rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 600 m Railway Railway m Road Road<br>- = Inland water Inland water water ® Maritime Maritime<br><<< 500 Air @ Pipeline<br>2c<br>5400400<br>5<br>= 300<br>g<br>ie)££ 200<br>°==<br>Oo 100<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2009 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2014 2015 2016 2017 2015 2016 2017 2016 2017 2017<br><!-- End of picture text --> <!-- Start of picture text --> 4500 Railway @ Road Road<br>_ 4000 = Inland water_TM water_TM Maritime<br>522 3500 m Airii @ Pipelineipeliipeli<br>c<br>2 3000<br>£ 2500<br>a<br>& 2000<br>o99 1500<br>& 1000<br>& 500<br>0<br>2007 2008 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2014 2015 2016 2017 2015 2016 2017 2016 2017<br><!-- End of picture text --> <!-- Start of picture text --> 600 m Railway Railway m Road Road 4500 Railway @ Road Road<br>- = Inland water Inland water water ® Maritime Maritime _ 4000 = Inland water_TM water_TM Maritime<br><<< 500 Air @ Pipeline 522 3500 m Airii @ Pipelineipeliipeli<br>2c<br>5400400 2 3000<br>5 £ 2500<br>= 300 a<br>g & 2000<br>ie)££ 200 o99 1500<br>==<br>°== & 1000<br>Oo 100 & 500<br>0 0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2009 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2014 2015 2016 2017 2015 2016 2017 2016 2017 2017 2007 2008 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2014 2015 2016 2017 2015 2016 2017 2016 2017<br>_ 0.17 0.17 0.164<br>g ois 0-147 3 0.15<br>5° 0.134 «0.431 5° 0.137 0.135 0.132<br>3®%®% 0.13 0.116 0.119 3% 0.13 0.114::<br>8rr0.11 8 0.11<br>30.090.09 3 0.09<br>& 0.07 & 0.07<br>££<br>>0.050.05 > 0.05<br>°°<br>2F0F00 oe”o”o” o”ioio o”9?9? 2== i.os”os” oe”2”2” o”o”o” ov©© 9?<br>Market Access Index (0 to to 1) Market Access Index (0 to to 1)<br><!-- End of picture text --> <!-- Start of picture text --> 0.17 0.164<br>3 0.15<br>5° 0.137 0.135 0.132<br>% 0.13 0.114::<br>3%<br>8 0.11<br>3 0.09<br>& 0.07<br>£<br>> 0.05<br>2== i.os”os” oe”2”2” o”o”o” ov©© 9?<br>Market Access Index (0 to to 1)<br><!-- End of picture text --> <!-- Start of picture text --> _ 0.17<br>g ois 0-147<br>5° 0.134 «0.431<br>3®%®% 0.13 0.116 0.119<br>0.11<br>30.090.09<br>& 0.07<br>>0.050.05<br>2F0F00 oe”o”o” o”ioio o”9?9?<br>Market Access Index (0 to to 1)<br><!-- End of picture text -->
Note that the coefficients in columns (1)-(3) as well as columns (5)-(7) imply that the number of exporters, the extensive margin, explains about two-thirds of the variation in total exports across countries of different size, and about 60 percent of the variation in total exports as a result of stage of development.

Columns (9)-(12) in Table 4 present results using the country-sector-destination-year level data for years 2004 to 2008 for the sample of 34 developing countries and 3 developed countries.<sup>24</sup> This allows us to control for interacted sector-destination fixed effects in the same regression (as well as for bilateral gravity determinants), but has the disadvantage of less variation in terms of stage of development, with only 3 developed countries included. The results confirm that within a given sector-destination, larger countries and richer countries have more exporters, larger exporters, and a greater share of exports controlled by top 5 percent of exporters. Moreover, the findings are maintained in specifications that include more stringent sector-destination-year fixed effects to account for the possibility of temporal shifts affecting sectors and destination markets and influencing exporter behavior due to trade networks and relationships, as seen in Appendix Table A2.2 (Panel A). Of interest, in the country-sector-destination regressions, the contribution of the intensive margin rises when we control for sector and destination, especially with respect to GDP per capita: the intensive margin now accounts for more than half of the variation in exports with GDP per capita.

