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- Raquel Fernandez and Dani Rodrik. Resistance to reform: Status quo bias in the presence of individual-specific uncertainty. _The American economic review_ , pages 1146–1155, 1991.

- Rosa L Figueroa, Qing Zeng-Treitler, Sasikiran Kandula, and Long H Ngo. Predicting sample size required for classification performance. _BMC medical informatics and decision making_ , 12(1):8, 2012.

- Manuel Garcia-Herranz, Esteban Moro, Manuel Cebrian, Nicholas A Christakis, and James H Fowler. Using friends as sensors to detect global-scale contagious outbreaks. _PloS one_ , 9(4):e92413, 2014.

- Chih-Wei Hsu and Chih-Jen Lin. A comparison of methods for multiclass support vector machines. _Neural Networks, IEEE Transactions on_ , 13(2):415–425, 2002.

- Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Diaz, and Patrick Meier. Practical extraction of disaster-relevant information from social media. In _Proceedings of the 22nd international conference on World Wide Web companion_ , pages 1021–1024. International World Wide Web Conferences Steering Committee, 2013.

- Maxime Lenormand, Miguel Picornell, Oliva G Cantu-Ros, Antonia Tugores, Thomas Louail, Ricardo Herranz, Marc Barthelemy, Enrique Frias-Martinez, and Jose J Ramasco. Cross-checking different sources of mobility information. _arXiv preprint arXiv:1404.0333_ , 2014.

- Alan Mislove, Sune Lehmann, Yong-Yeol Ahn, Jukka-Pekka Onnela, and J Niels Rosenquist. Understanding the demographics of twitter users. _ICWSM_ , 11:5th, 2011.

- Andr ́es Monroy-Hern ́andez, Emre Kiciman, Munmun De Choudhury, Scott Counts, et al. The new war correspondents: The rise of civic media curation in urban warfare. In _Proceedings of the 2013 conference on Computer supported cooperative work_ , pages 1443–1452. ACM, 2013.

- Anthony J Onwuegbuzie and Kathleen MT Collins. A typology of mixed methods sampling designs in social science research. _Qualitative Report_ , 12(2):281–316, 2007.

- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. _Journal of Machine Learning Research_ , 12:2825–2830, 2011.

38
34 -

# I&bILI: Private Investm,ent Regressions: Sub-Saharan Africa, 1965-82

|Independent<br>Variables|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Oil|7.468|6.424|4.870|4.549|
||(4.06)|(3.64)|(2.84)|(2.83)|
|Coup$|-1.652|-1.019|-0.334|-0.352|
||(2.12)|(1.33)|(0.47)|(0.53)|
|Population|1.146|0.712|-0.155|-0.475|
||(1.22)|0.80)|(0.18)|(0.58)|
|Terms of Trade|-0.235|-0.177|<br>0.024|-0.053|
||(1.56)|(1.24)|(0.17)|(0.39)|
|Government Share|0.386|0.256|0.028|0.047|
||(3.19|(2.08)|(0.24)|(0.41)|
|Government Growth||||0.132|
|||||(1.25)|
|Tax Effort||0.381|0.493|0.486|
|||(2.81)|(3.70)|(3.89)|
|Tax Growth||||0.198|
|||||(2.10)|
|Import Tax|||-0.249|-0.234|
||||(2.43)|(2.44)|
|Export Tax|||-0.328|-0.309|
||||(3.43)|(3.45)|
|Personal Tax|||-0.208|-0.052|
||||(1.11)|(0.28)|
|Corporate Tax|||-0.171|-0.161|
||||(2.00)|(2.00)|
|Sales Tax|||0.006|0.112|
||||(0.05)|(0.93)|
|Inflation|||<br>0.144|0.213|
||||(1.52)|(2.26)|
|1974-82 Interaction|<br>0.260|-0.467|<br>-0.883|-1.028|
||(0.21)|(0.40)|(0.73)|(0M9M)|
|Constant|1.971|-1.658|10.513|6.519|
||(0.58)|(0.20)|(2.59)|(1.59)|
|ft<sup>2</sup>|0.292|0.379|0.559|0.616|

Note: Dependent variable is the share of private investment to GDP. Absolute value of t-statistics in parentheses. N - 56.
Vulnerability to drought and food price shocks: Evidence from Ethiopia Ruth Vargas Hill Catherine Porter The World Bank Heriot-Watt University<sup>_∗_</sup>

