rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 6. Maturity Categories Correlations**

|||**Less t**|**han 1 year**|||**More t**|**han 1 year**|||**Les**|**s 1 year**|||**1 to**|**5 years**|||**More t**|**han 5 year**|**s**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variable name**|**World**|**HI**|**Developing**|**LAC**|**World**|<br>**HI**|**Developing**|**LAC**|**World**|**HI**|**Developin**|**g**<br>**LAC**|<br>**World**|<br>**HI**|**Developing**|**LAC**|**World**|**HI**|**Developin**|**g**<br>**LAC**|
|LN GDPPC|**0.41**|**0.50**|**0.39**|**0.47**|**0.55**|**0.51**|**0.66**|**0.26**|**0.41**|**0.50**|**0.39**|**0.47**|**0.46**|**0.50**|**0.60**||**0.38**|**0.23**|**0.61**||
|Inflation % Change|**-0.24**||**-0.25**||**-0.44**|**-0.19**|**-0.40**|**-0.42**|**-0.24**||**-0.25**||**-0.32**||**-0.36**||**-0.38**|**-0.27**|**-0.38**|**-0.36**|
|Voice and Account.|**0.18**||**0.20**|**0.47**|**0.35**|**0.19**|**0.48**||**0.18**||**0.20**|**0.47**|**0.25**||**0.42**||**0.41**|**0.40**|**0.48**||
|Political Stability|**0.25**||**0.17**|**0.20**|**0.37**||**0.44**||**0.25**||**0.17**|**0.20**|**0.29**||**0.39**||**0.30**||**0.45**|**0.27**|
|Trade/GDP|**0.15**|**-0.28**|<br>**0.28**|**0.23**|**0.13**|**-0.34**|**0.27**|**0.46**|**0.15**|**-0.28**|**0.28**|**0.23**|**0.12**|**-0.27**|**0.31**|**0.36**||**-0.24**|**0.13**|**0.37**|
|Dom. Deposits/GDP|**0.65**|**0.58**|**0.64**|**0.31**|**0.73**|**0.45**|**0.75**|**0.50**|**0.65**|**0.58**|**0.64**|**0.31**|**0.70**|**0.68**|**0.67**|**0.36**|**0.32**|**-0.15**|**0.71**|**0.46**|
|Foreign Own.|**-0.26**|**-0.24**|<br>**-0.31**||**-0.19**|**-0.18**||**-0.26**|**-0.26**|**-0.24**|**-0.31**||**-0.18**|**-0.24**||||||**-0.21**|
|Gov. Ownership|||**-0.31**|**0.29**|**-0.18**|**-0.16**|**-0.23**|**-0.29**|||**-0.31**|**0.29**|**-0.11**||**-0.32**||**-0.17**|**-0.21**||**-0.42**|
|NPLs/Total Loans||||||||**-0.38**|||||||||**-0.16**||**-0.24**|**-0.47**|
|3 Bank Asset Conc.|**0.08**||**-0.30**||**0.13**||**-0.22**||**0.08**||**-0.30**|||**0.14**|**-0.28**|||**-0.15**|||
|Strength Legal Rights|**0.10**||||**0.19**|**0.26**||**0.30**|**0.10**||||**0.10**||||**0.19**|**0.35**||**0.30**|
|Depth Credit Info.|**0.10**|**0.13**|**0.33**||**0.21**|**0.29**|**0.50**|**-0.53**|**0.10**|**0.13**|**0.33**||**0.26**|**0.24**|**0.46**||**0.20**|**0.20**|**0.43**|**-0.62**|
|No. of observations|**657**|**246**|**265**|**146**|**657**|**246**|**265**|**146**|**657**|**246**|**265**|**146**|**631**|**246**|**265**|**120**|**594**|**244**|**251**|**99**|
|No. of countries|**90**|**30**|**43**|**17**|**90**|**30**|**43**|**17**|**90**|**30**|**43**|**17**|**90**|**30**|**43**|**17**|**89**|**30**|**43**|**16**|

_Note:_ Variables as % of GDP. Only significant correlations are shown. HI is High Income 33
business only, 11 percent own a farm business only, and the rest own both. An estimated 26 percent of the pooled sample households do not own a business of any type.

