rafmacalaba/datause-encoder-data huggingface dataset:
significant ways. Studies vary across many dimensions, such as whether the analysis is carried out for representative households or actual households (i.e. micro-simulation), whether it is static or dynamic, single- or multi-region, partial- or general-equilibrium, and so forth.

Out of these possibilities, four broad categories of study are identified based upon the principal methodology employed. These categories are loosely defined and unlikely to satisfy every reader, but they provide a starting point for getting a grip on what is an extraordinarily wide range of approaches. The first category encompasses a broad array of _partial-equilibrium_ and/or _cost-of-living_ approaches. These studies are typically based on household expenditure data, and generally emphasize commodity markets and their role in determining poverty impacts, or at least as a measure of poverty across time. Factor earnings are frequently ignored.

In contrast, studies in the second category all involve some form of _general equilibrium_ model that accounts for commodity, terms of trade, and factor market effects. These studies are usually based on disaggregated economy-wide Social Accounting Matrices for individual countries.<sup>2</sup> They are also closely related to the third category of study, which represents a relatively recent approach – general equilibrium simulation coupled with incidence analysis based on household survey data. These studies may be thought of as involving a _micro-macro synthesis_ . While the term “micro-macro” has been used differently in other contexts, in this paper it is meant to refer to the linking of a model based on household survey (micro) data with a model based primarily on macro-level data.

The fourth category of study attempts to estimate the long run potential for poverty reduction through the effect of trade liberalization on economic growth. This is typically undertaken in a series of steps, whereby the effect of trade liberalization on capital accumulation, productivity and economic growth is first estimated, thereupon using some sort of reduced form relationship to estimate the poverty reduction that might occur as a result of this growth. Whereas the first three sets of studies tend to focus on changes in income distribution, this group abstracts from such changes, focusing instead on shifts in the aggregate income distribution.

> 2 Cloutier, Cockburn, and Decaluwé (2004) provide an extensive review of CGE models used in this field.

5
Considering these possibilities for the relaxation of local independence and the four possibilities for the number of classes, there were already 16 models to consider. With this starting point, we conducted an empirical exploration of whether there was a need to relax the local independence assumption on other pairs of answers to the sub-questions. To accomplish this, we estimated the 16 models and examined the size of bivariate residual correlations, a measure of the marginal increase in the log-likelihood function that could be obtained by removing the local independence assumption for any specific pair (Vermunt and Magidson, 2016: 83-5). The pairs that had particularly large bivariate residual correlations were then added to the set of pairs that were candidates for non-imposition of local independence.<sup>56</sup> Based on these residual correlations, we chose to consider a total of 20 models for each of supplier relations and consumer relations, where, for each of the four possibilities of class size, we allowed for the combination of correlations of 1-2, 2-3, 3-4, 3-5, 4-5 for supplier relations, and 1-2, 4-5, 4-6, 5-6 for customer relations, in addition to the four mentioned above. The selection of the best model was based on several information criteria, particularly the Bayesian information criterion, the consistent Akaike information criterion, and the approximate weight of evidence criterion.

Providing further details of the model selection process is necessary for completeness, but understanding these details adds little to understanding the paper's central substantive findings. Interested readers can consult the relevant appendixes, which describe the choice of the set of models considered (Appendix C.1), the statistical criteria used to compare the performance of the different models (Appendix C.2), and the final choice of the preferred models (Appendix C.3).<sup>57</sup>

> 56 The exploratory results suggested that answers to adjacent sub-questions were related. As already noted, this may be due to non-differentiation of responses. This effect is known to be smaller in face-to-face surveys (Holbrook et al, 2003), which may be a reason that only adjacent responses were correlated in our data. These correlations might also reflect an "anchoring effect" (e.g. Furnham and Boo, 2011), where a subsequent response is biased towards a previously selected response.

> 57 Four-class models were preferred, with the correlation structures obtained through the analysis of the bivariate residual correlations as described above.

30
performance of the PMT. We explore a binary dependent variable model, which models the probability of a discrete outcome, in this case household poverty status. Then we explore, several specifications that add a complete set of state and rural and urban indicator variables (i.e., dummy variables), as well as employment information, in the form of the proportion of number of household members who have ever worked for income to the total household members.

