rafmacalaba/datause-encoder-data huggingface dataset:
farm and nonfarm enterprises, which include access to finance, market, and transportation. Business service constraints are relatively perceived to be less critical (about 20%) by both farm and nonfarm entrepreneurs. Most farm and nonfarm entrepreneurs expect no support from any agencies/institutions in rural areas because there are almost no business service agencies exists in rural Yemen. This may be one of the reasons they did not perceive business services as a major constraint or obstacle. Our survey data shows that entrepreneurs recognize basic investment climate such as access to finance, access to market, and market demand as major obstacles.

# **Figure 3.2 Comparison of Business Constraints, Farm vs. Nonfarm**

<!-- Start of picture text --> Nonfarm<br>Licenses and permits Farm<br>Electricity Exports and Imports<br>Financing Land-use policy<br>Market Court<br>Transportation Postal/Delivery Services<br>Corruption Tax system<br>Telecommunication Business services<br>Water Security/Safety<br>Technology and IT Labor issue<br><!-- End of picture text --> _Source:_ Yemen RIC Survey 2010

# **3.1.3** **_Gender Difference in Obstacles Perceived by Entrepreneurs_**

Another interesting finding emerged regarding constraints to doing business in Yemen revealed a significant difference along gender lines. Because only one woman farm entrepreneur was represented in the sample data, the gender results were limited to nonfarm women entrepreneurs. While men and women entrepreneurs are confronted with similar types of constraints, those which women face are much larger. The investment climate constraints that men and women in Yemen appear to share in common include access to finance, electricity, markets, and transportation. Yet there is at least one major difference in the types of constraints that men and women face: labor and security/safety represent serious constraints to women whereas they do not appear to be major constraints for male entrepreneurs (figure 3.3).

7
the range of net transfers for different levels of pre-transfer income during the 1995 to 1998 period for households without and with migrants, respectively. Comparing the top two figures, note that more of the lower bound of transfers lies below the poverty threshold for households that do not have migrant family members. Indeed, during the earlier period, elderly households with migrant family members appear very unlikely to have income per capita below the poverty line after private transfers are included household income. Consistent with Giles (2006), the migrant labor market appears to be providing sufficient insurance against falling into poverty. Evident in the lower panel of Figure 5, households with migrant family members are actually at a greater risk of falling into poverty by the 2000 to 2003 period. Below, Table 5 summarizes the range of transfers at five multiples of the poverty line. From the values reported in Table 5, it is evident that elderly households with pre-transfer income per capita less than onehalf the nutrition-based poverty line are at particular risk of falling into poverty. From Table 3, we know that in the later period this amounts to 17.5 and 15.7 percent, respectively, of the elderly without and with migrant children. As transfers have become less responsive to pretransfer income even as risk of falling into poverty has increased, it is apparent that a positive role might be played by some type of government transfer, either the _dibao_ or a non-contributory component of a pension. Given that transfers are unlikely to completely crowd out private transfers, a low base level of support would provide insurance against falling into poverty.

When thinking about the descriptive results on financial transfers above, it is important to keep in mind that financial transfers are only one form of transfer received by elderly from their adult children. As enumerated in the RCRE dataset the value of in-kind transfers in the form of food-stuffs and other consumable goods are included among these transfers, but there is no attempt to value time spent providing instrumental care, labor input to the elderly person’s farm 22
families are more susceptible to face these conditions as they often lack the environment conducive to learning and caregiving necessary for proper hygiene.

The relevant nuances of hygiene information campaigns are found on the frequency and quality of information, and the means used to spread such information, but the program design and impact evaluation data did not permit identifying such nuances. Nevertheless there is some recent evidence on this issue. Regassa et al. (2011) found that both the educational status of the caregivers (or head of household) and the direct contact with health extension workers significantly affect the utilization decision of sanitation and hygiene information by rural households. However, little evidence exists on how different types of information campaigns (e.g. personalized, mass media, or a mix of both) can have differentiated impacts on households receiving hygiene or sanitation information, and subsequently how children’s caregivers within households provide adequate hygiene to children. Some information campaigns are designed to rely on the _means of communication_ to boost the effect of information on households. Different designs of campaigns thus attain different objectives according to the means of information transmission outside and inside the household.

