rafmacalaba/datause-encoder-data huggingface dataset:
As noted above, these potential tests of resilience require data that have multiple observations per household. Collection of comparable surveys before and after a shock can provide a lot of information on households’ resilience capacity. However, panel data are rare in developing countries, and are nonexistent in the study countries – with the exception of two rounds of panel in Nigeria. Therefore, there is a need to develop such a measure using more readily available cross section surveys. We now explain our empirical strategy for obtaining a measure of resilience in the West African Sahel.

# **4 Empirical Model**

In the absence of panel data, the major hurdle we face, when we use the cross section data to estimate resilience, is a missing data problem. Our interest is to estimate whether a household (or individual) exposed to a shock is resilient or not. Our empirical strategy makes use of easily available cross section data. However with cross sections, we only observe one observation of the outcome variable per household when in fact we need at least two (and in an ideal case many more), one before and another after the shock. Our proposal is to rely on the insights of literature on program evaluation. A central problem studied in that literature is how to recover the missing data for the outcome of interest (see a recent review by Imbens and Wooldridge, 2008). To see this, suppose we observe h=1,...,N households. These households face a positive probability of exposure to a shock. And for each we assume there are two potential outcomes, h(0) and h(1). Suppose that the first outcome is the welfare (consumption, income, nutrition, etc.) that would be observed if the household did not CC CC experience a shock, and the second outcome denotes the observed welfare after the household experiences a shock. At any given point in time, a household is either exposed to a shock or not, but not both at the same time. If a household is not exposed, then we observe the first outcome and the second outcome becomes the ex-post counterfactual outcome. If a household is exposed to a shock we observe the second outcome, and the first outcome becomes the ex-post counterfactual (Imbens and Wooldridge, 2008; Fortin, Lemieux and Firpo, 2010).

Given this setup, we have two challenges to estimate resilience. One is that we have to obtain an estimate of the counterfactual which in both subpopulations is not observed. Second, we have to determine a rule for considering a household resilient or not. To obtain the counterfactual we use the Oaxaca-Blinder framework, we subdivide households into two different groups based on their exposure to shocks. To stay with the usual language in the literature, denote the two subpopulations of treated and non-treated as, and , respectively. We define these subpopulations as } (5) ss, nnss ii iitt−1<sup>�</sup> ıı ii iitt−1 ii ss= {h : RR < RR } MMnnaa nnss= {h : RR ≥RR<sup>�</sup> That is, a household belongs to the treated subpopulation if last period (defined as the year before the start of the survey) shock (e.g. rainfall) is below a specific threshold, and it belongs to the 9
Urdal & Hoelscher: Urban Youth Bulges and Social Disorder levels of lethal events. The pacifying effect of secondary education also holds when the interaction term is excluded from the model. Income and education are often considered to both measure pacifying aspects of ‘development’, but similar to what has been found for armed conflict (Barakat & Urdal, 2008), educational attainment seems to curb political violence beyond the effect of income. Expansion in tertiary education, on the other hand, does not appear to affect levels of social disturbance when entered separately.

