rafmacalaba/datause-encoder-data huggingface dataset:
- 37 -

# Table 1: Definitions of Explanatory Variables

|EASTFOR|One if household lives in East Forest region, zero otherwise.|
|---|---|
|LAREA|Log of floor area square meters of household dwelling.|
|LPCAREA|Log of floor area (square meters) per capita of dwelling.|
|HOUSE|One if household lives in single house, zero otherwise.|
|APT|One if household lives in an apartment, zero otherwise.|
|WALLWOOD|One if dwelling has wooden walls, zero otherwise.|
|WALLSTONE|One if dwelling has stone walls, zero otherwise|
|CEMROOF|One if dwelling has cement roof, zero otherwise.|
|TOILET|One if household has flush toilet, zero otherwise.|
|NOCOVER|One if windows of dwelling have no covering, zero otherwise.|
|FAUCET|One if household's drinking water is from an indoor faucet,<br>zero otherwise.|
|OPENWELL|One if household's drinking water is from a well with pump,<br>zero otherwise.|
|VOLTAIC|One if head of household is of Voltaic ethnic group, zero<br>otherwise.|
|ELEM|One if head of household has elementary level of education,<br>zero otherwise.|
|JRSEC|One if head of household has junior secondary level of<br>education, zero otherwise.|
|SRSEC|One if head of household has senior secondary level of<br>education, zero otherwise.|
|_UNIV_|One if head of household has university level of education,<br>zero otherwise.|
|TV|One if household owns TV, zero otherwise.|
|BIKE|One if household owns bike, zero otherwise.|
|REF|One if household owns refrigerator, zero otherwise.|
**Dupas, Pascaline and Jonathan Robinson** , “Savings Constraints and Microenterprise Development: Evidence from a Field Experiment in Kenya,” _Forthcoming, American Economic Journal: Applied Economics_ , 2012.

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- **and Thomas Lemieux** , “Special issue editors’ introduction: The regression discontinuity design–

- Theory and applications,” _Journal of Econometrics_ , 2008, _142_ (2), 611 – 614.

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30
# **_Table 3: Balance across screening participants_**

