rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 2. Balance Tests**

||(1)|(2)|(3)|(4)|(5)<br>_P_-value|(6)|
|---|---|---|---|---|---|---|
|Variable|Mean<br>C|Mean<br>LOA|Mean<br>ESR|LOA vs.<br>C|ESR vs.<br>C|ESR vs.<br>LOA|
|**_PANEL A. Household Characteristics_**|||||||
|Household size (N)|3.723|3.762|3.643|(0.748)|(0.519)|(0.256)|
|Dependency ratio (%)|0.268|0.253|0.274|(0.274)|(0.665)|(0.212)|
|Households in rural area (%)|0.500|0.500|0.500|--|--|--|
|Households with assets index above the<br>median (%)|0.461|0.491|0.461|(0.476)|(1.000)|(0.449)|
|Moderate to severe food insecurity (%)|0.372|0.354|0.366|(0.637)|(0.883)|(0.722)|
|Remittances received (%)|0.324|0.369|0.366|(0.213)|(0.201)|(0.934)|
|Household has a non-farm enterprise (%)|0.333|0.327|0.315|(0.856)|(0.625)|(0.753)|
|Household engages in agricultural<br>activities (%)|0.560|0.491|0.574|(0.066)|(0.683)|(0.035)|
|Observations|336|336|336||||
|**_PANEL B. Individual Characteristics_**|||||||
|Female (%)|0.549|0.539|0.559|(0.494)|(0.512)|(0.195)|
|Age (years)|35.006|35.032|36.479|(0.990)|(0.014)|(0.018)|
|High school or higher education (%)|0.282|0.296|0.275|(0.610)|(0.705)|(0.400)|
|Read and write (%)|0.878|0.888|0.847|(0.638)|(0.066)|(0.023)|
|Never married (%)|0.333|0.351|0.320|(0.433)|(0.636)|(0.241)|
|Access to mobile phone (%)|0.892|0.864|0.888|(0.097)|(0.652)|(0.180)|
|Access to Internet (Wi-Fi) (%)|0.164|0.193|0.198|(0.241)|(0.110)|(0.721)|
|Observations|830|854|796||||

_Notes:_ This table shows the average characteristics of the household- and individual-level variables—Panels A and B, respectively—by treatment status. Columns (1) to (3) present the mean for each characteristic of households and individuals assigned to C, LOA, and ESR, respectively. Columns (4) to (6) present the _p_ -value associated with the hypothesis of the mean values across pairs of groups that are the same. For the estimation of _p_ -values, we control for enumeration area (EA) fixed effects (stratification variable) and estimate standard errors at the EA level in Panel A and at the household level in Panel B. The variables presented in this table were collected using the following modules: sociodemographic characteristics, education, food security, household characteristics, and assets. Appendix C contains definitions for each of the variables. All indices are estimated using Anderson’s (2008) approach of inverse covariance weighting and take a value between 0 and 1. All variables are dummies except when the unit of measurement is indicated in parentheses.

29
the inclusion of income, wealth, a range of demographic and household characteristics, and household composition variables.

# Table 4 here

At the time of the R3 survey implementation, IBLI policies from sales period 1 and sales � period 2 had already lapsed without payout. Thus, the coefficient estimate on _IBLIL_ captures the negative _ex post_ SWB effect of having bought an insurance policy that did not � pay out. Indeed, the negative and statistically significant coefficient estimate on _IBLIL_ indicates buyer’s remorse. Having bought an IBLI contract that lapsed without pay out reduces the log-odds of reporting high SWB by 0.42, which indicates that buyers of lapsed IBLI contracts are 1.5 ( _≈_ 1 _/e_<sup>_−_0</sup><sup>_._44</sup> ) times more likely to report lower SWB than higher SWB. In probability units, having bought a lapsed IBLI contract increases the probability of reporting low SWB ( _SWB ≤_ 3) by 5 percent and decreases the probability of reporting � high SWB ( _SWB ≥_ 5) by 5 percent. More importantly, the magnitude of the _IBLIL_ coefficient is statistically significantly smaller than that of _IBLI_<sup>�</sup> . This suggests that people are comforted by insurance coverage, and the positive _ex ante_ effect trumps the negative _ex post_ regret they feel once they realize that they paid for insurance that, in retrospect, they did not ultimately need.

As expected, SWB is positively correlated with various wealth measures. Both livestock and non-livestock assets are positively related to SWB. Male headed households are more likely to report higher SWB than their female headed counterparts. Household size is negatively correlated with SWB.

