rafmacalaba/datause-encoder-data huggingface dataset:
|weighted IO-RoO|3.1|3.9|3.7|5|4.7|4.5|3.8|4.5|4.3|4.1|4.3|5.6|4.6|3.1|4.3|4.2|otal number of IO relations).|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|IO-RoO|3.5|4.3|3.6|4.5|4.4|4.4|4.2|4.4|4.3|4.4|4.3|4.9|4.2|3.7|4.3|4.2|as a share of t|
|Total|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|rom 1 to 7,|
|RoO=7|8.3|7|7.5|13.1|6.4|5.2|5.6|8.4|8|15|5.2|20.1|6.2|6.8|8.2|8.3|RoO Index, f|
|RoO=6|7|3.6|12.9|7.6|3.7|3.2|3.1|4.9|4.1|7.4|2.4|7.5|4.2|10.3|4.4|4.4|ods which face a|
|RoO=5|23.2|41|15.1|36.4|49|48.9|40.9|44.8|41.8|33.1|47.2|37.4|45|18.9|41.9|42.8|enter fnal go|
|RoO=4|16.9|22.2|17.5|21.8|18.5|20.7|21.5|17.9|22.3|18.7|18.9|20.6|19.4|16.1|20.7|20.5|te goods that <br> Index.|
|RoO=3|9.7|16.4|17.5|8.9|13.9|14.3|19.7|14.6|13.5|12.4|19.4|8.4|12.4|24.8|14.2|14.6|er of intermedia<br>ghted IO-RoO|
|RoO=2|4|3.8|3.8|3|3.1|2.9|3.8|3|3.3|4.1|3.1|1.4|3.4|3.9|3.6|3.2|trictions (numb<br> simple and wei|
|RoO=1|30.9|5.9|25.7|9.3|5.5|4.8|5.5|6.5|7|9.3|3.8|4.6|9.4|19.2|6.9|6.3|re of RoO res<br> statistics for|
|ector|al Products|icals|tufs|ear/Headgear|inery/Electrical|s|al Products|llaneous|c/Rubbers|Hides,Skins,Leathers|/Glass|es|portation|ables|Products||Columns 1 to 8 report the sha<br>s 9 and 10 present descriptive|
|HS2 S|Anim|Chem|Foods|Footw|Mach|Metal|Miner|Misce|Plasti|Raw|Stone|Textil|Trans|Veget|Wood|Total|_Notes_: <br>Column|

11
The imputation process is a simplified version of the methodology developed in Elbers et al. (2003). Stifel and Christiaensen (2007) provide theoretical guidance regarding variables to be included in imputation models. They recommend including covariates that change over time, but call for excluding variables whose rates of return are likely to change markedly in the face of evolving economic conditions. Following Stifel and Christiaensen (2007), we included several household durables but excluded mobile phones, as their relationship with total household expenditure has been changing rapidly in the last ten years. Other variables include household characteristics, location, and zone interacted variables. Most of the variables are significant and show the expected sign, and, more importantly, the model yields a _R_<sup>2</sup> of 0.46 (see appendix).

The procedure follows two stages. First, we estimate the log per capita real expenditures on a sample from GHS-Panel Wave 1 using standard household characteristics and assets. Second, to control for location effect and heteroskedasticity we draw errors from the distribution of residuals for households in the same zone. We divide the sample into six groups based on six macro zones. The sample of the target distribution is also divided into six groups by the same methodology used for the original sample. Residuals are then drawn and imputed to households within each of the six groups. Following the bootstrap principle, the residual’s distribution is drawn for a number, _R_ = 50, of replications so as to obtain a number _R_ of distributions. Poverty rates and middle class shares are finally averaged over the replications (for a detailed explanation see Clementi et al., 2014).

## **IV. Results**

The results section is organized into three subsections. In the first one we discuss results related to the threshold definition and the share of the middle class over the whole population. In subsection two we test whether the three groups defined in subsection 1 (middle class, vulnerable and poor) present significant differences in terms of welfare covariates. The purpose is obtaining a middle class average profile. Subsection three looks at the dynamic evolution of the three groups over the last decade.

# _Middle class threshold and share over total population_

The VAMC method is based on the idea that only households with a limited risk of falling into poverty can qualify for middle class status. The panel nature of the data allows for 14
prefer not to list the worthy reforms that have already been adopted but will formulate a shorter list of elements that are still outstanding, in order to focus and incentivize efforts on the remaining agenda. Some of the latter prior actions may be likened to the wedge‐shaped keystone of a masonry arch: the keystone may be one of fifty or a hundred wedges but without it the structure would collapse.

