rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Descriptive Statistics

|(A) Full sample, post-planting|Mean|SD|Min.|Max|n|
|---|---|---|---|---|---|
|Own-account HH-enterprise work (Y=1)|0.22|0.42|0|1|21,988|
|Agricultural work (Y=1)|0.38|0.49|0|1|21,997|
|Work outside the home (Y=1)|0.07|0.26|0|1|21,989|
|Hrs. total worked|18.29|22.18|0|114|13,543|
|Hrs. own-account HH-enterprise work|6.57|15.70|0|100|13,543|
|Hrs. agricultural work|9.52|15.93|0|84|13,543|
|Hrs. work outside the home<br>|2.21|9.64|0|92|13,543|
|(B) Full sample, post-harvest|Mean|SD|Min.|Max|n|
|Own-account HH-enterprise work (Y=1)|0.20|0.40|0|1|21,756|
|Agricultural work (Y=1)|0.26|0.44|0|1|21,757|
|Work outside the home (Y=1)|0.07|0.26|0|1|21,758|
|Hrs. total worked|12.45|20.33|0|124|14,212|
|Hrs. own-account HH-enterprise work|5.50|14.87|0|105|14,212|
|Hrs. agricultural work|4.58|11.59|0|84|14,212|
|Hrs. work outside the home|2.37|9.99|0|96|14,212|
|(C) Men, post-planting|Mean|SD|Min.|Max|n|
|Own-account HH-enterprise work (Y=1)|0.19|0.39|0|1|10,913|
|Agricultural work (Y=1)|0.44|0.50|0|1|10,924|
|Work outside the home (Y=1)|0.09|0.29|0|1|10,920|
|Hrs. total worked|21.31|23.40|0|114|6,351|
|Hrs. own-account HH-enterprise work|5.70|14.98|0|98|6,351|
|Hrs. agricultural work|12.64|17.62|0|84|6,351|
|Hrs. work outside the home|2.97|11.17|0|92|6,351|
|(D) Men, post-harvest|Mean|SD|Min.|Max|n|
|Own-account HH-enterprise work (Y=1)|0.18|0.38|0|1|10,907|
|Agricultural work (Y=1)|0.30|0.46|0|1|10,908|
|Work outside the home (Y=1)|0.09|0.29|0|1|10,909|
|Hrs. total worked|14.11|21.61|0|118|6,780|
|Hrs. own-account HH-enterprise work|5.08|14.50|0|98|6,780|
|Hrs. agricultural work|5.86|13.17|0|84|6,780|
|Hrs. work outside the home|3.16|11.58|0|96|6,780|
|(E) Women, post-planting|Mean|SD|Min.|Max|n|
|Own-account HH-enterprise work (Y=1)|0.26|0.44|0|1|10,384|
|Agricultural work (Y=1)|0.31|0.46|0|1|10,382|
|Work outside the home (Y=1)|0.05|0.22|0|1|10,378|
|Hrs. total worked|16.61|20.98|0|104|6,147|
|Hrs. own-account HH-enterprise work|7.99|16.87|0|100|6,147|
|Hrs. agricultural work|7.02|13.98|0|72|6,147|
|Hrs. work outside the home|1.60|8.12|0|84|6,147|
|(F) Women, post-harvest|Mean|SD|Min.|Max|n|
|Own-account HH-enterprise work (Y=1)|0.24|0.42|0|1|10,432|
|Agricultural work (Y=1)|0.22|0.41|0|1|10,432|
|Work outside the home (Y=1)|0.05|0.22|0|1|10,432|
|Hrs. total worked|11.57|19.34|0|105|6,611|
|Hrs. own-account HH-enterprise work|6.31|15.63|0|105|6,611|
|Hrs. agricultural work|3.53|9.93|0|70|6,611|
|Hrs. work outside the home|1.74|8.40|0|96|6,611|

_Source_ : Authors’ tabulations of individual-level GHS data. All panels include observations that are observed in the 2018-2019 GHS round and also at least one previous round; the sample is restricted to those that were at least 5 years old as of their first appearance in the data (when the labor module is first applicable). Borno state is excluded.

26
households, respectively, are likely to be poor in Haiti. However, in rural areas femaleheaded households (62 percent of which are poor) are much more likely to fall below the indigence poverty line than are male-headed households (54 percent are poor). These income poverty figures are, however, only part of the myriad of factors that affect a poor woman’s well being.

