rafmacalaba/datause-encoder-data huggingface dataset:
# **_A Closer Look at the Impact on the Devaluation on Farm Households_**

This section of the paper discusses the trends in rural poverty evidenced by the household data. On the one hand, the data raise questions about the reliability of the survey results. On the other hand, they also suggest that many rural farm households may not have benefited -- at least in the short term from the devaluation -- to the extent commonly supposed (Goreux, 1995) because of export taxes imposed at the time of the devaluation.

The large increase in poverty in West Forest seems to be driven by the large decrease in home consumption. Consumption of home-produced food fell by 13 percent in East Forest and by 20 percent in the Savannah, compared to the 40 percent fall in West Forest (table 32). Breaking it down further, the fall was about 21 percent for food crop farmers in the West Forest, compared to 33 percent in the East Forest and 17 percent in the Savannah. For export crop farmers, the fall in consumption of home production was 43 percent in the West Forest, compared to only 6 percent in East Forest, and 13 percent in the Savannah (table 33). Thus, the huge decline in consumption of home production, particularly among West Forest export crop farmers, raises questions. The estimation of Engel curves shows that food shares drop significantly in the West Forest between 1993 and 1995, and not in the other rural areas (table 34).8 However, for West Forest, the dummy for 1995 becomes insignificant if a dummy is added for 1995 export crop farmers. The export crop-1995 dummy is significant and negative, indicating that in 1995, West Forest export crop farmers had significantly lower food shares than other West Forest residents.<sup>29</sup> Table **32 Mean Household Expenditure Per Capita** _(in 1985 CFAF per year)_

|||_199_|_3_|||_1995_|||
|---|---|---|---|---|---|---|---|---|
||_Consumption_||||_Consumption_||||
||_of home_<br>_production_|_Purchased_<br>_food_|_Non -_<br>_food_|_Household_<br>_expenditure_|_ofhome_<br>_production_|_Purchased_<br>_food_|_Non -_<br>_food_|_Household_<br>_expenditure_|
|Abidjan|142|78275|146857|<br>225274|364|64081|121805|<br>186251|
|Other Cities|5235|49835|70375|125445|3416|52931|62259|<br>118605|
|East Forest|29102|28657|43908|101667|25328|31885|41055|<br>98269|
|West Forest|43008|20480|42497|105986|25643|27008|43596|<br>96247|
|Savannah|35814|17109|33117|86040|28680|27066|35494|<br>91240|
|Cote d'lvoire|21544|39773|67989|129306|16104|41709|63673|<br>121486|

28 There is a significant, though much smaller drop, in food shares in Abidjan between 1993 and 1995.

29 The decline in consumption of home production does not appear to be related to the procedure used to correc **t** he months of home consumption in 1993. There is a decline in the home consumption of the major staple foods not only in the months of consumption, but also in the number of days per month of consumption and especially in the value per day.

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- `sae proc stats||inds :` The stats and inds sub-routines are useful for processing Mata formatted simulation output and producing indicators with new thresholds or weights, as well as profiling.

The routines and sub-routines are described in the sections below.

# **6.1 Preparing the Target Dataset**

Due to the potential large size of the target data set, the `sae` command comes with an ancillary sub-command ( `sae data import` ) useful for preparing the target dataset and converting it into a Mata format dataset to minimize the overall burden put on the system. The command’s syntax is as follows: ``` saedataimport,datain(string)varlist(string)area(string)uniqid(string) dataout(string) ``` The options of the command are all required.

- The `datain()` option indicates the path and file name of the Stata format dataset to be converted into a Mata format dataset.

- The `varlist()` option specifies all the variables to be imported into the Mata format dataset. The variables specified will be available for the simulation stage of the analysis. Variables must have similar names between datasets. Additionally, users should include here any additional variables they wish to include, as well as expansion factors for the target data.

- The `uniqid()` option specifies the numeric variable that indicates the unique identifiers in the target dataset. This is necessary to ensure replicability of the analysis, and the name should match the one of the unique identifier from the source dataset.

- The `area()` option is necessary and specifies at which level the clustering is done, it indicates at which level the _ηc_ is obtained. The only constraint is that the variable must be numeric and should match across datasets, although it is recommended it follows a hierarchical structure similar to the one proposed by Zhao (2006).

- The hierarchical id should be of the same length for all observations. For example: AAMMEEE.<sup>30</sup> This structure facilitiates getting final estimates at different aggregation levels.

- The `dataout()` option indicates the path and filename of the Mata format dataset that will be used when running the Monte Carlo simulations.

## **6.1.1 Example: Preparing the Target Data**

The `sae` command requires users to import the target data into a Mata data format file. This is done to facilitate the process of simulations in the second stage due to the potential large size of the target data.