It is worth noting that the coefficients on trade costs shown in columns (5)-(12) of Table 4 indicate that they have opposite effects on export-sector characteristics, relative to country size and stage of development. Higher trade costs (proxied by higher distance, absence of either

> 24 As noted above, we do not have the raw exporter-level customs data for all the rich countries to be able to calculate the corresponding measures to estimate these regressions for all countries. We obtained the measures at the country-sector-year level for three of the rich countries, Norway, Portugal, and Spain which are used in the regressions.

18
Fig. 1 Growth of Income of the Poor and Economic Policy in the 1990s <!-- Start of picture text --> Growth of income of the poor<br>(percent per year)<br>0.08<br>*Vietnam<br>0.04 *Ucanda Ghana<br>* Nepal _ ~<br>0 -_Guyana<br>-0.04 -0.04 ia Et+pa * Madagascar*~~j Zambia<br>* Cote d'lvoire<br>-0.08<br>Nigean:<br>-0.12<br>Poor < Good<br>Economic policy<br><!-- End of picture text --> Source: Collier and Dollar (2001) Collier and Dollar also find that aid effectiveness and the quality of policies are positively correlated. In a country with good policies, aid has double the impact on investment-an additional 1 percent of GDP in aid results in 0.9 percent additional gross investment-as it does in a country following bad policies. The implication is that countries with many poor citizens and good policies should receive more aid than they do today.<sup>7</sup> Collier and Dollar (2001) show that if poverty and the quality of policies are taken into account when distributing aid, the effectiveness of aid could be increased nearly two-fold. Were more aid is spent in good policy countries, where its marginal impact is greater, they estimate that 19 million people a year could be lifted out of poverty, instead of the current 10 million people. Furthermore, if the development community supports poor-policy countries in their efforts to improve their policies, the payoffs to poverty reduction and progress in the other development goals will be much greater.

# Box 1: **Making Progress Toward the Goals: Uganda**

In the 1990s, Uganda made substantial progress towards the international development goals. Between 1992 and 1999, the share of Ugandans living in poverty fell from 56 percent to 35 percent. Net primary school enrollments increased from 62.3 percent in 1992 to 83.8 percent in 1997. The ratio of girls to boys rose from 79.8 percent in 1990 to 97 percent in 1997, and continues to rise at 1 percent a year. Although morbidity increased from 1992 to 1997 (due mainly to HIV/AIDS and malaria), since then the health status of all Ugandans appears to be improving. Under-5 mortality has declined (from 165 to 162), There has also been an improvement in child malnutrition, resulting in gains in stunting.

7 Collier and Dollar (2001) also state that aid has a limited impact on poverty reduction in middle-income countries because a relatively small share of the population in these nations is in poverty, which suggests that aid to these countries would not be the most efficient allocation in terms of poverty reduction per dollar spent.

14
- 10 -

For production factors, various spatial and other data are used. For labor, the number of population engaged in crop _j_ production at location _i_ is calculated from the rural population estimate and the probability of household’s engagement in crop production. The latter comes from the national statistics of each country, and the level of aggregation differs across countries.<sup>7</sup> The pixel-level rural population data is taken from the Global Rural-Urban Mapping Project (GRUMP).<sup>8</sup> The land data comes from the same source as the output variable, i.e., SPAM. It provides an estimate of land use for each crop at each location. Land has two types: rain-fed ( _R_ ) and irrigated ( _I_ ). There is little irrigated area in East Africa (Figure 4). According to the SPAM data, there is no irrigated land in the region for bananas, for which the variable _I_ is omitted from the model.