||**T**|**otal**|**Co**|**ntrol**||**Movie**|||**MFB**|||**Movie + M**|**FB**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variable|**N**|**Mean**|**N**|**Mean**|**N**|**Mean**|**P-value**|**N**|**Mean**|**P-value**|**N**|**Mean**|**P-value**|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|
|_Personal characteristics_||||||||||||||
|Age of respondent|1243|38.27|309|38.13|327|38.46|0.79|287|37.92|0.81|307|38.52|0.60|
|Gender (male)|1261|0.28|313|0.25|333|0.26|0.78|292|0.30|0.21|310|0.30|0.19|
|<br>Married|1260|0.85|312|0.84|333|0.84|0.87|292|0.87|0.41|310|0.85|0.67|
|Widowed|1260|0.02|312|0.02|333|0.04|0.15|292|0.01|0.24|310|0.02|0.56|
|Single|1260|0.13|312|0.13|333|0.12|0.55|292|0.12|0.68|310|0.13|0.83|
|Muslim|1261|0.35|313|0.34|333|0.40|**0.080***|292|0.35|0.79|310|0.32|0.71|
|Christian|1261|0.64|313|0.66|333|0.60|**0.096***|292|0.65|0.72|310|0.67|0.78|
|Can speak English|1256|0.72|311|0.71|331|0.70|0.76|292|0.71|0.97|309|0.77|0.13|
|Igbo|1261|0.20|313|0.19|333|0.19|0.94|292|0.23|0.30|310|0.22|0.40|
|Yoruba|1261|0.75|313|0.78|333|0.77|0.88|292|0.74|0.34|310|0.73|0.15|
|Other ethnicitiy|1261|0.04|313|0.04|333|0.04|0.62|292|0.03|0.95|310|0.06|0.18|
|_Education_||||||||||||||
|No completed school education|1261|0.06|313|0.05|333|0.08|0.26|292|0.05|0.98|310|0.06|0.85|
|Primary school education|1261|0.22|313|0.24|333|0.23|0.96|292|0.22|0.61|310|0.19|0.15|
|High school diploma|1261|0.50|313|0.50|333|0.47|0.57|292|0.51|0.77|310|0.52|0.57|
|<br>Diploma|1261|0.11|313|0.11|333|0.11|0.94|292|0.12|0.76|310|0.11|0.92|
|Graduate school|1261|0.11|313|0.11|333|0.10|0.74|292|0.10|0.91|310|0.13|0.44|
|_Household characteristics_||||||||||||||
|Household (HH) size|1252|4.52|311|4.49|332|4.63|0.40|289|4.37|0.36|307|4.54|0.72|
|Number of children below 12 in HH|1235|1.31|307|1.39|331|1.31|0.34|285|1.27|0.19|299|1.26|0.20|
|Number of dependents in HH|1242|2.47|306|2.44|331|2.51|0.69|287|2.40|0.85|305|2.51|0.67|
|Number of dependents outside the|1180|1.52|297|1.50|308|1.47|0.94|274|1.65|0.43|288|1.51|0.93|
|_Business characteristics_||||||||||||||
|Months in operation|1420|97.23|350|96.94|369|101.37|0.47|334|96.54|0.95|352|95.03|0.76|
|Has a savings account|1448|0.59|356|0.58|378|0.61|0.30|343|0.58|0.80|356|0.60|0.48|
|Keeps written financial records|1442|0.39|355|0.40|377|0.36|0.36|341|0.40|0.89|354|0.42|0.48|
|Operating inside main market|1448|0.27|356|0.27|378|0.26|0.80|343|0.26|0.86|356|0.28|0.73|
|Number of employees|1448|1.52|356|1.58|378|1.56|0.89|343|1.47|0.54|355|1.48|0.53|
|Business experience inyears|1257|10.89|312|10.95|330|11.02|0.96|292|10.45|0.45|310|11.03|0.92|

*** p<0.01, ** p<0.05, * p<0.1

28
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concept of marginal social cost of taxation allows one to account for efficiency cost. This requires information of price elasticity of demand. Table 3.5 presents estimates of the marginal social cost associated with 16 food commodities for which we have estimates of price elasticity (see tables A.1 and A.2 in the annex). The ranking of commodities depends on the value judgments underpinning the poverty measure used. By and large, the commodities with the highest social cost tend to be the same as the ones identified on the basis of distributional characteristics. FGT measures rank roots, grains, cereals, vegetables and local rice as commodities with highest marginal social cost. To this list, the Watts index would add palm oil, smoked fish, oils and livestock products.

**Table 3.5: Guinea (1994): Marginal Social Cost for Some Components of Food Expenditure**

||Headcount|PovertyGap|Squared PovertyGap|Watts|
|---|---|---|---|---|
|Beef|0.00|0.19|0.09|0.26|
|Palm Oil|0.00|0.35|0.18|0.50|
|Bread|0.85|0.23|0.12|0.33|
|Fresh Fish|0.00|0.16|0.08|0.23|
|Smoked Fish|0.00|0.31|0.18|0.47|
|Local Rice|5.21|0.41|0.22|0.60|
|Cereals|1.42|0.44|0.24|0.66|
|Roots|0.00|0.49|0.27|0.73|
|Grains|0.53|0.47|0.27|0.72|
|Fruits|0.00|0.31|0.15|0.44|
|Vegetables|1.08|0.40|0.22|0.60|
|Meats|1.40|0.26|0.13|0.37|
|Livestock|0.00|0.31|0.17|0.47|
|Beverages|0.00|0.21|0.09|0.28|
|Oils|1.32|0.36|0.19|0.51|
|Sweets|0.17|0.33|0.18|0.49|