With this metaphor in mind, the count of prior actions and/or tranche conditions is an imperfect way of gauging the total reform effort. To quote the paper’s title, it could be that the journey (process) is what matters. The tally of reforms done, or papers produced, or policy actions agreed to, is a contributing factor but perhaps not a critical one.<sup>22</sup> An example is given from the Economic Management and Private Sector Operation (EMPSO) in Mozambique in the period 2000 to 2003 (P049878). As of 2000 there were only five fixed telephone lines per 100 inhabitants, among the lowest in Africa. There were high mobile and international tariffs, reflecting _inter alia_ the monopoly of the incumbent, Telecomunicações de Moçambique (TDM), over international services.

In 2000, a meeting was held with the Minister of Transport and Communications at which the World Bank telecommunications staff and the Country Director presented the advantages of opening up the system to competition. Although the Minister was persuaded, his senior staff were not convinced. Nevertheless, the exploratory discussions went ahead and eventually the continued debate paid off, shifting the mindset of government officials. Furthermore, the Bank offered technical assistance for the regulation of the sector. Later a Communications Sector Reform Project was initiated by the Bank so as to strengthen the telecoms regulator and provide the conditions for a competitive market.

The monopoly of TDM was ended by decree and the government agreed to an auction of a second mobile license (the first being held by the incumbent). A second license was granted to Vodacom in August 2002. Meanwhile the EMPSO recognized, as a second tranche condition, the submission of a new telecommunications law to parliament which would provide for competitive allocation of licenses. The EMPSO operation was approved by the Bank’s Board in late August 2002 and the second tranche was released in 2004.

The effects in the real economy took but a matter of months. For the first time ever, TDM ran an aggressive advertising campaign, sending out trucks of young people in orange uniforms to hold banners promoting the TDM mobile operation MCel. Prices of mobile phones fell even

> 22 One way of demonstrating directly that it is the process that induces policy improvement would be to obtain data on analytical work done by the World Bank, the government and other instances and construct appropriate variables which could be inserted into the main equation (8).

23
Table 1. Descriptive Statistics on Variables Used to Analyze 2016 Stove Use

|Variable description|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|
|Stove still in use (1 if yes, 0 otherwise)|0.62|0.49|0|1|
|Incentive treatment|0.33|0.47|0|1|
|Pay treatment|0.35|0.48|0|1|
|Free treatment|0.29|0.46|0|1|
|Group training dummy (1 if yes, 0 otherwise)|0.49|0.50|0|1|
|Male headed household (1 if yes, 0 otherwise)|0.90|0.30|0|1|
|Stove is in main house (1 if yes, 0 otherwise)|0.23|0.42|0|1|
|Participated in CCT (1 if yes, 0 otherwise)|0.32|0.47|0|1|
|Corrugated roof (1 if yes, 0 otherwise)|0.15|0.35|0|1|
|Distance to all weather road in hours (two-ways)|0.87|1.16|0|6|
|Injera baking freq. since receiving stove (1 if once a<br>month or more, 0 otherwise)|0.98|0.30|0|1|
|Frequency of stove use per week in Round 4|2.05|2.38|0|18.61|

N=327 households.

Table 2 presents results of probit regressions to examine correlates of dis-adoption after 3.5 years since the introduction, where stove still in place is coded as 1 and 0 otherwise. The results in column 1 show that none of the treatments are correlated with retention of the stove when compared to the reference category of Free Treatment. The results are unaffected when other covariates are added, as shown in column 2. Households with stoves in the main house were less likely to have the stove in place as of 2016 (p<0.10), which may imply they are more easily broken in main living quarters. Households that participated in the controlled cooking test (CCT) were more likely to still be using the stove in 2016 (p<0.05). In our models with selection discussed below, the selection equations include all the variables in Table 1.

15
# **Table 9: Piecemeal Sensitivity Analysis–Welfare effects**

|Parameter<sup>a</sup>|Pa<br>Lower|rameter value<br>Inter-<br>mediate<br>Upper|Hicksian<br>with corr<br>Lower|equivalent v<br>esponding p<br>Inter-<br>mediate|ariation<sup>b</sup><br>arameter<br>Upper|
|---|---|---|---|---|---|
|σ(va, bs)|0.5|1.25<br>2|8.5|11.1|15.6|
|σ (qi, qj)|2|3<br>4|55.1|11.1|7.2|
|σ(D, M)|2|4<br>6|11.0|11.1|11.3|
|σ(Ls, Lu, K)|0.7|1<br>1.3|11.1|11.1|11.2|
|σ(A1,...An)|0|0<br>0.25|11.1|11.1|11.3|
|σ(D, E)|2|4<br>6|10.8|11.1|11.3|
|ε(di)|2|4<br>6|8.9|11.1|13.0|
|ε(fi)|2|4<br>6|8.9|11.1|13.2|
|θm(i)|se|e table below|11.1|11.1|11.2|
|θfdi(i)|se|e table below|10.2|11.1|13.2|

a The piecemeal sensitivity analysis employs central values for all parameters (see below) other than the tested parameter and lump sum tax replacement.

b Mean value of Hicksian equivalent variation as a percent of the value of consumption in the benchmark equilibrium.

