rafmacalaba/datause-encoder-data huggingface dataset:
# **The labor market impact of mobility restrictions: Evidence from the West Bank**<sup>∗</sup>

_Massimiliano Calì_<sup>♠</sup> _and Sami H. Miaari_<sup>♦</sup>

**_JEL classification codes_ :** J21, J40, J61

**_Keywords_** _:_ Conflict, Palestine, Israel, mobility, closures, Intifada

> ∗ _Acknowledgements_ : We are particularly indebted to Jad Isaac, Fuad Issac and Issa Zboun of the Applied Research Institute of Jerusalem for the superb help with the barriers’ data. We are also grateful to the Palestinian Central Bureau of Statistics for providing the Palestinian Labor Force Survey data and to OCHA for the complementary data on barriers. We thank Michele Di Maio, Karim Nashashibi, Siddharth Sharma, Roy Van der Weide, Asaf Zussman and Noam Zussman for helpful comments. The views in this paper do not necessarily reflect those of the organizations the authors are affiliated to. All remaining errors are ours.

> ♠ World Bank, m.cali@worldbank.org.

> ♦ Tel-Aviv University, smiaari@gmail.com.
<u>Panel C: Adding cognitive skills (reading proficiency scores)</u>

||Total<br>Difference|Difference in<br>endowments|Difference in<br>coefficients|
|---|---|---|---|
|Bolivia|0.725|0.119|0.607|
||_0.205_|_0.051_|_0.190_|
|Colombia|-0.087|0.032|-0.119|
||_0.180_|_0.040_|_0.171_|
|Armenia|0.171|0.016|0.154|
||_0.290_|_0.024_|_0.289_|
|Georgia|1.129|-0.035|1.164|
||_0.306_|_0.039_|_0.302_|
|Ukraine|0.118|0.010|0.108|
||_0.126_|_0.030_|_0.117_|
|Vietnam|0.064|0.116|-0.052|
||_0.100_|_0.028_|_0.101_|
|Kenya|-0.063|0.179|-0.243|
||_0.438_|_0.045_|_0.434_|

<u>Panel D: Adding cognitive skills (complexity of computer use at work)</u>

||Total<br>Difference|Difference in<br>endowments|Difference in<br>coefficients|
|---|---|---|---|
|Bolivia|0.648|0.129|0.520|
||_0.338_|_0.053_|_0.329_|
|Colombia|-0.028|0.006|-0.034|
||_0.173_|_0.043_|_0.165_|
|Armenia|0.224|0.016|0.208|
||_0.273_|_0.027_|_0.271_|
|Georgia|0.950|-0.066|1.016|
||_0.274_|_0.044_|_0.269_|
|Ukraine|0.144|-0.001|0.144|
||_0.137_|_0.031_|_0.128_|
|Vietnam|0.066|0.107|-0.041|
||_0.097_|_0.028_|_0.099_|
|Kenya|0.150|0.192|-0.042|
||_0.210_|_0.049_|_0.201_|

_Note:_ The dependent variable is the log hourly earnings. Controls include potential experience, the quadratic of experience, and type of employment. The sample includes 25- to 64-year-old women in selfemployment or wage work. Noncognitive skill scores and reading proficiency scores are standardized with mean 0 and SD = 1. Binary variables indicate complexity of computer use at work where the reference category is ‘No computer use on the job’. Highlighted cells indicate statistical significance at the 1% level or lower.

_Source_ : STEP Surveys (2014).

22
**Table S6.1.** ITT and knowledge effects on number of adopted ISFM technologies and integrated adoption of the full ISFM package, ADE of treatments and ACME of overall, principles and how-to knowledge as mediating variables _(continued)_ .

