Although the permission to travel may be granted in most cases, the existence of restrictions adds to the costs and uncertainty of the migration decision. Moreover, the restrictions imposed on citizens of national service age may constrain the population in the age range at which individuals have the greatest propensity to migrate (McKenzie 2005).

> 8 While countries experiencing a military conflict or a major economic crisis are likely to experience both an increase in emigration and a decline in FDI inflows, this phenomenon would be captured by the other control variables in our regression.

5
holders. In other words, such investments in market facilities and infrastructure (including transportation) will be pro-poor.

While this paper can be regarded as a fi rst step towards quantifying the bene fi ts of facilities and infrastructure available at market place, the market access index is estimated by counting the total number of facilities at a given location and dividing it by square of distance from the village to the market. This formulation of a market access index does not take into account the quality and scale di ff erences in facilities across markets. Accounting for such di ff erences will be important for more practical policy simulations and remains as a major priority for future research.

# REFERENCES

1. Acharya, S.S., 2004, Agricultural Marketing in India, Part of the Millennium Study of Indian Farmers, Report # 17, Academic Foundation Publishers, New Delhi.

2. Badiane, O, and G.E. Shively, 1998, “Spatial integration, Transport costs and the Response of Local Prices to Policy Changes in Ghana," Journal of Development Economics, Vol. 56(2), p.411-431.

3. Baldwin, R. and D. Taglioni, 2006, “Gravity for Dummies and Dummies for Gravity Equations," NBER working paper # 12516.

4. Bardhan, P., 1989, The Economic Theory of Agrarian Institutions, Clarendon Press, Oxford.

5. Barrett, C.B. and D.C. Clay, 2003, “How Accurate is Food-for-Work Self-targeting in the Presence of Imperfect Factor Markets? Evidence from Ethiopia," Journal of Development Studies, Vol. 39(5), p.152-80.

29
# **Table A3: Panel B – Multinomial Regressions, 2005**

Log Odds ratio between Outright Owners and Mortgage holders (Marginal Effects)

|**Country**|**lage**|**female**|**hhsize**|**lnincome**|**hhnodep**|**hhdep**|**house**|**secedu**|**teredu**|**fulltime**|
|---|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_|||||||||||
|Czech Republic|0.2574 ***|0.0041|0.0321 **|0.0677 **|0.0271|-0.0594|0.5884 ***||0.8488 ***|-0.0258|
|Estonia|0.2328 ***|-0.0012|0.0304 ***|-0.0378 **|0.0996 ***|0.0366|-0.0288 *||-0.1745|0.0713 *|
|Hungary|0.1254|0.0052|0.0239 ***|-0.1594|0.0306|-0.0628 **|0.1120 ***|-0.6720|-0.9360 ***|-0.0172|
|Latvia|0.1815 ***|-0.0453 ***|-0.0036|0.0059|0.0479 **|0.0305|0.1674 ***|-0.4582|-0.7367 ***|0.0511|
|Lithuania|0.2860 ***|0.0327 ***|0.0203 ***|0.0152|0.0426 **|-0.0261|-0.0084||-0.0729|0.0221|
|Poland|0.2917 ***|-0.0067|0.0231 ***|0.0420 **|-0.0395 **|-0.0555 **|0.6128 ***|0.4187 *|0.1289|-0.0255|
|Slovak Republic|0.2313 ***|0.0285|0.0529 ***||0.2265 ***|0.1037 ***|0.4044 ***|0.2565||0.0810|
|Slovenia|0.1678 ***|0.0146|0.0385 ***|0.0153|0.0397 **|-0.1094 ***|0.1867 ***|-0.7034 ***|-0.9190 ***|-0.0959 ***|
|_EU-15 and Cyprus_|||||||||||
|Austria|0.2094 ***|0.0090|0.0449 ***|0.0801 ***|0.0224|-0.1294 ***|0.3545 ***||-0.5646 ***|-0.0625|
|Belgium|0.6986 ***|-0.0062|0.0466 ***|-0.0349|0.0685 **|-0.2316 ***|0.2864 ***|-0.9813 ***|-0.7037 **|-0.0569|
|Cyprus|0.2976 ***|0.0226|0.0316 ***|0.0349|0.0044|-0.0375|0.1570 ***|-0.6674 **|-0.7840 ***|-0.0428|
|Finland|0.7958 ***|0.0144|0.0151|0.0421|0.0421|-0.0835 **|0.1804 ***|-0.4694|-0.8413 ***|0.0204|
|France|0.7209 ***|-0.0021|0.0012|0.0154|0.1026 ***|-0.0235|0.2065 ***|-0.0154|-0.2465|-0.0270|
|Greece|0.3201 ***|0.0091|0.0688 ***|-0.0335|0.0169|-0.1400 ***|0.1666 ***|-0.7204 ***|-0.6621 **|-0.0461|
|Ireland|0.8212 ***|0.0056|0.0258 **|-0.0255|0.0386|-0.2204 ***|0.4648 ***|0.0243|-0.4000|0.0544|
|Italy|0.5062 ***|0.0186 *|0.0368 ***|-0.0180|0.0609 ***|-0.1021 ***|0.1615 ***|-0.1632|-0.6996 ***|-0.0682 ***|
|Luxembourg|0.7133 ***|0.0307|-0.0031|0.0212|0.0828 *|-0.0651|0.3013 ***|-0.9093 ***|-0.5908|-0.1480 ***|
|Portugal|0.4229 ***|0.0090|0.0478 ***|-0.0587 ***|0.0732 *|-0.1439 ***|0.3265 ***|0.4172|-0.3553|-0.2307 ***|
|Spain|0.5651 ***|0.0249|0.0797 ***|-0.0593 ***|0.0457 *|-0.1801 ***|0.0868 ***|0.0183|-0.3163|-0.0874 ***|
|Sweden|0.1073 **|-0.0062|-0.0085|0.0221|0.0082|-0.0131|0.0444 ***|0.8609 **|0.9971 ***|-0.0186|
|United Kingdom|0.8130 ***|0.0005|0.0192 **|-0.0679 ***|0.0764 ***|-0.1442 ***|0.1799 ***|0.9659 ***||0.1076 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

