rafmacalaba/datause-encoder-data huggingface dataset:
proxied by distance) will matter less for bilateral trade between NewZealand and Australia than for bilateral trade between Greece and Switzerland because Australia and New-Zealand are further away from their other trade partners than Greece and Switzerland. A large family of trade models satisfies the conditions necessary to yield a gravity equation at the product level.<sup>6</sup> Here is one. Take a one sector economy with a representative consumer with CES preferences with common elasticity σ among all goods. Impose symmetry of trade costs ( _tij_ = _tji_ ) and assume that trade costs are proportional to trade (no economies of scale in transport). Then the delivered price includes an ad-valorem equivalent of trade costs (tariffs, NTBs, etc.). With constant returns to scale in transport and marginal cost pricing in transport _pij_ = _p ti ij_ = _pi_ ( 1 +τ _ij_ ) and trade costs enter multiplicatively as in (2). Under these assumptions, outward   and inward trade costs _Pi_ , _Pj_ are symmetric and the theory-based gravity equation is composed of the system of equations: where _M ij_ is the imports of country _i_ from country _j_ , _Yi_ ( _j_ ) is the GDP of country _i (j)_ , _Y_ w is world GDP, t _ij_ is bilateral trade costs between _i_ and _j_ , _σ_ >1 is the elasticity of substitution in the CES utility function. According to (3) and (4), bilateral trade flows depend on the relative size of partners   and conditionally on relative trade costs where _Pi_ , _Pj_ respectively represent the inward and outward multilateral trade resistance.

A more satisfactory formulation of trade would recognize that transaction costs include several components, and that per-unit transport prices may not be equal to transport costs because of market power by transport carriers. Using disaggregated US ocean freight rates over the 1991-2004

> 6 See AvW (2003, 2004). These conditions are: (i) trade separability (i.e. separability in preferences and technology as in CES technology and utility); (ii) aggregator of varieties are identical and CES across countries; (iii) trade costs are proportional to trade and may include local distribution costs, but these costs do not affect trade flows; (iv) consumer have CES preferences with a common elasticity of substitution σ across commodities. Trade costs do not depend on the quantity of trade, a strong assumption since trade costs are likely to depend on the volume of trade.

6
15 Considering that _Language90_ may solely explain past migration and that return migrants make up only a selected sub-sample of the migrant population, we introduce an additional instrumental variable; _HouseholdKids,_ to specifically predict return migration without exerting any direct impact on the dependent variable. The utilization of _HouseholdKids_ is informed by the analysis of Carletto and Azzarri (2007), who use the ALSMS05 to model the determinants of out-migration (using the entire sample) and return (only among migrants) through single-spell hazard models. They find that while the number of household children in the age group of [0,14] is negatively associated with the likelihood of out-migration, the variable increases the probability of return among migrants. Hence, _HouseholdKids_ can be assumed to capture “return” if it assumes a negative coefficient in the first stage probit regressions that predicts past migration decision.

The results for all first-stage regressions are reported in Table A3, where instrumental variables take on statistically significant coefficients with expected signs. The joint tests of significance of our instruments following the first-stage regressions also yield F-statistics that are greater than 10; a common threshold employed in the literature (Staiger and Stock, 1997), to conclude that a particular set of instruments is sufficiently strong. Given the absence of formal instrumental variable ordered probit models, empirical tests for instrumental exogeneity also do not exist. To ensure that our instruments satisfy the requirement of exogeneity from an empirical standpoint, we first estimated Models 1 and 2 including the endogenous covariates and other independent variables of interest together with the instrumental variables that are excluded from the ordered probit regressions. In this context, we posit that successfully passing a basic test of instrumental exogeneity would require the instruments to assume statistically insignificant coefficients and fail to be jointly significant. In addition, we estimated Models 1 and 2 using the linear IV estimator which allows us to test the joint validity of our instruments under the assumption that the linear model of choice is correct. The null hypothesis of the overidentification tests is that the instruments are jointly valid, and that the excluded instruments are correctly excluded from the estimated equation. For instrumental validity and thereby the inability to reject the null hypothesis of the overidentification test, the resulting Hansen’s J test superior ability, and is rather indicative of innate cultural affinity. One may still argue that the exogeneity of the language instrument is questionable as individuals with exposure to foreign languages or of a specific ethnic background might have been members of wealthier households. We counteract this argument by specifically controlling for household economic status and dwelling conditions in 1990.
TABLE 6 EGYPT 1975-80 DECONPOSITION OF THE TOTAL IMPACT OF WEANING ON NEXr PREGNANCY OF MOTHER (before **age** 60 months) WEANED ceased breastfeeding prior to next pregnancy of mother DEATH child death prior to next pregnancy of mother and after weaning RR relative risk of pregnancy of mother (weaned / not weaned)

