rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 8 Compartive Wheat Price Increases**

_Source: Staff estimates_

**<u>Table. 6. Wheat Prices in Afghanistan</u>**

|Region|Prices|(Afs/kg)|Percent Change from May 2007|
|---|---|---|---|
||May 07|May 08||
|Kabul|13.3|39.0|193%|
|Kandahar|12.0|35.8|198%|
|Jalalabad|11.2|35.8|221%|
|Heart|11.7|34.0|190%|
|Mazar|9.9|35.5|259%|
|Faizabad|17.3|44.4|157%|

_Source: World Bank (2008)_ The movement in rice prices is shown in Figure 9. As in the case of wheat, South Asian rice prices on average rose less than in the global market and there are marked differences by countries. For the period as a whole, Pakistan witnessed the most rapid increase followed by Bangladesh, Sri Lanka, Nepal and India (Figure 10). Again, as in the case of wheat, India experienced the lowest price increase in the region, which is strikingly lower than the global price rise and the rest of the region.

There are two interesting questions: Why did South Asia experience substantially lower price increases of wheat and rice than the global market? And secondly, how did India manage to virtually insulate its economy from higher global rice and wheat prices? The answer to the first question lies in that all South Asian countries, except Afghanistan, largely rely on domestic grain production. The dependence on trade is low and most 12
Figure 5: Income distribution (Oportunidades vs. Public different to Oportunidades) <!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Income decil<br>Public different to Oportunidades Oportunidades<br>Density<br><!-- End of picture text -->

# **5.2 Motivation**

In this section we test the role played by motivation (or lack thereof) in explaining the ineffectiveness of the PROBEMS scholarships. A random sample of around 10% of the ENLACE takers were administered a survey, the so called _ENLACE de contexto_ that elicits a broad set of information about student sociodemographic characteristics, such as student effort, family composition and aspirations. We merge the information of the _ENLACE de contexto_ applied to students in our evaluation sample when they sat for the ENLACE 9th grade in 2009 to test a student motivation hypothesis.

The merged sample includes only 564 individuals, a sample substantially smaller than our original one but still well-balanced between treatment and control and showing the same lack of treatment effects as with the entire sample. Students’ motivation is proxied by the, self-declared, highest degree they expected to achieve, the options being: lower secondary, EMS, technical higher education, university or postgraduate. Of the students in our sample, 80% expected to finish, at least, a university degree and 47% expected 19
substantial empirical evidence that labor market reforms aimed at relaxing the conditions for the use of temporary contracts were important propellers of this increase (Kahn [2010]; Cahuc et al. [2016]), which may help explain why we do not find evidence that the Internet has played a major role in explaining such changes. Analyses of the drivers of second job holding are scarce. Zangelidis (2014) argues that job insecurity and volatility are important drivers of second job holding in Europe, as it provides a hedging strategy against labor market risk. Regarding self‐employment, it has declined or remained stable across most European countries during the last 20 years (OECD [2014]). However, standard labor force surveys may fail to capture the increase in self‐employment and multiple job holding allowed by online platforms (Katz and Krueger, 2016), which may contribute to explain why our findings do not support the hypothesis that the Internet has contributed to the rise of these forms of work.

The remainder of this paper is structured as follows. Section 2 describes the methodology and data sets used. They include the European Labour Force Surveys and the database on telecommunications reforms of Howard and Mazaheri (2009). Section 3 presents the main results and Section 4 concludes.

