rafmacalaba/datause-encoder-data huggingface dataset:
Table 12: The impact of PAL on parental investment

|_Outcome =_|(1)<br>Index of parental<br>activities[0-5]|(2)<br>Principal<br>Component|
|---|---|---|
|In-kind|0.459*<br>(0.266)|0.309*<br>(0.183)|
|Cash|0.343|0.238|
||(0.319)|(0.219)|
|State FE|YES|YES|
|Year FE|YES|YES|
|Grade FE|YES|YES|
|Pre-program controls|YES|YES|
|Observations|283|283|
|Outcome mean in control group|3.19|-0.02|
|**_Effect size:_ In-kind - Cash**|0.12|0.07|
|**_H 0 :_ In-kind = Cash (p-value)**|0.68|0.71|
|**_H 0 :_ In-kind = Cash (Randomization Inference p-**<br>**value)**|0.78|0.81|

Notes: *** p<0.01, ** p<0.05, * p<0.1 (1) Standard errors are clustered at the village level.

(2) Pre-program controls include gender, age, and indicators for whether the household head is married, whether the house has running water, and whether the closest school offers morning shift.

(3) Results based on the Enlace de Contexto for all years between 2008-13 (4) The _Index of parental activities_ equals the number of activities where parents are involved among the following: 1) helping with homework; 2) explaining topics that were not clear from the lecture; 3) inviting them to review class material that was not clear; 4) paying attention to student grades; 5) attending school meetings. The principal component in column (2) is the first component of the 5 indicators of parental activity.

39
find that, as in these studies from the trade literature, our canonical estimates are qualitatively similar, but smaller in magnitudes than the heterogeneity robust estimators (see Table C.1). This also applies to the results for the period 0 estimates in the event study framework (see Figures C.2 and C.3).<sup>8</sup> We also use our event-study framework to test for pre-trends using the heterogeneity-robust estimator and fail to reject the null of no pre-trends (figure C.1) with a p-value of 0.66.

# **5 Welfare impacts**

Large migration impact of a BLAs suggests that a BLA can have significant welfare implications. Here, we calculate the welfare impacts, in terms of increased incomes for migrants, of the migration impacts of signing a BLA.

In 2020, an average corridor with LLMIC origin country had a migrant stock of about 8,000 individuals. With the estimates in column 6 of table 4), this implies an increase of 25,000 migrants within a decade in response to a BLA. Corridors from LLMIC to GCC countries had a larger stock of 56,000 migrants in 2020. A BLA, based on the impacts in column 2 of table 4, would increase the migration stock by 472,000 migrants within a decade. The average Sub-Saharan African country had a lower migration stock of 3,500 and 11,500 to the average and GCC destinations, respectively. A BLA would increase this only by about 500 migrants given the low impacts of BLAs observed in the African origin countries (column 4, table 4). If migration impacts were the same as the average LLMIC country, then the number would be much higher at 11,000 to an average destination and 96,000 to a GCC destination.

To convert the migration impacts to earnings, we use the average nominal GDP per-capita to proxy for earnings at origin and the returns to migration estimated in the literature focusing on low-skilled migration from LLMICs. Rigorous empirical research on the returns to migration establishes that income increases several-folds even for low-skilled workers migrating to highand upper-middle-income destinations. As summarized in World Bank (2023), estimates range from 118 to 298 percent across these studies (Clemens, 2019, Clemens and Tiongson, 2017, Gaikwad, Hanson and T ́oth, 2023, McKenzie, Stillman and Gibson, 2010, Mobarak, Sharif and

> 8However, the results on persistence differ from the canonical estimates presented in figure 1. This could be driven by the fact that treatments are non-staggered in our settings and the estimators are not well adapted to this difference.

22
5 different social groups, and will then proceed to a comparison of poverty lines, and extent and the composition of poverty in the three countries.