**<u>Key:</u>**

|**Parameter**|**Central**|**Definitions of the parameter**|
|---|---|---|
||value||
|σ(va, bs)|1.25|Elasticity of substitution between value-added and business services|
|σ(qi, qj)|3|Elasticity of substitution between firm varieties in imperfectly competitive sectors|
|σ(D, M)|3|"Armington" elasticity of substitution between imports and domestic goods in CRTS sectors|
|σ(Ls, Lu, K)|1|Elasticity of substitution between primary factors of production in value added|
|||Elasticity of substitution in intermediate production between composite Armington aggregate|
|σ(A1,...An)|0|goods|
|||Elasticity of transformation (domestic output versus|
|σ(D, E)|5|exports)|
|ε(di)|7.5|Elasticity of Russian service firm supply with respect to price of output|
|ε(fi)|15|Elasticity of multinational service firm supply with respect to price of output<br>share of value added in multinational firms in sector I due to specialized primary factor imports,|
|θm(i)|varies|in the benchmark equilibrium|
|θfdi(i)|varies|share ofoutput ofservice sector Icaptured bymultinationalsfirmsinthe benchmarkequilibrium|

|Parametervaluesfor:|t|heta_fdi(i)|||theta_m(i)||
|---|---|---|---|---|---|---|
||low|central|high|low|central|high|
|comm|0.165|0.33|0.66|0.025|0.05|0.1|
|osrv|0.165|0.33|0.66|0.025|0.05|0.1|
|bnkg|0.165|0.33|0.66|0.025|0.05|0.1|
|insr|0.165|0.33|0.66|0.025|0.05|0.1|
|road|0.05|0.1|0.2|0.025|0.05|0.1|
|rail|0.05|0.1|0.2|0.025|0.05|0.1|
|mari|0.05|0.1|0.2|0.025|0.05|0.1|
|pipe|0.05|0.1|0.2|0.025|0.05|0.1|
|airl|0.05|0.1|0.2|0.025|0.05|0.1|

43
Figure 1 shows the AGEBs and municipalities that appear in the main sample. Panel A shows AGEBs, with individual AGEBs colored based on whether they are in the estimation sample (“Yes”), are not in our estimation sample but are urban (“No”), or are rural AGEBs that are not considered in the analysis (“Not in results”). Panel B shows the same, but for municipalities. Specifically, a municipality is coded as included in the sample if at least one AGEB within that municipality is contained in the sample. As such, a municipality in our sample could have a relatively small proportion of its total population actually included in our sample. There are 1,641 municipalities that contain at least one urban-AGEB with non-missing data. Of these, 1,034 – or 63.0 percent – have at least one AGEB in the sample.

# **b. Auxiliary geospatial data**

Auxiliary data is drawn from two sources: Google Earth Engine, and Open StreetMap, utilizing the 2020 shapefile of urban AGEBs provided by INEGI. These sources were largely selected because they are publicly available, cover a large portion of the world, and convenient to obtain. However, both contain a large number of candidate predictors that could be plausibly correlated with spatial patterns in labor force participation. From Google Earth engine, we extracted summary statistics by AGEB from six datasets: Nitrogen Dioxide from Sentinel 5P, Normalized Difference Vegetation Index (NDVI) from the Sentinel 2 Multi-spectral Instrument, Nighttime lights from VIIRS, estimated population from WorldPop, land cover classifications from the Copernicus dynamic land cover map, and the year of development, as proxied for by the change in pixels from pervious to impervious surfaces (Gong et al., 2020). All summary statistics were taken over March 2020, except for land cover, which pertains to the period between January and December 2019.