--------FERTILITY CONTROL-

ALL ln(RR) % NO ln(RR) % YES ln(RR) % WEANED RR at any age TOTAL 3.07 100.00 % 5.28 100.00 % 1.94 100.00 % DIRECT 3.02 98.51 % 5.21 99.16 % 1.94 100.00 % INDILE:CT 1.02 1.49 % 1.01 0.84 % * (through child death) WEANED RR before 12 months TOTAL 3.00 100.00 % 4.81 100.00 % 2.18 100.00 % DIRECT 2.91 97.12 & 4.69 98.38 % 2.06 92.70 % INDIRECT 1.03 2.88 % 1.03 1.62 % 1.06 7.30 % (through child death) WEANED RR at 12 months or later TOTAL 3.07 100.00 % 6.06 100.00 % 1.84 100.00 % DIRECT 3.07 100.00 % 6.06 100.00 % 1.84 100.00 % INDIRECT * (through child death) based on Weibull proportional hazards a.id log-logistic models controlling for: child death, weaning, parity, age of mother, preceding birth interval length, sex, region, urban/rural residence, fertility control, education of mother, education of father indirect effects evaluated at mean age of childreal based on data from the Egyptian Fertility Survey 1980 restrictions: children born w<sup>4</sup> thin 60 months of survey to married women last two children only no multiple births no marital disruption prior to child death Muslim only

*-not statistically distinguishable from 1.0

38
# **1. Introduction and summary**

On June 3, 2015, Accra was hit by a flood that claimed at least 152 lives and caused around US$100 million in asset losses. It was the most significant disaster to affect the city in recent times. An important fraction of the city was affected, and the impact on livelihoods and well-being was very large. Beyond the loss of life and direct impacts of the flood, a particular concern is the longer-term impact on the poorest and most vulnerable people in the city, who are likely to be less able to cope with and recover from a flood than the rest of the population.

There is little information on the relationship between poverty and flood risk in Accra. The CityStrength diagnostics study conducted after the 2015 flood identified knowledge gaps, including the need for (i) a systematic study on the geographic location of poor people in hazard prone areas and (ii) a better understanding of how floods affect poor households and the coping mechanisms they implement to deal with floods (World Bank, 2017). A previous study (Rain et al., 2011) assessed areas affected by floods in the Odaw River catchment and found that out of 172,000 people exposed to floods, approximately 20% lived in areas with the highest slum index, suggesting linkages between vulnerability to floods and poverty. This study aims at complementing this work, through a household survey focusing on the 2015 event.

The Ghana Poverty-DRM study was designed to assess the relationship between poverty and disaster risk in areas identified as informal settlements in the Greater Accra Metropolitan Area (GAMA). The analysis builds on the review of previous survey exercises conducted in a few post-disaster locations and reviewed in Hallegatte et al. (2017). Following these authors, we use a framework separating the hazard (“What are the characteristics of the flood?”), the exposure of the population and assets (“Who is affected by the flood?”), the vulnerability of the population and assets (“How much did the affected people lose?”), and the socio-economic resilience of the population (“Was the affected population capable of coping with and recovering from the losses?”); see Figure 1.

The study also investigates the effects of the flood – and of the risk of future floods – on households’ behaviors, and especially on decisions regarding investment in housing and businesses. The effect of risk in general on individuals’ or firms’ savings and investment decisions has been documented elsewhere, but never in an urban flood context (Elbers et al., 2007; Hallegatte et al., 2016; ODI and GFDRR, 2015).