_II. Impact of the Crisis on Population Incomes_ The analysis of poverty is conducted using 1978 as the benchmark year. The choice of the benchmark year Is made relatively simple by the fact that 1978 or 1979 (in the case of Yugoslavia) was the last year of relative prosperity, displaying most of the features of the decade of the 1970's. The period of the 1980's represents for Yugoslavia and Hungary a homogeneous period of stagnation and crisis. The situation Is slightly different for Poland where we can distinguish two subperiods. The first Is the subperiod 1978-82 during which real GDP contracted by one-fifth; the second is the subperiod of recovery when GDP grew on average by 5 percent annually. Polish current account deficit decreased in that subperiod from a very high level of almost 6 percent of GDP in 1979-82 to cnly 1.6 percent. The Impact of recovery on domestic demand and p4;sor.al Incomes was therefore less than the relatively high rate of growth of GDP alone would suggest. Table 2 illustrates tne effects of crisis on some of the variables most closely related with populr on welfare.

_Table 2. Crisis and Its Effects on Population Incomes_ _(levels in 1987 with 1978=100)_

||Poland|Hunfarv|Yugoslavia|
|---|---|---|---|
|Domestic demand per capitaa/|87.9|103.6|85.9|
|Consumption per capita|98.0|126.1|100.5|
|Real wagesb/|71.1|101.0|72.0|
|Real pensions|83.2|120.4|88.1|
|Real income of workers' HHC/|67.8|103.9|63.0|
|Real income of farmers' HH|81.5|101.0|102.5|
|Real income of mixed HH|84.5|--|81.8|
|Real income of pensioners' HHs|82.6|118.2|--|

Notes: All data are on per capita basis. Data on real Incomes of different types of households are obtained from household surveys. HH = household.

a/ For Yugoslavia, excludas changes in stocks.

b/ Real wages In the socialized sector.

c/ For Yugoslavia, includes all non-agricultural households, that Is, workers and pensioners.

____________________________
overall portfolio performance. We predict that households whose holding periods are positively correlated with transaction costs should have higher trading ability and earn higher _gross_ returns in addition to earning higher _net_ returns compared to households whose holding periods are negatively correlated with transaction costs. Hence, the second part of our second hypothesis is: **H2.b** : Households who pay closer attention to transaction costs earn higher gross returns before accounting for transaction costs.

In testing the above hypothesis, we consider both raw portfolio returns as well as characteristicsadjusted returns computed as in Daniel et al (1997).

# **3. Data**

# **3.1 Household Transactions and Demographics**

This study uses two distinct data sets in order to explore the trading behaviors of households. The first data set contains transactions for a subset of individual investors in the United States, while the second data set contains transactions of all investors in Finland. The individual trade data for the United States come from a major U.S. discount brokerage house. It records the daily trading of 78,000 households from January 1991 to December 1996 and this is the same data set used in Barber and Odean (2000). We focus only on the common stock transactions of households in this study, which account for nearly two-thirds of the total value of household investments. We exclude from the current analysis investments in mutual funds (both open-end and closed-end), American Depositary Receipts (ADRs), warrants, and options. About 66,000 of the 78,000 households trade common stocks, making about 800,000 transactions over the sample period. The transaction record includes the number of shares traded, the price traded, each household’s month-end positions, and 10
# **Table A9: Ordered Probit and Probit Regressions, 2006**