This paper relates the timing of a hand washing intervention and the influence it can have on a child’s cognitive and socio-emotional development during the first five years of his or her life. Taking advantage of an experimental intervention conducted in Senegal, this study estimated the impact of hand washing on cognitive development of young children. The evidence cannot reject the possibility that there is a relationship between hand washing and the developmental skills of the intervened children. However, the differences at the baseline between treatment and control groups on socio-emotional scores of children are influenced by both household communication assets and contamination in random assignment of program components. Also, significant differences in hand washing behaviors are explained by both the presence of certain communications assets within households and potential contamination biases. Still there is still room to conduct further research that addresses developmental skills driven by household choices or preferences. The existing household differences in communication assets tenure, independently of household income, and its interaction with handwashing promotion campaigns and behavior change confirm that the equal expectation assumption 29
- 25 -

schools, only the number of class periods the teacher taught elsewhere had an effect on student achievement, and the effect again was positive.

After holding these variables constant, it would be expected that some of the private school advantage would disappear. After all, these differences in teacher characteristics and teaching practices may account for a portion of that advantage. The results in Table 7 indicate that this hypothesis is indeed the case, at least for the F-type schools: the private F- type advantage over public schools falls from 7 points to about 4-5 points. However, it does not disappear, the implication being that there are unmeasured practices, teacher characteristics or factors that motivate teacher performance that account for a residual impact. For the private 0-type schools, holding these characteristics constant has virtually no impact on their advantage over public schools, which remains at about 2-3 points.

Table 7: Private School Effects After Holding Background, Teacher, and School Characteristics Constant, Dominican Republic, 1982-83.

Characteristics of the Randomly Predicted Score 1Differential Chosen Student, Set at Mean of: Public Private Private 0-Pub. F-Pub. 0-Type F-Type Public school subsample 9.72 12.76 14.32 3.04 4.60 Private 0-type subsample 10.34 12.90 14.59 2.56 4.25 Private F-type subsample 11.32 12.08 16.69 0.76 5.37 Peer Group Effects Because students interact with each other in school, the ability and backgrounds of fellow students could arfect individual achievement. To
Figure 3: NSB and MSB in South Africa data <!-- Start of picture text --> 0 1 2 3 4 5 6 7 8 9 10<br>Total number of switches<br>.5<br>.4<br>.3<br>Density<br>.2<br>.1<br>0<br><!-- End of picture text --> Notes: Total number of times the subject switched between options in the 10-item MPL. 0 switches indicate NSB. 0 or 1 switch are consistent with utility maximizing behavior. 2 or more switches constitute MSB and indicate inconsistent choices.

express a strong preference for the voucher, while 46% choose cash in all choices. Thus, on average, this group has a slight preference for electricity. However, the right panel reveals that the share of NSB who prefer the voucher varies depending on the order in which the choices were presented. The figure shows the share of never switchers who always choose the voucher conditional on whether the value of the voucher was descending or ascending in value. When the value is descending, the share of NSB choosing electricity is 63%. However, when it is descending, the share is only 45%. At the sample level, this suggests that order biases can be quite powerful and shift preferences towards the high-value option in the first MPL choice. At the individual level, however, we cannot distinguish errors or order biases from true preferences. In Jack et al. (2022), we consider reasons why respondents may have strong preferences for both vouchers or cash: frictions due to liquidity issues and transaction costs, combined with unexpected shocks, may lead households to experience unplanned shortages and exhibit a (potentially temporary) high marginal rate of substitution of one for the other.

25
25 Mexico MFA solely by fixed quality differences between U.S. and Mexican<sup>apparel. Furthermore, while we cannot</sup> distinguish between the fixed component of rent-sharing,<sup>and the fixed quality effect, the results seem</sup> to indicate that for the quota bound group this fixed effect<sup>is not very important. One interpretation of</sup> the coefficient on the U.S. price is that for these two groups (woven<sup>shirts and underwear), an</sup> increase of one dollar in the U.S. price has led to an average<sup>increase of 51 cents in the Mexican</sup> price. This may be an indication that U.S. retailers<sup>are receiving up to forty-nine percent of the rent</sup> from the MFA quota restrictions in these two groups of<sup>apparel.</sup> Table 6. Fixed Effects Quality Difference Regression Groups 3, 5, and 6