# **2. Data and methodology**

# **2.1 Telecommunications reforms and Internet use in Europe**

Following Howard and Mazaheri (2009), this paper uses four different types of telecommunications reforms. The first one is the first year the telecommunications sector was opened to competition, by allowing firms to build infrastructure and provide services and thereby undermine the monopoly power of the incumbent. The second type of reform is a variable to indicate regulatory depoliticization, which is measured by the time a regulatory authority is judged to be fully autonomous from the executive branch. The third type is an indicator of the privatization of the sector, measured by the year in which the government first sells a majority stake in the relevant state‐owned telecommunication provider. Finally, 6
|Table1.8:ImpactofCatastrophicEventsonPublican<br>DynamicPanelData(GMMEstimator)<br>CountrySpecificEffects<br>Sample:1963- 1997<br>Totalpanelobservations:545|dPrivateConsumption|
|---|---|
|Vanable<br>Pnvate<br>Consumption<br>_Acp1_|Public<br>Consumption<br>_Acg,_|
|073...<br>AYI-I<br>_<br>__<br>_<br>__<br>__<br>__<br>_(3<br>26~|_0.85)_<br>.1|
|0.226<br>AYt-2<br>(2.43)<br>|________<br>_____|
|-0.08<br>Sargantest<br>[°997l8(0.44)||
|_Acg,,r_senalcorrelation<br>16.75-0<br> <br>022|16...<br> <br>|
|_Acg,-,_<br>~ ~~~~~-...<br>Cat,____________(4.93)|<br>-%005)_<br>(2.68)|
|_**_**______**_**<br>_____ _<br>________Diagnostic<br>Statistics<br>||
|Wald test<br>~ ~~~48.26L<br>[0.0001<br>|15.78"<br>(0.0561|
|Sargan test<br>~~~~14.14|7.97|
|I1'-orderserialcorrelation<br>_[0056]_<br>_O052_|[0.005<br>[0901|
|_._<br>2'd-order senalcorrelation<br>0.52<br>[0.601]<br>Notes.|.<br>0.91<br>[0364]|

a Figures in parentheses ( ) are absolute t-ratios; figures in brackets [ ] are p-values *, "' indicate t, that a coefficient Is significant at the 10 percent, 5 percent and I percent level respectively b The Wald test is for the joint significance of the regressors.

- c. The Sargan test is of over-identifying restrictions, that is, for the validity of the set of instruments and is defined as Prob( J > XX _),_ where p Is the number of over-identifying instruments

- d The tests for I" and 2'd order of no serial correlation are asymptotically distnbuted as standard normal vanables (see Arellano and Bond, 1991). The p-values report the probability of rejecting the null hypothesis of senal correlation, where the first differencing will induce (MAI) serial correlation if the time-varying component of the error term in levels is a senally uncorrelated disturbance. I' and **2,** order of no serial correlation tests are related to the lags of the instruments (i.e _L,_ and t-, ), where the instruments are the lagged values of the explanatory varables and the lagged dependent vanable (except the catastrophe dummy, which is an exogenous shock to the system).

- e. The GMM estimations were performed by using the DPD model developed by Arellano and Bond (1991) Ox version 3 00 (Windows) for PcGive (C) by J.A Doomik (1 994-200 1).

Source Estimated based on an extended version of the Penn World Tables 5 6 (Summer and Heston, 1992), and data on catastrophic events from Table 1 6.

30
Global Estimates. Policy Research Working Paper No. 9284. World Bank, Washington, DC <u>https://openknowledge.worldbank.org/handle/10986/33945.</u> 48 Munich, D. 2020. 500 neviditelných miliard ve školství. Názory Aktuálně Blog <u>http://blog.aktualne.cz/blogy/daniel-munich.php?itemid=37080.</u> 49 Andresen M., Bensnes S., Løkken S. 2020. What does it cost to close the education sector? Calculation of costs of infection prevention measures against COVID-19 for human capital, study - progression and productivity. Statistics Norway: <u>https://khrono.no/files/2020/04/15/RAPP2020 15_web.pdf.</u> 50 Quartey, P., Mensah-Abrampah, K.E., Wong, B., Lomborg, B., and Nordjo, R. 2020. A rapid cost-benefit analysis of moderate social distancing in response to the COVID-19 pandemic in Ghana.” Ghana Priorities Project. Copenhagen Consensus Center. <u>https://www.copenhagenconsensus.com/sites/default/files/covid_brief_for_ndpc_ghana_final.pdf</u> 51 National Planning Commission. 2020. Medium and long-term impacts of a moderate lockdown (social restrictions) in response to the COVID-19 pandemic in Malawi: A rapid cost-benefit analysis. African Institute for Development Policy (AFIDEP). <u>https://www.afidep.org/publication/medium-and-long-term-impacts-of-a-moderate-lockdownsocial-restrictions-in-response-to-the-covid-19-pandemic-in-malawi-a-rapid-cost-benefitanalysis/.</u> 16
# **Annex 8: Multiyear results**

