rafmacalaba/datause-encoder-data huggingface dataset:
# **Food Insecurity Experience Scale (FIES)**

|**Statement**|**Share of**<br>**choosin**<br>**that ind**<br>**severe o**<br>_PPCS_|**respondents**<br>**g responses**<br>**icate more**<br>**utcomes (%)**<br>_Facebook_|**Difference**<br>**(Percentage**<br>**point)**|**N**<br>**(combined)**|
|---|---|---|---|---|
|You worried that the household would not|42.07|56.67|14.60|5775|
|have enough food|(1.68)|(2.18)|(2.75)||
|You or any household members were not able|43.00|62.36|19.36|5784|
|to eat the kinds of foods you prefer because of|(1.74)|(2.11)|(2.74)||
|a lack of resources|||||
|You or any household member ate a limited|42.71|60.59|17.89|5797|
|variety of food because of a lack of resources|(1.79)|(2.11)|(2.76)||
|You or any household member ate a smaller|31.16|56.11|24.95|5800|
|meal than you felt you needed because there<br>was not enough food|(1.51)|(2.14)|(2.62)||
|You or any household member ate fewer meals|26.96|53.85|26.88|5792|
|a day because there was not enough food|(1.36)|(2.16)|(2.55)||
|There was no food at all in your household|19.77|36.28|16.51|5788|
|because there were no resources to get more|(1.06)|(2.09)|(2.34)||
|You or any household member go to sleep at|8.97|35.94|26.97|5797|
|night hungry because there was not enough|(0.68)|(2.07)|(2.18)||
|food|||||
|You or any household member went for a|6.35|25.98|19.63|5795|
|whole day and night without eating because|(0.58)|(1.90)|(1.98)||
|there was not enough food|||||
|_Average _|34.28|54.31|20.03|5789|

Notes: Responses indicating more severe outcomes are operationalized in this module as responses of “once or twice”, “3 to 10 times”, and “more than 10 times”. Estimates are unweighted, and clustered standard errors are reported in parentheses.

# **Reduced Coping Strategy Index (RCSI)**

|**Statement**|**Share of r**<br>**choosing**<br>**that indi**<br>**severe ou**<br>_PPCS_|**espondents**<br>**responses**<br>**cate more**<br>**tcomes (%)**<br>_Facebook_|**Difference**<br>**(Percentage**<br>**point)**|**N**<br>**(combined)**|
|---|---|---|---|---|
|Reduced the number of meals for all|27.64|63.59|35.95|6450|
|household members per day|(1.31)|(2.01)|(2.40)||
|Reduced the quantity of meals eaten by adults|25.09|56.45|31.37|6434|
|in favor of children|(1.24)|(2.10)|(2.44)||
|Purchased low quality markets "Leftover"|35.99|46.21|10.22|6430|

24
individuals with more education. Professionals did indeed take a serious blow in 2009 and 2010. Furthermore, job losses were mostly among younger workers, both in 2009 and 2010.

Finally, changes in unemployment by location show a similar pattern to changes in employment. The largest unemployment changes occurred in the border, capital, and central regions. As explained before, the latter two concentrate the largest share of total Mexican population, which explains both their large share of total unemployment and the greater impact of unemployment in the lesspopulated border region.

