rafmacalaba/datause-encoder-data huggingface dataset:
21. Averaging within each detailed category gives us the PPP for each of about 150 detailed categories. Laspeyres, Paasche, and Fisher index number formulas were used to estimate the PPP for GNP and other aggregates. Thus, the PPPs for the detailed categories were aggregated first usmig U.S. expenditure weights and then the China expenditure weights. The formulas for the U.S. and China the U.S. and China weighted indexes are: <!-- Start of picture text --> =u x w, (3)<br>= 1 (4)<br>,-) X wic<br><!-- End of picture text --> where the indexi mrns over the categories, **_Li_** is the subscript for the United States and c for China The weights are: <!-- Start of picture text --> W. fU (5)<br>'lei,<br>e.c<br>wie (6)<br>leke<br>i-,<br><!-- End of picture text --> where e is per capita expenditue in local curncy. The Fisher index, which is the geometric mean of the China-weighted and U.S.-weighted index munbers was calculated for each sector. The aggregation methods suggested by ICP has a very intuitive interpetation, especially because the intnational coDmpasons can be regarded as the counterpat to the single county intertemporal comisons in a II
essential infrastructure and services or quickly developing the competence to do so. I measure three dimensions of government’s performance in fulfilling its fiscal obligations to citizens.

First, I include a measure of respondents’ food security, specifically, whether a respondent or a household member ever went without sufficient food in the year preceding the survey. Sacks and Levi (2010) find that food security is strongly related to government performance in providing and maintaining infrastructure, reliable bureaucracies, and law and order, controlling for income, rainfall levels, the competitiveness of elections, and the presence of civil liberties and political rights.

Second, I include two measures of citizens’ perceptions of government service delivery. I include an indicator of citizens’ perceptions of how well the government is maintaining roads and bridges. I also include an indicator of citizens’ perceptions of how well the government is providing water and sanitation services. Third, I include two objective indicators of government service delivery. Survey enumerators identified whether there was a sewage system and school in each of the primary sampling units.<sup>7</sup>

# **6.2.4 Perceptions of Administrative Competence and Honesty**

I include two measures of citizens’ perceptions of the honesty and transparency of the bureaucracy. First, I include a measure of whether citizens believe that a large portion of tax administrators is corrupt. Second, I include a variable indicating whether citizens approve of how well their local government is handling the collection of license fees on bicycles, carts and barrows.<sup>8</sup> Third, both the size of a country and the size of the government may affect a government’s ability to detect and punish evaders. I include the

> 7I also include a country-level indicator of government performance, the World Bank Governance indicator of government effectiveness, in the model. This indicator measures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies (Kaufmann, Kraay and Mastruzzi, 2006, 4). This variable is not significant at the p _<_ .05 level.

> 8I included two additional measures in the model neither of which were significant at the p _<_ 0.05 level. One is a measure of citizens’ approval of how well their local government council provides citizens with the information about the councils budget (i.e. revenues and expenditures). The other, the World Bank governance indicator, control of corruption, measures the extent to which public power is exercised for private gain, as well as capture of the state by elites and private interest (Kaufmann, Kraay and Mastruzzi, 2006, 4).

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three to five years for re-graveling and every five to ten years for resealing chip roads. The base cost assumptions associated with road works and their sources are summarized in Table 14. The details of these full cost inputs are included in Annex 1.

Table 14. Roadworks cost assumptions

|Road type|Capital Expenditure|Normal Maintenance Expenditure|Cost Source|
|---|---|---|---|
|Gravel|US$50,000/km for gravelling<br>and some improvements to<br>drainage|US$3,000/km per year for routine<br>maintenance<br>US$30,000/km every 3 years for<br>periodic re-gravelling|Existing road contracts in Malaita|
|Chip seal<br>(BTC)|US$350,000/km for BTC<br>paving|US$3000/km per year for routine<br>maintenance<br>US$150,000/km every 10 years for<br>resealing|SIRAP estimate for capital<br>expenditure<br>Existing road contracts in Malaita for<br>maintenance|
||US$500,000/km for concrete|US$3000/km per year for routine||
|Cement<br>concrete|paving|maintenance<br>US$20,000/km every 10 years for<br>repairs|Expert input|