29
Measuring the Upper Tail of the Income and Wealth Distributions Andrew Kerr<sup>1</sup> and Mxolisi Zondi<sup>2</sup> JEL Classification: D31, C81 Keywords: Income Distribution, Wealth Distribution, Top tail, Measurement, Household Surveys 1 School of Economics and DataFirst, University of Cape Town. andrew.kerr@uct.ac.za 2 Research Unit on the Economics of Excisable Products, University of Cape Town We thank Utz Pape, Jed Friedman and Matthew Wai-Poi from the World Bank for helpful comments and suggestions. Mxolisi Zondi was employed by DataFirst at the University of Cape Town when he worked on this project.
# **References**

[1] Abramovitz, J. , 2001. Unnatural Disasters. Worldwatch Institute Paper 158. [2] Alesina, A., Dollar, D., 2000. Who Gives Foreign Aid to Whom and Why? Journal of Economic Growth 5, 33—63.

[3] Berthélemy, J.-C., Tichit, A., 2004. Bilateral Donors’ Aid Allocation Decisions - a Three Dimensional Panel Analysis. International Review of Economics and Finance 13, 253—274.

[4] Darcy, J., Hofmann, C.A., 2003. According to Need? Needs Assessment and Decision-Making in the Humanitarian sector. HPG Report nr.15, London. [5] Easterly, W., Levine, R., 1997. Africa’s Growth Tragedy: Policies and Ethnic Divsions. The Quarterly Journal of Economics 112(4), 1203—1250. [6] Eisensee, T., Stromberg, D., 2007. News Floods, News Droughts, and U.S. Disaster Relief. Forthcoming: Quarterly Journal of Economics.

[7] Fearon, J.D., Laitin, D.D., 2003. Ethnicity, Insurgency and Civil War. American Political Science Review 97(1), 75—90.

[8] Freedom House, 2007. Freedom in the World 2006. http://www.freedomhouse.org/. [9] García-Montalvo, J., Reynal-Querol, M., 2005. Fractionalization, Polarization and Economic Development. Journal of Development Economics, 76, 293-323. [10] Gartzke,E., Dong-Joon, J., 2002. The Affinity of Nations Index, 1946-1996. http://www.columbia.edu/~eg589/datasets.html.

[11] Gleditsch, K. S., Ward, M. D., 2001. "Measuring Space: A Minimum Distance Database." Journal of Peace Research 38:749-68.

[12] Guha-Sapir, D., Hargitt, D., Hoyois, P., 2004. Thirty Years of Natural Disasters 1974-2003: the Numbers. UCL, Presse Universitaires de Louvain.

