rafmacalaba/datause-encoder-data huggingface dataset:
Table 1. Impacts of PEP Program on Electoral Outcomes

||Election|Sha|re of Votes|for|Electoral|
|---|---|---|---|---|---|
||Turnout<br>(1)|Left<br>(2)|Center<br>(3)|Right<br>(4)|Competition<br>(5)|
|_Panel A. Mayoral Election - Di_<br>|_screte Treat_|_ment Varia_|_ble_|||
|I(PEPm)_×_I(Post2018)t|-0.006|-0.008|0.015|-0.013|0.007|
||(0.003)|(0.019)|(0.025)|(0.020)|(0.014)|
|q-values|[0.653]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.853|0.450|0.363|0.422|0.386|
|Observations|6,174|6,174|6,174|6,174|5,969|
|_Panel B. Mayoral Election - Co_|_ntinuous Tr_|_eatment V_|_ariable_|||
|PEPm_×_I(Post2018)t|-0.004|0.001|0.011|-0.012|0.010|
||(0.002)|(0.007)|(0.009)|(0.008)|(0.006)|
|q-values|[0.283]|[0.490]|[0.357]|[0.283]|[0.283]|
|R-squared|0.853|0.450|0.363|0.422|0.386|
|Observations|6,174|6,174|6,174|6,174|5,969|
|_Panel C. Presidential Election -_|_Discrete Tr_|_eatment V_|_ariable_|||
|I(PEPm)_×_I(Post2018)t|0.002|-0.009|0.004|-0.001|-0.004|
||(0.004)|(0.007)|(0.008)|(0.007)|(0.013)|
|q-values|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.877|0.898|0.845|0.866|0.682|
|Observations|6,561|6,561|6,561|6,561|6,549|
|_Panel D. Presidential Election_|_- Continuou_|_s Treatmen_|_t Variable_|||
|PEPm_×_I(Post2018)t|-0.019|-0.038|0.008|0.022|0.050|
||(0.015)|(0.016)|(0.016)|(0.021)|(0.026)|
|q-values|[0.250]|[0.105]|[0.500]|[0.280]|[0.129]|
|R-squared|0.877|0.898|0.845|0.866|0.682|
|Observations|6,561|6,561|6,561|6,561|6,549|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Department FE_×_Year|Yes|Yes|Yes|Yes|Yes|
|Municipal controls_×_Year|Yes|Yes|Yes|Yes|Yes|

_Notes:_ This table presents the results of our main specification in equation 1. I(PEPm) is a discrete variable that takes the value of one if the municipality had positive take-up rates and PEPm is the standardized value of the ratio of PEP migrants to population. Electoral competition was estimated following Chac ́on et al. (2006):1 - (% winning candidate - % second-place candidate). The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

26
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78. Dijkstra, E. W. A note on two problems in connexion with graphs. _Numer. Math._ **1** , 269–271 (1959).

79. Angel, S. & Blei, A. M. The productivity of American cities: How densification, relocation, and greater mobility sustain the productive advantage of larger U.S. metropolitan labor markets. _Cities_ **51** , 36–51 (2016).

80. US Department of Commerce, N. Turn Around Don’t Drown®. https://www.weather.gov/safety/flood-turn-around-dont-drown.

81. Gao, J., Barzel, B. & Barabási, A.-L. Universal resilience patterns in complex networks. _Nature_ **530** , 307–312 (2016).

82. Peponis, J., Bafna, S. & Zhang, Z. The Connectivity of Streets: Reach and Directional Distance. _Environ Plann B Plann Des_ **35** , 881–901 (2008).

83. Jing, W., Xu, X. & Pu, Y. Route Redundancy-Based Network Topology Measure of Metro Networks. _Journal of Advanced Transportation_ **2019** , e4576961 (2019).

84. Bruneau, M. _et al._ A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities. _Earthquake Spectra_ **19** , 733–752 (2003).

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# **Data Availability**

GHS-SMOD global human settlement layer is available online for download by tiles or as a single file.

- Download by tile: https://ghsl.jrc.ec.europa.eu/download.php?ds=smod

- Download as single file: https://cidportal.jrc.ec.europa.eu/ftp/jrcopendata/GHSL/GHS_SMOD_POP_GLOBE_R2019A/GHS_SMOD_POP2015_GLOBE_R2019 A_54009_1K/V2-0/GHS_SMOD_POP2015_GLOBE_R2019A_54009_1K_V2_0.zip

GHS-POP global population multitemporal grid raster data is available online for download by tiles or as a single file.