**<u>Table 3 Interaction Models</u>**

|**Explanatory**<br>**Variables**|**Model 9**|**Model 10**|**Model 11**|**Model 12**|**Model 13**|**Model 14**|
|---|---|---|---|---|---|---|
||**Lethal**<br>**events**|**Nonlethal**<br>**events**|**Lethal**<br>**events**|**Nonlethal**<br>**events**|**Lethal**<br>**events**|**Nonlethal**<br>**events**|
|Urban male β|-0.015|-0.050**|-0.040|-0.060***|-0.062|-0.038|
|youthbulgesz|(-0.49)|(-2.34)|(-1.21)|(-2.76)|(-0.81)|(-0.81)|
|Secondary<br>education(inv)|-1.687***<br>(-2.66)|0.054<br>(0.10)|||||
|Urban yb*<br>secondary (inv)|-0.068<br>(-0.75)|-0.055<br>(-0.89)|||||
|Expansion<br>tertiary ed.|||1.842<br>(0.45)|3.049<br>(1.00)|||
|Urban yb* exp.<br>tertiary ed.|||0.113<br>(0.17)|0.406<br>(1.29)|||
|Informal<br>employment|||||-0.326<br>(-0.32)|1.920**<br>(2.11)|
|Urban yb*<br>informalempl.|||||-0.135<br>(-0.67)|0.163<br>(0.97)|
|City pop|-0.009**<br>(-2.12)|0.002<br>(0.64)|-0.001<br>(-0.17)|0.001<br>(0.22)|0.001<br>(0.13)|0.004<br>(0.71)|
|Real gdp pc|-0.033<br>(-0.59)|0.050<br>(1.51)|-0.067<br>(-0.97)|0.028<br>(0.81)|-0.172<br>(-0.54)|0.120<br>(1.05)|
|Real gdp per<br>capita growth|-0.032***<br>(-3.34)|-0.018**<br>(-2.06)|-0.027***<br>(-2.68)|-0.020**<br>(-2.18)|-0.030*<br>(-1.69)|-0.036**<br>(-2.41)|
|Regime type|0.003<br>(0.28)|0.012<br>(1.20)|0.005<br>(0.45)|0.013<br>(1.27)|0.026<br>(0.88)|-0.028<br>(-1.27)|
|Regime squared|-0.008***<br>(-3.05)|-0.010***<br>(-4.84)|-0.010***<br>(-3.58)|-0.013***<br>(-5.48)|-0.015**<br>(-1.99)|-0.004<br>(-0.68)|
|Missing regime|-0.364|-1.420*|-0.420|-0.990|||
|<br>data|(-0.63)|(-1.93)|(-0.72)|(-1.32)|||
|Ongoing armed<br>conflict|0.446***<br>(3.21)|0.091<br>(0.75)|0.388***<br>(2.73)|0.077<br>(0.62)|0.418<br>(1.36)|0.222<br>(0.82)|
|Lagged|0.080***|0.075***|0.067***|0.069***|-0.011|0.009|
|<br>dependent|(3.57)|(5.75)|(2.79)|(4.98)|(-0.15)|(0.20)|
|Constant|0.377|0.034|0.328|0.314|1.417|-0.267|
||(1.28)|(0.15)|(1.02)|(1.35)|(1.19)|(-0.49)|
|N|1,209|1,278|1,096|1,160|268|301|
|No. of cities|40|43|40|43|27|30|
|LogLikelihood|-921.14|-1236.28|-842.88|-1126.98|-206.00|-289.15|

* Sign at 0.1 ** Sign at 0.05 ***Sign at 0.01. Interaction and constitutive terms are centered on their means. Secondary education has been inverted so that a high value on the interaction term reflects high urban youth bulges and low secondary educational attainment.

15
**Table 6:  Second-Stage Instrumental Variable Regression Results**

|||**Log Total Non-**|**Food Ex**|**penditure P**|**er Capit**|**a**||
|---|---|---|---|---|---|---|---|
|||IHPS|||FIA|S||
||1|2||1||2||
|**Weather Shocks†**||||||||
|Weather Shock|-0.055|*||-0.233|***|||
||(.032)|||(.067)||||
|Current Shock||-0.038||||-0.220|<br>***|
|||(.033)||||(.071)||
|Sequential Shock||-0.040||||-0.108||
|||(.075)||||(.104)||
|**Aid * Shock**||||||||
|Aid|0.047|0.075||-0.235||-0.204||
||(.104)|(.107)||(.144)||(.146)||
|Aid * Weather Shock|0.011|||0.467|***|||
||(.124)|||(.173)||||
|Aid * Current Shock||-0.219||||0.337||
|||(.146)||||(.215)||
|Aid * Sequential Shock||0.113||||0.389|<br>*|
|||(.174)||||(.201)||
|**Explicit HH Targeting Characteristics**||||||||
|Dummy, Female Head|-0.131|***<br>-0.134|***|-0.144|***|-0.147|<br>***|
||(.034)|(.035)||(.055)||(.055)||
|Dummy, Children<3|-0.001|0.000||0.013||0.013||
||(.021)|(.021)||(.034)||(.034)||
|Dummy, All Members>59|-0.295|***<br>-0.299|***|-0.257|***|-0.252|<br>***|
||(.074)|(.075)||(.097)||(.096)||
|Dummy, HH Member Disabled|-0.012|-0.011||0.052||0.055||
||(.024)|(.025)||(.041)||(.041)||
|**Wealth**||||||||
|Wealth Index|1.028|***<br>1.027|***|0.792|***|0.794|<br>***|
||(.091)|(.092)||(.161)||(.163)||
|# Mobile Phones|0.098|***<br>0.097|***|0.098|***|0.094|<br>***|
||(.013)|(.013)||(.026)||(.026)||
|**Constant**|10.975|***<br>11.017|***|10.611|***|10.677|<br>***|
||(.308)|(.311)||(.549)||(.545)||
|# of Obs.|4412|4412||1999||1999||
|R2 (within)|0.491|<br>0.488||0.342||0.34|8|
|R2 (between)|0.764|<br>0.764||0.575||0.57|8|
|R2 (overall)<br>**Notes:*****/**/* denote statistical signi<br>**Shock**is a dichotomous variable that is|0.682<br>ficance at 1/<br>equal to 1 i|<br>0.682<br>5/10 percent respec<br>f the household has|<br>tively. St<br>been ex|0.473<br>andard Erro<br>posed to a|<br>rs in pa<br>weather|0.47<br>rentheses.**†**<br>shock in th|8<br>**Weather**<br>e current|