> 30In the case this were done for a specific country, AA stands for the highest aggregation level, MM stands for the second highest aggregation level, and EEE, stands for the lowest aggregation level.

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# **2. Overview of Protection**

An overview of protection in the LINKAGE model is offered in tables 2.7 and 2.8 of Anderson and Martin (2005), and so it will not be repeated here. However, it is important to bear in mind the main features of global protection gleaned from this earlier chapter as impacts of its removal are analyzed. Recall that the main source of protection resides in tariffs or border barriers, with many countries having significant domestic distortions in agriculture. As noted in Anderson and Martin (2005), the world average tariff on agriculture and food is 16.7 percent, with little difference between high-income and developing countries,<sup>9</sup> 16.0 percent and 17.7 percent, respectively. Of course, the averages obscure large variations across countries and commodities. Not surprisingly, the highest agricultural tariffs are in the high-income Asian economies, but several low-income countries also have high tariffs—for example India, Vietnam, the rest of Sub-Saharan Africa, and the rest of South Asia. These same developing countries tend to have high tariffs in other sectors as well—for example, India’s tariff on textile and clothing is more than 26 percent, and on other goods, it is nearly 25 percent. In summary, the patterns of protection are relatively clear, agriculture and food have the highest level of protection, followed by textile and apparel and then other goods. Developed-country protection is largely concentrated in agriculture and food, and developing-country protection is by and large more uniform across broad sectors.

Recall that precommitments (handled via a pre-experiment) represent those tariff changes that have been agreed to in 2001, but not yet fully implemented. These are implemented as part of the baseline scenario. As can be seen from table 2.7 in Anderson and Martin (2005), these precommitments are small except for the case of China. Under WTO accession, its average tariff

> 9. This paper uses the World Bank definitions for developing countries, unless otherwise stated. This means that East Asia’s newly industrialized economies are classified with the high-income countries.

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WPS3747 Despite this average decrease, import prices for agricultural goods increase. The impact on nominal GDP at factor cost is positive and both imports and exports increase by 4.3 and 2.1 percent respectively. In terms of absorption components, both government consumption and investment are assumed to be fixed while private consumption increases by 0.7 percent. At the sector level, changes are driven by the differential exposure to foreign trade and the terms of trade shocks and some reallocation of factors occurs, mainly between manufacturing activities. These shocks translate into a 0.5 percent increase in per capita income at the household level. The increase is much smaller in the urban sector and is accompanied by a worsening in the income distribution. As a result, the poverty impact is small in terms of incidence and both the poverty gap and squared poverty gap increase, suggesting that some of the poorest households fare badly.<sup>8</sup> This result stems from the adverse impact on urban households of rising food prices. Results in the rural sector are quite very different, with a 0.9 percent increase in the average per capita income and almost no change in inequality indicators. As a result, close to 690,000 people are lifted out of poverty in the rural area when the rest of the world liberalizes trade.

The full liberalization scenario (Full-Lib) combines ROW and Indonesian reforms and generates more favorable aggregate results with exports and imports increasing by 5.9 and 9.1 respectively. Total employment is assumed to fixed, but the full liberalization entails some reallocation of labor towards self employment for each labor category, namely a reallocation of labor out of formal sectors and towards agricultural sectors. This stems from the fact that import prices for nonagricultural good decrease more than for agricultural products as a result of the full liberalization. As a consequence there is an improvement in the distribution of income overall, as well as within urban and rural areas. Consequently poverty falls with nearly 1,400,000 people

> 8 The poverty gap measures the distance between of the average poor household income per capita and the poverty line while the squared poverty gap gives a measure of the distribution of income among poor households.

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revisiting rejected projects later on when underlying project circumstances change and they are likely to generate net positive benefits. Hence, all appraised projects should be recorded in a project database ranked by priority for budget consideration.

18. Projects involving non-standard procurement, such as public private partnerships (PPPs) and bundled “resources for infrastructure” projects, should be subject to the same appraisal process as standard public investment, and the costs and benefits of such projects should be compared against a public sector comparator project.

19. An important step after a project has been selected and before it is included in the budget is development of a detailed project design to ensure that the project is accurately costed and can be tendered and implemented (a “ready-to-go” check). Moreover, to facilitate project implementation, the project design should also provide a full risk assessment, performance indicators and an implementation strategy which should be used by an implementing agency.

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quality. The low quality leads to low returns on investments, and decreased profitability and earnings for everyone — lowering enthusiasm in consequence.

**iv.** Disturbance in family life : Entrepreneurs report that the uncertainties lead to distress and frequent disagreements in their personal, professional, and family lives. The unstable socio‐political environment, financial constraints, and long working hours are perceived to disturb their work‐life balance. This, in turn, impairs their family and social lives, and for some, the lack of family support can make this situation worse.

v. Gender‐specific issues for female entrepreneurs: Pervasive social and cultural barriers, lack of technical skills, limited availability of capacity building opportunities, household responsibilities, and lack of financial and emotional support from families were reported as additional barriers for female entrepreneurs.

