rafmacalaba/datause-encoder-data huggingface dataset:
# “an organized demand for enhanced services and government accountability”.

At this point it is worth noting that, even if we present here different indicators of civic participation, mainly drawing from opinion surveys such as Americas Barometer and Latinobarómetro, it does not necessarily mean that these indicators are a good proxy for collective action: social capital and participation in associations does not necessarily mean effective production of demands directed to the government, rather they indicate strong interdependences within homogenous groups.

First, in what regards civic participation, the Dominicans rank fourth (out of 24) in Latin America in participation in religious groups (60.5%), sixth in attendance to parents association meetings (27.1%), first in involvement in community improvement groups (25.0%) and fourth in participation in women ́s associations (13.3%). Thus, it may be argued that the Dominican Republic present a very high level of civic participation if we compare with the regional average.

Second, it seems quite difficult to portrait political participation at the different levels in the DR, since survey results are mixed. On the one hand, interest in politics is among the highest in Latin America (44.5% of respondents) and 19.9% of the population affirms to have done campaign work for candidates (the highest). However, this may not be a good proxy for social capital; conversely, it may rather reflect political clientelism. On the other hand, the average number of people that participates in protest is just of 5.4% (among the lowest) and the percentage of people that votes in the elections (76.1%) is just around the regional average.

Third, Dominican citizens seem to be very involved in <u>local politics. The DR enjoys the</u> highest level of participation in municipal meetings (27.3%) and also ranks high in number of formal demands presented to the municipal authorities. At the same time, however, Dominicans are among the most displeased with local government services<sup>13</sup> . This stark contrast between an active political and local participation and discontent with the quality of public services seems to confirm the view that the Dominican Republic operates under a limited access order model, as described by North (2007): demands by citizens are not often addressed by policies, and the existence of individualized private solutions within the middle class further reduce their collective incentive to engage the state.

> 13 Just 48.8% are satisfied.

[23]
# **Table 7A: First stage estimations instrumenting the likelihood that the household receives remittances**

This table shows the results of the first stage regressions instrumenting the likelihood that the household receives remittances. The dependent variable is a dummy for whether the household received remittances. The instruments used are _Real GDP per capita in US states_ and _Poverty in US states_ defined in Table 1. Columns (1)-(3) are the first stage regressions for the likelihood that the household has a deposit account. Columns (4)-(6) are the first stage regressions for the likelihood of receiving a loan, while columns (7)-(9) show the first stage regressions for the likelihood of applying for a loan. All independent variables are defined in Table 1. Department fixed effects are estimated but not reported. Robust z-statistics are shown in brackets. *,**, and *** denote significance at 10, 5 and 1 percent, respectively.

|Variables||Deposit accou|Dependent var<br>nt|iable: dummy f|or whether ho<br>Loan receive|usehold receives<br>d|remittances|Loan request|ed|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Real GDP per capita in US states|0.019||0.004|0.018||0.003|0.019||0.003|
||[6.46]***||[0.97]|[6.31]***||[0.44]|[6.37]***||[0.46]|
|Poverty in US states||-4.445|-3.716||-4.358|-3.889||-4.387|-3.905|
|||[-7.41]***|[-3.99]***||[-7.23]***|[-3.15]***||[-7.28]***|<br>[-3.21]***|
|Real per capita income|-0.005|-0.004|-0.004|-0.006|-0.005|-0.005|-0.006|-0.005|-0.005|
||[-1.44]|[-1.31]|[-1.34]|[-1.65]*|[-1.51]|[-1.53]|[-1.62]|[-1.48]|[-1.50]|
|House and land ownership|0.069|0.067|0.067|0.070|0.068|0.069|0.069|0.068|0.068|
||[4.47]***|[4.35]***|[4.39]***|[4.54]***|[4.44]***|[4.46]***|[4.49]***|[4.38]***|[4.40]***|
|Adults average education|0.000|0.001|0.001|0.002|0.002|0.002|0.001|0.001|0.001|
||[0.13]|[0.17]|[0.16]|[0.45]|[0.50]|[0.49]|[0.28]|[0.32]|[0.31]|
|Agriculture|-0.007|-0.008|-0.008|-0.005|-0.005|-0.006|-0.005|-0.005|-0.006|
||[-0.30]|[-0.32]|[-0.34]|[-0.20]|[-0.23]|[-0.24]|[-0.20]|[-0.23]|[-0.24]|
|Electricity|0.090|0.096|0.094|0.092|0.098|0.096|0.092|0.097|0.096|
||[5.56]***|[5.97]***|[5.80]***|[5.65]***|[6.05]***|[5.90]***|[5.62]***|[6.03]***|[5.88]***|
|Returned migrant|0.322|0.315|0.315|0.326|0.319|0.319|0.32|0.314|0.313|
||[6.50]***|[6.33]***|[6.34]***|[6.57]***|[6.40]***|[6.40]***|[6.46]***|[6.30]***|[6.31]***|
|Constant|-0.668|0.540|0.309|-0.660|0.524|0.373|-0.607|0.572|0.419|
||[-6.5]***|[6.06]***|[1.24]|[-6.43]***|[5.83]***|[1.05]|[-5.79]***|[6.07]***|[1.20]|
|Observations|2315|2315|2315|2287|2287|2287|2293|2293|2293|
|Wald test chi<sup>2</sup>|1353.0|1332.0|1339.0|57.3|62.8|61.6|64.9|73.6|72.1|
|Wald test p-value|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Cragg-Donald weak inst. F-stat|41.42|54.48|27.48|39.46|51.76|26.12|40.27|52.51|26.52|