|Independent<br>Variables|Coefficient|t Statistic|Intercepts ai|F Statistic (1,22)<br>Ho:a*=<br>0|
|---|---|---|---|---|
|a6|1.299<br>(0.4913)|2.644X|_||
|pjtus|-0.136<br>(0.1913)|-0.711|||
|Hjt|0.006<br>(0.0023)|2.603a|||
|ujt|-0.661<br>(0.4902)|-1.348|||
|V jt<br>_ _ _**_**_ _ _<br>_ _ _<br>_ _ _ _ _ _ _|-1.712x10-<sup>9</sup><br>(2<br>X10-8|-0.878|_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>|_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|
|Tt|0.082<br>(0.0809)|1.019<br>.|||
|D3<br>__________<br>_________(1.4815)|11.303|7.629 a<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|12.602|67.37<br>reject<br>|
|Ds<br>.___ <br>_|3.413<br> _ __ _ __ _ _<br> <br>(0.8368)|1.019 '|4.712|26.12**'**reject|

Aujusted R<sup>2</sup> 0.98, n=30. Standard errors in parentheses. **a** si-nifi:ant at one percent. **c** significant at ten percent.

# Hypothesis Tests

1. ' = 1, t statistic, -5.938 , reject at one percent.

2. ' = 1, F(1,22) = 154.778, reject at one percent.

3. ' - 1, y' = '= ' = 0, F(4,22)=48.97, reject at one percent.
**_Remarks:_** We make two further observations related to other poverty lines in the literature. First, if we calibrate strongly relative lines that are directly proportional to the (equallyweighted) mean then the best fit is to use a constant of almost exactly 0.5 (0.497; s.e.=0.008), i.e., to set the line at about half the mean. Figure 5, panel (a), compares this strongly relative line with the weakly relative lines implied by the analysis above; there is little difference for the OECD so we focus on the non-OECD sub-sample. We see that the strongly relative lines are considerably lower among the countries with a low mean; for the poorest 50 countries in terms of the mean the gap is about $0.70 a day, which is likely to be sizeable for poverty measures.<sup>48</sup> For 81% of the (non-OECD) countries the weakly relative line is above the strongly relative line; in other words, the fact that the share of the mean is higher for the weakly relative line generally outweighs the fact that we discount the mean for the extent of inequality. Panel (b) of Figure 5 compares two sets of weakly relative lines, one with our distributional correction and the other based solely on the (equally-weighted) mean. Given that the Gini coefficient is only weakly correlated with the mean ( _r_ =-0.15), there is less sign of a systematic difference between the two sets of relative lines, although a persistent gap is still evident among low income countries.

Second, in the light of these findings, consider again the World Bank’s $1.90 a day absolute line in 2011 prices. This is higher than the expected line in the poorest countries based on our preferred specification. Using our parameter estimate for the full sample (column (5) of Table 1) the expected value of the poverty line is $1.17 for the country with the lowest value of * * * * _m j_ ( _m_ min=min( _m j_ , _j_ =1,.., _n_ )), namely the DRC where _m_ =$0.44. However, there is naturally a

* degree of uncertainty in any estimate of _m_ min. As already noted, the DRC value is an outlier. The next smallest value is $0.86 and with the next 7 the values are found in the interval $0.86-$1.14. The mean of the poorest 10 is exactly $1.00, for which the predicted poverty line is $1.56, also well below the World Bank’s $1.90 line. The latter can still be interpreted as the average poverty line corresponding to a set of the poorest countries (in the spirit of Ravallion et al., 1991); $1.90 * lies between the mean line for the poorest 23 and 24 countries ranked by _m j_ (with means of

$1.86 and $1.92 respectively). Given that a degree of consensus has developed around the $1.90 line it is the natural choice for the lower bound.

> 48 To be more precise the mean gap for the 46 countries with a mean less than $6 per day is $0.69 (s.e.=$0.20).