Table 7 presents the evaluation metrics of the PMT using a binary dependent variable logistic model compared to the OLS regression model used in Section 4.<sup>19</sup> For comparison purposes, the PMT cutoff score for the logistic model was set so that the predicted household poverty rate was similar under both models. The results for total accuracy, poverty accuracy, undercoverage, and leakage, are very similar and always within 2 percentage points. Therefore, it does not appear to make much difference whether OLS or logistic regression is used to develop the PMT on targeting performance. However, in practice, OLS is easier to implement and interpret due to its linear functional form.

Table 8. Evaluation of a Binary Dependent Variable Model (Logistic Regression)

|**Binary Dependent Variable (Poverty**|**Status) Model**<br>**Logistic Mod**<br>**Poverty Line**<br>|**versus OLS Re**<br>**el**<br> **/ PMT Cutoff**<br>|**gression of Ho**<br>|**usehold Per C**<br>**OLS Model**<br>**Poverty Line**<br>|**apita Consum**<br>**/ PMT Cutoff**<br>|**ption**<br> <br>|
|---|---|---|---|---|---|---|
|**Evaluation Criteria**|**Bottom**<br>**20%**|**Bottom**<br>**40%**|**Bottom**<br>**60%**|**Bottom**<br>**20%**|**Bottom**<br>**40%**|**Bottom**<br>**60%**|
|Povertyheadcount(observed)|19.96%|39.95%|59.89%|19.96%|39.95%|59.89%|
|Household poverty rate (observed)|19.36%|37.59%|54.92%|19.36%|37.59%|54.92%|
|Household poverty rate (predicted)|12.88%|39.40%|59.40%|12.81%|38.76%|59.60%|
|Total accuracy|83.41%|78.39%|80.00%|83.01%|77.89%|79.26%|
|Povertyaccuracy|40.41%|73.67%|85.87%|39.21%|72.15%|85.38%|
|Undercoverage|59.59%|26.33%|14.13%|60.79%|27.85%|14.62%|
|Leakage|6.26%|18.76%|27.15%|6.47%|18.65%|28.19%|
|_Notes:_The logistic model is estimated<br>poverty line. For comparisons purpos<br>National Post-LASSO OLS model. Both<br>out-of-sample prediction on a random|using observe<br>es, the PMT cut<br>models includ<br>sample of 25|d poverty statu<br>off was selecte<br>e the same vari<br>% of household|s in the NHBPS<br>d to identify a<br>ables presente<br>s.|2014/2015 ba<br>similar share o<br>d in Table 3. T|sed on the na<br>f households<br>he results are|tional<br>as the<br>based on|

Table 9 presents the results of the Sudan national PMT with the inclusion of a complete set of state and rural and urban indicator variables. The inclusion of state and area indicator variables improves model accuracy by less than 2% with the largest gains observed when targeting the bottom of the consumption distribution. Similarly, poverty accuracy, undercoverage, and leakage all show modest improvements with

> 19 The regression results are available upon request.

19
We use the two-digit level of the NIC system for industry variation. This level of aggregation contains 22 manufacturing industries. Discussions of India’s industrial landscape and its long-run transformation often use the terms “traditional” and “modern”, although there are no established or precise definitions of these groups. Following Ghani, Kerr, and O’Connell (2013a), we classify an industry as being modern if its unorganized sector share is less than the unweighted average unorganized share across manufacturing industries. We report several traits below that utilize this separation, and the notes to Table 3a document the industries included in the two groupings explicitly.<sup>4</sup> The NSS surveys the ownership type of each establishment. Establishments can be listed as male proprietary, female proprietary, other owned, cooperative, household partnership, multihousehold partnership, private LLC, and unknown. In some of our analyses of the unorganized sector, we separately consider establishments listed as either male proprietary or female proprietary. These two groups constitute 98% of establishments in the informal manufacturing sector in 2000. Ghani, Kerr, and O’Connell (2012) in particular emphasize the partitioned business networks in India for female-owned businesses, and this work extends these earlier studies to understand the implications for input usage.

Finally, and most important, we collect raw material input counts information from the survey questionnaires (ASI ‘00 - Block H, NSS ‘00 (56<sup>th</sup> Round) - Block 3). In each survey, respondents are asked to list the first five major raw material inputs into their production, providing product codes, quantities and values. From this block of information we extract the total number of reported inputs (with a maximum of five as per the survey questionnaires). The minimum input count is zero and the median input count is two inputs.

We focus on the raw count of distinct materials inputs in this paper. As we describe at many points below, we typically consider deviations of these input counts from the average pattern of plants in each industry to control for differences in typical production techniques across industries. We will also control directly for the intensity of material use as input for firms. Thus, the input count measure in our strictest estimations is modeling the variety of raw material

> 4 For clarity, we use term “sector” when discussing organized and unorganized sectors, “industries” when discussing the 22 manufacturing industries within manufacturing, and “groups” when describing “traditional” and “modern” industry groups.

