rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 11. Breaking down ARG pension spending cyclicality: number and value of benefits per subsystem (2000-2022)**

<!-- Start of picture text --> 0,8<br>0,6<br>0,4<br>0,2<br>0,0<br>-0,2<br>-0,4<br>-0,6<br>-0,8<br>Correlation between cyclical components of each variable and real GDP<br><!-- End of picture text --> _(1)NCP stands for Non-Contributory pensions, including disability pensions_ _Notes:_ the cyclical components of GDP and public spending have been estimated using the Hodrick-Prescott filter. Public spending is considered in real terms, deflated by CPI. *, ** and *** indicate statistical significance at the 15%, 10% and 5% levels, respectively. _Source:_ Ministry of Economy, INDEC, ANSES.

What drives the cyclicality in pension spending? Similar to the wage bill, the dynamics of pension spending are determined by both "quantities" and "price" effects - the number of benefits/beneficiaries and the evolution of the real value of benefits. Theoretically, the number of benefits granted by a pension's retirement scheme should be determined by demographic trends (i.e. aging) as well as structural labor market dynamics, with little, if any, short-term determinants. Hence, an aging population or economies with a steady reduction in potential growth, which limits formal employment generation, will face higher fiscal pressure to support pension systems. Therefore, in the absence of structural reforms, the number of benefits should behave neutrally with respect to the cycle. Similarly, there is no social security rationale to see the real value of benefits co-moving with the cycle. Moreover, evidence shows that countries lacking automatic pension indexation mechanisms tend to exhibit more procyclical behavior (Galeano et al. 2021). Ideally, benefits should follow a stable path, ensuring income stability for pensioners. Paradoxically, Argentina does not fit well with this finding, as it has chosen inherently procyclical indexation mechanisms, as we will demonstrate later.

Interestingly, in the case of Argentina, the quantity effect - determined by the number of benefits - has been slightly procyclical, with two pension schemes standing out in particular: the semi-contributory scheme (moratorium) and the non-contributory pensions (i.e. disability and welfare pensions) ( **Figure 11** ). Data suggests that the procyclical performance of almost all the different pension schemes is not driven by the number of benefits, aligning with theoretical expectations. However, the evolution of benefits granted by the “moratorium” program does reflect a more procyclical behavior, with a cyclical correlation coefficient of +0.28, statistically significant at a 15% level. This program -that took place between 20062009 and a second phase since 2015- involved incorporating nearly 3.5 million new beneficiaries (almost
Figure 1: Iraq and Syria Oil Production, Fields, and Daesh Control, March 2016 <!-- Start of picture text --> IBRD 42122 | MARCH 2016 This map was produced by the Map Design Unit of The World Bank. 40°E 45°E DAESH STUDY<br>GSDPM Map Design Unit The boundaries, colors, denominations and any other informationshown on this map do not imply, on the part of The World BankGroup, any judgment on the legal status of any territory, or anyendorsement or acceptance of such boundaries. T U R K E Y DAESH CONTROLLED FLARESNON-DAESH CONTROLLED FLARESMAXIMUM EXPANSION OF DAESH<br>DURING PERIOD OF CONCERN<br>DAESH TERRITORY BY EARLY<br>Ibrahim Khalil SEPTEMBER 2015<br>Duhok MAIN CITIES<br>Al Yarubie NATIONAL CAPITALS<br>Azaz SECONDARY ROADS<br>Aleppo Al - Hasakah Sinjar Tall`Afar Mosul Arbil PRIMARY ROADSINTERNATIONAL BOUNDARIES<br>Idlib Ar Raqqah Shadadi<br>As - Sulaymaniyah<br>Latakia<br>Kirkuk<br>Dayr az Zawr<br>Hamah<br>35°N Tartus Al Mayadin Bayji 35°N<br>Aboudiye Homs<br>Tripoli SYRIAN Abu Kamal Abu Kamal<br>BEIRUTLEBANON ARAB REPUBLIC Sa`dah ThartharLake Samarra IRSELAP. OM IFC<br>Masnaa DAMASCUS Attanf Ar - Ramadi Al Fallujah Ba qubah I R AN<br>HabbānīyahLake BAGHDAD<br>Dar'a As Suwayda Al Karama I R A Q RazzazaLake Karbala' Al - Kut<br>West Bank Irbid Al - Hillah<br>Zarqa An Najaf Ad Diwaniyah<br>Gaza AMMAN Azraq Al Omari Al 'Amarah<br>ISRAEL JORDAN As - Samawah An - Nasiriyah<br>S A U D I Basra<br>A R A B I A<br>Um Qasr<br>30°N<br>ARAB KUWAIT<br>REP. OF Aqaba 0 150 300 KUWAIT<br>EGYPT KILOMETERS<br>35°E 40°E 45°E<br>Euphrates<br>gris<br>Ti<br>rates<br>Mediterranean<br>Sea<br>Epuh<br><!-- End of picture text -->