Source : Author’s calculations

# **_4.  Conclusion_**

The perspective of development as empowerment has prompted the international community to declare poverty eradication a basic objective of development, hence a benchmark measure of performance of development interventions. In the pursuit of this fundamental policy objective, many developing countries rely heavily on indirect taxes 21
Policy Research Working Paper 10661

# **Abstract**

Despite substantial progress in closing the gender gap, women’s labor force participation in the Middle East and North Africa remains one of the lowest globally, at a mere 18 percent. This paper investigates the effect of the introduction of unilateral divorce laws on women’s labor outcomes, using data from the Demographic and Health Survey program that spans decades and a quasi-experimental difference-in-differences design in three countries: Morocco, the Arab Republic of Egypt, and Jordan. The results highlight that no-fault divorce legislation was associated with a modest increase in mothers’ labor outcomes, measured by current employment, a few years after the reform. These findings are likely induced by a power shift and anticipatory effects that drive women into the labor force. However, when a longer time window is considered, 10 or more years after the reform, the study documents a negative effect of the reform on women’s labor outcomes in Morocco, and a positive effect in the Arab Republic of Egypt and Jordan. These differences can be attributed to a set of countervailing effects, including social norms, labor market dynamics, and evolution of the legislation, that make the derived utility from marriage, in some cases, more attractive than that derived from employment, and vice versa. These findings partially confirm results from previous research on the relationship between no-fault divorce and women’s agency and empowerment in the Middle East and North Africa region, but, at the same time, contrast with prominent perspectives on legislation that aims at reducing gender-based discrimination. Instead, they show that there might be undesired effects of legislation and provide a policy relevant discussion on that basis.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at najat.el-mekkaoui@dauphine.psl.eu or sara.loukili@dauphine.eu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<mark>On the brighter side, with support from the World Banak and other partners, the country is working to create a</mark> comprehensive, integrated, and effective SSN and with sufficient fiscal space the dedicated social protection could include the welfare and recovery of IDPs and returnees.

Plans to explore how to integrate the MPCA targeting models used by the humanitarian agencies (CLCIPMT) and the SSN targeting model used by the Government of Iraq (MOLSA-PMT) started in 2018 through a Social Protection workshop convened by FCDO (formally DFID, with the Ministry of Labor and Social Affairs ( MoLSA), the World Bank (WB), the Cash and Livelihoods Consortium for Iraq (CLCI), the CWG and key UN agencies in attendance. The workshop aimed to develop a roadmap document for the transition from MPCA to state-led SSN. Technically, it involves ways to identify beneficiaries of the MPCA caseloads eligible for assistance under the SSN. One of the initial outputs from the eligibility analysis exercise is the PPMT developed by the World Bank. PPMT is a probabilistic model that predicts the likelihood that a household eligible for MPCA would also be eligible for SSN. The PPMT results could assist the government in prioritization, either by ranking the most vulnerable households receiving humanitarian cash transfers and/or by identifying the conflict-affected areas where a larger number of IDPs receiving humanitarian cash transfers would likely be eligible for SSN assistance.

The procedure and result of applying the PPMT on two caseloads of the humanitarian data sets - the CLCI Legacy caseload and the new MPCA caseload are available in the associated desk review paper (Phadera, et al., 2022). Results from applying the PPMT models to these data sets suggest potential for both significant referral numbers and a sequenced referral strategy. To allow for budget flexibility, the desk analysis focused on the level of expansion (e.g. targeting the poorest 15, 20, etc. percent nationally) and the degree of confidence of a successful referral. However, given the small number of overlapping proxies between the MPCA and SSN targeting formula, the PPMT cross-eligibility results are probabilistic estimates.