6
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# **References**

- Daron Acemoglu and David Autor. Skills, tasks and technologies: Implications for employment and earnings. In _Handbook of labor economics_ , volume 4, pages 1043–1171. Elsevier, 2011.

- Daniel A Ackerberg. Timing assumptions and efficiency: Empirical evidence in a production function context. _University of Michigan mimeo_ , 2016.

- Daniel A Ackerberg, Kevin Caves, and Garth Frazer. Identification properties of recent production function estimators. _Econometrica_ , 83(6):2411–2451, 2015.

- Lewis Alexander and Janice Eberly. Investment hollowing out. _IMF Economic Review_ , 66(1):5–30, 2018.

- Dan Andrews, Chiara Criscuolo, Peter Gal, et al. _Frontier firms, technology diffusion and public policy: Micro evidence from OECD countries_ , volume 2. OECD Publishing, 2015.

- John Asker, Joan Farre-Mensa, and Alexander Ljungqvist. Corporate investment and stock market listing: a puzzle? _The Review of Financial Studies_ , 28(2):342–390, 2014.

- Andrew Atkeson and Patrick J Kehoe. Modeling and measuring organization capital. _Journal of Political Economy_ , 113(5):1026–1053, 2005.

- David Autor, David Dorn, Lawrence F Katz, Christina Patterson, and John Van Reenen. The fall of the labor share and the rise of superstar firms. NBER Working Papers 23396, National Bureau of Economic Research, May 2017. URL `http://www` _._ `nber` _._ `org/papers/w23396` .

- David H Autor, Frank Levy, and Richard J Murnane. The skill content of recent technological change: An empirical exploration. _The Quarterly Journal of Economics_ , 118(4):1279–1333, 2003.

- Meghana Ayyagari and Vojislav Maksimovic. Fewer and less skilled? human capital, competition, and entrepreneurial success in manufacturing. _SSRN Working Paper Series_ , 2017. Available at SSRN: https://ssrn.com/abstract=3093443.

- Meghana Ayyagari, Asli Demirguc-Kunt, and Vojislav Maksimovic. Corporate inequality and human capital accumulation. _University of Maryland mimeo_ , 2018.

30
For the 19 data sets with graded answer scales, we can compare the extent of the disability gap for the three disability categorizations. Comparing the top and middle graph, the first categorization (No difficulty vs Some difficulty) has less of a disability gap in ever attending school than the second categorization (No or some difficulty vs At least a lot of difficulty). For instance, in Namibia, the disability gap stands at 14 percentage points for No difficulty vs Some difficulty compared to 23 percentage points for No or some difficulty vs At least a lot of difficulty. The bottom graph in fact shows that there is a gradient with persons with at least a lot of difficulty having less often attended school than persons with some difficulty, who in turn have less often attended school than persons with no difficulty.

In Figure 4.2 and Appendices 4 and 9, educational attainment is presented in four mutually exclusive categories: 1/ less than primary school completion, 2/ primary school completion, 3/ secondary school completion and 4/ more than secondary school. In all countries, persons with functional difficulties tend to be overrepresented at the lowest educational level, i.e. less than primary school, while they are underrepresented at higher educational levels.

Figure 4.2: School completion rates across 'Any functional difficulty' status <!-- Start of picture text --> Less than primary school completion<br>No Difficulty Any difficulty<br>Afghanistan<br>Bangladesh<br>Dominican Rep.<br>Ethiopia<br>Gambia<br>Indonesia<br>Kiribati<br>Liberia<br>Malawi<br>Mexico<br>Namibia<br>Nigeria<br>Panama<br>Papua New Guinea<br>Philippines<br>Rwanda (Census)<br> Rwanda (LFS)<br>South Africa (Census)<br>South Africa (GHS)<br>Tanzania (Census)<br>Tanzania (LSMS)<br>Uganda<br>Vanuatu<br>Vietnam<br>0 0.2 0.4 0.6 0.8 1<br><!-- End of picture text --> 19
Table 5: Baseline Differences Across Above vs. Below Median Mobility Women

