rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1 Sectoral value added in Tunisia**

||_Millions of US dollars_|_Percentage of total_|
|---|---|---|
|Agriculture, other primary and food|3281|20.1|
|Other manufacturing|2111|12.9|
|Electricity|344|2.1|
|Gas and water|126|0.8|
|Construction|975|6.0|
|Trade|2744|16.8|
|Other transport|1194|7.3|
|Air transport|382|2.3|
|Communications|778|4.8|
|Other financial services|557|3.4|
|Insurance|67|0.4|
|Other business services|680|4.2|
|Other services|3098|19.0|
|**Total**|**16336**|**100.0**|

_Source_ : FTAP model database, based on the GTAP version 6 database (Hertel 1997).

30
PPPs, and by Ravallion, Chen, and Sangraula (2009) to $1.25 per person per day at 2005 PPPs, the latter of which is known today as the Global Extreme Poverty Line and is computed as the average of the poverty lines of the 15 poorest countries in the world. Recently, Jolliffe and Prydz (2015, 17) proposed an update of the Global Extreme Poverty Line which resulted in a poverty line of $1.92 per person per day in 2011 PPPs, while Ferreira et al. (2015) calculated it as $1.88 (rounded to 1.90) per person per day.<sup>3</sup> For the particular case of LAC, the majority of governments in the region use two poverty lines: an extreme (food) and a moderate (non-food) poverty line. Countries estimate their extreme poverty lines as the lack of per capita income required to access a basic food basket and expand them to non-food components using the Orshansky coefficient. The Global Extreme Poverty Line frequently used for international poverty comparisons in the developing world was derived from poverty lines set in the poorest Sub-Sahara African countries; therefore, they have limited applicability to the LAC context, a region composed mostly of urbanized middle-income economies. Consequently, almost all official poverty lines in the region are higher than $2 per person per day, and the share of the population under the Global Extreme Poverty Line is relatively low in most LAC countries. Furthermore, given the economic development of the region, analysts have used higher poverty lines in LAC. The World Bank currently uses an extreme and moderate poverty line of $2.5 and $4 per person per day in 2005 PPPs, respectively, which were widely accepted in LAC as the extreme and moderate regional poverty lines for the region.<sup>4</sup> However, little is known about the origin of these regional poverty lines since the approach, as well as the underlying information used to estimate them, have never been explicitly documented in the past.

This paper provides inputs to estimate both extreme and moderate poverty lines for LAC based on a consistent methodology and updated information that allows for meaningful comparisons of poverty across countries in the region. We use the most up-to-date _official_ extreme and moderate poverty lines chosen by LAC countries and used in their own poverty and social

> 3 Ferreira et al. (2015) updated to $3.10 in 2011 PPPs the $2 per person per day poverty line in 2005 PPPs, commonly used for middle-income countries.

> 4   López-Calva and Ortiz-Juarez (2014); Gasparini, Cicowiez, and Escudero (2013); Ferreira et al. (2012); World Bank (2015); World Bank (2014); World Bank (2013); World Bank (2011).

3
In this expression, _S_ is the set of economic activity sectors reported by DANE, and _GDPst_ is sector _s_ ’s national GDP in year _t_ . _GSdks_ is the share of SCP graduates who work in sector _s_ in department _d_ and have graduated from a field- _k_ program relative to all SCP graduates employed in sector _s_ across the 13 departments: These shares sum to one by sector when adding over all fields and departments in our sample. They are time-invariant and are calculated by pooling all years available in OLE (2007 through 2013).<sup>12</sup> Our GDP-by-field variable captures the notion that exposure to the aggregate, national shocks experienced by an economic sector varies across SCP graduates depending on their field of study, work location, and work sector (Bartik, 1991; Goldsmith-Pinkham et al., 2020). The aggregate shock to sector _s_ is captured by _GDPst_ . Although this sector-specific shock is common to all departments and fields, the degree of exposure for field- _k_ graduates who work in department _d_ is determined by the time-invariant employment share, _GSdks_ . In the simplest case, if all SCP graduates employed in sector _s_ in the country had graduated from the same field and worked in the same department, then that department and field would absorb the full shock to the sector.

