rafmacalaba/datause-encoder-data huggingface dataset:
# Table 1. Main estimation Results.

|Dependent variable: annualsumofearthquake deaths|model 1|model 2|model3|model 4|model5|
|---|---|---|---|---|---|
|max_magnitude|0.0000222*|0.0000367**|0.0000234*|0.0000450**|0.0000374*|
||(0.0000115)|(0.0000177)|(0.0000124)|(0.0000210)|(0.0000225)|
|magnitude_popdensity|0.0209**|0.0193**|0.0209***|0.0221***|0.0123|
||(0.00854)|(0.00772)|(0.00809)|(0.00700)|(0.0151)|
|quake propensity|-0.00366***|||||
||(0.00103)|||||
|quake propensity in developing countries||-0.00304***<br>(0.00104)||||
|quake propensity in developed countries||-0.00885***<br>(0.00143)||||
|quake propensity in autocracies|||-0.00293*<br>(0.00165)|||
|quake propensity in democracies|||-0.00445***<br>(0.00153)|||
|quake propensity in autocracies||||0.00256||
|w. low institutionalization||||(0.00259)||
|quake propensity in autocracies||||-0.00277***||
|w. high institutionalization||||(0.00103)||
|quake propensity in young democracies||||-0.00457***<br>(0.00158)||
|quake propensity in old democracies||||-0.00813***<br>(0.00107)||
|quake propensity in corrupt countries|||||-0.00189<br>(0.00262)|
|quake propensity in non-corrupt countries|||||-0.00639***<br>(0.00210)|
|ln gdppc|-0.513*|-0.331|-0.521*|-0.649**|-0.780***|
||(0.267)|(0.268)|(0.268)|(0.325)|(0.285)|
|democracy|-1.558***|-1.512**|-1.348*|0.531|-1.916**|
||(0.595)|(0.616)|(0.774)|(0.930)|(0.932)|
|ln pop|0.657***|0.675***|0.625***|0.994***|0.641***|
||(0.216)|(0.218)|(0.227)|(0.279)|(0.248)|
|lack of corruption|||||-0.177|
||||||(0.292)|
|Constant|-2.524|-4.245|-2.057|-9.444**|0.650|
||(3.957)|(4.278)|(4.109)|(4.141)|(4.777)|
|Observations|1696|1692|1696|1065|867|
|Period of study|1960-2005|1960-2005|1960-2005|1975-2005|1982-2005|

Notes: Standard errors clustered on countries. * significant at p<.1 ** at p<.05 *** at p<.01.