|||Numbe<br>|r of ISFM t<br>|echnologies<br>|adopted<br>|||Integrate<br>|d adoption o<br>|f full ISFM<br>|package<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Ove|rall|Princ|iples|Ho|w-to|Ove|rall|Prin|ciples|Ho|w-to|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|**Panel B: ACME an**|**d ADE Esti**|**mates of T1**|||||||||||
|ACME (T1)|0.147***|0.051***|0.050*|0.015|0.212***|0.073***|0.019**|0.009**|0.004|0.002|0.034***|<br>0.020***|
||(0.044)|(0.019)|(0.027)|(0.011)|(0.049)|(0.020)|(0.008)|(0.004)|(0.005)|(0.002)|(0.010)|(0.006)|
|ADE (T1)|0.553***|0.410***|0.651***|0.441***|0.488***|0.383***|0.094**|0.077***|0.107**|0.085***|0.079*|0.069***|
||(0.153)|(0.080)|(0.165)|(0.085)|(0.153)|(0.078)|(0.045)|(0.027)|(0.045)|(0.028)|(0.045)|(0.026)|
|Total effect (T1)|0.700***|0.460***|0.700***|0.456***|0.700***|0.457***|0.109**|0.085***|0.111**|0.086***|0.108**|0.086**|
||(0.163)|(0.080)|(0.168)|(0.083)|(0.161)|(0.079)|(0.045)|(0.028)|(0.045)|(0.028)|(0.045)|(0.027)|
|Share of T1 effect<br>explained by|20.9%|**11.0%**|7.1%|3.2%|30.2%|**16.1%**|17.3%|**11.1%**|4.0%|2.1%|31.6%|**23.0%**|
|knowledge|||||||||||||
|**Panel C: ACME an**|**d ADE Esti**|**mates of T2**|||||||||||
|ACME (T2)|0.240***|0.097***|0.105***|0.041***|0.281***|0.102***|0.038***|0.020***|0.017***|0.008**|0.049***|<br>0.028***|
||(0.042)|(0.019)|(0.025)|(0.010)|(0.052)|(0.022)|(0.008)|(0.006)|(0.005)|(0.003)|(0.011)|(0.007)|
|ADE (T2)|0.616***|0.493***|0.751***|0.546***|0.575***|0.483***|0.125***|0.106***|0.142***|0.115***|0.115**|0.097***|
||(0.152)|(0.079)|(0.161)|(0.082)|(0.148)|(0.078)|(0.048)|(0.027)|(0.049)|(0.027)|(0.048)|(0.026)|
|Total effect (T2)|0.856***|0.590***|0.856***|0.586***|0.855***|0.585***|0.155***|0.122***|0.155***|0.121***|0.154**|0.120***|
||(0.160)|(0.078)|(0.165)|(0.080)|(0.159)|(0.080)|(0.048)|(0.027)|(0.049)|(0.027)|(0.048)|(0.026)|
|Share of T2 effect|||||||||||||
|explained by|28.0%|**16.4%**|12.3%|**6.9%**|32.8%|**17.5%**|24.9%|**16.9%**|11.2%|**6.4%**|32.1%|**23.3%**|
|knowledge|||||||||||||
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|2,334|2,334|2,334|2,334|2,334|2,334|2,116|2,116|2,116|2,116|2,116|2,116|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note_ : Causal mediation estimates for overall, principles, and how-to knowledge variables on number of practices adopted (OLS specification) and integrated adoption of full ISFM package (probit specification, AME shown). ACME stands for average causal mediation effect, ADE for average direct effect. Additional controls identical to those listed in notes of Tables 2 and 5. Robust standard errors in parentheses, clustered at the mws level. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.

31
Similarly, consider the seminal work of Miguel and Kremer (2004). In developing countries there tends to be a correlation between the use of deworming medicine by school children and superior participation in schools by children. Why may this be so? Regular participation in school may teach the children the importance of good hygiene and, in particular, keeping oneself free of stomach worms and so they take deworming medicines. It can be that some households are smarter and smartness means being aware of the importance deworming and going to school regularly. It can also be that deworming makes children fit and fitness enables them to attend school regularly. Miguel and Kremer used an excellently designed RCT. They administered deworming medicine to children in a bunch of randomly selected schools in Kenya<sup>4</sup> and found that participation rates of students improved. This clearly ruled out the first two causal hypotheses.<sup>5</sup> It should be clear that in neither of the two studies just discussed is causality established. Based on these findings, we cannot assert that electing a woman leader in a Tamil Nadu village will improve water provision there and administering deworming medicine to all school kids in a school in Kenya in 2014 will improve school attendance. All these two RCTs do—and RCTs are ideal for this—is to rule out certain possible causal links. They give the impression of having pinned down causality only because we carry certain priors about causal links and when all but one of our priors are ruled out, our mind jumps to the conclusion that causality has been established;<sup>6</sup> and we feel that we are now ready to use these findings to design actual policy interventions.<sup>7</sup>

> 4 In this brief statement I am, of course, leaving out important details of the experiment.

> 5 These are just two papers I am using for my argument. But RCTs have given us a rich haul of findings on past regularities in a range of domains in development economics: see, for instance, Ashraf, Karlan and Yin (2006), Bernard, Dercon, Orkin and Taffesse (2013), Habyarimana and Jack (2012), Jensen (2012), Hoff and Pandey (2006), Ifcher and Zarghamee (2011).