**Field relative employment.** Similar to the construction of employment shares for GDP by field, we use OLE data from 2007 to 2013 to obtain the total number of SCP graduates employed in a given department, independently of the department where they studied. Among them, for a given year we calculate the proportion of graduates with a field- _k_ degree as follows:

The GDP-by-field and relative employment variables seek to capture local labor market

> 12If we had used time-invariant shares from a single year rather than pooling all available years, this would have yielded shares equal to zero or one for some small departments. Among the years with OLE availability, 2013 is the one where these issues are least concerning. When we use shares for 2013 rather than pooling 2007 through 2013, results are virtually unchanged. While we would have preferred to have used shares from some year prior to the beginning of our study period (2003), OLE is only available beginning in 2007. Note, however, that our regressors include a rich set of time, location, and field-category fixed effects.

11
5 REVIEW AND FUTURE DIRECTIONS Table 4: Export Processing Zones sector contribution in 2012 (%)

|Sector|Number<br>Firms|of|Employment|Exports|Total Sales|Investment|
|---|---|---|---|---|---|---|
|Agro<br>pro-|22||11|15.9|14.7|16.1|
|cessing|||||||
|Beverages|4||0.5|0.9|0.8|0.6|
|Chemicals|1||0.2|0.1|0.1|2.9|
|Dartboard|1||0.7|1.5|1.4|1.8|
|Electricals|3.7||0.1|6.9|6.3|1.2|
|Food<br>Pro-|3.7||0.9|1.0|1.5|5.6|
|cessing|||||||
|Garments|26.9||79.7|53.1|48.9|27.9|
|Garment|6.1||0.1|0|0.1|0.2|
|supported|||||||
|services|||||||
|Minerals,|4.9||1.2|8.2|7.5|20.3|
|Metals,<br>and|||||||
|Gemstones|||||||
|Medicine|2.4||0.8|1|1.4|2.7|
|and<br>medical|||||||
|services|||||||
|Plastics|6.1||1.3|1|1.3|3|
|Printing|1.2||0.7|1.5|7.5|6.2|
|Relief<br>sup-|2.4||0.3|2.5|2.5|1.4|
|plies|||||||
|Services|12.2||2.5|6.4|6.0|10|
|Other|2.4||0.02|0.01|0.01|0.01|
|**Total**|100||100|100|100|100|

_Source:_ Adapted from Export Processing Zones Annual Report 2012

# **5.4.4 Conclusion**

Greater competition from imports lowers consumer prices and gives some producers access to cheaper inputs and capital goods. Chinese imports do not necessarily displace domestic production, as they may replace imports from other countries, lowering the cost of imports. The imports could also boost labor productivity within certain sectors and increase employment in services. But Kenya must still work to improve its manufacturing sector to compete with China’s low cost manufacturing; Kenyan producers have to upgrade their skills or specialize in areas where they have a comparative advantage. To improve exports, Kenya could negotiate for duty free access for cut flowers as part of the 404 primary products that China already allows to enter duty free. Exports to China, especially of services, may increase once China transitions to a consumptiondriven economy closer to 2030. When focusing on trade, policy makers should shift attention to improving export competitiveness and the overall balance of trade. Kenya’s exports need to improve overall, not just to China.

41
in the sample. The final sample comprised a total of 14,306 individual records. Of these, approximately 80% represented individuals aged 18 years and over and the remainder, individuals aged 10 to 17 years. The diary had a fixed time interval of 30 minutes. A respondent could name up to three activities in each time slot, with a code to denote whether they were conducted simultaneously or sequentially. Fieldworkers administered face-to-face questionnaires that contained separate sections for questions at the level of the household, for those asked of each respondent and for the time use diary of each respondent.

The survey instruments for the Nicaraguan EMNV98 included three questionnaires: a household questionnaire, an anthropometric questionnaire and a price questionnaire. The time use module was included in the household questionnaire and administered to half the households in the surveyed dwellings, selected on the basis of “every other” household. The module was administered to all individuals in the household aged 6 and older and collected information on the time spent the day previous to the interview. The anthropometric information was collected from every household member. The price questionnaire was administered at the community level in rural areas and at the municipality level in urban areas.<sup>44</sup> The final full sample contained 23,643 individual records and the sample for which time use data were collected, 9,390 individual records. Of these, approximately 60% represent individuals aged 18 years and over and the remainder, 6 to 17 years.

The two surveys illustrate different problems. In the first instance, because the SSA TUS2000 collected data by diary, almost all records satisfy the time constraint and no record exceeds it. In the EMNV98 over 10% of records do not satisfy the time constraint, and of these almost half exceed it. A second advantage of the SSA file is that, as noted above, it contains information on up to three simultaneous activities in a given (half hour) episode. Information on a second simultaneous activity is particularly important because

> 44 The goods included were based on the basic consumer basket used by the National Institute for Statistics and Census (Instituto Nacional de Estadistican y Censos) (INEC)

44
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6 more readily available on micronutrient interventions, and there are also more cost-effectiveness studies in this area. Data on the other nutrition interventions (nutrition education, breastfeeding promotion, growth monitoring) listed earlier are even more scanty.