Furthermore, the lack of trust in the government or fear of persecution, for example due to perceived ISIS affiliation or being ISIS sympathizers, may mean not all identified likely eligible households might be willing to be referred to the formal government social protection system. To confirm the promising referral percentages and to determine the proportion of the likely households who are also willing to be referred, a field test was carried out during the CLCI’s assessment process in late 2020. The field survey collected information on the full set of questions required to construct both the MOLSA-PMT and the CLCI-PMT scores from the same households and their willingness to be referred to the MOLSA program.

8
# Appendices

## A. Survey data collection and sampling

|**Table A.1**<br>**Region**|**: Total and sampled number of firm**<br>**Stratum (sector)**|**s in Tanzania, by**<br>**Number of firms**|**stratum and reg**<br>**Percentage in**<br>**region total**|**ion**<br>**Number of**<br>**firms surveyed**|**Percentage in**<br>**region sample**|
|---|---|---|---|---|---|
|**Dar es**<br>**Salaam**|Accommodation and food service<br>activities, Construction|5,105|12.8|84|13.5|
||Communication and other services|6,385|16.1|120|19.3|
||Manufacturing, Mining, Water,<br>Energy, Agriculture|10,458|26.2|170|27.3|
||Transportation and storage|1,115|2.7|48|7.7|
||Wholesale and retail trade, Repair<br>of motor vehicles and motorcycles|16,844|42.2|201|32.3|
||**_Subtotal_**|**_39,907_**|**_100_**|**_623_**|**_100_**|
|**Dodoma**|Accommodation and food service<br>activities, Construction|941|10.6|11|10.9|
||Communication and other Services|3,469|39.2|40|39.6|
||Manufacturing, Mining, Water,<br>Energy, Agriculture|2,100|23.7|22|21.8|
||Transportation and storage|42|0.5|4|4.0|
||Wholesale and retail trade, Repair<br>of motor vehicles and motorcycles|2,295|26|24|23.8|
||**_Subtotal_**|**_8,847_**|**_100_**|**_101_**|**_100_**|
|**Tanga**|Accommodation and food service<br>activities, Construction|1,289|12.6|15|13.3|
||Communication and other Services|3,898|38.2|38|33.6|
||Manufacturing, Mining, Water,<br>Energy, Agriculture|2,517|24.7|31|27.5|
||Transportation and storage|236|2.3|2|1.8|
||Wholesale and retail trade, Repair<br>of motor vehicles and motorcycles|2,265|22.2|27|23.9|
||**_Subtotal_**|**_10,205_**|**_100_**|**_113_**|**_100_**|
|**Total**||**58,959**|**100**|**837**|**100**|

## B. Descriptive results on the scale and type of disaster losses

## b.1. Firms perceive high levels of flood risk

Flood risks are high throughout most of Tanzania, and the survey data confirm that firms are aware of these risks. Indeed, 47.7 percent of firms stated that they perceive at least moderate risk of on-site flooding, and 26.8 percent perceive the risk to be high or very high. Firms are also aware of the potential indirect effects on their operations—for example, due to supply chain or infrastructure disruptions. Indeed, 64.2 percent of firms stated that they perceive at least moderate risk of indirect risks; 33.6 percent perceive the risk to be high or very high. The perception of disaster risks differs significantly across regions (figure B.1 and B.2), with firms in Dodoma exhibiting consistently higher levels of perceived risk. However, 25
problem of consumption turns into a problem of consumers.14 This can lead to rejecting the message, rather than acting upon it.