Financial Vulnerability Regressions (Marginal Effects)

|||**arrears**|**2**<br>|||**arrears**|**3**<br>|||**himedc**|**ost**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**mortgage**|**renter**|**Pseudo r**<sup>**2**</sup>|**t-test**|**mortgage**|**renter**|**Pseudo r**<sup>**2**</sup>|**t-test**|**mortgage**|**renter**|**Pseudo r**<sup>**2**</sup>|**t-test**|
|_New EU Countries_|||||||||||||
|Czech Republic|-0.0009|0.019 **|0.15|-3.09 ***|0.0120|0.0217|0.13|-0.48|0.0002|-0.0005|0.12|0.92|
|Estonia|-0.0268 ***|0.0134|0.13|-3.73 ***|-0.0084|0.0289|0.14|-1.64|-0.0060|0.0077|0.22|-2.26 **|
|Hungary|0.0392 ***|0.0322 **|0.11|0.40|0.0145|0.0663 *|0.08|-1.31|0.0067|0.0129 ***|0.07|-0.86|
|Latvia|0.0310 **|0.0728 ***|0.11|-1.95 *|0.0029|0.0596 ***|0.09|-2.26 **|0.0006|0.0343 ***|0.16|-1.96 **|
|Lithuania|0.0577|0.0022|0.11|1.08|0.0473|0.0272|0.08|0.33|-0.0017|-0.0110|0.12|0.46|
|Poland|0.0148|0.0317 ***|0.10|-0.67|-0.0215|0.0343 **|0.06|-1.57|-0.0132|-0.0050|0.09|-0.62|
|Slovak Republic|0.0250 ***|0.0396 ***|0.08|-0.69|0.0099|0.0294 *|0.07|-1.28|-0.0008|0.0040|0.12|-0.95|
|Slovenia|0.0068|0.0149|0.07|-0.31|0.0278|-0.0344 **|0.07|1.56|||||
|_EU-15 and Cyprus_|||||||||||||
|Austria|0.0009|0.0049 **|0.22|-1.56|0.1148|0.1075 ***|0.23|-1.99 **|-0.0008|-0.0001|0.19|-1.22|
|Belgium|0.0160 **|0.0329 ***|0.17|-2.15 **|0.0086|0.0198|0.14|-0.94|0.0029 *|0.0057 ***|0.24|-1.23|
|Cyprus|-0.0102|0.0211 *|0.08|-2.05 **|-0.0377|0.0046|0.11|-1.33|-0.0005|0.0016|0.11|-0.27|
|Denmark|-0.0236 ***||0.18||-0.0333 ***||0.14||-0.0004 ***||0.18||
|Finland|0.0309 ***|0.0218 ***|0.11|1.29|0.0171|0.0694 ***|0.15|-4.71 ***|-0.0036|0.0053|0.08|-1.87 *|
|France|0.0034|0.0466 ***|0.16|-6.20 ***|-0.0033|0.0197 *|0.13|-3.12 ***|0.0077 **|0.0157 ***|0.14|-2.27 **|
|Greece|-0.1175 ***|0.0659 ***|0.16|-5.69 ***|-0.2192 ***|-0.0004|0.18|-6.07 ***|0.0023|0.0186 **|0.07|-1.10|
|Ireland|0.0053|0.0628 ***|0.22|-4.29 ***|-0.0010|0.0327 ***|0.17|-3.19 ***|0.0035|0.0051|0.14|-0.30|
|Italy|0.0415 ***|0.0627 ***|0.12|-2.52 **|0.0044|0.0415 **|0.08|-1.86 *|0.0227 ***|0.0234 ***|0.08|-0.47|
|Luxembourg|-0.0139 **|0.0064|0.15|-3.03 ***|-0.0047|0.0133 *|0.19|-2.19 **|0.0255 **|0.1011 **|0.29|-2.24 **|
|Netherlands|-0.0221 ***||0.12||-0.0661 **||0.08||-0.0002||0.20||
|Portugal|-0.0010|-0.0038|0.05|0.30|0.0127|0.0059|0.10|0.75|0.0249 ***|0.0244 ***|0.14|0.25|
|Spain|0.0193 ***|0.0298 ***|0.07|-0.98|0.0289 **|0.0636 ***|0.08|-1.72 *|0.0014|0.0008|0.12|0.75|
|Sweden||0.0514 ***|0.11||-0.0733 ***||0.15|||0.0178 ***|0.10||
|United Kingdom|0.0002|-0.0005|0.10|1.32|0.0130 **|0.0328 ***|0.10|-3.67 ***|0.0005|0.0011 *|0.13|-0.81|