||(1)<br>|(2)<br>|(3)|(4)|
|---|---|---|---|---|
||Below<br>Mobility|Above<br>Mobility|Diference|N|
|**Demographics**|||||
|Lives in rural area|0.34|0.22|_−_0_._12<sup>_∗∗∗_</sup>|1122|
|Lives around clan community<br>|0.66|0.52|_−_0_._14<sup>_∗∗∗_</sup>|1122|
|Age|40.20|43.03|2_._83<sup>_∗∗∗_</sup>|1122|
|Married|0.84|0.74|_−_0_._10<sup>_∗∗∗_</sup>|1122|
|Has children|0.92|0.87|_−_0_._05<sup>_∗∗∗_</sup>|1122|
|Educ: less than high school|0.26|0.24|-0.02|1122|
|Educ: high school|0.50|0.44|_−_0_._06<sup>_∗∗_</sup>|1122|
|Educ: more than high school|0.23|0.29|0_._06<sup>_∗∗_</sup>|1122|
|Currently employed (in addition to business)|0.09|0.08|-0.00|1102|
|Ever employed|0.30|0.41|0_._11<sup>_∗∗∗_</sup>|1122|
|Husband works (if married)|0.66|0.60|_−_0_._06<sup>_∗∗_</sup>|1084|
|HH income (usd), 95p|678.37|780.95|102_._58<sup>_∗∗∗_</sup>|1122|
|HH debt and rent (usd), 95p|443.90|494.03|50_._13<sup>_∗∗∗_</sup>|1100|
|**Agrees with Statements**|||||
|If a mother works outside the home, children sufer|0.79|0.68|_−_0_._12<sup>_∗∗∗_</sup>|1122|
|Society judges a mother that works outside the home|0.50|0.36|_−_0_._13<sup>_∗∗∗_</sup>|1122|
|Better to work from home to conserve own value|0.38|0.29|_−_0_._10<sup>_∗∗∗_</sup>|1122|
|**Digital Practices, Personal**|||||
|FB: personally uses it daily/weekly|0.84|0.86|0.02|1112|
|% of your acquaintances using FB daily/weekly|83.71|85.07|1.36|972|
|FB: profle shows her real name|0.61|0.65|0.04|1122|
|FB: profle picture shows her face|0.07|0.16|0_._09<sup>_∗∗∗_</sup>|963|
|**Digital Practices, Business**|||||
|Markets on FB|0.42|0.48|0_._06<sup>_∗∗_</sup>|1119|
|Has FB business page|0.19|0.25|0_._06<sup>_∗∗_</sup>|1122|
|Posts on FB groups|0.21|0.24|0.03|982|
|FB page: willing to reveal name|0.58|0.71|0_._13<sup>_∗∗∗_</sup>|1122|
|FB page: willing to reveal picture|0.22|0.42|0_._19<sup>_∗∗∗_</sup>|1054|
|**Business Characteristics**|||||
|Home-based|0.87|0.70|_−_0_._17<sup>_∗∗∗_</sup>|1122|
|Product (partially) made|0.45|0.41|-0.04|1122|
|Product bought & traded|0.32|0.23|_−_0_._09<sup>_∗∗∗_</sup>|1122|
|Product is a service|0.11|0.21|0_._10<sup>_∗∗∗_</sup>|1122|
|Business is registered|0.06|0.14|0_._08<sup>_∗∗∗_</sup>|1103|
|Has partners or paid help|0.35|0.40|0_._05<sup>_∗_</sup>|1122|
|Currently ofers delivery|0.57|0.67|0_._10<sup>_∗∗∗_</sup>|1122|
|At least half of clients are friends/neighbors|0.71|0.65|_−_0_._07<sup>_∗∗_</sup>|1122|
|At least half of clients ask for store credit|0.30|0.20|_−_0_._09<sup>_∗∗∗_</sup>|1122|
|Monthly proft (usd), 95p|115.24|167.16|51_._93<sup>_∗∗∗_</sup>|1122|
|Observations|567|555|1122||

Notes: * p _≤_ 0.1, ** p _≤_ 0.05, *** p _≤_ 0.01. Column (3) is the coefficient from a regression of the outcome on a binary variable for above median mobility index, holding strata fixed effects. Mobility index is defined as the average of z-scores from mobility variables (a)-(c) in table 1. Continous variables are winsorized at the 95th percentile (noted by 95p).