[13] IASC, 1994.Consolidated Appeal Process Guidelines. Inter-Agency Standing Committee. [14] IFRC, 2003. World Disasters Report 2003. International Federation of Red Cross and Red Crescent Societies, Geneva.

[15] Macrae, J. (ed.), 2002. The New Humanitarianism: A Review of Trends in Global Humanitarian Action. HPG Report nr.11. London: ODI.

[16] McKinlay, R.D., Little, R., 1977. A Foreign Policy Model of U.S. Bilateral Aid Allocation. World Politics, Vol. 30(1), 58-86.

[17] Neumayer, E., 2003. Do Human Rights Matter in Bilateral Aid Allocation? A Quantitative Analysis of 21 Donor Countries. Social Science Quarterly, vol. 84(3), 65066.

[18] Neumayer, E., 2003a. The Determinants of Aid Allocation by Regional Multilateral Development Banks and United Nations Agencies. International Studies Quarterly, 47 (1), 101-122.

[19] OECD, 2007. International Development Statistics (IDS) Online. http://www.oecd.org/dac/stats/idsonline.

[20] Olsen, G.R., Carstensen, N., Høyen, K., 2003. Humanitarian Crises: What Determines the Level of Emergency Assistance? Media Coverage, Donor interests and the Aid Business. Disasters, vol.27, no.2.

17
being tested increases both for currently and formerly married women. Compared to individuals with no education, men who have been to primary school are more likely to be tested, but women who have been to secondary school are less likely to be tested. The latter result could suggest that educated women are more likely to be tested before the survey and they think they do not need to be tested again or they may have a greater probability of working outside the household and therefore were absent during the test. Wealth and religion do not seem to have an important role in the probability of being tested for HIV.

The fact that some socio-economic factors (location, marital status and education) have an impact on the probability of being tested indicates that there is a potential risk of bias in the interpretation of the results in table 3. The sign and the magnitude of the bias would depend on whether we expect individuals who were not tested even though they were selected to be more or less likely to be HIV positive than the average individual.

# **4.2 Condom use**

We move next to the analysis of the determinants of reported behaviors. It is important to note that all those behaviors are self-reported and therefore more subjective than the result of an HIV test. The first set of regressions focuses on the probability of using a condom during the last intercourse. This aspect is particularly interesting for Lesotho, where condoms use is still relatively limited. The Demographic Health Survey asks whether the last intercourse occurred with a spouse or with another partner, thus we can compare this behavior inside and outside the marriage. The use of a condom is recommended in both situations, but the absence of a condom during extra-marital sex is considered even more risky. Unfortunately, in the survey, precise questions about condom use at the last intercourse were only asked to females, so that we have to limit our analysis to them.

[Insert table 5] Table 5 reports the results of the analysis. Urban status is positively and strongly correlated with the likelihood of using a condom, both inside and outside 13
Table 7 provides information on the importance of intra-industry trade in Central America based on the adjusted Grubel-Loyed intra-industry trade index.<sup>5</sup> This index can take values between 0 (no intra-industry trade) to 1 (all trade is intra-industry). There appears to be some importance of intra-industry trade within Central America, however, with the exception of Costa Rica (0.3) there is virtually no evidence of intra-industry trade with the United States. For El Salvador and Guatemala intra-industry trade appears to be quite high with Mexico and Brazil.

**Table 7. Intra-industry Trade**

||**Costa Rica**|**El Salvador**|**Guatemala**|**Honduras**|**Nicaragua**|**Argentina**|**Mexico **|**Canada**|**France**|
|---|---|---|---|---|---|---|---|---|---|
|**El Salvador**|0.36|||||||||
|**Guatemala**|0.38|0.45||||||||
|**Honduras**|0.40|0.27|0.33|||||||
|**Nicaragua**|0.34|0.15|0.21|0.15||||||
|**Mexico**|0.18|0.43|0.42|0.11|0.02|0.26||0.49|0.57|
|**Brazil**|0.08|0.43|0.51|0.03|0.28|0.39|0.51||0.11|
|**USA**|0.30|0.05|0.05|0.06|0.02|0.10|0.46|0.66|0.56|
|**Germany**|0.06|0.02|0.01|0.02|0.01|0.13|0.79|0.33|0.70|

Source: Own calculation based on trade data from UN COMTRADE for the year 2001. A 5 digit level of disaggregation is used for this exercise.

