rafmacalaba/datause-encoder-data huggingface dataset:
which produced 3,093 studies. The search was complemented by a database search. The keyword search in the databases generally aimed to look for the “poor’s exposure to natural disasters” and the “impact of natural disasters on the poor.” The database searches produced an additional 880 studies: 80 studies from _Scopus_ , 320 studies from CORE, and 450 studies from _Jstor_ .<sup>2</sup> After duplicate citations were removed, 1,303 articles and reports remained. We also complemented our search with studies in a review of climate adaptation by Rexer and Sharma (2024). Citation results were restricted to those in English, published in the field of economics or general-interest peerreviewed journals, and reports published between 2000 and 2023.<sup>3</sup> The list of journals was based on the list developed by Rexer and Sharma (2024). These restrictions excluded 518 studies, leaving 785 studies for abstract screening.

In our search, "poor" refers to poor households, poor regions, or poor countries. This broad definition allows us to capture a wide range of studies that examined the distributional impacts of climate change at different levels of analysis. An artificial intelligence (AI) model, GPT4-32k, was used to screen abstracts for climate shocks. We conducted a validation test on the AI output by comparing a reviewer’s binary coding with the AI output and reached agreement in 87.2 percent of the validation sample. We then used the AI response to filter the remaining abstracts, leaving 361 abstracts for further review. Two reviewers then excluded 272 studies because they did not analyze the poor’s exposure to climate shocks or the impact of such shocks on the poor. Conflicts in inclusion and exclusion at this stage were reconciled by a third reviewer to yield 89 studies for the full text screening stage. Each study was then examined by two reviewers. Studies were excluded if they did not analyze the poor’s exposure to climate shocks or compare the impact of such shocks on the poor relative to the non-poor. Conflicts at this stage were reconciled by a third reviewer to yield 70 studies and 701 regression results for the analysis. Figure 1 describes the number of studies at each screening stage.

> 2 The keyword search in _Scopus_ included ‘natural disaster’ and ‘poverty’ in the fields of Economics and Econometrics. The keyword search in CORE included ‘effect of natural disaster’ and ‘poor’ in the field of Economics. The keyword search in _Jstor_ included ‘natural disaster’ and ‘poverty’ in the field of Economics. The item type in each database was restricted to articles. The search in _Scopus_ and _Jstor_ was done on November 14, 2023 and the search in CORE was done on November 15, 2023.

> 3 While it is possible to examine studies from before 2000, we believe more recent studies would be more similar to current policy settings.

