rafmacalaba/datause-encoder-data huggingface dataset:
Treatment allocation was altered in 5 instances and we explain possible reasons underlying these changes. 87 (95%) of the 92 villages that we can match from List 1 to List 4 have the correct treatment allocations<sup>3</sup> . Villages included in the RCT, despite not being in the initial list, do not differ significantly from villages initially listed. We find evidence suggesting that some Tigre villages received preferential treatment, which underlines the importance of controlling for this ethnic group in all our regressions.

# **A.2**

Differences between village lists may have arisen from a variety of situation-specific problems. Those issues were discussed at length with the NMCP and analysed with the help of local staff. The following are the main issues that we identified for each village list:

1. The initial list was outdated, possibly from the Census of 2002 or 2003. For example, a sub-zone had changed name since then, from Omhajer to Goluj, and village sizes do not correspond to the current situation (e.g., Omhajer had only 70 household at the time, while some 1,200 households lived there in 2009). Some villages switched from a sub-zone to another (e.g., Hawashait moved from sub-zone Dighe to sub-zone Laelay Gash) and some became part of another country (Sudan or Ethiopia). Existence and location of treatment and control villages were not checked or recorded prior to the beginning of the study. However, notice that, in a setting like ours, the problem of missing some migrant villages could be expected. Due to a sustained process of villagization, several villages may have merged into a new one. Villages may also have changed name or may even have several names, so that the same village could be recorded in two lists under very different names. We were able to reconcile some, but not all, of these cases.

2. When spraying teams tried to reach the treatment villages in List 2, sometimes they could not find one or a village may have moved abroad and be out of reach. Migrant villages were followed whenever possible and missing treatment villages were replaced with the closest available village.

> 3Villages 56 and 59 (reallocated to treatment) and 72 and 16 (reallocated to control) have matching names in Lists 1 and 4. Village 19 (reallocated to treatment) may be matched using the sub-zone where it is located.

2
return for the pension system and for the economy as a whole, a major reason for choosing a multi-pillar system in which the funds are concentrated in the private pillar. This will also help to develop financial markets, an essential prerequisite for economic growth at the stage where China is now. In order to achieve these benefits, Singapore and Malaysia, which until recently featured central control over their pension funds, are now decentralizing, allowing greater diversification of investments and giving workers greater control over the investment strategy.

Besides the advantage of competitive management of pension reserves, there is also an advantage to having part of the pension system set up as defined contribution rather than defined benefit. The hidden, sometimes perverse redistributions that are inherent in defined benefit plans are thereby avoided. The close link between benefits and contributions, both en toto and at the margin, means that workers are more likely to regard the required contribution as their own saving, rather than a tax, hence it is less likely to reduce employment or induce evasion. And the evasion that remains does not undermine the financial viability of the system, as it would in a PAYG defined benefit plan. This paper does not quantify the reduction in evasion but it does quantify the financial improvement for the system when the evading individual bears the cost.

In addition, defined contribution plans automatically avoid higher costs as longevity increases, without the politically difficult decision to raise the retirement age periodically- Instead, either the retirement age shifts upward or the annual benefit shifts downward automatically when the worker's accumulation is converted into an annuity upon retirement, as life expectancy rises. Similarly, policy-makers need not make a universal choice between wage and price indexation as real wages rise, since each worker can choose a different time profile of consumption. Those who want their living standards to keep pace with wages will have to forego withdrawals at the beginning of retirement rather than placing a financial burden on the over-all system. (See World Bank 1994 for a fuller description of and rationale for this multi-pillar system).

For all these reasons, the uniform contribution rates given in Table 4-which are based on a partial equilibrium model with many variables treated as exogenous that are in fact endogenous--underestimate the real values that would be needed in a single-pillar partially funded defined benefit publicly managed 20
**Table 9. Effects of Agatha on Consumption and Poverty (Subsample of Households Located Less than 50 Kilometers Away from the Closest Weather Station)**

||_Total Consumption_|_Moderate Poverty_|
|---|---|---|
|**Measure of Shock**|(1)|(2)|
||**Panel A: Total**||
|t * (rainfall z-score> 2)|-79.983***<br>[28.177]|0.033*<br>[0.019]|
|Baseline Mean/Median|603.3|0.448|
||**Panel B. Urban**||
|t * (rainfall z-score> 2)|-176.094***<br>[44.263]|0.047*<br>[0.025]|
|Baseline Mean/Median|799.4|0.303|
||**Panel C. Rural**||
|t * (rainfall z-score> 2)|-1.191<br>[35.389]|0.022<br>[0.028]|
|Baseline Mean/Median|499.1|0.558|

Observations: 25,803 Panel A; 11,021 Panel B; 14,782 Panel C. _Notes_ : Results from diff-diff regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. Baseline median for Total Consumption and baseline mean for Moderate Poverty. The Z-score indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy. *** p<0.01, ** p<0.05, * p<0.1. _Source:_ calculations by the authors based on data from Encovi (INE) and Insivumeh.