26
Table 6: Border discontinuity analysis of state political reservations on women's economic activity

||<br>|<br>DV is the ratio o|<br>f the indicated tr|<br>ait across the b|<br>orderingdistrict|<br>s|
|---|---|---|---|---|---|---|
||Log count of|Log|Log count of|Log women|Log women|Log women|
||<br>new women-<br>owned<br>establishments<br>in unorganized|<br> <br>employment in<br>new women-<br>owned<br>establishments<br>|<br> <br> <br> <br>women-owned<br>establishments<br>in unorganized<br>sector|<br>employed in<br>unorganized<br>sector|<br>employed in<br>male-owned<br>establishments<br>in unorganized|<br> <br> <br>employed in<br>women-owned<br>establishments<br>in unorganized|
||sector|in unorganized|||sector|sector|
|||<br>sector<br>|<br>||||
||(1)|(2)|(3)|(4)|(5)|(6)|
||||A. Base e|stimation|||
|Gap in years of implementation|0.555+++|0.732+++|0.226++|0.212+++|0.033|0.347+++|
||(0.139)|(0.157)|(0.105)|(0.062)|(0.043)|(0.101)|
|Observations|352|352|352|352|352|352|
|Adjusted R-squared|0.271|0.296|0.398|0.573|0.481|0.383|
|Controls<br>|State fixed|effects and the<br>B. Panel A inc|ratio of the speci<br>luding male anal|fied DV in 199<br>ogue of activity|4 for the borderi<br>in district pair|ng districts|
|Gap in years of implementation|0.458+++|0.664+++|0.178+|0.002|-0.287+++|0.184+|
||(0.130)|(0.147)|(0.098)|(0.067)|(0.072)|(0.100)|
|DV analogue for male-owned|0.502+++|0.505+++|0.956+++|0.806+++|0.992+++|0.288+++|
|establishments in 2005|(0.085)|(0.073)|(0.140)|(0.100)|(0.156)|(0.058)|
|Observations|352|352|352|352|352|352|
|Adjusted R-squared|0.387|0.424|0.507|0.714|0.676|0.503|
|<br>Controls|State fixed|effects and the|ratio of the speci|fied DV in 199|4 for the borderi|ng districts|

Notes: Regressions consider the impact of state political reservations using district pairs along state borders in 2005. Column headers indicate dependent variables, which are constructed as log ratio of the two variables in the adjacent districts. Regressions contain 352 unique district pairs, include fixed effects for the two states from which the district pair is drawn, and include the specified dependent variable as calculated in 1994 for the district pair. Panel A includes a variable that measures the gap in years between how long the two states have implemented reservations. A positive coefficient indicates that a longer history of implementing the reservations for one district on the border is associated with a greater gap in that district's values for women's manufacturing outcomes compared to its adjacent pair. Panel B further includes a control for the analogue of the dependent variable in 2005 for male-owned establishments along in the two districts. Regressions are weighted by an interaction of log initial employment in each district in 1994 and cluster standard errors by the bordering state pair. +++, ++, and + indicate statistical significance at a 1%, 5%, and 10% level, respectively.
incorporated in the data (Jolliffe, 1990; Mishra, 2007; Jolliffe and Cadima, 2016). Jolliffe (1990) argued that it is important to standardize the data prior to applying the PCA procedure for two reasons. First, standardization allocates the same weight to all the variables while using the original data could potentially lead to the high-variance variables dominating the first few principal components. Second, the measurement units of the different variables can affect the relative sizes of variances and covariances, a problem that can be overcome by standardization.

With respect to the first argument put forward by Jolliffe (1990), Gewers et al. (2021) pointed out that this practice could potentially lead to erroneous conclusions on the data generating process. These authors argue that standardization should be used only if the data variability is meaningful and intrinsic. Otherwise, if this variability is only a consequence of an unwanted effect, for instance, if it is due to an experimental error or noise, then Gewers et al. (2021) claim that ''... standardization may emphasize what should have been otherwise eliminated.'' In our case, because most of our variables are obtained by estimation, they are likely to be exposed to significant noise and therefore we would tend to avoid standardization. However, Jolliffe (1990)'s second argument strongly militates for standardization. Indeed, from Table 2 we see that there are significant differences in the variances of some of the variables due to the different units in which these variables are measured.<sup>28</sup> We thus apply a (rescaling) procedure referred to as ''min-max scaling.'' Let indicate a country and a year. For any variable , define its rescaled version as: ii ff ppvvrr i i ≡ ppvvrr −mmiiuu{ppvvrr} (1) rrff_ppvvrr mmvvmm{ppvvrr} −mmiiuu{ppvvrr} where i is rescaled i , and are respectively the minimum and maximum realizations of the variable for all countries and all years in our dataset, rrff_ppvvrr ppvvrr mmiiuu{ppvvrr} mmvvmm{ppvvrr} corresponding to the worst and best performance across countries and years in terms of the ppvvrr ii ff variable . Note that when i , i = 0 and that when i = , i = 1. This transformation enables us to normalize the values of each ppvvrr ppvvrr = mmiiuu{ppvvrr} rrff_ppvvrr ppvvrr variable to a unitary segment and evaluate the performance of a particular country in a relative mmvvmm{ppvvrr} rrff_ppvvrr sense by comparing it to the highest and lowest levels of the variable under consideration. As a result, this method also enables the comparability of each variable across different countries.

