rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Summary statistics

|Variable|Mean|Std. Dev.|Min.|Max.|
|---|---|---|---|---|
|**Education sector: 1082 observation**|**s (108 countries, an**|**nual data for 1990-2003)**|||
|Public education expenditure|4.02|1.92|0.38|13.61|
|Education aid|1.13|1.45|0.01|14.19|
|Education IP|0.13|0.23|0|3.6|
|Education SP|0.04|0.09|0|0.95|
|Education TC|0.81|1.1|0|10.85|
|Education ONM|0.16|0.34|0|5.83|
|General aid|1.2|1.92|0|22.78|
|Support to NGOs|0.13|0.24|0|3.02|
|Other non-education aid|5.84|6.78|0.01|62.84|
|Real GDP per capita|3.63|2.98|0.47|17.96|
|Real GDP per capita growth|1.6|5.46|-30.28|49.86|
|Urbanization|42.4|20.36|6.3|91.56|
|Trade|78.11|41.06|10.83|280.36|
|PV debt|52.15|60.07|0.09|892.12|
|Public debt service|4.02|3.47|0|35.24|
|**Health sector: 1087 observations (1**|**08 countries, annua**|**l data for 1990-2003)**|||
|Public health expenditure|1.96|1.25|0.17|7.44|
|Health aid|0.44|0.54|0|3.63|
|Health IP|0.11|0.18|0|1.69|
|Health SP|0.05|0.1|0|1.75|
|Health TC|0.18|0.23|0|1.91|
|Health ONM|0.1|0.18|0|1.46|
|General aid|1.21|1.97|0|22.78|
|Support to NGOs|0.13|0.24|0|3.02|
|Other non-health aid|6.56|7.5|0.02|66.11|
|Real GDP per capita|3.64|2.98|0.47|17.96|
|<br>Real GDP per capita growth|1.58|5.4|-30.28|28.5|
|<br>Urbanization|42.24|20.4|6.3|91.56|
|Trade|77.8|41.2|10.83|280.36|
|PV debt|51.12|59.14|0.09|892.12|
|Public debt service|3.91|3.24|0|35.24|

Note: All variables as % of GDP except real GDP per capita (thousands of constant 2000 international dollars) and its growth rate and urbanization (urban population, % of total). Source: author’s analysis based on data described in the text.

training and scholarships in donor countries, remunerating experts and consultants, and financing equipment and administrative costs associated with TC primarily involve direct payments from donor governments rather than transfers of money to recipient governments. In fact, Sundberg and Gelb (2006) argue that many aspects of TC, such as finance for training programs, analytical reports and expert advice, involve resources that never even leave donor countries. For the seven countries that they study, IDD and Associates (2006, p. 23 in annex B) indicate that off-budget aid is explained, among other things, by “aid in kind e.g. TA [technical assistance] and other aid where expenditure is undertaken directly by the donor”. Similarly, Fagern ̈as and Roberts (2004b) argue that technical assistance involves donors making direct payments that are not reflected in budget documents, and Feeny (2007, p. 442) states that “the salaries of external consultants will not enter public sector accounts”. Feeny argues that a larger share of aid is off-budget in Fiji and Vanuatu compared with Papua New Guinea and the Solomon Islands because the former two countries receive a large proportion of their aid in the form of technical assistance. In addition, Fagern ̈as and Roberts (2004a) attribute discrepancies between donor 12
61 TABLE 2: Proportion<sup>of Farms In the Ejido Sector by State and Region, 1981*</sup>

|States and Regions|EJldo/Total %|
|---|---|
|All of Mexico<br>|63.8<br>770|
|North Pacific<br>|.<br>470|
|Sonora<br>|.<br>810|
|Slnaloa<br>|.<br>690|
|California Norte<br>|.<br>|
|Baja Calfornia Sur|23.9<br>|
|Nayarft|92.8|
|North<br>|68.9<br>653|
|Chihuahua|.<br>|
|Coahulla<br>|78.1<br>357|
|Nuevo Leon<br>|.<br>650|
|Tamaulipas<br>|.<br>806|
|San Luis Potosi<br>|.<br>830|
|Durango<br>|.<br>628|
|Zacatecas|.|
|Center<br>|56.8<br>562|
|Jalisco<br>|.<br>722|
|Aguascallentes<br>|.<br>650|
|Guanajuato<br>|.<br>695|
|Queretaro<br>|.<br>612|
|Hidalgo<br>|.<br>86|
|Talaxcala<br>|.<br>800|
|Morelos<br>|.<br>|
|Puebla<br>|48.0<br>720|
|Michoacan<br>|.<br>463|
|Mexico City<br>|.<br>486|
|Mexico|.|
|South Pacific<br>|66.9<br>824|
|Coikna<br>|.<br>746|
|Guerrero|.<br>584|
|oaxaca<br>|.<br>731|
|Chlapas|.|
|Gulf<br>|64.5<br>668|
|Veracruz<br>|.<br>427|
|Tabasco<br>|.<br>819|
|Campeche<br>|.<br>723|
|Yucatan<br>Quintana Roo|.<br>87.9|

