rafmacalaba/datause-encoder-data huggingface dataset:
this period. All other covariates remain at the levels of the (last observed) period 2010-14 and are summarized in Table 4.

**Table 4** – Values of covariates in Scenario 0

|**Variable**|**Value assumed for all periods**<br>**between 2015-2044**|
|---|---|
|Share of upper secondary and tertiary educated in the labour force|34.0%|
|Government consumption as share of GDP|9.0%|
|Investment as share of GDP|15%|
|Openness indicator|-12.8|
|Infation|26.6|
|Oil rents as share of GDP|8.2%|
|Non-oil natural resource rents as share of GDP|2.8%|
|Rule of law|17.4|
|Corruption|-0.3|

The resulting forecasts of GDP per capita growth rates are graphically displayed in Figure 5.<sup>17</sup> The dotted line corresponds to Scenario 0. Under this scenario, growth rates are projected to rise only moderately as compared to the current levels. Low oil prices, which are assumed to remain at the low level over the next decades, and stagnating human capital stocks, investment, and institutional quality keep growth prospects below 3% until 2040-44. The top left graph in Figure A.7 in the Appendix shows the contributions of the main explanatory variables to the growth predictions in Scenario 0, and to what extent they compensate for the significantly negative fixed effect of Nigeria. Inherent to the underlying assumptions is that the contributions remain constant over time, and the variation in the expected growth rate results from income convergence alone. The factors that contribute most to Nigeria’s growth rates are human and physical capital, oil and non-oil natural resource rents, institutional quality, and corruption.<sup>18</sup>

**Scenario 1:** That scenario differs from the baseline scenario with respect to the underlying expected oil rents in GDP. The future development of oil rents is tied to the oil price forecasts of the World Bank’s Commodity Price Forecasts (see Figure A.5 in the Appendix). All other covariates remain at their 2010-14 level as displayed in Table 4, including the non-oil natural resource rents.

> 17Growth rates used in the regressions as well as for the predictions are growth rates of _per capita_ GDP, that correspond to yearly averages over five-year periods. With an annual population growth of roughly 2.7%, GDP growth rates are considerably higher.

> 18It might seem puzzling that the institutional environment in Nigeria positively contributes to growth. This finding is explained by the construction of the underlying indices: they are measured as deviations from countryspecific means. As Nigeria’s institutional quality as well as corruption levels improved since the 1980s and 1990s, the current deviation from the mean is positive (negative) for the rule of law (corruption) index. If this improvement did not happen, growth rates were predicted to be considerably lower.

20
_lri*rdAwtIond Pl of do Piper_ I Pm .tL Natue Jd 1. Inmalaaiatd _3_ 3. Integation 4 4. **JflflvatiO** _5_ & **ASh** of the Mar8t nd hae at Delopg comtrie PatM BaRaonals rPPardelpaM- _i_ 6. External Refourcess _1_ 7. xtna DeMant 8. Manament of fign Exchmg Reserves _16_ 9. import uand Expot Possbilite _1_ Part im Prrequlsa for Ppm AgndafchRssah X 10. Establiablng and Mintainng Credit Worthinss _N 1L_ Integrtion _X_ 12. Attracing Forign Investors to the Indinn Market 21 13. Integrti and Intest Rate Poliy 2 Pat IV. a U the Domestic 1an- da Marketb s 1i Competitive Impulses 15. Money and Capital Markets 27 16. Strictuare of Int:st Rat.S 17. Prudential Regulation and Superision _30_ PartV. ob _Si_ PartVL Stisil3 Appendiz N
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- - <u>Panel D: Regulatory framework for deposit taking (0 4)</u>