33
Table B7: Summary statistics by country (cont.)

||LSO|MRT|MWI|NER|SEN|TCD|TZA|UGA|
|---|---|---|---|---|---|---|---|---|
|Number of households|4,294|9,341|12,439|6,024|6,843|7,493|9,463|15,572|
|Survey years<br>|2017_/_18|2014|2016_/_17|2018_/_19|2018_/_19|2018_/_19|2017_−_1|9 2016_/_17|
|Lowest geographic unit avail-|HH|HH|HH|PSU|PSU|PSU|HH|Parish|
|able<br>|||||||||
|**DOU urban classifcation:**|||||||||
|Urban center|0.11|0.29|0.11|0.17|0.44|0.18|0.17|0.15|
|Urban cluster|0.17|0.09|0.22|0.09|0.13|0.09|0.28|0.39|
|Rural|0.71|0.62|0.67|0.74|0.43|0.73|0.56|0.46|
|**DB urban classifcation:**|||||||||
|Core|0.04|0.33|0.07|0.15|0.39|0.20|0.22|0.06|
|Suburb|0.21|0.04|0.12|0.09|0.12|0.10|0.13|0.11|
|Town|0.11|0.09|0.08|0.17|0.16|0.10|0.27|0.32|
|Other rural|0.75|0.63|0.82|0.76|0.49|0.70|0.65|0.82|
|**Consumption**<br>**(per**<br>**day**|||||||||
|**USD in 2017 PPP terms):**|||||||||
|Real per capita consumption|4.64|5.71|2.40|2.66|5.57|3.77|3.39|3.52|
|expenditures|||||||||
|Nominal per capita consump-|4.62|5.74|2.44|2.79|5.82|3.90|3.50|3.54|
|tion expenditures|||||||||
|**Demographic:**|||||||||
|Household size|5.27|7.47|5.19|7.58|12.69|6.88|6.18|5.89|
|Age of household head|51.07|49.92|43.12|46.01|53.44|43.96|47.38|43.43|
|Household head is male|0.63|0.74|0.75|0.87|0.76|0.81|0.76|0.74|
|**Marital status (of house-**|||||||||
|**hold head):**|||||||||
|Married|0.58|0.83|0.78|0.93|0.86|0.86|0.75|0.79|
|Never married|0.06|0.01|0.01|0.00|0.02|0.01|0.03|0.03|
|Living together|0.00|0.00|0.00|0.00|0.00|0.00|0.04|0.00|
|Divorced/Separated|0.05|0.06|0.11|0.02|0.02|0.05|0.07|0.07|
|Widowed|0.31|0.09|0.10|0.05|0.11|0.08|0.11|0.10|
|**Education (of household**|||||||||
|**head):**|||||||||
|No education|0.00|0.75|0.16|0.68|0.55|0.52|0.20|0.13|
|Primary incomplete|0.48|0.09|0.55|0.17|0.17|0.21|0.14|0.40|
|Primary complete|0.16|0.02|0.06|0.01|0.05|0.03|0.52|0.18|
|Secondary incomplete|0.19|0.08|0.12|0.10|0.15|0.18|0.10|0.19|
|Secondary complete|0.08|0.02|0.07|0.00|0.01|0.01|0.00|0.02|
|Post secondary but not uni-|0.03|0.00|0.02|0.01|0.01|0.01|0.02|0.05|
|versity|||||||||
|University incomplete and<br>complete|0.05|0.04|0.01|0.02|0.06|0.05|0.02|0.03|
|Can read and write|0.79|0.61|0.72|0.36|0.47|0.32|0.76|0.72|
|**Employment (of house-**|||||||||
|**hold head):**|||||||||
|Not in labor force|0.26|0.20|0.48|0.02|0.17|0.02|0.29|0.08|
|Unemployed|0.04|0.17|0.06|0.00|0.00|0.00|0.09|0.01|
|Employed in Agriculture|0.24|0.19|0.06|0.66|0.31|0.69|0.36|0.54|
|Employed in Industry|0.20|0.07|0.07|0.07|0.12|0.06|0.05|0.09|
|Employed in Services|0.26|0.37|0.32|0.25|0.40|0.23|0.21|0.29|
|**Access to basic services**|||||||||
|Improved water|0.86|0.61|0.87|0.62|0.85|0.66|0.71|0.77|
|Improved sanitation facility|0.45|0.51|0.52|0.15|0.63|0.13|0.28|0.22|
|Access to electricity|0.41|0.46|0.93|0.21|0.74|0.10|0.56|0.39|

Source: International Urban Poverty Database. Note: Real per capita consumption expenditures are deflated using a spatial deflator calculated based on the GHSPOP 1km dataset and the DOU method.

65
|Table 4: Efect|s of indoct|rination|and ”critic|al citizenship”|
|---|---|---|---|---|
|Dependent vari-|Coef.|Std.|P>t|95% conf. int.|
|able - dif||Err.|||
|freedom|−0.03<sup>∗∗∗</sup>|0.007|0.000|-0.05<br>-0.02|
|cr<br>~~c~~it|0.09<sup>∗∗∗</sup>|0.025|0.001|0.04<br>0.14|
|F(6,157)|17.65||||
|Prob¿F|0.00||||
|R-squared|0.4||||
|Adj. R-squared|0.38||||
|No.<br>of observa-|164||||
|tions|||||

Note: *** - significant at less than 1% level. Method of estimation - OLS. Sample - countries surveyed by World Values Survey during all 3 waves.

where freedomi is an index of Freedom House, cr ~~c~~ iti is the ”critical citizenship” dummy defined above, and demogri is a set of demographical country-specific variables (average level of education, share of married population, share of males, average age).