- Download by tile: https://ghsl.jrc.ec.europa.eu/download.php?ds=pop

- Download as a single file: https://cidportal.jrc.ec.europa.eu/ftp/jrcopendata/GHSL/GHS_POP_MT_GLOBE_R2019A/GHS_POP_E2015_GLOBE_R2019A_5400 9_250/V1-0/GHS_POP_E2015_GLOBE_R2019A_54009_250_V1_0.zip

# **Code Availability**

The Python code used to complete the analysis and produce the figures in this study will be available in the following online repository [https://github.com/jesuslovesyiyi/global-road-network-resilience-to-floods].

26
**Table 16. Child development production function for 4 year old cohort (early beneficiaries)**

|||Total months of<br>N Pt|Enrolment between 20|09 and 2013 in<br>Rlii||
|---|---|---|---|---|---|
||Project playgroup|on rojec<br>playgroup|Kindergarten|egous<br>playgroups|Primary school|
|**EDI**||||||
|Physical Health and Well-being|0.011*|0.011|0.004|0.005|0.009*|
||(0.006)|(0.008)|(0.005)|(0.009)|(0.005)|
|Social Competence|0.006|0.011|0.001|-0.002|0.016***|
||(0.006)|(0.007)|(0.005)|(0.013)|(0.005)|
|Emotional Maturity|-0.000|0.006|-0.001|-0.016*|0.011*|
||(0.006)|(0.007)|(0.005)|(0.009)|(0.006)|
|Language and Cognitive Development|0.006**|0.002|0.006**|0.007**|0.024***|
||(0.003)|(0.004)|(0.002)|(0.003)|(0.004)|
|Communication and General Knowledge|0.004|0.011|0.014**|0.031***|0.022***|
||(0.007)|(0.010)|(0.006)|(0.009)|(0.006)|
|**SDQ**||||||
|Total Difficulties|0.018***|0.003|0.008*|0.010|-0.003|
||(0.006)|(0.007)|(0.005)|(0.009)|(0.006)|
|Emotional Problems|0.011|0.012|0.007|0.017**|0.001|
||(0.007)|(0.008)|(0.005)|(0.008)|(0.006)|
|Social Conduct|0.015**|-0.004|0.006|-0.001|0.001|
||(0.007)|(0.007)|(0.005)|(0.008)|(0.006)|
|Hyper activity|0.008|0.005|0.007|0.005|-0.003|
||(0.006)|(0.008)|(0.005)|(0.009)|(0.005)|
|Peer problems|0.008|-0.006|0.001|0.003|-0.009|
||(0.007)|(0.007)|(0.005)|(0.008)|(0.006)|
|Pro social behavior|-0.003|0.006|0.002|0.013*|-0.001|
||(0.006)|(0.008)|(0.005)|(0.008)|(0.005)|

Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors are in the parentheses. Each row is the result of a separate regression. The dependent variables are child development outcomes as measured by the EDI and the SDQ. Key variables of interest are months of enrollment in each type of service. Each regression controls for child and household characteristics as specified in the text.

52
- c) The third case, diffuse seasonality, generates intermediate results. The bias is substantial in short samples (131⁄2 percent and 8 percent respectively using five and 10 years data) and the regression _F_ statistic does a poor job in confirming the presence of seasonality.

The third set of results (those for diffuse seasonality) are in some respects the most disturbing. The results in the no seasonality case suggest discarding the dummy variable estimates when the estimates fail to reject the hypothesis of no seasonality. Yet that rule would also lead to an estimate of zero seasonality in cases of diffuse seasonality. Finally, note that the _R_<sup>2</sup> statistics in short samples tend to attribute much more explanatory power to seasonality than it actually has, as becomes apparent when the sample size increases. The seemingly high degree of explanation obtained in short samples is entirely spurious in the “no seasonality” experiments and largely so in the other two experiments.