Table 15 provides the details of the cost estimates with annualized operational expenditures. In addition, costs associated with repairs due to 3-, 10-, and 30-year rain events are incorporated to provide the complete lifecycle cost associated with the type of road upgradation.

|Tab|le 15. Road Life|cycle Costs|,includingrepairs for|rain events<br>Uni|t Cost||Estimated||
|---|---|---|---|---|---|---|---|---|
|||||(US$|per km)|Total Cost|Rain Event|Whole Lifecycle<br>|
||||f|||*||Cost|
|||Kms|Speciications|||CAPEX+OPEX|Repair||
|||||CAPEX|Annualized<br>OPEX|30yrs))*Kms|<br>Costs, 30<br>years|(Total Cost +<br>Repair Cost)|
|0|Status Quo<br>(SQ)|41.533|Gravel, earth<br>drains|50,000|15,000|20,766,500|1,490,603|22,257,103|
||||Total|||US$20,766,500|1,490,603|US$22,257,103|
|||29.546|Gravel, earth<br>drains(<9%)|50,000|15,000|14,773,000|117,827|14,890,827|
||Medium<br>|7.335|Chip seal, lined<br>drains(9-15%)|350,000|18,000|6,528,150|641,865|7,170,015|
|1|Design<br>(MD)|4.652|Cement concrete,<br>lined drains<br>(>15%)|450,000|5,000|2,791,200|209,342|3,000,542|
||||Total|||US$24,092,350|969,034|US$25,061,384|
|||22.355|Gravel, earth<br>drains(<6%)|50,000|15,000|11,177,500|27,944|11,205,444|
||High Design|14.526|Chip seal, lined<br>drains(6-15%)|350,000|18,000|12,928,140|704,783|13,632,923|
|2|(HD)||Cement concrete,||||||
|||4.652|lined drains<br>(>15%)|450,000|5,000|2,791,200|209,342|3,000,542|
||||Total|||US$26,896,840|942,069|US$27,838,909|
||Sealed Long,<br>Steep|25.34|Gravel, earth<br>drains|50,000|15,000|12,670,000|312,873|12,982,873|
|3|Sections<br>(SLS)|16.193|Chip seal, lined<br>drains|350,000|18,000|14,411,770|1,168,821|15,580,591|
||||Total|||US$27,081,770|1,481,694|US$28,563,464|

The results show a significant reduction in both disrupted length and resulting repairs costs as a result of paving for slopes over 15% grade. For slopes from 9-15%, the repair cost savings associated with reduced 27
36 Source: Authors’ calculations from data in Anderson and Croser (2009) based on NRA and CTE data in Anderson and Valenzuela (2008).

- a. Country contributions are computed by converting national percentage WRIs to dollar values by multiplying by the average of the gross value

of production and consumption at undistorted prices. The country contributions therefore capture both the magnitude of the WRI and the share of a country’s gross value of production and consumption in the regional value of production and consumption.

- b. The total for all countries does not necessarily sum to exactly 100 for five-year averages, but it does sum to 100 for individual years.

- c. This column gives the country contribution to the fall in the WRI from 1975–79 to 2000–04.
<!-- Start of picture text --> 100<br>88.0 86.6<br>90<br>80 71.5<br>67.5<br>70 64.758.3 59.9 64.3<br>55.5<br>60 52.0<br>48.0<br>50 40.1 41.744.5<br>35.3 35.7<br>40 32.5<br>28.5<br>30<br>20 13.3 12.0<br>10<br>0<br>Children Adult IDP/Refugee Non-IDP/Host<br>Age Displacement Status<br>Ethiopia N.E. Nigeria Somalia South Sudan Sudan<br><!-- End of picture text --> Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018).

