rafmacalaba/datause-encoder-data huggingface dataset:
a) <!-- Start of picture text --> Agricultural Land Area, Food Crops (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>100<br>Range of all sample paths<br>1.9 average<br>80 10% quantile<br>50% quantile<br>90% quantile<br>60 Stochastic-optimistic<br>1.8 Stochastic-pessimistic<br>40<br>1.7 20<br>0<br>1.6<br>-20<br>-40<br>1.5<br>-60<br>Deterministic-baseline<br>1.4 Deterministic-optimisticDeterministic-pessimistic -80<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>billion hectares million hectares<br><!-- End of picture text -->

# b)

<!-- Start of picture text --> Agricultural Land Area, 2G Biofuels Crops (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>0.55<br>Deterministic-baseline 80 Range of all sample paths<br>0.5 Deterministic-optimistic average<br>Deterministic-pessimistic 10% quantile<br>60 50% quantile<br>0.45 90% quantile<br>0.4 40 Stochastic-optimisticStochastic-pessimistic<br>0.35 20<br>0.3 0<br>0.25 -20<br>0.2 -40<br>0.15<br>-60<br>0.1<br>-80<br>0.05<br>-100<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>c)<br>Pasture Land Area (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>2.7 Range of all sample paths<br>average<br>2.65 6 10% quantile50% quantile<br>90% quantile<br>2.6 Stochastic-optimistic<br>4 Stochastic-pessimistic<br>2.55<br>2.5<br>2<br>2.45<br>2.4 0<br>2.35<br>2.3 -2<br>2.25<br>Deterministic-baseline<br>2.2 Deterministic-optimistic -4<br>Deterministic-pessimistic<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>billion hectares million hectares<br>billion hectares million hectares<br><!-- End of picture text --> 17
participatory, the final say for these projects rested with the village head and in some cases the' village choice was overridden by project staff in the interests of budget and timetable concerns. Even though both projects intended to be "participatory", the VIP projects in which villagers had greater say operated substantially better, had higher citizen satisfaction (38 percent were "very satisfied" with VIP versus 24 percent in WSSLIC), and had a greater impact on health (54 percent reported improved health in VIP versus 33 percent in WSSLIC).

Qualitative results for the second LLI Study indicate that projecl designs in the research area have grown increasingly participatory. Before 1998, villagers reported only 12% of projects giving them a direct say in project planning decisions. After 1998, they were given the opportunity to participate directly in planning in 22% of government projects. There has also been a simultaneous shift in satisfaction with project outcomes (37% satisfied or somewhat satisfied with pre- 1998 projects vs. 50% for post-1998 projects) (Wetterberg 2002).

In Indonesia it is recognized that for decentralization to lead to better governance the pre-existing _desa_ institutions will have to undergo major changes. Indonesia has embarked on a radical decentralization of power and responsibility to its regions (districts). The success of this decentralization will to a large extent depend on the extent to which changes from top down (creating democratically elected district legislative councils) and bottom up (creating effective _desa_ structures) can be integrated.

The qualitative data from LLI2 show that while some modifications to _desa_ structures are underway, the direction of change is not yet clear. The main innovation 24 The empirical evidence is the strongest for rural water supply (Briscoe and Gamn 1995, Narayan 36
reforms literature. We also study price hikes below, for example due to increases in trade protection as a result of the recent waves of protectionism. Using tari data from Brambilla, Galiani and Porto (2018), we report in Table 3 the average sectoral tari in place in Argentina during 1994-2000 (the period spanned by our data). There is a fair degree of protection, with average taris of 19.4 percent in textiles, 13.9 percent in food and beverages, 14.2 percent in metals, 14.1 percent in other manufactures, and 11.6 percent in minerals. The tari on capital goods is also fairly high, 12.3 percent.

