rafmacalaba/datause-encoder-data huggingface dataset:
**<mark>Table 7. Regression: Vignettes</mark>**

|**Variables**|(1)<br>**Latrine use**<br>**(baseline**<br>**survey)**|(2)<br>**Latrine use**<br>**(endline**<br>**survey)**|
|---|---|---|
|Most use latrine|0.138***|0.143***|
||(0.0211)|(0.0173)|
|Most think OD is wrong|0.158***|0.160***|
||(0.0211)|(0.0173)|
|Constant|0.729***|0.776***|
||(0.0122)|(0.00998)|
|Observations|812|1,010|
|Standard erro|rs in parenthes|es|
|*** p<0.01, *|* p<0.05, * p<0|.1|

<mark>We also explored mental models by asking narrative questions, for example, associations that respondents made with the word “ganda” (dirty). Analysis at baseline suggest a strong association of the word “ganda” with latrines. For example, a word frequency count showed that latrine was the third most mentioned word, after feces and drainage, when people were asked to talk about things that they considered “ganda”. Topic modeling using latent dirichlet allocation showed that the word latrine appeared frequently in three of the top five topics, along with words such as feces, garbage, drains, etc.</mark> This <mark>demonstrates the importance of changing social norms but also mental models regarding latrine use.</mark>

# **<mark>Intervention</mark>**

<mark>Social norms</mark> appear to matter when it comes to beliefs and behaviors regarding open defecation in rural UP. While the majority of latrine owners use latrines, and do so frequently, individuals do not necessarily know this (perhaps because latrine use is a less visible practice than open defecation). They may also be making inferences about latrine use on the part of latrine owners from the observed behavior of non‐ latrine owners. This points to a potential mismatch between empirical expectations and reality, where updating the former could have the effect of communicating to people who continue to defecate in the open that they are, in fact, deviating from the norm. Similarly, our findings confirm that weakening social norms and changing mental models that get in the way of a permanent shift away from open defecation require direct confrontation with “gandagi” and related terms regarding impurity and dirtiness, insofar as they relate to latrine <mark>s. To address these behavioral barriers, we piloted two simple informational interventions and evaluated their short‐term impact p</mark> eople’s behavior and beliefs around latrine use.

<mark>The goals of the interventions were to: i) re‐brand latrine use by associating it with cleanliness; and ii) make information about growing latrine use among latrine owners salient. Three types of messages were delivered—the first cued empirical expectations about open defecation and latrine use by informing people about prevalent norms among latrine owners in order to update their beliefs about latrine use within their appropriate reference groups; the second and third challenged existing mental models that automatically associate latrines with</mark> _<mark>gandagi</mark>_ <mark>by highlighting the relative desirability of latrines vis‐à‐vis open defecation through direct association of latrine use with cleanliness and other “clean” practices.</mark> 14
# **Table 7:** Impacts on household member time use

||**_Pro_**|**_ducer_**|**_First_**|**_Spouse_**|**_Other_**|**_Spouses_**|**_G_**|**_irls_**||**_Boys_**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Outcome**|**Control**<br>**Mean**<br>**[1]**|**Estimated**<br>**Impact**<br>**[2]**|**Control**<br>**Mean**<br>**[3]**|**Estimated**<br>**Impact**<br>**[4]**|**Control**<br>**Mean**<br>**[5]**|**Estimated**<br>**Impact**<br>**[6]**|**Control**<br>**Mean**<br>**[7]**|**Estimated**<br>**Impact**<br>**[8]**|**Control**<br>**Mean**<br>**[9]**|**Estimated**<br>**Impact**<br>**[10]**|
|HH plot activities|43.11|-0.48|35.31|-2.14<sup>_∗∗_</sup>|35.34|-1.50|26.68|-2.65<sup>_∗∗_</sup>|34.54|-0.96|
|||(0.86)||(0.91)||(1.38)||(1.12)||(1.21)|
|Other agric work|1.32|-0.30|1.19|-0.06|1.12|-0.05|0.28|0.00|0.20|0.05|
|||(0.23)||(0.21)||(0.30)||(0.11)||(0.08)|
|Non-farm labor|11.38|-0.61|5.47|0.61|5.09|0.02|4.10|0.06|12.90|-1.20|
|||(0.85)||(0.59)||(0.82)||(0.56)||(0.95)|
|Household chores|1.62|0.19|28.52|-0.53|28.87|0.06|16.37|-0.02|1.79|0.55<sup>_∗_</sup>|
|||(0.31)||(0.82)||(1.38)||(0.81)||(0.31)|
|Hobbies|9.26|-0.00|4.50|-0.02|5.63|-0.04|9.69|-0.63|9.47|-0.37|
|||(0.55)||(0.38)||(0.66)||(0.79)||(0.71)|
|Education|0.62|-0.02|0.19|-0.02|0.07|0.06|0.79|-0.19|0.69|-0.14|
|||(0.14)||(0.11)||(0.11)||(0.15)||(0.12)|
|Total inactive time|100.49|1.17|92.82|2.20|92.18|1.01|110.06|3.67<sup>_∗∗_</sup>|108.38|2.23|
|||(1.47)||(1.46)||(2.26)||(1.74)||(1.69)|
|Observations|1|836|1|781||757|1|199||1352|

