rafmacalaba/datause-encoder-data huggingface dataset:
|**Table1:Socio-economiccha**|**racteristicso **|**fsamplehouseh**|**oldsin2000,to**|**talandby sub-**|**sample**|
|---|---|---|---|---|---|
||Total<br>Sample|Established<br>Producers|Landscarce<br>households|Titlingprog.<br>Participants|Land buyers|
|**HouseholdCharacteristics**||||||
|No.ofhouseholdmembers|9.03|9.18|8.94|9.05|8.44|
|Meanage|27.80|29.79|24.82|25.40|26.06|
|Femaleheadedhousehold|12%|12%|15%|12%|11%|
|Head'seducation(years)|2.48|2.60|2.25|1.77|3.36|
|Parentsinagriculture|86%|90%|60%|90%|91%|
|Accesstopipedwater|73%|70%|94%|65%|72%|
|Accesstoelectricity|43%|42%|53%|33%|48%|
|Dirtfloor|63%|57%|68%|79%|57%|
|Clayorwoodwalls|_52%_|_50%_|52%|63%|45%|
|Had TVin1996|23%|24%|24%|18%|27%|
|Had radioin1996|79%|82%|70%|76%|86%|
|**Infrastructure**||||||
|Distancetopavedroad(km)|82|75|31|149|75|
|DistancetoNGO (km)|58|51|36|100|52|
|Participationinchurches|11%|14%|11%|4%|6%|
|ParticipationinNGO|4%|4%|2%|7%|5%|
|**Incomelevelandstructure**||||||
|Totalannual income (C$)|34,523|37,466|23,349|16,296|53,997|
|Agriculturalprofits(C$)|23,846|27,543|7,773|10.665|32,485|
|fromcropproduction|8,525|9,141|5,731|5,983|10,579|
|fromlivestockproduction|15,322|18,402|2,042|4,682|21,906|
|Nonagricultural profits (C$)|6,228|5,167|7,590|2,618|17,206|
|Agriculturalwages (C$)|1.043|876|3,225|1,231|916|
|Non-agriculturalwages(C$)|2,915|3,419|4,525|1,571|2,206|
|Non-earnedincome(C$)|490|460|236|211|1,184|
|Assetportfolio(C$)||||||
|Totalassets owned|163,812|209,421|14.680|85,573|268,923|
|Land|62,122|80,743|4,633|48,524|70,607|
|Livestock|62,381|78,195|5,195|26,578|120,298|
|Machinery|26,307|33,596|2,246|5,436|57,013|
|<br>Land-related investments|8,782|11,060|877|3,993|16,049|
|Non-agric.enterpriseassets|4,220|5,826|1,729|1,041|4,958|
|<br>No.ofobservations|2475|1360|372|461|282|

Source: Own computation from 2000 World Bank/University of Wisconsin survey. Notes: IUS$=12.3 C$ in 2000 A househo]d was defined to participate in church activity if this was reported for at least one member of the household.

Agricultural profits are defined as the difference between the total value of production and total costs for the entire year (** explain). Non-agricultural profits are defined similarly for activities in commerce, service, small industry, handcrafts, and food processing. Agricultural wages correspond to earnings in money or kind received for activities outside the farm. Non-earned income includes income from remittances, interest, and pensions.

The value of assets in the portfolio are self reported for a large number of categories (** give for both agricultural and nonagricultural).

Non agricultural enterprise assets corresponds to facilities located in urban areas 23
> We examine the empirical link between hiouseholds' social activities4 and responses about four elements of the workings of village government: _information_ about government activities (two questions), _participation_ in decision making (two questions), voice and expression of discontent (three questions), government responsiveness to local problems (three questions). We make two key distinctions. First, we distinguish the _private_ impact of social activities-whether households who are more socially active report higher quality village government-from the _community_ impact of social activities-whether households who live in communities where _other households_ are more socially active report higher quality village government. Second, we: distinguish the impact of social activities (e.g. participation in public meetings) that are directly related to _village government structures_ from that of other social activities (that are not explicitly related to _village_ government). The "endogenous" social activities are further divided into three types: (i) socializing with friends or neighbors; (ii) participating in group activities within a network (usually organized around a specific event, such as harvest or

> prayer); and (iii) participating in social activities related to organizations (such as farmers' groups, formal religious groups, and crecdit unions that are distinguished by having a permanent leadership). Both of these distinctions prove empirically important-as the estimated associations of private and community and of social organizations and village government organizations with the proxies we use for governance are frequently not even of the same sign.