The biases obtained using the MAD procedure are similar to those using the stochastic trend model (Table 2). They are slightly higher when there is no seasonality, slightly lower with clear seasonality, and virtually the same when seasonality is poorly defined. The notable difference is that the MAD estimates exaggerate the statistical significance of the results. For large samples, the _R_<sup>_2_</sup> statistics converge to the values obtained with the stochastic trend model, though they are systematically higher for shorter samples. Second, in the case of no seasonality, exactly 5 percent of experiments should reject the hypothesis of no relationship. Instead, absence of seasonality is rejected in around 8 percent of cases using the MAD procedure (compared with 5 percent using the stochastic trend model) (Tables 1 and 2, column 3). This indicates mild over‐sizing, arising from autocorrelation in the error terms generated by the moving average transformation.

Overall, three conclusions emerge. First, on model choice, the stochastic trend model is slightly preferred. It is more reliable in its statistical inference. It is also more parsimonious in its data use, which the analysis above has abstracted from.<sup>14</sup> Second, on the matter of whether there is

> 14 It remains that the MAD procedure uses up twelve monthly observations in estimating the trend. The estimates reported in Table 2 relay on 5,10,20 and 40 years of detrended data equivalent to 6, 11, 21

12
- _Economics and Statistics_ , _98_ (2)arXiv arXiv:1011.1669v3, 268–284. https://doi.org/10.1162/REST

- Johnson, H. B. (1967). The location of Christian missions in Africa. _Geographical Review_ , _57_ (2), 168–202. https://doi.org/10.2307/213158

- Kiszewski, A., Mellinger, A. D., Spielman, A., Malaney, P., Sachs, S. E., & Sachs, J. D. (2004). A global index representing the stability of malaria transmission. _American Journal of Tropical Medicine and Hygiene_ , _70_ (5), 486–498. https://doi.org/70/5/486[pii]

- Kudo, Y. (2017). Missionary influence on marriage practices: Evidence from the Livingstonia mission in Malawi. _Journal of African Economies_ , _26_ (3), 372–431.

- Mantovanelli, F. (2014). _Christian missions, HIV and sexual behavior in subSaharan Africa_ [Unpublished manuscript]. Retrieved September 12, 2017, from https://sites.google.com/site/fedmantovanelli/home/ research

- Masaki, T. (2018). The political economy of aid allocation in Africa: Evidence from Zambia. _African Studies Review_ , _61_ (1), 55–82.

- Meier zu Selhausen, F. (2014). Missionaries and female empowerment in colonial Uganda: New evidence from Protestant marriage registers, 1880–1945. _Economic History of Developing Regions_ , _29_ (1), 74–112. https://doi.org/10.1080/20780389.2014.927110

- Michalopoulos, S., Naghavi, A., & Prarolo, G. (2018). Trade and geography in the spread of Islam. _The Economic Journal_ , _128_ (616), 3210–3241.

- Michalopoulos, S., & Papaioannou, E. (2013). Pre-colonial ethnic institutions and contemporary African development. _Econometrica_ , _81_ (1), 113– 152. https://doi.org/10.3982/ECTA9613

- Murdock, G. P. (1959). _Africa: Its peoples and their culture history_ . New York, NY, McGraw-Hill.

- Nunn, N. (2008). The long-term effects of Africa’s slave trades. _The Quarterly Journal of Economics_ , _123_ (1), 139–176.

- Nunn, N. (2010). Religious conversion in colonial Africa. _American Economic Review: Papers & Proceedings_ , _100_ (2), 147–152. https://doi.org/10. 1257/aer.100.2.147

- Nunn, N. (2014). Gender and missionary influence in colonial Africa. In E. Akyeampong, R. H. Bates, N. Nunn, & J. A. Robinson (Eds.), _Africa’s development in historical perspective_ (pp. 489–512). Cambridge, England, Cambridge University Press.

44
**10**

> and amended the Law [Berman **1997].** In the **17 'h** and **18'h** centuries the Scandinavian countries formalized their own legal codes. The Scandinavian legal systems have remained relatively unaffected from the far reaching influences of the German and especially the French Civil Codes.

Napoleon directed the writing of the French Civil Code in 1804. He made it a priority to secure the adoption of the Code in France and all conquered territories, including Italy, Poland, the low countries, and the Habsburg Empire. Also, France extended her legal influence to parts of the Near East, Northern and Sub-Saharan Africa, Indochina, Oceania, French Guyana, and the French Caribbean islands during the colonial era. Furthermore, The French Civil Code was a major influence on the Portuguese and Spanish legal systems, which helped spread the French legal tradition to Central and South America.