**Keywords:** Vulnerability, Poverty, Drought, Inflation, Ethiopia. **JEL Codes:** I31, I32, I38

> _∗_ Many thanks to Christopher Gaukler and Radhika Goyal for excellent research assistance, in particular for their dedication and patience in accessing and compiling the many datasets used in this study. Thanks to Jose Cuesta, Tassew Woldehanna, Matthew Hobson, Camilla Holmemo, participants at CSAE conference Oxford 2015 and anonymous reviewers for comments on earlier drafts. Comments welcome to catherine.porter@hw.ac.uk
We conduct a regression analysis to estimate the cyclical behavior of government consumption expenditure before and after 2002 (our estimated time break). Our coefficient estimates suggest a shift in the stance of fiscal policy along the business cycle across developing regions. The instrumental variable (IV) regression estimates in Table 3 show that, on average, fiscal policy has become counter-cyclical in Latin American (after 2005) and Asia (1999) —although the negative coefficient is statistically not different from zero in the latter group. In Sub-Saharan Africa, the magnitude of the fiscal policy procyclicality bias has decreased over time. After 2002, the coefficient estimates of (the cyclical component of real output) is still positive but it is smaller (in magnitude) and no longer statistically significant. The reduction in the degree of procyclicality across developing regions in recent years has been the result of better fiscal management, particularly during the strong global growth experienced in 2000-08, which better prepared many developing countries (including those in in Sub-Saharan Africa) to face the 2008-09 global financial crisis. In sum, there has been an overall change to more acyclical behavior of the government consumption expenditure in developing regions in recent years, a phenomenon that does not exclude African countries.

**Figure 4.** Sub-Saharan Africa: change in the correlation coefficients between the cyclical components of Government Consumption Expenditure and GDP by country, 1970-2001 vs. 2002-2014

<!-- Start of picture text --> Change to more Still in Procyclical<br>Procyclical region<br>BDI SLE<br>GMB<br>MLI UGA CIV<br>BFA COD GHA<br>GNB<br>TGO SEN BEN MDG<br>TZA NER<br>COM CMR COG CAF GAB<br>MOZ<br>MRT<br>BWA<br>RWA<br>NGA<br>ZWE ZMB MWI LBR<br>GIN<br>ZAF CPV<br>TCD<br>AGO<br>KEN MUS<br>Still in Countercyclical Change to more<br>region Countercyclical<br><!-- End of picture text --> <!-- Start of picture text --> Classification for the 2002-2014 period: Procyclical Acyclical Countercyclical<br><!-- End of picture text --> _Note: The government final consumption expenditure and GDP are expressed in local currency at constant prices (source: UN-Statistics database). Their cyclical components are obtained using the Hodrick-Prescott filter. Countercyclical government consumption means that the correlation coefficient is negative and statistically significant at 5% level. It is procyclical when it is positive and significant, and acyclical when the coefficient is not statistically different from zero._

# **_3.4 The effect of financial openness on the fiscal cyclical stance_**

Table 4 examines whether the degree of cyclicality of fiscal policy is different between countries with low and high levels of financial openness. Our IV estimates show that government consumption expenditure is procyclical among countries with low financial openness and acyclical among countries with high 13
the projected fiscal deficits. Room for additional borrowing is limited without causing macroeconomic sustainability: the country is already rated as being at high risk of debt distress by the latest Joint World Bank-IMF Debt Sustainability Analysis (DSA), and further domestic borrowing will worsen the inflationary pressure, increase exposure of domestic banks to the public sector, and risk crowding out private sector borrowing.

