rafmacalaba/datause-encoder-data huggingface dataset:
others provides cross-sectional (spatial) variation in the degree to which each Palestinian location was obstructed by obstacles.

# _Completing the road closure obstacles database_

The original database we received did not include: (a) construction dates for each segment of the separation barrier (needed to account for changes in optimal routes as segments are added to the wall over time), (b) obstacles prior to 2008, and (c) estimates of the “crossing times” associated with crossing any given checkpoint (which vary over time). We completed (a) and (b) by inspecting the obstacle maps UNOCHA has published over the years, and by manually: (1) dating each segment of the separation barrier, and (2) adding the geolocations and attributes of any obstacles appearing on published UNOCHA maps prior to 2008 into our shapefile. Moreover, obstacles whose locations did not perfectly align with the road network were snapped to the most plausible road segment based on available documentation on these obstacles and consultation with UN-OCHA officials.

Since checkpoints play a critical role in controlling Palestinian traffic in the West Bank, accounting for the time it takes to traverse active checkpoints improves our estimates of optimal travel times over the road network. Working in coordination with UNOCHA, we conducted repeated retrospective interviews between 2009 and 2013 of their field workers in an effort to estimate average “crossing times” for each checkpoint and for each year between 2005 and 2012. For selected checkpoints the reported crossing times are found to range between 15 minutes and 240 minutes depending on the time period, highlighting the importance of accounting for fluctuations in checkpoint traversal times.

# _Updating optimal routes as obstacles are relocated or removed_

Between 2005 and 2012 obstacles are frequently relocated, replaced by other types of obstacles, or removed altogether. As the spatial configuration and intensity of obstacles changes, we recalculate the optimal routes between origin-destination pairs (by minimizing travel times). Figure 4 provides an example, plotting the optimal route between Tulkarm and Nablus as it evolves over time. Nablus is Palestine’s historical economic capital and the West Bank’s second largest city after Hebron. It was surrounded by a large number of obstacles in the years following the uprising that made it difficult for commercial (and civilian) traffic to travel to and from Nablus; all traffic was forced to travel through the Huwwara and Awarta checkpoints, located to the south of the city, significantly prolonging travel times. As some of these obstacles were being removed during the 2009-2010 period, more direct routes to/from Nablus were being permitted 14
# **7. Figures**

_Figure 1._ Mean household per capita consumption expenditure in Rural India, across surveys <!-- Start of picture text --> 130<br>120<br>117.0<br>110<br>100<br>93.9<br>96.1<br>90 87.3<br>86.9<br>80.0 80.3<br>80<br>74.7 79.2<br>70<br>68.7 62.9<br>60<br>50<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>CES SCS health SCS education IHDS<br>2011 USD PPP per month<br><!-- End of picture text --> _Note_ : CES refers to Consumption Expenditure Surveys, SCS Health and Education to the Surveys on Social Consumption and IHDS to India Human Development Survey. Consumption expenditures are in 2011 USD PPP using price deflators as in Atamanov et al. (2020).

_Figure 2._ Mean household per capita consumption expenditure in Urban India, across surveys <!-- Start of picture text --> 160<br>152.0<br>150.3<br>150<br>143.7 147.7<br>140<br>131.4<br>130 127.1 128.4<br>125.0<br>120<br>111.0<br>110<br>106.6<br>100 98.8<br>90<br>80<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>CES SCS health SCS education IHDS<br>2011 USD PPP per month<br><!-- End of picture text --> _Note_ : CES refers to Consumption Expenditure Surveys, SCS Health and Education to the Surveys on Social Consumption and IHDS to India Human Development Survey. Consumption expenditures are in 2011 USD PPP using price deflators as in Atamanov et al. (2020).