**Table 3.5: Decomposition of Annual Changes in Unemployment by Personal Characteristics**

||year en<br>|dingthe secondqua<br>|rter of...<br>|
|---|---|---|---|
||2008|2009|2010|
|**TOTAL**|**88,111**|**771,767**|**120,851**|
|**BY SEX**||||
|Female|46,341|152,988|113,699|
|Male|41,770|618,779|7,152|
|**BY EDUCATION LEVEL**||||
|Without instruction|11,743|21,522|-5,593|
|Primary|26,805|187,998|22,458|
|Secondary|30,827|313,027|32,773|
|High school|39,754|171,422|27,342|
|Professional|-21,018|77,798|43,871|
|**BY AGE GROUP**||||
|Less than 26|31,778|284,352|9,549|
|From 26 to 35|8,148|180,417|66,278|
|From 36 to 45|22,443|129,941|40,089|
|From 46 to 55|15,395|118,860|1,750|
|More than 55|10,347|58,197|3,185|
|**BY GEOGRAPHIC LOCATION**||||
|Capital|-37,911|240,194|5,394|
|Center region|23,423|114,121|24,558|
|Center gulf region|-1,310|34,349|20,172|
|North central region|14,242|30,074|7,641|
|North pacific region|-1,385|25,571|12,240|
|Central pacific region|29,402|79,003|19,278|
|South pacific region|-4,104|1,648|16,611|
|Peninsula region|5,261|25,126|6,919|
|Border|60,493|221,681|8,038|

**Source: Own calculations using data from ENOE.**

19
# **_6.5 Likely effects of climate change on net farm revenues_**

# _6.5.1 Uniform climate scenarios_

The study examined the sensitivity of net farm revenues to adverse changes in temperature and rainfall. The changes in net farm revenues as a result of changes in the climatic variables were simulated using the estimated models. The scenarios that were examined included changes in temperature by 2.5 _°_ C and 5 _°_ C and decreases in rainfall by 7% and 14%. The scenarios were based on scenarios used in the study by Kurukulasuriya and Mendelsohn (2006), who estimate the impacts of climate change on African cropland.

Following Kurukulasuriya and Mendelsohn (2006), we used the estimated regression coefficients in Table 7 to examine how changes in climate change net revenue per hectare in each district throughout the country (Equation 6). We then multiplied the change in net revenue per hectare by the number of hectares of cropland in each district to get an aggregate impact for each district. This value was then summed across all the districts of the country to get a total impact for the country: Aggregate climate impactd= Sum (ΔYi<sup>*W</sup> j<sup>)</sup> (7) where ΔYi = change in net revenue per hectare from a climate change Wj =hectares of cropland, irrigated cropland or rainfed cropland d= district d The values for cropland were downloaded from FAO statistics (2006). The results of the uniform scenarios are shown in Table 10. These ‘uniform’ scenarios assume that only one aspect of climate changes and that the change is uniform across the country. They show that a 2.5 _°_ C increase in temperature would result in a decrease in net farm revenues by US$0.4 billion for all farms but increase net revenue for farms with irrigation by US$0.3 billion. The study also examined the effect of a 5 _°_ C increase in temperature and the results show that net revenues would decrease across all farms, rainfed farms and farms with irrigation by US$0.4 billion, US$0.5 billion and US$ 0.003 billion respectively. A 7% and a 14% decrease in precipitation would result in a decrease in net farm revenue by US$0.3 billion for all farms.

As indicated in Table 10, it is evident that further changes in adverse climate variables (temperature and rainfall) are detrimental to crop production in the country. Rainfed farming is affected most by further increases in temperature and decreases in rainfall. Increases in temperature tend to be beneficial for farms with irrigation, implying that irrigation is important for sustaining agricultural production and as an adaptation option for smallholder farmers. It plays an important role as an additional source of water for crop production, particularly during the dry season and during mid-season dry spells that can affect agricultural production.

With the country having experienced successive droughts since 2000, any further adverse climatic conditions that worsen the already bad conditions would significantly reduce net farm revenues emanating from very low agricultural productivity. The implication from these 20
likelihood estimator is used. The PPML specification is given by the following: The other specification is the zero-inflated Poisson model (ZIP) that allows for frequent zero-valued observations. The model applies when the excess zeros are generated by a separate process from the count values and that the excess zeros can be modeled independently. This model allows for overdispersion assuming that there are two different types of individuals in the data: those who have a zero count with a probability of 1 (Always-0 group), and those who have counts predicted by the standard Poisson (Not always-0 group). The overall model allows for both the overdispersion and excess zeros that cannot be predicted by the standard Poisson model. The ZIP model therefore mixes two zero generating processes. The first process generates zeros through a logit model. The second process is governed by a Poisson distribution that generates counts, some of which may be zero. A household in the "Always-0 group" is a binary outcome that can be predicted by a logit model. The probability _φi_ that observation _i_ is in Always-0 group is predicted by the characteristics of observation _i_ and can be written as: with _β_<sup>0</sup> , _δ_<sup>0</sup> , _γ_<sup>0</sup> , and _φ_<sup>0</sup> the coefficients of the first-stage logit regression. Then the probability that observation _i_ is not in the "always-0" group becomes 1 − _φi_ . For observations in the "Not always-0" group, their positive count outcome is predicted by the standard Poisson model.