news outlets give numbers around 50-60,000 bpd yielding an income of US$2.5m per day [33] to more than US$3m per day [1]. Early estimates by the US Departments of State and Treasury put the organization’s oil revenues at around US$1m per day [42]. These estimates were then revised down to “a couple million dollars a week” after the U.S. started air-strikes against the organization’s assets [4]. Views as to whether Daesh was financing itself through oil, external support, extortion, or taxes then evolved, with higher emphasis put on taxes and extortion as primary sources of revenues over time. Die Zeit for instance reported December 2014 oil revenues to be a mere US$370,000 per day or even lower at US$260,000 [9]. An October 2015 article however gives an estimated output of 34-40,000 bpd, earning the organization an average of 5
context of ecological systems by Holling (1973), who refers to resilience as a system’s ability to persist without eventually moving to a different state of behavior when exposed to changes or shocks (Zou et al., 2018). Later, Bruneau et al. (2003) adapt the resilience concept in earthquake engineering, as the ability of a system to resist and absorb the impact of disruptions. Focusing on infrastructure systems, the definition of resilience is enriched by Faturechi and Miller-Hooks (2014) who argue that resilience accounts for possible interventions that help return system performance to near pre-disruption levels. The authors stress the importance of resilience measures in quantifying the potential benefits of predisruption mitigation actions for increasing the system’s ability to cope with disaster impact, as well as post-disaster adaptive actions that aim to restore system functionality. Another widely-used definition of resilience is from the US National Academy of Science (2012), which relates resilience to the ability of a system to plan for, absorb, recover from, and more successfully adapt to adverse events.

While the definitions/descriptions of resilience vary in emphasis depending on the application context, resilience covers two essential aspects: 1) preparing a system before disruptions; and 2) recovering the system after disruptions occur. The preparation aspect can be alternatively interpreted as enhancing the resistance of a system to attacks. The aim of implementing a comprehensive resilience plan, therefore, is to reduce the potential damages caused by attacks through resistance and enhance fast recovery (Linkov et al., 2013). This two-aspect view is echoed by Zhou et al. (2019), who focus on transportation systems resilience and conclude that all resilience definitions for transportation systems take the following two perspectives: i) the ability to maintain the system functionality under disruptions; and ii) the time and resources required to restore performance after disruptions. As shown in Fig. 5, the first perspective is related to a disruption phase, beginning from the occurrence of a disruption and ending when the system performance reaches the minimum level. In this phase, robustness and redundancy of system functionalities are key to minimize the consequence of disruptions. The second perspective is associated with a recovery phase. When it starts, system performance is expected to improve, till return to the original performance level which means full recovery. This phase usually takes a longer time than the disruption phase. The abundance and optimal and speedy use of resources are critical to recovery.

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Policy Research Working Paper 5018

# **Abstract**

The paper assesses the costs and household level benefits of migrating overseas from Bangladesh. The authors survey households who have had overseas migrants to assess their characteristics compared to non-migrants. They also compute various types of migration and remittance related transaction costs and discuss the channels by which overseas migration is financed, remittances sent and the constraints faced by the poorest.