|||Dep var: % of W|<br>omen borrowers||
|---|---|---|---|---|
||Version 1 (|Ful samples)|Version 2 (Dro<br>sam|pping Rural bank<br>ples)|
||OLS|Robust Reg|OLS|Robust Reg|
||(1)|(2)|(3)|(4)|
|Regulatory framework for deposit-taking (0-4)|-0.0644*|-0.0131|-0.0734**|-0.0062|
||[0.084]|[0.446]|[0.037]|[0.690]|
|NBFI dummy * Regulatory framework for deposit-taking (0-4)|-0.0234|-0.0328|-0.0156|-0.0268|
||[0.566]|[0.156]|[0.693]|[0.197]|
|Combined group dummy *Regulatory framework for deposit-taking (0-4)|-0.1050**|-0.1272***|-0.0011|-0.0102|
||[0.021]|[0.000]|[0.976]|[0.756]|
|NBFI dummy|-0.0491|-0.009|-0.0516|-0.0019|
||[0.510]|[0.839]|[0.487]|[0.962]|
|Combined group dummy|0.0075|0.0417|-0.1160*|-0.082|
||[0.921]|[0.475]|[0.084]|[0.194]|
|Observations|611|611|557|557|
|Number of countries|39|39|39|39|
|R-squared|0.449|0.542|0.454|0.583|
|Adjusted R-squared|0.432|0.527|0.435|0.569|
|_Microscope index+ interaction term (P-value in brackets)_|||||
|NBFI dummy|-0.0878|-0.0459**|-0.089|-0.0329**|
||[0.140]|[0.029]|[0.136]|[0.081]|
|Combined group dummy|-0.169***|-0.14***|-0.0745|-0.0163|
||[0.008]|[0.000]|[0.213]|[0.609]|

36
- (e) Satisfaction with services {i}

- i. The last time you visited the clinic in the past one month, how satisfied were you with the care that you received at the clinic?

- ii. The next time you need medical attention for some other reason, would you visit [CLINIC] again?

- (f) The last time you visited the clinic in the past one month, how would you rate the attitude of the staff? {i}

# **7. Community Support**

- (a) Reported engagement index [standardized summary index of i-iii] {com}

- i. Health monitoring facility (HMF)/clinic monitoring facility (CMF) exists

- ii. Number of HMC/FMC meetings

- iii. Contributions to clinic (e.g. expenditures, nurse veg garden, etc.)<sup>A2</sup>

- (b) Reported community engagement index (past 6 months)[standardized summary index of ii-vi] {phu}

- i. Has the community helped clean this facility?

- ii. Has the community helped you with your personal work? E.g. Farm, back garden... etc.

- iii. How often have community members helped you with your personal work?

- iv. How often has the facility had disputes/conflicts with the community?

# **8. Community Development and Political Engagement (CDPE)**<sup>A3</sup>

- (a) Development projects (Excluding NGOs) {com}<sup>A4</sup>

- i. Has [the Local Council/the Paramount Chief] done any projects that this community (In the past year, starting May 2012)

- ii. Did community members contribute labour, money or local materials for this project (Including work for food and work for pay)?

- iii. Were any community members involved in the planning of this project?

- (b) Collective action {com}

- i. Has this community worked together to address any problem facing this community? For each project: (In the past one year since May 2012)

> A2 The analysis plan mis-specified financial contributions as originating from the community survey, but it was part of the clinic survey and is included in index ii accordingly.

> A3 Originally, both the CDPE and Community Support indices included the HMC / HMF meetings variables. We retain these as a part of the Community Support index, as this index is intended to more directly gauge the monitoring mechanism more directly. However, we verify that omitting these variables from the CDPE index has no consequence on the estimated effect. (These results are available upon request).

> A4 The baseline survey asked for projects in the past two years while the endline survey asked for the past year. In the ANCOVA specification, we therefore control for projects in the past two years.