4
# **Table 2. Mapping:** **_Accounting Services_**

|**Australian categories**|**Weight**|**Score**|**Weighted score**|**The World Bank data**|**Weight**|**Score**|**Weighted**<br>**score**|
|---|---|---|---|---|---|---|---|
|**BARRIERS TO ESTABLISHMENT**||||||||
|**Formof establishment**|**0.08**|||**Formof establishment**|**0.08**|||
|Prohibition on incorporation|0.08|1||Prohibition of separate legal entity|0.08|1||
|Some form of incorporation permitted|0.08|0.5|**NA**|||||
|No restrictions|0.08|0||Separate legal entity allowed|0.08|0||
|**Foreign partnership or joint**<br>**venture**|**0.08**||||**0.08**|||
|Prohibition on partnership with foreign<br>professionals<br>hi i ih ll|0.08|1||Limit on ownership or control by<br>foreign national is not allowed|0.08|1||
|Partnersp or jont venture wt oca<br>professionals required|0.08|0.5|NA|hi l b fi||||
|No restrictions|0.08|0||ownersp or contro y oregn<br>national is allowed|0.08|0||
|**Investment and ownership by**<br>**foreign professionals**|**0.05**|||**Investment and ownership by**<br>**foreign professionals**|**0.05**|||
|Firms must be owned or controlled by<br>_local_professionals. The score is||||The score is inversely proportional to<br>the maximum foreign equity<br>participation permitted in a||||
|<br>inversely proportional to the maximum<br>||||<br>professional firm. If s = maximum<br>||||
|foreign equity participation permitted in<br>a professional firm|0.05|||foreign equity participation permitted<br>in a professional firm|0.05|1-s||
|**Investment and ownership by non-**<br>**professional investors**|**0.05**|||**Investment and ownership by non-**<br>**professional investors**<br>|**0.05**|||
|||||The score is inversely proportional to<br>||||
|Firms must be owned or controlled by<br>||||the maximum equity participation<br>||||
|professionals. The score is proportional<br>to the non- rofessional euit||||permitted for non- locally licensed<br>rofessionals in a rofessional firm||||
|p qy<br>permitted in a professional firm. For<br>||||p p .<br>Let p = the maximum equity<br>||||
|example , equity participation to<br>maximum of 75 percent in existing firm||||participation permitted for non- locally<br>licensed professionals in a professional||||
|<br>receives a score of 0.25|0.05|||<br>firm|0.05|1-p||
|**Nationality or citizenship**<br>**requirements**|**0.135**|||**Nationality or citizenship**<br>**requirements**|**0.135**|||
|Nationality required to qualify or to<br>||||Market closed nationality of host<br>||||
|practice|0.135|1||country is required|0.135|1||
|Nationality required for use of<br>||||||||
|professional title, but practice is<br>relatively free|0.135|0.25|**NA**|||||
|**No restrictions**|0.135|0||No restriction|0.135|0||
|**Residency and localpresence**|**0.135**|||**Residency and localpresence**|**0.135**|||
|Permanent or prior residency (more<br>than 12 months)|0.135|1||Residency in host country is required|0.135|1||
|Less than 12 months prior residency|0.135|0.75|**NA**|||||
|Prior residency required for local<br>training|0.135|0.5|**NA**|||||
|Domicile or representative office only|0.135|0.25|**NA**|||||
|No restrictions|0.135|0||No restriction|0.135|0||

24
∂ _s L_ ∂ _sU_ Note that having < 0 and at the same time > 0 requires ∂ _n_ ∂ _n_ to see that which proves (a). Coupling this with the non-increasing density assumption then proves (b).

# **References**

Ai, Chunrong and Edward Norton (2003) “Interaction terms in logit and probit models”, _Economics Letters_ 80: 123-129.

Angrist, Joshua (1991) “Instrumental Variables Estimates of Average Treatment Effects in Econometrics and Epidemiology”, Working Paper 115, Cambridge, MA: National Bureau of Economic Research.

Bauer, Thomas, Gil S. Epstein and Ira N. Gang (2002): Herd effects or migration networks? The location choice of Mexican immigrants in the US, IZA Discussion Paper No 551, August.

Bauer, Thomas, Gil S. Epstein and Ira N. Gang (2005): Enclaves, language and the location choice of immigrants, _Journal of Population Economics_ , 18, 4: 649-62.

Borjas, George J. (1987): Self-selection and the earnings of immigrants, _American Economic Review_ , 77, 4: 531-53.

Borjas, George J. (1999): _Heaven’s door: immigration policy and the American economy_ , Princeton, N.J.: Princeton University Press.

Caponi, Vincenzo (2006) “Heterogeneous Human Capital and Migration: who migrates from Mexico to the U.S.?”, _IZA Discussion Paper No. 2446._ Carrington, William J., Enrica Detragiache and Tara Vishwanath (1996): Migration with endogenous moving costs, _American Economic Review_ , 86, 4: 909-30.

Chiquiar, Daniel and Gordon H. Hanson (2005): International migration, self-selection, and the distribution of wages: Evidence from Mexico and the United States, _Journal of Political Economy_ , 113, 2: 239-81.

Chiswick, Barry (1999): Are immigrants favorably self-selected?, _American Economic Review – Papers and Proceedings_ , 89, 2: 181-85.

Chiswick, Barry and Paul W. Miller (2005): Do Enclaves Matter in Immigrant Adjustment?, _City and Community_ , 4, 1: 5-35.

Cuecuecha, Alfredo (2005a): The immigration of educated Mexicans: the role of informal social insurance and migration costs, Mimeo, ITAM..

Cuecuecha, Alfredo (2005b) “The Educational Characteristics of the Immigrants from Mexico to the United States”, Mimeo. ITAM.