The HLCS data on domestic violence shows that the most common forms of violence that women have experienced are forced sex, being pushed or kicked and slapped. Women experienced slightly more of these forms of violence than men claimed to have committed. Of those women who were at any time beaten, around 50 percent were beaten roughly every month.

Migrants experience less poverty than non-migrants do in Haiti as a whole. Household heads who migrated from one region to another experienced 24-percentage points less poverty than did heads that never left, 30 percent of the former and 54 percent of the latter group fell below the US$1 a day poverty line. That is a difference of 80 percent. One explanation for this is that migrants are better endowed than non-migrants (Justesen & Verner 2005, Egset 2004). For rural areas the difference is much smaller (12 percent) between _stayers_ in rural areas in one region and _leavers_ to rural areas in another region. Hence, there is little difference between rural dwellers who never migrated and those who did migrate to a region different from that of birth.

Religious belief has only marginal impact on poverty status. The three major religions by number of adherents in Haiti are Catholicism, Baptism and Voodoo and 49, 51, 47 percent are poor respectively for each religion.

<!-- Start of picture text --> Figure 6.2: Headcount Poverty and Religious Affiliation by Location in Haiti, 2001<br>Other<br>Metropolitan<br>Voodoo<br>Baptist<br>Urban Catholic<br>Rural<br>Total Haiti<br>0 10 20 30 40 50 60 70<br>Source: Own calculations based on HLCS 2001.<br><!-- End of picture text --> Household heads with social capital are less likely to fall into poverty in Haiti as a whole. This finding also holds true for heads in metropolitan areas, but even more so in rural areas. The relationships among extended family members and neighbors form an important informal social safety net for sharing assets, responsibilities, and risks.

34
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Jencks, C. (1979). Who Gets Ahead? NewYork: Basic Books.

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21
(Wacziarg and Welch 2008). Between 2000 and 2018, the employment share in agriculture declined from 55 percent to 33 percent (or by 22 percentage points), while the employment share in services increased from 28 percent to 47 percent (19 percentage points). After a long period of stagnation, the employment share in industry rose slightly after 2013, from 17 percent in that year to 20 percent in 2018 (3 percentage points).

Figure 2b plots trends in average labor productivity per worker by sector between 1990 and 2018. Agriculture and industry have seen steady gains in average labor productivity over this subperiod. Starting from the early 2000s, average labor productivity in services declined slightly (from about 18,300 cedis per worker in 2000 to about 17,600 cedis per worker in 2018). Between 1990 and 2000, when the country exhibited little structural transformation, a basic decomposition of annual average labor productivity growth shows that virtually all of the gain in average labor productivity is attributable to within-sector productivity gains.<sup>4</sup> Between 2000 and 2018, when structural transformation was much stronger in the country, roughly 46 percent of the gain in average labor productivity is attributable to between-sector gains (that is, the movement of workers between sectors, or structural transformation) while the remaining percentage is attributable to within-sector gains.

Turning to the picture across regions within Ghana, Figure 3 plots trends in employment shares in agriculture, industry, and services by region, based on data from the 2005/06, 2012/13, and 2016/17 GLSS rounds.<sup>5</sup> These rounds of data are also used for other results presented in the remainder of this section.

Based on the figure, we note three general patterns across the regions. First, the employment share in agriculture has declined. Second, employment has mainly shifted from agriculture to services. Third, in richer regions, services have replaced agriculture as the dominant sector in terms of employment.

The shift to nonfarm employment across regions can be more clearly seen in Figure 4, which plots trends in nonfarm employment shares by region between 2005/06 and 2016/17. There is some indication that the shift to nonfarm employment has been generally stronger among poorer than richer regions.

C. _Regional informal shares of nonfarm employment_

> 4 See de Vries et al. (2015) for a description of the decomposition approach. 5 See GSS (2014) for survey design and implementation details for the 2012/13 GLSS.

7
for and supply of social services.

Source: Authors’ compilation based on program information.

The essence of _Chile Solidario_ is not a specific program or social benefit, but a management model based on the articulation of institutional and local networks to provide social protection to the poorest families. It aims to coordinate mechanisms to organize the delivery of integrated services to support families in extreme poverty (though, where gaps have been identified, _Chile_ Solidario led to the creation of additional programs), by guiding them to effectively use the social services network to overcome factors responsible for their poverty.