**Table 2. Medium-Term Fiscal Projection: Baseline Scenario**

<u>(in percent of GDP unless otherwise indicated)</u>

||Prelim.<br>2012|Proj.<br>2013|2014|2015|2016|2017|
|---|---|---|---|---|---|---|
|**Revenue and Grants**|**33.0**|**29.4**|**26.6**|**25.9**|**25.8**|**24.2**|
|Revenue|14.8|14.8|14.8|14.8|14.8|14.7|
|Tax revenue|13.8|13.8|13.8|13.8|13.8|13.8|
|Non-Tax Revenue 1/|1.0|1.0|1.0|1.0|1.0|0.9|
|Grants|18.2|14.6|11.8|11.1|11.0|9.4|
|**Total expenditure**|**34.6**|**31.0**|**30.4**|**29.5**|**29.4**|**27.7**|
|Current expenditure|22.2|19.9|19.7|18.7|18.6|17.5|
|Wages and salaries|8.0|7.3|7.5|7.4|7.3|7.0|
|Interest|0.9|0.8|0.8|0.7|0.7|0.6|
|Domestic interest|0.8|0.7|0.6|0.6|0.6|0.5|
|External interest|0.1|0.1|0.1|0.1|0.1|0.1|
|Other|13.3|11.7|11.5|10.6|10.6|9.9|
|Capital Expenditure|12.4|11.2|10.7|10.8|10.9|10.2|
|**Overall surplus/ deficit**|**-1.7**|**-1.6**|**-3.8**|**-3.6**|**-3.7**|**-3.5**|
|Primary surplus/ deficit|-0.7|-0.7|-3.1|-2.9|-3.0|-2.9|
|**Financing**|**1.7**|**1.6**|**3.8**|**3.6**|**3.7**|**3.5**|
|External borrowing, net|0.4|1.5|3.6|3.2|3.8|3.1|
|New borrowing|0.9|2.2|4.5|4.5|5.1|4.4|
|Amortization (- entry)|-0.5|-0.8|-0.9|-1.3|-1.3|-1.3|
|Domestic borrowing, net|0.8|-0.1|0.2|0.4|-0.2|0.4|
|New borrowing|1.3|0.1|0.7|0.8|1.0|1.0|
|Amortization (- entry)|-0.6|-0.2|-0.4|-0.4|-1.2|-0.6|
|Changes in financial assets 2/|-0.5|-0.2|0.0|0.0|0.0|0.0|
|**Financing gap**|**0.0**|**0.0**|**0.0**|**0.0**|**0.0**|**0.0**|
|_Memorandum items:_|||||||
|Nominal GDP (in BIF bn)|3,566|4,138|4,690|5,242|5,833|6,662|
|Government Debt (% GDP)|35.2|33.7|33.9|34.1|34.5|33.6|
|External debt|21.4|21.9|23.2|24.1|25.7|25.4|
|Domestic debt|13.8|11.8|10.7|10.0|8.8|8.1|

_Source_ : IMF (March 2013), DSA (January 2013), and the author's calculation.

_Note_ : 1/ Includes sale of fixed capital assets. 2/ Negative sign denotes a reduction of financial assets.

9
Foreign aid is an important issue for both developing and developed countries. Aid in the form of money, goods or technical assistance can develop infrastructure, strengthen institutions, or address humanitarian crises in recipient countries. Foreign aid can exceed 10% of a recipient country’s national income in many instances (World Bank 2005). There is growing evidence that aid can produce positive outcomes, from democracy (Finkel, Pérez-Liñán, and Seligson 2007) to economic growth (Burnside and Dollar 2000; but see Roodman 2007). Foreign aid can help donor countries as well. In donor countries, foreign aid may be viewed as a strategic foreign policy tool (USAID 2008). When foreign aid is used by recipient countries to finance public goods, it can lead to welfare improvement in _both_ the recipient country and in the donor country (Hatzipanayotou and Michael 1995).

The supply of foreign aid to recipients is influenced by public opinion in the donor countries. Public opinion influences public policies of many types (Shapiro and Jacobs 2000; Jencks 1985; see Burstein 1998 for an excellent review). In the area of foreign aid, Mosley (1985) demonstrates that states are influenced by citizens in determining the quantity (and quality) of distributed foreign aid. Other theorists view foreign aid as a public good with a market (Dudley and Montmarquette 1976; Mosley 1985).

The level of public support for aid affects not only the quality of aid but also the effectiveness of each dollar spent on aid. Paul Collier in his book, _The Bottom Billion_ (2007: 183), asserts: “The key obstacle to reforming aid is public opinion” in donor countries. Where there is relatively little support for aid, aid agencies are overly risk averse, according to Collier, and are constrained to deliver aid in sub-optimal ways – e.g. on projects that provide photo opportunities for politicians. Aid in general may be more effective in reducing poverty when channeled through multilateral institutions such as the World Bank or the regional development banks, because national donor agencies such as USAID often must pursue diplomatic, commercial or other objectives that sometimes conflict with development objectives. However, where public opinion is less supportive of aid, governments may be less willing to yield control over how it is spent to multilateral institutions. Figure 1 depicts a strong relationship between support for
- _increasingpressure on land:_ Orissa's population, growing at an annual rate of 2 percent, continues to depend substantially on agriculture for their livelihoods. The 'cultivable land' frontier is therefore being pushed progressively further onto government and common land.

# _4.3.1 Typology and extent of encroachment_

In Orissa, all types of common land are under threat of encroachment. In private discussions, government officials freely acknowledge the widespread encroachment of government land, but official data is either not gathered or not readily forthcoming. For example, survey and settlement operations do not recognise claims established through encroachment. The reason for this is to preserve original rights in land, but the opportunity is missed to acquire more reliable aggregate data on the extent and forms of encroachment. An encroachment register is maintained at tehsil level, for purposes of proceeding with eviction cases from government land. Owing to the incentives for rent-seeking at this level, these data must be regarded as highly suspect. They are not, in any case, aggregated at any higher level, although in principle some monitoring of encroachment is carried out at district level.

