rafmacalaba/datause-encoder-data huggingface dataset:
# References

<mark>Arneson, R.J., 1989. Equality and equal opportunity for welfare.</mark> _<mark>Philosophical studies</mark>_ <mark>,</mark> _<mark>56</mark>_ <mark>(1), pp.7793.</mark> Asian Development Bank. 2010. The Rise of the Asia’s Middle Class, Philippines: Asian Development Bank.

<mark>Alesina, A. and Angeletos, G.M., 2005. Fairness and redistribution.</mark> _<mark>The American Economic Review</mark>_ <mark>,</mark> _<mark>95</mark>_ <mark>(4), pp.960-980.</mark> <mark>Alesina, A., Stantcheva, S. and Teso, E., 2017.</mark> _<mark>Intergenerational Mobility and Preferences for Redistribution</mark>_ <mark>(No. w23027). National Bureau of Economic Research.</mark> <mark>Altham, P.M., 1970. The Measurement of Association of Rows and Columns for an r× s Contingency Table.</mark> _<mark>Journal of the Royal Statistical Society. Series B (Methodological)</mark>_ <mark>, pp.63-73.</mark> <mark>Azam, M., 2015. Intergenerational Occupational Mobility among Men in India.</mark> _<mark>The Journal of Development Studies</mark>_ <mark>,</mark> _<mark>51</mark>_ <mark>(10), pp.1389-1408.</mark> <mark>Azam, M. and Bhatt, V., 2015. Like father, like son? Intergenerational educational mobility in India.</mark> _<mark>Demography</mark>_ <mark>,</mark> _<mark>52</mark>_ <mark>(6), pp.1929-1959.</mark> Azevedo, Joao Pedro, Minh Cong Nguyen, and Viviane Sanfelice. 2012. Shapley Decomposition by Components of a Welfare Aggregate. Washington, DC: World Bank. Mimeo.

<mark>Banerjee, A.V. and Duflo, E., 2008. What is middle class about the middle classes around the world?.</mark> _<mark>The Journal of Economic Perspectives</mark>_ <mark>,</mark> _<mark>22</mark>_ <mark>(2), pp.3-41A.</mark> <mark>Bane, M.J. and Ellwood, D.T., 1983. Slipping into and out of poverty: The dynamics of spells.</mark> <mark>Birdsall, N. 2007. The Macroeconomic Foundations of Inclusive Middle-Class Growth. 2020</mark> <mark>Birdsall, N., Graham, C. and Pettinato, S., 2000. Stuck in tunnel: Is globalization muddling the middle? The Brookings Institution Center on Social and Economic Dynamics, Working Paper No.</mark> 14 <mark>Black, S.E., Devereux, P.J. and Salvanes, K.G., 2011. Too young to leave the nest? The effects of school starting age.</mark> _<mark>The Review of Economics and Statistics</mark>_ <mark>,</mark> _<mark>93</mark>_ <mark>(2), pp.455-467.</mark> <mark>Black, S.E., Devereux, P.J. and Salvanes, K.G., 2013. Under pressure? The effect of peers on outcomes of young adults.</mark> _<mark>Journal of Labor Economics</mark>_ <mark>,</mark> _<mark>31</mark>_ <mark>(1), pp.119-153.</mark> <mark>Bowles, S. and Gintis, H., 2002. The inheritance of inequality.</mark> _<mark>The Journal of Economic Perspectives</mark>_ <mark>,</mark> _<mark>16</mark>_ <mark>(3), pp.3-30.</mark> 40
Table 5: Child characteristics by transfer status

||No|n-transf|ers||Transfer|s|Di¤e|rence|
|---|---|---|---|---|---|---|---|---|
||Comp|Treat|All (1)|Comp|Treat|All (2)|(1)-(2)|t-value|
|Girl|0.49|0.50|0.49|0.44|0.53|0.49|0.00|0.01|
|Female headed household|0.07|0.13|0.10|0.11|0.17|0.14|0.05|1.80|
|Number of children under 6|2.66|2.48|2.57|2.30|2.17|2.22|-0.35|-4.59|
|Enrolled in February 2000|0.46|0.45|0.45|0.99|0.91|0.94|0.49|17.27|
|Age|4.52|4.53|4.52|5.28|5.10|5.18|0.65|11.03|
|Nobs|2899|2824|5723|106|151|297|6020||

Notes: Table reports percentage of observations in each category. The sample is the set of baseline pupils found in school in 2000 and 2001. Transfers are pupils who transferred in or out of the program schools, in 2000 or 2001. Non-transfers are school participants who did not transfer in 2000 or 2001.

transferred. Hence it is unlikely that transfers between schools bias the estimation results when the estimation is con...ned to the sample of baseline children.