26
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ability to mobilize information and monitor disbursements. This could affect costefficiency and create further opportunities for corruption and capture by elites.

What do evaluations of community-based targeting mechanisms say on this issue? Galasso and Ravallion (forthcoming_ examine the targeting performance of a decentralized poverty program in Bangladesh, the Food for Education program.<sup>8</sup> First participating Union Parishads (which typically have about 15 villages) were identified by the center and then eligible households were identified at the community level. Using both household and community data, Galasso and Ravallion examine how much of the program’s performance in reaching poor families was due to the center’s efforts to reach poor communities and how much to the communities’ efforts to reach their own poor. They also look at the factors that influenced targeting at both levels. They are specifically interested in the role of village institutions and land inequality within villages as determinants of targeting performance.

While they find a positive targeting differential (the difference in optimal spending on the poor and the non-poor), with more poor people than non-poor people receiving the program, the size of the effect is small and most of the targeting differential arises from targeting within villages. They find no evidence that the center is targeting villages at all. The program’s eligibility criteria for selecting participating Union Parishads, for example, cannot account for any of the variation in the targeting 8 Ravallion and Wodon (2000) show that the program accounted for some 13 percent of primary school enrollment.

19
As of June 2009, the latest estimate by the head of the Spanish Savings Bank Association was the loan losses were expected to more than double to over 9 percent in 2010, from 4% in 2009; according to the same report, a rise of NPLs to 10% of assets would put about 20 banks below the minimum 8% capital ratio.<sup>3</sup> What is not yet clear is the extent to which the housing boom was financed through the banking system and how much larger a decline of housing prices – which fell by about 7% in the year ending in March 2009 -- will occur by the end of the crisis.

Importantly, even if the Spanish banking system fares well during this cycle, it is by no means clear that their provisioning system is or would be the principal cause. First, as noted above, Spanish banks had been through their own serious crisis in the early 1980s and then experienced a series of losses related to their Latin American exposure in the 1990s and early 2000s. Banks traditionally become more risk averse following crises – as the world is discovering in the current one! – and tend to reduce the rate of growth of lending (and/or provision more conservatively), even when regulator policy is being relaxed. Second, similar to Canada, Spanish authorities adopted a relatively conservative position related to securitization, so that off-balance sheet securitization accounts for less than 7% of total securitization (Fernandez de Lis and Garcia Herrero, 2009) and involves far less complicated instruments than in the United States. This policy change could easily have been more important in reducing the scope of the crisis in Spain, compared with a realistic counterfactual. Third, there may be other regulatory and supervisory factors, should Spanish banks emerge from this crisis in better condition than those elsewhere, that account for their better performance. Thus Canadian banks, which seem to be faring well, do not have a system of counter cyclical provisioning, yet seem to be among the better performing banking systems in the OECD region, notwithstanding the economic performance of their neighbor. Among the factors that could account for their relative stability are a higher franchise value (less competition), less financial innovation, avoidance of policies to encourage home ownership (though with the same home ownership rate as in the United States), and a policy of not allowing capital relief to banks who purchased CDS. In sum, _aficionados_ of the Spanish approach must acknowledge a serious identification problem and be less quick to claim victory for countercyclical provisioning.