> 28 For instance, we see from Table 2 that there is an extreme difference in the standard deviations of the variables m and that are both proxies for the Provision dimension of an SC. ff iiffppi 16

16
In a response to this, an effort to collect comprehensive NTM data is currently being undertaken jointly by the World Bank, UNCTAD and the African Development Bank. Since 2009, local consultants have been tasked with drawing up NTM inventories in collaboration with ministries and agencies in 48 countries. One of the challenges related to data collection is that most countries do not have a national repository of NTM data. This is because laws and regulations affecting trade are often developed by different government agencies and regulatory bodies (for instance, in Lao, 14 organizations oversee the 27,975 NTMs, and in Tunisia, there are 10 authorities responsible for the 3,361 regulations). In practice, the data has to be carefully scrutinized for possible duplications, omissions, or any other problems in order to minimize inaccuracies. Once the gathered data are verified and formatted into a common, consistent nomenclature by UNCTAD and also verified by the relevant government, it is uploaded onto WITS, the World Bank’s portal for trade data, where it is publicly available. Variables in the WITS database include reporter name, NTM code, HS code, the year in which the measure was implemented, legal sources, enforcing agencies, and a list of affected partner countries.

# **c. Data Availability**

At the time of writing, NTM data has been collected for 48 countries, with 33 of the data files publically available on the WITS website. The remaining data files are in the process of verification by UNCTAD. Table 2 shows the availability of data across each of the six World Bank regions, along with the year in which the data was collected. The table separates out the five Central American countries that are the subject of our analysis. However, they are officially classified under the Latin America and Caribbean region by the World Bank.

**Table 2. NTM Data Availability (by World Regions)**

|**Sub-Saharan**<br>**Africa**|**East Asia**<br>**and Pacific**|**Europe and**<br>**Central Asia**|**Rest of**<br>**Latin America**<br>**and Caribbean**|**Central**<br>**America**|**Middle East**<br>**and North**<br>**Africa**|**South Asia**|**High Income**<br>**Countries**|
|---|---|---|---|---|---|---|---|
|Burkina Faso*|Cambodia|Kazakhstan*|Argentina*|Costa<br>Rica*|Egypt*|Afghanistan*|EU*|
|(2010)|(2011)|(2012)|(2008)|(2012)|(2011)|(2012)|(2010)|
|Kenya|China*|Russia|Bolivia*|El Salvador|Lebanon*|Bangladesh|Japan*|
|(2011)|(2012)|(2009)|(2008)|(2012)|(2011)|(2011)|(2009)|
|Madagascar*|Indonesia|Turkey|Brazil*|Guatemala*|Morocco*|India*||
|(2011)|(2011)|(2010)|(2008)|(2012)|(2011)<br>|(2012)||
|Malawi|Lao PDR*||Chile*|Honduras|Syrian Arab<br>Republic|Nepal*||
|(2011)|(2011)||(2008)|(2012)|(2011)|(2012)||
|Mauritius*|Philippines||Colombia *|Nicaragua|Tunisia*|Pakistan*||
|(2011)|(2008)||(2008)|(2012)|(2011)|(2012)||
|Namibia*|||Ecuador*|||Sri Lanka*||
|(2011)|||(2008)|||(2012)||
|Rwanda|||Mexico*|||||
|(2011)|||(2008)|||||
|Senegal*|||Paraguay *|||||

8