The Family Support component and the preparation of a family-specific development plan ( _Programa Puente_ ) are the backbone of _Chile Solidario_ . So is the definition of common and measurable objectives for both the institutions and the beneficiaries to achieve, structured around 53 “minimum quality of life” conditions, organized in 7 dimensions: identification, health, education, family dynamics, housing, work and income (see Table 4). By reducing the number of conditions from thousands to only 53, the program addresses the “choice overload” problem that prevents the extreme poor from effectively using the social protection system. Some conditions were already well covered, for example, more than 95 percent of the target population was already registered in the civil registry, while on the other hand only 33 percent had an income above the poverty line and 39 percent had an identify card.

Participation in the system is voluntary. The Family Support component is the entry point to the system, and is provided by a Family Counselor for 2 years, implementing a methodology based on family visits, information and guidance, including referrals to social services and benefits. The frequency of contact with each family decreases over time and each work session has a specific methodology. The main objective of this service is to provide psychosocial support to the family in the development of the skills necessary to access and use the network of available services.

The Family Support Service is complemented by a family cash transfer (flat amount per family), called _Bono de Protección Familiar_ , delivered on a monthly basis, preferably to a woman, that is intended to help finance the costs associated with access to services. The 12
27

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- ———. 2011. _Bolivia-Tanzania South-South Exchange for Low Cost Rural Electrification Strategies_ . Washington, DC.

———. 2012a. “Tanzania Affordability Analysis and Options for a Program to Make the Cost of Rural Household Grid Connections Affordable.” Draft report, Washington, DC.
To test if this is the case, variations of the basic specification are used to analyze affiliate R&D. These specifications take the form: The dependent variable measures the level of R&D spending conducted by affiliate _l_ of parent _i_ in year _t_ . The right hand side variables remain as they were in specification (1).

Changes in technology transfer are not constrained to take place between parents and their affiliates. Firms also license intangible assets to arm’s length parties. The prior literature, intuition, and the formal model laid out in Appendix B predict that this type of licensing may also increase after IPR reform. Equation (1) above can be slightly altered to study these flows: Here, the dependent variable measures royalty payments received by parent firm _i_ from unaffiliated parties in country _j_ in year _t_ . Unfortunately, the surveys that collect these data do not record detailed information on the characteristics of licensee or licensor firms. As a result, firm/country fixed effects ( α _ij_ ) take the place of affiliate fixed effects, and it is not possible to control for characteristics of firms that pay or receive licensing fees. Other right hand side variables include a vector of host country characteristics ( _Hjt_ ) , and host-country specific time trends ( _yjt_ ).

If multinationals do change the volume of technology transferred in a sustained manner and if these changes are a consequence of stronger IPR, then this should be reflected in the data on foreign patenting in reforming countries. In the absence of a truly international patent system, firms have to seek patent grants in each country in which 10
**Table 4. Conditional Correlations of Measures of Cognitive and Socioemotional Skills with Labor Outcomes, Controlling for Schooling**

|Outcomes|Log hourly labor<br>earning|Being a formal<br>worker|Being a high-<br>skilled worker|Being employed|
|---|---|---|---|---|
|Method|OLS|Logit|Logit|Logit|
||(1)|(2)|(3)|(4)|
|Reading proficiency|0.065|0.014|**0.061*****|-0.009|
||(0.06)|(0.02)|(0.02)|(0.02)|
|Extraversion|0.000|0.005|-0.000|-0.007|
||(0.04)|(0.02)|(0.01)|(0.02)|
|Conscientiousness|-0.034|-0.003|-0.002|**0.045*****|
||(0.04)|(0.02)|(0.01)|(0.02)|
|Openness to experience|**0.078****|-0.022|0.017|0.010|
||(0.03)|(0.02)|(0.01)|(0.01)|
|Emotional stability|-0.015|0.016|-0.007|0.015|
||(0.04)|(0.02)|(0.01)|(0.02)|
|Agreeableness|0.015|-0.014|-0.003|-0.016|
||(0.03)|(0.02)|(0.01)|(0.02)|
|Grit|-0.043|-0.025|0.007|0.004|
||(0.04)|(0.02)|(0.01)|(0.02)|
|Hostile attribution bias|0.023|**-0.030***|-0.003|-0.008|
||(0.03)|(0.02)|(0.01)|(0.01)|
|Decision making|-0.007|0.002|-0.002|**-0.033****|
||(0.04)|(0.02)|(0.01)|(0.02)|
|Education: below primary|0.015|0.103|0.091|-0.063|
||(0.11)|(0.08)|(0.07)|(0.06)|
|Education: upper secondary|**0.289*****<br>(0.10)|**0.198*****<br>(0.05)|**0.163*****<br>(0.04)|0.003<br>(0.04)|
|Education: vocational tertiary|**0.371*****<br>(0.11)|**0.263*****<br>(0.05)|**0.278*****<br>(0.04)|0.053<br>(0.05)|
|Education: general tertiary|**0.880*****|**0.348*****|**0.566*****|0.066|
||(0.15)|(0.06)|(0.05)|(0.07)|
|Observations|1,372|1,576|1,801|2,117|
|R-squared|0.16||||