**Notes:** ***/**/* denote statistical significance at 1/5/10 percent respectively. Standard Errors in parentheses. **† Weather Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in the current period (irrespective of potential exposure to past weather shocks), and 0 otherwise. **Current Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock _only_ in the current period, and 0 otherwise. **Sequential Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in consecutive periods, and 0 otherwise.

31
# **3 Related Literature**

Our analysis relates to several literatures. First, we contribute to the literature on social cohesion and interactions with host communities.<sup>7</sup> Alrababa’h et al. (2021) conduct a large-scale representative survey of public attitudes toward migration in Jordan. The authors find that while economic concerns do not drive Jordanians’ attitudes toward Syrian refugees, humanitarian and cultural factors matter. In particular, Jordanians who are more exposed to refugees’ challenging living conditions and who are less sensitive to cultural threat demonstrate more positive attitudes toward refugees. Ghosn et al. (2019) explores how an individual’s contact with refugees influence their attitudes about hosting refugees. They find that attitudes towards refugees are associated with whether individual respondents have had contact with Syrians in Lebanon —those with such interactions are significantly more likely to support hosting refugees, to consider hiring a refugee, or to allow one of their children to marry a refugee.<sup>8</sup> Alan et al. (2021) presents an experimental evidence from an educational program in southeastern Turkey that aims to build social cohesion in schools by developing perspective-taking ability in children. The authors find that the intervention increased the likelihood of forming inter-ethnic friendship ties as well as reducing ethnic segregation in the classroom and lowering victimization in school grounds.<sup>9</sup> Second, there is a recent literature on the impact of refugee movements and

> 7De Berry and Roberts (2018) argue that several factors mediate social relations in the context of forced displacement. These factors are: (i) perceptions of identity; (ii) pre-existing relationships between displaced and host communities; (iii) capacity/readiness of communities to host displaced people; (iv) duration of displacement; (v) perceived/real disparities between different groups affected by forced displacement; and (vi) patterns of settlement.

> 8In a conjoint survey experiment, Allen et al. (2021) study preferred policy responses of Colombians in response to the large inflow of Venezuelans into their country. They find that those who have less contact with Venezuelans, those who put more weight on economic priorities, and those who see the situation in Venezuela as mainly an economic problem, tend to support policies that are more restrictive.

> 9There is also the literature on the effectiveness of cash transfers in refugee-hosting settings. Valli et al. (2019) examine whether a short-term transfer programme targeted to Colombian refugees and poor Ecuadorians led to changes in social cohesion measures. They find that the programme contributed to reported improvements in social cohesion among Colombian refugees in the hosting community but had no impact on social cohesion among Ecuadorians. See also Devereux et al. (2017) for review article on the targeting effectiveness of social protection programmes.

6
**TABLE 11: BOLIVIA SENSITIVITY ANALYSIS**

||IncreaseinCosts<br>0 %||20 %||
|---|---|---|---|---|
||NPV(US$million)|ERR(%)|NPV(US$million)|ERR(%)|
|0 %<br>Decrease<br>in|21.76|27.0%|17.08|22.2%|
|Benefits<br>- 20 %|12.73|21.2%|8.05|16.9%|
|_Source: World Bank, 2001: Bolivia_|_, R_<br>_oad Rehabilitation & M_|_aintenance Proje_|_ct._||

Switching values were calculated for the increase in costs or the reduction in benefits. In the case of the Calamarca-San Pedro sub-project, costs would have to increase by more than 72 percent or benefits reduced by more than 42 percent for the sub-project to yield a rate of return lower than 12 percent. In the case of the Boyuibe-Yacuiba sub-project, costs would have to increase more than 1.5 times, or benefits reduce by more than 61 percent for the project to become not economically feasible. These results show the robustness of the economic worth of the rehabilitation investments.