## **Table A8.1: Multiyear analysis for countries with a low QFD (less than 1 percent of GDP in the reference year)**

<!-- Start of picture text --> QFD as percent of GDP QFD as percent of revenues<br>-1.0% -0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% -20.0% 0.0% 20.0% 40.0% 60.0% 80.0% 100.0%<br>LOW QFD AND IMPROVING LOW QFD AND IMPROVING<br>Uganda_2011 0.0% Uganda_2011 2.0% More than 50 percent of units<br>Uganda_2012 0.0% H Uganda_2012 2.5% H generated are from hydro<br>Uganda_2014 -0.1% Uganda_2014 -5.0%<br>Mauritius_2010 0.7% Mauritius_2010 18.8%<br>Mauritius_2012 0.7% Mauritius_2012 17.0%<br>Mauritius_2013 0.4% Mauritius_2013 11.4%<br>Seychelles_2011 1.9% Seychelles_2011 29.5%<br>Seychelles_2012 0.9% Seychelles_2012 12.4%<br>Seychelles_2013 -0.6% Seychelles_2013 -7.2%<br>Seychelles_2014 -0.3% Seychelles_2014 -3.6%<br>Tanzania_2011 1.1% Tanzania_2011 86.5%<br>Tanzania_2012 1.3% Tanzania_2012 81.0%<br>Tanzania_2013 1.4% Tanzania_2013 93.9%<br>Tanzania_2014 0.3% Tanzania_2014 17.3%<br>LOW QFD AND STEADY LOW QFD AND STEADY<br>Benin_2011 0.2% Benin_2011 6.7%<br>Benin_2012 0.3% Benin_2012 10.7%<br>Benin_2013 0.3% Benin_2013 12.1%<br>Cameroon_2013Cameroon_2014 0.6%0.7% H Cameroon_2013Cameroon_2014 36.2%41.4%<br>Central African Republic_2013 0.4% Central African Republic_2013 70.9%<br>Central African Republic_2014 0.4% H Central African Republic_2014 68.1%<br>Gabon_2012 0.3% Gabon_2012 17.7%<br>Gabon_2013 0.3% Gabon_2013 18.2%<br>Gabon_2014 0.4% Gabon_2014 19.2%<br>Niger_2012 0.6% Niger_2012 39.2%<br>Niger_2013 0.6% Niger_2013 40.9%<br>Niger_2014 0.5% Niger_2014 31.0%<br>Mozambique_2011 1.0% Mozambique_2011 50.4%<br>Mozambique_2012Mozambique_2013 0.8%0.9% H Mozambique_2012Mozambique_2013 39.5%44.2%<br>Mozambique_2014 0.9% Mozambique_2014 45.3%<br>LOW QFD AND MIXED LOW QFD AND MIXED<br>Liberia_2012 0.5% Liberia_2012 40.2%<br>Liberia_2013 0.2% Liberia_2013 20.9%<br>Liberia_2014 0.4% Liberia_2014 33.4%<br>Kenya_2012 0.8% Kenya_2012 31.1%<br>Kenya_2013 0.7% Kenya_2013 33.6%<br>Kenya_2014 0.6% Kenya_2014 28.6%<br>Kenya_2015 0.8% Kenya_2015 40.4%<br><!-- End of picture text --> _Source_ : World Bank staff calculations using utility data.