Finally we discuss the performance of each model based on tests applied to discriminate between these two models. The usual AIC and BIC tests are based on the likelihood function and are therefore not valid if at least one of the models is estimated by pseudo maximum likelihood, as it is the case for the PPML model. Following Santos Silva et al. (2015b), we therefore compare the performance of the two models for non-negative data with many zeros using the HPC test developed by Santos Silva et al. (2015a) and the Stata module available from Santos Silva and Tenreyro (2015).

# **4.2 The determinants of transport expenditures and its composition**

**Tests** Following Santos Silva et al. (2015a), we use the HPC test to choose between competing models for non-negative data with many zeros. The question is whether the zero and positive observations are generated by the same mechanism (PPML) or whether the zeros are somehow different (ZIP). The HPC test can be used to test whether or not the zero and positive observations are generated by different mechanisms. Table 3 shows the results for several sets of simulations for all transport expenditures, transport services expenditures, and private transport expenditures. For each type of expenditure, the PPML model is tested against the ZIP model, and vice-versa. The table includes the R-Squared for each model (computed as the

24
# **Online Appendix A.1 Summary Statistics**

Table A.1: Annual Participation Rates in Pension Programs and Health Insurance

||Pa|nel 1: Full S|ample|Panel 2:|2008 and N|ew Sample|Panel 3:|2008 and O|ld Sample|
|---|---|---|---|---|---|---|---|---|---|
|**Panel **|**A: Ove**|**rall partici**|**pation**|||||||
||Total|Employee|Self-emp|Total|Employee|Self-emp|Total|Employee|Self-emp|
|**Panel **<br>2008|**A1: H**<br>11.50|**ealth insur**<br>14.71|**ance**<br>3.24|11.50|14.71|3.24|11.50|14.71|3.24|
|2009|14.14|17.97|5.19|14.04|16.83|5.25|14.30|20.39|5.13|
|2010|22.75|29.48|7.12|20.52|24.90|6.47|25.00|35.05|7.55|
|2011|20.09|27.33|5.91|18.23|22.26|7.30|21.34|31.40|5.25|
|2012|27.28|37.46|7.86|31.35|39.02|7.98|25.23|36.48|7.82|
|2013|30.16|42.99|8.22|27.26|35.93|6.16|31.50|46.89|8.91|
|2014|29.71|41.12|11.38|28.32|35.31|12.06|30.40|44.62|11.14|
|2015|30.78|43.28|11.48|29.36|36.10|11.87|31.48|48.00|11.36|
|2016<br>**Panel **|33.72<br> **A2: Pe**|48.62<br>**nsion**|11.63|31.19|40.34|8.93|34.85|53.45|12.40|
|2008|18.53|23.58|5.46|18.53|23.58|5.46|18.53|23.58|5.46|
|2009|19.82|25.26|7.24|20.24|24.53|6.85|19.12|26.79|7.64|
|2010|21.66|27.99|6.94|18.53|22.11|7.05|24.83|35.13|6.86|
|2011|24.83|32.62|9.59|24.48|28.98|12.28|25.06|35.54|8.31|
|2012|30.82|40.57|12.02|33.19|40.06|12.13|29.61|40.90|11.99|
|2013|31.85|44.14|10.87|28.44|36.17|9.64|33.44|48.56|11.29|
|2014|30.95|41.54|13.93|29.27|35.43|14.87|31.79|45.25|13.60|
|2015|31.38|43.11|13.21|30.45|37.30|12.55|31.84|46.94|13.41|
|2016|34.11|48.92|12.12|31.66|40.71|9.59|35.22|53.70|12.85|