# **3.3. Business Cycle Synchronization and Trade**

Empirical evidence on trade integration and business cycle synchronization is somewhat mixed. While Frankel and Rose (1998), Choe (2001), Calderon, Chong and Stein (2002) and Calderon (2003) find that a higher trade intensity tends to increase business cycle synchronization, Shin and Wang (2003) find that increasing trade itself does not necessarily lead to more synchronized business cycles, evidence for East Asia suggests that only the expansion of intra-industry trade had such an effect. However, Garnier (2003) find only weak or no relations between intraindustry trade and business cycle synchronization for 16 industrialized countries and conclude that intra-industry trade at most only partially explains business cycle transmission; the low respectively. The adjusted Grubel Llyod index makes an adjustment for trade imbalances.

11
**Table 6 (Continued)**

|||**Log Total Value**|**of Food**|**Purchases P**|**er Capita**|
|---|---|---|---|---|---|
|||IHPS|||FIAS|
||1|2||1|2|
|**Weather Shocks†**||||||
|Weather Shock|-0.043|||-0.170||
||(.050)|||(.111)||
|Current Shock||-0.033|||-0.162|
|||(.051)|||(.115)|
|Sequential Shock||-0.308|**||-0.325<br>*|
|||(.145)|||(.189)|
|**Aid * Shock**||||||
|Aid|-0.323|*<br>-0.345|*|-0.304|-0.335|
||(.189)|(.190)||(.259)|(.266)|
|Aid * Weather Shock|-0.241|||-0.010||
||(.233)|||(.341)||
|Aid * Current Shock||-0.379|||-0.085|
|||(.275)|||(.421)|
|Aid * Sequential Shock||0.644|*||0.385|
|||(.364)|||(.344)|
|**Explicit HH Targeting Characteristics**||||||
|Dummy, Female Head|-0.142|**<br>-0.151|***|-0.051|-0.054|
||(.057)|(.057)||(.081)|(.080)|
|Dummy, Children<3|-0.025|-0.023||-0.011|-0.007|
||(.034)|(.033)||(.061)|(.060)|
|Dummy, All Members>59|-0.292|*<br>-0.308|*|-0.332|-0.349|
||(.162)|(.162)||(.235)|(.238)|
|Dummy, HH Member Disabled|-0.068|*<br>-0.071|*|0.026|0.025|
||(.039)|(.038)||(.064)|(.063)|
|**Wealth**||||||
|Wealth Index|1.119|***<br>1.130|***|1.303|***<br>1.317<br>***|
||(.138)|(.139)||(.283)|(.286)|
|# Mobile Phones|0.047|***<br>0.048|***|0.025|0.027|
||(.017)|(.017)||(.035)|(.035)|
|**Constant**|10.595|***<br>10.522|***|9.685|***<br>9.631<br>***|
||(.516)|(.515)||(.899)|(.907)|
|# of Obs.|4412|4412||1999|<br>1999|
|R2 (within)|0.307|0.312||0.231|<br>0.234|
|R2 (between)|0.603|0.605||0.404|<br>0.406|
|R2 (overall)<br>**Notes:*****/**/* denote statistical sig<br>**Weather Shock**is a dichotomous varia|0.506<br>nificance at<br>ble that is e|0.509<br>1/5/10 percent res<br>qual to 1 if the house|<br>pective<br>hold ha|0.325<br>ly. Standard<br>s been expo|<br>0.328<br>Errors in parentheses.**†**<br>sed to a weather shock in|

**Notes:** ***/**/* denote statistical significance at 1/5/10 percent respectively. Standard Errors in parentheses. **† Weather Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in the current period (irrespective of potential exposure to past weather shocks), and 0 otherwise. **Current Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock _only_ in the current period, and 0 otherwise. **Sequential Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in consecutive periods, and 0 otherwise.

33
endogenous variable. We now find that the size of the wage loss for smokers is equal to a 23.4 percent wage reduction (bootstrapped standard error of 9.8) compared to wages of non-smokers.<sup>10</sup> This large estimated loss in wages is comparable with the estimates by Auld (2005) who finds a 30 percent earning loss for smokers in Netherlands and is higher than the effects found in studies of smoking in the US and Canada. The results show a positive selection bias into smoking as found in other studies (such as Auld, 2005, and Van Ours 2002)<sup>11</sup> ; OLS results underestimate the negative wage effect of smoking.

The results of the FIML estimation of the switching regression model (2) are shown on Table 3.<sup>12</sup> The Log-likelihood test of independence is rejected in favor of joint dependence of the error terms in system (2). Hausman over-identification test (Hausman 1983) confirms the validity of our instruments with 5 percent significance level.