The estimation results are presented in the Table 4. As one can see from the table, both freedom of the county and its being in the stage of ”critical citizenship” are highly statistically significant in explaining biases on responses on trust-related questions in the WVS surveys. The directions of the effects are what would be intuitively foreseen. In the Freedom House ranking a country has the higher score the less civil and political rights it’s citizens have: 1 is the best score, 7 is the worst. Therefore, negative γ in our estimation means that the intimidation effect plays a greater role in less free countries. 1 score up in the Freedom House ranking of a country makes the citizens of this country to be more cautious in answering governmentrelated questions in a survey, and consequently overestimate their governments in trust-related questions by 0.03 points. For a completely depressed country (with the score 7) the effect on our governance estimate would be -0.07 points. From the other side, residents of the countries, which are in a stage of ”critical citizenship”, do have significantly less confidence in their governments then they should have had. If not too ”critical”, residents of these countries would give their governments score 0.09 points higher, which would be reflected in the increase of citizen-centric indicator on about 0.03.

Even though we find statistically significant effects of indoctrination, intimidation and ”critical citizenship” in some countries, the magnitude of these effects is not particularly immense. For example, Vietnam with our score of 0.72 is not a free country based on criteria of Freedom House (it had rank 6 in 2005), and there is a moderate (0.05) effect of indoctrination on television. Together these effects would cut citizen-centric governance indicator in Vietnam by 0.07 points. New indicator would be 0.65 - still in the highest 20th percentile of the sample. Apparently, there are other reasons for some governments to score so high in the public opinion polls. In case of East Asia the main of them is probably last decade’s stable economic 22
# **Table 5:  Significant Changes in WGI Estimates of Governance, 1998-2008, Cont'd**

||**Gov**|**ernance S**|**core**||||**Agree/**||||
|---|---|---|---|---|---|---|---|---|---|---|
||**2008**<br>**(Level)**|**1998**<br>**(Level)**|**Change**|**Agree**|**No**<br>**change**|**Dis-**<br>**agree**|<br>**(agree+**<br>**Disagree)**|**Sources**<br>**Added**|**Balanced**<br>**Change**|**Bal Chng/**<br>**Actual Chng**|
|**Rule of Law**|||||||||||
|ZIMBABWE|-1.81|-0.53|-1.29|9|0|1|0.90|7|-1.33|1.04|
|ERITREA|-1.24|-0.21|-1.03|4|0|0|1.00|8|-0.67|0.65|
|VENEZUELA|-1.59|-0.71|-0.88|10|1|0|1.00|5|-0.88|1.00|
|BOLIVIA|-1.12|-0.28|-0.84|7|2|0|1.00|0|-0.76|0.91|
|ARGENTINA|-0.61|0.08|-0.69|8|2|1|0.89|8|-0.64|0.93|
|COTE D'IVOIRE|-1.52|-0.90|-0.62|5|3|0|1.00|0|-0.49|0.79|
|TRINIDAD AND TOBAGO|-0.25|0.37|-0.62|5|1|1|0.83|3|-0.48|0.77|
|ECUADOR|-1.23|-0.63|-0.60|6|2|1|0.86|3|-0.49|0.82|
|KYRGYZSTAN|-1.26|-0.67|-0.59|5|0|2|0.71|0|-0.39|0.65|
|THAILAND|-0.03|0.48|-0.51|5|2|3|0.63|0|-0.45|0.87|
|LATVIA|0.73|0.20|0.52|8|1|0|1.00|5|0.60|1.15|
|ESTONIA|1.05|0.52|0.54|7|2|0|1.00|6|0.55|1.02|
|ALBANIA|-0.60|-1.21|0.61|4|1|2|0.67|9|0.62|1.02|
|SERBIA|-0.46|-1.27|0.81|4|0|1|0.80|9|0.78|0.97|
|LIBERIA|-1.23|-2.07|0.84|4|1|0|1.00|6|0.94|1.11|
|GEORGIA|-0.34|-1.21|0.87|4|1|2|0.67|4|0.75|0.86|
|RWANDA|-0.50|-1.48|0.98|5|0|0|1.00|0|0.88|0.90|
|_Average_||||_5.88_|_1.12_|_0.82_|_0.88_|_4.29_|||
|**Control of Corruption**|||||||||||
|ERITREA|-0.38|0.78|-1.17|2|1|0|1.00|7|-1.34|1.15|
|ZIMBABWE|-1.37|-0.43|-0.95|7|0|1|0.88|7|-0.97|1.03|
|COTE D'IVOIRE|-1.17|-0.38|-0.79|3|2|1|0.75|0|-0.50|0.63|
|GREECE|0.10|0.69|-0.59|6|1|0|1.00|4|-0.63|1.07|
|ESTONIA|0.94|0.45|0.49|5|1|1|0.83|5|0.32|0.66|
|INDONESIA|-0.64|-1.16|0.51|7|1|1|0.88|7|0.56|1.08|
|TANZANIA|-0.51|-1.12|0.61|5|2|0|1.00|9|0.50|0.82|
|ALBANIA|-0.45|-1.07|0.62|4|0|1|0.80|8|0.61|0.98|
|GEORGIA|-0.23|-0.86|0.63|4|0|1|0.80|1|0.30|0.47|
|HONG KONG|1.88|1.16|0.71|6|0|2|0.75|1|0.67|0.95|
|RWANDA|0.03|-0.87|0.89|3|0|0|1.00|0|0.86|0.96|
|SERBIA|-0.16|-1.08|0.92|4|0|0|1.00|8|0.85|0.93|
|LIBERIA|-0.60|-1.73|1.13|4|0|0|1.00|5|1.37|1.21|
|_Average_||||_4.62_|_0.62_|_0.62_|_0.90_|_4.77_|||
|**Overall Average**||||4.47|0.48|0.54|0.90|3.71|||