2
- Levy, Philip I. 1997. “A political-economic analysis of free-trade agreements.”American Economic Review 87(4): 506-19.

- Levy, Philip I. 2006. “Do We Need an Undertaker for the Single Undertaking? Angles of Variable Geometry.” In Economic Development and Multilateral Trade Cooperation, edited by Simon Evenett and Bernard Hoekman, 417-437. London: Palgrave Macmillan.

- Limão, Nuno. 2005. “Trade policy, cross-border externalities and lobbies: do linked agreements enforce more cooperative outcomes?” Journal of International Economics 67(1): 175-99.

- Limão, Nuno. 2006. “Preferential Trade Agreements as Stumbling Blocks for Multilateral Trade Liberalization: Evidence for the U.S.” American Economic Review 96(3): 896-914.

- Limão, Nuno 2007. “Are Preferential Trade Agreements with Non-Trade Objectives a Stumbling Block for Multilateral Liberalization?” Review of Economic Studies 74(3): 821-55.

- Limão, Nuno and Giovanni Maggi. forthcoming. “Uncertainty and Trade Agreements.”American Economic Journal: Microeconomics.

- Limão, Nuno and Kamal Saggi. 2008. “Tari¤ retaliation versus ...nancial compensation in the enforcement of international trade agreements.” Journal of International Economics 76(1): 48-60.

- Limão, Nuno and Kamal Saggi. 2013. “Size inequality, coordination externalities and international trade agreements.” European Economic Review 63(C): 10-27.

- Limão, Nuno and Patricia Tovar. 2011. “Policy Choice: Theory and Evidence from Commitment via International Trade Agreements.” Journal of International Economics 85(2): 186-205.

- Liu, Xuepeng and Emanuel Ornelas. 2014. “Free Trade Agreements and the Consolidation of Democracy.” American Economic Journal: Macroeconomics 6(2): 29-70.

- Ludema, Rodney D. 1991. “International Trade Bargaining and the Most-Favored Nation Clause.” Economics and Politics 3(1): 1-20.

- Ludema, Rodney D. and Anna Maria Mayda. 2009. “Do Countries Free Ride on MFN?” Journal of International Economics 77(2): 137-50.

- Ludema, Rodney D. and Anna Maria Mayda. 2013. “Do Terms-of Trade E¤ects Matter for Trade Agreements? Theory and Evidence from WTO Countries.” Quarterly Journal of Economics 128(4): 183793.

- Magee, Christopher and Stephen Magee. 2008. “The United States is a Small Country in World Trade.” Review of International Economics 16(5): 990-1004.

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113
- 16 -

about 0.5. Adjustment in the real wage was, thus, a major channel through which the adjusfing countries improved the interational price compettiveness of their exports.-L

**Tb** **_10t_** EFFECT OF **_EXBCAMJ_ RATE** DEVALUATION ON WAGES Olpxdfs wibb: ad wmug)

||RER(1-I)|**EzIDfl-I)**|C|No. droW|32|SY-IL|D.W|1Fabtis|
|---|---|---|---|---|---|---|---|---|
|**(A)**Five diuses|0.5<br>_oar_|0.3n2<br>**_(o1l3r_**|Q2D<br>**_rsm3_**|30|0269|0.46|IS.IS|720|
|(B) Inteeivcude_a.Ljsh_|||||||||
|hmesia|eCItIZ<br>(0.43|0.473<br>a-=0)|0.59<br>(L|6|_0.4_|2|1.747|3.1106|
|Maxi|LB1<br>(L451)r|-ISi.5<br>(L17r|2.752<br>OMP|6|O9tJ|0.017|_ZAGI_|31-C06|
|_Tay_|_IJ09_<br>_n3s6>,_|2.3<br>¢Lr|_-5953_<br>¢us}|6||0.1129|**2.54**|**3mA**|
|(C) Lan hasive udeaduusa|||||||||
|Caxitla|**0.631**<br>(1.36)|**-2.6**<br>M.17s5r|**7.6I3**<br>**_(7AW_**|6|a.im|**02i6**|2.311|**15.066**|
|_PAbm_|0.0113<br>--M|13<br>a -or|-4.Itl<br>0-2ae|6|ODil|0.06|1.774|6&796|