**Table 2. Distribution of household business ownership (household business definition)**

||Share (%)|Observations|Countries|
|---|---|---|---|
|Owns a business|73|3008|11|
|Farm business|11|3008|11|
|Nonfarm business|42|3008|11|
|Farm & Nonfarm business|20|3008|11|
|Does not own a business|26|3008|11|

Source: Own calculations based on HFPS-Wave 1.

Notes: Urban observations only. Countries include Chad, Ethiopia, Madagascar, Malawi, Nigeria, Sierra Leone, Somalia, South Sudan, Sudan, Zimbabwe.

# **These patterns are similar when using the household head definition to identify household**

**enterprises** . Table 3 presents the distribution of respondent employment type from wave 1 of the HFPS from 11 countries.<sup>27</sup> Most respondents are self-employed, from which we infer that they own or work in a household enterprise (79 percent). An estimated 58 percent the sample are selfemployed in non-agriculture sectors, similar to the estimates using the household business definition. The remaining observations correspond mainly to wage employees (19 percent).<sup>28</sup>

**Table 3. Distribution of Urban Household Enterprises (Household Head Definition)**

||Share(%)|Observations|Countries|
|---|---|---|---|
|Self-employed/Family agricultural business|21|2,812|11|
|Self-employed/Family non-agricultural business|58|2,812|11|
|Wage employee|19|2,812|11|
|Seasonal or Temporaryworkers|2|2,812|11|

Source: Own elaboration based on HFPS-Wave 1. Notes: Urban observations only. Countries include Burkina Faso, Ethiopia, Madagascar, Malawi, Nigeria, Sudan, Senegal, Sierra Leone, South Sudan, Sao Tome and Principe, Zimbabwe.

# **Income declines were most prevalent among households that owned a non-farm business,**

**which are also the vast majority of households** . Among households that own a business, those with non-farm businesses were most likely to experience an income decline (88 percent) as compared to 78 percent of those with only a farm business (Table 4). If we consider a slightly larger sample of countries that asked about farm and non-farm income declines (9 versus 3 countries) or a decline in business revenue, we see the same trends: greater incidence of income loss among nonfarm enterprises (83 percent and 82 percent, respectively) than farm enterprises (46 percent and 55 percent, respectively).

> 27 The list of countries is similar to that when using the household business definition with five exceptions: data from Burkina Faso, São Tomé & Príncipe, and Senegal are added while data from Chad and Somalia are dropped.

> 28 The share of self-employed is much higher in the HFPS sample than in comparable samples before the pandemic, when more than half the sampled household heads reported being wage workers. This may reflect that job loss was higher among wage workers than among the self-employed at the onset of the pandemic or other characteristics of the survey that were not corrected by the weighting.

13
**_Table 2: Firm characteristics by_** **_<u>group</u> and percentage of_** **_<u>firms in each group</u>_**

|||**Size**<br>_employees_|**Age**<br>_years_|**Exporter**<br>_% of firms_|**Foreign**<br>_% of firms_|**Manuf.**|**Wholesale**<br>**and retail**|**Other**<br>**services**|
|---|---|---|---|---|---|---|---|---|
||no>no|51.85|15.32|12.08%|8.41%|12.04%|19.15%|16.15%|
|**Ni**|yes>no|59.76|16.50|18.81%|7.28%|11.83%|22.51%|17.78%|
|**on‐nnovators**|y>y>n|127.67|18.88|20.09%|5.99%|10.24%|12.53%|10.82%|
||n>y>n|62.94|18.11|13.47%|3.64%|2.19%|2.51%|2.30%|
|**It**|y>y>y|119.98|17.84|21.61%|8.40%|54.52%|37.70%|45.65%|
|**nnovaors**|n>y>y|63.45|18.21|12.85%|3.77%|9.18%|5.60%|7.29%|