Note: Shaded countries correspond to significant increases in WGI estimates of governance (at the 90 percent level), and non-shaded areas correspond to significant declines.

35
number of potential beneficiaries is 22,546. In the sample of interest, we observe a total of 294 municipalities. Of these, 124 are only observed in one round, 26 in two, 77 in three, and 67 in all four rounds. Most of the municipalities that are only observed once appear in 2011, while only one municipality that is sampled in 2012 is not observed in 2013 and vice-versa. The total number of municipalities sampled each year is 117 in 2010 (of which only 15 are never repeated in the following years), 218 in 2011, and 170 in both 2012 and 2013.

The treatment variable is binary, indicating whether or not any member in the household receives the Colombia Mayor benefit. The ENCV captures information on the receipt of government benefits only among households. This makes it impossible to determine who among the various potential beneficiaries in the same household receives the benefit. The instrumental variables (IVs) are described below. Table 8 illustrates the full set of control variables. In addition, all specifications include yearand municipality-specific fixed effects, which are omitted from the table. The first five variables (other urban, rural, age, Sisben score, and woman) are included in all specifications, while the remainder are additional control variables. The former directly determine treatment eligibility; their omission would thus result in a clear omitted variable bias.

**Table 8: List of Included Control Variables**

|_Control variable_|_Description_|
|---|---|
|Other urban<br>Rural|Categorical variables indicating two of the three geographical areas that<br>define eligibility for the Colombia Mayor benefit. The omitted category is<br>residence in one of the 14 major cities.|
|Age|Age inyears|
|Sisben|The household’s Sisben III score|
|Woman|A binary variable indicating gender. It is omitted from estimations by<br>gender.|
|Widowed<br>Divorced<br>Single|Categorical variable indicating each of the three civil states. The omitted<br>variable is married.|
|Potential beneficiaries,<br>number<br>Other adults, number<br>Minors,number|The number of household members in each group: members old enough<br>to qualify for Colombia Mayor; other household members 18 years of age<br>or older; members younger than 18|
|Number of women|The number of women household members|
|Primary<br>Secondary<br>Postsecondary|Set of categorical variables indicating the highest attained certificate or<br>degree. The omitted category is no completed level of education.|
|Incapacitated|A binaryvariable indicatingthat aperson ispermanentlyunable to work.|