Source: World Bank Group, 2018; Saraf, Rahman, Hamdani, Jamison, and others, 2018 3. Psychosocial Training Interventions

# 3.1 Literature on Relevant Interventions

<mark>Behavioral economics and cognitive psychology provide a set of tested tools to address stress and cognitive resource depletion, including restoration of emotional well‐being in the workplace. One key approach found across psychological interventions is CBT. It has been commonly used in psychological settings, and more recently, in economic interventions with youth and adults in FCVs. Greater exploration of this approach in the context of mental health improvements for SME entrepreneurs in FCV situations is documented in the following paragraphs.</mark> <mark>The simple idea behind CBT (Figure 1) is that an individual’s unique patterns of thinking (cognition) and feeling are significant factors in how they experience their surroundings and the type of actions they take (behavior), both desirable and undesirable. As these patterns have such a significant impact on performance, it follows that addressing these patterns — where they have become maladaptive due to mitigating circumstances — can change their experience of the world and hence, actions and performance (Martin 2016). CBT workshops can help to generate a more positive outlook, developing resilience and improving decision‐making abilities and will‐ power. CBT tends to be short (3‐4 months) and inexpensive (US$300‐$750 per participant). It can include somatic interventions, such as deep breathing exercises. In some cases, CBT has been delivered by NSPs with positive results.</mark> 7
**Figure 9: Rates of Increase per Year of Age in COVID-19 Mortality vs. Rate of Increase per Year of Age in All-Cause Mortality by Country**

<!-- Start of picture text --> y y y<br>CHE<br>CAN<br>NLD<br>DNK<br>AUS<br>SWE<br>ESP<br>DEU KOR ITA<br>HUN PRT NOR<br>FRA<br>JPN<br>CHL<br>ARG<br>PHL<br>BRA<br>MYSCOL<br>ZAF<br>AFG PER<br>PAK<br>MEX<br>.06 .07 .08 .09 .1<br>Rate of increase in all-cause mortality rate<br>.15<br>.1<br>Rate of increase in COVID-19 mortality rate<br>.05<br><!-- End of picture text --> Source: Author’s analysis. LMICs are shown in red and HMICs in blue.

The pattern of mortality risk by age across countries from COVID-19 resembles the pattern for all-cause mortality, matching the observation that the patterns of mortality from COVID-19 resemble those for mortality generally. The disease kills principally those who were already at highest risk of death: the aged and those with comorbidities. Likewise, in the United States African-Americans are at higher risk of death from COVID-19 and have higher mortality risk overall at all ages (Yancy 2020). Similarly, ethnic minorities in the United Kingdom are at highest risk of dying of COVID-19 (Public Health England 2020).

# **5 Possible Explanations for Variation across Countries**

Rules and practices for classifying deaths by particular causes vary by country, and the same is true for classification of COVID-19 deaths. The extent to which deaths are attributed to COVID-19 varies depending on the overall effectiveness of the country’s vital statistics system in generating cause of death information, the country’s guidelines for attributing deaths to COVID19, and the extent to which accurate COVID-19 diagnosis through testing and other means is available. In virtually all countries with a substantial number of COVID-19 deaths, it is likely that 13
# **Figure 4: Diet quality (restricted nutrients separately), by socioeconomic neighborhood**

<!-- Start of picture text --> Fat (absolute) Fat (per price) Fat (per 100g)<br>Socioeconomic groups Socioeconomic groups Socioeconomic groups<br>Carbohydrates (absolute) Carbohydrates (per price) Carbohydrates (per 100g)<br>Socioeconomic groups Socioeconomic groups Socioeconomic groups<br>Sodium (absolute) Sodium (per price) Sodium (per 100g)<br>Socioeconomic groups Socioeconomic groups Socioeconomic groups<br>low mid-low<br>mid high<br>.2 .01 .02<br>.1 0 .010<br>0 -.01 -.01<br>PDRNI (mean) -.1-.2 PDRNI/price (mean) -.02-.03 PDRNI/100g (mean) -.02-.03<br>2 .4 .4<br>.3 .3<br>1 .2 .2<br>PDRNI (mean) .1 .1<br>0 PDRNI/price (mean) 0 PDRNI/100g (mean) 0<br>2 .4 .4<br>.3 .3<br>1 .2 .2<br>PDRNI (mean) .1 .1<br>0 PDRNI/price (mean) 0 PDRNI/100g (mean) 0<br><!-- End of picture text --> _Source:_ Estimates based on data of ENCONUT 2013.