The third class of transfers is hidden transfers. A hidden transfer is the enrollment of a baseline child in a non-program school, when that child has never attended a program school. This child would be counted as absent in my school participation data. If the feeding program a¤ected the rate at which baseline children enroll in a non-program school, the estimates will be biased. The bias in the estimate of the school participation e¤ect will be upward if children in the baselines of treatment schools were less likely to enroll in a non-program school than children in the baselines of treatment schools. To try to get at this problem, retrospective data were collected on the whereabouts of baseline children. In 2002, 40 households from each school were sampled and the NGO sta¤ tried to identify and interview one child from each household. The child was asked about the whereabouts of the household’s children who were recorded in the baseline. This information was not yet available at the time of writing.

# 5 Econometric modeling and results

We estimate the impact of the program on the average school participation rate of a child, on the probability of ...nding a child in school at least once, on height and weight and on teacher absenteeism. In section 6, we estimate the impact of the program on the price of preschool.

19
solely from beneficiary identification using the UDB lists by computing UDB undercoverage and leakage at an unchanged coverage level (i.e., holding constant the number of beneficiaries).

Lastly, we also address an important limitation of standard undercoverage and leakage measures (see, for example, Coady and Skoufias, 2004; Coady et al., 2004), which weight equally all households regardless of their position in the consumption distribution. For instance, when measuring undercoverage for a program intended to cover the poorest three deciles of the consumption distribution, no distinction is made between the exclusion of a household at the 5<sup>th</sup> versus the 29<sup>th</sup> percentile, even though from a welfare perspective, excluding the former represents a more serious error. We therefore present a nonparametric analysis of the expected incidence of benefits to provide a more detailed assessment of the distributional performance of the UDB.

# **III. Results: UDB Targeting Performance**

This section presents initial results on the targeting performance of the UDB, taking advantage of the matched SUSETI-UDB-pre-listing data. First, we focus on the registration stage and assess to what extent registered households are poorer than non-registered households, highlighting differences across registration methods. Second, we present the overall UDB performance by comparing program baseline and expected UDB targeting accuracy.

## **Are UDB-Registered Households Poorer than Non-Registered Households?**

An initial glimpse into the UDB’s performance in reaching the poorest households is provided in Figure 2. First, UDB households are significantly poorer than non-UDB households (Figure 2(a)). However, there is considerable overlap in the consumption distributions, suggesting that a large share of poor households is not in the UDB. Second, there is a clear inverse relationship between the probability of being in the UDB and per capita consumption (Figure 2(b)). Households with the lowest consumption levels have a probability of more than 60 percent to be in the UDB compared to less than 20 percent for households with the highest consumption levels. This roughly linear figure is far from the “perfect” targeting case, which would look more like a step function with those below the 15
quite contractionary whereas reducing spending in good times would have little, if any, e¤ect.

What happens in developing countries? Figure 3, panels A and B, shows the ...scal multipliers associated with expansion and recession. In line with our ...ndings for OECD countries, the ...scal multiplier in recessions is larger than in good times (particularly in the medium and long run). Speci...cally, the impact multiplier in recessions and expansions is fairly similar at, respectively, 0.24 and 0.21, and signi...cantly di¤erent from zero. However, the peak multiplier during recessions (0.81) is two times and a half as high as the peak multiplier during expansions (0.32). Furthermore, the multiplier during expansions becomes insigni...cantly di¤erent from zero after 4 quarters, whereas the multiplier during recessions remains signi...cantly di¤erent from zero throughout our estimated horizon.

# 3.3 Multipliers when government spending is increasing/decreasing

Following the empirical speci...cation (3), we will ...rst evaluate whether government spending going up or down matters for the size of the ...scal multiplier (i.e., even before taking into account the stance of the business cycle). For the case of industrial countries, Table 1 indicates that government spending is above the long-term trend 48 percent of the time and below such trend 52 percent of the time.