96
of the different sectors in consumption, we choose values similar to other studies like Betts et al. (2017), _ωa_ = 0 _._ 07, _ωm_ = 0 _._ 15. The elasticity of substitution between consumption goods comes from Herrendorf et al. (2009). Using a value-added approach and the U.S. data these authors estimate _ε_ = 0 _._ 002.

To obtain a value for the international interest rate, we follow a standard procedure by looking at the prediction given by the Euler equation for consumption assuming that aggregate consumption and output grow at the same constant rate.<sup>18</sup> If this is the case, condition (4) implies that where _ZYm_ is the rate at which these two variables grow. Equation (41) predicts that an interest rate net of depreciation of 6.79% is the one compatible with the 2.52% average annual growth rate of real GDP per capita for the Brazilian economy over the period 19702007 obtained fitting an exponential regression model to Penn World Tables data, version 8-0. The capital depreciation rates are the ones that generate an average investment share – in gross fixed capital formation in Brazil for the period 1971-2007 of 0.194 this number is calculated from the IBGE data. Applying the fact that estimated depreciation rates for public capital (including the electricity infrastructure) are usually about half those of private capital (see, e.g., Kamps 2006), we obtain _δk_ = 0 _._ 0452 and _δg_ = 0 _._ 0226.

As we mentioned previously, not all investment comes from manufacturing because the service sector is an increasingly important component. Next, we search for the share of investment that needs to be assigned to each of these two sectors. Following Herrendorf et al. (2014), we allocate investment value added to each sector using constant shares. As these authors argue, the quantitative relevance of this assumption should be relatively small because total investment is a relatively small share of GDP. To estimate the share that we should attribute to services, we use input-output data for Brazil from the World Input-

> 18Recall that this is not the case in our model of unbalanced growth. Nevertheless, we believe that this strategy is reasonable for the purpose of calibrating the interest rate.

33
and _Tj =_ **_GJP_** - _GJ._ is the absolute difference<sup>between the average allocation to the poor and</sup> that to the non-poor in provincej.<sup>If</sup><sup>_Tj_is negative then the program favors the non-poor in</sup> absolute terms; if _Tj_ is positive, then the program<sup>favors the poor, and the higher the targeting</sup> differential, the more provincial spending<sup>favors the poor.</sup> How can the targeting differential<sup>be estimated? Under the horizontal equity</sup> assumption, the error tern in (12)<sup>has zero mean for any given province and is uncorrelated</sup> with _H,_ since the es are zero-mean<sup>errors within any given province and are uncorrelated</sup> with _Hy_ (and its squared value). Thus<sup>_H,_is exogenous in (11) and so one can estimate TJ</sup> _Hi_<sup>across all departments within a given province.12</sup> from an OLS regression of G, on Provincial performance in reaching poor<sup>areas can thus be measured by the regression</sup> coefficient of spending per capita on the<sup>poverty rate, estimated across all departments in</sup> each province. Call this the "targeting<sup>differential" (TD) for provincej. This is given by:</sup> One can similarly define a national inter-departmental<sup>targeting differential, by calculating</sup> (13) over all departments nationally<sup>(ignoring province boundaries).</sup> The targeting differential can be interpreted<sup>as a measure of absolute progressivity,</sup> namely the difference between per capita<sup>spending on the poor and that on the non-poor. A</sup> TD of zero indicates that there is no difference<sup>in Trabajar spending on the poor versus non-</sup> poor. A positive TD means that the program<sup>favors poor areas; a negative coefficient means</sup> it favors non-poor areas. Poverty is measured<sup>by the proportion of the population deemed to</sup> have unmet basic needs (IJBN), based<sup>on the 1991 census.</sup>