Source: Agricultural Census,<sup>1981.</sup> *Refers to number<sup>of enterprises, not area occupied.</sup>
**Figure 7: Dirty versus Clean Manufacturing Industries Output (in BVD) and Exports (in**

## **10,000 VD),  1997-2002**

<!-- Start of picture text --> 20000 70000<br>18000<br>60000<br>16000<br>14000 50000<br>12000<br>40000<br>10000<br>30000<br>8000<br>6000 20000<br>4000<br>10000<br>2000<br>0 0<br>1997 1998 1999 2000 2001 2002<br>Output_dirty Output_Clean<br>Exports_Dirty Exports_Clean<br>Output (in BVD) Exports (10K VD)<br><!-- End of picture text -->

# **_b. Regression Approach_**

Previous empirical studies on the relationship between trade and the environment have found varying results. Dean (2002) uses provincial level data on water pollution from China and found support for the idea that trade liberalization has both a direct and an indirect effect on emission growth and these could be opposite in sign. In contrast, Grossman and Krueger (1993) examined the environmental impacts of NAFTA and found no evidence that a comparative advantage is being created by lax environmental regulations in Mexico. Using data across different countries from 1960-1995, Mani and Wheeler (1999) found that ‘pollution haven effects’ are insignificant in developing countries because production is mainly for domestic consumption, not for export.

In a more closely related study, Gamper-Rabindran and Jha (2004) empirically analyzed the relationship between trade liberalization and the environment in the Indian context. Their findings indicate that exports and FDI grew in the more polluting sectors relative to the less polluting sectors between the pre and post liberalization periods. This evidence provides some support for concerns raised about the environmental impact of trade liberalization. Specifically, they find that trade liberalization has resulted in an increase in exports from industries that are more water and air pollution-intensive relative to less 12
based on known people can take the form of “honor” (or trust) among thieves (Heller, and Sieberg, 2010). Particularized trust is associated with <mark>corruption (Tanzi, 1998). Generalized trust is, in contrast, strongly negatively associated with corruption (Bjørnskov and Svendsen, 2013).</mark><sup>12</sup> <mark>Particularized trust is a substitute for absent generalized trust when elite extended families dominate the economic life of a country through vertical control over industries and interlocking family ownership. The elite families are usually associated with the political regime, often through representation of the family in the government and in the bureaucracy. There may be no generalized trust in the society at large but particularized trust facilitates economic activity by the elite extended families.</mark> When particularized trust extends beyond family ties to common group identity, anonymity can be partial and group-related.<sup>13</sup> <mark>The two groups that participated in our experiments are not as distinct as family-based economic and political elites or groups with common ethnicity or links of common heritage.</mark> 12 Uslaner (2002) found that the weak association between social trust and network activity arises due to a selection effect whereby people with high trust are more likely to engage in network activity. Bjørnskov and Sønderskov (2013) conclude that neither social nor political trust is associated with network activities, nor by extension with particularized trust of any kind. Naef and Schupp (2009) test the relation between trust in family and friends and social trust and show using the German Socio-Economic Household Panel that standard questions intended to reveal social trust indeed do so but are entirely uncorrelated with trust in friends, family or colleagues. Alesina and Guiliano (2011) using detailed Italian data found support for the proposal by Banfield (1967) of ‘amoral familism’ in showing that people with little social trust tend to develop strong bonds to family and friends. For another perspective on dimensionality in Putnam’s concept, see Bjørnskov (2006).

> 13 The particularized trust among members of particular groups can be advantageous in allowing trade but with limited market participants. See Greif (1989). Bouckaert and Dhaene (2004) report trust among businessman from different ethnic origins in the same city in contemporary Belgium. The results suggest particularized trust in a small city among people engaged in business who are likely to know one another.

13
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE around $30 per month. France Telecom also offers WiMAX<sup>19</sup> Internet access through its Orange subsidiary.