From Open StreetMap, indicators were obtained representing the total length and number of highways in each AGEB. Two measures of road length were calculated: The first is the total length of the portions of highways contained within the AGEB, and the second is the total length of all highways that intersect the AGEB, including the portions of the road outside the AGEBs. In addition, the total counts of open StreetMap amenities and points of interest, such as hospitals, offices, schools, churches, and so on, were calculated for each AGEB. A final auxiliary variable, besides the Open StreetMap indicators, is the total area of the AGEB of the AGEB in sq km, which was calculated directly from the AGEB level shapefile provided publicly by INEGI. For each auxiliary variable, municipal variables were constructed by taking either population-weighted averages or simple sums (for counts and area) of the AGEB values.

# **c. Model selection**

Variables for prediction were selected using the LASSO (Least Absolute Selection and Shrinkage Operator), with the penalty chosen to minimize the Bayesian Information Criteria (BIC) (Zhang et 7
While parental perceptions about their child’s chance of future success are correlated with the child’s current school enrollment, these perceptions may or may not be accurate or wellinformed. There may also be significant differences across households in how parents perceive their own children and what factors they take into account in formulating perceptions. To further explore these issues, we incorporate an externally validated measure of the child’s cognitive ability using the Raven’s CPM test. These tests were administered during the baseline survey to every child age 5 to 15 regardless of their current enrollment status and provide a consistent measure of child ability across children in all households. There is a strong positive relationship between the ability measure and parent perceptions. Higher ability children are viewed by their parents to have a higher chance of future success. However, after controlling for gender and age, the ability measure only explains about 20 percent of the variation in parental perceptions.

In Table 3, we estimate the relationship between child ability (as measured by the Raven’s age adjusted z-score) and current school enrollment using a household fixed effects logit as described in Equation 2 and a logit regression with alternative sibling ability measures as described in Equation 3.<sup>13</sup> The household fixed effects logit results in column 1 indicate that a child with one standard deviation higher own ability compared to the average of his siblings has a 16.5 percentage point higher likelihood of being currently enrolled, corresponding to 30.6 percent of the base enrollment. The coefficient is significant at the one percent level. This is evidence parents take into account a child’s cognitive ability in deciding enrollment, and the magnitude of the effect is large.

> 13 All regressions include child gender and age dummies, and the regressions estimating Equation 3 also include village fixed effects, parent schooling, household assets, and family demographic composition measures. Results presented in Table 3 are consistent when using the number of siblings and the number of sisters age 5 to 15 rather than the number of siblings and sisters age 0 to 15. Correlation among the error terms of children in a given village experiencing the same enrollment environment might bias the standard errors downward, so in all regressions we cluster the standard errors by village.

16
elevation (mountains) comes from the UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC, 2002). Rainfall data were obtained from the GPCC Global Precipitation Climatology Centre (Schneider et al., 2015). We also collected data on Tunisia’s socio-political landscape, using legislative election data for 2014 from the national election authority (ISIE, 2015). Finally, alternative measures of violent extremism comprise information on political protests, riots, and political violence from the Armed Conflict Location & Event Data (ACLED) Project database (Raleigh et al., 2010) and Zelin (2020)’s data on the locations where extremist group Ansal al-Sharia Tunisia (AST) delivered jihadist religious lectures - a vehicle for the group’s proselytization and recruitment activities. We also compiled data from the National List of Persons, Organizations and Entities Associated with Terrorist Crime curated by the anti-terrorism commission, which tracks all individuals indicted for terrorism as listed in the Official Gazette (CNLCT, 2021). It also lists their addresses, which allows us to georeference them.<sup>6</sup>

# **4 Results**

Our main finding that internet access predicts recruitment is shown in Table 2. Columns 1-6 show progressively more stringent OLS estimation results. Column 1 documents a positive and statistically significant correlation between (the inverse hyperbolic sine of- henceforth ihs) the number of Daesh recruits and internet access rates, while normalizing by the population of each cell by controlling for the logarithm of the number of men in a particular delegation-educationage cell as enumerated in the census. Standard errors are clustered at the delegation level. Recognizing that the nature of internet access might lead error terms in (1) to be spatially correlated, the specification in column 1 is replicated in column 2 but following Conley (1999) to account for spatial correlation of the error term with a 50-km buffer area. The results are very similar. Given the scope for spatial correlation in internet access, we henceforth adopt Conley (1999) standard error correction procedures.

This association between internet access and jihadist recruitment is robust to including addi-

> 6Note that the terrorist acts documented therein were committed between 2004 and 2018; the timespan the data cover is thus different from that covered by our data on Daesh recruitment

10
- 26 -

Using the Allen’s partial elasticities of substitution between inputs _i_ and _j_ (e.g., Uzawa, 1962; Berndt and Wood, 1975), the implied own price elasticity of demand for factor _i_ is computed: The estimated elasticities are relatively moderate than the above ( **Table 11** ). The elasticity associated with transport inputs is estimated at -0.52. Not only labor but also water and communications are found to be price-elastic under this specification.