bg Rod Wage - **t** + al hgft-1 + **_ii_** hgEuiloyt-I

**_1._** Sak n :w **ISS--itl. maul da** alu e ia

s iuface: SS Int

**2.** Eatnamd by OLS; _CwlCaur_ Mari to e cias.

3. **SYER:** Stanar Enr orRnasae 4. D.W.: DuabinWm D dfmiim .fv rbbL

1. Reat Wag It! egnin per Iae in d-me nunab ribg seewr (U-lUtl: **WOld This.** WOdd Pa 2. RER R:sI Pd of (1933-lOD a fa1 iaa reinda

3. EumpIer Epkomn _bab._ mm ___ wn _c macb_ (1911-1)- _Weld Tab. Woldd_ fL Ls The regression results for the individual adjusters suggest a far closer relationship between exchange rates and wages for Turkey, Mexico and Colombia as compared to that of Pakistan _and_ Indonesia.
**Table A3: Impacts of mental health on labor outcomes – Results of FE and IV-FE models**

||Employm<br>|ent status<br>|Labor<br>partici<br>|force<br>pation<br>|Perm<br>|anent job<br>|Skilled o<br>|ccupation<br>|Work in<br>|agriculture<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|FE|IV-FE|FE|IV-FE|FE|IV-FE|FE|IV-FE|FE|IV-FE|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Standardized mental health|||||||||||
|scores|-0.082***|-1.947***|-0.033***|-2.657**|-0.010|-0.443|-0.039***|-0.600***|0.068***|1.329***|
||(0.013)|(0.544)|(0.006)|(1.086)|(0.011)|(0.273)|(0.010)|(0.189)|(0.012)|(0.385)|
|_First stage of 2SLS (depende_|_nt variable is_|_standardized_|_mental heal_|_th scores)_|||||||
|Trauma experience*Year<br>after arrival||-0.058***||-0.058***||-0.058***||-0.058***||-0.058***|
|||(0.016)||(0.016)||(0.016)||(0.016)||(0.016)|
|Kleibergen-Paap F stat.||17.692||17.692||17.692||17.692||17.692|
|||[-4.014, -||[-10.924, -||[-1.811, -||[-1.355, -||[0.837,|
|AR 95-CIs||1.262]||1.498]||0.070]||0.351]||2.754]|
|Dep. Mean|0.455|0.455|0.418|0.418|0.163|0.163|0.135|0.135|0.746|0.746|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|3,687|3,687|7,768|7,768|3,687|3,687|3,687|3,687|3,687|3,687|
|Number of individuals|1,609|1,609|2,182|2,182|1,609|1,609|1,609|1,609|1,609|1,609|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