We find similar results for the volume of TLUs insured (columns 4-6). Vignette corrected SWB is increasing in the predicted number of TLUs insured. In the full model in column 6, a unit increase in the volume of TLUs insured increases the log-odds of reporting higher SWB by 0.14, which translates to 1.15 times more likelihood of reporting higher SWB than lower SWB. The corresponding column in panel (b) shows that an additional unit of TLUs insured reduces the probability of reporting low SWB ( _SWB ≤_ 3) by 2 percent and increases the probability of reporting high SWB ( _SWB ≥_ 5) by 2 percent. Yet, as IBLI policies lapse without paying, the more TLUs one had insured, the greater the buyer’s remorse one experiences. A unit increase in lapsed TLUs insured reduces the log-odds of reporting higher SWB by 0.07. An IBLI buyer with a unit more lapsed TLUs insured is 1.08 times more likely 20
**Figure 8: Composition of Security Expenditures, 2003/04-2004/05**

<!-- Start of picture text --> National Police and<br>Law Enforcement<br>28%<br>Justice<br>3%<br>Afghan National Mine Action<br>Army 6%<br>60%<br>DDR<br>3%<br><!-- End of picture text --> _Source: Afghanistan Ministry of Finance budget documents and fiscal reports; authors’ estimates._ As for the national budget as a whole, there are no easy answers or technical fixes for allocating public spending, either between security and other sectors or within the security sector. Clearly, the very large share of security spending in the External Budget (nearly 80% in 2004/05), and fragmented donor decisionmaking on expenditures, make the task of the National Security Council, the Ministry of Finance, and other oversight bodies especially difficult. Some points with respect to intersectoral and intrasectoral prioritization are outlined below (see World Bank, 2005b, Volume I, Chapter 5 for further discussion).

**Prioritization across sectors:** Sophisticated technical approaches may require substantial resources and even so are highly imperfect and can result in misleading conclusions.  Simpler rules of thumb may work better, and judgment is called for.

- Intersectoral allocation decisions need to be anchored in the overall national security strategy and national development strategy.

- Identifying and mitigating gross anomalies in intersectoral allocations is very important (e.g. the serious underfunding of the justice sector, noted earlier).

- International patterns of spending and outcomes (for example for police) may provide some guidance on where funds should be allocated (see Section 3.2).

- Identifying bottlenecks and directing expenditures to alleviate them may be a useful approach in the short run.

- Some linkage between sectoral allocations and performance may make sense but needs to be watched carefully, and corrective actions taken if required, to ensure that large imbalances between sectors do not emerge over time.

- Finally, it is essential that intersectoral prioritization be comprehensive—no sector (e.g. Defense) should be considered sacrosanct or immune from scrutiny.

**Prioritization within sectors:** Technically, prioritization within sectors is more manageable than intersectoral prioritization because the alternatives tend to be more comparable.

- Intrasectoral allocation decisions need to be anchored in a sound sector strategy.

31
**Table 1: 1Triffs in Latin America: Pre-Reform and Recet Levels**

||**Average**<br>**(Arith**<br>**Unwei**|**ariff Levl**<br>**n**<br>**,**<br>**ghted)**|**Range of T**<br>**(Mm.**|**wff Schedule**<br>- **Ma.)**|
|---|---|---|---|---|
|**Country and Pariod Dellnition**|**Pre-**<br>**Reform**|**Recent**|**Pre-**<br>**Reform**|**Recent**<br>_|
|Argentina (1987, 1991)|42|15|15-115|5-22|
|Bolivia' (1985, 1991)|20|8|0-20|5-10|
||80|21|0-105|0-65|
|Brazil (1985, 1992)|35|11|35|11|
|Chile' (1984, 1991)|83|12|0-220|5-20|
|Colombia (1984, 1992)|92|15|1-100|5-20|
|Costa Rica (1985, 1992)|50|18|0-290|2-40|
|Ecuador (1985, 1991)|50|19|1-100|5-20|
|Guatemala (1985, 1992)|NA|20|NA|0-45|
|Jamaica (1991)|34|4|0-100|0-20|
|Mexico (1985, 1991)|72|16|0-44|3-86|
|Paraguay (1985, 1991)|64|15|0-120|5-25|
|Peru (1987, 1992)|32|18|10-55|12-24|
|Uruguay (1987, 1992)|37|19|0-135|0-50|
|Venezuela (1989, 1991)|||||

' For Bolivia and Chile, the earlier years too are post rather than Pe reform.