The present study is therefore forced to use cost data which are less than ideal, in an effort to learn something about operation of projects other than research ones. There is a fair amount of consistency even with these less-than-ideal data, but there is also a lot of noise in the data. It is also not clear whether it is worth advocating that agencies operating nutrition interventions expend a great amount of effort in collecting cost data. USAID did a field test of a cost information system developed for PL-480 Title II programs operated by NGO's, and came to the conclusion that it was not worth the effort (Bremer and Gilmore, 1986). The authors argued that although the on-site costs of different interventions differed (school feeding, MCH, and food-for-work), this information was not used as an allocative devices for food aid. The same authors concluded that cross-country comparisons were also potentially dangerous. It would be politically extremely difficult to make value judgements about aid allocations, if for example it were shown that programs in Africa were more costly or less cost-effective that those in South Asia. However some of the programs (in particular the WFP and the USAID-funded PVO Child Survival Program) have made more efforts at standardizing cost and beneficiary data, and this lead might be followed especially by the larger NGO's and by the USAID title II food aid program.

Well-controlled impact data are even scarcer than good cost data. Indeed, some nutritionists have argued that one should not expect to see impacts of feeding programs on children's growth, when children are over 3 years old (Beaton, 1990). Haaga et al (1984) and others have discussed the methodology involved in interpreting change in nutritional status in the program setting. One problem with existing data is that the definition of severe malnutrition (in terms of number of standard deviations below the median, or percentiles of the reference population) is not always
Table 2: Business Characteristics of Participants

|**Variable**|**Categories**|**Control**<br>**(%)**|**Intervention**<br>**(%)**|**X**<sup>**2**</sup>|**p‐value**|
|---|---|---|---|---|---|
|Business|Manufacturing|33.3%|31.4%|4.911|0.427|
|sectors|Retailing|13.7%|17.0%|||
||Services|12.9%|17.8%|||
||Wholesale|5.1%|6.8%|||
||Other|35.0%|27.1%|||
|Size|Micro (0‐20)|86%|88%|1.131|0.770|
||Small (21‐50)|7.6%|7.7%|||
||Medium (50+)|5%|4%|||

_Note:_ The value displayed for the t‐tests are the p‐values of difference across the groups. ***, **, and * indicate significance at the 1, 5, and 10 percent levels, respectively Source: Saraf, 2019; World Bank Group, 2018 Table 3: Psychological Characteristics of Participants

||**Co**|**ntrol**|**Tre**|**atment**|**t‐test**|
|---|---|---|---|---|---|
|Variable|N|Mean/SE|N|Mean/SE|p‐value|
|PHQ‐ADS score|117|7.215|118|6.678|0.639|
|||[0.816]||[0.769]||
|PHQ‐ADS prevalence|117|29.9%|118|22.0%|0.170|
|||[0.043]||[0.038]||
|Transformed WHO‐5 score|116|77.931|117|82.393|0.096*|
|||[1.984]||[1.786]||

_Note:_ The value displayed for t‐tests are the p‐values of difference across the groups. Standard errors in brackets. ***, **, and * indicate significance at 1, 5, and 10 percent levels respectively. Source: Saraf, 2019; World Bank Group, 2018

# 5.5 Intervention Implementation, Take‐up, and Attendance Rates

A total of 118 participants received the intervention in 12 groups at 9 locations . Training was organized in the form of ` _affinity groups_ ` based on the gender and location of the participating entrepreneurs based on inputs from the rapid needs assessment. Fourteen trainers participated in the _training of trainers,_ and each group (with 10 participants on average) was led by one of these trainers and a co‐facilitator. The five interactive sessions were, on average, approximately three hours each.

Overall, workshop attendance exceeded the usual rates seen in FCV contexts.<sup>6</sup> However, attendance was lower than the assumed 75 percent, with 63.5 percent of the 118 participants

> 6 Typically, due to implementation challenges, attendance rates are rarely above 50 percent for interventions carried out in emergency areas.

14
of women at the top of the income distribution and discuss why female presence in the top 1% highest earners in Honduras seems to be larger than in many high-income countries (Neef & Robilliard, 2021).