24
# **<u>Table A.7. Poverty Lines, Mauritania, in LCU, Per Capita Annual Consumption in Survey Year Prices</u>**

||**Extreme poverty line**|||**Pove**|**rty line**||
|---|---|---|---|---|---|---|
||**2004**|**2008**|**2004**|**2008**|**2014**|**2019**|
|**Total**|70400|96000|94650|129000|169445|191000|

Source: IMF (2011) Table 1.1.

50
# **1. Introduction**

The world is entering the third year of the global COVID-19 pandemic, which has caused enormous devastation to both people’s health (450 million cases and 6 million deaths as of March 2022) and national economies, in the form of a global recession that has pushed millions into poverty (Dong, Du and Gardner 2020; World Bank 2022). With the continued emergence of new variants and limited treatments available, it is commonly accepted that widespread vaccination is the world’s best bet to contain the virus and it is also expected to play an important role in economic recovery, as widespread vaccinations would enable the reopening of the economy (IMF 2021; Hoogeveen and Lopez-Acevedo 2022).<sup>2</sup> As of early March 2022, over 60 percent of the world’s population had received at least one dose of a COVID-19 vaccine. However, there are stark disparities in vaccination rates across countries: only 14 percent of people in low-income countries had received at least one dose as of March 22, 2022, compared to 79 percent in high-income countries and 81 percent in upper-middle income countries (Ritchie et al. 2022) (Figure 1).

The lag in vaccine distribution in lower income countries put the focus mainly on supply-side constraints until recently. However, the latest industry estimates predict that total vaccine production will exceed global demand by mid-2022, more than two years after the start of the pandemic and about a year and half since a COVID-19 vaccine was first authorized for emergency use. In fact, there are multiple reports of a possible “supply glut” and doses going to waste (Sanjay and Bloomberg 2022). Moreover, low- and middle-income countries have also been catching up with high-income countries in terms of the rates of vaccine rollout (Glassman, Kenny and Young, 2022; see also Figure 2). Despite this progress, a significant share of the world’s population remains unvaccinated, and if COVID-19 becomes endemic, the necessity of periodic boosters will reinforce the importance of addressing barriers to vaccination both on the supply and demand sides. To this end, it is increasingly important to understand the extent to which individuals in low- and middle-income countries elect to be vaccinated, and if not, to understand the nature of the concerns of those who choose not to. This will help address underlying bottlenecks to achieving immunity among the wider population, especially given the fear that persistent pools of unvaccinated people around the world present a greater risk for the emergence of new SARS-CoV-2 variants (Mallapaty 2022).

> 2 Note that lagged increases in vaccination rates for other diseases were also found to be positively associated with increases in GDP growth rates across countries, and increasingly so over time (Masia et al. 2018), but for nonCOVID-19 vaccines the primary channel would be that of improved population health, rather than an enabling environment for economic activity.

2
As an alternative to the fixed-effect models presented above, which assume that the slope coefficients across countries are identical, one can assume that only a common long-term relationship holds, whereas short-run coefficients are different in each country. Table 7 presents such pooled mean-group (PMG) estimation results for the error correction model.<sup>8</sup> The short-run coefficient θ for government expenditure increases from 1.28 in the fixed-effect estimation to 1.43, with the highest individual coefficients in Bangladesh and Pakistan. The short-run coefficient for the tax revenue also increases, with the highest individual values found in India and Nepal, but it remains smaller than the expenditure coefficient. Overall, the PMG estimator yields higher estimates, but leads to similar conclusions.

**Table 7** Error-correction model results (PMG estimator)

|**Dependent variable**|**μ**|**θ**|**γ**|**β**<br>|**Observations**|
|---|---|---|---|---|---|
|Government expenditure|1.99*|1.43***|-0.27**|1.23**|162|
|Inflation tax|0.018|-0.4|-0.24***|0.18***|157|
|Tax Revenue|-1.13**|1.18***|-0.17**|1.17**|162|

Note: ***, **, * denote significance at the 1, 5 and 10 percent levels respectively.

