rafmacalaba/datause-encoder-data huggingface dataset:
Over time, the extensive reliance of developing country governments on donor assistance, combined with the willingness of donors to provide emergency grants and loans, has created an environment in which countries that are prone to disasters have few or no incentives to take proactive measures to manage disaster risks. While ex post disaster funding is an important element of a risk management strategy, over-reliance on this approach by both governments and donors provides no incentives for the implementation of policies to reduce risks (such as better urban planning, higher construction standards, etc.).

In the authors’ view this approach is becoming increasingly unsustainable. Over time, as developing countries accumulate more assets (buildings, infrastructure) in limited geographic spaces, their potential loss exposure increases. With sub-standard construction and land-use practices, and the propensity of some regions to experience more natural disasters, the level of countries’ vulnerability and risk exposures also increases. These two factors together point to the likelihood of growing real losses from natural catastrophes in the future. Moreover, the capacity and willingness of donors to fund disaster relief and reconstruction is ultimately constrained<sup>2833</sup> . Therefore, funding gaps between available donor resources and post-disaster funding needs are likely to appear (or grow if already present) if disaster prone countries continue to rely on ex post donor funding without engaging in ex-ante risk management, including risk financing.

In addition to being potentially unsustainable, experience across a broad range of countries has demonstrated that an over reliance on reactive, ex post approaches to natural disasters can be sub-optimal. Ex post funding approaches are subject to the following risk enhancing possibilities:

**Slow release of funds.** Multilateral post-disaster reconstruction aid can take a relatively long time to be negotiated and to disburse, relaxed requirements notwithstanding. Perhaps more importantly  there is strong evidence that some disaster related loans have not disbursed because of a fundamental lack of local human and institutional capacity to deploy the available funds. Regardless of the cause the human impact and the level of disruption of economic activity may be far greater due to a delayed response.

**Ineffective use of funds.** Resource allocation after a catastrophe may be partly ad hoc (and subject to political agendas) due to extreme urgency, which often precipitates a lack of public scrutiny and leaves ample room for political considerations in determining postdisaster investment priorities. For example, in some countries, governments have committed to incur the costs of rebuilding destroyed housing, regardless of the economic status of the owners. This diversion of limited fiscal resources away from productive development projects which are likely to restore economic activity and support the poor can have longer-term adverse economic and poverty effects.

> 28 See data in the IMF’s paper “Fund’s Assistance To Countries Facing Exogenous Shocks”, August 8, 2003

10
Table 4: Balance: Transfer size and persistence

||log baseline<br>consumption|log GDP<br>per<br>capita|Africa|Transfer<br>duration<br>(years)|Year of<br>last<br>transfer|Intervention<br>cost per unit<br>of transfer|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Panel A: No interaction**|||||||
|Years since last transfer|0.093|0.010|0.014|-0.084|-1.03|-0.003|
||(0.051)|(0.038)|(0.016)|(0.025)|(0.19)|(0.008)|
||[0.089]|[0.795]|[0.403]|[0.004]|[0.000]|[0.659]|
|log transfer size|0.153|-0.141|0.045|-0.290|0.60|-0.074|
||(0.168)|(0.178)|(0.059)|(0.161)|(1.07)|(0.030)|
||[0.377]|[0.442]|[0.456]|[0.093]|[0.583]|[0.025]|
|**Panel B: Interaction**|||||||
|Years since last transfer *|-0.126|-0.012|-0.028|-0.126|-0.98|-0.008|
|log transfer size|(0.131)|(0.130)|(0.038)|(0.269)|(0.985)|(0.036)|
||[0.357]|[0.928]|[0.470]|[0.646]|[0.336]|[0.822]|
|Controls|||||||
|Years since last transfer|X|X|X|X|X|X|
|log transfer size|X|X|X|X|X|X|
|Mean dep. var.|7.358|7.644|0.944|0.676|2014.83|1.179|
|# of RCTs|6|7|7|7|7|7|
|# of observations|16|18|18|18|18|18|

_Notes:_ Columns 1 through 6 present regression coefficients, with robust standard errors clustered at the RCT-level in parentheses and p-values in brackets.