_Source:_ Authors’ calculations based on Colombia STEP Household Survey (2012).

_Note:_ Standard errors are in parentheses. Conditional correlations are computed from ordinary least squares (OLS) regressions for labor earnings and logit regressions for labor supply outcomes. The bottom and the top 1 percent of the log hourly labor earnings distribution are trimmed. The outcomes of being employed and having attended tertiary education are restricted to adults ages 19–64 and ages 25–64, respectively.. OLS calculations control for being a woman (dummy), age, age-squared, mother’s education (dummies; primary education is the reference category), and cities of living and their metropolitan areas (dummies; Bogota-Barranquilla-Villavicencio is the reference category). Logit regressions control for the same variables and a self-reported categorical variable on parents' involvement in one’s education at the age of 12 (three levels). Average marginal effects are reported for logit regressions and reflect the changes in the probability of being observed in a labor or school participation situation with respect to the variables evaluated at the mean. Measures of reading proficiency and socioemotional skills are standardized. Regressions coefficients and standards errors of reading proficiency are the average of the 10 estimations using plausible values.

* _p_ < 0.1  ** _p_ < 0.05  *** _p_ < 0.01

18
**Figure 1. Common reduced VAT rates in OECD countries as at 1 January 2022**

<!-- Start of picture text --> Number of countries<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> Source: Author’s calculations based on OECD (2022) and European Commission (2021).

Most countries also use reduced VAT rates to encourage the consumption of certain goods and services with perceived social or cultural benefits. The most common examples are books and newspapers, which are subject to reduced VAT rates in 29 and 28 (out of 37) countries, respectively. As Figure 1 shows, countries often also provide reduced rates for cinema, theatre and concerts, and to a lesser extent for amusement parks, museums, sporting events, sporting facilities and zoos.

Some countries also introduce reduced rates on services that are close substitutes with home supply in order to encourage (market) employment, such as repair, domestic cleaning and hairdressing services. Many countries also provide reduced rates with a less clear policy rationale. The most common of these are for hotel (and/or other) accommodation, restaurant food, and passenger transport. Reduced rates for hotels and restaurant food may, for example, be introduced to encourage the employment of low-skilled workers, or for a perceived social benefit. Reduced rates for (domestic) passenger transport may be motivated by environmental concerns, but they may also be justified on employment or distributional grounds (on the basis that they lower the cost of commuting to work).<sup>6</sup> The existence of these reduced rates may also partially result from interest group pressure rather than from a clear policy rationale.

Though not the focus of this paper, it is also common for countries to exempt certain expenditures from VAT (i.e. zero rating with no ability to deduct input tax). Again, it is European countries that tend to have the greatest number of exemptions, whereas Chile, Japan and New Zealand tend to have the fewest.<sup>7</sup> Most countries exempt certain sectors considered essential for social reasons – particularly education, healthcare and charities. In some cases, practical reasons have led countries to use exemptions – for example, in every OECD country, most or all financial services are exempted due to the difficulty in

> 6 International air travel is universally zero-rated due to the practical difficulty in assigning taxation rights to a service that may occur across multiple countries. Some countries also apply zero rates to other forms of international passenger travel.

> 7 Unlike a zero rate, some VAT will still generally be payable on exempted goods and services. This is because the inability to deduct input tax will increase the cost, and hence the price, of the final good or service (unless the cost is fully borne by a party within the supply chain).

6