Our benchmark simulation is the full elimination of taris on textiles. We chose textiles because it is the most heavily protected sector of the economy and because this simulation allows us to document in a neat way the generic dynamic responses of the economy to a tari cut. For a small country and homogeneous goods, we have that _pjt_ = _p_<sup>_∗_</sup> _jt_<sup>(1 +</sup><sup>_τjt_)and,consequently,theimpactof</sup> the elimination of taris on prices is given by _d_ ln _pjt_ = _−τjt/_ (1 + _τjt_ ) . As a result, such a cut in taris would bring textiles prices down by 16.2 percent. The other taris and prices of traded goods remain unaected, while the price of the non-traded goods responds in equilibrium.

Figure 1 illustrates the mechanics of the impacts on capital (panel a), real wages (panel b) and employment (panel c). Note that, in the model, imperfect factor mobility creates cross-industry spillover eects. To show these general equilibrium responses, we consider impacts on other tradables (for simplicity of exposition we group the four non-textile traded sectors using employment weights) and on the non-tradable sector. In each panel, the solid line corresponds to textiles, the dashed line to other tradables, and the dotted line to non-tradables. The magnitudes of the responses are given in Table 4. All the results in the paper are very robust to the Leontief production function assumption. Indeed, a Cobb-Douglas specication delivers very similar results both qualitative and quantitatively.

The immediate implication of lower textile prices is a decrease in protability for rms in the sector. Textile rms want to contract. However, capital and employment are predetermined and do not respond initially (i.e., at _t_ = 1 ).<sup>14</sup> The nominal wage in the textile sector goes down proportionately to prices due to the decrease in the value of the marginal product of labor. But lower textile prices imply a decline in the price index that brings up real wages in all sectors. In addition, the price of non-traded goods declines by 1.36 percent because of the negative income

> 14Note that investment at _t_ becomes productive capital in _t_ + 1 . In consequence, while there is an investment response in the rst year of the shock, the capital stock remains at the steady state level for one period before adjusting.

19
# BelIaWo _hid Am_ **_4i_** _CANINO_

|_Cowuniy_|_Unlwty & Locadon_|_yew_|_Lagg_|_Acad_<br>_staff_|_Studens_|
|---|---|---|---|---|---|
|_Alprk_|U.of Al&_s,A_<br>M|1859/<br>1909|_A,F_|_c_<br>1,530|_a_<br>17,086|
||_U. of_Scibn_h_TechL<br>I. Doumie,<br>Ag|1974|A, F|c<br>1,300|c<br>13,000|
||U. of'"Aim"_,'An_<br>ba|1975|A,F|c.<br>9"0|c<br>8|
||U. ofCOmtal<br>Cbutmanins|1961/<br>1969|A,F|c<br>1,000|c<br>12,000|
||_U.ofOesn,i 0n_<br>Es Senia|1961/<br>1966|_A,F_|c<br>1,000|c<br>9000|
||U. of Sci<br>and Todb<br>'OfAn, Oran|1975|F||844<br>c. 828|
||U.ofSO"f,SeO"|1978|A.F|cc<br>_48D_|_5,800_|
||Univ. Center of<br>Th-Oi<br>Tha-OMu|1977|A, F|883-84<br>465 F.T.|4§72|
|Bahmij|UnoWahy of 1bra,<br>ha Town, Mamah|1978/<br>I16|A, E|||
||GOf Uniwaity,<br>MsUmah|||||

a MOBMiny of Bducti **MOHUSR-Mlkuqy** of Hih dcatio ad Scientif RPser b Ag-Apctur; Ar-ArlUa_ A-As; Cs-Comp_w S; D-Dentt;, Ed-Educaio; E-Engleu Ft-Food Techn.; La-Laaapg L-Law **M-Msdd_a;** P-Pb,miLc R-ReIgo SPdea S-Sckec T-TechoolW, Vm-Vtecuay **MedN** _34_
Policy Research Working Paper 10456