> 3 See Thomson Reuters, UPDATE 2:  CECA says Spanish banks may post losses in 2010, June 17, 2009.

14
- 4) How likely do you think it is that you will not catch any fish in the month of August if you go fishing 6 days a week?

- 5) How likely it is that you will eat fish at least once during the month of August?

The first question serves to assess numeracy, and whether respondents can express a known probability with the visual aid. The second and third questions can be used together to evaluate whether respondents respect a basic property of probability by asking about nested events: if respondents understand the concept of probability, they should allocate a larger number of beans (or at least as many) to the likelihood of going to town in the next 2 weeks as they do to the next two days. Finally questions 4 and 5 ask about events which are likely to be zero probability (catching no fish in the month) and certain events (eating fish at least once in the month) for the target population, allowing us to see whether they can use the visual aid to accurately represent zero and unity probability events.

# **2.4. Eliciting the Distribution**

After these preliminary questions, respondents were asked to report their subjective distribution of future catches during one day in August by allocating the total number of beans on a sheet of paper divided into intervals to express the likelihood that the catches will fall into various intervals, expressed in Rupees. We compare two different methods for providing the support and number of intervals that should be used for this: a pre-determined support with a large number of intervals, and a self-anchored support with a small number of intervals. These reflect the main two approaches used in the literature.

The pre-determined support is the same for all respondents, and contained 20 intervals. These intervals were chosen based on previous catch data, which gave the range of reasonable possibilities for the value of daily catches.<sup>5</sup> The first interval ranged from 0 to 150 rupees, with the first 14 intervals (up to 2100 rupees) all having width of

> 5 When prior data is not available, one can use pilot testing to obtain a reasonable range.

9
# **6.  Summary and Conclusions**

In this paper, we have analyzed 36,491 case histories drawn from individual and household records in the Bangladesh Demographic and Health Surveys for 2000, 2004, 2007, 2011 and 2014. We address three related questions: What are the socioeconomic determinants of consumption of animal-source foods? Among animal-source foods, what are the socioeconomic determinants of the fish share? What are the impacts of consumption of animal-source foods (fish, meat, eggs) on child mortality and resistance to infectious disease? The paper focuses particularly on fish consumption and its health effects for children in their first years of life. Critical factors examined include maternal education, family economic status, child gender, and temporal/spatial variations in fish supply.

We find that maternal education and family economic status are highly-significant determinants of animal-source food intake, along with annual, seasonal and local-area fish supply conditions. After controlling for these factors, we find that children’s animal-source food intake is gender-neutral. Our results for the fish share of animal-source food consumption assign significant roles to family economic status, maternal education and seasonal and spatial variations in fish supply. The importance of fish supply for poverty alleviation is highlighted by another finding: The share of fish in consumption of animal-source foods is highest for the poorest households, and declines significantly with income. By implication, programs that increase fish supply and lower fish prices will have a significant impact on intake of animal-source foods, particularly for the poorest households.

We also find a highly-significant, negative relationship between maternal education and the share of fish in animal-source food consumption. In the lowest income groups, mothers shift strongly toward meat and eggs as their education level increases, suggesting a perverse “status bias” against fish despite their lower cost and health advantages.

27
common factor captures fluctuations in global economic activity and represents the world business cycle. I include the estimated common factor and oil market variables (the real price of oil, U.S. oil consumption, and U.S and non-U.S. oil production) in a factor-augmented VAR model. Exploiting a set of sign and elasticity restrictions, I identify structural shocks, including U.S. energy efficiency and U.S. oil supply shocks.

My empirical results support previous findings in the oil market literature and provide additional evidence of the effects of U.S. energy efficiency and U.S. oil supply shocks. For example, I find that U.S. energy efficiency shocks have a larger impact on global output and the real price of oil than U.S. oil supply shocks. I reaffirm the key result from Kilian (2009) that it is important to disentangle the underlying drivers of oil price movements. The impulse responses indicate that both shocks decrease real price of oil and generate a favorable effect on global output. However, U.S. energy efficiency shocks decrease total world oil production and consumption, whereas U.S. oil supply shocks have the opposite effect. Moreover, results from a set of FAVAR models augmented with country GDP data indicate that the GDP implications of U.S. energy shocks are heterogenous across net oil-importing and net-oil exporting countries.