Davis, Benjamin and Paul Winters (2001) “Gender, Networks and Mexico-US Migration”, _Journal of Development Studies_ 38(2): 1-26.

Dirección General de Estadística (DGE) (1941) _Anuario Estadístico de los Estados Unidos Mexicanos 1939._ Secretaría de la Economía Nacional, DGE: Mexico City.

Dolfin, Sarah and Garance Genicot (2006) “What do networks do? The role of networks on migration and 'coyote' use”, Mimeo. Georgetown University.

20
23 result seems difficult to explain within the framework of the FWS system.32 It is possible, however, that the CONFLICT variable is capturing other influences which are playing a major role in the spending decision. Specifically, species in conflict may generate extra political attention. Then, through a variety of mechanisms, such political attention might translate into increased spending.

There are also indications that species in conflict receive higher priority numbers than they objectively deserve. As mentioned earlier, the NC endangerment rank (NCRANK) and FWS's "degree of threat" component of PRIORITY attempt to measure the same thing. Nevertheless, the correlation between the two measures is far from perfect, and some of the deviation can be explained by the existence of conflict. To iliustate this point, we estimate an OLS regression of the FWS degree of threat (DEGREE) on independent variables NCRANK and CONFCT.33

# REGRESSION #4 - DETERMINATION OF DEGREE OF THREAT

|DEREE<br>|<br>Coef.|Std. Er.|t|P>ItI|
|---|---|---|---|
|NCRANK<br>I<br>.20|.05|4.333|0.000|
|CONFLICT<br>-.41<br>|.07|-5.637|0.000|
|CONSTANT<br>I<br>1.28|.47|15.394|0.000|

2 Adjusted R<sup>2</sup> = .17 Dependent variable is DEGREE. Method of estimation is OLS. 237 observations.

The coefficient on NCRANK is positive and significant, but considering that a coefficient of 1 would indicate a perfect correlation, the size of the coefficient seems low.

32Mann and Plummer (1993) were the first to indicate the importance of te CONFLICT variable. Their results motivated us to include CONFLICr in our analysis.

33An OLS regression implies that we take the actual numencal DEGREE ranings seriously. If we believe that DEGREE ranldngs are only ordered casses, then the proper estimation procedure would be ordered logit Since, in this case, the results of an ordered logit estimation are very similr to OLS, we only report the latter. In either case, the indicated choices of idependent and dependent varibles aue natural beCaUSe DEGREE is a somewhat subjective measure created by the FWS, wbile NCRANK and CONFLICT are more objectively detrmned. No specific standards have been published by the FWS to explain why species receive different DEGREE ranks. NCRANK, by contrast, has fairly specific guidelines summaized in Nadonal Heritage Data Center (1992). Also, CONFLICT is the most objective of the FWS ranks; the published guidelines state that 'Any species identified ... as having generad a negative biological opinion which concluded that a given proposed project would violate Section 7(a)(2) of the Endangered Species Act or resulted in the e of reasonable and prudent alernatives to avoid a negative biological opinion would be assigned to the conflict category ... ". Fay and Thomas (1983).
sectoral switches alone are more frequent than occupational switches alone; for other cells the pattern is the reverse; but for most cells the frequency of switching along both dimensions is either between the frequency of switching only sector and the frequency of switching only occupation or – more often – higher than either. Put differently, the probability that a worker switches sector is quite similar to the probability that a worker switches occupation, and a worker who switches in one dimension is at least as likely also to switch in the other dimension as not. This all suggests that the costs of switching along either dimension are likely quite similar, and the cost of switching both is likely not significantly greater than the cost of switching only one, which would imply a negative value of _C_<sup>4</sup><sup>_,s_</sup> . This is all borne out in the estimates, as will be seen shortly.