a one year increase in years since last transfer is associated with a 1.05 year decrease in the year of the intervention. This is because longer run estimates (with larger years since last transfer) must come from interventions that occurred more years ago. Second, a 10% increase in transfer size is associated with a 0.009 decrease in cost per unit of transfer. This is because many of the costs of UCT are fixed with respect to transfer size, such as targeting and delivery, so larger transfers are cheaper to deliver per unit of transfer. Third, a one year increase in years since last transfer is associated with approximately one month shorter duration of transfers. This correlation is potentially because UCT with longer duration often have the objective of increasing consumption at shorter time horizons, and so often include relatively short run follow up surveys. For robustness, we therefore also estimate specifications that include transfer duration as a control. Fourth, a one year increase in 30
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18
**Table 2. Trade Partners Predicted to See the Largest Increases in Exports to Russia, 201220**

|**Partner**|**Change in Value of Russian Imports**<br>**(US$, millions)**|**Increase in Russian Imports from**<br>**Partner, 2012 (Percent)**|**Increase in Import Value Explained**<br>**by Increased imports from Partner**<br>**(Percent)**|
|---|---|---|---|
|EU-27|2,115.4|1.6|48.8|
|_Of which:_||||
|Germany|476.2|1.2|11.0|
|France|286.3|2.2|6.6|
|Italy|246.9|1.9|5.7|
|Poland|204.0|2.7|4.7|
|Netherlands|105.6|1.8|2.4|
|Spain|94.7|1.9|2.2|
|United Kingdom|85.0|1.0|2.0|
|Belgium|75.7|1.7|1.8|
|Hungary|62.8|2.0|1.5|
|China|1,000.1|1.9|23.1|
|United States|201.2|1.3|4.6|
|Turkey|116.4|1.7|2.7|
|Korea, Rep.|109.9|1.0|2.5|
|Canada|92.5|4.2|2.1|
|Japan|83.1|0.5|1.9|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 29
with higher moving costs are hurt more in the metal sector, as they are less likely to leave their sector and suffer from the lower wages. Intriguingly, the workers with higher moving costs benefit more in the manufacturing sector. Because workers with higher moving costs are less likely to leave manufacturing, and more likely enjoy higher wages in the long run compared to others. The manufacturing workers with lower moving costs are more likely to end up in the metal sector; this decreases the change in their expected welfare relative to the workers with higher moving costs. This result can be generalized for age as well: As workers get older their moving costs increase and the impact of trade shocks increases. However, at the same time workers’ time horizon gets shorter; this decreases the impact simultaneously. The second effect starts to dominate after workers reach age 50. Therefore, in general, large moving costs magnify the impact of trade shocks for both winners and losers (with an exception of workers who are close to retirement).

# 7 Conclusion

We estimate a dynamic model of labor mobility using US data, CPS and NLSY. We find that moving costs increase with age and experience, but are unchanged with workers’ education level. Then, we simulate a counterfactual trade liberalization in the metal manufacturing sector using the estimated labor mobility model and calibrated production function parameters in a general equilibrium setting. We consider two alternatives with different levels of heterogeneity and find that aggregating worker types does not change the welfare implications of the model.

We show that trade shocks affect workers with higher mobility costs more, for both winners and losers of the policy shocks. In the metal sector, workers with higher moving costs are hurt more, while in the manufacturing they benefit more. But the effects are non-monotonic and they taper off over a worker’s lifetime, especially when they are close to retirement. We also find that a worker’s sector is the main determinant of how he is affected; this shows that moving costs are large enough to cause significant wage differentials across sectors, in contrast with models that have perfect labor mobility and factor price 25
econometric estimates have an unexpected sign, implying, if taken at face value, that some of the projects may have made some health outcomes worse.