4 It should be noted that the general term "social activities" includes all group activities that households reported participating in, not that the activities have a "social" purpose. Some, such as water user groups or credit cooperatives, serve primarily economic functions while others are mixed (e.g. a prayer group that includes a rotating credit scheme as part of its activities).

4
determined in part by the productivity of its firms relative to those in other countries. Ricardian specialization by comparative advantage suggests that differences in relative productivity can play an important role in shaping the pattern of production and trade across countries. Such is the basic approach of the Eaton and Kortum (2002) model, upon which our paper builds (see further in the next section). A second aspect of technology that is relevant is whether or not it is technically feasible to trade a particular service via Mode 1. By using micro-data on domestic trade, the type of analysis conducted by Jensen and Kletzer (2005), Gervais and Jensen (2013), and Borchsenius et al. (2010) sheds significant light on this question. A sector is considered to be in principle tradable internationally if it is already heavily traded across geographical entities domestically. Since domestic trade costs are likely to be relatively low for services, however, such an approach must be interpreted primarily in terms of technological feasibility—as the authors themselves suggest—rather than the likelihood that particular services will in fact be traded on international markets by a given country. To take just one example, regulatory differences are far more significant between countries than within them—even in the case of federations like the USA—which means that international trade costs due to a lack of regulatory harmonization are likely to be very significant.

The second set of factors influencing a country’s ability to export services is trade costs. Natural and regulatory barriers to services trade remain significant, and some evidence suggests that despite recent technological advances, they remain much higher in ad valorem equivalent terms than trade costs in goods markets (Miroudot et al., Forthcoming). In a world without trade costs, the cross-country pattern of specialization and trade is determined by comparative advantage (relative productivity and relative endowments). In a world with trade costs, however, they interact with comparative advantage factors to produce the final pattern of production and trade. The likelihood that a country can trade in a particular type of service therefore depends not only on its endowments and productivity, but also on the level of trade costs faced by its firms (actual and potential exporters).

7
trends in geographical coverage and use by labor and enterprises may lead to changes in data at unchanged labor market conditions. It is not clear from the source either whether applications and placement data include emigrants. For applications and placements I have tried ':o sidestep coverage and use problems by computing two auxiliary series, one resembling the unemployment rate, albeit computed on flow data, and defined as the difference between applications and placements over applications, the other one simply as the ratio between applications and placements. The two auxiliary series are closely related, of course, the former being nothing but one minus the inverse of the latter. The placement offices cover the whole country and, albeit located in cities and towns they do pick up some demand and supply from the surrounding countryside. The comparison is therefore naturally with the SP0 estimates which show an increase by 152 percent from 1967-69 to 1982-85 while the auxiliary series show increases by 95 and 140 percent, the ratio series thus very close to the SPO estimates for this period. Even more interesting are the short term tendencies in both auxiliary series to decline, weakly,so.: from 1965 to 1973, strongly so after 1982. This contradicts the steady upward trend in the SP0 series, prevailing also from 1965 to 1973. Considering the relatively high GDP growth in the economy during these years and the strong increase in emigration, slowing down during the early 1970s, it seems plausible that unemployment should indeed have been declining during these

- _ 17
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larger focus on countries with high poverty rates.

Once we have selected the model that minimizes the mean absolute deviation following the details above, we will apply the selected model to predict poverty in the nearcasting and nowcasting years, which for the present paper are 2020 and 2021.

