rafmacalaba/datause-encoder-data huggingface dataset:
|DUMfootwear||(0.018)**<br>0.157<br>(0.025)**||(0.018)**<br>0.151<br>(0.024)**||(0.018)**<br>0.150<br>(0.024)**||(0.019)**<br>0.118<br>(0.021)**|||
|---|---|---|---|---|---|---|---|---|---|---|
|DUMmachinery||-0.162<br>(0.004)**||-0.161<br>(0.004)**||-0.160<br>(0.004)**||-0.101<br>(0.004)**|||
|DUMwood||0.058||0.055||0.049||0.016|||
|||(0.014)**||(0.014)**||(0.014)**||(0.009)|||
|DUMmineral||-0.089||-0.086||-0.088||-0.051|||
|||(0.013)**||(0.014)**||(0.012)**||(0.008)**|||
|DUMclothing||0.364||0.370||0.373||0.305|||
|||(0.013)**||(0.013)**||(0.013)**||(0.014)**|||
|agripref<br>textilepref<br>footpref<br>machinerypref<br>woodpref||||||||||0.050<br>(0.002)**<br>0.002<br>(0.000)**<br>0.062<br>(0.008)**<br>0.071<br>(0.003)**<br>0.040<br>(0.004)**|
|mineralpref||||||||||0.016<br>(0.010)|
|clothpref||||||||||0.069<br>(0.003)**|
|Observations|23631|23631|23631|23631|23631|23631|23631|23631|23631|23631|

Marginal effects are presented in the table. Standard errors in parentheses, *significant at 5%, ** significant at 1% In terms of our control variables it can be seen that the coefficient of the distance variable is significant and negative in all equations except when country specific dummies are included (equation (4) and (5)) when it becomes insignificant and positive. The variable Preferences, which measures the hypothetical value of preferences, is significant and has a positive sign implying that the higher the value of the preferences the more likely that traders request preferential treatment. However, the amount of duty reduction (the difference between preferential and MFN tariffs) does not have a significant effect on the amount of utilization. In other words, while the value of preferences influence whether preferences are requested or not, once this decision has been made, the duty reduction does not influence any more how much preferential trade is requested.

The variable measuring the economic freedom of the ACP countries is highly correlated with their GDP, income per capita, and distance (the correlation coefficients with income, income per capita, and distance are -0.19, -0.24, and -0.56, respectively). In the first and third column the freedom index is significant and negative when distance is not included in the regression (selection equation). In the second column, when income per capita was included instead of GDP, the freedom index variable was significant and positive, which might be the result of high correlation between income per capita and the economic freedom of the country. Nevertheless 24
The second contribution is methodological: we are not aware of other studies applying causal machine learning to open the black box of climate-induced migration. Specifically, we propose a systematic, rather than _ad hoc_ , approach to shed light on the irreducible heterogeneity of climateinduced mobility outcomes. Our approach improves upon traditional _ad hoc_ subsample analysis (or use of interaction terms) typically employed in the specialized literature to uncover underlying heterogeneity because it limits researchers' discretion when selecting the relevant dimensions and thresholds of heterogeneity. Given such heterogeneity, traditional econometric tools for the estimation of average treatment effects are not fit for purpose, at least not without strong _a priori_ assumptions by the analyst. The tree-based machine learning techniques we employ (Athey & Imbens, 2016; Wager & Athey, 2018) are, instead, agnostic regarding functional forms, easily capture nonlinearities and interactions among key household characteristics, and identify the main heterogeneity drivers and thresholds in a data-driven manner. This enables us to estimate policy relevant group-average treatment effects and show how average impacts can be misleading, since they conceal substantial heterogeneity, with opposite effects for different groups. <mark>Importantly, given the multidimensionality of the migration phenomenon, this approach presupposes that the researcher has access to longitudinal multi-topic and multi-purpose household survey data, which collect information about the most important mediation channels and transmission mechanisms, including agricultural variables.</mark> The last contribution is conceptual: we frame and explore the climate-migration nexus through the lens of the poverty traps approach. Specifically, we provide an extension of the asset-based and geographic poverty traps literature (Carter & Barrett, 2006; Barret & Carter, 2013; Jalan & Ravallion, 2002) to the immobility framework, coupled with a perspective of the migration phenomenon as a household-level risk management strategy inspired by the NELM (Stark & Bloom, 1985). Such a framework serves as the domain knowledge basis for our empirical approach aimed at detecting potential regime shifts due to the existence of "climate immobility thresholds", below which households fall into immobility traps. If trapped populations are concentrated in areas plagued by recurrent shocks, this may, in turn, give rise to geographic poverty traps, which have been assessed as the most likely form of poverty trap by leading scholars and one for which policies promoting migration might prove particularly beneficial (Kraay & McKenzie, 2014).