**<u>Table 10. Three Stage Least Squares Regression Result</u>**

||Coef.|Std.Err.|||Coef.|Std.Err.||
|---|---|---|---|---|---|---|---|
|βRAIL|-0.7691|(0.4651)|*|βWfWw|-0.0036|(0.0021)|**|
|βZ|0.0534|(0.0210)|**|βWfWt|-0.0030|(0.0023)||
|βY|2.3370|(0.2945)|***|βWfWc|0.0019|(0.0022)||
|βYY|-0.0822|(0.0156)|***|βWfWm|-0.0088|(0.0025)|***|
|βWl|0.0978|(0.0831)||βWwWw|0.0135|(0.0036)|***|
|βWe|0.1531|(0.0635)|**|βWwWt|0.0051|(0.0027)|*|
|βWf|0.2572|(0.0530)|***|βWwWc|0.0076|(0.0026)|***|
|βWw|0.0691|(0.0594)||βWwWm|-0.0038|(0.0029)||
|βWt|0.3189|(0.0879)|***|βWtWt|0.0444|(0.0045)|***|
|βWc|0.1823|(0.0668)|***|βWtWc|0.0014|(0.0028)||
|βWlWl|0.0268|(0.0018)|***|βWtWm|-0.0281|(0.0044)|***|
|βWlWe|-0.0055|(0.0017)|***|βWcWc|0.0092|(0.0038)|**|
|βWlWf|-0.0051|(0.0013)|***|βWcWm|0.0017|(0.0034)||
|βWlWw|-0.0031|(0.0015)|**|βYWl|-0.0211|(0.0042)|***|
|βWlWt|-0.0116|(0.0022)|***|βYWe|0.0024|(0.0032)||
|βWlWc|-0.0101|(0.0017)|***|βYWf|-0.0013|(0.0026)||
|βWlWm|-0.0137|(0.0035)|***|βYWw|0.0050|(0.0029)|*|
|βWlWe|0.0403|(0.0033)|***|βYWt|0.0033|(0.0044)||
|βWeWf|-0.0011|(0.0020)||βYWc|0.0023|(0.0034)||
|βWeWw|-0.0036|(0.0025)||βAGE|0.2161|(0.0632)|***|
|βWeWt|-0.0054|(0.0027)|**|βEDU|0.1745|(0.0357)|***|
|βWeWc|-0.0063|(0.0026)|**|βGRGL|0.6518|(0.1448)|***|
|βWeWm|-0.0102|(0.0032)|***|βFORN|0.0015|(0.0019)||
children to identify (point to or name) the larger of two numbers; the test ended when the child failed four consecutive items. The _Addition_ task included 20 equations, and the child was instructed to say the sum for each item in the maximum time allowed (60 seconds). Passed items for each subscale were summed to create subscale scores.

In order to aggregate the scores of _Number Recognition_ , _Quantity Discrimination_ , and _Addition_ into one single EGMA score, we used the Inverse Covariance Weighting (ICW) methodology in Casey et al. (2012), which is described in detail under _Kaufman Assessment Battery-Children, 2_<sup>_nd_</sup> _Edition (KABC-II)_ above.

All enumerators were trained for a minimum of two weeks at each data collection time point, and all followed standardized procedures for administering each measure. Inter-rater reliability, as indicated by the correlation between scores obtained by two different testers for the same child, was estimated by having the enumerators observe and score videotaped administrations. Average inter-rater reliabilities were 0.95 (for MDAT Fine Motor at baseline and Round 2), 0.88 (for MDAT Language at baseline and Round 2), 0.94 (PPVT, baseline), and 0.96 (Triangles, Round 3).

# **Primary caregiver measures**

The following scales were administered to each caregiver:

1. _The Center for Epidemiological Studies, Depression (CESD) (Radloff, 1977),_ a 20-item, selfreport scale that assesses the frequency of common depressive symptoms experienced in the past week. The CESD has been widely used with adults throughout the world, including in low- and middle-income countries (e.g. (Baker-Henningham et al., 2005; Black, 2007). Items were translated and back translated. Items included, “Did you feel sad?” “Did you feel that everything you did was an effort?” and “Were you happy?” For administration, all items were read to the caregiver. Higher scores indicated the presence of more depressive symptoms.<sup>31</sup> The final index was obtained by standardizing the scale using the control group’s mean and standard deviation.

> 31 The CESD can be downloaded through <u>http://cesd-r.com/.</u>

60