Figure 4 shows the ...scal multipliers associated with government spending decreases (panel A) and increases (panel B). Interestingly enough, we ...nd that the ...scal multiplier associated with increases in government spending is larger than the one associated with decreases in government spending. In fact, the multiplier for government spending increases is 0.48 on impact (and signi...cantly di¤erent from zero) and reaches a maximum of about 1.36 after four semesters. In sharp contrast, the multiplier when government spending falls is never signi...cantly di¤erent from zero.

When comparing these multipliers to the ones obtained in section 3.2, interesting similarities emerge. The pro...le obtained for the ...scal multiplier in periods of expansion (Figure 2, 14
|Financial Sector Adjustment Project|Andre Ryba|
|---|---|
|Competitiveness and RegulatoryReform Adjustment Project|Elke U. Kreuzwieser|
|Competitiveness and RegulatoryReform Adjustment Project|Elke U. Kreuzwieser|
|Competitiveness and RegulatoryReform Adjustment Project|Elke U. Kreuzwieser|
|Competitiveness and RegulatoryReform Adjustment Project|Elke U. Kreuzwieser|
|Competitiveness and RegulatoryReform Adjustment Project|Elke U. Kreuzwieser|
|Privatization Support Project|Marie Francoise Marie-Nelly|
|Privatization Support Project|Marie Francoise Marie-Nelly|
|Privatization Support Project|Marie Francoise Marie-Nelly|
|Privatization Support Project|Marie Francoise Marie-Nelly|
|Privatization Support Project|Marie Francoise Marie-Nelly|
|Privatization Support Project|Marie Francoise Marie-Nelly|
|Privatization Support Project|Marie Francoise Marie-Nelly|
|Privatization Support Project|Marie Francoise Marie-Nelly|
|Privatization Support Project|Marie Francoise Marie-Nelly|
|Privatization Support Project|Marie Francoise Marie-Nelly|
|Human Resources Management Project|Catherine M. Laurent|
|Human Resources Management Project|Catherine M. Laurent|
|Human Resources Management Project|Catherine M. Laurent|
|Human Resources Management Project|Catherine M. Laurent|
|Human Resources Management Project|Catherine M. Laurent|
|Human Resources Management Project|Catherine M. Laurent|
|Rural Savings and Loans Rehabilitation and Promotion Project|Bleoue Nicaise Ehoue|
|Rural Savings and Loans Rehabilitation and Promotion Project|Bleoue Nicaise Ehoue|
|Rural Savings and Loans Rehabilitation and Promotion Project|Bleoue Nicaise Ehoue|
|Rural Savings and Loans Rehabilitation and Promotion Project|Bleoue Nicaise Ehoue|
|Rural Savings and Loans Rehabilitation and Promotion Project|Bleoue Nicaise Ehoue|
|Rural Savings and Loans Rehabilitation and Promotion Project|Bleoue Nicaise Ehoue|
|Rural Savings and Loans Rehabilitation and Promotion Project|Bleoue Nicaise Ehoue|
|Rural Savings and Loans Rehabilitation and Promotion Project|Bleoue Nicaise Ehoue|
|Private Sector Development Adjustment Credit Project|Elke U. Kreuzwieser|
|Private Sector Development Adjustment Credit Project|Elke U. Kreuzwieser|
|Private Sector Development Adjustment Credit Project|Elke U. Kreuzwieser|
|Private Sector Development Adjustment Credit Project|Elke U. Kreuzwieser|
|Integrated Health Services Development|Ibrahim Magazi|
|Integrated Health Services Development|Ibrahim Magazi|
|Integrated Health Services Development|Ibrahim Magazi|
|Integrated Health Services Development|Ibrahim Magazi|
|Integrated Health Services Development|Ibrahim Magazi|
|Integrated Health Services Development|Ibrahim Magazi|
|Integrated Health Services Development|Ibrahim Magazi|
|Integrated Health Services Development|Ibrahim Magazi|
|Integrated Health Services Development|Ibrahim Magazi|
|Integrated Health Services Development|Ibrahim Magazi|
|Tana River Primate National Reserve Conservation Project|Richard John Kaguamba|
|Tana River Primate National Reserve Conservation Project|Richard John Kaguamba|
|Tana River Primate National Reserve Conservation Project|Richard John Kaguamba|
coordination among economic agents. Buyers and sellers incur these costs in search of information on input and output prices and other market and non-market information. Second, it allows better management of the supply chains and thereby improves producers’ productive efficiencies. Third, it is associated with job creation potential – not just for telecom related services but much more widely. Fourth, improved information exchanges that come with mobile phone communication enable better assessment of shocks and mitigation of related risks across households and market makers. Lastly, beyond the demand-side channels emphasized here whereby less price information asymmetry boosts competition and firm performance, there are supply channels that also enhance productivity through relaxation of financial constraints, better functioning factor markets (including the labor market), as well as cheaper and better inputs.