**Panel B: Participation Rate among Employees**

||overall|with<br>contr.|without<br>contr.|overall|with<br>contr.|without<br>contr.|overall|with<br>contr.|without<br>contr|
|---|---|---|---|---|---|---|---|---|---|
|**Panel **|**B1: Hea**|**lth insur**|**ance**|||||||
|2010|29.48|50.65|9.20|24.90|42.79|8.09|35.05|59.76|10.63|
|2011|27.33|47.16|8.59|22.26|39.11|6.62|31.40|53.49|10.22|
|2012|37.46|63.18|11.88|39.02|63.03|13.36|36.48|63.27|11.00|
|2013|42.99|71.44|14.21|35.93|64.20|9.51|46.89|75.17|17.17|
|2014|41.12|72.08|11.85|35.31|64.97|9.10|44.62|76.08|13.66|
|2015|43.28|72.68|13.08|36.10|61.84|10.77|48.00|79.57|14.69|
|2016<br>**Panel **|48.62<br> **B2: Pen**|75.92<br>**sion**|15.32|40.34|67.61|10.15|53.45|80.43|18.62|
|2010|27.99|48.98|7.94|22.11|39.22|6.23|35.13|60.22|10.16|
|2011|32.62|55.73|10.81|28.98|49.04|10.29|35.54|61.03|11.24|
|2012|40.57|67.41|14.01|40.06|64.33|14.10|40.90|69.48|13.95|
|2013|44.14|72.93|14.85|36.17|64.10|9.54|48.56|77.49|18.18|
|2014|41.54|71.61|12.96|35.43|63.33|10.73|45.25|76.30|14.44|
|2015|43.11|71.85|13.54|37.30|63.60|11.38|46.94|77.11|15.05|
|2016|48.92|75.77|16.06|40.71|67.93|10.21|53.70|80.03|19.77|

Source: RUMiC Migrant Survey 2008 and 2016.

A-2
play a large role by providing the necessary checks and balances. In cases where the government is willing to reform but the judiciary is closely linked to the private sector, other methods which affect the incentives of judges are also warranted. The good news is that there are many things that can be tried.

# **Conclusion.**

Integral components of an effective institutional reform strategy are countryspecific data which illustrate how a given system is working, comparative data which help suggest ways in which a system may improve, and an analysis which identifies the importance of the data collected and hypotheses formed relative to competing hypotheses.

Any reform of the judiciary should ideally take into account the net benefits to society, to assess which an empirical and comparative approach is critical. These depend on how the change may affect behaviour (for example, will more people be less likely to default, and if they default will more creditors be more likely to use the courts) as well as the total cost of administering the change. To do this job more effectively is needed a deeper and more concrete understanding of how institutions work in other countries, how the various pieces fit together.

As governments move forward they will need to think which changes would complement their existing endowments. Reforms in the judiciary, just like reforms in any other field can be greatly enhanced if key players are open to innovation and to new ways of doing things. One of the ways in which ideas can be stimulated and consensus for reform brought about is through greater openness- both with the rest of the world and 36
In equation (4), β=β2 and _xi_ ( γ ) = _xi d i_ ( γ ) , and _d i_ ( γ ) = { _qi_ ≤ γ} is a dummy variable. The first step is to identify the threshold value γ and the other coefficients. This is done by using the algorithm provided by Hansen (2000), which searches through the values of γ until the splitting value is found (this is the value of γ which minimizes the concentrated sum of squared errors based on an OLS regression).

Most of the _xi_ variables included in our threshold regression are typical variables used in gravity models—probably the most robust empirical relationship explaining the volume of bilateral trade flows. To identify the threshold value in the preferential margin we estimate to following equation: In equation (5), _Yi_ jk is the utilization rate of Cotonou preferences for product _k_ , in other words the percentage of country _i's_ imports of product k from country _j_ which requested preferential access. The data originates from Eurostat and contains import data at 8-digit level for the year 2001. _Dutydifference_ k is the difference between MFN and preferential tariffs for product k. This is the variable ( _qi_ ) for which we identify a threshold value.