5
20 Ferraro, P. J., & Price, M. K. (2013). Using nonpecuniary strategies to influence behavior: evidence from a large-scale field experiment. _Review of Economics and Statistics, 95_ (1), 64-73.

Foster, V. (2005). Ten Years of Water Service Reform in Latin America: Toward an Anglo-French Model. Water Supply and Sanitation Sector Board Discussion Paper Series No. 3, World Bank.

<mark>Galiani, S., Gertler, P., Orsola-Vidal, A. (2012). Promoting Handwashing Behavior in Peru. World Bank Policy Research Working Paper No. 6257.</mark> Harding, M., & Hsiaw, A. (2014). Goal Setting and Energy Conservation. _Journal of Economic Behavior & Organization, 107_ (A), 209-227 <mark>La Nacion (2014). Concejo Municipal de Barva suspende permisos de construcción ante escasez de agua. Retrieved from http://www.nacion.com/m/nacional/servicios-publicos/Concejo-Municipal-Barvasuspende-construccion_0_1417058546.html</mark> <mark>Olmstead, S. M., Hanemann, W. M., & Stavins, R.N. (2007). Water Demand under Alternative Price Structures. NBER Working Paper No. 13573</mark> Municipalidad de Belén (2010). Plan Maestro de los Sistemas de Abastecimiento de Agua Potable de Belén de Heredia.

Rogers, T., Milkman, K., John, L., & Norton, M. I. (2013). Making the Best Laid Plans Better: How PlanMaking Increases Follow-Through.

<mark>Scanzerla, T. (2014). Sao Paulo is Running Out of Water, But Authorities Say There’s No Need for Rationing. Retrieved from http://globalvoicesonline.org/2014/10/17/sao-paulo-is-running-out-of-waterbut-authorities-say-theres-no-need-for-rationing/</mark> Solano, H. (2014). Concejo Municipal de Barva suspende permisos de construcción ante escasez de agua. Retrieved from http://www.nacion.com/m/nacional/servicios-publicos/Concejo-Municipal-Barvasuspende-construccion_0_1417058546.html <mark>Times News Network. (2013). Water Board to Launch ‘Save Water’ Campaign. Retrieved from http://timesofindia.indiatimes.com/city/hyderabad/Water-Board-to-launch-save-watercampaign/articleshow/18839731.cms</mark> U.N. (2014). Progress on Drinking Water and Sanitation: 2014 Update. Retrieved from http://www.unwater.org/fileadmin/user_upload/unwater_new/docs/jmp.2014_eng.pdf U.N. Department of Economic and Social Affairs (2014). World Urbanization Prospects: 2014 Revision. Retrieved from http://esa.un.org/unpd/wup/
socio-economic status, hence nutritional status, does not signi fi cantly affect the age at menarche. While this issue is open for discussion, empirical evidence from the Matlab data seems to con fi rm the absence of such correlation. We also control for various measures of socio-economic status, without this affecting both fi rst and second stages. The results of the IV estimation are presented in Column (5) and (6) of Table 7. Note that the second stage estimates of the coefficient for the age at marriage are very close to the estimates obtained from the OLS speci fi cation and that the coefficient is still signi fi cant at the 5 percent level. We interpret this as evidence in support of the existence of credit constraints. In other words, we believe fi rmly that a family may well delay a girl’s marriage in order to build up and to consolidate the equity that is required to pay her dowry at the time of marriage.