**Table 15. Mobile penetration in West African countries**

|Subscribers per 100 people||||||Al|
|---|---|---|---|---|---|---|
|**Country**|2005|2006|2007|2008|2009|nnua average<br>growth (%)|
|Benin|8.4|12.8|23.1|44.7|46.7|54|
|Burkina Faso|5.3|6.9|11.7|17.9|25.3|48|
|Côte d’Ivoire|12.3|20.2|36.6|45.4|54.4|45|
|Cape Verde|17.2|22.5|31.0|55.6|68.1|41|
|Ghana|11.5|22.6|32.4|51.4|63.0|53|
|Gambia, The|15.4|25.9|33.4|51.3|59.6|40|
|Guinea-Bissau|6.3|9.0|19.2|23.9|29.5|47|
|Liberia|3.2|11.9|15.0|19.0|26.3|70|
|Mali|7.6|12.6|20.5|27.0|32.3|43|
|Mauritania|22.9|34.8|48.3|62.7|75.0|35|
|**Niger**|**2.4**|**4.1**|**6.7**|**11.6**|**18.4**|**67**|
|Nigeria|12.7|23.1|27.8|42.6|48.1|40|
|Senegal|16.3|27.3|36.8|43.1|53.8|35|
|Sierra Leone|4.6|5.0|13.2|16.2|20.0|44|
|Togo|8.6|13.7|22.4|27.7||48|
|**Simple average**<br>_Source:_AICD.|**10.3**|**16.8**|**25.2**|**36.0**|**44.3**|**44**|

Providing a high-speed fiber-optic backbone for the country remains a challenge. Domestic backbone connectivity in the western part of the country is partly served by fiber-optic cable, but most of Niger is linked by microwave or domestic satellite connections. Despite its landlocked situation, Niger has access to an international submarine cable via Benin and Burkina Faso. Currently connections are to the South Atlantic 3 (SAT-3) cable, but this will expand as additional cables are laid along the west coast of Africa over the coming years. One potential bottleneck is of the incumbent SONITEL controlling the fiber backbone. It is important to achieve open access by either ensuring that wholesale access pricing is competitive or by encouraging other operators to construct their own fiber networks. The regulator needs to ensure that wholesale access to fiber-optic connectivity is priced competitively.

> 19 Worldwide interoperability for microwave access.

33
- 18 -

In general terms, and **as** argued earlier, NWC and job security regulations are a dominant feature of formal labor markets. According to estimates of ILO (Riveroa, 1988c), the **size** of the formal urban economy in Latin America is about 60 percent of total employment, the public sector constituting about half of this. In South **Asia,** this proportion is even smaller, **as** well **as** in the case of African countries, **as** suggested by thke high share of agricultural productlon in GDP.<sup>15</sup> Thus, taking into account the relative size of the urban formal sector, it **is** also a sensible hypothesis that enforcement of all legal labor regulations imposed on the formal manufacturing sector is not at all difficult from the viewpoint of the required administrative machinery. FLnally, it is important to note that in almost all countries, procedures exist which allow workers to initiate legal demands if regulations are not complied with by the firm.

In order to test for the likely significant distortionary effect of NWC of labor one may use the existing differential between labor costs -- which are basically urban-formal wage plus NWC items -- and agricultural wages. If enforcement of NWC keeps labor costs (LC) growing substantially above agricultural wages, one could reasonably argue that this is indicating the existence of a distortion. However, the existence of labor costs growing faster than agricultural wages (WA) may also be due to more rapidly growing productivity in manufacturing **as** well **as** to faster human capital growth in urban **areas** which would be reflected in higher wages. **WV** conducted a simple analysis of this issue by exploring the statistical relationship between the ratio (LCIWA) and the ratio formed by the **average** labor products in manufacturing and agriculture. We first regressed the ratio (LCIWA) against a time trend in order to test the statistical significance of observed growth rates. If a statistically significant growth trend for a given country is not
migration. Since there are both individual and province level variables in my regressions, the standard errors are adjusted for within province and urban/rural correlation (clustering). It is found that younger and more educated individuals are more likely to migrate; these results are in line with theory (e.g., Sjaastad, 1962). The result of a regression model that includes dummies to denote the levels<sup>35</sup> of education (column 2) confirms that the probability of migration increases monotonically as the education level increases (the upper secondary education is the omitted category). Females are more likely to migrate relative to males, which is in line with the trend found in the 1999 and 2009 census data (GSO/UNDP, 2001; GSO, 2011). Consistent with Nguyen-Hoang and McPeak (2010), higher unemployment at the place of origin appears to be a factor which generates out-migration.