Recently, there is a growing body of literature that examines the effect on economic behavior and outcomes of improvements in information communication technology (ICT) using micro level data. For example, Jensen (2007), based on microlevel survey data from fishing industry in India, reports substantial price reduction as well as welfare gains because of adoption of mobile phones by producers and wholesalers. Based on survey data from Southeast Asian countries (Thailand, Vietnam, the Lao People’s Democratic Republic and Cambodia), Hubler and Hartje (2016) document an economically large positive and significant positive effect of smartphone ownership on household income. Evidence from Sub- Saharan African studies points in the same direction. Muto and Yamano (2009), using data from households in Uganda, find that mobile phone network expansion has led to increased market participation of farmers as well as sales of perishable crops in remote areas. Indeed, sales of a perishable crop (banana) increased relative to a storable one (maize).

These results are attributed to decline in marketing costs of agricultural products because of increased flow of information that comes with the mobile phone network coverage. Furthermore, the results indicate that simple expansion of mobile phone coverage itself (not necessarily ownership and usage of mobile phone) gave rise to better market participation and revenue outcomes for producers of perishable products.

Aker (2010) attributes 10-16 percent of the grain price reduction in Niger to the introduction of mobile phones between 2001 and 2006. This effect is more pronounced in markets 3
Policy Research Working Paper 6969

# **Abstract**

This paper uses a panel of annual observations for 198 banks in 19 Middle East and North Africa countries over 2001–12 to develop an early warning system for forecasting bank insolvency based on a multivariate logistic regression framework. The results show that the traditional CAMEL indicators are significant predictors of bank insolvency in the region. The predictive power of the model, both in-sample and out-of-sample, is reasonably good, as measured by the receiver operating characteristic curve. The findings of the paper suggest that banking supervision in the Middle East and North Africa could be strengthened by introducing a fundamentals-based, off-site monitoring system to assess the soundness of financial institutions.

This paper is a product of theFinance and Markets Global Practice.. 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://econ.worldbank.org. The author may be contacted at pcalice@worldbank.org.

_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
# **2.3 The “Domestic Firms” Spreadsheets – AD-CTY-Domestic-Firms**

For many user countries, we also have a spreadsheet presenting a common set of information on domestic firms, trade associations and/or labor unions that are part of the filing of the antidumping investigation petition. The AD-CTY-Domestic-Firms spreadsheet has the same 2 initial column headings (Columns A and B) for each of the using countries, described in table 2.3.1.

**Table 2.3.1 : Variables in the AD-CTY-Domestic-Firms Spreadsheets**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**A.**|**CASE_ID**|Case identifier used to link observations across different elements of the database<br>[generated by us]|
|**B.**|**D_FIRM**|Domestic firm, trade association, industry group, labor union, etc. that is part of the<br>antidumping petition requesting the investigation|

# **2.4 The “Foreign Firms” Spreadsheets – AD-CTY-Foreign-Firms**

For many user countries, we also have a spreadsheet presenting a common set of information on foreign firms subject to the antidumping investigation, and, where available, the foreign-firm specific outcomes (e.g., level of duties imposed) at the resolution of the investigation. The AD-CTY-Foreign-Firms spreadsheet has the same 3 initial column headings (Columns A through C) as described in table 2.4.1.