_Source: Enterprise Surveys and Innovation Enterprise Surveys_

**_Table 3: Knowledge activities by group (percentage of firms)_**

|||**R&D**|**R&D**<br>**internal**|**R&D**<br>**external**|**Training**|**Equipment**|**Purchase of**<br>**knowledge**|
|---|---|---|---|---|---|---|---|
||no>no|7.65%|6.75%|2.88%|10.32%|11.14%|2.96%|
|**Non‐innovators**|yes>no|11.32%|10.36%|3.25%|14.27%|17.69%|4.83%|
||y>y>n|32.88%|30.72%|9.53%|34.67%|43.00%|7.25%|
||n>y>n|25.70%|23.27%|5.74%|25.79%|44.05%|7.54%|
|**Innovators**|y>y>y|41.14%|38.42%|9.85%|41.80%|60.93%|7.70%|
||n>y>y|31.29%|28.15%|7.84%|29.30%|61.88%|7.01%|

_Source: Enterprise Surveys and Innovation Enterprise Surveys_

**_Table 4: Knowledge activities by group (percentage of firms)_**

|||**No show**<br>**cards**|**Show**<br>**cards**|
|---|---|---|---|
||no>no|18.14%|18.32%|
|**Non‐innovators**|yes>no|21.32%|23.04%|
||y>y>n|9.31%|8.94%|
||n>y>n|1.83%|1.85%|
|**Innovators**|y>y>y|43.28%|42.32%|
||n>y>y|6.13%|5.54%|

_Source: Enterprise Surveys and Innovation Enterprise Surveys_ 28
We begin by evaluating the relative importance of country fixed effects and either HS2digit sector fixed effects or destination fixed effects in explaining export-sector characteristics and dynamics. Specifically, we perform an ANOVA with country and sector fixed effects, and separately with country and destination fixed effects.<sup>15</sup> Panel A of Table 3 shows the results of the ANOVA with country and sector fixed effects. On average, country and sector effects explain about 60 percent of the variation in export-sector characteristics, though somewhat less of the variation in exporter dynamics.<sup>16</sup> With the exception of average exporter size and entrant survival, country effects explain a greater share of the variation than sector effects. Still, the significance of sector effects implies that it will be important to control for them as we examine the relationships between country characteristics and export-sector characteristics.

We next repeat the ANOVA exercise using country-destination level data. The results, reported in Panel B of Table 3, show that country and destination fixed effects together explain somewhat less of the variation in export-sector characteristics than country and sector fixed effects. However, with the exception of number of exporters, destination effects prevail over country effects. In that sense, it will be important to control for destinations to ensure the results are not being driven by variation in trade barriers or other destination-specific market access costs.

We now examine the hypotheses discussed in the previous section and present stylized facts, first focusing on basic characteristics of the export sector and then on exporter dynamics. In the analysis below, we use data from the World Development Indicators (WDI) of the World Bank

> 15 The ANOVA analysis focuses on the measures’ averages in 2006-2008 for simplicity. The sample used for concentration (fourth column) is relatively smaller because the share of exports accounted for by the top 5 percent of exporting firms can be calculated only if a country-sector-year cell includes more than 20 exporters. This remark applies naturally to all specifications in the rest of the section whose dependent variable is exporter concentration.

> 16 The percentages accounted for by country, sector, and residual effects in Panel A or country, destination, and residual effects in Panel B do not add up to 100 percent due to the unbalanced nature of each of the cross-sectional datasets. Specifically, in Panel A not all sectors are exported by all countries while in Panel B not all destinations are served by all countries.

15
demand for other goods and spur industrialization, but only if income is distributed broadly enough (Murphy, Shleifer, and Vishny 1989). In addition, productivity improvements through learning by doing can reduce the production costs and prices, making the goods affordable to more people. This can trigger mass production and industrialization provided that inequality is not too severe (Matsuyama 2002).

# **_Risk Aversion and Decision-Making Capabilities_**

Inequality and poverty might also have a long-term impact on growth through the effects on individuals’ decision-making processes. In order for people to overcome poverty, they must save and reinvest continually in order to earn higher wages, which also contributes to higher economic growth rates. However, living in impoverished conditions can prevent individuals from making the best decisions to escape poverty.