20
# 3 Empirical Model

The empirical specication is derived from a model of malaria that is based on previous models used in D. L. Smith and McKenzie (2004), Cosner et al. (2009) and Torres-Sorando and Rodriguez (1997). Four key assumptions allow me to simplify the model so that incidence in the current month is dependent on incidence in the last month using a linear functional form. Expected imported cases enter as a linear additive term as in Torres-Sorando and Rodriguez (1997). The model, assumptions and implications are described in detail in Appendix A. I start out estimating equation 1 using OLS, with imported incidence calculated using equation 2: In this model, _xit_ represents the incidence, or number of humans infected in location i at time t per 1000 people in location i.<sup>18</sup> Location i is one of the 36 health post catchment areas and t is at the monthly level. The _β_ 1 parameter estimated tells us on average how many new cases per 1000 are generated in the following month from cases in the current month. In contrast to epidemiological models, where this parameter would be estimated separately for each area, I want to causally estimate the secondary cases generated. Therefore, I estimate an average eect across locations and time in order to be able to include health post area xed eects, _γi_ and month xed eects, _δt_ . This allows me to control for malaria seasonality and unobservable characteristics of a health post area that might impact incidence.

I use the mobile phone data to calculate expected imported malaria cases entering and divide them by the population of the area they enter, _Hit_ , to calculate the expected imported incidence, E( _Iit_ ) . The likelihood of an infected case entering depends on the origin of an

> 18Population is based on the known population of each health facility catchment area. The appendix includes a robustness check where the annual population is adjusted monthly using the number of people entering and leaving each month based on the mobile phone data.

15
categories, indicating no observable effect of respondent fatigue from the detailed food list on subsequent non-food categories. Clearly, at least in the context of Tanzania, the 17 most important food items (a subset of the full list of 58) performs only marginally worse than the full detailed listing.

Table 5 compares results among the three diaries. In Panel A, columns 1 and 2, we find that household diaries record significantly less total and food consumption than the personal diary, from 13 to 20 percent lower. Between the two types of household diaries, the frequency of interviewer visits makes little difference in total consumption, but food consumption is 7 percent higher in the infrequent diaries than the frequent household diary (and this difference is marginally significant at the 10 percent level; results available upon request), suggesting that the net impact of telescoping and recall error in the infrequently supervised households is positive but not especially large. Interestingly, the frequent non-food consumption exhibits the largest deviations in the household diaries from the individual diary, suggesting that a relatively large amount of these goods are consumed outside the purview of the household respondent.

In panel B of Table 5, we subdivide the household diaries on the basis of the literacy status of the household. Approximately 16 percent of households had no literate adults to fill in diaries. Households assigned to the diary with infrequent field visits had more visits if the household was deemed illiterate (see Table 1), although not as many as for households in the frequent visits subsample (module 6). The presence of an illiterate member does not affect reported consumption when households are regularly supervised (panel B columns 1-4, module 6). However, infrequent supervision results in significantly lower reported consumption among illiterate households, even for non-food non-frequent items, which are asked only in recall format. Clearly, leaving a diary with a household lacking a literate member will yield greater mis-measurement unless this difficulty can be overcome through frequent visits, thus converting the survey format to a de facto high frequency recall.

# _Consumption Measurement and Household Characteristics_

The literacy of household members is one characteristic that may affect the relative performance across modules. Table 6 explores the influence of additional characteristics by estimating equation (2) for the following: total household size, the number of adult household members (age 15 and above), urban location, education of the household head, and an asset index as an alternative measure of household resources (asset wealth) derived from housing conditions and household durable goods (Filmer and Pritchett, 2001). All of these characteristics are strongly
Wu, Z., S. Yao, 2003. Intermigration and intramigration in China: A theoretical and empirical analysis. China Economic Review, 14(4), 371-385.

- Zhang, K.H., S. Song, 2003. Rural–urban migration and urbanization in China: Evidence from time-series and cross-section analyses. China Economic Review, 14(4), 386-400.

- Zhao, Z., 2005. Migration, labor market flexibility, and wage determination in China: A review. Developing Economies, 43(2), 285-312.

- Zhu, Y., 2000. In Situ Urbanisation in Rural China : Case Studies from Fujian Province. Development and Change, 31(2), 413-434.