A second key contribution is the construction and use of a shareholder dataset to assign undistributed corporate profits to firm owners in proportion to their ownership shares. Distributing corporate income to individuals is a challenging task – Alvaredo _et al._ (2020) note how being able to ”match the individual profit of firms to the tax record of the owners (...) is very rare” and Piketty _et al._ (2022), in reference to Bruil _et al._ (2022), mention their study is ”the most sophisticated, as it links individual business owners to their businesses and hence can assign business-level taxes to individual owners with great precision”. Other than a handful of exercises in high-income countries such as Norway (Alstadsæter _et al._ , 2016), Chile (Fairfield & Jorratt De Luis, 2016) and Uruguay (De Rosa & Vil ́a, 2023), to our knowledge we provide the most detailed account of the importance of business income at the top of the distribution for a middle-income country.

Finally, this paper builds on the broader DINA initiative, providing a new data point on top income shares using high-quality tax records combined with survey and national accounts data.<sup>1</sup> Recent studies have provided valuable insights on income inequalities across the Middle-East (Alvaredo _et al._ , 2019, Assouad _et al._ , 2018), Asia, Africa (Czajka, 2020) and Europe (Bruil _et al._ , 2022). In Latin America, only 11 countries are considered to have ”high-quality” measures of income inequality by the World Inequality Database project, due to the lack of available tax records.<sup>2</sup> Recent research on top income inequality in the region includes Alvaredo & Londo ̃no Velez (2014) on Colombia, Zu ̃niga Cordero (2022) on Costa Rica, Alvaredo _et al._ (2017) on Mexico, Burd ́ın _et al._ (2022) on Uruguay, Morgan (2018) on Brazil and Flores _et al._ (2020) on Chile. As an illustration, the current level of top 1% income share in Honduras in the WID is 18.3%, obtained by ”regional imputation”.<sup>3</sup> As with other recent studies in the region, we show that new estimates using detailed tax records might provide a very different perspective on the levels and trends of top income shares in Latin America.

# **2 Context**

Honduras is a lower-middle-income country in Central America with approximately 9.8 million inhabitants. Economic activity is linked to low value-added agricultural

> 1The existing literature in Honduras exclusively exploits survey data (see for instance Ham (2011), World Bank (2016) and S ́anchez & Urrea (2019))

> 2Still, this figure includes countries where there is no micro-data but tax-tabulations e.g., Brazil, and interpolation techniques are used to estimate incomes at the top.

> 3Both values, the number of countries identified as with ’high-quality’ data, and the Honduran top income share, are obtained from the WID website (October 2023).

3
|**Table 9: Split sam**|**ple results fo**|**r the interact**|**ion term betw**|**een matern**|**ity leave and w**|**age rate**|
|---|---|---|---|---|---|---|
||**Low skill**<br>**educated**<br>**minor,**|**s obstacle: Ina**<br>**workers is no**<br>**or moderate**|**dequately**<br>**obstacle,**<br>**obstacle**|**High ski**<br>**educated**|**lls obstacle: Ina**<br>**workers is a ma**<br>**severe obstacle**|**dequately**<br>**jor or very**<br>|
|Dependent variable:<br>Female workers|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Maternity leave<br>(logs)*Wage rate<br>(real, logs)|**-1.874****<br>**(0.803)**|**-1.649****<br>**(0.676)**|**-1.227***<br>**(0.642)**|**-2.699*****<br>**(1.012)**|**-2.174****<br>**(0.933)**|**-1.656***<br>**(0.861)**|
|<br>Maternity leave (logs)|19.119**|16.478***|64.567|25.050***|20.111**|60.415|
||(7.815)|(6.305)|(44.662)|(9.214)|(8.714)|(60.519)|
|Wage rate (real, logs)|9.718***|7.092**|5.377*|13.005***|9.557**|7.995**|
||(3.710)|(3.114)|(2.833)|(4.674)|(4.300)|(3.949)|
|GDP per capita (logs)||3.878***|31.511**||1.389|43.907**|
|||(1.440)|(12.299)||(1.730)|(17.151)|
|Growth rate of GDP||0.594***|0.555***||0.153|0.095|
|per capita (%, annual)||(0.164)|(0.174)||(0.260)|(0.268)|
|Females in adult<br>||1.683***|1.837***||1.171**|1.184**|
|population (%)|||||||
|||(0.469)|(0.481)||(0.526)|(0.541)|
|Number of workers||-0.065|-3.014**||-0.647|-7.398**|
|(logs)||(0.277)|(1.366)||(0.439)|(3.241)|
|Female owner present||4.400***|5.992**||3.795***|-14.839**|
|<br>Y:1 N:0||(0.570)|(3.037)||(0.989)|(6.540)|
|Top manager is<br>female Y:1 N:0||13.665***|13.831***||12.592***|12.710***|
|||(0.846)|(0.829)||(1.433)|(1.416)|
|Age of firm (logs)||-0.283|-0.215||0.371|0.372|
|||(0.432)|(0.430)||(0.609)|(0.613)|
|Exports (% of sales)||0.033**|0.030**||0.019|0.014|
|||(0.015)|(0.015)||(0.022)|(0.022)|
|Foreign ownership||0.029***|0.023**||0.021|0.017|
|(%)|||||||
|||(0.011)|(0.010)||(0.015)|(0.016)|
|Firm offers formal<br>training to workers||2.337***|2.256***||1.581|1.565|
|Y:1 N:0|||||||
|||(0.618)|(0.614)||(1.061)|(1.061)|
|Inadequately educated<br>workers: Obstacle||-0.744**|-0.675*||1.215|1.116|
|severity (0-4 scale)|||||||
|||(0.341)|(0.344)||(0.985)|(0.981)|
|Firm registered when||0.659|0.577||2.474|2.474*|
|started operations Y:1<br>N:0|||||||