_Sources: Edwards (1993), Ch. 5, Table 5.2 Primo Braga, Safadi and Yeats (1994), Table 1;_ _These sources have drawn partly on Erzan et.al. (1989) and on Alam and Rajapatirana (1993)_
|Study and Location|Definition of<br>Empowerment|Measurement<br>Concept|Data Sources|Focus|Conclusions|Implication|Scope|
|---|---|---|---|---|---|---|---|
|(1995)<br>_US_|influence over<br>events and<br>outcomes of<br>importance|empowerment<br>measures using self<br>assessment scales<br>for five components<br>(perceived<br>knowledge and skill<br>development,<br>perceived<br>participatory<br>competence,<br>expectancies for<br>future individual<br>contributions,<br>perceived<br>group/organization<br>accomplishments,<br>and expectancies for<br>future<br>group/organizational<br>accomplishments);<br>organizational<br>empowerment<br>derived from two<br>items (key<br>informant telephone<br>survey) rated on a<br>four-point scale<br>concerning the<br>impact of the task<br>force on their<br>organization<br>policies or<br>procedures and<br>resources|members of 35<br>community<br>coalitions<br>organized for the<br>prevention of<br>alcohol and other<br>drug problems;<br>key informant<br>telephone survey|empowerment<br>of individuals<br>and<br>organizations<br>in the context<br>of community<br>coalitions|individual<br>psychological<br>empowerment<br>(participation<br>levels, sense of<br>community,<br>perceptions of a<br>positive<br>organizational<br>climate),<br>collective<br>empowering of<br>members (net<br>benefits of<br>participation,<br>commitment and<br>positive<br>organization<br>climate), and<br>empowered<br>organizations<br>(psychological<br>empowerment<br>and positive<br>organization<br>climate)|interactional<br>process linking<br>the individual to<br>the collective,<br>that is both<br>multilevel and<br>context specific|organizational level;<br>empowerment seen<br>mostly as outcome<br>(influence,<br>achievements);<br>participation,<br>perception of<br>community, and<br>inclusion treated<br>separately. Social<br>domain.|
|Albertyn (2001)<br>_South Africa_|Effective<br>empowerment|Questionnaire<br>refined to 61|Questionnaire<br>implemented in|Developing a<br>standardized|Instrument that<br>can be used for|Using local and<br>personal|_[NOTE: Potentially_<br>_interesting. Need to see_|

39
<!-- Start of picture text --> 0,80%<br>0,60%<br>0,40%<br>0,20%<br>0,00%<br>Consumption Government Investment Export Import GDP<br>Expenditure<br>-0,20%<br>-0,40%<br>-0,60%<br>-0,80%<br>irap_bas irap_ires irap_vat<br><!-- End of picture text --> Figure 2: Macroeconomic impacts on GDP and its components in 2030, measured as percentage change with respect to the baseline scenario, in all considered simulations.

21
# **References**

Acs, Zoltan J. and David B. Audretsch, 1987, Innovation, Market Structure and Firm Size, _Review of Economics and Statistics,_ 69, 567-574.

- Adams, Renée B., Heitor Almeida, and Daniel Ferreira, 2004, Understanding the relationship between founder-CEOs and firm performance, working paper, New York University.

- Aghion, Philippe; Bond, Stephen;  Klemm, Alexander, and Ioana Marinescu, 2004, Technology and Financial Structure: Are Innovative Firms Different? _Journal of the European Economic Association,_ 2, 277-288.

- Aghion, Philippe and Steven Durlauf, 2005, _Handbook of Economic Growth_ , Eds., Amsterdam: North-Holland Elsevier Publishers, forthcoming.

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28
We recognize that there are many other considerations that policy makers must evaluate when deciding on possible liberalization of such rules, such as a desire to maximize funding of domestic growth. While we discuss other likely causes of resistance to international diversification, given the magnitude of the rapid demographic aging dynamics that are causing unprecedented strain on scheme finances, it becomes increasingly difficult to ignore the levels of opportunity illustrated by our analysis.