An added challenge in moving from concern to understanding has to do with how the mind perceives the problem. The dynamics of climate change stretch our mental capacities in several ways.<sup>15</sup> Psychological research shows that individuals are ill equipped to deal with multiple-cause problems.<sup>16</sup> Simplifying by adopting single-cause explanations in turn leads to searching for individual solutions and focusing on (often nonexistent) easy technological silver bullets. The inertia affecting adaptation responses can be linked to our incapacity to understand stock-and-flow relationships of the kind governing GHGs concentration, removal, and stabilization. The fact that even the most drastic and sudden emissions reductions will not prevent further warming and make the need for adaptation disappear in the short and medium terms is something we struggle with, and without careful explanation, simply do not understand.<sup>17</sup>

# **4. Understanding does not lead to action**

Secondly, understanding does not always lead to action. Knowledge is mediated through value systems shaped by psychological, cultural, and economic factors that determine whether we act or not. Again, the idea here is not that individuals are not rational, but that we need to understand better how we make decisions. Our evolution as a species has shaped the way our brains work and explains why we set priorities and act on certain issues and overlook others. We are particularly good at acting upon threats that can be linked to a human face—that present themselves as unexpected, dramatic and immediate, rather than problems which emerge and develop slowly and gradually—that present obvious links to human health—that challenge our moral framework involving visceral reactions—that are spurred by recent personal experience.<sup>18</sup> The slow-changing quality of climate change, as well as the delayed, intangible, and statistical nature of its risks, simply do not “move us”.

Behavioral economics shows that features of human decisionmaking under uncertainty tend to constrain our instinct to adapt.<sup>19</sup> We underestimate cumulative probabilities (the sum of the probabilities of an event occurring over a period of time), which explains why building continues in areas prone to fires, flooding, and earthquakes. We strongly favor the status quo, and agree only to make small incremental adjustments to it. We are at loss when the lack of a clear counterfactual makes measuring achievements difficult because, as in disaster preparedness. We are “myopic decisionmakers” who strongly discount future events to assign higher priorities to spatially and temporally closer problems. For instance, the public tends to be mobilized by “visible” environmental problems (urban air

> 14 Miller 2008. 15 Bostrom and others 1994. 16 Bazerman 2006. 17 Sterman and Sweeney 2007. 18 Ornstein and Ehrlich 2000; Weber 2006. 19 Repetto 2008.

4
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<!-- Start of picture text --> Guess of Common Worktype for Women by Gen<br>Second guess (SEE4B)<br>Business 57<br>Craft 100<br>Specialist a<br>Beauty 8% 45<br>Security and Army /@ 4<br>Seller S2n0 44<br>Health ad 57<br>Factory |me S<br>Public Sector 19<br>Education Lon 15<br>Agriculture | ?<br>Don't know 1<br>ie) 50 100<br>frequency<br><!-- End of picture text -->
monotonically with educational achievement: whereas those who attended college or have at least some secondary education tend to go to the areas with high state presence (as the coefficients for state sector for these individuals are positively significant), the coefficients for state sector employment for those without secondary education are found to be negatively significant. Perhaps, these results reflect that the governments and SOEs tend to hire educated individuals.

The coefficients for expected income, based on the predicted income equations, are positive and significant for most of the cases confirming the importance of the income differential as a determinant of destination choice. With the inclusion of the income variable, the magnitudes of the coefficients for foreign job are reduced, but remain positively significant implying that the foreign job opportunities are an additional determinant of migration. As a robustness check, I inserted the province (and urban/rural) specific per capita expenditure (the same variable used at the first-stage) in place of the individual specific expected income, as the former variable may more generally represent the standard of living in the potential destination area (the regressions are not reported). The coefficients for the per capita expenditure turn out to be highly positively significant suggesting that the individuals tend to move to the areas with higher standards of living, but the coefficients for the foreign job remain essentially unchanged.<sup>47</sup> In terms of the impact of land differential, a robust pattern emerges: the less educated the migrant, the more important is land availability as a determinant of destination choice. In particular, as the level of educational attainment increases, the sign of the coefficients for land availability changes from positive to negative and the magnitudes of the coefficient tend to decrease. A potential explanation is that land availability reflects land-intensity of jobs that

> 47 The coefficients for the foreign job are stable even I adjust for both expected incomes and the standards of living in destination areas. However, the coefficients for the state job turn out to be sensitive to the choice of control variables.