**Table 17.  Cost-Benefit Analysis of Social Fund for Development Programs**

|||A vs. B|||D vs. F|||C vs. E||
|---|---|---|---|---|---|---|---|---|---|
||Regres-<br>sion|Nearest<br>Neigh-<br>bor|Kernel|Regres-<br>sion|Nearest<br>Neigh-<br>bor|Kernel|Regres-<br>sion|Nearest<br>Neigh-<br>bor|Kernel|
|**Education (LE 200m)**||||||||||
|Per one less illiterate (LE 1,157)|126|||63|||43|||
|Per one more job (LE 10,700)|||||||-220|||
|Per one fewerpoor(LE 248/yr × 5)|-3|-106|-72|||||||
|**Total **|**123**|**-106**|**-72**|**63**|||**-177**|||
|**Total, with lower value of life**|**123**|**-106**|**-72**|**63**|||**-177**|||
|**Health (LE 390m)**||||||||||
|Per one less U-5 death (LE 0.796m)|1,695||||||1,583||1,680|
|Per LE1 saved on health spending (× 5)|149|||58|52|74|89||79|
|Per one fewerpoor(LE 248/yr × 5)||||33|53|41|-48|-63|-45|
|**Total**|**1,844**|||**91**|**104**|**115**|**1,624**|**-63**|**1,714**|
|**Total, with lower value of life**|**319**|||**91**|**104**|**115**|**200**|**-63**|**202**|
|**Potable water (LE 620m)**||||||||||
|Per LE1 saved on water (× 5)|21|24|20|12||8|10|18|13|
|Per one less U-5 death (LE 0.796m)|||||||894|||
|Per LE1 saved on health spending (× 5)|212|330|191|93|71|74|115|168|120|
|Per one fewerpoor(LE 248/yr × 5)|13||||||20|||
|**Total **|**247**|**354 **|**211**|**106**|**71**|**82 **|**1,039**|**186**|**133**|
|**Total, with lower value of life**|**247**|**354**|**211**|**106**|**71**|**82**|**234**|**186**|**133**|
|**Sanitation (LE 220m)**||||||||||
|Per LE1 saved on sanitation spending (× 5)<br>Per person fewer with renal disease<br>(0.0945 × life)<br>Per person fewer with bilharzia (0.006 ×<br>life)|5<br>-11,928|||-44,269||-39,271<br>312|2<br>4,091||2<br>4,674|
|Per one fewerpoor(LE 248/yr × 5)|8||12||||2||3|
|**Total **|**-11.915**||**12**|**-44,269**||**-38,960**|**4,094 **||**4.679**|
|**Total, with lower value of life**|**-1,180**||**12**|**-4,427**||**-3,896**|**413**||**472**|
|**Roads (LE 500m)**||||||||||
|Per LE1 of transport spending saved (× 5)|90|41|80||||53|37|50|
|Per one more job (LE 10,700)|334||367||||228||214|
|Per LE1 of additional farm income (× 5)|193|207|145|144||151|71|||
|Per LE1 of additional non-farm inc (× 5)|-70||-79|90|||-61|-59|-66|
|Per one fewer poor (LE 248/yr × 5)||||-7||||||
|**Total**|**548**|**248**|**514**|**227**||**151**|**291**|**-22**|**198**|
|**Total, with lower value of life**|**548**|**248**|**514**|**227**||**151**|**291**|**-22**|**198**|

_Source:_ Authors’ calculations based on HIECS and community survey 2004/2005.

_Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 28 of 36_
WPS3784 headcount index in rural Rwanda stands at 47 percent, against only 13 percent in urban areas<sup>2</sup> (Table 1). The EICV also shows that poverty varies substantially across provinces. The poverty incidence is highest in Gikongoro (57 percent) and lowest in Kigali Ville (10 percent).

Denoting _y_ as the poverty status (incidence index) of observed individuals within the sample, and _X_ as a vector of major individual, household, community and regional characteristics, the probability of escaping poverty is defined by the probit model: Pr( _y_ = 1 _X_ ) = Φ ( _Xb_ ) (1) Φ is the standard cumulative normal probability distribution and _Xb_ is the probit score or index. The probit coefficient, _b_ , shows the impact of a one-unit increase in the predictor on the probability of not being poor. A one-unit increase in the predictor leads to an increase in the probit score by _b_ standard deviations. We model the probability of being out of poverty as a function of the following predictors: market access community variables (such as, access to roads, access to electricity, proximity of markets), household and individual characteristics (demographic profile, head of household education, land size, livestock, etc.), and a set of control variables such as provincial dummies.

Table 2 shows the results of the probit estimation. A key feature of these results is the central role of community variables in escaping poverty. Access to year-round roads, credit and electricity increase significantly the chances of escaping poverty. Of particular interest in the context of this paper is that all of the market access variables significantly influence the chance of escaping poverty. Households that do not have easy access to a road and/or have to use impracticable earth roads to connect to secondary or main roads have limited or no access to product and factor markets, substantially reducing the opportunities for escaping poverty. Regional disparities in the probability of being poor partly reflect this result. Residents of Kibuye, Kigali, Gikongoro, Byumba, and Butare that are more likely to be in poverty have poor access to community infrastructures, rural

> 2 For our analysis the poverty line is conventionally defined as the per capita income of the final individual in the 4<sup>th</sup> decline of the income distribution. The headcount index, P0, is the proportion of population with per capita expenditure below the poverty line. The poverty-gap, P1, estimates the average distance separating the poor from the poverty line, as a proportion of that line.

4
# **<u>DMFAS</u>**<sup>**18**</sup>

**Number of versions:** 6 main versions, first version launched in in 1983. Latest version is DMFAS 6 for which most recent release is 6.1.3 (distributed in April 2018).