The fertilizer variable represents the quantity of plant nutrients applied to the land area for each crop. This is calculated by multiplying the land area (ha) where fertilizer is used by the national average fertilizer consumption (kg per ha). The land area with fertilizer application is derived from SPAM, which generates the land area for each crop depending on 7 For Tanzania, the district-level share of households engaged in each crop production is calculated with the 2010 LSMS data. For Uganda, the LSMS 2010 is similarly used to calculate the shares at the first sub-national level. For Rwanda, the subnational statistics are available at the 30-district level. For Burundi, the national-level data are directly used because no subnational data are available. For Kenya, household-level agriculture production data are not available, either. In this paper it is assumed that the average plot size per household is the same across crops in each administrative unit. As in other countries in the region, the vast majority of farmers are small landholders with an average of 2.5 ha in Kenya. While some wealthy households sometimes own a significant amount of land, the variation of land per household is relatively small (Jayne et al. 2006; Salami et al. 2010). Thus, our assumption may not always hold but can generate a reasonable proxy.

8 The Global Rural-Urban Mapping Project (GRUMP) is a derivative product of the Gridded Population of the World (GPW) data, which is based on 2000 round census data and has actual 1990, 1995, and 2000 population grids and projected 2005, 2010, and 2015 grids at a 2.5 minute resolution (approximately 5km at the equator). GRUMP is at a 30 arc-second (1km) resolution (the same as Landscan) and includes an urban-rural reallocation.
2010 HIES were surveyed. Within each PSU, six households were randomly allocated to each the diary and recall collections.

The experiment shows that per capita food consumption collected with the diary survey is higher than that of the recall survey. Figure 3 shows the histograms and kernel distribution of per capita food consumption for all households. The natural logarithm was taken and outliers removed. The mode of the distribution for the diary approach is to the right of that of the recall approach.

Table 13 contains the medians of per capita total, food, and nonfood consumption that were collected by the diary and recall methods for all four areas. Medians were used because they are less vulnerable to outliers. For all the areas combined, the median per capita food consumption from the diary collection was 10.1 percent larger from the recall collection. This difference is statistically significant. In all four areas, median per capita food consumption expenditure collected by diary was larger than that collected by recall although the difference is not statistically significant except for the Khulna rural stratum, possibly because of limited sample sizes. Results from the full sample suggest that collecting consumption data via the recall would result in the underestimation of per capita food consumption.

**Table 13: Comparison between the Diary and Recall Based Collections for Food and Nonfood Expenditure per Capita (medians)**

|||Per cap<br>me<br>|ita, taka,<br>dian<br>|Differenc<br>diary an<br>|e between<br>d recall<br>|
|---|---|---|---|---|---|
|Areas|Category|(D)iary|(R)ecall|Difference|% of D - R|
||Food|1,557.0|1,406.8|150.3|10.1|
|All|Nonfood|1,061.7|908.0|153.7|15.6|
||Total|2,662.0|2,414.6|247.4|9.7|
||Food|1,570.3|1,455.7|114.7|7.6|
|Dhaka rural|Nonfood|1,051.4|833.8|217.7|23.1|
||Total|2,650.7|2,326.1|324.6|13.0|
||Food|1,373.4|1,208.1|165.3|12.8|
|Khulna rural|Nonfood|950.9|750.1|200.8|23.6|
||Total|2,448.9|2,097.6|351.2|15.4|
||Food|1,454.0|1,369.5|84.5|6.0|
|Rajshahi rural|Nonfood|875.0|909.5|-34.5|-3.9|
||Total|2,466.1|2,394.8|71.3|2.9|
||Food|2,115.2|1,913.5|201.7|10.0|
|Dhaka urban|Nonfood|2,125.7|1,597.7|528.0|28.4|
||Total|4,127.6|3,499.1|628.5|16.5|

_Source_ : Authors’ calculations based on pilot survey. _Note_ : Outliers are defined as = 1 if logy > 2.5 × sd (logY) + mean (logY) in each division. P-values are calculated based on bootstrap resampling in the median regression. Percentage of D - R = (D - R) / mean (D, R).