72
23 Table 4 Indexation of Bonds Principal

|Years (JanIst)|1989|1990|1991|1992|1993|1994|1995|1996|1997|1998|<br>1999|<br>2000|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Present System|||||||||||||
|Total Portfolio|274.0|245.1|223.2|204.9|186.8|169.6|1'4.0|139.7|127.0|115.5|106.3|<br>97.4|
|Installments|37.6|29.5|25.2|24.4|22.9|20.6|18.8|16.7|15.0|12.2|11.8|<br>11.1|
|-->Interests:|8.7|7.6|6.9|6.3|5.7|5.1|4.5|4.0|3.5|3.1|2.8|2.6|
|-->Pincipal|28.9|21.9|18.3|18.1|17.2|15.6|14.3|12.7|11.5|9.2|8.9|8.5|
|With Indexation,|||||||||||||
|Interests Paid|(a)|12.3|13.7|14.9|16.0|16.9|17.6|18.3|19.0|19.5|20.2|21.0|
|OutstandingPrincipal|||||||||||||
|in nominal termns|(b):|245.1|273.1|298.9|319.6|337.6|352.4|366.3|379.2|390.5|404.7|420.1|
|in real terms||245.1|217.9|196.8|178.4|162.4|148.7|136.8|126.4|117.3|109.5|<br>102.4|
|Subsidies|(c)|4.6|5.2|_5.8_|6.3|6.8|7.3|7.8|8.5|9.1|9.4|9.7|
|Years|2001|2002|2003|2004|2005|2006|2007|<br>2008|2009|2010|2011|2012|
|Present System|||||||||||||
|Total Portfolio|88.9|80.9|73.0|65.3|57.7|50.3|43.8|37.3|31.2|25.2|19.2|13.4|
|Installments|10.4|10.1|9.6|9.3|9.0|7.9|7.6|7.1|6.9|6.7|6.4|5.5|
|->Inteiests:|2.4|2.2|2.0|L.8|1.6|1.4|1.2|1.0|0.9|0.7|_0.5_|0.4|
|->Principal:|8.0|7.9|7.7|7.6|7.5|6.5|6.4|6.1|6.0|6.0|5.9|5.2|
|With Indzxation,|||||||||||||
|interests<br>:|21.8|22.7|23.7|24.8|25.9|27.0|28.3|29.7|31.2|32.8|34.4|36.2|
|nominal debt:|436.8|455.0|474.4|495.2|517.3|540.9|566.8|594.4|624.0|655.4|688.7|724.2|
|real debt|95.9|90.9|84.6|79.5|74.8|70.5|66.6|62.9|59.5|56.3|53.3|50.5|
|Subsidies<br>:|10.1|10.5|11.0|11.4|11.9|12.4|12.9|13.4|13.9|14.4|14.9|15.0|

Notes: (a): On the indexed outstanding principal, interest rate: 5% (b): if P(t) represents this nominal outstanding indexed debt at year t, and I(t) the market interest rates at date t (here the scenario A of bond rates), then: P(t+ 1)= P(t) * (I +lftl) - interests(t) - Repayments of Principal under present System. I (a) 5% * P(t) (c): Subsidies just compensate :he differences of intcrest rates between the Average Rate of corresponding Portfolio and these 5%: the low initial subsidies result of this indirect recapitalization of interests.