In terms of practical advice for policy makers looking to take action on international diversification, we would suggest the following broad principles. First, any change envisioned should be implemented gradually, ideally in a way that is triggered automatically perhaps based upon size of pension assets relative to size of the economy or based upon a combination of more than one of the major variables discussed above. Secondly, progress should be tracked, perhaps against the opportunity as portrayed in Table 3, with a goal of closing any opportunity gaps vs. the wider benchmarks. Finally, implementation should be encouraged in a manner suitable to the capabilities of the funds investing overseas. Specifically, as is apparent from the LAC examples in Appendix B and the Slovenia case, countries that have higher levels of international pension fund investing are often achieving this via international mutual funds, and in many cases indexed funds. This type of lower cost instrument is likely most suitable to pension funds new to international investing and should be encouraged.

# **6. Bibliography**

Blake, D. (2000). _Financial Market Analysis_ . Wiley. Retrieved from https://books.google.com.co/books?id=rsfuAAAAMAAJ Cetorelli, N., Hirtle, B., Morgan, D.P., Peristiani, S. and Santos, J.A., (2007). Trends in financial market concentration and their implications for market stability. Economic Policy Review,
and distributed nature of some renewable energy technologies, particularly solar and micro-hydro, has great value for providing access to electricity in more remote and rural areas around the world. These are important issues to consider in extending this study.

# **References**

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- Apostoleris, Harry, Sgouris Sgouridis, Marco Stefancich and Matteo Chiesa (2018). Evaluating the factors that led to low-priced solar electricity projects in the Middle East. Nature Energy, Vol. 3, pp. 1109-1114.

Bellini, Emiliano (2019). Winners, projects, prices of Portugal’s record PV auction. pv magazine, August 9, 2019. <u>https://www.pv-magazine.com/2019/08/09/winners-projects-prices-ofportugals-record-pv-auction/</u>

- Branker, K, M.J.M. Pathaka and J.M. Pearce (2011). A review of solar photovoltaic levelized cost of electricity. Renewable and Sustainable Energy Reviews 15:4470–4482.

- Candelise, Chiara, Mark Winskel and Robert J.K. Gross (2013). The dynamics of solar PV costs and prices as a challenge for technology forecasting. Renewable and Sustainable Energy Reviews 26: 96–107.

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34
# **J Characterization of Venezuelan Refugees with Formal Jobs**

Table J.1. Descriptive Statistics – Main Outcomes: Refugees with Formal Jobs

||Formal Job|Informal Job|Mean Difference<br>(p-value)|
|---|---|---|---|
||(1)|(2)|(3)|
|_A. Socioeconomic Well-being_||||
|Well-being (Index)|1.731|1.101|0.000|
||(0.974)|(0.877)||
|Consumption per Capita (log)|1.821|1.661|0.003|
||(0.489)|(0.566)||
|Labor Income (log)|0.671|0.464|0.000|
||(0.320)|(0.231)||
|_B. Rights and Services_||||
|Rights and Services (Index)|3.528|3.486|0.899|
||(2.573)|(3.525)||
|SISBEN Access|0.512|0.506|0.897|
||(0.502)|(0.500)||
|Subsidized Healthcare|0.09|0.344|0.000|
||(0.288)|(0.475)||
|Financial Products|0.637|0.23|0.000|
||(0.483)|(0.421)||
|Transfers from Government|0.056|0.147|0.006|
||(0.232)|(0.354)||
|_C. Labor Outcomes_||||
|Labor Outcomes (Index)|11.924|0.116|0.000|
||(0.751)|(0.639)||
|Hours Worked (log)|3.867|3.912|0.388|
||(0.439)|(0.542)||
|Job Satisfaction|0.694|0.425|0.000|
||(0.463)|(0.495)||
|Self-Employed|0.008|0.345|0.000|
||(0.090)|(0.476)||
|Observations|124|940|1,064|

_Notes:_ The table depicts the difference between Venezuelan refugees with PEP by having a formal job. The variable Formal Job is a indicator [=1] if the respondent has a written or verbal contract and saves in a pension fund.

89
truncate the joint distribution of consumption for each individual. Though analytical expressions for such truncated distributions are unattainable, we recover the distributions by randomly drawing the error terms from the five-variate truncated normal with 1,000 replications. This way, we generate the simulated universe of 3,620,000 household expenditures with a different realization of conditional errors. The poverty rates and Gini coefficients (or any other statistic) could then be calculated for the particular counterfactual scenario. Confidence intervals for the inequality and poverty measures are estimated by the jackknife method (see Appendix 2 for the detailed description of the simulation technique).