23
patory budgeting meetings in Porto Alegre.<sup>24</sup> Half of the respondents report they are working, while around a third report that they are studying or teaching. The number of participants that declare themselves unemployed is 7 percent and the proportion of retired respondents is around 5 percent. The unemployment rate in the state of Rio Grande do Sul is 6 percent, suggesting that the survey represents the general population relatively well with respect to employment status. The median age of participants is 31 years and three-quarters of the participants are between 25 and 44. The median age in the state is slightly higher around 35 years old. Quite surprisingly, a vast majority of the participants are not very active in the community. More than nine out of ten declare that they had not participated in any discussion regarding the budget before the vote.

With regard to Internet usage, more than 97 percent of the participants indicated that they had used the Internet before, but one in five had not used it in the previous three months. In this sense, it seems that i-voting attracts a non-negligible number of people who are traditionally considered as non-Internet users<sup>25</sup> . Slightly more than half of the participants said they were voting from home, while around 40 percent were voting from their place of work. Most importantly, a stunning 63.1 percent of all respondents identified themselves as online-only voters by answering no on the question about whether they would have voted if they had not been able to cast a vote online.<sup>26</sup>

# 4.2 Statistical Model and Results

In this section we present the results of the following logistic model: logit(πi) = α + β1Xi1 +... + βk Xik + εi where π is the probability that individual i self-identifies as an online-only voter, and Xik are common predictors of engagement. The models include dummy variables for each of the regions (COREDES), meaning that we are only looking at variance between individuals within a region rather than explaining regional level variation.

> 24 The proportion of females in the PB sessions in Porto Alegre in 2009 (the last year for which there is survey data), was 55.0 % (Fedozzi et al. 2013).

> 25 Individuals are traditionally considered Internet users when they have accessed the Internet in the last three months. See, for instance, ITU Manual for Measuring ICT Access and Use by Households and Individuals, 201 4 Edition.

> 26 Note that, as would be expected, the proportion of ‘online-only’ voters is slightly higher among those who at the time of the vote were outside the state. As already mentioned, we focus only on survey responses coming from an IP address within the boundaries of Rio Grande do Sul.

14
# **The Socioeconomic Impacts of Energy Reform in Tunisia: A Simulation Approach**

José Cuesta _The World Bank_ AbdelRahmen El-Lahga _University of Tunis_ Gabriel Lara Ibarra _The World Bank_ JEL: D1, D31, D6, H2, H6, I3 Keywords: Tunisia, subsidies, energy, welfare, income distribution

*World Bank,<sup>+</sup> University of Tunis
## **Chart III.3 DUMPING INVESTIGATIONS BY SECTORS**

# **1990-2004**

|**Sectors**|**Number of I**|**nvestigations**|**Percentage **|**breakdown**|**Wo. Duties/ **|
|---|---|---|---|---|---|
||**Conducted**|**Wo. Duties**|**Conducted**|**Wo. Duties**|**Conducted**|
|Agricultural products|3|3|8.1|21.4|100.0|
|Chemicals|10|2|27.0|14.3|20.0|
|Petrochemicals|7|1|18.9|7.1|14.3|
|Tires|2|2|5.4|14.3|100.0|
|Textiles|3|3|8.1|21.4|100.0|
|Tableware and crockery or china|2|0|5.4|0.0|0.0|
|Iron & Steel Products|9|2|24.3|14.3|22.2|
|StationaryBatteries|1|1|2.7|7.1|100.0|
|**TOTAL**|**37 **|**14**|**100.0**|**100.0**|**37.8**|

Source: Calculations by the authors based on data from the Ministry of Trade.

As for the safeguards, the sectoral breakdown is relatively different according to the type of instrument (Chart III.4). Most of the investigations under the WTO safeguard were made for textiles and apparel, and home appliances. Besides these two sectors, special safeguard investigations focus on chemicals and petrochemicals. In the case of the Andean safeguard, almost 60% of the cases are agricultural and focus on two products: rice and vegetable oils.

Summarizing, most of the dumping and safeguards investigations have involved industrial products. Additionally, the investigations on agricultural products have mainly concentrated on safeguards and in those few cases in which the application of anti-dumping duties were requested for this sector, they were denied. Finally, textiles, apparel, iron and steel products, and chemicals and petrochemicals are the sectors requesting more investigations, which is consistent with international patterns. In effect, data for dumping investigations in the western hemisphere shows that these tend to concentrate on chemicals, plastics, paper, textiles and basic metals.<sup>20</sup>

# **Chart III.4     SAFEGUARD INVESTIGATIONS BY SECTORS**

> and steel the products are steel bars, chrome plated sheets, iron or steel wire rods, billets, tin sheet and hotrolled steel.

> 20 Tavares et al (2001) Antidumping in the Americas.

25