# _7.3 Alternative model specifications to estimate fiscal multipliers_

In the following, we will modify some of the identification assumptions of the PVAR model to understand how sensitive the results are to these assumptions. First, we will vary the contemporaneous tax elasticity; second, we will vary the contemporaneous government spending elasticity; and, third, we will change the ordering of expenditure and tax decisions.

**Figure 2** IRFs for different values of the contemporaneous tax elasticity (a��)

<!-- Start of picture text --> a) Government expenditure b) Tax revenues<br>1 0<br>0.8 -0.2<br>0.6 -0.4<br>0.4 -0.6<br>0.2 -0.8<br>0 -1<br>0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10<br>Years Years<br>0.75 1 1.25 0.75 1 1.25<br><!-- End of picture text --> In the baseline analysis, we assume that the elasticity of tax revenues is equal to 1. In Figure 2 we show impulse response functions for alternative values of 0.75 and 1.25. Little surprising, the spending multiplier does not change with different values for the tax elasticity. The tax multiplier, on the other hand, increases in absolute values with higher elasticity values. For an elasticity of 1.25, the impact of

> 8 See Pesaran, Shin and Smith (1999) for a discussion of the methodology. The MG estimation of the simple regression is included as Table A6 in the Appendix.

14
|UG Teacher and School Effectiveness Project|Kirill Vasiliev|
|---|---|
|UG Teacher and School Effectiveness Project|Kirill Vasiliev|
|UG Teacher and School Effectiveness Project|Kirill Vasiliev|
|UG Teacher and School Effectiveness Project|Kirill Vasiliev|
|UG Teacher and School Effectiveness Project|Kirill Vasiliev|
|UG Teacher and School Effectiveness Project|Kirill Vasiliev|
|UG Teacher and School Effectiveness Project|Kirill Vasiliev|
|UG Teacher and School Effectiveness Project|Kirill Vasiliev|
|UG Teacher and School Effectiveness Project|Kirill Vasiliev|
|UG Teacher and School Effectiveness Project|Kirill Vasiliev|
|Open Government and Public Financial Management|Chiara Bronchi|
|India Ecosystems Service Improvement Project|Andrew Michael Mitchell|
|India Ecosystems Service Improvement Project|Andrew Michael Mitchell|
|India Ecosystems Service Improvement Project|Andrew Michael Mitchell|
|India Ecosystems Service Improvement Project|Andrew Michael Mitchell|
|India Ecosystems Service Improvement Project|Andrew Michael Mitchell|
|India Ecosystems Service Improvement Project|Andrew Michael Mitchell|
|India Ecosystems Service Improvement Project|Andrew Michael Mitchell|
|EmergencyCrisis RecoveryProject|Lire Ersado|
|EmergencyCrisis RecoveryProject|Lire Ersado|
|EmergencyCrisis RecoveryProject|Lire Ersado|
|EmergencyCrisis RecoveryProject|Lire Ersado|
|BANGLADESH - BUILDING SAFER CITIES BY IMPROVING EART|HQUAKMarc S. Forni|
|Irrigation and Land Market Development Project|Wilfried Hundertmark|
|Irrigation and Land Market Development Project|Wilfried Hundertmark|
|Irrigation and Land Market Development Project|Wilfried Hundertmark|
|Irrigation and Land Market Development Project|Wilfried Hundertmark|
|Irrigation and Land Market Development Project|Wilfried Hundertmark|
|Irrigation and Land Market Development Project|Wilfried Hundertmark|
|Irrigation and Land Market Development Project|Wilfried Hundertmark|
|Irrigation and Land Market Development Project|Wilfried Hundertmark|
|Irrigation and Land Market Development Project|Wilfried Hundertmark|
|Irrigation and Land Market Development Project|Wilfried Hundertmark|
|Irrigation and Land Market Development Project|Wilfried Hundertmark|
|Irrigation and Land Market Development Project|Wilfried Hundertmark|
|Kosovo Water Securityand Canal Protection Project|Trandelina Baraku|
|Kosovo Water Securityand Canal Protection Project|Trandelina Baraku|
|Kosovo Water Securityand Canal Protection Project|Trandelina Baraku|
|Kosovo Water Securityand Canal Protection Project|Trandelina Baraku|
|Kosovo Water Securityand Canal Protection Project|Trandelina Baraku|
|Kosovo Water Securityand Canal Protection Project|Trandelina Baraku|
|Kosovo Water Securityand Canal Protection Project|Trandelina Baraku|
|Kosovo Water Securityand Canal Protection Project|Trandelina Baraku|
|Preparation of SREP Investment Plan|Artur Kochnakyan|
|Preparation of SREP Investment Plan|Artur Kochnakyan|
|Preparation of SREP Investment Plan|Artur Kochnakyan|
|Lao PDR Ninth PovertyReduction Support Operation|Richard James Lowden Record|
When implementing the BMA estimators we only consider models with the two‐way fixed‐ effect structure in (7), so that the effects are to be interpreted as refering to the within‐country dimension and therefore explain differences in school enrollment for a given country over time. The results of the exercise for the five‐year and ten‐year panels are presented in Table 3, which has the same structure as Table 2. The analysis in the panel setting does not include the variables _war_ , which presents low variability in time, and _gini_ , for which a single comparable observation was available for most countries in the sample.