# **3 Data**

All poverty estimates used in this paper come from PovcalNet, which contains the World Bank’s official country-level, regional, and global estimates of poverty. Most of the data in PovcalNet comes from the Global Monitoring Database, which is the World Bank’s repository of harmonized multitopic income and expenditure household surveys used to monitor global poverty. PovcalNet contains more than 2,000 surveys from 168 countries covering 98% of the world’s population. The data available in PovcalNet are standardized as far as possible but differences exist with regards to the method of data collection, and whether the welfare aggregate is based on income or consumption. By relying on the PovcalNet database, we ensure consistency with the official numbers used by the World Bank and United Nations for monitoring poverty, inequality, and related goals.

To predict poverty, we rely on variables from three databases. First, the World Bank’s World Development Indicators, which contain country-year information on nearly 1,300 variables covering a wide range of topics, such as health, agriculture, education, climate change, infrastructure and more (but as we will explain below, only a small selection of these can be used for the present exercise). Second, we rely on the IMF’s World Economic Outlook (WEO) database, which contains country-year information for about 50 variables related to macroeconomic outcomes, such as inflation, government debt, and unemployment. Third, we rely on remote sensing data from the Google Earth Engine, particularly, data on nighttime lights, rainfall, land surface temperature, impervious surface, cropland, and normalized difference vegetation, snow, and water indices. In contrast to WEO and WDI, the remote sensing data are both more granular spatially and more frequent temporally. To fit into our framework, they need to be aggregated to the country-year level. We first aggregate them to annual data by calculating the mean, max, min, and standard deviation of each location over a year. Afterwards, we aggregate spatially by taking the mean, max, min, and standard deviation of the annual data for a country. This gives 16 features for each type of variable. Some of these combinations will not be relevant for global poverty 13
- Bertrand, M. (2011). New Perspectives on Gender. In Ashenfelter and Card, ed. Handbook of Labor Economics, 4:1543- 1590. North Holland: Elsevier.

- Broeck, V., & Kilic, T. (2018) Dynamics of off-farm employment in Sub-Saharan Africa: A gender perspective. World Bank Group Policy Research Working Paper. 8540

- Bolino, M.C. and Turnley, W.H. (2005). The personal costs of citizenship behavior: The relationship between individual initiative and role overload, job stress, and work-family conflict. _J. Appl. Psychol. 90_ , 740–748.

- Campbell, D.J. (2000). The proactive employee: Managing workplace initiative. _Acad. Manag. Perspect. 14_ , 52–66.

- Carneiro, P., Crawford, C. and A. Goodman (2007). The Impact of Early Cognitive and NonCognitive Skills on Later Outcomes, CEE Discussion Paper 0092.

- Contreras, D., de Mello, L. & Puentes, E (2011) The determinants of labor force participation and employment in Chile. _Applied Economics_ , 43:21, 2765-2776, DOI: 10.1080/00036840903373303

- Corrigall, E. and Konrad, A.( 2007). Gender Role Attitudes and Careers: A Longitudinal Study. _Sex Roles 56_ (11-12): 847–855.

- Drentea, P. (1998). Consequences of women’s formal and informal job search methods for employment in female-dominated jobs. _Gender & Society_ , 12(4), 466–468.

- DTDA (2020). Ghana Labor Market Profile, 2020. Danish Trade Union Development Agency

- Fadayomi, T. O. & Olurinola, I. O. (2014). Determinants of Labor Force Participation in Nigeria: The Influence of Household Structure. _Journal of Economics and Development Studies_ , Vol. 2, No. 2, pp. 169-19

- Fay, D. and Frese, M. (2001). The concept of personal initiative: An overview of validity studies. _Hum. Perform. 14,_ 97–124

- Fernandez, R. and Fogli, A. (2005.) Culture: an Empirical Investigation of Beliefs, Work, and Fertility. NBER Working Paper No. 11268.

- Fortin, N. M. (2005). Gender role attitudes and the labor-market outcomes of women across oecd countries _. Oxford review of Economic Policy, 21_ (3):416–438.