An interesting observation is that even though nonfood consumption data were collected by the recall, irrespective of how food consumption data were collected, median per capita nonfood consumption expenditure was larger when food consumption was collected via the diary approach. This is puzzling, but a similar result in the nontreated part (nonfood) was also found in Beegle et al. (2011).

Switching the data collection methods from diary to recall likely underestimates household consumption data, overestimates poverty rates, and thus makes poverty estimates incomparable with those of previous 24
||PPR|Working Paper Series|||
|---|---|---|---|---|
||Title|Author|Date|Contact|
|WPS25|A Comparison<br>of Alternative<br>Training<br>Modes for Youth in Israel: Pesults<br>from Longitudinal<br>Data|Adrian Ziderman|July 1988|W. Ketema<br>33651|
|WPS26|Changing Patterns in Vocational<br>Education|John Middleton|July 1988|W. Ketema<br>33651|
|WPS27|Family Background<br>and Student<br>Achievement|Marlaine E. Lockheed<br>Bruce Fuller<br>Ronald Nyirongo|<br>July 1988|R. Rinaldi<br>33278|
|WPS28|Temporary Windfalls and Compensation<br>Arrangements|Bela Balassa|June 1988|N. Campbell<br>33769|
|WPS29|The Relative Effectiveness<br>of<br>Single-Sex<br>and Coeducational<br>Schools<br>in Thailand|Emmanuel Jimenez<br>Marlaine E. Lockheed|August 1988<br>|T. Hawkins<br>33678|
|WPS30|The Adding Up Problem|Bela Balassa|July 1988|N. Campbell<br>33769|
|WPS31|Public Finance and Economic Development|Bela Balassa|August 1988|N. Campbell<br>33769|
|WPS32|Municipal Development<br>Funds and<br>Intermediaries|Kenneth Davey|July 1988|R. Blade-Charest<br>33754|
|WPS33|Fiscal Policy in Commodity-<br>Exporting LDCs|John Cuddington|July 1988|R. Blade-Charest<br>33754|
|WPS34|Fiscal Issues in Macroeconomic<br>Stabilization|Lance Taylor|||
|WPS35|<br>Improving<br>the Allocation<br>and Manage-<br>ment of Public Spending|Stephen Lister|August 1988|R. Blade-Charest<br>33754|
For all countries analyzed in this paper, increases in employment appear to reduce poverty and social exclusion more than increases in education. A Fairlie decomposition analysis (Table 6) shows that employment status is, by far, the dominant factor explaining changes in absolute poverty between 2005 and 2008. This is not surprising given the short time span considered in the simulations, in which improvements in education indicators are not given the time needed to show measurable poverty and social exclusion impacts.

**Table 6: Fairlie decomposition: Contribution of education and employment change in explaining change in Absolute poverty between 2005 and 20008 (percent of total change)**

||**Countries**|**At most lower**<br>**secondary**|**Higher**<br>**secondary**|**Tertiary**|**Employed**|**Total**|
|---|---|---|---|---|---|---|
|**NMS**|Bulgaria|0.0|−14.3|0.0|114.3|100|
||Czech Rep|−20.0|–20.0|40.0|100.0|100|
||Estonia|0.0|0.0|−16.7|116.7|100|
||Hungary|−42.9|−14.3|57.1|100.0|100|
||Latvia|0.0|0.0|−14.3|114.3|100|
||Lithuania|0.0|0.0|−14.3|114.3|100|
||Poland|−16.7|−33.3|50.0|100.0|100|
||Romania|–100.0|−33.3|66.7|166.7|100|
||Slovakia|0.0|−25.0|0.0|125.0|100|
||Slovenia|−0.03|−0.04|0.03|104.0|100|
|**OMS**|Greece|−36.4|−36.4|54.5|45.5|100|
||Ireland|−71.4|−57.1|100.0|128.6|100|
||Italy|−166.7|−100.0|166.7|200.0|100|
||Portugal|0.0|−20.0|0.0|120.0|100|
||Spain|−66.7|−100.0|66.7|200.0|100|
|_Source:_|Sweden<br>World Bank staff|0.0<br>calculations using|0.0<br>EU-SILC data.|0.0|100.0|100|