Table 3: BMA results

||**5‐yearpanel, w**|**ithin variation**||
|---|---|---|---|
||PIP/PM(PSD)|PIP/PM(PSD)|PIP/PM(PSD)|
|0<br>_e_|_0.974 / 0.415 (0.191)_|_0.974 / 0.415 (0.191)_|_0.975 / 0.414 (0.190)_|
|0<br>_y_|0.100 / 0.435 (3.116)|0.100 / 0.434 (3.114)|0.100 / 0.428 (3.102)|
|0<br>_life_|0.122 / ‐0.05 (0.313)|0.122 / ‐0.05 (0.313)|0.121 / ‐0.05 (0.310)|
|_vol_|_0.826 / ‐2.17 (1.743)_|_0.826 / ‐2.17 (1.743)_|_0.826 / ‐2.16 (1.740)_|
|_polity_|0.293 / ‐0.26 (0.603)|0.294 / ‐0.26 (0.604)|0.288 / ‐0.25 (0.594)|
|_pavroad_|0.072 / 0.001 (0.061)|0.072 / 0.001 (0.061)|0.072 / 0.001 (0.060)|
|_cred_|0.109 / 0.011 (0.067)|0.109 / 0.011 (0.067)|0.108 / 0.010 (0.066)|
|_t_<sup>_d_</sup>|0.079 / 2.488 (33.57)|‐|‐|
|_c_<br>_d_|‐|0.081 / 2.844 (34.12)|‐|
|_g_<br>_d_|‐|‐|0.091 / ‐60.4 (493.9)|
|_g_‐prior|BIC|BIC|BIC|
|Prior model size|4|4|4|
|Obs.|214|214|214|
|Number of models|256|256|256|
# Table of Contents

||Introduction|2|
|---|---|---|
|1.|Specification of the Model|3|
||1.1 The private Sector<br>1.2 The Banking and the External Sectors<br>1.3 The public Sector|4<br>7<br>9|
||1.4 Remaining Equations|11|
||1.5AnOverview of the Model|12|
|2.|AnEmpirical Test : Argentina (1962-86)|14|
||2.1 Data and Fj.timation<br>Results|14|
||2.2 Goodness-of-Fit|17|
||2.3 Total Effects of Debt-Reduction<br>and Capital Inflows<br>Policies on Domestic Variables|17|
|3.|A Policy Implication : The Efficient Use of International<br>Resources in a Debt-Reduction Program|19|
|4.|Conclusion|22|
|Tabl|es|24|
|Appe|ndix 1|27|
|Appe|ndix 2|30|
|Bib|liography|30|

*I am grateful to S. Claessens for his helpful comments. I also wish to thank J-C. Berthelemy and the participants of seminars at the World Bank and the Instituto di Tella.
Urdal & Hoelscher: Urban Youth Bulges and Social Disorder Urdal Henrik, 2006. ‘A Clash of Generations? Youth Bulges and Political Violence’, _International Studies Quarterly_ 50(3): 607–630.