# _Identification Strategy_

Our theoretical model guides an identification strategy for the empirical estimation. The fact that migration and consumption decisions are separated in time allows us to assume that certain factors (variables) affecting the migration decision in time period 1 have no direct impact on household consumption in period 2. Such variables could be used as instruments in the FIML estimation of equations (2)–(4). A variation in these instrumental variables would identify the causal effects of migration and remittances on household consumption because the effect of this variation is entirely channeled through household migration decision. We use two instruments to identify the separate effects of international and domestic migration on household consumption.

The first instrument, the proportion of migrants in a ward in 2001, is constructed based on information from 2001 Nepal Census (Central Bureau of Statistics 2003). The proportion of households with migrants in a village in 2001 could be interpreted as a proxy for the extent of village-level networks. We argue that 2004 household consumption should not be directly affected by the migration networks in 2001. Carringon, Detragiache, and Vishwanath (1996) and Munshi (2003) test the role of networks in promoting migration and find a greater propensity toward migration in villages with existing migrants––meaning that there is propensity for new migrants to follow in the footsteps of existing migrants. When in the host country, Nepali migrant workers develop extensive social networks that link them with their relatives and friends 14
consumption goods for 1980 from Summers<sup>and Heston (1988). It is</sup> then transformed to measure the movement<sup>toward urnity. The sixth,</sup> index of price distortion, is a modified<sup>version of the index</sup> used in Dollar (1991). The relative price<sup>of consumption goods</sup> from Summers-Heston is "purged" of its<sup>non-traded component by</sup> taking the residual from a regression<sup>of this index on</sup> urbanization, land, and population.<sup>The seventh, bias against</sup> agriculture, measures the indirect bias<sup>against agriculture from</sup> industrial sector protection and overvaluation<sup>of the exchange</sup> rate (source: Krueger, Schiff, and Valdes<sup>(1991)).</sup> Annual observations were available for<sup>time periods which</sup> ranged from 1960-87 for trade shares to<sup>1978-88 for the Thomas et</sup> al. trade liberalization index. The number<sup>of countries</sup> available for each index varies, ranging<sup>from 60 (for trade</sup> shares) to 19.

Tables 2 and 3 examine the rank correlation<sup>between</sup> these seven different measures of openness.<sup>Table 2, which</sup> examines the rank correlation between<sup>openness measures in</sup> levels, excludes the measure for movement<sup>towards international</sup> prices, which is an estimate of changes<sup>in policy. The results</sup> suggest that there is generally a statistically<sup>significant (and</sup> positive) correlation between the two<sup>measures of trade reform,</sup> minimal disprotection of agriculture,<sup>and trade shares. This</sup> positive relationship exists for both<sup>levels of openness as well</sup> as changes, for a pure cross section<sup>as well as for a panel.</sup> However, the majority of the rank correlations<sup>using the pure</sup> 12
32 Appendix Table 2. Distribution of Remittance-Receiving Households by Decile Group, Ranked by Observed Per Capita Household Expenditure, Excluding Remittances

|**Rank**|**Households**<br>**receiving**<br>**internal**<br>**remittances**<br>**(from**<br>**Guatemala)**|**Internal**<br>**remittances as**<br>**percent of total**<br>**per capita**<br>**household**<br>**expenditure**<br>**(including**<br>**remittances)**|**Households**<br>**receiving**<br>**international**<br>**remittances**<br>**(from USA)**|**International**<br>**remittances as**<br>**percent of total**<br>**per capita**<br>**household**<br>**expenditure**<br>**(including**<br>**remittances)**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|(decile)|(percent)|(percent)|(percent)|(percent)|
|Lowest 10|9.7|70.5|15.2|80.5|
|Second 10|9.0|33.5|8.1|46.3|
|Third 10|8.8|12.8|8.6|32.7|
|Fourth 10|8.7|21.3|6.8|43.1|
|Fifth 10|9.0|14.0|7.4|21.3|
|Sixth 10|8.6|15.1|9.8|21.4|
|Seventh 10|10.4|13.2|9.2|18.4|
|Eighth 10|14.1|9.1|13.7|18.4|
|Ninth 10|12.1|19.5|12.2|13.3|
|Top10|9.6|10.7|9.0|11.5|
||100.0||100.0||

Notes: All values based on observed – not predicted – data. Households ranked into decile groups on the basis of observed per capita household expenditure (excluding remittances). For those households receiving internal remittances (from Guatemala), column (2) shows the percent of total per capita household expenditure (including remittances) coming from internal remittances. For those households receiving international remittances (from USA), column (4) shows the percent of total per capita household expenditure (including remittances) coming from international remittances.

Source: Calculated from Guatemala 2000 ENCOVI Survey.