# **Abstract**

Over the past decade, national statistical offices in low- and middle-income countries have increasingly transitioned to computer-assisted personal interviewing and computer-assisted telephone interviewing for the implementation of household surveys. The byproducts of these types of data collection are survey paradata, which can unlock objective, module- and question-specific, actionable insights on respondent burden, survey costs, and interviewer effects. This study does precisely that, using paradata generated - by the Survey Solutions computer-assisted personal inter viewing platform in recent national household surveys implemented by the national statistical offices in Cambodia, Ethiopia, and Tanzania. Across countries, the average household interview, based on a socioeconomic household questionnaire, ranges from 82 to 120 minutes, while the average interview with an adult household member, based on a multi-topic individual questionnaire, takes between 13 to 25 minutes. Using a multilevel model that is estimated for each household and individual questionnaire module, the paper shows that interviewer effects on module duration are significantly larger than the estimates from high-income contexts. Food consumption, household roster, and nonfarm enterprises consistently emerge among the top five household questionnaire modules in terms of total variance in duration, with 5 to 50 percent of the variability being attributable to interviewers. Similarly, labor, health, and land ownership appear among the top five individual questionnaire modules in terms of total variance in duration, with 6 to 50 percent of the variability being attributable to interviewers. These findings, particularly by module, point to where additional interviewer training, fieldwork supervision, and data quality monitoring may be needed in future surveys.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ardina.hasanbasri@yale.edu or tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Lasso Index: CPI in Costa Rica**

<!-- Start of picture text --> 2.5<br>cpi<br>LASSO GT Index<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>0.5<br>1.0<br>2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Figure 8: Lasso model index created for CPI series from Costa Rica. Conservative fit. Does not vary much.

21
Table 2: Test sample of FTS

|Year of conversion to FTS|Tested in 1996 and 2002|
|---|---|
|1997|2|
|1998|2|
|1999|8|
|2000|3|
|2001|12|
|2002|1|
|2003|1|
|2004|2|
|Never a FTS|187|
|Total|218|

school and learning; (2) in the head of household questionnaire: household demographics, family assets, parental education and occupation, preschool attendance; (3) in the teacher questionnaire: training, experience, opinions on pedagogy, and interactions with parents; (4) in the director’s questionnaire: training and experience of the school director, school infrastructure and equipment, school problems, interactions with parents, director’s opinion on pedagogy.

# 3.2 National Census of Schools

The National Census of Schools<sup>3</sup> is a registry that contains yearly information on all public school in Uruguay, from 1996 to 2004. The registry contains data on pupil enrollment, repetition, grade promotion, repetition, drop-outs and attendance at each grade level. We use the 1996 data as controls for the initial conditions at the school. As these variables are potentially endogenous to participation in the full-time model, we use only the 1996 data. The Census of Schools also contains a classi...cation of the sociocultural context of the school on a scale of 1 through 5, which is available for 1996 and 2002. In 1996, the classi...cation was computed on the basis of data from the additional questionnaires for the National Evaluation of Learning Achievements for Sixth Grade. (Cf. 3.1) In 2002, it was computed on the basis of data reported by the school director in the newly introduced Educational Monitor instrument<sup>4</sup> .

> 3 Hiperbase Primaria, Gerencia de Investigación y Evaluación

> 4 Monitor Educativo, Gerencia de Investigación y Evaluacion ANEP-CODICEN

6
**Table 1:** Overview of Indonesian targeting survey variables

|**Type**|**Theme**|**Variables included**|
|---|---|---|
|Core|Demographics|Household size & HH size<sup>2</sup>, number of<br>people by age groups by gender,<br>ur-<br>ban/rural, family structure, family smart<br>card|
|Optional|Housing|Materials used in the foor, wall, roof,<br>source of drinking water and cooking wa-<br>ter, type of lighting & cooking fuel, toi-<br>let facilities, septic tank, foor space per<br>capita, ownership status.|
||Assets|Household<br>ownership<br>of:<br>motorcycle,<br>car, computer, fridge, boat, motorboat,<br>phone, water heated, air conditioning|
||Education|Household members’ total levels of educa-<br>tional attainment and enrolment|
||Employment|Employment status, employment sectors|