In order to proxy for the standard of living in the location where an individual resides, Model (3) introduces per capita expenditure as a set of dummy variables by four quartiles.<sup>36</sup> (The uppermiddle quartile is the omitted category). The coefficient estimates turn out to be consistent with the “liquidity constraint hypothesis” (Phan and Coxhead, 2010) and with the existence of a “migration hump” (Nguyen _et al._ , 2008), but are not statistically significant.<sup>37</sup> Table 4 presents the results of multinomial logit model relating four migration outcomes to a set of personal and origin characteristics. For the purpose of identification, non-migrants constitute the base category. The coefficients are reported in terms of the impacts of the variables

> 35 Five levels of education - college (and above), upper secondary (grade 12 completed), lower-secondary (grade 9- 11 completed), primary (grade 5-8 completed) and no primary (less than grade 5 completed) - are considered.

> 36 The lowest quartile represents those who live in a location where the average monthly per capita expenditure is less than 298,000 dong. The ranges of average expenditures of the lower-middle, upper-middle, and highest quartiles are 298,000-339,000, 339,000-449,000 and above 449,000 dong respectively.

> 37 The results are sensitive to adjustment for within province and urban/rural correlation. In terms of the coefficient estimates, the coefficient for the lower-middle quartile is found to be greater than one and those of the lowest and the highest quartiles turn out to be less than one, suggesting that individuals residing in the areas with a living standard belonging to the lower-middle quartile are most likely to migrate. I also run an alternstive regression including the log of the monthly per capita expenditure and its square terms. In line with Nguyen _et al_ . (2008), my coefficient estimates imply that the probability of out-migration peaks when the monthly expenditure level reaches around 356,000 dong. Whereas these coefficients are found to be significant without the adjustment, they lose statistical significance when corrected for clustering.
# **Figure 5. WFH amenability and GDP per capita**

<!-- Start of picture text --> (a) PIAAC surveys (b) STEP surveys<br>0.6 0.8<br>SRB<br>0.6<br>0.4 NLD KOS MKD<br>BEL 0.4 GEO<br>FIN DNK PHL<br>0.2 ESTHUN CZEISRJPNFRACAN SWE NOR 0.2 VNM ARM UKR<br>AUT SGP<br>LTU KOR DEU<br>0 SVK SVN NZL GBR 0<br>9 9.5 10KAZ POL ESP10.5 ITA IRL USA11 11.5 -0.2 7.5 8 KEN 8.5 9 9.5COL 10<br>GRC<br>-0.2<br>RUS -0.4 BOL<br>CHL GHA<br>SLV<br>-0.4 -0.6<br>TUR LAO<br>-0.8<br>-0.6<br>ECU LKA<br>PER -1<br>MEX<br>-0.8 GDP per capita PPP (log) -1.2 GDP per capita PPP (log)<br>(c) LMPS<br>0.4<br>JOR<br>0.3<br>0.2<br>0.1<br>0<br>9 9.05 9.1 9.15 9.2 9.25 9.3 9.35<br>EGY<br>-0.1<br>-0.2<br>TUN<br>-0.3 GDP per capita PPP (log)<br>WFH index - adjusted by home internet access WFH index - adjusted by home internet access<br>WFH index - adjusted by home internet access<br><!-- End of picture text --> Note: The vertical axis measures the corresponding task index, in standard deviations from the mean of the (A) PIAAC, (B) STEP and (C) LMPS samples. A higher value indicates that jobs are more amenable to WFH.

# **3.2 Within-country findings**

There are large disparities in terms of jobs’ amenability to working from home within countries. Figure 6 shows differences with respect to the average worker for the whole PIAAC, STEP and LMPS data sets.<sup>7</sup> Across most countries, women are more likely to have jobs more amenable to WFH. This is because they are less likely to have jobs intensive in physical/manual work than men. Educational attainment is strongly linked

> 7 Tables showing country-level findings are available in the Online Appendix

> (http://www.hernanwinkler.com/uploads/5/5/1/1/5511764/appendix_jobs_amenability_to_wfh_v13.xlsx).

14
applying the indicated weights and summing the scores.<sup>9</sup> The linear scores for each included property ranged from 0 to 1 and were determined by dividing all observations by the highest value in the sample for soil properties in which a higher value is more beneficial (carbon

**_Table 4._** _Correlation of Soil Quality Indices_

||SA SQI|WA SQI|Organic<br>Carbon (%)|
|---|---|---|---|
|SA SQI|1|||
|WA SQI|0.659|1||
|Organic Carbon (%)|0.830|0.800|1|

_Note: All significant at the 1% level._ and nitrogen) and dividing the all observations by the lowest value in the sample for properties in which a lower value is preferred. Soil electrical conductivity and pH have an optimal range, and these were treated as such.<sup>10</sup> This method follows Mukherjee and Lal (2014), who learn from Karlen and Stott (1994) and Fernandes et al. (2011). WA SQI scores range from 0.32 to 0.86, with a mean of 0.46.