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Table 1: Correlating Main and Additional Outcomes: Individual Level

|Outcome|OLS|Year FE|Region FE|Region-<br>Year FE|Obs|Years|Regions|
|---|---|---|---|---|---|---|---|
|Immigration should be decreased|-0.145***|-0.145***|-0.105***|-0.099***|192,813|11|1,712|
||(0.008)|(0.008)|(0.005)|(0.005)||||
|Immigrants take jobs|-0.048***|-0.047***|-0.025***|-0.025***|135,629|9|1,602|
||(0.006)|(0.006)|(0.005)|(0.005)||||
|Immigrant neighbors is “bad thing”|-0.274***|-0.272***|-0.192***|-0.191***|116,902|3|1,917|
||(0.006)|(0.006)|(0.005)|(0.005)||||
|Immigrants in country is “bad thing”|-0.290***<br>(0.006)|-0.287***<br>(0.006)|-0.206***<br>(0.005)|-0.206***<br>(0.005)|116,285|2|1,918|
|Immigrant marrying relatives is “bad thing”|-0.261***<br>(0.007)|-0.258***<br>(0.007)|-0.168***<br>(0.005)|-0.168***<br>(0.005)|114,426|2|1,918|
|Too many immigrants|-0.045***|-0.048***|-0.017*|-0.013|43,346|3|285|
||(0.013)|(0.013)|(0.010)|(0.009)||||
|Oppose citizenship for immigrants|-0.047***<br>(0.014)|-0.046***<br>(0.014)|-0.035***<br>(0.012)|-0.034***<br>(0.013)|20,047|5|172|
|Immigration is a serious problem|-0.207***<br>(0.016)|-0.207***<br>(0.016)|-0.156***<br>(0.013)|-0.156***<br>(0.013)|11,247|1|135|
|Oppose taking Syrian refugees|-0.213***<br>(0.027)|-0.213***<br>(0.027)|-0.195***<br>(0.019)|-0.195***<br>(0.019)|11,152|1|223|

Notes: * significant at 10%; ** significant at 5%; *** significant at 1%. Each cell is a separate regression at the individual level with the binary dependent variable listed in the left-most column and the independent variable is our main outcome: 1 if the respondent answers yes to the question “is the city or area where you live a good place to live for immigrants from other countries?” and 0 otherwise. _Years_ refers to the number of survey-years the outcome variable is included in GWP. _Regions_ refers to the number of sub-national regions observed in GWP. Results use the Gallup sampling weights and standard errors are clustered at the region (sub-national) level.

immigrants to live, correlates strongly with perceptions toward immigrants. Regionyears where more respondents report their area is a good place for immigrants have a smaller percentage of the population reporting that they would not want immigrants as neighbors, believe immigrants take jobs, oppose further immigration, etc. This holds across regions and within-region changes over time.

Based on the strong individual- and regional-level relationships, we argue that our main outcome serves as a useful proxy measure for a general perception toward immigrants and refugees. We believe it is likely that many respondents approximated the question as a version of “are immigrants good or bad”. Where possible, however, we support our main findings with the additional related outcomes.

# **4.1.3 Policies**

For data on refugees’ right to work, we utilize the data and methodology from the Developing World Refugee and Asylum-Seeker Policy (DWRAP) dataset by Blair 12
In addition to the performance tests, PIRLS provides a rich set of background information. This background information was gathered through a student questionnaire, a home questionnaire (completed by parents), a teacher questionnaire and a school questionnaire (completed by school principals). By merging these data with the student achievement data, the database used in this paper contains extensive information on the family background of students, on the resource endowment and teacher characteristics of the class and school and on schools’ institutional features.

A problem immanent in any empirical work based on survey data is non-response and thus missing data. Table A1 in the appendix reports the share of missing values for each variable used in this paper. While there are only very few missing values on such variables as student age and gender, kindergarten attendance and teacher gender and education, the amount of missing data on some variables in some countries is quite substantial, reaching more than 30 percent in Argentina in the case of books at home and parental education, working status and occupation. In such a situation, dropping any student that misses data on at least one variable in multivariate analyses such as the ones conducted in this paper means that one is left with relatively small samples. Furthermore, since data points are unlikely to be missing at random, such a procedure could introduce severe biases and reduce the representativeness of the results. In effect, when sticking to the original data, there is an inescapable trade-off between controlling for many variables and keeping many observations. Even more, dropping all observations with some missing values always means dropping relevant information, because there is abundant information on other variables for each student that misses data on one or a few of the numerous considered variables.