Who encroaches on what types of land? Table 13 summarises the current status of encroachment on different types of public and common land. An important type of interaction between the state and the land user is the de facto 'privatisation' of common lands through encroachment. Successful encroachment _of peromboke_ lands and other common lands (such as uncultivated wastelands) depends upon the degree of influence the individual encroacher enjoys, both with regard to other villagers and to government revenue department officials. Individuals of widely differing socio-economic status encroach upon _peromboke_ land. Landless and near-landless households are perhaps the most numerous, but their position is vulnerable to better-off and relatively more powerful groups who are able to employ intimidatory tactics to evict them. Those with a greater degree of influence may be in a position to see that weaker groups are evicted, yet also manage to evade the law themselves through bribery to allow their encroachment on public and common land to go unnoticed, and eventually to have the encroached land registered in their own names. A lengthy process of de facto occupation _ofperomboke_ land and payment of annual fines for encroachment may thus eventually lead to a change in the revenue classification of the land and acquisition of legal title. Revenue records inevitably lag significantly behind the true extent of encroachment at any given moment.

From our field investigations, it appears that opportunities for further encroachment are now generally limited. Local revenue records and discussions with Rls and tehsildars reveal that most of the encroachment takes place on land designated as _peromboke,_ cultivable waste, permanent pastures and other grazing lands, and only to a very limited extent on land under the jurisdiction of the forest department. Focus group discussions with _50_
(now allowed for loans from public banks) and limited transferability (with special permission from the government which was uncommon during the period covered by the survey), the basic provisions of unitary succession and ban on subdivision and sales of plots and land rental remain largely intact (see Peebles, 2006; De Silva, 1981; World Bank, 2008 for the history of land reforms). Distribution of LDO land took place mainly after Sri Lanka’s Independence in 1948 and much of the land was distributed under various settlement schemes. The settlement schemes brought landless Sinhalese people from the relatively malariafree south to the historically malaria infested DSDs in the dry zone. The LDO leases today coexist with complete private holding in the same location (World Bank, 2008). The share of land under LDO leases varies significantly across areas in our sample (the maximum is 63 percent and minimum 0.2 percent) which is critical for estimation of the effect of LDO restrictions on equilibrium wages.

36
# **Figure 3. Integrating customs/immigration for shared targeting can reduce costs...**

<!-- Start of picture text --> TARGETING<br>IMMIGATION CUSTOMS<br>RED LANE<br>GREEN LANE<br><!-- End of picture text --> _Source:_ Michel Zarnoweicki (World Bank) presentation on ‘Borders and Shared Facilities’ One option to border-sharing is to carry out a phased introduction that initially focuses on simplified processing for traffic (using red and green channels, targeting booth for customs and border police, examination bays), and then graduating to the joint facility. Prior to establishing a joint facility, it is important to determine the type and volume of traffic and trade, time taken to cross the border, relations between the border agencies and with neighboring country.

However, sharing facilities can often increase the time required for clearance and create legal hassles concerning the applicability of country legislation. Problems might arise with respect to administration and responsibility issues between the two countries sharing the facility e.g. consensus on the road between the facility and the actual border.

The Greater Mekong Sub-region (GMS)<sup>32</sup> Cross-Border Transport Agreement (a multilateral agreement ratified by all GMS countries) brings to light issues related to sovereignty and conformance with national laws; consensus on modalities of SingleWindow Inspection (SWI) and Single Stop Inspection (SSI); measures to facilitate transit traffic etc. It covers in one document all the relevant aspects of cross-border transport facilitation, which includes transit traffic regimes, requirements for road vehicle, SSI, SWI, and cross border movement of persons. The agreement applies to selected and mutually agreed upon routes and points of entry and exit in the signatory countries, with initial implementation at select border crossings is scheduled for 2005-07.

# **_....for which Single Window inspection_**<sup>**_33_**</sup> **_is key_**

Given the plethora of shipping documentation, a single window would provide a one-stop shop solution to the trader, and balance trade facilitation with controls dictated by

> 32 Please see Presentation by <u>ADB on its efforts towards single stop customs inspection and agreement on</u> GMS

> 33 Please refer to presentation by Tom Butterfly of <u>UNECE</u> on the single window approach, including guidelines for its establishment.