Turning to the estimations of the earning functions, controlling for other personal characteristics respondent’s age has a negative and significant effect on individual earning. Married non-smoking males earn significantly more than single nonsmokers and whereas marital status of smokers has no effect on their earnings. The relationship between wages and education of non-smokers is stronger (i.e. wage-education profile is steeper) than for smokers. An ability to speak English is associated with a significant wage premium for both groups. Knowledge of Italian and Greek, on the other hand, is beneficial in term of higher wages only for non-smokers. Overall we find a stronger dependence between wages and productive human capital characteristics for non-smokers than for smokers. That could support the hypothesis about the differences in the discount rates of the future between smoking and non-smoking individuals (Evans and Montgomery 1994).

We evaluate the effect of smoking on wages by simulations based on estimates in Table 3 of the parameters of equations (4) and (5). For every smoking respondent we

> 10 We use simulated effects instead of approximate effects given by the coefficient on the smoker dummy in Table 2.

> 11 Levine _et al._ (1997) show mixed results in regards to evidence of the positive selection but find no evidence of negative selection.

> 12 The estimation of the switching regression model and simulation are based on the FIML algorithm implemented as a Stata program _movestay_ (Lokshin and Sajaia 2004). We also tried to estimate the simultaneous model with three wage equations for smokers, quitters, and those who never smoked by FIML. Aside from the issue of finding valid instruments for identification, the model failed to converge, most likely due to the small number of quitter in our sample.

14
# <u>Table 7: Structure and information</u>

The dependent variable is the standard deviation of the rate of growth of real value added for each 4 digit ISIC industry in each country. Regressions included a country and industry specific effect. Liquidity needs is the ratio of total inventories over annual sales for U.S. corporate firms in the same industry between 1980 and 1989. The row “Differential in volatility” measures how much volatile (as a fraction of the interquartile range of volatility) is an industry at the 75th percentile level of liquidity needs with respect to an industry at the 25th percentile level when it located in a country at the 75th percentile of financial development rather than in one at the 25th percentile. All columns report the results obtained when the measures of financial development are instrumented using a country’s legal origin.

|Variables|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Interact. (Liquidity needs|-1.206*|||-0.964*|-1.360*|
|X private credit)|(0.227)|||(0.301)|(0.244)|
|Interact. (Liquidity needs||-1.845*||-1.058*||
|X accounting standards)||(0.383)||(0.427)||
|Interact. (Liquidity needs|||-0.446*||-0.396|
|X Stock Mkt. cap.)|||(0.220)||(0.280)|
|Diferential in volatility||||||
|Private credit||||||
|Percentage points|4.1|||3.3|4.7|
|Fraction of IQR|59|||47|67|
|Accounting standards||||||
|Percentage points||2.7||1.6||
|Fraction of IQR||39||22||
|Stock Mo. capitalization||||||
|Percentage points|||0.5||0.4|
|Fraction of IQR|||8||6|
|Obs.|2301|1670|1965|1844|2137|

Note:*=Significant at 5% confidence. Robust standard errors in parenthesis.

34
occurring at one price were assigned a lower price by USDOC which in effect creates margins.

# **6.2 Evidence from WTO dispute documents**

While the Lindsey and Ikenson study is compelling, it involves a small sample of firms. We have also reviewed the WTO disputes for evidence on the impact of zeroing. We found reports of the impact of zeroing in the public documents for only three cases: _U.S. - Stainless Steel_ (Mexico) (dispute 344), _U.S. - Zeroing_ (Japan) (dispute 322), and _U.S. -_ _Zeroing_ (EC) (dispute 294). From these three disputes we have information on the impact of zeroing for 74 separate margin calculations.

The tabulation of the findings is given in Table 4. For each margin calculation we report the name of the product under investigation, the name of the company subject to the investigation, and the AD duty as calculated by the USDOC (inclusive of zeroing). For original investigations this is the final AD duty for each firm; for administrative reviews this is the duty margin actually imposed by USDOC. In the final column we report the results of the counterfactual exercise – what the margin would have been if zeroing were not performed. Given the individual firms’ sensitivities about revealing confidential pricing information, many times we do not know the exact “what if no zeroing” margin. Instead, the public documents often simply report “lower,” “negative,” or _de minimis_ . “Lower” simply means the margin would have been lower but would have still been above the _de minimis_ level; “negative” means the margin would have been negative (i.e., no – 35 –