Thus, to ensure the usage and exploitation of all the information available in the dataset, we imputed missing values in the data using a simple median imputation method. This method assigns the median of the available observations at the lowest possible level, i.e. either the class, school or country median, for any missing observation. In this procedure, we used sampling weights to take account of the survey structure of the PIRLS dataset (see Section 3 below). In the estimated regressions, we include imputation dummies which ensure that none of the substantive results are driven by imputed rather than original data, and we do additional robustness tests of the imputation data (again, see Section 3 below).

7
N − 1 observations on Mis<sup>handtheregressors,whereNisthenumberofpossiblelocations.5</sup> We then estimate (3) by logit.<sup>6</sup> Since the same individual appears N − 1 times, we have to correct for correlation between the di ff erent choices for the same individual h. We do so fi rst by adding individual fi xed e ff ects. This takes care of much of the correlation. We also correct standard errors for clustering by district of origin. This takes care of possible peer e ff ects, as would arise if individuals from a given location all tend to migrate to the same destination. Robust standard errors that cluster by district of origin also correct for negative correlation in errors across choices for the same individual, a possibility that fi xed e ff ects do not control for. Negative correlation is a serious issue here, a point that is discussed in more detail in the next section.

We worry about possible circularity resulting from general equilibrium e ff ects (Dahl, 2002; Hojvat-Gallin, 2004; Borjas, 2006; Bayer, Khan and Timmins, 2008). If many people migrate to a speci fi c location, such as the capital city, this is likely to a ff ect wages, incomes, and access to amenities in that location.<sup>7</sup> This would generate a potential endogeneity bias due to the fact that incomes and amenities in that location result in part from the decision of many migrants to locate there.

To eliminate this bias, we use past data to estimate the income regression. More precisely, let T be the period for which we have income information and T + t the period at which we

> 5 The dropped observation corresponds to the location of origin Mii h<sup>which,asexplainedearlier,wedonot</sup> include in the analysis since including Mii<sup>hwouldmeandefactoincludingthedecisionofwhethertomigrateor</sup> not.

> 6 McFadden (1974) has shown that, in multiple choice problems of the kind studied here, the application of logit estimation is justi fi ed if (1) the errors in each latent choice equation follow the extreme value distribution and (2) errors are independent across choices. See Train (2003), Chapter 3 for a detailed discussion. The estimation of models with correlated errors across choices requires either multiple integration or the use of Bayesian estimation techniques relying on Gibbs sampling. With a choice of over 70 possible destinations, multiple integration is out of the question. Gibbs sampling remains a possibility but would require extensive programming. We choose instead to keep the logit approach but to correct the standard errors for possible correlation in errors across choices. In our case the possible e ffi ciency gain achieved by Bayesian methods does not appear to justify the programming cost.

> 7 The e ff ect could be negative — e.g., congestion — or positive — e.g., agglomeration externalities.

10
19 we see households falling below 120 percent of the poverty line yet not incurring catastrophic payments according to the first definition; by contrast in Figure 8 we see households incurring spending in excess of 40 percent of the discretionary consumption but not classified as having incurred catastrophic spending under the second definition because their nonmedical consumption was not below 120 percent of the poverty line.

Figure 9 shows – for the 51 countries for which the WHS has out-of-pocket spending data – the average<sup>11</sup> breakdown across the various groups in Figure 1 and in Table 1 for different poverty lines, along with the simple financial protection index. The first two panels examine the effects of changing the definition of catastrophic spending: the first panel, catastrophic payments are defined as payments that absorb more than 40 percent of discretionary consumption; in the second panel, they are defined as payments that leave nonmedical consumption below 120 percent of the poverty line. The second two panels examine the effects of using the individual spending data rather than the household spending data: when the individual questionnaire is used as the source of the spending data, we are able to separate the no-spenders into (a) those who received care but did not pay and (b) those who did not spend because they did not receive care. In the computation of the financial protection index in the individual data, group (b) is excluded from the calculation.

11 The numbers are mean shares across the 51 countries with no weighting for population size.
# _5.5 Summary of cultural priors_

Trust has been found to be a societal characteristic that persists across generations. Vestiges of traditional society and socialism of the labor-management type could have provided foundations for particularized trust. There could have been cynicism because of the inability of the socialist system to provide declared equality. Past regional war and internal tensions could have affected trust. The retained political dominance of the successor of the socialist party and also distributive aspects of transition could have affected trust, as could have the financial crisis, but the lack of trust would be expected to be more in institutions and government rather than in other individuals.