Note: *, **, and *** indicate significance at the 90, 95, and 99 % confidence intervals, respectively. _⋄_ indicates variables that have been winsorized at the 5% level. Regression reports impacts of treatment on weekly average household time use for given tasks: for households with multiple wives, daughters, or sons, the time use is averaged and regressions are reported at the household level. All regressions control for whether household head completed primary school, number of boys between 6 and 16 years old, and stratum fixed effects.
demands.<sup>24</sup> Interestingly, Ecuador has a (relatively young) autonomous regulator but Paraguay has a Ministry regulator.

For the pooled sample, the elasticity of generation capacity with respect to real per capita GDP was 0.89 (with a standard error of 0.02). The cross-section elasticities for selected years were as follows:

**Table 3:  Generation Capacity Real Income Elasticities**

|Year|Estimated Generation Capacity/ Real GDP|
|---|---|
||Elasticities|
||(Standard error in parentheses)|
|1980|0.78|
||(0.09)|
|1990|0.90|
||(0.11)|
|2000|0.94|
||(0.11)|

Table 3 shows a trend towards the simple elasticity rising towards unity i.e. faster growth in generation capacity than in real income over the period. It remains to be seen whether this is in any way related to the spread of electricity regulatory reform.

# **4.1.6 Generation Capacity Utilisation 1980 and 2000**

Beginning and end-of-period generation capacity utilisation rates are shown in Figure 6 below. In general, there has been a noticeable increase in capacity utilisation, but there are country exceptions (e.g. Colombia).

**Figure 6:  Utilisation of Generation Capacity 1980 and 2000**

<!-- Start of picture text --> Utilisation 1980 and 2000<br>16<br>Utilisation 2000<br>14<br>12 Utilisation 1980<br>10<br>8<br>6<br>4<br>2<br>0<br><0.1 0.1- 0.2- 0.3- 0.4- 0.5- 0.6- 0.7- 0.8- 0.9-<br>0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br>Utilisation rate<br>Frequency<br><!-- End of picture text -->

> 24 See IEA country analysis brief :Paraguay at www.eia.doe.gov

23
presence of covariates. This models alpha as a multinomial logit function of the covariates X:

> where φ is an ancillary scale parameter required to identify the model.

The main variable of interest in the vector of independent variables X is log cohort size, but we also include dummies for each country, each year, and gender.

where CS stands for log cohort size, g is age and gender group, c is country, and t is year. The estimated proportions are therefore modeled as a function of log cohort size at birth, gender, and a set of dummy variables indicating country and year.

We estimate these models separately for seven five-year age groups, ranging from age 15 to 19 to age 40 to 44. Each observation is weighted by the square root of the number of observations used to construct the proportion, to correct for the heteroscedasticity arising from differences in the size of the household surveys used to construct the cell means. The standard errors are robust and clustered on country. Below, we report the estimated marginal effect, and its standard error, of log cohort size.<sup>19</sup> Therefore, the reported effect represents a rough approximation of the percentage point change in an outcome when cohort size doubles, and dividing the reported coefficients by ten gives a more accurate approximation of the percentage point change when the cohort size increases by 10 percent.

# 4 Results

We first consider the relationship between cohort size and primary activity and school attendance, before turning to sector, status, and the earnings of wage workers. In each case, we also present interactions of cohort size with dummy variables indicating the gender of the cell, and whether the country, based on its average per capita GDP, is in the poorer half of our set of countries.<sup>20</sup>

## 4.1  Primary Activity

Table 1 presents the results from regressing labor market status on log cohort size as

> 19 We use the stata command margins, which applies the delta method for continuous variables, to calculate marginal effects.

> 20 We use a threshold of US $1500, which is roughly the median. We use a country’s average GDP because, conditional on country fixed effects, it is orthogonal to the error term by construction.