This faulty decision making can occur as a result of the particularly burdensome risks and uncertainty imposed by poverty. As noted by Banerjee (2000), the poor might be more risk averse than the rest of the population because they have more to lose if a bad shock materializes, even risking malnourishment or starvation. In the absence of developed financial and insurance markets, the poor will avoid investing in profitable investment opportunities that are intrinsically risky. That behavior self-perpetuates poverty, as the poor do not engage in risky activities that might boost their income. Dercon (2005) surveys several studies conducted in developing countries that support this hypothesis. He finds that if the poor could insure against risks in the same way as the rich, their income could be higher by at least 25 percent.

An alternative behavioral channel through which poverty is perpetuated and economic growth prospects is curtailed is through the lack of self-control in consumption and saving decisions. Banerjee and Mullainathan (2010) develop a model with “temptation” goods (such as cigarettes or alcohol) that provide utility in the present, but not in the future. Under the assumption that the share of expenditures on temptation goods declines with the level of income, the model can lead to poverty traps, whereby the poor overvalue the present and undervalue the future, and thus decide not to make investments that could yield a higher income later. Their model is consistent with the evidence that the poor spend a large fraction of their income on goods that are not survival necessities such alcohol, tobacco, and festivities (Banerjee and Duflo, 2007).

Shah, Mullainathan, and Shafir (2012) study an alternative mechanism through which poverty affects the decision-making process. Through several experiments, they illustrate how the poor devote a significant fraction of their attention span to satisfying basic needs, such as obtaining food, leaving them with less attention to handle other problems, such as investment decisions that would enable their businesses to expand and grow.

32
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.93*** 0.95*** 0.93***<br>H<br>Kendall tau-b 0.79*** 0.83*** 0.8***<br>Spearman 0.98*** 0.87*** 0.83***<br>A<br>Kendall tau-b 0.91*** 0.71*** 0.72***<br>Spearman 0.96*** 0.98*** 0.94***<br>M0<br>Kendall tau-b 0.84*** 0.89*** 0.81***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C4.1. FOSD: Counting Vector C4.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C4.3. SOSD: Social Sustainability Index C4.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.97*** 0.93***<br>H<br>Kendall tau-b 0.89*** 0.89*** 0.83***<br>Spearman 0.93*** 0.9*** 0.58*<br>A<br>Kendall tau-b 0.83*** 0.83*** 0.56**<br>Spearman 0.93*** 0.98*** 0.93***<br>M0<br>Kendall tau-b 0.83*** 0.94*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .2<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference 0<br>.2<br>0 -.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C5. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Quality of housing indicator removed (weights within the Resilience dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru C5.1. FOSD: Counting Vector C5.2. Dominance Analysis 51
# **Table A2: Official poverty**