# 5 Conclusion

This paper has argued that consanguinity is a response to a marriage market failure in developing countries. The starting point of our analysis is the recognition that dowries exist across many societies, and that consanguinity is also pervasive across many parts of the world. We propose a theoretical model of a marriage market to reconcile the existence of these two facts. We argue that these two social practices together address an agency problem between spouses’ families and then provide empirical evidence that corroborates the central predictions of the model. By focusing on the economic underpinnings of consanguineous marriage, we help explain the seeming puzzle of why consanguineous marriage continues to take place in modern times in developing countries, despite the greater knowledge (from the medical and biological sciences) that such marriages may lead to a greater likelihood of congenital birth defects. By providing a rationale for consanguinity that does not rely on an exogenous preference argument, we encourage a reappraisal of the welfare implications of regulating marriage markets in such contexts.

women) appear to be ‘child’ marriages, i.e. cases of marriage where the age at marriage was less than 11.

18
to consider cyclical episodes like these is the core advantage of NO2 versus lights. Moreover, in each case there is at least one country in a data quality ranking for which lights have no explanatory power, and thus to these NO2 is uniquely suited.

# **6.1 Instances Where GDP Is Particularly Suspect**

**Economic Crises.and Hyperinflation** First, we consider the cases of the Rep ́ublica Bolivariana de Venezuela and Zimbabwe, which are at the top of the list in grades C and D respectively. A shared feature of these countries is that they both experienced periods of significant economic distress, hyperinflation, and social unrest.

Figure 8 depicts the annual percentage change in reported GDP versus the composite estimate for these countries. The Rep ́ublica Bolivariana de Venezuela’s data indicates tremendous losses in GDP during a period of crisis from 2016-2019, culminating with a remarkable -43.08% change in GDP in 2019. While we confirm a large downturn in that year, we find that this figure is a significant overstatement of the true decline, which was likely closer to -27.71%. To understand the source of this error, it is worth noting that the while the data was compiled and published by the IMF, the Venezuelan government has refused to cooperate with IMF authorities in the calculation of GDP since 2004.<sup>19</sup> The fact that 2019 followed several years of large declines raises the question of whether several years of underestimating the extent of the recession culminated in one year when authorities were forced to reconcile previous errors.

Zimbabwe, on the other hand, reported a remarkably high growth rate of 46.70% in 2009, which is especially surprising given the effects of the 2009 Financial Crisis elsewhere in the same year. We find that the true growth in output was closer to 19.89%. We also considered a grade C normalization for Zimbabwe, which subsequently weights GDP more (*). Even in this case, our estimated composite would still only be 29.81%. To explain this, we note that over 60% of Zimbabwe’s economy may be a shadow economy (Schneider and Enste, 2013). Our finding suggests that this segment of the economy may have responded more elastically to the 2009 global downturn than the measurable, formal economy.

**Conflict** . Figure 9 considers the Syrian Arab Republic and the Republic of Yemen, which are at or near the top of the list in Table 7, in Grades C and E respectively, and both endured civil war during this period. Conflict has direct consequences for macroeconomic performance (H ̈onig, 2021) and is also known to cause changes in NO2 (Lelieveld et al., 2015). Both in the Syrian Arab Republic over 2012-2013, and the Republic of Yemen in 2015, we find that actual output was likely higher than reported in GDP. This may be due to informal economies, characterized by unincorporated businesses that can not be counted in GDP, and which are known to arise during conflict (Peksen and Early, 2020; Galdo et al., 2021). It is especially

> 19The IMF’s estimate differs somewhat from the Penn World Tables, at -35%, closer to our estimate.