_Table 3: Sex ratios_

||**Ethiopi**|**a**|**N.E. Ni**|**geria**|**Somalia**||**South S**|**udan**|**Sudan**||
|---|---|---|---|---|---|---|---|---|---|---|
|Sex Ratio (%)|Male|Female|Male|Female|Male|Female|Male|Female|Male|Female|
|Population|48.9|51.1|48.9|51.1|50.4|49.7|46.8|53.2|50.0|50.0|
|Children (0-17)|52.0|48.0|50.3|49.7|52.1|48.0|49.4|50.6|50.5|49.5|
|Adults (18+)|43.2|56.8|47.0|53.0|48.2|51.8|45.0|55.0|49.6|50.4|
|IDP Pop.|49.0|51.0|53.4|46.6|49.9|50.2|47.4|52.6|50.7|49.3|
|Non-IDP Pop.|48.3|51.7|47.1|52.9|50.6|49.4|46.6|53.4|49.7|50.3|
|Sample Size (_n_)|||||||||||
|Population|13,952|13,518|9,063|8,882|16,408|16,739|2,356|2,549|9,215|9,319|
|Children (0-17)|8,683|7,782|5,525|5,142|9,343|9,081|982|965|4,613|4,634|
|Adults (18+)|5,269|5,646|3,538|3,740|7,065|7,658|1,374|1,584|4,602|4,685|
|IDP Pop.|10,198|9,519|4,314|4,210|3,019|3,062|231|253|5,887|5,967|
|Non-IDP Pop.|3,754|3,999|4,749|4,672|13,389|13,677|2,123|2,295|3,328|3,352|

Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018). The percentages of the population shares (%) are weighted and the sample observations ( _n_ ) are unweighted.

Table 4 describes the difference in means for several key household characteristics. In Ethiopia, South Sudan, and Sudan, female-headed households are more common among the forcibly displaced population. For other aspects of household compositions – age of the household head, household size, one-adult households, one-adult households with children, two-or-more-adult households with children – there are significant differences among the countries by displacement status, but the variation in absolute terms is minor.

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earnings: based on estimates of the supply elasticity of women’s labor in Brazil (Vick, 2017), such a wage penalty would imply a 0.4 - 0.5 percent reduction in the labor supply of a woman working at minimum wage.

Finally, we account for the possibility that bystanders may _implicitly_ view women outside the reserved space as provoking harassment and assign responsibility for harassment to the victims. We interview men and women commuters on the platform and administer 948 social norm surveys and 291 Implicit Association Tests (IATs) to establish whether women face norms against riding in the public space when they have the choice of a reserved space, and whether this relates to their demand for the reserved space. Results suggest that men and women respondents both _implicitly_ and _explicitly_ associate women traveling in the public space with sexual provocation.

Overall, we find that a majority of men and women commuters consider using the womenreserved space as the “proper” choice for a woman commuter. Surveying women’s second order beliefs reveals that women commuters who perceive this attitude against women riding in the public space to be the prevailing norm are 79 percent more likely to report taking most of their rides in the reserved space. Our back-of-the-envelope estimates suggest that complying with this perceived norm may account for more than half of the demand for riding in the reserved space.

Taken together, our results paint a nuanced picture of the impacts of women-reserved spaces. On the one hand, the incidence of harassment is lower in the reserved space than in the public space, in line with the degree of adherence to the reservation rule. However, by playing into latent prejudice, the reservation policy appears to reinforce norms limiting women’s safe travel in the public, non-reserved space. We show that perception of this norm reflects back on women’s demand for the reserved space, imposing an additional cost on women traveling in the public space. Quantitative estimates of this cost suggest that it is at least as large at the protective value generated by the reserved space.

This study therefore makes four central contributions to the economics literature on 5
If panel data is available, the joint and conditional probabilities defined above can be computed directly. If not, one can obtain estimates of these quantities by constructing synthetic cohorts based on repeated cross-sectional data. Using the data from BLSS 2017 and 2022, we estimate 2017 and 2022, as well as the mean squared errors and , using OLS on the following linear regression ββ̂ ββ̂ model: εεii,2017 εεii,2022 σσ� σσ� where _yij_ denotes the real per capita consumption in survey round _j_ for household _i_ , with _i_ = _1_ , ..., _n_ , and _j_ = _2017,2022_ . _xij_ is a vector of time-invariant household characteristics observed in both survey rounds, which include the age, gender and education of the household head, the district of residence of the household, and whether the household lives in a rural or urban area. Household heads’ ages are restricted to between 25 and 55 for the first period, and the age group is then adjusted for the second period according to the time elapsed between survey rounds ( _i.e._ household heads aged 30 to 60), so as to focus on a consistent group of households. The estimation of joint and conditional probabilities rests on the assumption that the population underlying the 2017 and 2022 BLSS samples is the same. If verified, this assumption allows to exploit the time-invariant household characteristics used to define the cohorts as the connectors of household consumption between the two periods. This is a strong and crucial assumption in the context at hand, due to the unprecedent shock generated by the COVID epidemic. In particular, we are concerned with any migration from rural to urban areas (between 2017 and 2022, the share of population living in rural areas decreased from 59.8 to 56.3 percent; Amendola et al. 2023). Even though the Monte Carlo simulations presented by Dang and Lanjouw (2023) show that results remain reasonably robust when this assumption is violated, we extend their framework by using a simulation based on actual survey data for 2022, to provide evidence of robustness. More details are provided in section 4.3.