# **Baseline PMT**

The Indonesian PMT implemented in the country’s social registry provides the baseline method for the early stopping algorithm’s performance. Its construction uses the data described above, and follows the canonical PMT approach in Algorithm 1 except for two significant changes. The first is in line with McBride and Nichols [2018], who pointed out that PMT is an outof-sample prediction task and that a cross-validation approach to variable selection leads to better performance than in-sample measures such as information criteria. Accordingly, we substitute selection via an information criterion with 5-fold cross-validation and a mean squared error (MSE) selection criterion. The stopping criterion is triggered when the cross-validated error no longer declines. MSE is the preferred metric as it aligns with the objective function of predictive model OLS<sup>4</sup> . The second change is to start estimation from a set of core questions required for administrative reasons or known up front. The rationale is that the algorithm should leverage both mandatory and pre-existing data to achieve a higher starting accuracy, rather than to ignore available information and start estimation from scratch. Table 1 displays the assignment of the two variable types.

A separate PMT model is built for each of the 514 districts. Although our baseline follows the current policy practice, there are also minor differences 4Areias and Wai-Poi [2022] show that optimizing for MSE does not necessarily minimize EER, which is coverage-level dependent, but it provides a consistent objective for multiprogramme targeting across coverage levels 12
the company to put a major emphasis on sewerage expansion in El Alto, rather than La Paz. Nonetheless, it can be said that those coverage increases that did take place in La Paz almost certainly benefited poorer households. The reason is that the coverage rate for all three of these services is much higher than the proportion of non-poor households in the city (i.e. 60%). This suggests that those households remaining uncovered are very likely to be poor households.

Telephony is the one service that has shown substantial coverage improvements in La Paz following sector reform. The gain in coverage appears to have been of the order of 15-20 percentage points. However, there are serious discrepancies between data sources as to the baseline level from which coverage increased. According to data from COTEL, fixed line telephone coverage in 1996 was already at the 50% level. While, according to household survey data, fixed _plus_ cellular telephone coverage at that time was only 30%.

**Figure 2.4: Evolution of coverage in La Paz**

<!-- Start of picture text --> 100%<br>90% Water<br>80%<br>70% Sewerage<br>60%<br>Electricity<br>50%<br>40%<br>Telephone<br>30% (COTEL)<br>20% Telephone<br>10% (Survey)<br>0%<br>Sources: INE, Electropaz, AISA and COTEL, 2000<br>Figure 2.5: Evolution of coverage in El Alto<br>100%<br>90% Water<br>80%<br>70% Sewerage<br>60%<br>Electricity<br>50% (Electropaz)<br>40% Electricity<br>30% (Survey)<br>20% Telephone<br>(COTEL)<br>10%<br>Telephone<br>0% (Survey)<br>199219931994199519961997199819992000<br>Coverage rate<br>Coverage rate<br>1992 1993 1994 1995 1996 1997 1998 1999 2000<br><!-- End of picture text --> _Sources:_ INE, Electropaz, AISA and COTEL, 2000 15
Paper No. 8977.

- La Croix, S. and A. Kawaura, 1996.  Product Patent Reform and Its Impact on Korea's Pharmaceutical Industry. _International Economic Journal_ 10: 109-124.