**1Z** Equation (12) indicates that the error term will<sup>not be homoskedastic. Standard errors of the</sup> targeting differential were corrected for<sup>heteroscedasticity.</sup>

18
This suggests that entrepreneurs in the treatment group are substantially less likely (half as likely) to experience depression and anxiety symptoms compared to those in the control group, and that the effects improve over time. In absolute levels of prevalence, this means that one‐third more entrepreneurs in the treatment group experienced improved quality of life as a result of their improved mental health status compared to entrepreneurs in the control group. Even though the effects are substantial in size, they are not statistically significant in both the 5‐week and the 3‐month mark, likely due to the small sample size and higher than expected levels of attrition.

The results are confirmed by using the alternative approach of the pooled regression<sup>10</sup> (Columns 3 and 4). Consistent with the earlier impact estimates, SME entrepreneurs in the treatment group have 0.56 times the odds of experiencing depression and anxiety symptoms compared to those in the control group. The effect is statistically significant at the 10% level (p=0.054) and remains statistically significant at the 10% level (p= 0.097) when standard errors are clustered at the individual level.

Table 5. Impact of Treatment on Prevalence

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**ANC**<br>|**OVA**<br>|**Poo**<br>|**led**<br>|
||**5 weeks**|**3 months**|**Pooled**|**Clustered SEs**|
||**PHQ‐ADSprev.**|**PHQ‐ADSprev.**|**PHQ‐ADSprev.**|**PHQ‐ADSprev.**|
|Treatment|0.649|0.459|0.563*|0.563*|
||(0.252)|(0.219)|(0.168)|(0.195)|
|Baseline prev.|4.684***|11.04***|6.503***|6.503***|
||(1.848)|(5.301)|(1.931)|(2.215)|
|Round‐ ERKF|1.484|0.684|1.120|1.120|
||(0.584)|(0.345)|(0.341)|(0.385)|
|Age|1.004|1.001|1.002|1.002|
||(0.0186)|(0.0227)|(0.0143)|(0.0162)|
|Education|0.972|1.009|0.983|0.983|
||(0.0459)|(0.0610)|(0.0364)|(0.0413)|
|0.127***0.127**Constant|0.155|0.0759*|0.127***|0.127***|
||(0.158)|(0.0986)|(0.101)|(0.120)|
|Observations|206|200|406|406|

_Note:_ Standard errors in parenthesis.

Column 4 shows results of pooled regression with standard errors clustered at the individual level *** p<0.01, ** p<0.05, * p<0.1 Source: Saraf, 2019; World Bank Group, 2018 Figure 4 below show prevalence trends across the three rounds for all participants.

> 10 The pooled sample comprises of all respondents from both follow‐up panels (i.e. 5 weeks and 3 months). Table 5, Column 3 shows the result of the pooled logistic regression, and Column 4 shows the same result after clustering standard errors at the individual level.

18
because of a high occurrence of missing data and the data collection design,<sup>8</sup> our analysis is restricted to outcomes for which there are sufficient observations in terms of the ECT treatment. The structure of the survey and the issue of missing data also lead to highly unequal sample sizes across the different outcomes.<sup>9</sup> Therefore, our active sample ranges from 187 to 913 observations depending on the outcome of interest and the specification (i.e., cross-sectional or pooled cross-sectional estimation). Moreover, some of the child human capital outcomes were exclusively collected in Round 1 whereas others in Round 2, limiting the scope of the analysis to only one of the two rounds. These characteristics of the CIMS data limit the scope of the analysis presented in this paper. A discussion on the data and future efforts for data collection that could make the analysis more robust are discussed in Section 6.