# **6. Results**

This section presents the simulated poverty and social exclusion indicators (at-risk-of-poverty rates, low work intensity rates, and anchored poverty rates) that would result if each country achieved the Europe 2020 national targets in the following areas by 2020: (i) four separate employment outcomes; (ii) early school leaving rates; and (iii) tertiary school completion rates.<sup>14</sup> The three employment outcomes are described below and depicted in Figure 2. First, we assume that Europe 2020 national targets for employment rates are met by 2020. For the NMSs, achieving the national targets represents the best simulated outcome for employment rates. Second, we assume that, up until 2020, current employment rates will remain equal to the growth rate observed during the 2000–11 period. This is the least

> 14 As discussed above, the poverty and social exclusion indicators are not as sensitive to the education variables in the short run and therefore, those simulations are not carried out in this paper.
the business was closed, hours per day spent on paid work, hours per day spent on care and domestic responsibilities by the respondent, and hours spent on care by the respondent’s spouse. We conduct this analysis among the relevant sample of entrepreneurs i.e. those who are married or living with a partner and have child dependents in their household.

Data on norms towards gender equality in opportunities were collected from both male and female entrepreneurs in 2019 prior to the onset of the COVID-19 pandemic and these measures are described and analyzed in the Data Section and Appendix C of the paper. In Table 7a Men’s Individual Beliefs is a country-level continuous variable between 0 and 1 indicating the attitudes of men in the country towards gender equality in opportunities i.e. the proportion of men that agreed with the statement “men and women should have equal opportunities.” Higher values of Men’s Individual Beliefs indicate that a higher proportion of men in that country agree with gender equality i.e. the gender attitudes of men are more liberal. In the sample the mean for Men’s Individual Beliefs is 89.69% and the observed scores range between 74.49% in Egypt and 99.22% in Canada.

# [Table 7a about here]

Table 7a examines whether the effects of school closure differ by Men’s Individual Beliefs separately for female and male firms. Overall, these beliefs do seem to be correlated with behavior – in more liberal societies both men and women report that men do more care work (columns 6 and 7). In column 1 we can see that with a coronavirus-induced school closure, female firms are significantly more likely to close than men. As societies become more liberal (in terms of men’s beliefs on gender equality) this effect declines. Indeed, moving from Egypt to Canada would diminish the effect by around 12 percentage points. The care mechanism tracks the closure effect as women in more liberal societies show a significantly lower increase in the amount of care they provide in response (column 5). Their spouses, however, also – provide significantly less (column 7) suggesting in these societies there was more outsidethe-couple provided care than in more conservative societies. Overall, these results indicate 28
Tables