28
# **Table 4: Panel A – Nested Logit Regressions**

Log Odds ratio between Renting and Owning

|**Country**|**lage**|**female**|**hhsize**|**lnincome**|**hhnodep**|**hhdep**|**house**|**secedu**|**teredu**|**fulltime**|
|---|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_|||||||||||
|Czech Republic|-0.20 *|0.16 **|-0.09|0.09|-0.48 ***|-0.05|-2.89 ***||0.94|-0.21|
|Estonia|-3.03 **|0.22|-0.63 ***|0.97 ***|-1.71 ***|0.15|0.53 **||8.63 ***|0.59|
|Hungary|-2.42 *|-0.16|-0.05|0.48|-0.38|0.94|-1.66 ***|14.20 ***|8.27 **|-0.16|
|Latvia|-0.57|-0.16|0.11|0.39|-0.33|-0.41 *|-0.84|7.73|1.84|-0.23|
|Lithuania|-14.15|-0.86|-3.89|1.63|-0.48|5.19|0.24||-3.45|30.88|
|Poland|5.52 ***|0.66|1.49 ***|-0.65|-0.22|-3.51 ***|-0.97 *|25.55 ***|9.50 *|-3.28 **|
|Slovak Republic|-0.31|-0.22 *|0.14|-0.05|-0.30|-0.01|1.12|7.97 ***|11.62 ***|0.48|
|_EU-15 and Cyprus_|||||||||||
|Austria|-0.79|-0.06|-0.25 ***|0.33|-0.15|0.99 ***|-2.85 ***|16.25 ***|11.00 ***|1.16 **|
|Belgium|0.80|0.22 **|0.01|-0.14|-0.29 *|-0.29|-1.99 ***|15.15 ***|14.43 ***|-0.83 ***|
|Cyprus|-4.97|-0.16|-0.43 **|1.47|-0.10|0.77|-0.98 ***|26.76 ***|17.00 ***|0.53|
|Denmark|-1.43|0.43 ***|-0.27 ***|0.80|-0.35 **|0.30|-2.32 ***|8.69 ***|6.28 ***|-0.86 ***|
|Finland|5.12 ***|0.01|0.04|-1.83 ***|0.60 **|0.03|-1.68 ***|21.43 ***|18.16 ***|-0.70 ***|
|France|2.12 ***|0.13|-0.06|-0.55 ***|0.00|-0.41 **|-1.81 ***|21.59 ***|18.34 ***|-0.36|
|Greece|0.15|-0.44|-0.31|0.42|-0.65|-0.63|-0.73|19.26|14.35|-1.15|
|Ireland|0.45|0.67 ***|0.17|0.21|0.06|-0.63|-2.59 ***|21.97 ***|25.05 ***|-1.74 ***|
|Italy|3.03 ***|0.15|0.24 **|-0.79 ***|-0.10|-0.84 **|0.02|11.92 ***|7.43 ***|-1.04 **|
|Luxembourg|1.32 ***|-0.06|0.05|-0.44 ***|0.19|0.05|-2.13 ***|14.90 ***|6.10 *|0.28|
|Netherlands|0.70 **|0.03|0.00|-0.06|-0.20 *|-0.22|-1.31 ***|17.05 ***|20.41 ***|-1.06 ***|
|Portugal|1.47 ***|0.36 **|0.16 *|-0.52 ***|-0.17|-1.16 ***|1.12 ***|13.16 ***|4.87|-0.90 *|
|Spain|-0.80|0.19 **|-0.04|0.14|-0.49 ***|-0.36|-0.46 ***|7.12 ***|8.85 ***|0.55 **|
|Sweden|1.44 ***|0.10|0.00|-0.49 **|-0.15|0.19|-2.64 ***|11.77 ***|11.63 ***|-0.45 ***|
|United Kingdom|-0.65 ***|0.43 ***|-0.04|0.36 ***|-0.43 ***|0.31 **|-1.70 ***||19.27 ***|-1.39 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

7
recorded while the event is ―active‖ (Combs et al. 1998; Jonkman and Kelman 2005). Most events in our sample killed at least one person, as the proportion of events with zero deaths was just 9.9 percent. Table 3 shows that the mean number killed was large, 119, with great variation: the standard deviation was more than 20 times as large. The frequency distribution of this variable shows that the large mean and standard deviation are due to a long right tail, as more than 90 percent of the values are under 100. One particular event, flooding from a 1991 Bangladesh cyclone that had a death toll of 138,000, has an especially large impact on the mean and standard deviation. As a robustness check, we estimate models that exclude events with unusually large death tolls.

As water bodies are not confined to national boundaries, some floods in the archive (less than 10 percent) are regional in scope. For these events, the reported number of deaths is the aggregate figure per event, with no available split between the countries affected. Because of this restriction, our sample excludes multi-country floods from all models.