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

Source: EU Statistics on Income and Living Conditions (SILC) database, 2006 48
50 more often. The data from a number of Swiss pension funds show that this is not the case. Bütler and Teppa (2005) argue that this may be due to a lack of alternative longevity insurance by the family, and an absence of a bequest motive. It is also not clear, how much individuals realize the large differences in the annuity values, especially as a uniform conversion factor suggests an equal treatment. Moreover, there are hardly any arbitrage opportunities (take the lump sum and buy a private annuity contract), as the offered conditions in private annuity contracts are rarely more favorable than those of second pillar providers even for single men, possibly due to selection effects.

# **_4.3.3  The Recent Reform_**

Table 13 reports the MWR based on the slowly decreasing conversion factors and the increase in the female retirement age over the next ten years, based on a flat discount rate of 3.5 percent. The overall picture is that despite the relatively high chosen discount rate, MWRs stay very high. The reduction in the conversion factor comes too late and is not large enough. As can be seen from the two columns “single male” and “joint”, the reduction is barely able to compensate the expected increase in longevity over the next ten years. It is too small to offset the increase in life expectancy since 1985.

**Table 13  Projected Money's** **<u>Worth Ratios</u>**

|Year|Gender|R.A.|C.F.|Single<br>FIF|female<br>(SFSO)|Singl<br>FIF|e male<br>(SFSO)|Joint (<br>FIF|F=61.3)<br>(SFSO)|
|---|---|---|---|---|---|---|---|---|---|
|2004|F<br>M|62<br>65|0.0720<br>0.0720|1.175|(1.191)|0.959|(0.904)|1.120|(1.104)|
|2005*|M|65|0.0715|||0.952|(0.902)|1.117|(1.110)|
|2006|F<br>M|63<br>65|0.0715<br>0.0710|1.148|(1.153)|0.953|(0.899)|1.113|(1.096)|
|2007**|F<br>M|64<br>65|0.0720<br>0.0710|1.133|(1.144)|0.957|(0.904)|1.117|(1.100)|
|2008|F<br>M|64<br>65|0.0710<br>0.0705|1.122|(1.132)|0.954|(0.901)|1.113|(1.097)|
|2009|F<br>M|64<br>65|0.0700<br>0.0705|1.111|(1.120)|0.958|(0.905)|1.117|(1.100)|
|2010|F<br>M|64<br>65|0.0695<br>0.0700|1.107|(1.116)|0.955|(0.903)|1.112|(1.096)|
|2011|F<br>M|64<br>65|0.0690<br>0.0695|1.103|(1.112)|0.952|(0.900)|1.108|(1.092)|
|2012|F<br>M|64<br>65|0.0685<br>0.0690|1.099|(1.108)|0.948|(0.898)|1.104|(1.088)|
|2013|F<br>M|64<br>65|0.0680<br>0.0685|1.095|(1.104)|0.945|(0.895)|1.099|(1.084)|
|2014|F<br>M|64<br>65|0.0680<br>0.0680|1.098|(1.108)|0.941|(0.892)|1.095|(1.079)|

MWRs for the conversion rates specified in the law. The computations are based on a constant nominal interest rate of 3.5 percent. Joint annuities are for men only (number in parentheses = age of spouse at retirement of main claimant). * = MWR for female is not reported as there are no women to reach the statutory retirement age. ** = women born in 1943 can choose to retire at 63 (at a slightly reduced conversion factor) or at age 64. Mortality rates taken from FIS and SFSO (in parenthesis), they are updated by improvement rates from SFSO.