A8
**<u>Table 6. Matching results—indirect taxes</u>**

|**Treatment Variable:**<br>**Non-Cooperation**|**1-Nearest**<br>**Neighbor**<br>**Matching**|**2-Nearest**<br>**Neighbor**<br>**Matching**|**3-Nearest**<br>**Neighbor**<br>**Matching**|**Ra**|**dius Matchi**|**ng**|**Local**<br>**Linear**<br>**Regression**|**Kernel**<br>**Matching**|
|---|---|---|---|---|---|---|---|---|
|||||**r=0.005**|**r=0.01**|**r=0.05**|**Matching**||
|||**Depend**|**ent variable**|**: General Go**|**ods & Servi**|**ces Taxes (%**|<br>**of GDP)**||
|[1] Average Treatment on the Treated (ATT)|-2.181***<br>(0.671)|-2.639***<br>(0.622)|-2.593***<br>(0.546)|-2.511***<br>(0.528)|-2.489***<br>(0.471)|-2.608***<br>(0.391)|-2.659***<br>(0.406)|-2.581***<br>(0.401)|
|Number of observations|483|483|483|483|483|483|483|483|
|Number of Treated observations|105|105|105|105|105|105|105|105|
|Number of Control observations|378|378|378|378|378|378|378|378|
|Rosenbaum bounds sensitivity tests|2.5|2.5|2.5|2.5|2.5|2.5|2.5|2.5|
|Standardized biases (p-value)|0.87|0.68|0.72|0.95|0.96|0.97|0.87|0.97|
|Pseudo R2|0.01|0.02|0.02|0.01|0.09|0.07|0.01|0.07|
|||**Depe**<br>|**ndent varia**<br>|**ble: Value A**<br>|**dded Taxes (**<br>|**VAT) (% of**<br>|**GDP)**<br>||
|[2] Average Treatment on the Treated (ATT)|-1.595***<br>(0.574)|-1.586***<br>(0.484)|-1.593***<br>(0.486)|-1.646***<br>(0.546)|-1.549***<br>(0.446)|-1.635***<br>(0.408)|-1.648***<br>(0.418)|-1.615***<br>(0.415)|
|Number of observations|411|411|411|411|411|411|411|411|
|Number of Treated observations|84|84|84|84|84|84|84|84|
|Number of Control observations|327|327|327|327|327|327|327|327|
|Rosenbaum bounds sensitivity tests|1.8|2|2|2.1|2.1|2.3|2.4|2.3|
|Standardized biases (p-value)|0.60|0.79|0.70|0.56|0.77|0.91|0.60|0.90|
|Pseudo R2|0.03|0.02|0.02|0.03|0.02|0.01|0.03|0.01|
||||**Dependen**|**t variable: E**|**xcise Taxes (**|**% of GDP)**|||
|[3] Average Treatment on the Treated (ATT)|-0.780**|-0.725***|-0.650**|-0.705**|-0.662***|-0.522***|-0.504***|-0.503***|
||(0.336)|(0.269)|(0.272)|(0.293)|(0.248)|(0.178)|(0.178)|(0.173)|
|Number of observations|425|425|425|425|425|425|425|425|
|Number of Treated observations|81|81|81|81|81|81|81|81|
|Number of Control observations|344|344|344|344|344|344|344|344|
|Rosenbaum bounds sensitivity tests|1.8|1.9|2.1|2.4|2.4|1.8|2|1.8|
|<br>Standardized biases (p-value)|0.78|0.90|0.96|0.99|0.93|0.99|0.78|0.99|
|Pseudo R2|0.02|0.01|0.01|0.008|0.01|0.002|0.02|0.002|

Note: Standard errors in parentheses computed using 500 bootstrap replications. * _p_ <0.10, ** _p_ <0.05, *** _p_ <0.01.

19
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categories: requirements on the legal form of entry and restrictions on foreign equity; limits on licenses and discrimination in the allocation of licenses; restrictions on ongoing operations; and relevant aspects of the regulatory environment.

Measures governing mode 1 are slightly different in that they typically stipulate conditions under which cross-border trade may take place, rather than conditions imposed on the service provider. Mode 4 measures, covered only in professional services, focus on qualification and (re-) certification requirements as well as entry and immigration rules, all of which strongly affect the movement of service supplying individuals. The challenge in evaluating policy measures is to assess whether prudential or regulatory measures affect contestability of the market by restricting entry of foreign suppliers (Findlay and Warren, 2000). While we make an effort to capture the important licensing regulations in professional services where they have a significant impact on trade, in future work more could be done to improve the coverage of such measures in areas like financial services. Finally, to understand how the policy was measured and became the services trade restrictiveness indices (STRI), please see the detailed note on the scoring in the Appendix.