Table 4: Sector and Occupation Change (Percent)

||||**No-C**<br>|**ollege**<br>|||**Co**<br>|**llege**<br>||
|---|---|---|---|---|---|---|---|---|---|
|||_Sec_|_Occ_|_Both_|_Stay_|_Sec_|_Occ_|_Both_|_Stay_|
|_White_|_Aggr/Cons_|1.1|1.8|2.0|95.2|2.9|0.5|1.2|95.3|
||_Manuf_|1.2|1.1|1.2|96.5|1.9|0.4|0.7|96.9|
||_Non-traded_|1.1|1.7|2.6|94.5|3.1|0.8|1.1|94.9|
||_Traded_|0.9|2.0|1.7|95.5|1.1|0.9|0.6|97.3|
|_Tech/Sales_|_Aggr/Cons_|2.5|2.0|3.0|92.4|3.7|1.3|4.1|90.9|
||_Manuf_|1.4|1.5|2.1|95.0|3.0|1.8|2.4|92.8|
||_Non-traded_|1.6|1.3|2.8|94.3|2.8|1.4|3.5|92.4|
||_Traded_|1.2|1.9|1.7|95.2|1.5|3.3|1.6|93.7|
|_Service_|_Aggr/Cons_|1.3|2.2|6.0|90.4|0.9|1.3|9.7|88.1|
||_Manuf_|1.5|1.6|1.7|95.2|2.5|1.7|4.2|91.5|
||_Non-traded_|1.2|1.3|2.7|94.8|0.9|1.4|4.1|93.6|
||_Traded_|1.3|1.9|2.7|94.2|1.5|8.9|4.6|85.0|
|_Craft_|_Aggr/Cons_|1.8|1.8|2.9|93.4|1.4|2.2|7.6|88.8|
||_Manuf_|0.9|1.2|1.4|96.5|0.8|2.6|2.6|94.1|
||_Non-traded_|2.3|1.1|1.7|94.9|2.0|2.3|2.6|93.0|
||_Traded_|2.0|1.4|1.6|95.0|1.8|3.5|2.7|92.0|
|_Laborer_|_Aggr/Cons_|3.2|3.2|2.9|90.7|3.4|4.3|12.0|80.4|
||_Manuf_|1.1|0.7|1.9|96.2|0.6|2.2|6.0|91.2|
||_Non-traded_|2.6|1.6|2.8|93.0|2.1|4.2|7.5|86.2|
||_Traded_|2.3|1.6|2.2|93.9|1.5|5.2|3.8|89.4|

In Table 5, we present the ratio of industry-occupation cells for each industry and occu16
Table 13: **Effective Protection Coefficients for Cane**

||1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|Avg.|
|---|---|---|---|---|---|---|---|---|---|---|---|
|UP|0.56|1.05|1.11|1.98|8.13|6.18|3.98|1.80|0.94|0.80|2.65|
|Maharashtra|0.46|0.79|0.95|1.39|2.29|1.81|1.74|1.35|0.73|0.53|1.20|
|TN|0.47|0.83|1.18|1.67|3.47|2.35|1.45|0.88|0.48|0.60|1.34|
|**All India**|**0.54**|**0.99**|**1.12**|**1.89**|**5.66**|**4.21**|**2.85**|**1.60**|**0.84**|**0.75**|**2.04**|

Sources and notes: See Annex 14.

Figure 22 **Effective Protection Coefficients for Sugarcane** <!-- Start of picture text --> w~~~ UP<br>| Maharashtra<br>- TN<br><!-- End of picture text -->

Sources and notes: See Annex 18.

s

# **EFFECTIVE SUBSIDY FOR** SUGARCANE, **1981-90**

Effective subsidy coefficients ( ESCs) are are estimated by adding the value of non - traded subsidies (NTS) to the numerator of the effective protection calculation i.e. the subsidies on non-traded inputs are treated as though they are equivalent to an increase in the selling price of the sugarcane . The subsidies quantified in this way (Annex 22) are the subsidised supply of canal irrigation water, the subsidised price of electricity (mainly used for pumpsets) and subsidised credit.The estimated values of these subsidies for 1981-87 are from the Goldar and Gulati study. The estimates for 1988-90 have been extrapolated from these using the wholesale price index. On average over the period the canal irrigation subsidy forms was nearly 80 percent of the total subsidy to cane cultivators in UP, 67 percent in Maharashtra and 50 percent in TN. The credit subsidy was relatively more important in Tamil Nadu and Maharashtra than in UP, accounting for 10, 24.6 and 49 percent respectively of the total subsidy in the three states. In real 50
- _95_ -