Source: Authors’ Calculations
document harassment as a demand mechanism for the reserved space. We start by estimating the impact of riding in the reserved space on the incidence of harassment relative to riding in the public space. We then combine data from our revealed preference experiment with experimental variation in riding location to relate incidence of harassment to riders’ demand for the reserved space. Finally, we explore the role of perceived norms around a woman’s choice of space in driving demand for the reserved space.

# **6.1 Experimental Assignment to the Reserved or the Public Space**

We run an experiment in which we assign riders a paid offer to ride in either the reserved space or the public space at random, and ask them to report on their ride experience through the app.

In contrast with the setup in the previous phase, riders are now offered a ride task for a specific space for a fixed payment of $4.70 per ride through the same app.<sup>30</sup> Upon logging into the app, a participant could see only whether she had an offer on that day and, if so, in which space she was assigned to ride (Panel (c), Figure 1). Each participant was offered several iterations of each car type in a random sequence across days and could not predict their sequence of rides. The space assignment on a given day was independent of previous assigned spaces and a rider’s decision to take up those tasks. At the end of each ride, participants were asked to report experiences of harassment on their journey, including whether any stranger had “made comments that made you uncomfortable”, “touched you intentionally in a way that made you feel uncomfortable”, or “stared at you”. Whenever a rider reported any harassment, the app directed her to resources available in the Rio area. Participants were also asked if they felt concerned about physical harassment and to report their emotional state on a scale of 1 to 10, overall and on specific items: happy, sad, tense, relaxed, frustrated, and satisfied.

> 30Pay-out at this stage was fixed at the highest pay-out from the previous phase, to avoid discouragement (Table A2).

23
producer and consumer prices of rice, a set of variables that describe the income structure, demographic characteristics, and the presence of irrigation and other infrastructure. We examine the correlation between these factors and the change in total consumption in the framework of a simple OLS regression, using a community-level panel data set with approximately 230 _thana_ in rural areas that were included in the 199596 and 2000 HIES. In order to explore the differential effect of changes in prices and yields on different household categories within the framework of community-level panel data, we create three data sets by averaging the characteristics of the poor and the nonpoor and then pooling them into a single data set with approximately 460 observations. We proceed in the same way for farmers versus non-farmers and net sellers versus net buyers. We include a set of demographic control variables in the regression, as well as variables reflecting the production structure, dummy variables to control for unobserved heterogeneity by location and the economic variables of interest, such as percentage change in yields and prices and interactive variables of these changes with different household categories.

Regression results, shown in Appendix Table A3, reinforce the earlier finding that the poor have experienced significantly higher income growth than the non-poor (Model 1). Share of rice in irrigation, which reflects the production structure and the role of irrigation in the production process, has a positive significant effect on income growth for farmers compared to non-farmers (Model 2) and net sellers compared to net buyers (Model 3). Interestingly, having more women as a share of household size is associated with higher rates of income growth in Models 2 and 3, while the share of children has a positive effect on income of the net sellers/net buyers group and a negative effect on the poor/non-poor group. Furthermore, higher shares of remittances, agricultural wages and other income from non-agricultural sources income are associated with higher income growth. This finding might point to the significant role of multiplier effects and indirect labor market effects of the improvement in rice productivity observed in Bangladesh during this period.

Turning to the variables of most interest, we find that, as expected, the change in the price of paddy (i.e., producer price) has a significant impact only on farmer households and net sellers. It means that a smaller decline in the price of rice is 22
Tenreyro 2006). To make sure the results are not subject to aggregation bias, we also run the same specifications as in equation (3) but at the product level and using exporter-product and importer-product fixed effects, which also vary for online and offline flows.

To uncover what drives the difference in distance coefficients online and offline we use interaction terms between distance and country or product characteristics that capture information asymmetries and trust problems. The idea is that if the results are partly driven by technologies that reduce information asymmetries and trust problems, the differences in the distance coefficients online and offline should be larger for countries and products affected by information asymmetries.