_Note:_ The diet quality index, expressed in the Proportional Difference of Relative Nutrient Intake (PDRNI) index on the restricted nutrients, measures the distance to the ideal intake. The further away the value is from zero (ideal value), the less nutritious is the average menu offered in restaurants in the nutrients considered. Data are weighted.

In order to understand the potential relevance of the diet quality of nearby restaurants, figure 5 displays the share of women ages 15–49 who consume FAFH on a daily basis. It appears that nearly half of all women living in neighborhoods of low socioeconomic background consume FAFH every day, while the share is smaller in mid–socioeconomic status (43 percent) and high socioeconomic status (38 percent) neighborhoods.

**Figure 5: Percentage of women (age 15–49) who consume FAFH every day, by socioeconomic neighborhood**

<!-- Start of picture text --> Socioeconomic groups<br>low mid-low<br>mid high<br>70<br>60<br>50<br>40<br>30<br>20<br>Percentage of women having FAFH every day (mean) 10<br>0<br><!-- End of picture text --> _Source:_ Estimates based on data of ENAHO 2013. _Note_ : Data are weighted.

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|Planning<br>(10)|9.1**|(4.1)||Yes|Yes|50|0.44<br>ility, number of villages in|
|---|---|---|---|---|---|---|---|
|Family <br>(9)|5.5|(4.9)|15.2***|(3.5)<br>No|No|50|0.03<br> health fac|
|atal<br>(8)|17.4*|(9.6)||Yes|Yes|50|0.65<br> at the|
|Anten<br>(7)|16.1|(16.1)|78.9***|(11.9)<br>No|No|50|0.02<br>nking water|
|(6)|3.45*|(2.03)<br>1.01***|(0.09)|No|No|50|0.66<br>ity of dri|
|Delivery<br>(5)|7.3**|(2.5)<br>||Yes|Yes|50|0.61<br>, availabil<br>ty unit.|
|(4)|6.3*|(3.3)|9.2***|(1.6)<br>No|No|50|0.07<br>evel.<br>lth facility<br>te materni|
|nt<br>(3)|123.3***|(47.8)<br>0.31***|(0.05)|No|No|50|0.49<br>0) percent l<br>e at the hea<br>has a separa|
|ut-Patie<br>(2)|137.0**|(54.4)||Yes|Yes|50|0.40<br>he 1 [5] (1<br>sis.<br>watersourc<br>h facility|
|O<br>(1)|107.4*|(65.0)|660.8***|(35.0)<br>No|No|50|0.05<br>i...cance at t<br> in parenthe<br>de: type of <br>er the healt|
|t<br>ion|mpact|ation|||xed e¤ects|ons|) denote sign<br>andard errors <br>riables inclu<br>ea and wheth|
|en<br>e<br>at|m i|liz|nt|ls|...|ati|] (*<br> st<br> va<br> ar|
|d<br>l<br>...c|a|ti|a|o|ct|v|*<br>st <br>ol<br>nt|
|pen<br>iab<br>ci|gr|-u|nst|ntr|tri|ser|[*<br>bu<br>ntr<br>me|
|De<br>var<br>Spe|Pro|Pre|Co|Co|Dis|Ob|R2<br>a. ***<br>b. Ro<br>c. Co<br>catch|

52
|||**Model 2**||**Model 3**|
|---|---|---|---|---|
||**2012**|**2018**|**2012**|**2018**|
|**N**|1222|1149|1221|1107|
|**Pseudo R-squared**|0.222|0.168|0.262|0.209|

Source: Authors’ calculations based on ELMPS 2012 and ELMPS 2018 Notes: *p<0.05; **p<0.01; ***p<0.001.

# **4.3.2. Are informal firms like formal firms?**

Using the preceding models, we now turn to considering whether the predicted probabilities of being formal overlap between formal and informal firms. We take the extent of this overlap to be a metric of the potential to formalize. However, we recognize that this is entirely driven by the predictive power of the models, which is in turn driven by data availability. The pseudo-R-squareds of the models are another metric of this; consistent with our finding that location and industry were poor predictors of formality, Model 1, which includes these, firm size, and firm age, has pseudo-R-squareds that range from 0.0820.119. Model 2a for EcC is 0.133, and the ELMPS model 2b 0.221 (2012) and 0.168 (2018). Owner characteristics in Model 3 raise this to 0.262 (2012) and 0.209 (2018).

Consistent with the low R-squareds from Model 1, Figure 10 shows high overlap between predicted probabilities, ranging from a 67% overlap in the EcC to an 76% overlap in ELMPS 2012. Since these are the easily observable characteristics, this bodes poorly for the potential of policymakers to target firms that are likely to formalize based on readily observed characteristics, whether for incentives or enforcement. Model 2a (Figure 11) for the EcC with firm characteristics shows 65% overlap. There is a distinct mode at 0.2 probability for informal firms and a mode at 0.4 for formal firms.

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