14
**Appendix Table A3: Sectoral Export Shares and Female Attainment Measures**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||Panel A|: OLS||||
|Dep. Var.: Sectoral Share of|Exports||||||
|Female Labor Intensity *|-0.01***|-0.00|||||
|Fertility|(0.001)|(0.001)|||||
|Female Labor Intensity *|||-0.00|-0.00|||
|Female Labor Force|||(0.000)|(0.000)|||
|Participation|||||||
|Female Labor Intensity *|||||0.03*|-0.00|
|Ratio of Female to Total|||||(0.020)|(0.027)|
|Education<br>|||||||
|Capital Intensity * Capital||0.38***||0.38***||0.45***|
|Abundance||(0.166)||(0.168)||(0.200)|
|Skill Intensity * Skill||9.63***||9.60***||10.42***|
|Abundance||(2.112)||(2.091)||(2.251)|
|Country and Sector|Yes|Yes|Yes|Yes|Yes|Yes|
|Dummies<br>|||||||
|_R_<sup>2</sup>|0.229|0.131|0.229|0.131|0.212|0.134|
|Observations|13,269|8,055|13,269|8,055|11,468|7,309|
|||Panel B|: 2SLS||||
|Dep. Var.: Sectoral Share of|Exports||||||
|Female Labor Intensity *|-0.01***|0.00|||||
|Fertility|(0.004)|(0.003)|||||
|Female Labor Intensity *|||0.01***|-0.00|||
|Female Labor Force|||(0.000)|(0.000)|||
|Participation|||||||
|Female Labor Intensity *|||||0.08***|-0.03|
|Ratio of Female to Total|||||(0.031)|(0.028)|
|Education|||||||
|Capital Intensity * Capital||0.36**<br>||0.38**<br>||0.43**<br>|
|Abundance||(0.165)||(0.167)||(0.196)|
|Skill Intensity * Skill||9.23***||9.64***||10.01***|
|Abundance<br>||(2.149)||(2.074)||(2.180)|
|Country and Sector<br>|Yes|Yes|Yes|Yes|Yes|Yes|
|Dummies<br>|||||||
|Observations|13,269|8,055|13,269|8,055|11,468|7,309|
||||First|Stage|||
|Dep. Var.:|_FLi_*|_FLi_*|_FLi_*FLFP|_FLi_*FLFP|_FLi_*Ratio|_FLi_*Ratio|
||Fertility|Fertility|||of Female<br>to Total|of Female<br>to Total|
||||||Education|Education|
|Female Labor Intensity *|0.02***|0.03***|-0.27***|-0.28***|-0.01***|-0.01***|
|Muslim Share of|(0.001)|(0.001)|(0.007)|(0.010)|(0.000)|(0.000)|
|Population|||||||
|Female Labor Intensity *|0.01***|0.01***|-0.15***|-0.15***|0.01***|0.01***|
|Christian Share of|(0.001)|(0.001)|(0.005)|(0.007)|(0.000)|(0.000)|
|Population|||||||
|F-test|599.40|442.34|852.97|444.94|898.03|1053.12|
|Partial _R_<sup>2</sup>|0.913|0.927|0.950|0.949|0.976|0.981|

Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. _Female Labor Intensity_ ( _FLi_ ) is the average fraction of female employees in a sector at the fourdigit level of the US Census classification; _Fertility Rate_ is total births per woman; _Female Labor Force Participation (FLFP)_ is female labor participation rate (% of female population ages 15+); _Ratio of Female to Total Education_ is the ratio of the Barro-Lee measures of female to total educational attainment. All of 37 the variables are averages over the period 1962-2007, except for _Female Labor Force Participation,_ which is averaged over the period 1980-2007. Variable definitions and sources are described in detail in the text.
namely **by** taking **q** in (3.4) to equal the replacement cost of capital. 12 **In** turn, we value physical gold stocks (taken from the IMFs **IFS)** at current market (London) prices. **13** Using these estimated wealth stocks, we construct the foreign wealth **/** domestic wealth ratios of country _i_ as the sum of wealth across all sample countries other than _i_ divided **by** the wealth of country _i._

# **3.2** Measures of return and risk

Apart from wealth ratios, the two key explanatory variables in our model of net foreign asset positions are the measures of anticipated risk and return for each country. In practice, these likely depend on a large variety of variables reflecting relative prices, profitability, transaction costs, property rights, tax regimes and so on. However, degrees-of-freedom considerations prevent us from including a large number of variables in the empirical estimation. In order to reconcile the need to consider all relevant variables with the requirement to maintain a sensible number of degrees of freedom, we summarize the information provided **by** pertinent variables in a few indices. These indices correspond to the categories introduced in equation **(2.6).** That is we construct, respectively, indices for expected returns _(REl)_ and perceived risks _(RIiy)._ We construct each index as a weighted average of the principal components of its respective underlying indicators, where the weights are given **by** the share of the indicators' overall variance explained **by** each principal component. The principal components are derived from the indicators data pooled for all countries in the sample. The resulting index for each country would then be relative to the world and time sample average.