# **Cost-Benefit Analysis Case 2: Mexico’s Federal Highway Maintenance Project**

The main objective of this project is the reduction of road transport costs and the preservation of the road network in an efficient and sustainable manner. The proposed investments would reduce road user transport costs by lowering vehicle operating and travel time costs. An economic analysis was done for the civil works program under the project consisting of: (i) rehabilitation and maintenance program (85 percent of civil works); (ii) comprehensive maintenance by contract program (7 percent of civil works); and (iii) bridge rehabilitation program (8 percent of civil works).

Table 12 below summarizes the net present value (NPV), internal rate of return (IRR), and modified internal rate of return (MIRR) of the programs.

## **TABLE 12: MEXICO CASE, SUMMARY OF ECONOMIC ANALYSIS**

|PresentValue of Flow|Rehab.<br>&<br>Maint. Program|Comprehensive<br>Maint. Program|Bridge<br>Program|Maint.<br>|All Programs|
|---|---|---|---|---|---|
|Benefits:US$million|304|77|231||612|
|Costs: US$ million|54|12|11||77|
|Net benefits (NPV):US$million|250|65|220||535|
|IRR(%)|114%|%<br>140|68%||111%|
|MIRR(%)|36%|38%|57%||40%|
|_Source: World Bank, 2000: Mexico, Fede_|_ra_<br>_y Maintena_<br>_l Highwa_|_n_<br>_t_<br>_ce Projec_||||

The Ministry of Communications and Transport of Mexico (SCT) estimated maintenance and rehabilitation costs in financial and economic terms (net of taxes), economic costs being on average 85 percent of financial costs; and defined vehicle fleet characteristics and unit costs for five vehicle classes. Traffic growth rate was set to 3 percent based on past trends for the duration of the analysis period. The discount rate was set to 12 percent.

37
# **TABLE 1. Indicators of the Public Company: OSE**

||**Units**|**2002**|**2003**|**2004**|**2005**|**2006**|
|---|---|---|---|---|---|---|
|**Service area**|||||||
|Total population - water supply|'000 inhabitants|3,158.4|3,178.2|3,100.7|3,245.0|3,253.7|
|Total population - wastewater|'000 inhabitants|1,791.6|1,805.9|1,541.2|1,919.0|1,935.7|
|Total number of staff|# FTE|4.816|4.508|4.362|4.174|4.280|
|**Water service**|||||||
|Population served - water|'000 inhabitants|3,041.6|3,063.8|2,833.5|2,980.9|3,055.3|
|Water connections year end|'000 connections|735.1|732.9|737.7|799.9|822.5|
|Volume of water produced|Million m3/year|282.7|275.1|288.1|309.4|320.0|
|Total volume of water sold|Million m3/year|135.3|131.0|132.3|142.0|147.1|
|**Sewerage Service**|||||||
|Population served - sewerage|'000 inhabitants|467.8|479.7|531.3|633.8|729.1|
|Sewerage connections year end|'000 connections|162.2|166.1|169.5|195.6|205.9|
|Length of sewers|Km|1,683.0|1,759.6|1,872.5|2,410.8|2,494.5|
|**Financial information**|||||||
|Total operating revenues|Million URY of<br>2010|3,951.3|6,015.2|6,036.5|627.3|6,848.2|
|Total billings to residential<br>customers|Million URY of<br>2010|2,252.1|3,417.8|3,537.0|3,718.0|4,070.0|
|Total billings to industrial<br>customers|Million URY of<br>2010|840.9|12,19.0|1,269.5|1,340.1|1,499.5|
|Total water and wastewater<br>operational expenses|Million URY of<br>2010|2,499.8|3,736.6|3,733.9|3,689.6|4,010.7|
|Labor costs|Million URY of<br>2010|1,342.1|1,851.9|1,842.9|1,688.4|1,848.8|
|Total gross fixed assets including<br>work in progress|Million URY of<br>2010|17,907.5|24,051.4|2,356.2|24,330.8|2,4604.8|
|**Tariff information**|||||||
|Fixed charge per month for water<br>and wastewater services for<br>residential customers|URY of 2010 per<br>month|86.4|140.7|144.5|145.7|145.0|
|Connection charges - water|URY of 2010|1,414.1|1,814.4|1,803.5|1,891.0|1,957.6|
|Connection charges - sewers|URY of 2010|567.3|726.7|722.3|757.1|784.3|

_Source:_ OSE.

26
Ellsberg, D. (1961). "Risk, Ambiguity, and the Savage Axioms". Quarterly Journal of Economics. 75 (4): 643–669 Fisher, I. (1892): “Mathematical Investigations in the Theory of Value and Prices,” Transactions of the Cunnecticut Academy of Sciences and Arts, 9, 1–124.