Urdal, Henrik, 2008. ‘Population, Resources and Violent Conflict: A Sub-National Study of India 1956– 2002’, _Journal of Conflict Resolution_ 52(4): 590–617.

Winckler, Onn, 2002. ‘The Demographic Dilemma of the Arab World: The Employment Aspect’, _Journal of Contemporary History_ 37(4): 617-636.

World Bank, 2008. _The Road Not Travelled: Education Reform in the MENA Region_ . MENA Development Report. Washington DC: World Bank.

Xenos, Peter, 2004. ‘Demographic Forces Shaping Youth Populations in Asian Cities’, in Lisa M. Hanley, Blair A. Ruble, Joseph S. Tulchin & Diana H. Varat, eds, _Youth, Poverty, and Conflict in Southeast Asian Cities_ (7–36). Washington DC: Woodrow Wilson International Center for Scholars.

Yousef, Tarik, 2003. ‘Youth in the Middle East and North Africa: Demography, Employment and Conflict’ in Blair A. Ruble, Joseph S. Tulchin, Diana H. Varat & Lisa M. Hanley, eds, _Youth Explosion in Developing World Cities: Approaches to Reducing Poverty and Conflict in an Urban Age_ . Washington DC: Woodrow Wilson International Centre for Scholars 22
|How did latest returning migrant learn about opportunity to work abroad:|||||||
|---|---|---|---|---|---|---|
|found information on their own||||1.209**||1.247**|
|||||(0.485)||(0.485)|
|Main reason why latest returning migrant returned to Indonesia: contract not|||||||
|renewed||||0.266||0.264|
|||||(0.187)||(0.189)|
|Respondent’s migrant network faced issues abroad: long work hours, heavy|||||||
|workload||||0.908***||0.878***|
|||||(0.285)||(0.290)|
|Madurese ethnicity||||-0.529***||-0.529***|
|||||(0.144)||(0.149)|
|Respondent has friends/acquaintances currently working abroad||||-0.092||-0.093|
|||||(0.423)||(0.425)|
|Reason why current migrant is working abroad: to earn higher income than|||||||
|possible in Indonesia||||0.613***||0.601***|
|||||(0.191)||(0.195)|
|No knowledge of the risks of working abroad without all the necessary|||||||
|documents||||-0.438**||-0.455**|
|||||(0.187)||(0.188)|
|Elementary school diploma||||-0.206**||-0.212*|
|||||(0.095)||(0.121)|
|Reason why Respondent did not travel abroad despite applying: for family|||||||
|reason other than caring for children||||1.743*||1.712|
|||||(1.049)||(1.052)|
|Constant|1.454***|2.827***|1.302***|2.585***|1.551***|2.900***|
||(0.410)|(0.438)|(0.439)|(0.601)|(0.469)|(0.668)|
|Observations|8,186|5,093|8,059|5,049|8,059|5,011|

Source: Authors’ calculations from DESMIGRATIF (2018). Note: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 26
**In Search of the Missing Resource Curse**<sup>∗</sup>

Daniel Lederman Development Economics Research Group The World Bank William F. Maloney Office of the Chief Economist for Latin America The World Bank

> ∗ This paper is partly financed by the Regional Studies Program of the Latin American and Caribbean Region of the World Bank. We would like to thank Joana Naritomi for inspired research assistance. Cameron Shelton, Thad Dunning, and Associate Editor Francisco Rodríguez provided invaluable feedback and criticisms during and after the 17<sup>th</sup> Economía Panel Meeting, held May 2-3, 2008 at Yale University. We also acknowledge the comments from other participants at this event, and the comments from an anonymous reviewer.
31 out by the fact that those with more education earn more. If schooling were really of very poor quality then why would private employers pay more for more educated workers?