As a proxy for the trading countries' _income_ and _size_ the GDP ( _GDPi_ is the level of income in country _i)_ and population ( _POPi_ is the population in country _i_ ) of both partner countries are included in the regression. GDP and population data are taken from the World Development Indicators database. _Dij_ is the distance between the partner countries and is a proxy for _trading costs_ . Previous empirical results showed that distance had an important negative impact on trade. The further away the trading partners are located from each other the higher the costs will be for transporting the products. Distance is expected to have a significant and negative impact on preferential trade. The distance data originates from the CEPII distance database and calculated following the great circle formula, which uses latitudes and longitudes of the capital cities.

11
such spending is additional or a diversion of existing funds and the practical problem of how to measure financial additionality remains.

As discussed, the decision by delegates to require project level reviews of project level baselines was prompted by a desire to safeguard the environmental integrity of the climate change framework. Even so, several authors have advocated setting sectoral or industry level baselines. For example, Zhang, Heller and May (2005) discuss how this might be accomplished for electricity producers in China. Largely, the arguments for doing so are based on arguments that project baselines are costly, arbitrary and unlikely to fully safeguard environmental integrity.<sup>36</sup> Moreover, project-by-project approval precludes the potential for the restructuring of transport systems, which may have substantial positive spillover effects in the long-run. In contrast, the environmental safeguards associated with sectoral baselines may be second best, but may substantially lower costs and regulatory uncertainty and could potentially play a large role in establishing low-emission development pathways.

# <u>Managing tradable units inventories under Kyoto</u>

The Kyoto Protocol and a series of rules issued as part of the Marrakesh Accords govern how countries manage their inventories of measured emissions and tradable permits. For one, in order to participate in emission trading, Annex I countries must create a national registry to keep track of their tradable units and must file annual reports on emissions. In addition, a series of rules govern how countries must mange their inventories within a reporting period and across reporting periods. How countries manage their inventories within the first commitment period is governed by the _commitment period reserve rule_ . This rule is designed to address concerns that incautious sales of excess permits would leave some parties unable to meet their targets. Keeping in mind that the current commitment period spans five years while greenhouse gas inventories are reviewed annually, each Annex I Party must maintain in its national registry a “commitment period reserve,” which cannot be below 90 percent of the Party’s assigned amount or five times its most recently review inventory, whichever is lowest.<sup>37</sup> How inventories can be managed between commitment periods are governed by the banking rule, the suspension rule and the restoration rule. The _banking rule_ is straightforward and set out in

> 36 Geres and Michaelowa (2002) and Shrestha and Timilsina (2002) discuss indirect leakages related to project baselines. Transaction costs and barriers that may not be fully accounted when investment additionality is determined are discussed by Renz (1998); Heller (1998); Michaelowa and Fages (1999); Woodward (2000); and Brechet and Lussis ( 2006).

> 37 Net holdings of ERUs, CERs, AAUs and RMUs for the relevant commitment period comprise the commitment period reserve.

19
BOX 2.1 TEXT REFERENCE FOR THIS BOX How some governments are using transfer payments to increase financial inclusion Digitizing transfer payments is one step governments can take to increase account ownership, and in recent years many countries have made this a policy priority. In Mexico, for example, the development bank Bansefi makes digital payments to 6.5 million social transfer recipients as part of the Oportunidades program, using the retailer Diconsa as part of its distribution network.<sup>a</sup> In Brazil the Bolsa Família program delivers financial assistance to nearly a third of the total population, and 99 percent of the recipients receive digital payments into a card or bank account.<sup>b</sup> The South African Social Security Agency distributes all funds to accounts.<sup>c</sup> And Mongolia’s Child Money Program delivers transfers by depositing the funds into savings accounts opened in children’s names. The goal is to ensure that all young adults will be banked, with a transaction history that can serve as a springboard from which to manage their financial needs.<sup>d</sup> The large share of government transfer recipients who receive transfer payments directly into an account in all four of these countries reflects these policy priorities (figure B2.1.1).