6 The reason magic surprises us has roots in a similar phenomenon of the human mind. The skillful magician instills in us certain prior beliefs about how a rabbit may have got into a hat. Then he or she demonstrates that none of these are possible and when the rabbit is pulled out of the hat, we are left wondering if this was a miracle.

> 7 The journey from (allegedly) finding causes to designing policy is fraught with dangers and is the subject matter of considerable writing (see, for instance, Cartwright, 2007; Basu, 2000; Das, Devarajan and Hammer, 2011). I shall return to this later.

6
Intemational Country Risk, etc. After numerous<sup>tests, we selected theHeritage Indexas</sup> it had added the highest explanatory power to our model.<sup>7Similarly, we used threeas</sup> alternative indicators for the local market size: GDP, population,<sup>and incomeper capita.</sup> All these variables appear highly correlated for<sup>our sample of countries,and the levelof</sup> income generated the most robust results per capita,<sup>hence we retained thisvariable.</sup> We explore the relationship between the investment<sup>promotioneffort and FDI inflowsin</sup> 58 countries during 2001.8 Our sample is diversified<sup>in termsof regions,levelof income</sup> per capita, as well as the magnitude of investment<sup>promotionefforts.To keepsome</sup> homogeneity, we include only countries that have<sup>reportednationalinvestment</sup> promotion agencies. We exclude countries, such<sup>as the US,China,Brazil, andIndia,that</sup> have only regional agencies since it was difficult<sup>to assess theircontributionto FDI flows</sup> at the national level. This omission may be important<sup>because thesecountries accountfor</sup> a substantial share of FDI worldwide.

While the data obtained from the FIAS survey are<sup>unique, they areonly availablefor the</sup> year 2001. For this reason, our approach can<sup>only be cross-sectional.By doing this,we</sup> do not necessarily assume that promotion has an immediate<sup>effect on overallFDI flows</sup> because we use a cross-country rather than time-series<sup>analysis.Our results would be</sup> significantly biased only if we had depicted large<sup>variationsin the promotioneffort over</sup> the last few years. Fortunately, it appears that variations<sup>in the promotioneffort across</sup> 7 Indeed, as recently confirmed by Batra and Moody,<sup>2002,variationsin cross-countryFDIflowsare well</sup> explained by these factors over the past decade. The Heritage<sup>FoundationIndexcaptures50 independent</sup> variables divided into 10 broad factors of economic freedom<sup>related to internal andexternal</sup> macroeconomic conditions, economic openness,<sup>and politicaland institutionalhealth.The higher thescore</sup> on a given factor, the greater the level of government interference<sup>in theeconomyand theless economic</sup> freedom a country enjoys. The index assigns scores<sup>inversely to the state of the investmentclimate;hence</sup> we expect a negative sign on the coefficient of the<sup>estimation.</sup> 8 'he list of countries is the following: Armenia, Bangladesh,<sup>Belgium,Benin, Bolivia,Bosnia And</sup> Herzegovina, Bulgaria, Cape Verde, Chile, Colombia,<sup>Costa Rica,CoteD'Ivoire,Czech Republic,</sup> Denmark, Dominican Republic, Ecuador, El Salvador,<sup>Estonia, Ethiopia, Finland,Georgia,Greece,</sup> Guyana, Honduras, Hong Kong,, Indonesia, Ireland,<sup>Israel, Jamaica,Jordan, Kenya,Korea, Latvia,</sup> Lesotho, Malawi, Maldives, Mali, Mauritius, Morocco,<sup>Mozambique,Nepal, Pakistan,Panama, Paraguay,</sup> Peru, Portugal, Senegal, Slovakia, Slovenia, South<sup>Africa,Sri Lanka, SaintLucia,Thailand,Trinidad and</sup> Tobago, Tunisia, Uganda, Vanuatu, Venezuela.
# Identifying the Vulnerable to Poverty from Natural Disasters: The Case of Typhoons in the Philippines

**Emmanuel Skoufias* Yasuhiro Kawasoe***

**Eric Strobl** Pablo Acosta***

**JEL Classification:** I3, O12, Q54,Q58

**Keywords:** Philippines, Poverty, Vulnerability, Targeting, Typhoons

*The World Bank, Washington, DC USA

** University of Bern, Switzerland
We use as exchange rate regimes variable, the classification by Ilzetzki et al. (2017). The classification ranges from 1 to 15, with lower values representing more fixed exchange rate regimes and higher values representing more flexible regimes (See Appendix 4).