# **6. The data**

The trust experiments were conducted with World Bank support in the capital city of Montenegro, Podgorica, as part of a broader study of development constraints and opportunities in Montenegro (World Bank 2012). The first round of experiments was in May 2012 and a second round in September 2012.

# _6.1 Group-size effects_

The participants were drawn from the State University of Montenegro and the private Mediterranean University. The state university has a considerably larger student body.<sup>22</sup> Group-size effects might lead students at the private university to have a more pronounced sense of mutual identity and therefore to exhibit greater particularized trust among themselves than students at the state university.

> 22 The State University of Montenegro had 18,390 students at undergraduate level and 1,653 students at postgraduate and doctoral levels – a total of 20,043 students. The Faculty of Economics at the state university had in recent years enrolled 1,000 to 1,200 new students per year. The private Mediterranean University had a smaller student body of 1,450 undergraduate students and 200 postgraduate and doctoral students - the total of 1,650 students. In the Faculty for Business Studies there were 450 students in all programs, with 100 new students (1st year of study) and 138 new students in the previous year. The data are from Monstat – the statistical office of Montenegro.

18
differences in observed pro-forest behaviors between CFP forests and non-CFP forests due to different rules, norms or habits created by Community Forestry Programme forest participation.<sup>8</sup> The dependent variables that directly affect community forests (ANYTREE_CF, MEETING_DUM, MONITORING, NUMTREES_CF) have timeframes during the previous month and NUMTREES_FARM focuses on the previous 5 years. As all Community Forestry Programme groups were formed well before one month ago, even going back to 1995, but in only one case less than 5 years ago, it is impossible to have reverse causality with respect to the first 4 variables and very unlikely with regard to NUMTREES_FARM. That BIOGAS_DUM has very important private good elements makes such endogeneity or other confounding factors highly unlikely. Omitted independent variables are, of course, always possible.

In our models of the six forestry behaviors we also include a dummy variable for whether the respondent is a CFP management committee member (CF_OFFICER), because officers who are in effect group leaders are likely to work harder at and adjust more quickly to forest collective action than other members. The second variable included in the forest behavior models, but not in the contribution and belief models, is MONITOR_PAY, which is a dummy variable for whether the respondent received pay from the forest user group for monitoring and patrolling in the forest. This variable indicates whether the respondent is essentially a part-time forest user group employee, which could affect participation in community forest activities. For example, those receiving pay for monitoring may spend more time monitoring than those who are not paid forest guards. All those receiving pay are CFP members.

In terms of econometric methods, we estimate OLS models of CONTRIBUTION1 and BELIEF1 that first include only participant characteristics and then add household characteristics

> 8 We tested whether Community Forestry Programme experience might influence experimental cooperative behavior or beliefs, but in no models was CF_MEM significantly correlated with CONTRIBUTION1 or BELIEF1. Including CF_MEM in models did not substantively change coefficient estimates of other variables.

15
15. Jacoby, Hanan G., 2000, “Access to Markets and the Bene fi ts of Rural Roads,” Economic Journal, Vol.110, p.713-737.

16. Johnson, D.G, 2000, “Population, Food and Knowledge", American Economic Review, Vol. 90(1), p.1-14.

17. Key, N., E. Sadoulet and A. de Janvry, 2000, " Transaction Costs and Agricultural Supply Response," American Journal of Agricultural Economics, Vol. 82(2), p.245259.

18. Kherallah, M.C., E. Delgado, E. Gabre-Medhin, N. Minot and M. Johnson, 2000, “Agricultural Market Reforms in Sub-Saharan Africa: A Synthesis of Research Findings," mimeo, IFPRI, Washington DC.

19. Lanjouw, Peter and Martin Ravallion, 1999, “Bene fi t Incidence, Public Spending Reforms, and the Timing of Program Capture,” The World Bank Economic Review, Vol. 13, p. 257-73.

20. McCallum, J., 1995, “National Borders Matter: Canada-US Regional Trade Patterns”, American Economic Review, Vol. 85, 615-623.

21. Rivers, D., and Q. H. Voung (1988), “Limited Information Estimators and Exogeneity Tests for Simultaneous Probit Models,” Journal of Econometrics, vol. 39, no. 3, 347-66.

22. Smith, R. and R. Blundell, 1986, “An Exogeneity Test for a Simultaneous Equation Tobit Model with an Application to Labor Supply," Econometrica, Vol. 54, p.679-685.

23. von Braun, J, and E. Kennedy,1994, Agricultural Commercialization, Economic Development and Nutrition, The Johns Hopkins Press, Baltimore.

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