The second step of the empirical strategy proposed by Dang and Lanjouw (2023) consists in computing the unconditional measures of poverty mobility as: 10
data analysts. Our study (i) documents the way in which the process of selecting plots for GPS-based land area measurement in accordance with an arbitrary distance criterion results in a non-random sample of plots with GPS-based area measures, (ii) multiply imputes missing GPS-based plot areas in two recent, public use household survey datasets from Uganda and Tanzania, and (iii) presents evidence, in context of land productivity estimates and the inverse scale-land productivity relationship, on why dealing with missing data in a statistically valid fashion matters at the analysis stage. We find that the missing GPS-based plot areas could be reliably simulated by MI, and that the effects of the imputation on key agricultural statistics are non-trivial. The empirical application of the method on the estimation of the scale-productivity relationship confirms and strengthens the presence of such relation in two African countries of interest. Overall, our study demonstrates the usefulness of judiciously reconstructed GPS-based areas in alleviating concerns over potential measurement error in farmer–reported area assessments, and with regards to systematic bias in plot selection for GPS-based area measurement.

The paper is organized as follows. Section 2 provides a literature review on (i) the use of GPS-based land area measurement in agricultural productivity analysis, and (ii) multiple imputation. Section 3 describes the data and provides summary statistics. Section 4 details the multiple imputation approach, followed by an empirical application focused on the inverse scale-land productivity relationship presented in Section 5. Section 6 concludes.

# 2. LITERATURE REVIEW

This paper falls at the intersection of two different strands of literature. The first strand relates to the improvements in methodological tools available to agricultural statisticians in the area of land measurement. The second concerns the advances in statistical procedures to deal with missing data.

Land area measurement is one of the fundamental components of agricultural statistics, which have traditionally been fraught with large measurement errors. It is therefore not surprising that the interest of agricultural statisticians in applying technological innovations such as satellite imagery and GPS technology to land area measurement is growing rapidly with the increasing affordability, precision and applications of these tools. Kelly et al. (1995) have long identified the use of GPS technology as having the potential to render land area measurement a significantly less costly and time-consuming exercise than traditional methods such as rope and compass. An early study mentioning a comparison of GPS-based and farmer-reported plot area measures is Goldstein and Udry (1999). The authors touch upon the issue as part of a study on agrarian innovation in the Eastern Region of Ghana, reporting a correlation coefficient of only 0.15 between the two variables. They attempt to reason the finding in the context of traditional regional land area measures 4
**Figure 1. 9  Basic access indicators**

<!-- Start of picture text --> No. of accounts per 100 inhabitants No. of cards per 100 inhabitants<br>35 30.5 30.6 30.5 28.0 35<br>30 30<br>25.5<br>25 25<br>18.6 18.7 19.0 18.7 21.0<br>20 20<br>15 15<br>105 4.7 4.7 4.4 4.6 4.4 105 4.8 4.6 4.8 5.1 5.6<br>0 0<br>1999 2000 2001 2002 2003 1999 2000 2001 2002 2003<br>Current Account Saving Accounts Credit Card Debit Card<br>No. of transactions per capita Population per branch, ATM and point of<br>10 sale<br>8.9<br>98 7.7 7.6 7.9 8.4 14,00012,000 10,863 10,982 11,295 11,767<br>7<br>10,000 8,718<br>6 8,000 7,440 7,663 7,973 8,071 7,986<br>5<br>4 3.1 3.1 6,000<br>32 1.8 2.1 2.2 1.32.6 4,0002,000 925 882 836 812 811<br>1 0.0 0.3 0.8 -<br>0 1999 2000 2001 2002 2003<br>1999 2000 2001 2002 2003<br>ATM Internet POS Branch ATM POS<br>% %<br>Nos. ('000)<br>No transactions per capita<br><!-- End of picture text --> _Source: Asobancaria_ 29. **In conclusion, the structure of Colombia’s financial system still reflects the consequences of the banking crisis of the late 1990s** , as financial depth is relatively low compared to other countries in the region and financial intermediation remains below pre-crisis levels. Accordingly, access to basic financial services has been declining, as reflected in a declining number of current and savings accounts over the past years, and in a decreasing share of credit available to the economy.