Using the Propensity Score Matching method, the paper finds that overseas migration conveys substantial benefits to families as measured by household consumption, use of modern agricultural inputs, and level of household savings. The authors also offer some possible policy directions to strengthen the returns from migration as well as reduce some of the costs.

This paper—a product of the Poverty Reduction Group, Poverty Reduction and Economic Management Network —is part of a larger effort in the group to examine the distributional impacts of migration. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at msharma5@worldbank.org and hzaman@worldbank.org. This work was initiated when the authors were respectively at the International Food Policy Research Institute and the South Asia region of the World Bank _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
# _7.3 Reciprocating behavior_

Receivers, as is usual in these types of experiments, exhibited reciprocating behavior (cf. Guerra and Zizzo, 2004).<sup>26</sup> In figures 1a and 1b, the horizontal axis is the amount in euros that receivers received in the experiment and the vertical axis is the amount that a responder returned to the initiator.

# _7.4 Earnings and survey responses: Initiators_

Figure 2a suggests that, on average, those participants expressively declaring generalized trust through “most people can be trusted” in fact earn more by sending more. The correlation between earnings and declared trust is 0.35. There were self-fulfilling expectations. Since trust was rewarded with trustworthiness, trust that translated into giving more yielded a higher return – in within-group behavior. Results from mixed-group behavior depicted in figure 2b are weaker.

# _7.5 Earnings and survey responses: Receivers_

Figures 3a and 3b show that declared generalized trust of receivers is not highly correlated with the actual behavior of receivers in returning money to initiators (r= 0.17). The correlation is greater when for particularized trust (r= 0.31).

26 There is however wide variation in response of receivers to receiving the maximum 30 euros (after an initiator had transferred 10). The amounts returned by receivers range from 5 to 24. An amount returned under 10 imposed a loss for the maximally trusting initiator. In one case 5 was returned. In another case the initiator received 10. Only one S-initiator was sufficiently trusting to transfer 10 and in this case the S-responder provided a minimal gain to the trusting initiator.

27
# 3. **The State Pension Fund**

**Since 2003, VEB has acted as the pension fund management company for the workers who do not exercise their right to choose a private asset manager (AM), a nongovernmental pension fund (NPF), or who have explicitly selected VEB as their pension management company.** VEB is a state development bank that provides financing to diverse areas of the economy, including manufacturing, infrastructure and housing sectors. VEB acts as the agent for the government in the management of pension savings of the second pillar and the country’s external and internal debt. The Trust Management Department of VEB is the unit of the bank in charge of managing the pension assets.

**VEB has been unsuccessful in providing adequate returns to contributors and with this pattern contributors are likely to receive very low pensions in the future** . The real return since inception of VEB pension fund has been -3.9 percent. As shown in Table 2, the negative real returns are not a consequence of a sharp deterioration during the financial crisis, but have shown a regular trend since the inception of the system in 2004. The low returns are explained by the narrow investment alternatives from which VEB is permitted to select. Between 2003 and October 2009, the pension fund managed by VEB was allowed to invest basically in Russian government securities. At the end of 2008, 96 percent of the portfolio was invested in federal and state bonds and the rest in cash and bank deposits. The investment regulations of the pension fund have been set in the Law.

Table 2 Real and <u>Nominal Returns of the pension fund managed by VEB 2004-1009</u>

||Nominal return|Inflation|Real return|
|---|---|---|---|
|2009|9.5%|8.8%|0.6%|
|2008|-0.3%|13.3%|-12.0%|
|2007|5.6%|11.9%|-5.7%|
|2006|5.5%|9.0%|-3.2%|
|2005|12.2%|10.9%|1.2%|
|2004|7.3%|11.7%|-3.9%|