2) Is. qtchlin 8<sup>**jnlra**</sup> **soegn2I 9** The most plausible explanation of the micro data consistent with education having no impact on productivity and yet having a large impact on individual wages is a signaling model. Spence (1974) has a model in which education has no impact on skills or productivity, but if workers with high initial (or innate) ability have an easier time investing in school than workers with low initial ability, employers will nevertheless pay more for high schooling workers.

The empirical difficulty with answering this objection is having measures of innate ability, and cognitive skills and schooling and wages to control for the self selection of the more able into greater years of schooling. However, whenever the requisite data have been available there has generally been little support for the signaling model. In the U.S. a variety of samples have been exploited to identify the pure education effect and at least some conclude that there is no evidence that the estimated rates of return to schooling are biased by signaling.45 One detailed study of workers in Kenya and Tanzania using data on ability, schooling, skills and wages shows that, by and large, the effect of schooling on wages is not because of signaling, but rather because schooling raises skills and skills raise wages (Knight and Sabot, 1991). Glewwe (1991) with data on skills, ability, and wages also finds no evidence of screening using data from Ghana. Alderman, Behrman, Ross and Sabot (1996) find in a sample from rural Pakistan that cognitive achievement, and not schooling attainment apart from achievement, raises wages.

A second argument against the importance of screening in explaining private wage effects is the 45 Ashenfelter and Krueger (1994) use data from a sample of twins raised apart to control for innate ability, while Angrist and Knreger (1991) use variations in the amount of compulsory schooling required. On the other hand Behrman, Rosenzweig, and Taubman (1994) testing different samnples of twins found their ability controlled estimates to be significant **l** y ower than the conventional estimates.
Collecting information on the HIV status of individuals in household surveys is difficult for a variety of reasons, including costs, logistics (especially before the development of rapid tests methods, including dried blood spot rather than venous blood samples), and human subjects considerations. As a result researchers often substitute measures of the proximate determinants of HIV for actual HIV status. These measures include behaviors that are biologically associated with risk of HIV, including risky behaviors (not using condoms, having multiple partners) and circumcision status. Mishra et al. (2007) and Boerma and Weir (2005) discuss the application of the proximate determinants approach, which has been adapted from the study of fertility by demographers to the study of HIV.<sup>4</sup> Identifying the socioeconomic status–HIV link using risk behaviors rather than actual HIV status is potentially problematic in ways that differ from those faced by demographers studying fertility. This is because observational data on behaviors and the prevalence of HIV are not always consistent with what is known about how HIV is contracted.

For example, clinical trials on the efficacy of circumcision in preventing HIV reveal that circumcised men are less likely to contract HIV (Auvert et al. 2005; Williams et al. 2006). But in some countries the opposite relation is observed: in Malawi the incidence of HIV in 2004 is estimated at 13.2 percent among circumcised males and 9.5 percent among uncircumcised males (NSO and ORC Macro 2005; Poulin and Muula 2007 present similar findings based on other data from Malawi). In Cameroon the incidence of HIV in 2003 is estimated at 4.1 percent among circumcised and 1.1 percent among uncircumcised males (INS and ORC Macro 2004). In Ethiopia the difference in HIV status between circumcised and uncircumcised males is negligible (0.9 percent among circumcised and 1.1 percent among uncircumcised males) (CSA and ORC Macro 2006).

> knowledge of HIV status informs decisions to participate in sero-prevalence surveys and therefore could produce bias in estimates of HIV prevalence.

> 4 This approach is drawn from the seminal work of Bongaarts (1978) on the proximate determinants of fertility to understand fertility patterns. These determinants include the exposure risk of conceiving, usually measured by cohabitation; use of contraceptives; rates of abortion; pathological sterility; and postpartum infecundability.

4