First-hand information for ASEAN member states was collected by administering a questionnaire in 2012 which was completed by local law firms familiar with the policy regime in the respective countries and sectors. The information on policies received was evaluated, and its restrictiveness assessed, by a team of World Bank economists. To ensure data accuracy, the policy information was reviewed by government officials between March and May of 2013. Upon receiving government comments, the policy information and scores have been revised. This paper is based on the data that have been reviewed and validated by the government officials.

It is notoriously difficult to measure policies affecting services trade because of their variety and complexity (see, for example, Hoekman (1996) and the overview by Deardoff and Stern (2008)). We rely on a measure of the restrictiveness of a country’s policy regime for any subsector-mode, the “services trade restrictions index” (STRI), which has the weakness of being subjective but the virtue of being simple, transparent and robust (Gootiiz et al. 2013).<sup>6</sup> This measure is most

> 6 The OECD has also developed a measure of services trade restrictiveness drawing upon the more detailed data available for industrial and more advanced developing countries (OECD 2009, 2011).

7
but a major concern here is that parental (usually father's) education may give us only a partial measure of socioeconomic status of a child. To address this, one can include a vector of observable family characteristics in a model of intergenerational mobility by adopting the latent socioeconomic approach developed by Lubotsky and Wittenberg (2006) (henceforth called LW approach following Emran and Shilpi (2021)).<sup>7</sup> A second solution is oered by the literature on inequality of opportunity (henceforth IOP) that grew out of the inuential work of Roemer (1998) and Coleman et al. (1966). The IOP approach uses multiple indicators such as father's education and occupation, sex of a child, ethnicity etc. as the circumstances for which a child should not be held responsible as they did not choose them, rather inherited by birth. A practical challenge for both of these approaches (IOP and LW) is that the set of family background indicators one can use is dictated by the lowest common denominator across the surveys in dierent countries. In practice, these approaches thus rely on a limited number of observed family characteristics when studying inequality of opportunity and intergenerational mobility across countries (see the discussion by Bjorklund and Jantti (2020) on IOP). A related important limitation is that these approaches often fail to take into account the eects of unmeasured and dicult to measure family characteristics (e.g., cultural inheritance) and neighborhood characteristics (e.g., school and peer quality). It is especially dicult to get reliable and comparable data on these tacit aspects of family and neighborhood environment when studying a large number of countries.

In a forthcoming paper, Deutscher and Mazumder (2022) discuss 19 measures of intergenerational mobility and nd that some of them are only weakly correlated with each other, suggesting that they measure very dierent concepts of economic mobility. When dierent studies use dierent measures of mobility capturing disparate economic concepts, it is impossible to rank countries in terms of intergenerational mobility based on the evidence from individual country studies. Many recent studies on developing countries use measures of intergenerational association based on the conditional expectation function of children's schooling given father's schooling. However, even among the subset of studies focusing on the conditional expectation function of schooling, comparability is compromised in two ways. First, some studies use years of schooling, some use years of schooling normalized by standard de-

> 7Notwithstanding its advantages, the LW approach has not been adopted widely yet. We are aware of only two studies that used this approach: Neidhofer et al. (2018) and Vosters and Nybom (2017).