- a. Babatz 2013. b. CGAP 2012. c. MasterCard 2013.

# FIGURE B2.1.1

Government transfer recipients and how they receive payments in selected countries Adults receiving transfers in the past year, by method (as % of all adults), 2014

- d. Hodges and others 2007; Fritz 2014.

<!-- Start of picture text --> Using other method<br>30 In cash only<br>20<br>Into an account<br>10<br>0<br>Brazil Mexico Mongolia South<br>Africa<br><!-- End of picture text --> <!-- Start of picture text --> Source: Global Findex database.<br><!-- End of picture text -->

> THE GLOBAL FINDEX DATABASE 3939
<!-- Start of picture text --> Localized wars and conflicts<br>180<br>160<br>£ 140<br>S<br>§ 120<br>2 100<br><!-- End of picture text --> <!-- Start of picture text --> = Non-localized<br>=hadoa 80 § Localized<br>2 60<br>E<br>3 40<br>.0 |<br>Conflict (25< BRD <1000) War (>1000 BRD)<br><!-- End of picture text -->
|Central Rangelands Development Project(02)|Preeti S. Ahuja|
|---|---|
|Central Rangelands Development Project(02)|Preeti S. Ahuja|
|Central Rangelands Development Project(02)|Preeti S. Ahuja|
|Baardhere EngineeringCredit Project||
|Baardhere EngineeringCredit Project||
|Baardhere EngineeringCredit Project||
|Baardhere EngineeringCredit Project||
|Baardhere EngineeringCredit Project||
|Education Rehabilitation Project||
|Education Rehabilitation Project||
|Education Rehabilitation Project||
|Education Rehabilitation Project||
|Education Rehabilitation Project||
|Education Rehabilitation Project||
|Economic Rehabilitation and Modernization Project||
|Economic Rehabilitation and Modernization Project||
|Economic Rehabilitation and Modernization Project||
|Economic Rehabilitation and Modernization Project||
|Economic Rehabilitation and Modernization Project||
|Economic Rehabilitation and Modernization Project||
|Economic Rehabilitation and Modernization Project||
|Cocoa Rehabilitation Project||
|Cocoa Rehabilitation Project||
|Cocoa Rehabilitation Project||
|Cocoa Rehabilitation Project||
|Cocoa Rehabilitation Project||
|Cocoa Rehabilitation Project||
|Agricultural Privatization and Smallholder Development Project|Dirk Nicolaas Prevoo|
|Agricultural Privatization and Smallholder Development Project|Dirk Nicolaas Prevoo|
|Agricultural Privatization and Smallholder Development Project|Dirk Nicolaas Prevoo|
|Agricultural Privatization and Smallholder Development Project|Dirk Nicolaas Prevoo|
|Agricultural Privatization and Smallholder Development Project|Dirk Nicolaas Prevoo|
|Agricultural Privatization and Smallholder Development Project|Dirk Nicolaas Prevoo|
|Agricultural Privatization and Smallholder Development Project|Dirk Nicolaas Prevoo|
|Agricultural Privatization and Smallholder Development Project|Dirk Nicolaas Prevoo|
|Agricultural Privatization and Smallholder Development Project|Dirk Nicolaas Prevoo|
|Agricultural Privatization and Smallholder Development Project|Dirk Nicolaas Prevoo|
|Structural Adjustment Credit Project(01)||
|Structural Adjustment Credit Project(01)||
|Structural Adjustment Credit Project(01)||
|Structural Adjustment Credit Project(01)||
|Structural Adjustment Credit Project(01)||
|Structural Adjustment Credit Project(02)|Jennifer R. Edwards|
|Structural Adjustment Credit Project(02)|Jennifer R. Edwards|
|Structural Adjustment Credit Project(02)|Jennifer R. Edwards|
|Structural Adjustment Credit Project(02)|Jennifer R. Edwards|
|Structural Adjustment Credit Project(02)|Jennifer R. Edwards|