**Table 2.4.1 : Variables in the AD-CTY-Foreign-Firms Spreadsheets**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**A.**|**CASE_ID**|Case identifier used to link observations across different elements of the<br>database [generated by us]|
|**B.**|**F_FIRM**|Foreign firm under investigation in the antidumping petition|
|**C.**|**F_AD_MEASURE_FIRM**|Foreign firm-specific final antidumping measure imposed|

12
There are some limited improvements in other dimensions of human capital as a result of the ELA program. These dimensions include business skills, attitudes towards gender norms/roles, and health-related knowledge, and are part of the life skills component of ELA. The gains to young women on these dimensions of human capital occur in both high and low disruption villages. Overall, the picture that emerges is that ELA clubs foster multiple dimensions of human capital accumulation in a time of aggregate crisis.

This all links to the potential role ELA clubs play in offering girls and women alternative uses of their time to spending it with men or in other leisure activities. Our remaining results examine this aspect more closely.

We find that women in ELA treatment villages significantly reduce time spent with men or time spent alone. These reductions occur for both age groups, and in low and high disruption villages. As a consequence of these effects on time use of ELA clubs, we observe subsequent impacts on pregnancy and risky behaviors. Most importantly, the impact on out-of-wedlock births is completely reversed in treated high disruption villages. The magnitude of the fall in out-of-wedlock pregnancies caused by ELA (7 _._ 5pp) closely matches the rise in school enrolment caused by ELA (8 _._ 5pp) in high disruption villages. This is as expected given the ban on visibly pregnant girls attending school that was in place post-crisis.

The protective time effects of ELA for young women have consequent impacts for older girls (those aged 18-25 at baseline). In particular, as the availability of younger girls is reduced in the presence of ELA, men likely shift attention to older girls and seek out alternative ways to satisfy their sexual demands. In treated villages, older girls report increases in unwanted sex (by 5 _._ 3pp or 40% of the baseline mean), and report engaging in more transactional sex (by 5 _._ 4pp, 120% of the baseline mean). As in Dupas and Robinson [2012], the use of transactional sex is thus one form of income generation available to women in a time of crisis when economic opportunities become scarce and the ability to use mechanisms to smooth consumption in the face of idiosyncratic shocks is limited. Reassuringly, ELA clubs prevent these changes translating into higher fertility because older girls take on the life skills provided by ELA and increase their use of female controlled forms of contraception.<sup>5</sup> Taken together, our findings show how the Ebola crisis impacts the economic lives of women through time spent with men, and how interventions in a time of crisis might interlink human capital and fertility outcomes across younger and older cohorts.

> 5Dupas and Robinson [2012] study the impacts of the political crisis of Kenya’s 2007 elections, where civil unrest led to the closure of markets. They collected data from sex workers and other samples, shortly after the political and social upheaval (in Busia). They found large reductions in income and consumption expenditures among rural households. The main coping strategy for women was transactional sex and engaging in higher risk sex.

5
risk of income losses due to health shocks. However, health insurance often fails to cover informal sector and, even among formal sector, the coverage can be very low due to low enrollment (Wagstaff, 2009). Thus little is known about the extent to which health insurance reduces vulnerability to the health shocks.

# **3. Data and Institutional Background**

# <u>Data</u>

This study uses a nationally representative Yemen Household Budget Survey (HBS) conducted in 2006. In examining health risks by work status, the data should contain information on both health and labor market outcomes. The wealth of information on health as well as individual demographic and socio-economic characteristics, labor market indicators, and household living conditions, income, and expenditure enable to analyze the health implications of informality.<sup>12</sup> The Yemen HBS collected information on 13,121 households and 98,845 individuals. Among 56,600 adults aged fifteen or above, 20,888 individuals are working as either wage employed or selfemployed.<sup>13</sup> For all workers, the data contain information on job duration for the current occupation, the total number of jobs held, and an indicator of agriculture sector. In addition to this, wage workers are asked about social insurance coverage, benefits such as paid leaves, and earnings and working hours are included. For the self-employed, the information on the size of enterprises and rough measures of revenue and costs are collected.

> 12 A Labor Force Survey includes detailed information on labor market indicators including employment status and sector, job search efforts, and earnings, but often lacks information on health. On the other hand, Demographic Health Survey has detailed information on health, but misses labor market indicators.

> 13 Among those not working, it is unclear whether they are still in the job search process.  Thus, defining an economically active population using a standard definition is not possible here. The majority of non-working adults are either women or students.

7