Source: VEB

## _Investment Portfolio_

**While in most countries, investments in government securities are enough to provide positive real returns, the structural issues in the Russian economy make government instruments scarcely attractive to domestic investors.** Sound fiscal accounts and the limited borrowing needs of the government typically lead to an excess demand for government securities, which results in negative real rates of return for investors. However, the still relatively high yields of Russian government securities make them highly attractive for international banks and other foreign investors looking at benefiting from the carry trade. Capital inflows from these investors looking for opportunities of arbitrage put additional pressure on the yields, resulting in even lower returns for domestic investors. The aftermath of the financial crisis has worsened the situation, as interest rates in developed economies have moved to a low level equilibrium, and is likely to remain like that in the coming future (IMF (2010)). Other large

7
stronger knowledge flows (e.g., Carlino et al. 2007, Arzaghi and Henderson 2008), and Audretsch and Fritsch (1994) use density as one source of convexity in local production that link to entry rates. Thus the conditional relationship between entry and population density is unclear, although many studies of advanced economies link higher population density to reduced manufacturing entry rates, especially for larger plants that are using established production techniques and seeking to minimize costs.

# _District-Level Conditions_

Beyond these basic demographics, we consider five primary traits of districts: education of the local labor force, quality of local physical infrastructure, access or travel time to major Indian cities, stringency of labor laws, and household banking conditions. While these traits do not constitute an exhaustive list of local conditions, they are motivated by the literatures on entrepreneurship and India‘s development. Unless otherwise noted, these traits are taken from the 2001 Population Census data for India.

We first measure the general education level of the local labor force. Doms et al. (2010) find that local skill levels correlate with higher rates of self-employment and better start-up performance in the United States, and Glaeser et al. (2010) associate higher education levels with higher entry rates using employment-based entry metrics. Many local policy makers stress developing the human capital of their workforces, and India is no different (Amin and Mattoo 2008). We measure the general education level of a district by the percentage of adults with a graduate (post-secondary) degree. All of our results below are robust to alternatively defining a district‘s education as the percentage of adults with higher secondary education.

Our second trait is the physical infrastructure level of the district. Basic services like electricity are essential for all businesses, but new entrants can be particularly dependent upon local infrastructure (e.g., established firms are better able to provision their own electricity if need be, which is quite common in India). Aghion et al. (2011) provide a theoretical model of this dependency. Many observers cite upgrading India‘s infrastructure as a critical step towards economic growth, and the Indian government has set aside substantial funds for this investment. The population census provides figures on the number of villages in a district which have telecommunications access, electricity access, paved roads, and access to safe drinking water. We calculate the percentage of villages that have infrastructure access within a district and sum across the four measures to create a continuous composite metric of infrastructure which ranges from zero (no infrastructure access) to four (full access to all four infrastructure components).<sup>5</sup>

> 5 In six districts (major cities) which were not further subdivided into separate geographic units, these indicators were not reported in the Census data. In these cases we assign the infrastructure access components as 100%. Our results are fully robust to excluding these major cities from the analysis sample.

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Policy Research Working Paper 8554

# **Abstract**

This paper investigates the consequences of granting politicians power over bureaucrats in the implementation of small-scale public infrastructure projects. While potentially bolstering the incentive for the executive to perform, increased legislative oversight may lead to distortions in the technocratic process for political gain. By assembling a nationally representative data set from Nigeria that tracks public projects from inception to audit, the paper finds evidence of a clear trade-off between political oversight and bureaucratic autonomy. Using an instrumental variables strategy in which early career choices of politicians are key determinants of legislative committee membership, the analysis finds that legislative influence increases the likelihood that a project is launched by 18 percent, but at the cost of reducing project quality by 15 percent and increasing the reported misuse of funds. The results highlight the fundamental tension between bureaucratic inaction and political corruption.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/research. The author may be contacted at drogger@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
# **Table 2c Plan to Return (Marginal Effects reported)**