As aforementioned, the set of control variables that we employ includes an inflation targeting dummy, the number of years prior to elections, an index for government polarization, the output volatility, the logarithm of GDP per capita, the ratio of public debt to GDP and the ratio of natural resource rents to GDP.

The use of inflation targeting is motivated by the fact that this institutional reform may influence the conduct of fiscal policy by limiting the scope of seigniorage revenues and placing other constraints on expenditure (Combes et al. (2017)). To address this point, we use a dummy variable equal to 1 if the country has implemented inflation targeting at time t and 0 otherwise. The data are taken from Aizenman et al. (2011), Samarina and De Haan (2014), and Laurens et al. (2015).

In addition, we take account of the electoral cycles to explain fiscal discipline since the main theoretical explanations of fiscal indiscipline emerge from the political budget cycle models. We use the variable _elections_ which is the number of years prior to executive elections. We construct this variable using the database of Hyde and Marinov (2012): The National Elections across Democracy and Autocracy (NELDA) data set.

The literature on government fragmentation suggests that fragmentation has harmful effect on public finances (Kontopoulos and Perotti (1999); Volkerink and De Haan (2001)). Thus, government polarization may be associated with higher fiscal discipline. We use the index _Polariz_ from the World Bank Database of Political Institutions to control for government polarization. It represents the maximum polarization between the executive party and the four principle parties of the legislature. The higher the index, the more polarized the government is. The index is zero if the chief executive’s party has an absolute majority in the legislature.

Moreover, we follow Frankel et al. (2013) and Talvi and Vegh (2005) by controlling for the variability of tax revenues that we proxy using output variability. This channel has been emphasized by 12
answer the research questions of interest. Moreover, the type of migrants captured in the data can vary from round to round. Therefore, empirical analysis is restricted to the 2007 and 2000 IFLS rounds for a consistent definition of migration. In the household-level data, the definition of households with international migrants refers to those families with parents, children, or spouses who are not co-residents and are abroad.<sup>6</sup> Given the way the questions about remittances are asked, in the data, households receiving international remittances are a strict subset of households with international migrants.

The individual-level panel, however, is less useful for this paper because not all individuals abroad are surveyed. An international migrant can only be defined as an individual who was present in the household in the previous IFLS round, but is abroad in the following round. The rate of migrants captured in this data is very small. Only descriptive statistics, without regression analysis, about individual migrants are reported.

Among households with migrants, the data allows further disaggregation by gender of the migrants. We can classify those families into (i) those with only female migrants, (ii) those with only male migrants, and (iii) those with both male and female migrants. The third category has very few households. In the regression analysis, we will combine categories (ii) and (iii) as households with mainly male migrants, for easy interpretation. Alternatively, the data also allows calculation of the female share among migrants, that is, for each household with migrants, what is the fraction of migrants that are female (mother, daughter, and wife).

# 4. **Descriptive Statistics**

Analyzing the differential impacts of female migrants requires a good understanding of the socio-economic factors of the migrants and migrants’ origin households in determining their international labor migration. This section presents descriptive examinations of the profiles of migrants and migrants’ origin households, including their trend over times. Some key characteristics of the migrants and migrants’ households are analyzed to identify who among migrants are likely to search for jobs overseas, and from which households they are, differentiating between male and female migrants. This

> 6 The 2000 data also identifies a fourth source—siblings—but for consistency purposes, we keep the same definition between the 2000 and 2007 data.

7
treated materials has been<sup>unclear since previous studies have</sup> based their results on the<sup>effects on the vectors rather than on</sup> the malaria morbidity and mortality<sup>rates in the human</sup> population. In 1988, therefore,<sup>the UK Medical Research Council</sup> began a systematic trial of<sup>a combined intervention for the</sup> control of malaria in central<sup>Gambia around the small town of</sup> Farafenni. On the North<sup>Bank, some earlier work had suggested</sup> that chemoprophylaxis with<sup>Maloprim (dapsone+pyrimethamine) and</sup> Permethrin-treated bed-nets<sup>had reduced malaria morbidity but the</sup> intervention was too small<sup>scale for much to be said about</sup> mortality. 5 For the new double-intervention<sup>trial with</sup> permethrin-treated bed-nets and<sup>Maloprim used in combination, a</sup> new site was chosen south the<sup>Gambia river and east of Farafenni</sup> town. The area included 73<sup>villages in all, of which 17 of the</sup> larger villages, generally<sup>those with 400 or more people, had</sup> joined the government's Primary<sup>Health Care scheme. The 56</sup> smaller villages are here referred<sup>to collectively as 'non-PHC'</sup> villages. Both the interventions,<sup>the treated bed-nets plus the</sup> chemoprophylaxis, were conducted<sup>in the PHC villages with the</sup> non-PHC villages serving as<sup>controls (see Table 1).</sup> of vector control, ed. CF Curk. i,<sup>CRC Press, Florida, 1990:5-46;</sup> WHO. The use of impregnated<sup>bednets and other materials for</sup> vector-borne disease control.<sup>(WHO/VBC 89.981). WHO, Geneva,</sup> 1987. For a recent general<sup>review, see SC Oaks et al, 1991,</sup> Malaria: obstacles **and** opportunities,<sup>Inatitute of Medicine, US</sup> National Academy Press.