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41
Table 23: IV estimates of the effect of the share of labor with Internet on labor outcomes by quartile of income-per-worker growth: Commerce

||1st quartile|2nd quartile|3rd quartile|4th quartile|
|---|---|---|---|---|
|**Dependent variable:** ∆**ln(Numb**|**er of white-c**|**ollar workers**|**)**||
|∆Share of labor that uses Internet|0.0089***|0.0019***|0.0002|-0.0014**|
||(0.0020)|(0.0006)|(0.0004)|(0.0007)|
|F- frst stage|69.89|403.459|588.263|424.73|
|Sargan p-value|0.4155|0.7261|0.5122|0.7041|
|**Dependent variable:** ∆**ln(Numb**|**er of blue-co**|**llar workers)**|||
|∆Share of labor that uses Internet|0.0230***|0.0018***|-0.0017***|-0.0073***|
||(0.0023)|(0.0004)|(0.0003)|(0.0006)|
|F- frst stage|75.62|387.54|331.82|406.88|
|Sargan p-value|0.3642|0.4130|0.3901|0.3294|
|**Dependent variable:** ∆**ln(Wage **|**of white-coll**|**ar workers)**|||
|∆Share of labor that uses Internet|-0.0142***|-0.0006|0.0008*|0.0093***|
||(0.0022)|(0.0006)|(0.0004)|(0.0007)|
|F- frst stage|69.82|405.46|588.26|423.86|
|Sargan p-value|0.8328|0.5629|0.5028|0.8573|
|**Dependent variable:** ∆**ln(Wage **|**of blue-colla**|**r workers)**|||
|∆Share of labor that uses Internet|-0.0196***<br>(0.0021)|-0.0040***<br>(0.0005)|-0.0012***<br>(0.0003)|0.0073***<br>(0.0006)|
|F- frst stage|75.54|389.01|568.87|405.73|
|Sargan p-value|0.1923|0.3044|0.9288|0.7811|
|**Dependent variable:**∆**white/blu**|**e-collar work**|**ers**|||
|∆Share of labor that uses Internet|-0.0033***|-0.0000|0.0003**|0.0010***|
||(0.0007)|(0.0002)|(0.0001)|(0.0002)|
|F- frst stage|62.44|367.19|554.69|341.20|
|Sargan p-value|0.9529|0.8944|0.1750|0.1086|
|**Dependent variable:**∆**white/blu**|**e-collar wage**|**s**|||
|∆Share of labor that uses Internet|0.0172***|0.0081***|0.0053***|0.0053***|
||(0.0038)|(0.0013)|(0.0009)|(0.0014)|
|F- frst stage|73.70|386.34|585.84|299.24|
|Sargan p-value|0.1689|0.1290|0.3202|0.3571|
|Observations|2,083|2,084|2,083|2,083|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

52
|**Table 3: Impact on Start‐up and Survival**<br>Dependent Variable: Operates a Firm at|the Time of<br>Round 1|the Survey<br>Round 2|Round 3|
|---|---|---|---|
|**Panel A: New Firms**||||
|Experimental Treatment Effect|0.213***<br>(0.029)|0.358***<br>(0.023)|0.373***<br>(0.024)|
|Lee Bounds|[0.19,0.30]|[0.36, 0.38]|[0.37, 0.43]|
|Sample Size|1021|1181|1085|
|Control Mean|0.550|0.569|0.540|
|PSM Impact for National/Zonal winners|0.250***<br>(0.040)|0.414***<br>(0.023)|0.382***<br>(0.035)|
|**Panel B: Existing Firms**||||
|Experimental Treatment Effect|0.082***<br>(0.027)|0.130***<br>(0.025)|0.196***<br>(0.031)|
|Lee Bounds|[0.07, 0.12]|[0.13, 0.15]|[0.19, 0.24]|
|Sample Size|432|505|477|
|Control Mean|0.871|0.844|0.759|
|PSM Impact for National/Zonal winners<br>Notes: Robust standard errors in parent<br>significance at the 10, 5, and 1 percent le<br>PSM is propensity score matching comp<br>to experimental control group|0.097***<br>(0.024)<br>heses, *, **,<br>vels respec<br>aring non‐e|0.134***<br>(0.029)<br>*** indicate<br>tively<br>xperimental|0.200***<br>(0.035)<br> <br>winners|

40