30. **As private banks provide the majority of financial services, the structure of the financial system offers an explanation for the low level of financial access in Colombia** . In light of profitable alternative investment opportunities such as government bonds, the main actors in Colombia’s financial system currently have limited incentives to provide services to low income clients, in particular access to credit services. This is exacerbated through the absence of regulation on financial access, as discussed in Chapter 2.

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**Table 1: General characteristics of the sample**

||**Total **|**North **|**East**|**Center **|**West**|
|---|---|---|---|---|---|
|**Household characteristics**||||||
|Family size|8.45|9.15|7.65|7.77|8.79|
|Age of head|48.36|46.25|51.55|49.06|47.53|
|Head’s education (years)|5.82|6.87|5.29|5.49|5.66|
|Female headed|25.35%|26.09%|25.58%|26.83%|24.12%|
|Rural|70.70%|68.48%|66.28%|73.17%|72.94%|
|Have a plot affected by conflict now|23.11%|11.67%|11.21%|30.97%|36.82%|
|Have a plot affected by conflict in the past|24.97%|34.01%|18.97%|17.41%|27.80%|
|**Asset endowments**||||||
|Permanent house in 1993|23.67%|9.88%|18.42%|37.33%|27.08%|
|Permanent house in 2001|35.35%|16.30%|25.58%|50.00%|43.53%|
|Bicycle owned in 1993|58.78%|62.96%|27.63%|84.00%|59.72%|
|Bicycle owned in 2001|58.84%|71.74%|31.40%|73.17%|58.82%|
|Radio owned in 1993|63.66%|44.87%|52.06%|73.97%|74.65%|
|Radio owned in 2001|68.37%|53.26%|53.49%|79.27%|78.82%|
|Land owned in 1993 (acres)|4.58|4.40|3.40|5.15|5.01|
|Land owned in 2001 (acres)|4.76|4.11|2.89|5.83|5.56|
|**Land market participation**||||||
|Leasing land in, 1993|3.19%|3.70%|5.26%|4.00%|1.39%|
|Area leased in, 1993 (acres)|1.31|1.42|1.19|0.92|2.00|
|Leasing land in, 2001|7.44%|8.70%|13.95%|9.76%|2.35%|
|Area leased in, 2001 (acres)|1.49|1.59|1.02|1.16|3.38|
|Sold land since 1994|14.65%|14.13%|15.12%|12.20%|15.88%|
|Area sold (acres) mean|3.94|2.77|2.31|4.21|5.19|
|Area sold (acres) median|1.5|1|2|2.8|1.75|
|Lost land since 1994 due to conflict|31.16%|31.52%|46.51%|24.39%|26.47%|
|Area lost (acres) mean|22.99|3.78|3.94|19.74|53.75|
|Area lost (acres) median|2|1|2|2|2|
|Number of plot lost per household (conditional)|1.45|1.34|2.03|1.15|1.13|
|**Knowledge about the land law and its provisions**||||||
|Husband knows about ’98 law|26.5%|30.4%|10.5%|24.4%|33.5%|
|Land belongs to the People|23.7%|25.0%|10.5%|20.7%|31.2%|
|Ownership after 12 years tenancy|19.3%|22.8%|4.7%|18.3%|25.3%|
|Co-ownership of spousal land|4.6%|5.4%|2.3%|3.7%|5.9%|
|<br>Payment of ground rent|13.5%|20.7%|0.0%|19.5%|13.5%|
|Consent clause for sale of family land|23.7%|29.4%|10.5%|19.5%|29.4%|
|Compensation on eviction|22.8%|28.3%|8.1%|18.3%|29.4%|
|Certificates for_bibanjia_holders|20.9%|26.1%|5.8%|19.5%|26.5%|
|Number of households|430|92|86|82|170|

Source: Own analysis based on the 2001 EPRC/World Bank survey.

19