- Zhu, Z., H. Wang, 2006. To Foresee the Demands and Supplies of Rural Labor Forces in the Coming Years by Looking at the Phenomena of “Shortage of Farmer-Turned Workers”. Statistical Research, 2006(2), 17-20.

19
Figure 3: Actual to Predicted ratios for Intra- and Interregional Trade<sup>1</sup> <!-- Start of picture text --> %<br>7<br>6<br>5<br>Intra-regional Trade<br>4<br>Trade with the Rest of the<br>3<br>World<br>2<br>1<br>0<br>South Asia East Asia<br><!-- End of picture text --> Source: Authors’ calculation based on United Nations COMTRADE data and World Development Indicators Note: Actual trade implies value of trade recorded in COMTRADE data. The predicated trade represents the value of trade that is predicted by a gravity model by taking geographical distance, gross domestic product, and other relevant factors into account.

There are a number of barriers to promoting intraregional trade and expanding exports as a whole. Tariff rates are one of the highest among developing countries. They have been reduced in the past decade, however, and are no not the only constraint to growth.<sup>2</sup> This implies the presence of other obstacles to trade liberalization and nontariff barriers—including transactions costs and behind the border barriers.

Specifically, constraints in supply chains and trade logistics include a number of increasingly important barriers to exports for South Asian countries. The lack of harmonized transport systems, frequent reloading of goods, port congestion affecting turnaround time for ships, complicated customs-clearance procedures, and nontransparent administrative procedures at customs are often at the center of trade constraints.<sup>3</sup> These constraints are often more serious in developing countries than in developed countries. One study shows, for example, that for 168 out of 215 U.S. trading partners, transport cost barriers outweigh tariff barriers (World Bank 2002a). Given that income growth is greater with more cross-border trade, at least in theory, eliminating nontariff barriers contributes to trade integration. Until recently, trade facilitation generally referred to policy measures that aimed to reduce the costs of transportation. It now encompasses a broader set of interrelated factors that reduce nontariff barriers in order to lower the cost of moving goods between destinations and across international borders.

This paper provides a summary overview of trade facilitation in South Asia. The primary question addressed here is “What are the potential gains to trade in the region with

> 1 The bilateral value of trade is predicted according to GDP, population, distance, and arrangements between importer and exporter. The share of observed trade that is smaller than 100 percent infers the presence of trade barriers.

> 2 Cite tariff data, World Bank sources.

> 3 For a more detailed description of these factors see: World Bank (2004a).

4
was created for firms with sales less than 700 Tax Units (nearly US$1 million) per year, reducing their income tax from 30 percent (in the general regime) to 10 percent.

Along the labor dimension, a special SME labor regime ( _Regimen Mype_ ) has been in place since 2003, in which the labor costs are reduced drastically, to almost to one-seventh of those in the general regime. Initially, the _Regimen Mype_ was created for firms with fewer than 10 employees. The SME Law of 2008 added an intermediate regime for firms with more than 10 employees but fewer than 100, reducing labor costs to one-half those in the general regime. In the Peruvian case, these reductions in labor costs implied fewer workers’ benefits.

In 2007, an electronic payroll system was created for firms with three or more employees, which is mandatory: this not only provides more information on firms’ workers, but also increases the government’s capacity for oversight and enforcement of labor rights. More recently, the National Superintendence of Labor Inspection was created, and a proposal is under discussion for giving local governments oversight capacity for firms with fewer than 10 workers.

Figure 2: Firm and Business Formality Rates, Peru, 2015 <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Registered firm Formal employment<br>Number of employees<br>1 2 to 5 6 to 10 11 to 20 21 to 30 31 to 40 41 to 50 51 to 75 76 to 100 101 to 150 151 to 200 201 to 300 301 to 400 401 to 500 501 to 1000 1001 or more<br><!-- End of picture text --> _Source:_ ENAHO.

_Note:_ Firm formality rates: proportion of firms declaring they are registered with SUNAT. Labor formality: persons with formal employment (registered with social security).

Until recently, business and labor promotional programs have operated independently from one another in Peru, which may have contributed to the low impacts of formalization policies.

10