5 RW Snow, SW Lindsay, RJ Hayes,<sup>BM Greenwood. Permethrin-</sup> treated bed-nets (mosquito nets)<sup>prevent malaria in Gambian</sup> children. Trans R Soc Trop<sup>**Ned Hyg**1988: 82:838-42.</sup> 3
capital flows, and increasing competitive pressures in export markets, it is not enough for countries to simply make progress towards a more open trade and investment regime; exporting economies must devote equal attention to what their competitors are doing.

This situation seems particularly prevalent in East Asia. Without question, this region has benefitted enormously from rapid growth in world trade: The World Bank (1994) reports that over the last twenty-five years, the region's exports have grown by a factor of thirty, corresponding to an increase in the share of world exports from 7 to 21 percent. This report goes on to argue forcefully that East Asia should aggressively pursue liberalization at a pace faster than that promised in the URA in order to provide a locomotive to pull the world trading system towards greater openness. But running counter to this sentiment is a less optimistic perception that the ordered historical progression of export-led growth from Japan to the Asian "tigers" to the next tier has now been supplanted by a more chaotic scramble for advantage in an increasingly competitive world. The response to trade liberalization tor one country in Asia depends not only on its own actions, but also on what other countries do as well. For example, the impact of China's resurgence on the region (as well as the possible costs of any renewed isolation or exclusion from the WTO), the potential competition from Vietnam, and the long-run effect of changes in the distribution or magnitude of foreign investment flows into the region were important factors behind the recent ASEAN decision to accelerate the timetable for its own free trade area.

It can be argued that concern over the policy environment in a country's economic neighbors may be beneficial, and even create momentum towards a self-fulfilling or virtuous circle of liberalization. But there is also the danger that such concerns can increase exclusionary pressures, or encourage strategic behavior that benefits the individual country but at the expense of the broader region.<sup>3</sup> While rarely openly identified as such, these pressures are evident in East Asia in areas ranging from the debate over ' For example, Hinojosa-Ojeda, Lewis, and Robinson (1994) analyze the potential for welfare-reducing "prisoner's dilemma" outcomes in an analysis of regional integration options for Central America and the Caribbean after NAFTA.

3 _Lewis, Robinson, and Wang_
1 replicates our baseline scenario from Table 1, column 1. Under that no-tax scenario, 33 million permit sellers receive an annual rent of US$19,700. By setting the tax to 13% (column 2) and increasing it to 141% (column 5) we can see how tax policy can improve targeting by reducing the number of “beneficiaries” and henceforth increasing the basic guaranteed income of the targeted group.

For instance, with a 13% tax, the market shrinks to 31 million permit sellers and the price of the work permit drops to US$18,000. Yet, they receive the revenues from the tax amounting to US$2,300 per person and per year. The basic income guarantee, therefore, goes up to US$21,000 and poverty decreases by an extra 1.2 percentage point to 8.1%. Increasing the tax up to 141% further targets the 15 million poorest by reducing the permit price down to US$ 10,000. However, with the tax revenues hence generated, such a scheme allows a basic income guarantee as high as US$24,000 per year. Such a policy would bring the poverty rate down to 2.8% from a baseline of 12.3%.

# **6 Informal Labor Markets and the Limits of a Market for Work Permits**

We have proposed a market for work permits as a mechanism to redistribute part of the benefits of migration from migrant to native workers to compensate them for the risk of being displaced and for the potential loss in earnings. The illustration using data from the US-Mexico migration corridor gives estimates that suggest the market price for working permits would constitute a sizeable basic income guarantee for native workers who decide to sell their right to work.

The plausibility of the quantitative results hinges on the model’s assumptions. In particular, we assume the perfect enforcement of immigration and labor laws. Imperfect enforcement would result in informal markets that alter the agents’ incentives. It would primarily incentivize irregular migration pathways when the regular alternative, i.e. buying a work permit, 16