Historical decompositions indicate that the importance of U.S. energy efficiency and U.S. oil supply shocks has varied over time. For the period 2010–2019, positive U.S. oil supply shocks, led by productivity gains in the U.S. shale sector, resulted in an increase of 0.27 percentage points in global GDP growth. During the same period, a series of negative energy efficiency shocks decreased global GDP growth by 0.19 percentage points. The latter results are most prominent during the second shale boom, 2017–2019, when negative energy efficiency shocks cancelled out the positive impact on the global growth of U.S. oil supply expansion.

Using indirect inference, I estimate key parameters of U.S. energy efficiency and U.S. oil supply shocks in a general equilibrium multi-country model that incorporates a global market. The model provides an explicit interpretation of the empirical results and highlights channels and mechanisms via which U.S. energy efficiency and U.S. oil supply shocks impact international macroeconomic aggregates and the global oil market. For example, the model reveals that a decrease in the real price of oil following a positive U.S. energy efficiency shock is a result of a combination of the low elasticity of substitution of oil with other factors of production and a positive correlation of energy efficiency improvements across countries. I also use the model to discuss the role of U.S. energy efficiency improvements in decreasing the real price of oil and increasing global output as the world transits away from fossil fuels, such as crude oil. My results show that increasing the transferability of U.S. energy efficiency improvements to other countries can be used as a policy instrument in mitigating the negative global economic implications of a cutback in U.S. oil production.

34
# Annex 2: Additional tables and figures

## **Table A1: Cash income, in real terms (2014q1 set to 100)**

|||201|4|||20|15|||201|6|||20|17||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|1|2|3|4|1|2|3|4|1|2|3|4|
|||||||<br>Plac|<br>e of re|<br>sidence|<br>||||||||
|Minsk|100.0|110.2|117.1|108.8|111.3|110.9|114.7|114.3|103.0|108.4|101.6|102.5|97.0|102.3|99.1|103.9|
|Large cities|100.0|104.5|106.1|107.0|96.7|98.4|99.4|99.3|93.3|97.1|95.4|93.1|89.6|94.7|95.7|96.9|
|Small cities|100.0|105.1|106.6|105.0|92.3|96.4|96.9|97.3|87.0|89.9|90.4|88.9|85.7|88.7|91.9|93.8|
|Rural area|100.0|105.1|107.8|106.0|95.3|99.9|101.7|98.7|89.3|94.2|94.0|91.0|88.2|94.5|97.3|95.4|
||||||||Age||||||||||
|Child|100|106.6|109.0|106.6|100.1|103.8|108.0|104.3|93.5|98.4|98.2|95.7|91.3|100.0|100.0|102.6|
|working age|100|107.5|110.1|108.2|98.3|101.7|103.2|102.4|93.9|97.9|96.0|94.8|89.7|95.5|97.0|97.6|
|Retiree|100|103.2|107.2|104.0|100.2|100.2|100.1|100.8|93.6|97.8|94.0|92.0|91.0|93.1|93.1|95.6|
|||||Em|ployme|nt statu|s for w|orking a|ge pop|ulation|||||||
|Employed|100|106.5|108.8|106.5|97.4|100.0|100.6|100.8|92.7|96.5|93.6|93.5|89.6|94.5|94.9|96.5|
|Partly employed|100|107.5|103.2|103.3|89.2|94.7|116.8|96.8|89.0|109.3|99.6|94.1|77.1|83.1|98.2|89.4|
|Nonemployed|100|105.8|111.5|108.3|103.3|107.7|107.8|107.4|94.8|98.4|98.1|93.8|90.6|97.3|98.7|99.2|
|||||E|ducatio|n level|for wor|king age|popul|ation|||||||
|high education<br>secondary|100|106.3|109.2|107.4|96.6|100.8|103.6|101.6|97.5|100.4|95.4|97.8|90.6|97.4|92.8|96.7|
|specialized|100|108.2|110.4|109.5|99.7|101.0|101.5|101.0|89.5|94.5|93.9|91.7|87.1|91.5|96.1|95.0|
|Other|100|107.7|110.5|107.6|96.8|101.1|102.5|101.5|91.6|96.3|96.1|92.6|87.5|93.7|98.7|98.0|
|||||||Type o|f house|hold (H|H)||||||||
|HH without|||||||||||||||||
|children|100|105.3|109.5|105.7|98.1|100.4|100.1|100.5|94.2|97.9|94.0|93.0|90.3|92.8|94.2|95.9|
|Retiree HH|100|100.5|104.1|102.0|98.5|97.4|97.1|97.2|92.8|92.5|91.0|89.7|89.1|90.7|90.2|93.4|
|HH with children|100|107.0|108.1|107.6|100.2|103.6|106.8|104.3|93.1|97.8|97.5|95.2|90.9|99.4|99.2|101.2|
|1 child|100|107.2|106.6|109.5|98.1|101.1|106.9|103.7|91.9|96.5|94.8|93.5|88.3|97.5|96.4|97.7|
|2 children<br>3 and more|100|108.6|111.1|108.5|103.7|108.2|104.4|102.8|92.7|97.7|101.0|96.2|95.2|102.4|103.7|107.0|
|children|100|116.5|129.5|112.7|97.6|103.8|109.8|109.5|101.7|105.3|104.6|103.6|91.5|97.3|102.7|103.6|
|singleparent|100|101.8|109.1|101.8|97.6|100.7|130.1|106.7|89.7|96.4|94.6|91.5|90.5|107.0|99.1|114.3|