The underlying indicators for each index are listed below.'<sup>4</sup> We have selected these indicators on the basis of both their relevance in previous theoretical and empirical work and

> X2This valuation of capital is admittedly crude. We are aware that, if adjustment costs are present, conventional investment theory predicts that the market value and replacement costs of capital should diverge in the short run.

> We should note one major caveat concerning our constructed wealth measure, namely the neglect of nonreproducible assets (land, oil, etc), on whose volume and value little information is available for the vast majority of countries. Our definition of wealth in (3.4) above is admittedly incomplete in this regard, especially for resourceintensive countries.

> 14 The main data sources are the World Development Indicators (World Bank), International Financial Statistics

> (IMF), Exchange Rate Arrangements (IMF), Civil Liberties Index (Freedom House), and Kaufman et al. **(1999).**

**16**
cost of childbearing, even though it decreased the overall number of children that eligible women would have.

The next sections explore how PEP shaped fertility among Venezuelan migrants and suggest possible mechanisms for the observed responses.

# **IV DATA: VENREPS**

Our main source of data is the Venezuelan Refugee Panel Study (VenRePS), a longitudinal study of irregular Venezuelan migrants in Colombia. The survey was conducted to examine PEP’s impacts on migrant well-being and consisted of two waves of data collection, starting in October 2020 and one year later. The data represents four geographical areas: Bogot ́a, Medell ́ın, Barranquilla, and a group of smaller cities that together comprise an area.<sup>8</sup> The first three cities are large urban centers in Colombia that host the most Venezuelan migrants in the country. In Figure 2, the location of each city in the VenRePS sample is compared with the location of Venezuelan migrants in Colombia based on the 2018 population census (the last one available).

Roughly half of the individuals interviewed in VenRePS were randomly selected from the RAMV survey. The other half originated from a “snowball” sample of referrals from local migrant organizations and respondents in the RAMV sample. Ibanez et al. (2022) show that migrants surveyed in VenRePS who were contacted through the RAMV survey or “snowball” referrals were comparable in terms of sociodemographic characteristics before the program’s rollout. All migrants in the survey had no passport, were at least 18 years old, provided documents to prove they were born in Venezuela, and had arrived in Colombia between January 2017 and December 2018. In other words, they were irregular migrants living in Colombia at the time of PEP’s implementation.

Table A.1 presents summary statistics distinguishing by gender. Panel A shows descrip-

> 8This includes migrants interviewed in ten municipalities including C ́ucuta, Villa del Rosario, Cali, Cartagena, Riohacha, Maicao, Uribia, Valledupar, Santa Marta, and Arauca.

8
Table D.19. Inequality of the Wealth Index at the Census Tract Level

|||_Wealt_|_h Index_||
|---|---|---|---|---|
||Gini Index|Interquartile Range<br>(p75-p25)|Percentile Range<br>(p90-p10)|Percentile Range<br>(p90-p50)|
||(1)|(2)|(3)|(4)|
|Guerrilla control|-0.00345|66.52|82.59|0.0679|
||(0.0104)|(67.52)|(68.28)|(0.116)|
|Observations|2,975|2,975|2,975|2,975|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|
|Dependent mean|0.278|3.520|47.23|1.981|

_Note:_ The table presents the results of equation 1 for the wealth index constructed from Census of 2007. Each Column represents a different measure of inequality using the real per capita income. Column 2 report the interquartile range, calculated as the difference of the wealth index in percentile 75 minus the wealth index in percentile 25 for each census tract. Column 3 shows the percentile range of the difference between percentile 90 and 10 for each census tract. Column 4 reports the percentile range of the difference between percentile 90 and 50 for each census tract. The unit of observation is at the census tract level. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The estimates use triangular kernel weights. Robust standard errors in parentheses.

Table D.20. Cooperatives

||Has a|Producer belongs|Commercial producer|Subsistence producer|Producer belongs|
|---|---|---|---|---|---|
||cooperative|to a cooperative|belongs to cooperative|belongs to cooperative|to association|
||(1)|(2)|(3)|(4)|(5)|
|Guerrilla control|0.00545|0.00226|0.00879|-0.00301|-0.00480|
||(0.00354)|(0.00960)|(0.0158)|(0.00429)|(0.00579)|
|Observations|929|2,400|2,400|2,400|2,400|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|2.266|
|Dependent mean|0.00900|0.0520|0.0750|0.00800|0.0110|

_Note:_ The table presents the results of equation 1 for outcomes related to cooperatives. Information was taken from the Agrarian Census of 2007. The unit of observation is at the census tract level. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The estimates use triangular kernel weights. Robust standard errors in parentheses.