17
From a practical point of view, it is typically not feasible to collect the type of detailed expenditure data we use here to construct the poverty variable for the purpose of program targeting. Instead, programs often use proxy means measures based on easily observables baseline characteristics to obtain a proxy measure of poverty that can then be used for targeting. In Table 7 we show heterogeneity by poverty using the official proxy means formula developed in Nicaragua (see above). Overall, while the results point in the same direction as those obtained with actual per capita expenditures, they are generally not significant.<sup>13</sup>

# _Endogenous Aspirations_

Heterogeneity in outcomes hence appears related to characteristics that are harder to target on. This could be either because they were unobserved at baseline, because they were not revealed by stated baseline demand, or because they can be affected by the intervention itself. Recall that stated demand for the productive investment intervention was lower among the poor (Table 2). Low baseline demand for productive interventions does not necessarily imply that expected returns to productive interventions are low. Indeed, such low demand could also be driven by low aspirations. If aspirations change once households start participating in program activities, preferences for the productive interventions may in turn evolve, which can lead to higher returns. Such shifts in preferences due to the intervention can make baseline demand information ineffective for targeting.

To shed suggestive light on this hypothesis we rely on the data collected in 2006, during the implementation of both the training and the business grant interventions. When the data was collected, households eligible for training had selected and enrolled in the courses, but the courses had not yet started. Households eligible for the business grant had received the grant 2 to 3 months prior to data collection. Some had started to make the investments, but in many cases it was too early to have income from these investments. As such, by analyzing impacts of the intervention on households’ perspectives on the future at that moment, we arguably have an indication of how participation in the intervention itself might have changed households’ aspirations.

Table 8 shows that both the training and the business grant positively affected the way households were thinking about their own future. On average, the grant increased positive attitudes with .22 of a standard deviation, while the training increased them with .13. Beneficiaries were in particular more optimistic and had stronger feelings that they were moving forward in life. While these impacts are similar for the poor and the less-poor (not shown), these changes potentially could have been more consequential for the poor,

> 13 But programs such as TUP that use a combination of participatory and proxy means methods might be able to identify poor households using these alternative methods.

16
# **Energy Subsidies Reform in Jordan: Welfare Implications of Different Scenarios**

Aziz Atamanov, Jon Jellema and Umar Serajuddin* JEL codes: D1, D3, D4, D6, H2, H4, H6, I3, O2.

Key words: Jordan, subsidies, energy, income distribution.

* World Bank, Washington DC. Email: <u>aatamanov@worldbank.org, jrjellema@gmail.com, userajuddin@worldbank.org.</u>

** The authors acknowledge useful comments and suggestions from Paolo Verme, Masami Kojima, Eric Le Borgne, Ferhat Esen, Daniel Camos Daurella, and Husam Mohamed Beides. The authors would like to thank Ambar Narayan and Ferid Belhaj for guidance and advice. We are grateful to Mukallad Omari, Zein Soufan, and Orouba Al Sabbagh of the Ministry of Planning and International Cooperation (MoPIC) of the Government of Jordan (GoJ) for helpful discussions and for sharing data. All simulations have been carried out with SUBSIM (see <u>www.subsim.org).</u>
- 22 -

Table 6 (Con't). Central Budget Base Infrastructure Regressions

|Variables|Model 1|Model 2|Model 3|Model 4|Model_5_|
|---|---|---|---|---|---|
|GASTIL|-2.69-|-2.52|-2.47"|-2.52-|-2.58~|
||(-4.27)|(-4.08)|(-4.00)|(-4.19)|(-4.30)|
|PCOM|7.71w|8.09|7.70w|6.49|8.49w|
||(2.29)|(2.45)|(2.31)|(1.98)|(2.53)|
|PCOMSQ|-1.07-|-1.-14w|1.08|-8.65E-1-|-1.23w|
||(-2.08)|(-2.26)|(-2.13)|(-1.72)|(-2.37)|
|NFFCAP1K|-|2.03E-2*<br>(2.67)|2.30E-2-<br>(2.82)|1.39E-2*<br>(1.78)|27'<br>(2.66)|
|NOFGDP1|-|-|-3.37E-1<br>(-0.92)|-||
|NCBGDP||-|-|1.29-<br>(2.73)|-|
|DFIGDP|-|-|-|-|-3.12w<br>(-2.10)|
|DFIGDPSQ|-|-|-|-|.l10<br>(3.02)|
|R<sup>2</sup><br>|0.8097|0.8195|0.8207|0.8293|0.8323|
|Adjusted R<sup>2</sup>|0.7852|0.7947|0.7945|0.8044|0.8063|
|F- Value<br>N|33.042<br>150|33.050<br>150|31.319<br>150|33.240<br>150|32.010<br>150|

Significant at .01 level or better

- Significant at .05 level or better Significant at .10 level or better