|VARIABLES|Model (11)|Model (12)|Model (13)|Model (14)|Model (15)|Model (16)|
|---|---|---|---|---|---|---|
|lnearngap6|-0.081***|-0.086***|-0.078***|-0.082***|-0.084***|-0.083***|
|assist|-0.017|-0.027|-0.029|-0.026|-0.023|-0.026|
|othermig1|0.053*|0.048*|0.046|0.044|0.042|0.047|
|othermig2|0.061**|0.059**|0.069**|0.067**|0.064**|0.066**|
|YrAgoMigr|-0.001|-0.000|-0.001|-0.000|-0.000|-0.000|
|Fluent and Language<br>Training abroad|-0.149***|-0.157***|-0.159***|-0.158***|-0.154***|-0.141***|
|Fluent and no|-0.128***|-0.130***|-0.130***|-0.126***|-0.125***|-0.113***|
|Language Training<br>abroad|||||||
|Not Fluent and|-0.186|-0.181|-0.139|-0.170|-0.166|-0.155|
|Language Training<br>abroad|||||||
|Other training while<br>abroad|-0.028|-0.025|-0.025|-0.025|-0.021|-0.027|
|Skills and experienced<br>effected investment<br>interest|0.019|0.015|-0.014|-0.016|-0.016|-0.016|
|Invested in host<br>country|-0.046|-0.047|-0.075*|-0.073|-0.070|-0.078*|
|Legal Migrant|-0.147***|-0.151***|-0.149***|-0.151***|-0.148***|-0.146***|
|Spouse in Romania|0.073*|0.058|0.067*|0.060|0.060|0.041|
|Child <18 years|0.091***|0.070**|0.065*|0.076**|0.079**|0.069**|
|Child >18 years|0.171***|0.149***|0.145***|0.152***|0.154***|0.150***|

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probability models of the poverty headcount using the D-D model defined in equation (1) indicate that overall poverty increased by 3 percentage points or 7% (column 5, Table 3).<sup>12</sup> In line with the heterogeneity of the impacts on consumption, the result is driven entirely by a higher incidence of poverty in urban areas. Indeed, households in urban centers were 5.5 percentage points or 18% more likely to be poor after being hit by Agatha (p-value = 0.026). Roughly speaking, these effects translate into roughly 80,000 more families falling into poverty. Results from the treatment dose specification (equation 2) are also indicative of negative effects on poverty, particularly among households exposed to rainfall intensity Low (2<s.d.≤3) and High (s.d.≥5).

Finally, in trying to qualify the deterioration of household welfare in urban centers, we examined which expenditure items were more heavily compromised by the shock. We find statistically significant evidence that food expenditures among affected households fell by around 10% of the baseline level, accounting for close to 40% of the total reduction in consumption (Table 4). As a way of illustration, urban and rural poor households devote 42.3% and 47%, respectively, of their budget to food expenditures in the baseline sample. Unfortunately, there is not data to formally test whether households were able to protect calorie intake despite the lower expenditures by, for instance, substituting away from expensive calories towards cheaper calories. However, the existing literature on the relationship between income or expenditures and calories suggests that the effects on nutrition may not be trivial. Whereas estimates of calorie-income elasticities for developing countries vary considerably, largely due to methodological reasons, those obtained from calorie demand equations and identification strategies that address nonrandom measurement error and other possible biases fall within the 0.2-0.5 range (Strauss and Thomas 1995, Subramanian and Deaton 1996). Taking this range as a reference, the effect of Agatha implies that the consumption of calories would have fallen by 2% to 5% among affected households, equivalent to anywhere from 43 to 108 fewer calories per day based on the representative dietary energy consumption per capita of Guatemalans.<sup>13</sup> These results are not trivial. At 43.4%, stunting (low height-for-age z-score) in children 0-5 years of age at baseline was endemic nationally and equally high in urban settings (28.8%) at baseline (2008-2009 Maternal and Infant Health Survey).

> 12 Compared to the consumption models, the analysis on poverty require stronger statistical power because only households crossing the poverty threshold provide variation useful to identify �� (equation 1) and ��, �� and �� (equation 2).

> 13 The daily dietary energy consumption per capita in Guatemala is estimated at 2,170 calories. Food and Agriculture Organization of the United Nations, February, 2009, "Compendium of food and agriculture indicators – 2006”.

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