The timing of the implementation of the ECT and of the CIMS data collection allow us to analyze two cohorts of beneficiaries: households that received the ECT before the first round of CIMS (hereafter referred to as ECT-R1 beneficiaries) and households that received the ECT after the first round of CIMS (hereafter referred to as ECT-R2 beneficiaries). The timing of the survey and the data collection (see Figure 1) allows us to analyze short-term ([0;3] months lapse between receiving the ECT and the survey) and medium-term ([3;7] months lapse) impacts of the ECT for ECT-R1 beneficiaries. We consider the impact of the ECT on ECT-R2 beneficiaries as a medium-term impact ([3;4] months lapse).

Table 2 shows that the ECT was mostly used for food (76 and 80 percent respectively in Rounds 1 and 2) and investing in an existing business (73 and 86 percent respectively in Rounds 1 and 2). The other

> 8 Part of the questionnaire was administered to all respondents (i.e., Basic information, information about school-aged children and income), but only one of the four following sets of questions was asked to each respondent: “Education, Child Welfare”; “Employment, KAB, Lockdown”; “Health, Access”; “Agriculture, Food Security, Social Protection”.

> 9 As a robustness check, we compare the characteristics of the full sample to a sample restricted to observations for whom we have information on labor market participation (i.e. the sample which corresponds to a large set of the estimations presented in this paper). The results presented in Supplementary materials 1 show no significant differences between the two samples.

10
At the regional level, despite the generous financial and fiscal incentives (such as the multiple regional development areas) approved by local authorities, foreign actors continue to be reluctant to invest in the inland regions of the country. Only 13% of all foreign firms established in Tunisia (about 16% of created jobs) are presently operating in those regions (World Bank, 2014a). Table 2 shows the importance of regional disparities in Tunisia in terms of attracting foreign investments to the 24 governorates of the country. As shown below, The North-West governorates (Beja, Jendouba, Le Kef and Siliana) have attracted about 100 foreign companies. In contrast, the coastal cities located along the regions of Grand Tunis, North East and Center East, have totaled 3,197 foreign companies (91.5%). The 13 remaining governorates (in the Center and South West) host, altogether, the other 289 firms.

**Table 2.** _Regional distribution of foreign firms in Tunisia in 2018_

|**Regions (****_governorates_) **|**Number of firms**|
|---|---|
|**Grand Tunis**_(Tunis/ Ariana/Manouba/ Ben Arous) _|1234(35,5%)|
|**North-East**_(Bizerte/ Nabeul/Zaghouan)_|873(25%)|
|**North-West**_(Beja/ Jendouba/Le Kef/ Siliana)_|101(3%)|
|**Center-East**_(Sousse/ Monastir/Mahdia/ Sfax)_|1090(31%)|
|**Center-West**_(Kairouan/Sidi Bouzid/ Kasserine)_|69(2%)|
|**South-East**_(Gabes/ Medenine/Tataouine)_|80(2,5%)|
|**South-West**_(Gafsa/ Tozeur/Kebili)_|39(1%)|
|**Total**|**3486(100%)**|

The economic development model adopted by Tunisian authorities since the 1970s was based on the industrial competitiveness and trade openness through manufacturing exports. This policy has resulted in a clustering of offshore companies near the ports in the capital city of Tunis and along coastal regions of the Center and Northeast. This has left the interior regions trailing behind in terms of development of productive activities, job creation and standard of living. Over the years, a growing part of the local population in the interior regions has chosen to migrate to coastal cities seeking decent job opportunities and a better standard of living for their families. Not surprising to find that today nearly 56% of the total population is located within an hour's drive from the country's three largest coastal cities: Tunis, Sfax and Sousse (OECD, 2018). According to the Tunisian Ministry of Regional Development (MDR, 2011), the spatial analysis of FDI distribution shows that most capital inflows are directed toward coastal regions in contrast to the small share channeled toward the interior of the country despite the fiscal and financial incentives offered to foreign firms under the investment incentive code.

The concentration of FDI in those regions is also explained by the ripple effect created by the establishment of foreign firms in the coastal regions since the 1970s. This effect has promoted the attraction of new foreign investors who chose to settle in the same cities in order to benefit from the presence of other