- Friedman, W., Kremer, M., Miguel, E. and Thornton, R. (2011). “Education as Liberation?” _Economica_ , https://doi.org/10.1111/ecca.12168

- Ghana Statistical Service- GSS (2014), Ghana Living Standards Survey, Wave 6, GLSS 6, Labor Force Report

- Giannetti, M., & Andrei, S. (2004). On the determinants of entrepreneurial activity: Social norms. Economic Environment, and Individual Characteristics. S _wedish Economic Policy Review_ , 11(2), 269–313. https://doi.org/10.2139/ssrn.554511

- Giuliano, P. (2004). On the Determinants of Living Arrangements in Western Europe: Does Cultural Origin Matter? Unpublished paper

- Glick, P & Sahn, DE, 1997. Gender and education impacts on employment and earnings in West Africa: evidence from Guinea. _Economic Development and Cultural Change_ , 45(4): 793–823.

- Greene W.H. (2008). Econometric Analysis. 7<sup>th</sup> ed. New Jersey: Prentice Hall.

20
# **Table 1. Country and Industry Coverage**

This table presents the distribution of observations and unique firms among the countries. Panel A presents the country coverage. Panel B presents the industry coverage. Industry definitions are from Institutional Shareholder Services (ISS) database.

||**Pane**<br>|**l A: Country Covera**<br>|**ge**<br>||
|---|---|---|---|---|
|**Countries**|**# of Observations**|**% of Observations**|**# of Unique Firms**|**% of Unique Firms**|
|Australia|479|1.42|125|1.48|
|Austria|95|0.28|23|0.27|
|Belgium|121|0.36|30|0.35|
|Canada|858|2.54|221|2.61|
|Denmark|116|0.34|26|0.31|
|Finland|153|0.45|33|0.39|
|France|413|1.23|97|1.15|
|Germany|427|1.27|100|1.18|
|Greece|193|0.57|43|0.51|
|Hong Kong SAR, China|252|0.75|65|0.77|
|Ireland|143|0.42|34|0.40|
|Italy|297|0.88|77|0.91|
|Japan|2,668|7.91|596|7.05|
|Netherlands|251|0.74|58|0.69|
|New Zealand|74|0.22|20|0.24|
|Norway|103|0.31|25|0.30|
|Portugal|70|0.21|15|0.18|
|Singapore|271|0.8|58|0.69|
|Spain|245|0.73|60|0.71|
|Sweden|220|0.65|54|0.64|
|Switzerland|304|0.9|72|0.85|
|United Kingdom|1,798|5.33|531|6.28|
|United States|24,163|71.67|6090|72.05|
|Total|33,714|100|8,453|100|

8
# **Figure 10. Private debt and investment growth**

_Domestic private debt has risen sharply in EMDEs since the global financial crisis. EMDEs with larger private debt experienced slower investment growth during 2010‐15._

## **A. Private debt in EMDEs**

**B. Investment growth in EMDEs with high and low private debt‐to‐GDP ratios, 2010‐15**

<!-- Start of picture text --> Percent of GDP Percent<br>60 7<br>6<br>40 5<br>4<br>20 3<br>Commodity importers 2<br>Commodity exporters<br>1<br>0<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Low High<br><!-- End of picture text -->

### Source: World Bank.

Notes: Private debt refers to domestic credit to private sector by banks as percent of GDP. A. Unweighted averages. Includes 115 EMDEs.

B. “Low” and “High” indicate median credit‐to‐GDP ratios over 2010‐15 in the bottom and top one‐thirds of the distribution, respectively. Difference in medians between “high” and “low” subsamples is significant at the five percent level. Group medians for 107 EMDEs during 2010‐15.

# **Figure 11. Correlates of investment growth**

_Slowing output growth, declining FDI inflows, and worsening terms of trade (for commodity exporters) are associated with lower investment growth in EMDEs. Rising private debt and deteriorating political stability are additional headwinds for many EMDEs._

## **A. Correlates of investment growth**

|**Variable**|**Effect**|
|---|---|
|Real GDP growth|+|
|Increase in FDI inflows|+|
|Political stability|+|
|Private debt|...|
|Private debt squared|—|
|Terms of trade improvement|+|
|Reform spurt|+|

Source: World Bank.