|Table<br>Price Indices in Uklaine<br>...............................<br>_10_<br>Table 2 Distribution of Average Income by Income and Expenditure Components .<br>_13_|
|---|
|Table 3 Real Average Per Capita Income and Components ...<br>.. .....<br>.. ...<br>_14_|
|Table 4 Annual Growth Rate, of Per Capita Real Income and Components ......<br>_15_|
|Table_5_Aggregate Income Per Family Member by Types of Workers:|
|At Current Prices - Ukmaine .............<br>....................<br>.......<br>17|
|Table 6 Per Capita Income of State and Collective Farm Workers|
|and Pensioner Families as a Percentage of Industrial Worker Families ...<br>....<br>17|
|Table 7 Average and Minimum Wages in Ukrine .....................<br>_19_|
|Table 8 Average Wage Eaming and It's Inequality by Age and Education|
|for Male and Female Workers and Employees: Ukamine 1989 .............<br>_20_|
|Table 9 Inequality of Per Capita Aggregate Family Income: Ulaine 1980 to 1992 .<br>_21_|
|Table 10 Income Inequality by it's Components ......................<br>_24_|
|Table 11 Income Inequality by Expenditure Components .................<br>_25_|
|Tablei2Explaining Changes in Inequality in Terms of Income Components ....<br>_28_|
|Table 13 Redistributive Effects of Direct Taxes<br>.<br>.<br>.<br>_30_|
|Table 14 Welfare in kllraine 1980-92 ....................<br>............<br>_30_|
|Table 15 Welfare by Income and Expenditure Components ............<br>...<br>_32_|
|Table 16 Explaining Changes in Welfare in Tenns of Income Components ....<br>_34_|
|Table 17 Explaining Changes in Welfare in Terms of Expenditure Components ...<br>_35_|
|Table 18 Povertyin Ukraine ..................................<br>_37_|
|Table 19 Explaining Changes in Poverty ...........................<br>_40_|
|Table20 InequalityinUSSR Republicsin 1990......................_42_|
|Table 21 Real Per Capita Aggregate Family Income and Welfare|
|inUSSRRepublicsinl98 UkainianPrims......................._43_|
|Table 22 Annual Growth Rates of Real Per Capita National Income|
|forUSSR Republics............<br>_45_|
|Table23 Poverty inUSSR Republicsin199 ......................._46_|
|Table 24 Ratio of Average Salis<br>of State Farmers**to**Blue and White Collor|
|WorklsbyUSSR'equblics<br>.................................<br>47|
|Table 25 Ratio of Average Salaries of Collctive Farmers to Blue and White Color<br>WorkersbyUSSRRepublics.................................<br>_48_<br>Table 26 Consumption of Foodstuffs by the Popatuim of Ukmine (Kilos Per Year)_51_|

ii
<!-- Start of picture text --> +<br>te<br>S<br>S<br> s . *° os 4 .<br>S ., SR .<br>RX ge D . oe I + eae<br>cs X . @BHR<br>2 . . e Zo @KWT<br>© A @SAU<br>= . °<br>o gt . . y °<br>a 9 ° ry oa<br>> . *. .? 2. ° el/** ,. «<br>3 . ame Se, etl SLBY<br>= . ry . : oe * £@JOR® © J @DZA@iRa<br>. : . @PSEs OMAR, KG<br>s®<br>° 9 wv<br>Log of GDP per capita in 2017, PPP (current international $)<br>95% Cl ——— Fitted values<br>. ROW ° MENA countries<br><!-- End of picture text -->
**Figure 1. Incidence of renters by income quintile in selected Latin American countries, 2006**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>Quintile1<br>25 Quintile 2<br>20 Quintile 3<br>15 Quintile4<br>10 Quintile 5<br>5<br>0<br><!-- End of picture text --> _Source:_ Rojas and Medellin 2011.

Rental housing has been neglected not only in policy discussion but also in recent analytical work on housing in developing countries, beyond reviews of rent-control regimes. An important exception is Gulyani and Talukdar’s (2008) study of Nairobi’s slum rental sector, which makes up 30 percent of the city’s housing. The study describes the sector as “a low quality–high price trap” for tenants, with absentee landlords, poor services and housing quality, little tenure security, and few incentives for either consolidation or improvement. This may not be the norm for rental housing in informal areas, but it is not unique to Nairobi.<sup>13</sup> The results, nonetheless, highlight the importance of understanding the dynamics of this market, which challenges not only the conventional wisdom about informal housing and its inevitable progressive improvement but also the presumed distributional effects of traditional remedies, such as upgrading and tenure security. Fay’s (2004) study of urban poverty in Latin America, on the other hand, reports that absentee slumlords are no longer the norm in informal settlements. Owners tend to be individuals living in the same informal areas as their tenants, have similar or slightly higher socioeconomic status, and own few rental properties.

Rental housing in 19th- and early 20th-century European, U.S., and many colonial cities was built by investors for the middle and upper classes. With the expansion of alternative investment opportunities, the increased access to finance for homeownership, and the enactment of rent controls in many countries, the role of investors has declined substantially. Today, small-scale individual landlords are the main suppliers of private rental housing, even in developed countries 8