35
13-a

**Table** 3

Individual **Inflation** Cycles in Argentina **and** Brazil

|Period<br>Equ|ation|Indep<br>----------------------------<br>Constant|endent<br>**Varia**<br>CPINF(t-1)|**bles**<br>_-----<br>TREND|Adjusted<br>R-Square|D.W.|
|---|---|---|---|---|---|---|
|1. Argentina<br>_______________|||||||
|1986.09-1986.08|(1)|0.6658<br>(.36)||0.5091<br>(5.43)|0.7216<br>(29.50)|1.38|
||(2)|0.1348<br>(.16)|0.3841<br>(1.06)|0.3841<br>(2.S5)|0.7249<br>(16.49)|1.78|
|1987.04-1987.10|(1)|0.1893<br>(.12)||2.4746<br>(8.86)|0.8844<br>(48.91)|3.03|
||(2)|0.7203<br>(.3484)|-0.1678<br>(-.49)|2.69e8<br>(4.47)|0.8636<br>(19.99)|2.94|
|1987.12-1988.08|(1)|2.2123<br>(1.44)||2.7098<br>(9.90)|0.9238<br>(97.96)|1.80|
||(2)|2.7599<br>(1.48)|-0.1612<br>(-.59)|3.0463<br>(4.77)|0.9160<br>(44.80)|1.63|
|1988.11-1989.07|(1)|-51.7816<br>(-2.0)||20.8247<br>(4.52)|0.7087<br>(20.48)|0.66|
||(2)|-12.5410<br>(-1.38)|1.4550<br>(8.42)|3.8108<br>(1.56)|0.9735<br>(147.91)|3.11|
|2.Brazil|||||||
|1986.04-1987.05|(1)|-7.1441<br>(-3.25)||1.9385<br>(7.52)|0.8103<br>(S6.51)|0.42|
||(2)|-0.5881|1.099d|0.2784|0.96|1.38|
|||(-.42)|(6.77)|(1.02)|(156.85)||
|1986.10-1987.05|(1)|-3.3267<br>(-2.25)||3.5226<br>(12.01)|0.9534<br>(144.18)|1.69|
||(2)|-2.3238<br>(-.94)|0.2737<br>(.52)|2.7393<br>(1.79)|0.947<br>(63.52)|1.S9|
|1987.08-1989.01|(1)|6.6856<br>(6.07)||1.4063<br>(13.83)|0.9179<br>(191.15)|0.91|
||(2)|4.705<br>(2.14)|0.3066<br>(1.04)|1.017<br>(2.62)|0.9183<br>(96.59)|1.16|
|1989.03-1989.12|(1)|0.795<br>(.20)||6.2562<br>(8.20)|0.8805<br>(67.30)|0.99|
||(2)|0.7958<br>(.20)|0.331<br>(1.07)|3.7191<br>(2.37)|0.8827<br>(34.86)|1.03|

**Notes:** Equation (1): CPINF(t) = C + TREND Equation (2): CPINF(t) a C **_*_** CPINF(t-1) **_*_** TREND Figures in **the parentheses under the estimated** coefficients **are** t-statistics. Figures in **the** parenthoses under tho adjusted R-square **are** F-statistics. Notatiors: CPINF r CPI Inflation
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**Figure 13. Economies ranked according to their need for migrants (economies with the greatest need are ranked first)**

|1 Japan|35 Australia|
|---|---|
|2 Italy|36 New Zealand|
|5 Czech Republic|37 Costa Rica|
|6 Greece|38 Venezuela, RB|
|7 France|39 Canada|
|9 Austria|41 Thailand|
|11 Hungary|42 Ecuador|
|11 Ukraine|43 Ireland|
|14 Bulgaria|44 Singapore|
|15 Germany|46 Mexico|
|17 Netherlands|47 Chile|
|18 United Kingdom|48 South Africa|
|18 Russian Federation|50 Ghana|
|22 Spain||

Source: Economist Intelligence Unit, 2008. Global Migration Barometer The underlying economic and social attractiveness of an economy will certainly be an important driver of migration flows. Once such attractiveness is accounted for, however, analysis of the data yields an interesting result: there is no correlation between the ESE index and an economy’s need for migrants. Consider two economies with the same level of attractiveness to migrants, but with different degrees of need. As measured by the Global Migration Barometer, Italy and New Zealand have the same level of attractiveness to migrants.

However, they have a different need for migrants: Italy is ranked second and New Zealand is ranked 36 on the need for migrants. A more open immigration policy would be one way for the country with a greater need to attract more skilled migrants; but there is no difference on average between such economies’ immigration policies as measured by the ESE index. This implies that there is room for economies with a need for migrants to improve their immigration regimes as measured by the ESE index as one means of attracting more skilled migrants.

Economies with a greater need for migrants and that have restrictive migration policies could do better by being more liberal. For instance, Italy could improve its migration policies by reducing the time required to obtain a temporary work permit (it takes on average 13 weeks to obtain a temporary work permit) and could also reconsider the use of quotas or how they apply to skilled expatriates.

# **6.  Conclusion**

The importance of skilled immigration reform cannot be underestimated and is an essential building block for the sustained global competitiveness of any economy. This paper has highlighted the main components of the current skilled immigration regimes in 93 economies which companies take into consideration when deciding where to invest (how easy is it to hire skilled expatiates). In addition, we measured components which influence the relocation-decision of the skilled expatriate (is there a path leading to permanent residency and citizenship and is a spousal work permit available).

20
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