Notes: Estimated impact of explanatory variables on investment growth in 73 EMDEs during 1998‐2015, based on a panel regression with country fixed effects. The explanatory variables denoted with plus/minus signs are significant at the five percent level. Details are discussed in Annex 1.

33
about respondents’ choices, we use multiple approaches, including econometrically controlling for biased program perceptions and misinterpretation of survey information and examining the sensitivity of our results to alternative estimation data sets that include/exclude potential problem cases, as explained below.

When constructing alternative estimation data sets, we considered two options: data on “ _all respondents_ ” and “ _validated respondents_ ”. _All respondents_ comprise data from each respondent who completed the choice experiments (1,373 respondents in total). _Validated respondents_ include data only from the respondents whose choices we expect most reliably reflect true preferences for the Amazon rainforests (849 respondents in total). To identify _validated respondents_ , we used debriefing questions, at the end of the survey, to recognize and drop respondents who: (i) indicated “infinite WTP” by stating that that they would always support increased protection, no matter how high its costs would be (277 respondents); (ii) chose the Status Quo option mostly because “countries in the Amazon region, not U.S. or Canada, should finance Amazon protection” (334 respondents); or (iii) stated that they did not understand that the implementation of increased protection in the Amazon would incur additional expenses to their household (120 respondents). Note that the classifications are not mutually exclusive; each respondent can qualify under more than one criterion.

We use data on _all respondents_ for our main estimation results, as those models most fully address the general public. Using those data also produces a more conservative approach to estimating of WTP (more likely to under- than overestimate WTP), as using data on _validated respondents_ , a subset of _all respondents_ , generates somewhat higher estimates (see Supporting Information), although the differences between results from alternative estimation data sets are not large.

15
however, that with institutional development for trade facilitation associated with a deep and comprehensive trade agreement with the EU, that Armenia could reduce its border costs of exporting to the 2010 costs of Ukraine ($1230) or Georgia ($1270). We assume, therefore, that the impact of a deep and comprehensive free trade agreement with the EU would reduce the border costs of exporting to the EU to $1250 from $1731. That is, the costs will fall by 28 percent of their present value.<sup>34</sup> Thus, in the scenario for a deep and comprehensive free trade agreement with the EU, we assume that, due to cost reduction, exporters save 2.5 cents of each dollar of export revenue from sales to the EU (28 percent of 8.9 percent), and importers will save 2.5 cents on each dollar of import costs (28 percent of 9.1 percent).

Improved institutional development for trade facilitation is likely to reduce trade facilitation costs for imports from and exports to all regions. If customs is more efficient in processing imports from the EU, these procedures will generally facilitate trade with all regions. For example, if trucks with imports from the EU can pass through Armenian borders more quickly, trucks with imports from other countries are also likely to see reduced delays. Given that the EU will monitor trade with the EU much more carefully, it is possible that not all institutional reforms in trade facilitation will transmit to trade with non-EU countries. So we shall assume that the border costs of exporting to or importing from non-EU countries will fall by 25 percent. Then, we assume that exporters save 2.2 cents of each dollar of export revenue from sales to the EU (25 percent of 8.9 percent), and importers will save 2.3 cents less on each dollar of import costs from non-EU countries (25 percent of 9.1 percent).

We summarize these calculations in the table below.

# **Summary of Estimates of the impact of a Deep and Comprehensive Free Trade Agreement with the European Union on Armenian Border Costs**

||percentage o<br>and import re|f export<br>venues|
|---|---|---|
|Border costs of trade in 2007||10.4|
|Border costs of exportingin 2010||8.9|
|Border costs of importingin 2010||9.1|
|**Reduction in exporting border costs for EU trade**||**2.5**|
|**Reduction in exporting border costs for non-EU trade**||**2.2**|
|**Reduction in importing border costs for EU trade**||**2.5**|
|**Reduction in importing border costs for non-EU trade**<br>**Source: Authors' calculations based on sources describe**|**d above.**|**2.3**|

> 34 Maliszewska _et al_ ., (2008) assume that a deep and comprehensive free trade agreement between Armenia and the EU would reduce Armenian border costs of exporting to the EU by 50 percent.

98