|||20|14|||20|15|||20|16|||20|17||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|1|2|3|4|1|2|3|4|1|2|3|4|
|||||||Place|of resi|dence|||||||||
|Minsk|1.2|0.6|1.4|0.9|0.8|1.2|1.0|0.7|0.9|1.9|1.6|1.8|1.5|1.4|1.5|1.2|
|Large cities|5.9|4.9|5.3|4.3|4.9|5.8|5.4|5.6|5.0|5.4|6.3|7.0|6.5|7.1|7.5|5.7|
|Small cities|5.1|6.4|5.4|5.6|4.5|5.2|7.2|5.2|7.1|6.7|8.1|8.7|7.7|6.6|6.8|6.5|
|Rural area|8.3|8.4|8.6|8.1|10.8|10.2|9.9|9.3|10.2|10.9|11.1|11.1|11.1|11.3|11.5|9.6|
||||||||Age||||||||||
|Child|10.9|10.6|10.3|9.9|10.5|11.2|11.7|10.4|11.4|12.9|13.1|14.3|12.9|13.4|13.5|11.3|
|working age|5.3|5.4|5.7|5.2|6.1|6.5|6.5|6.1|6.5|7.0|7.8|8.1|7.8|8.0|8.4|6.8|
|Retiree|1.6|1.2|1.3|0.8|0.9|1.0|1.2|1.1|1.1|0.8|1.2|1.3|1.5|1.1|1.3|1.2|
|||||Emplo|yment|status f|or wor|king ag|e popu|lation|||||||
|Employed|2.9|3.3|3.6|3.4|3.7|4.1|4.1|3.7|4.2|4.9|5.3|5.4|5.4|5.5|5.9|4.7|
|Partly employed|11.1|11.4|8.6|7.3|9.5|14.0|7.5|9.6|13.8|10.3|8.4|13.8|12.9|16.1|11.8|12.5|
|Nonemployed|10.2|9.7|10.1|9.7|11.5|11.5|12.3|11.8|10.8|11.3|14.5|14.4|12.6|13.2|13.7|10.9|
|||||Educ|ation l|evel for|worki|ngage|popula|tion|||||||
|high education|2.0|1.4|2.0|1.2|2.0|2.3|2.6|1.8|1.3|1.4|1.5|2.2|1.8|1.9|2.9|1.9|
|secondary|||||||||||||||||
|specialized|4.8|4.2|4.3|4.3|5.3|6.5|6.6|6.0|6.2|7.2|7.5|7.3|8.8|8.2|9.4|7.2|
|other|7.9|8.9|9.2|8.6|9.7|9.9|9.4|9.5|10.4|10.7|12.5|13.0|11.6|12.4|11.7|9.9|
||||||T|ype of|househ|old (HH|)||||||||
|HH without children|1.7|1.6|1.9|1.6|2.1|2.2|2.0|2.2|2.3|1.7|2.6|2.5|2.8|2.6|2.5|2.3|
|Retiree HH|0.3|0.0|0.1|0.1|0.1|0.2|0.3|0.2|0.3|0.2|0.2|0.3|0.2|0.2|0.3|0.1|
|HH with children|9.1|9.0|9.0|8.3|9.1|9.6|10.2|9.1|9.6|11.1|11.5|12.5|11.1|11.5|12.1|9.5|
|1 child|5.4|5.2|5.7|4.5|5.8|5.5|6.1|5.9|5.6|6.4|7.3|7.6|7.2|7.2|8.3|6.2|
|2 children<br>3 and more|10.8|11.0|11.0|10.3|11.4|13.2|13.9|11.4|12.7|15.4|15.0|17.5|14.5|13.8|14.8|11.1|
|children|28.9|23.6|20.0|21.0|25.5|25.8|26.1|24.4|27.6|30.0|29.4|29.0|27.6|34.8|30.7|29.8|
|singleparent|13.3|14.4|15.1|14.7|13.4|12.8|12.7|12.0|15.9|15.1|15.2|18.7|15.9|14.3|15.0|14.1|

Source: Authors’ estimates based on HSS data.

29
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# **3.3.3 Data models, digital services trade, and development variables**

Digital trade is strongly connected with a country’s level of development. Figure 5 shows that richer countries tend to exhibit a greater level of per capita digital services trade. The dashed vertical line in the figure marks the difference between high-income and middle-income countries. Interestingly, the three data models are applied by countries across all income groups, except for countries following the limited model for which Brunei is the only high-income country with this model in place.<sup>15</sup> The other countries applying a limited model are middle income countries, falling below the dashed vertical line.

A further interesting insight from this graph is that almost all countries sharing the limited model are placed below the fitted values line, indicating that they trade less digital services than it would be expected on the basis of their per capita income levels. This is not always the case for countries sharing the other models, where in fact countries are placed both above and below the dashed fitted values line.

Figure 5: Digital services trade, data models, and level of development (2015) <!-- Start of picture text --> Digital services trade and data models<br>By level of development<br>100<br>10<br>1<br>.1<br>4000 8000 16000 32000 64000 12800<br>GDP per capita PPP USD (log)<br>Open Conditional Limited<br>Per capita exports in 1000 Mln. USD (log)<br><!-- End of picture text --> Source: Authors’ calculations using TiVA trade data and World Bank WDI. Countries are categorized on the basis of the data model regarding the cross-border data transfers.

Digital services trade is also strongly associated with technological capabilities of a country, which in turn is typically connected with the level of development. Figure 6 shows that countries with a higher level of per capita digital services trade also exhibit greater levels of technological development. Here too, the open model and the conditional model are applied by countries placed across all levels of digital technology capabilities, while the limited model is more common among 15 The EU countries, which all follow the conditional model and are high-income countries that are omitted from Figure 4.

17
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