The German Civil Code _(Birgerliches Gesetzbuch)_ was completed almost a century later in **1896.** The German Code exerted a big influence on Austria and Switzerland, as well as China (and hence Taiwan), Czechoslovakia, Greece, Hungary, Italy, and Yugoslavia. Also, the German Civil Code heavily influenced the Japanese Civil Code, which helped spread the German legal tradition to Korea. Unlike these Civil Law countries, the English legal system is common law, where the laws were primarily formed **by** judges trying to resolve particular cases.

> This paper takes national legal origin as an exogenous "endowment" since the English, French, and German systems were spread primarily through conquest and imperialism. It is critical to recognize, however, that exogeneity is not a sufficient condition for economically meaningful instrumental variables. It must also be the case that there are good reasons for believing that legal origin is closely connected to factors that directly affect the behavior of financial intermediaries. Here, we rely on LLSV **(1998).** They trace differences in legal origin through to differences in the legal rules covering secured
we use fixed effects to average out time-constant unobserved heterogeneity at the district level and use the available data from the census to control for time varying factors at the district level, there may still be a bias in the estimates due to time varying unobservables at the district level correlated with the implementation of the reform and fertility rates. In this section, we assess the likelihood that the estimates are biased by time varying unobservables. We follow the idea that selection from observables can be used to assess the potential bias from unobservables (Altonji et al., 2005; Bellows and Miguel, 2008; Nunn, 2011), i.e., how much stronger does selection on unobservables need to be relative to the selection on observables, to explain away the estimated impact.

We consider two regressions per outcome variable: one with a full set of control variables and the other with a restricted set of control variables. The assessment on the selection of unobservables follows from a comparison of the estimated betas across regressions for the same outcomes, but with different sets of control variables. We then calculate a ratio with the estimated β<sup>full</sup> 3 from the model with a full set of controls, relative to the difference between the estimated βs for the restricted and the full model, (β<sup>restrict</sup> 3 − β<sup>full</sup> 3 ), i.e., β<sup>full</sup> 3 /(β<sup>restrict</sup> 3 − β<sup>full</sup> 3 ). The smaller (bigger) the difference between β<sup>restrict</sup> 3 and β<sup>full</sup> 3 relative to β<sup>full</sup> 3 , the lower (higher) the effect of the selection-ofobservables on the estimates. Put differently, a large (small) ratio implies a weak (strong) selection-on-unobservables. The ratio tells us how strong the selectionon-unobservables has to be to explain away the estimated reform impact.

We use two restricted sets of variables: one that consists of controls for the demographic composition of the district and another that includes the controls for the religious composition of the district. The full set of variables consists of the two restricted sets together. Irrespective of the control sets, all regressions include district fixed-effects. The estimated ratios for the TFR in the full, rural and urban samples are reported in Table 14.

## [Insert Table 14 about here]

Across all comparisons of control sets, the selection on unobservables would need to be at least eleven times greater than the selection on observables to explain away the effect. These findings imply that the estimated impact of the implementation of the reform on fertility is unlikely to be driven by selection due to time varying unobservables.

# 5 Conclusions

We have studied the impact of a land reform on the fertility outcomes of households in rural Ethiopia using a natural experiment. Public policies and customs had created a situation where Ethiopian households could influence their usufruct rights to land via a demographic expansion of the family. In this paper we have evaluated the impact on fertility outcomes of the abolishment of these pronatal property rights. Matching aggregated census data with administrative 18
Financial assets<sup>4</sup> represent a sizable proportion of the aggregate nonhuman wealth of households—over 50 percent in Norway (Jantti and Sierminska, 2006) and over 40 percent in the US, even if we take the results of household surveys, which may disproportionately understate financial assets if compared to data from national balance sheets computed from national accounts sources (Davies et al. 2006). The share of financial assets in total wealth tends to increased with mean income. For Australia, Canada, Germany, Italy the Netherlands and Sweden, the figure obtained from household surveys is about 30 percent (Guiso et al. 2003, Jantti and Sierminska, 2006)<sup>5</sup> ; in China it is 22 percent (Li and Zhao, 2006), in Korea only 17 percent (Yoo, 2005) and in India about 4 percent (NSSO, 2005, Subramanian and Jayaraj, 2006).