Source: Authors’ estimates based on HBS data.

28
-27-

the Philippines, among developing countries, are women more fully represented (Swanson and Rassi, 1981).

2.47 Newly recruited and existing VEWs, men and women alike, underwent a three-month intensive training program in groups of about 45 at a time. This pattern of sequential training fits into the phased approach HIDENO followed in implementing its activities. The training program included basic agriculture, cash and food crop production, and extension methods, and also modules on rural sociology and farming systems. Thus the VEWs were given an appreciation of the complexity of traditional agriculture and a fuller understanding of the relationship between agriculture and tribal customs and institutions. VEWs who successfully completed the course were equipped with basic agricultural tools and a small off-road motorcycle (125 cc), and posted to one of the agricultural posts in their tribal area.

**Tabie** 7_

|Division|Mcn-<br>Men|UDEZO1**V**<br>**Wo.nen**|**YiJs'**<br>Pnrrantoge<br>of**Womn**|iRacrited/Tr<br>**Men**|NIDM<br>e.In*d<br>**Women**|VVWW<br>Percentage<br> <br>of**Women**|To<br>**Men**|tal<br>VEW<br>**Womn**|S<br>Percentage<br>of**Womn**|
|---|---|---|---|---|---|---|---|---|---|
|**But**|28|4|12.5|31|6|16.2|59|10|**14.5**|
|Donga-Mantung|28|2|6.7|24|10|29.4|52|12|18.8|
|Menchum|47|1|2.1|32|9|22.0|79|10|11.2|
|**Mezam**|45|13|22.4|39|17|30.4|84|30|26.3|
|Momo|40|5|11.1|21|8|27.6|61|13|17.6|
|TOTAL|188|25|11.7|147|50|25.4|335|75|18.3|

-----------------------------------_--------------------------------__------__---------------------------------

Source: Provincial Service of Extension **and** Training **Not** * Non-MIDENO VEWs include those extension workers **eiployed at the** start of **the** project **and** those extension **agents** put **at** the disposal of the Provincial Service of Extension **during** the project.

# Supervisory Staff

2.48 The extension component also aimed to strengthen the supervisory personnel. Prior to the start of MIDENO activities, MINAGRI's extension agents had reported to one of the 59 Chiefs of Agricultural Post (CAPs) located throughout the province. With the planned increase of VEWs to 375, the number of VEWs each CAP would supervise would jump from three to six, a ratio considered unacceptably high by the PDA,9 Plan MIDENO called for supplementing the 59 CAPs with 22 Extension Supervisors, which lowered the ratio of first line supervisor to VEW to 1 to 4. To maintain this ratio as the number of VEWs expanded to 405, the number of CAPs and ESs increased to 65 and 28 respectively.

9 **Cernea** (1981) reports that the **average** ratio of supervisors to VEWs for **six** World Iank-funded projects in India **was one** to **eight.**