2
Table B10: Which villages were reallocated across treatments? - Household Variables

||(1)|(2)|(3)|
|---|---|---|---|
|Subsample:|All villages|Treatment group|Control group|
|Variables(Y)||||
|HOUSEHOLD LEVEL VARIABLES||||
|12. Household size|-0.8342***|-0.5932**|-1.2288***|
||(0.2902)|(0.2295)|(0.4477)|
|13. Household members under 5|-0.1453|-0.0428|-0.2987**|
||(0.0954)|(0.0852)|(0.1343)|
|14. Household members under 18|-0.8098***<br>(0.2020)|-0.5737***<br>(0.1506)|-1.1750***<br>(0.2461)|
|15. Main source of drinking water:||||
|15.1.Public tap|0.1895|0.1207|0.2919**|
||(0.1515)|(0.2349)|(0.1166)|
|15.2.Unprotected well|-0.2030***|-0.1837**|-0.2362***|
||(0.0475)|(0.0699)|(0.0400)|
|15.3.Unprotected spring|-0.0324|0.0482|-0.1451***|
||(0.0674)|(0.0927)|(0.0292)|
|16. Has any toilet|-0.0325|-0.0060|-0.0680***|
||(0.0282)|(0.0409)|(0.0193)|
|17. Has radio|-0.1080*|-0.0090|-0.2529***|
||(0.0607)|(0.0431)|(0.0240)|
|18. Firewood is main fuel|0.0107|-0.0104|0.0452***|
||(0.0419)|(0.0667)|(0.0118)|
|19. Has no window|0.4261***|0.3127|0.5853***|
||(0.1255)|(0.1889)|(0.0496)|
|20. Number of separate rooms|-0.5183***<br>(0.0882)|-0.5669***<br>(0.1047)|-0.4557***<br>(0.1507)|
|21. Number of sleeping rooms|-0.2773***<br>(0.0472)|-0.3001***<br>(0.0626)|-0.2461***<br>(0.0657)|
|22. Number of sleeping spaces|-1.1402***<br>(0.4100)|-0.9049<br>(0.6611)|-1.4443***<br>(0.1808)|
|23. Asset index|-0.3498***|-0.3021**|-0.4310***|
||(0.0994)|(0.1495)|(0.0763)|

Note: Variables 12-23: one observation per household. For each variable Y, we report the coefficient _β_ 1 estimated from LS regression _Yi_ = _β_ 0 + _β_ 1∆ _i_ + _✏i_ , where ∆ _i_ is a dummy =1 if person _i_ lives in a village whose treatment status was changed, and =0 otherwise. Sample restricted to treatment group in column (2) and to control group in column (3). Robust standard errors in parentheses. Observations clustered at village level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

22
7 overstated. If estimates fail to take into account that very sick children are those most likely to be taken to the health clinic, the positive impact of clinic visits in reducing child mortality will be understated. Worse still, if resoarchers fail to take into account the possibility that governk:ents are giving priority to placing health services where sickness and mortality are high, the possible positive effects of services on mortality may be understated.8 Epidemiological studies of specific diseases without adequate control groups are likely to suffer from these types of estimation problems.<sup>9</sup> In short, household level analyses have not been definitive on the effects of variables that represent policy interventions such as availability or price of health services. However, in these analyses there has been a constant process of advance in unaerstanding, as conceptual and methodological problems have been successively encountered, grappled with, and overcome. Conceptual advances have led to changes in data collection efforts, which have in turn simplified methodological problems. An example is the growing emphasis in data collection efforts on incorporation of community-level variables that represent weli exogenous "prices" to the household, including most recently, information on actual cash prices of alternative health services. 10 8 Rosenzweig and Wolpin (1986) pro-? de some impressive evidence on the endogenous location of public health serv-ces. This could be a problem with the results of Rosenzweig and Schultz (1982), reported in the preceding footnote.

9 Non-economists have also developed models that take into account endogeneity. See for example Mosley and Chen (1984), and Briscoe et. al. (1990); the latter paper is entitled "People Are Not Passive Acceptors of Threats to Health: Endogeneity and its Consequences." 10 Such community-level data have been collected in a few settings, most notably for health in the case of the Cebu (Philippines) study undertaken by researchers at the University of North Carolina, and in surveys sponsored by the World Bank as part of its LSMS program (Living Standards Measurement Study).
**Table 1. The Impact of Education on Smoking Initiation and Smoking Cessation**