Household financial liabilities also need to be considered for a rounded view; indeed, it would be very artificial to look only at the asset side when it comes to financial assets and liabilities, since both are endogenous and thought to be determined in large part by the same explanatory variables. In aggregate, household financial liabilities amount to as much as 30 percent of gross household financial assets in the US, where consumer and household finance is very well developed. Indeed, for all but about the top decile of the _income_ distribution in that country, borrowing exceeds gross financial asset holdings. In other words, _net_ financial assets for the lower 90 percent are negative. The largest category of debt is mortgage and home equity debt, reflecting the fact that a homeowner’s residence can represent valuable collateral. Installment credit (mainly related to the purchase of automobiles and other household durables) and credit card debt are also used in rich countries, but amount to smaller sums.

Borrowing possibilities for low-income households from formal intermediaries are more limited in less advanced economies. In these countries, middle class employee households may borrow unsecured, but often under arrangements whereby servicing charges are deducted from wage payments by the employer and remitted to the financial intermediary.

> 4 See Appendix 1 for a description of how financial assets are defined.

> 5 The figure for Japan has been highly volatile in view of the housing boom and bust of the 1980s and 1990s (Iwaisako, 2003).

8
_5_ correlation between federal budget transfers to regions and the volume of the regional budget transfers to local enterprises.

_Table 2. Inter-government Budget Arrangements in Russia and Regional Budget Expenditure on Subsidy Programs, 1992-95_

||**1992**|**1993**|**1994**|**1995**|
|---|---|---|---|---|
|Regional budget expenditure as % of<br>consolidated budget expenditure|34.02|46.07|42.90|43.66|
|Regional budget subsidies as % of total<br>regional expenditures|26.03|32.51|33.92|35.99|
|Overall regional governments' expenditure<br>subsidy programs, as % of GDP|s on 3.4|5.5|6.2|5.2|
|Federal government budget transfers to<br>regions, as % of GDP|1.75|2.70|3.84|1.82|
|Source:the World Bank|||||

Total subsidies amounted to 36% of the regional budget expenditures.<sup>4</sup> This is clearly an unsustainable level of subsidy financing. The remaining subsidies comprise the largest single source of the current crisis in regional finance. In addition, such a large degree of subsidization is highly distortive. By depressing prices in housing, agriculture, and transportation, regional governments depress corresponding market activities, which leads to large efficiency losses.

Housing, agriculture and public transportation are the sectors which received the largest amounts of subsidies explicitly fumded by regional governments from their budgets (Table 3). Housing subsidies amounted to about 4% of GDP in 1994 and to more than 3.1% of GDP in 1995. They constituted on average 60-65% of the overall budgeted subsidies in 1993-95. The share of agriculture amounted to 15% of the total, and the share of transportation was 7-8%. While steadily increasing since 1994, cost recovery in housing is still on average at the level of 30-35% of actual costs, while average cost recovery in public transportation seems to be less than 50%.

4 This in fact underestimates the actual volume of regional subsidizes because it does not include budget arrears to municipal utilities, housing maintenance organizations, as well as to other providers of subsidized goods. For instance, by the end of 1994 six cities participating in the World Bank Housing Divestiture Project accumulated in their budget arrears to local utilities and housing maintenance enterprises on average about 50% of their funded expenditures on housing subsidies (World Bank, 1996b). The case study of North Caucasus Railway reveals that the actual disbursed subsidies amounted to only 11 % of the actual losses of the railway from commuter transportation, which were supposed to be fully financed by the regional administrations.
# **Figure 14**

<!-- Start of picture text --> (a) (b)<br><!-- End of picture text --> <!-- Start of picture text --> Bolivia<br>Number of destroyed illegal laboratories<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> <!-- Start of picture text --> Source: UNODC and FELCN.<br><!-- End of picture text --> <!-- Start of picture text --> Colombia<br>Number of destroyed illegal laboratories<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> <!-- Start of picture text --> Source: UN ODC and DNE.<br><!-- End of picture text -->

# **Figure 15**

# **(a)                                                             (b)**

<!-- Start of picture text --> Bolivia<br>Coca base and cocaine seizures (kgs.)<br>12000<br>10000<br>8000<br>6000<br>4000<br>2000<br>0<br>Coca base seizures Cocaine seizures<br>1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> <mark>Source: UNO</mark> DC <!-- Start of picture text --> Colombia<br>Coca base and cocaine seizures (kgs.)<br>200000<br>180000<br>160000<br>140000<br>120000<br>100000<br>80000<br>60000<br>40000<br>20000<br>0<br>Coca base seizures Cocaine seizures<br>1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> <mark>Source: UNO</mark> DC 59