The DFO reports the magnitude of a flood as the log of the product of three terms: area affected by the flood (in km<sup>2</sup> ) × flood duration (in days) × flood severity. Floods are divided into three severity classes depending on their estimated recurrence interval. Class 1 floods have a 10-20 year-long reported interval between similar events, class 1.5 have a 20100 year recurrence interval, and class 2 have a recurrence interval greater than 100 years. Flood magnitude varies much less than either the number of floods or the number of deaths, with the standard deviation being smaller than the mean.

# _3.2 Exposure: Population in flooded areas_

Each entry in the DFO‘s register of major flood events has an associated GIS polygon representing the area affected by that event. The DFO uses news and governmental sources 10
variation in eligibility to FA across more than 100,000 voting booths in both OLS polling station fixed effect regressions and in an instrumental variable setting to look at the effect of FA on voter turnout in the 2010 presidential election. OLS and 2SLS results indicate that, in addition to encouraging people to register to vote, the receipt of FA transfers has a positive effect in the probability of casting a vote (Table 4). An increase of one standard deviation in the proportion of FA beneficiaries at the voting booth (around 10%), results in an increase in turnout of .07 and .09 of a standard deviation respectively. This corresponds to nearly a 2 percentage point increase in the turnout rate or 600,000 additional votes in the 2010 elections.<sup>18</sup> We also disaggregate the effects of FA on turnout by gender (Table 4, Panel B). Consistent with the findings on the probability of being registered to vote, we find that the increase in turnout rates among FA beneficiaries is mostly explained by women voting more.

# **5.2 Political Support**

The FA program was originally conceived, designed and piloted in 2001 under the administration of the Conservative party. One year later, the independent candidate Alvaro Uribe was elected president for the period between 2002 and 2006. He then ran successfully for a second term and was president until 2010. FA became the government’s flagship anti-poverty program during his two terms, which led to a notable expansion until achieving almost national coverage by 2009.

The results discussed above indicate that FA beneficiaries signaled a greater intent to vote but actually were more likely to vote in the Presidential elections. Seeking to shed light on the possible influence of targeted transfers on voter’s choices, we examine whether FA fostered political support for the incumbent party candidate that implemented and expanded the program between 2002 and 2009.

Two variables are used to measure political support in each of the electoral rounds: the per-

> 18The total number of registered people to vote in the 2010 elections was 29,853,299, and of those, 13,296,924 voted in the 2010 runoff.

17
9 Vouchers were distributed in 39 out of the 45 villages and in each of these villages about a quarter of the households received at least one voucher. To shed a first light on the criteria used in practice to identify the beneficiaries, Table 1 divides the sample in three groups: 1) those the village determined non-eligible for the input vouchers; 2) those determined eligible, but who declined the vouchers; and 3) those determined eligible who also took up the voucher. The middle group contains a rather small number of households (18 households), so the statistics from this group need to be treated with caution.

To help organize the discussion, the factors posited in the literature to affect eligibility are grouped under different headings including 1) politically oriented factors such as being (or being associated with) local elite, being informed, feeling empowered; 2) economic factors such as efficiency (having high marginal productivity for input use) and equity (poverty); 3) social factors such as community homogeneity, trust, gender and ethnicity; 4) village characteristics to explore geographic targeting criteria and 5) program design factors such as cropping patterns, education levels, and ability to co-finance. Where appropriate (and available), information preceding the voucher uptake is used i.e. the value of the indicator from the second (2004) as opposed to from the third survey round (2009).

The table begins by examining the likelihood of eligibility among the local elites (and those closely associated with them). Much of the literature on decentralized targeting has derived conclusions about elite capture based on indirect evidence inferred from the estimated relation between within community inequality and targeting performance (Bardhan and Mookherjee, 2005, 2006; Galasso and Ravallion, 2005).<sup>13</sup> Here we explore more directly whether certain groups of people, likely to belong to or being associated with the local elites, are also more likely to receive the input voucher. In particular, we look at differences in

> 13 Another indirect way of inferring the role of elite capture followed by Araujo et al. (2008),  is to compare the link between community inequality and the adoption of projects which do and do not benefit the rich. Studying social investment fund projects in Ecuador, they find that more unequal communities are less likely to adopt projects that provide excludable goods to the poor.