|Cohort born in|All|1910-19|1920-29|1930-39|1940-49|1950-59|1960-69|
|---|---|---|---|---|---|---|---|
|Age in 1950||31-40|21-30|11 to 20|0 -11|n.a.|n.a.|
|Age in 1970||51-60|41-50|31-40|21-30|11 to 20|0 -11|
|Dependent variable|: Ever started s|moking||||||
|High school|–0.028***|0.006|-0.018***|-0.037***|-0.037***|-0.047***|-0.057***|
||[–12.63]|[1.09]|[-3.59]|[-6.90]|[-6.73]|[-9.40]|[-9.57]|
|Some college|–0.043***|0.042***|0.007***|-0.01|-0.044***|-0.091***|-0.126***|
||[–17.04]|[5.32]|[1.09]|[-1.49]|[-7.41]|[-17.42]|[-21.76]|
|College and above|–0.178***|-0.027***|-0.064***|-0.112***|-0.17***|-0.253***|-0.295***|
||[–72.58]|[-3.20]|[-9.09]|[-16.63]|[-29.01]|[-51.10]|[-46.35]|
|Male|0.177***|0.336***|0.296***|0.221***|0.173***|0.103***|0.036***|
||[107.14]|[62.75]|[67.26]|[50.05]|[46.68]|[31.28]|[8.75]|
|Black|–0.044***|-0.06***|-0.034***|-0.039***|-0.034***|-0.032***|-0.1***|
||[–18.14]|[-7.57]|[-5.02]|[-5.95]|[-6.13]|[-6.82]|[-18.03]|
|Other race|–0.122***|-0.097***|-0.118***|-0.167***|-0.148***|-0.112***|-0.111***|
||[–28.20]|[-4.77]|[-8.07]|[-13.31]|[-14.88]|[-13.96]|[-13.27]|
|Pseudo R-square|0.04|0.09|0.07|0.04|0.04|0.03|0.05|
|Observations|348,198|35,991|50,255|49,774|68,778|89,381|54,019|
|Observed<br>robabilit|0.52|0.472|0.561|0.586|0.57|0.502|0.421|
|py<br>Dependent variable|: Stopped smo|king (for those|who ever started)|||||
|High school|0.068***|0.024***|0.049***|0.091***|0.097***|0.097***|0.091***|
||[24.34]|[3.99]|[8.47]|[13.90]|[14.19]|[15.19]|[8.40]|
|Some college|0.128***|0.041***|0.073***|0.136***|0.159***|0.178***|0.164***|
||[40.45]|[5.03]|[9.87]|[17.52]|[21.69]|[25.00]|[16.38]|
|College and above|0.244***|0.075***|0.136***|0.222***|0.295***|0.328***|0.298***|
||[75.32]|[8.76]|[18.49]|[29.75]|[42.92]|[43.65]|[24.68]|
|Male|0.05***|0.105***|0.118***|0.091***|0.032***|-0.004***|-0.011**|
||[22.89]|[16.55]|[22.09]|[16.45]|[6.75]|[-1.14]|[-1.96]|
|Black|–0.095***|-0.072***|-0.072***|-0.075***|-0.104***|-0.104***|-0.104***|
||[–28.63]|[-6.46]|[-7.96]|[-8.68]|[-13.99]|[-18.31]|[-12.28]|
|Other race|–0.03***|-0.007|-0.02|-0.034*|-0.036**|-0.023**|-0.015|
||[–4.49]|[-0.27]|[-0.94]|[-1.73]|[-2.42]|[-2.08]|[-1.18]|
|Pseudo R-square|0.09|0.07|0.07|0.06|0.05|0.05|0.04|
|Observations|181,222|16,988|28,212|29,177|39,206|44,884|22,755|
|Observed<br>probability|0.464|0.677|0.599|0.514|0.442|0.365|0.308|

***, **, * denote statistical significance at the 1%, 5% and 10% confidence levels, respectively.

_Note_ : Analysis from smoking histories from the 1978, 1979, 1980, 1983, 1985, 1987, 1988, 1990, 1991, 1992, 1994, 1995, 1997, 1998, 1999 and 2000 National Health Interview Surveys for individuals age 25 and above at the time of the survey. Average of marginal coefficients, observation by observation, from a logit estimation. Robust z-statistics in square brackets. Survey and year of birth dummies included. The excluded dummies are less than high school, female and white.

22
<!-- Start of picture text --> i)<br>SHGs —e—<br>Education<br>Service Failures —e—<br>Housing and Land Titles —+-e—<br>Beneficiary & Voter Lists |——*——<br>Sanitation ——~e+——<br>Environmental Protection ——~*—-<br>Toilet Construction —~*——<br>Water '<br>Employment& Wages ——*— |}<br>Ration Shop }=——*e— '<br>1<br>-0.06 -0.04 -0.02 0.00 0.02 0.04 0.06<br>Male Female<br><!-- End of picture text -->