90
< [

-shares that occurred during the past decade." In 1970, approximately 53 percent ($6.8 billion) of all USSR exports went to Eastern Europe; by 1990 this share had dropped to under 20 percent. While JUN ECE, 19911 and the UN COMTRADE data base suggest the FSU share of world trade peaked in 1983 at 3.4 percent, and steadily declined thereafter to 1.8 percent in 1990, this dramatic decline was accompanied by a rapid increase, from 22 to 53 percent, in the share of FSU exports going to OECD countries. Similarly, other Eastern European countries' intra-trade (plus exports to the FSU) fell froin 64 to 36 percent of total exports. As was the ase with the PSU, the OECi) was the "other" Eastein <; \ff& ean countries' major source of growth as their sham Xof exports to these markets wert from 26 to 51 percent.4

Table 2 shows the importance of individual OECD markets in 199', and provides details 1on the commodity composition of their imports from the FSU. Over two thirds of FSU exports ($29.4 billion) to the OECD went to the European Community (EC), with Germany receiving about 40 percent ($,.S billion) of the latter's total. The six EFI'A members received 14 percent ($4.2 billion went to these countries), with Finland accounting for about 45 percent of the EFTA total. With imports of $900 millio in 1991 the United States was a relatively unimportant market for the FSU, ranking slightly ah4d of Austria and about $200 million below the combined imports of Switzerland and Sweden. However, 'Statistics on developments in foreign trade of the FSU are subjec to considerable error, mainly because of difficulties involved in estimating trade based on 'soft' payments arangements with CMEA members and some developing countries (India being the most notable case). The estimates using the official 'transferrable ruble' exchange rate yield higher values of Soviet trade (the soft component is significantly larger) than those based on the 'conectod rate. Thus, the estimte of the UN ECE (Economic Commission for Europe), which are not based on the Soviet official cxchange rate, show lower value of Sovie **t** rade in 1990 than the Soviet or World Bank/IMF estates. The difference is significant: according to the UN ECE, the value of Soviet exports was USS 59.1 billion as compared with USS 104.7 billion (see Michalopoulos, 1993, table 1). Leaving aside the issue of which estimates bRtor reflected the actual trade flows, we use UN ECE estimates because they cover a longer time span. However, both estimates provide support to the conclusion about the growing importance of OECD markets to the FSU.

'While the data on 1992 exports are subjec **t** o a significant margin of error, they do sugges **t** hat the fall in the NIS8 exports to the former CMEA and developing countries was significastly larger than the decline in exports to the OECD. According to a preliminary estimate, OECD wqrkets received more than 50 percent of Russia's total exports.
export price within each subheading _k_ at the border of the exporting country. The data has been downloaded from the EUROSTAT database. The data has been downloaded in June 2007 from the website: http://epp.eurostat.ec.europa.eu.

_distij_ measures the logarithm of distance (unit: km) between country _i_ and country _j_ . The distance is measured using the great-circle formula (denoted G.C. Dist. in the tables) or by taking into account the demographic distribution within each country of the trading pair (denoted Wei. Dist. in the tables). The data on the geographical position of cities is taken from the encyclopedia Encarta.

_prodk_ stands for the fixed effect dummies of each subheading (eight digit CN). If a subheading is _k_ , the dummy is one; otherwise, the dummy takes the value of zero.

_exi_ and _imj_ are the fixed effect dummies for the exporting and importing countries. If the exporting country is _i_ , then _ex_ is 1 and zero otherwise. The same holds for the importing countries. The data is freely available.

_msi_ , _msj_ represent the market size of the exporting country _i_ and importing country _j_ . The market size has been calculated in two ways: First, the market size is measured by the logarithm of GDP. The GDP data come from EUROSTAT and are recorded in current euros. The second measure of market size is the logarithm of the population size. The data on population size come also EUROSTAT. Both samples have been downloaded in June 2007 from the following website: http://epp.eurostat.ec.europa.eu. The data is freely available.

_dbi, dbj_ stand for the doing business data on the number of procedures to register a firm or the official cost of each procedure as percentage of per-capita gross national income. The doing business data measure the cost in launching a commercial or industrial firm with up to 50 employees and start-up capital of 10 times the economy's per-capita gross national income (World Bank, 2007).

41