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Table 5: Eect of a direct SLM training on contact farmers' adoption of individual SLM techniques

|Adoption on<br>any plot|Ctrl. Mean|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Mulching|0.929|0.026<br>[0.046]|347|0.040|
|Strip-tillage|0.548|0.159*<br>[0.072]|347|0.125|
|Pit planting|0.560|0.159***<br>[0.023]|347|0.093|
|Contour farming|0.226|0.147*<br>[0.067]|347|0.241|
|Crop rotation|0.726|0.066<br>[0.051]|347|0.166|
|Row planting|0.440|0.096<br>[0.058]|347|0.081|
|Improved fallowing|0.310|0.080<br>[0.070]|347|0.169|

Sources: Contact farmer survey, 2010, 2012, 2013; Household survey 2012,2013. Notes: Regressions include the same explanatory variables as models in Table 4. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. ITT=intent-to-treat; SLM=sustainable land management.

36
TabLe 1: Basic regression on the gender disparity in mortality, enroLLment, and treatment

|VariabL|e<br>Chi|ld<br>Mortal|ity<br>(4q1)|<br>Enr|oLled<br>in s|chooL|No treat|ment<br>for fe|ver or ARI|
|---|---|---|---|---|---|---|---|---|---|
||ALL*|<br>Non<br>South<br>Asia|South*<br> <br>Asia|<br>ALI*|<br>Non<br>South<br>Asia|South*<br> <br>Asia|<br>All*|<br>Non<br>South<br>Asia|South*<br>Asia|
|Income|0.031|0.022|0.117|<br>0.056|<br>0.043|<br>0.133|<br>0.052|<br>0.064|-0.010|
|(In)|C 0.68)|( 0.72)|t0.51)|( 1.41)|( 1.01)|( 1.26)|( 0.65)|( 0.78)|(-0.05)|
|Dummy for|0.405|.|.|-0.067|.|.|0.230|||
|5thAsia|( 7.04)|(<br>.)|(<br>.)|(-1.58)|t<br>.)|(<br>.)|( 2.74)|(<br>.)|(<br>.)|
|Cons|0.785<br>|0.847<br>|0.568<br>|<br>0.449<br>|<br>0.543<br>|-0.174<br>|<br>0.690<br>|0.601<br>|<br>1.364<br>|
||( 2.39)|( 3.79)|( 0.34)|( 1.56)|(1.76)|(-0.23|) ( 1.23)|(1.04)|( 0.89)|
|R-sq<br>A R-sq|.3891<br>.3736|<br>.0096<br>-.0087|<br>.0107<br>-.0305|<br>.0639<br>.0346|<br>.0292<br> <br>.0006|<br>.0518<br> <br>.0191|<br>.1491<br> <br>.1164|<br>.0207<br>-.0131|<br>.0001<br>-.0454|
|Num. Obs|.<br>82|56|26|67|36|31|55|31|24|

t-statistics in parentheses

* Includes regional subgroupings within India and Pakistan but excludes observations at the nationaL LeveL for those countries.

44
# Housing Subsidies for Refugees: Experimental Evidence on Life Outcomes and Social Integration in Jordan

## Last modified: January 14th, 2025

|**C**|**ontents**||
|---|---|---|
|**1**|**Appendix D: Midline Results**|**4**|
|**2**|**Appendix E: Endline Results**|**14**|
|**3**|**Appendix F: Follow-Up Results**|**31**|
|**4**|**Appendix G: Social Integration Results**|**39**|
||4.1<br>Social Cohesion Heterogeneity . . . . . . . . . . . . . . . .|. . . . . . . . . . . .<br>49|
|**5**|**Appendix H: Ethics Appendix**|**53**|
|**6**|**Appendix I: Pooled Results**|**54**|
||6.1<br>Pooled Results Heterogeneity . . . . . . . . . . . . . . . . .|. . . . . . . . . . . .<br>54|

1
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