Table 4 presents the correlation matrix of the three abovementioned soil quality indicies, as well as the organic carbon content. All correlation coefficients are significant at the 1 percent level.

# _3.2.1 Soil quality indices: Regression analysis_

In order to determine how well subjective soil indicators are correlated with objective measures, including the soil quality indices and organic carbon content, basic ordinary least squares regression analysis is conducted. The primary objective of the regression analysis is to determine how well subjective soil assessments predict soil quality index measures, and which subjective questions perform best. The following model is executed: where _SQI_ is one of the two soil quality indices defined above, � is a constant, X is a matrix of subjective soil indicators, and � is a random error term with the usual desirable characteristics. Organic carbon content is also run as a dependent variable for robustness. While several factors, such as plot slope and various agricultural practices, may influence the soil quality on the plot, those covariates are excluded

> 9 Scores for each of the four soil properties normalized as (observation score – sample min)/(sample max – sample min). Weights were applied as follows: pH (0.3); electrical conductivity (0.3); organic carbon (0.2); total nitrogen (0.2). Scores and weights taken from Mukherjee and Lal (2014).

> 10 For properties that have an optimal range, the observations were split into those above and below the critical thresholds (as defined by Mukherjee and Lal, 2014), with those below the threshold treated as though a higher value is preferred and those above the threshold treated as though a lower value is preferred.

20
22 control for endogeneity and selection bias, we find that any differences in the marginal spending behavior between remittance-receiving and non-receiving households are explained completely by the observed and unobserved characteristics of the households. The relevance of our instrumental variables in the expenditure functions leads us to conclude that any evidence from the OLS regressions of differences in expenditures in food, education and housing between households receiving and not receiving remittances can be accounted for by the correlation between unobserved components in the expenditure equations and the decisions of the households to receive remittances.

In contrast with earlier studies cited above, we have found that remittances do not have a statistically significant impact at the margin on the consumption and investment behavior of households in Ghana. Household expenditures in Ghana change proportionately with income, and since remittances are fungible, we observe no changes in marginal budget shares for households receiving income from internal or international remittances.

There are several possible reasons why our results indicate that households in Ghana do not treat remittances differently from other sources of income. The first may be that our sample of 3,884 households is not large enough to generate sufficient variation in our variables. This may imply that our instruments do not have enough independent variation to identify the effects of remittances on consumption and investment. A second possible explanation is that once selection bias and endogeniety have been addressed remittances do not have a statistically observable impact at the margin on the consumption and investment behavior of Ghanaian households. A third reason may be that while our study is based on a low-income country, Ghana, most other studies of remittances and household behavior have been based on middleincome countries with substantially higher incomes. It is entirely possible that households with
and after the event is used to calculate the pre- and post-event means. In the case of crises, we define the event as the beginning of a crisis, and study the behavior of the cross-market premium during the crisis period relative to the pre-crisis mean. The length of the window is determined by the duration of the crisis; the pre-crisis window is equal to the post-crisis one.

In terms of data, we work with countries that have experienced the introduction (or lifting) of capital controls and/or financial crises during the sample period, such that we are able to analyze the effects of both. We also work with stocks with a long history of DR listings with important trading volume. Thus, we use stocks that are publicly traded in the United States, either on the NASDAQ or the New York Stock Exchange (NYSE). In total, we work with 98 stocks (out of 133 DRs that trade in the NYSE and NASDAQ) from nine emerging economies: Argentina (8 stocks), Brazil (30), Chile (20), Indonesia (2), Korea (6), Mexico (23), Russia (2), South Africa (8), and Venezuela (3). The cross-market premium is calculated only on days when both the underlying stock and the DR trade.<sup>11</sup> The data needed to calculate the premium (the dollar price of the stock in the domestic market, the price of the DR in New York, and the number of underlying shares per unit of the depositary receipt) come from Bloomberg. For Argentina, Brazil, Chile, and Venezuela we use the closing price both in the domestic market and in New York. For Asian markets, which are already closed when the New York stock market opens, as well as for Russia and South Africa, we use instead the closing price (and the exchange

> 11 For a detailed description of which companies are included in the respective portfolios and the period for which the cross-market premium is calculated, see Levy Yeyati, Schmukler, and Van Horen (2008a). In that paper, we also discuss the effects of using observations for which trading occurs in only one market.

8