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Kreibaum, M. (2016) Their Suffering, Our Burden? How Congolese Refugees Affect the Ugandan Population, World Development, Volume 78, February 2016, Pages 262–287 McClure, S. M., Ericson, K. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2007). Time Discounting for Primary Rewards. Journal of Neuroscience, 27(21), 5796‐5804.

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25
revenues of social insurance funds. Second, the SOEs which are receiving large subsidies from central government are a direct burden on the state. SOEs are not included in the general government fiscal data. However, under Eurostat rules, an SOE should be included in the general government data if more than half of its revenues are derived from government subsidies over a three year period. Around 30 SOEs are likely to be included in the general government budget in 2011, which will worsen the recorded general government fiscal balance by about 0.5 percent of GDP.

# **4. The fiscal challenges of an aging population**

# **4.1 Long-run demographic trends**

Romania’s long term demographic forecasts are a source of concern for both economic growth and public finances. The population has already fallen by about 10 percent since 1990, from 23 million to the current level of 21 million, of whom 2-3 million are working abroad. Romania’s population is forecast to shrink further, by 20 percent between 2008 and 2060.<sup>7</sup> The contraction in the population of working age over this period is even larger – about 35 percent – the second largest in the EU, after Bulgaria. The share in the total population of people aged 65 and above will rise from the current level of about 15 percent to over 30 percent by 2060, the third oldest in the EU.<sup>8</sup> The old age dependency ratio – the ratio of people aged 65 and above to the working age population is forecast to rise from the current level of about 20 percent to 65 percent by 2060 (figure 5).

Demographic trends of this nature are clearly an impediment to long-term growth. With the workforce contracting by an average of 0.8 percent a year over the next half century, it is unlikely that Romania will be able to sustain the 4 percent real GDP growth which is forecast for

> 7 The population has already fallen by about 10 percent since 1990, from 23 million to the current level of 21 million.

> 8 The data are from Eurostat.

14
# **5. SPECIAL AGRICULTURAL SAFEGUARDS (SSG)**

The Uruguay Round agreements Agreement on Agriculture (AoA) provide for a ‘special safeguard’<sup>30</sup> (SSG) that allows the imposition of an additional import duty in case of an import surge (volume trigger) or when the price of an import shipment falls markedly below historical prices (price trigger).

The SSG is intended nominally to deal with problems liberalization of agriculture might create;<sup>31</sup> the SSG provisions to ‘remain in force for the duration of the reform process,’<sup>32</sup> but the agreement provides no end date for the reform process. Legally speaking, the SSG mechanism is in place until members, by formal agreement, end it.<sup>33</sup> The provisions were agreed in conjunction with tariffication, and recourse to SSG is limited to those countries that undertook tariffication in line with Uruguay Round modalities and who, in their schedules, marked tariff lines on which each reserved the right to apply SSG measures. Thirty-nine Members, 9 developed (counting the EC-15 as 1), 24 developing and 6 transition economies, reserved that right.<sup>34</sup> (The 6 transition economies have since become Member States of the EC-27.) The share of agricultural tariff lines so reserved ranged from 66 percent for Poland to 1 percent for Indonesia.<sup>35</sup> Many developing Members tariffied by adopting ceiling bindings and are thus not eligible to apply SSG measures, some who did designate products as SSG-eligible complain that the complexity of the formulas and the data requirements make the SSG system difficult to use. For whatever reason, few developing Members are in the list of users provided in Annex 1.

I review briefly in this section the design and application of the SSG system, Annex 1 provides details.

## **Properties of the SSG**

The price trigger allows every import shipment to be screened against previously determined and posted trigger prices. Major users of the system (e.g., the EEC the Rep. of Korea, the United States) posted trigger prices in 1995, when the

> 30 Agreement on Agriculture, Article 5, titled “Special Safeguard Provisions.’

> 31 Article 5.9 specifies that the SSG  provisions ‘shall remain in force for the duration of the reform process as determined under Article 20.’  Article 20 notes agreement to continue the negotiating process, but provides no end date for ‘the reform process.’ 32 AoA Article 5.9

> 33 The December agriculture Chairs paper (WTO 2008b, paragraphs 126, 127) calls for elimination of developed Member’s application within 7 years of entry into force of a Doha Round agreement, for tightened limits on use by developing Members.

> 34 WTO document G/AG/NG/S/9 Table 2.

> 35 WTO document G/AG/NG/S/9 Table 2.

21