# **Cost of installation and maintenance:** Around $350K

|**Modules**||
|---|---|
|**Loans module**|Loans (the parties, the reference number, the date of signature drawing limit, and other<br>characteristics such as purpose, economic sector, and classification in addition to the<br>principal, interest and commission terms)|
|**Debt securities**<br>**module**|All types of debt securities from short-term to long-term options, under the following<br>categories: money market instruments, bonds and notes, promissory notes and others|
|**Sukuk**|Record different types of Islamic securities (short or long term)|
|**Grants module**|Record general and specific data relating to a grant|
|**Private Non-**|Record and monitor data on PNED. It offers two modes for data recording: either|
|**Guaranteed**<br>**External Debt**<br>**module**|manual (aggregated or detailed) or via import from Excel with pre-defined templates.|
|**Short-term**<br>**External Debt**<br>**module**|Recording short-term external debt with an original maturity of one year or less as<br>aggregated data (manual entry or import from Excel)|
|**On-lent Loans**|Record on-lent loans and their relationship with the original loan|
|**Auction module**|Record auctions (competitive and non-competitive bids, yield, clean<br>price, dirty price, etc. Linked to debt securities module|
|**General**<br>**agreements**<br>**module**|General information about frame agreements linked to related credit agreements (can<br>then be linked to the individual agreements)|
|**Debt**<br>**reorganization**<br>**module**|Handles all types of debt reorganization, including refinancing, rescheduling, forgiveness,<br>debt conversion and prepayments or buybacks for debt securities. Relationship between<br>the old and new debt is maintained.|
|**Mobilization**<br>**and Debt**<br>**Service module**|Recoding real drawings on loans, grants, on-lent loans, and debt securities in addition to<br>the debt service operations on principal, interest, commissions, and penalty interest.<br>Budget period allocations can also be used and linked to each transaction.|

> 18 Provided by DMFAS team and DMFAS 6 training catalogue, April 2015.

38
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Kpaka, Henry Musa. 2011. _Report on the IRCBP 2008 Decentralization Stakeholders Survey_ .

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- Olken, Benjamin. 2010. "Direct Democracy and Local Public Goods: Evidence from a Field Experiment in Indonesia." _American Political Science Review_ 104(2): 243-267.

- Ostrom, Vincent, and Elinor Ostrom. 1999. “Public Goods and Public Choices.” In _Polycentricity and Local Public Economies: Readings from the Workshop in Political Theory and Policy Analysis_ , ed. Michael McGinnis. Ann Arbor: University of Michigan Press, 75–105.

- Peters, Krijn, and Paul Richards. 1998. “‘Why We Fight’: Voices of Youth Combatants in Sierra Leone.” _Africa: Journal of the International African Institute_ 68(2): 183–210.

- ———. 2011. “Rebellion and Agrarian Tensions in Sierra Leone.” _Journal of Agrarian Change_ 11(3): 377–95.

- Rehavi, M. Marit. 2008. _Sex and Politics: Do Female Legislators Affect State Spending?_ University of California, Berkeley. Mimeo.

33
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that are being captured instead by the regional dummy.<sup>4</sup> And these variables appear to affect the impact on child mortality of public spending on health. However, the basic model remains robust in the presence of the MENA dummy.

**Table 5: Determinants of changes in school enrollment: MENA region and oil resource effects**

|Variable|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Ln Initial school enrollment|-0.776***<br>(0.039)|-0.770***<br>(0.039)|-0.595***<br>(0.041)|-0.597***<br>(0.040)|
|Education spending to GDP ratio [1]|0.080***<br>(0.028)|0.066**<br>(0.029)|0.040***<br>(0.011)|0.039***<br>(0.011)|
|GDP PC growth|0.051***|0.055***|0.045***|0.047***|
||(0.013)|(0.013)|(0.012)|(0.012)|
|Undernourishment|-0.013***|-0.013***|-6.00e-03***|-5.70e-03***|
||(2.30e-03)|(2.29e-03)|(2.20e-03)|(2.10e-03)|
|Population density|4.00e-06|1.40e-05|||
||(1.18e-04)|(1.20e-04)|||
|Control of corruption|4.84e-03|0.017|0.083**|0.089**|
||(0.037)|(0.039)|(0.037)|(0.037)|
|MENA dummy [2]|-0.090<br>(0.061)||||
|Oil-rich dummy [3]||0.024<br>(0.072)|||
|Interaction [1] and [2]|||-4.10e-03<br>(0.013)||
|Interaction [1] and [3]||||0.010<br>(0.012)|
|Constant|3.233***|3.247***|2.569***|2.570***|
||(0.170)|(0.178)|(0.203)|(0.202)|
|Observations|78|78|109|109|
|Adjusted R-squared|0.885|0.883|0.790|0.791|

Notes: Regressions are run on global samples including developing and high-income countries. The dependent variables are absolute log changes in secondary school enrollment (increase) over 19802014.

The oil resource dummy does not have an effect in either regression, not by itself and not in interaction with the public spending variable. This suggests that, once the other variables in the basic model have been included, whether or not a country is oil-rich does not matter. Most probably, the effect of oil resources is fully picked up by the public spending and income growth variables.

> 4 When we use the Gulf Cooperation Council (GCC) countries instead, we get a stronger effect. The coefficient increases from 0.370 [s.e=0.123] to 0.554 [s.e.= 0.221]. But this stronger effect is unlikely to be related to oil resources, given that the coefficient on the Oil-rich dummy is statistically insignificant.

16