# **4 Results**

Table 1 provides the results of the estimation of (3) using distance as the only trade costs in columns (1) and (5). The elasticity of distance is 61 percent smaller online than offline. In columns (2) and (6) of Table 1 we provide the estimates of (3) including the other usual trade costs variables. When we introduce these additional trade costs, the coefficient on distance declines both online and offline. Still it remains around 65 percent smaller online, suggesting a flatter world on the eBay platform.

Some interesting patterns emerge regarding the other trade-cost variables. Common legal systems, trade agreements, colonial links and borders seem to matter much more offline. On the other hand the absence of a common language seems to matter more online than offline. We test for the statistical significance of these differences by appending the online and offline datasets and estimating the gravity equation including interactions of each trade cost with an eBay dummy which takes a value of one if the flow on the left-hand side is the eBay flow and zero if it is the offline flow. As argued above we also include importer-eBay and exporter-eBay fixed effects that control for any country-level differences between importers and exporters online and offline. As seen in Table 2, we find that the difference in the effect of distance is statistically significant. What is more, we find that the absence of colonial links and common legal systems also matter significantly less online, though only at the 90 10
<!-- Start of picture text --> Quarterly Poverty Estimates: Urban regions 1-4 and 7<br>5 4<br>—<br>3 a \<br>&<br>T T T T T T<br>2000 2002 2004 ' 2006 2008 2010<br><!-- End of picture text --> <!-- Start of picture text --> Quarterly Poverty Estimates: Urban regions 5, 13 and 14<br>5<br>8 T T T T T T<br>2000 2002 2004 ' 2006 2008 2010<br><!-- End of picture text --> <!-- Start of picture text --> Quarterly Poverty Estimates: Urban regions 6 and 10-12<br>~<br>g<br>oN<br>8 ws<br>: eeea<br>8 T T T T T T<br>2000 2002 2004 ' 2006 2008 2010<br><!-- End of picture text --> <!-- Start of picture text --> Quarterly Poverty Estimates: Urban regions &, 15 and 16<br>3<br>5<br>8<br>3 —<br>8 T T T T T T<br>2000 2002 2004 ' 2006 2008 2010<br><!-- End of picture text --> <!-- Start of picture text --> Quarterly Poverty Estimates: Urban region 9<br>s-<br>8 ———<br>3<br>8<br>a<br>g ie<br>5 —<br>2000 2002 2004 ' 2006 2008 2010<br><!-- End of picture text -->
where ln( _online exports_ ) _x,m,t_ and ln( _exports_ ) _x,m,t_ are respectively the online and total exports from country _x_ to country _m_ in year _t_ . _βx,m_ and _αx,m_ control for time-invariant country-pair specific determinants of trade flows such as distance, colonial linkages, common language, etc. _βx,t_ and _αx,t_ are year and country specific shocks in the exporting country. We use the estimate in equations (2)(7) to predict the log of bilateral online and total exports. To estimate the impact of online trade on the wage gap, we need to aggregate the bilateral exports flows at the exporter and year level. We first take the exponential of the predicted bilateral trade flows estimated by equations (2)-(7). We then aggregate these trade flows at the exporter and year level by summing the trade flows over all importers each year. After taking logs we use these six variables to instrument the log share of online and total exports in GDP.

# **3 Data**

The variable of interest is the wage gap between skilled and unskilled workers. We borrow the data and methodology from Cruz and Milet (2017). The data source is the International Income Distribution Database (I2D2) put together by the World Bank which compiles more than 1,000 household and labor force surveys from 164 countries over the period 1960-2014 covering more than 120 million individuals. The data have been harmonized to the extent possible ensuring that conceptual variables have similar meaning, allowing for cross-country analysis.<sup>4</sup> For the purpose of this study, we selected a restricted sample of 22 countries for which sufficient data are available.<sup>5</sup>

> 4See Montenegro and Hirn (2009) and Montenegro and Patrinos (2014) for more information about the database.

> 5See table 1 for a list of countries and surveys.

6