rafmacalaba/datause-encoder-data huggingface dataset:
and also invited to the study site. If necessary, the backup participants would substitute for missing study participants from their village, following a precisely specified replacement protocol. The groups for the experiment were formed by randomly dividing the 20 study participants in a municipality into four groups of five individuals, subject to the constraint that the five members of a group had to be from different villages in the municipality. The groups were strictly separated to prevent information spillovers.

Prior to the decision exercise, all study participants completed a baseline survey. The baseline survey gathered information about local governance quality in the municipality and citizens’ experiences with municipal authorities, as well as data on individual-level characteristics, such as age, education, occupation, household size, leadership experience, electoral participation in the last elections, and their trust in the municipal administration.

Following the baseline survey, the baseline decision exercise and the actual experiment were carried out under the supervision of two facilitators. Study participants were not allowed to communicate during the exercise, except when they were asked to. To minimize facilitator effects, the instructions for the decision exercise were video recorded, dubbed in nine vernacular languages, and played back to the study participants in their respective language. The sequencing of instructions, comprehension checks and decisions was programmed into a tablet computer application that forced the facilitators to abide by the exact same procedures in every group. The tablet app simultaneously collected background data through which the facilitators’ performance could be monitored.

The video instructions were divided into multiple logical blocks. Each video block was followed by a set of comprehension checks. If a study participant in the group did not pass a comprehension check, the video block was re-played to the entire group until all participants had understood the instructions. Voting and allocation decisions were made on specifically designed touch screen interfaces. The video instructions included demonstrations of the tablet interface. Additionally, a practice tablet was passed around so that study participants could familiarize themselves with the interface. Pilot tests confirmed that the touch screen ballots and allocation interfaces were easily understood, especially by illiterate study participants.

16
- [18] BLOOM, N. AND J.VAN REENEN (2007) “Measuring and Explaining Management Practices Across Firms and Countries,” _Quarterly Journal of Economics_ 122 (4): 1351-1408.

- [19] BLOOM, N., B.EIFERT, A.MAHAJAN, D.MCKENZIE AND J.ROBERTS (2013) “Does Management Matter? Evidence from India,” _Quarterly Journal of Economics_ 128 (1): 1–51.

- [20] BLOOM, N., R.SADUN AND J.VAN REENEN (2012) “The Organization of Firms Across Countries,” _Quarterly Journal of Economics_ 127 (4): 1663-1705.

- [21] BLOOM, N., C.PROPPER, S.SEILER AND J.VAN REENEN (2015) “The Impact of Competition on Management Practices in Public Hospitals,” _Review of Economic Studies_ 82 (2): 457–489.

- [22] BROWN, M., W.SCHALLER, S.WESTERFIELD AND M.HEUSLER (2012) “Information or Insurance? On the Role of Discretion in Relationship Lending,” University of St. Gallen Working Paper.

- [23] BRYNJOLFSSON, E. AND K.MCELHERAN (2016) “The Rapid Adoption of Data-Driven DecisionMaking,” _American Economic Review_ 106 (5): 133-39.

- [24] CAMPBELL COLLABORATION (2019) “The Campbell Collaboration Online Library,” Available at https://campbellcollaboration.org/library.html (Accessed 4th February 2019)

- [25] CALLEN, M., S.GULZAR, A.HASANAIN, M.Y.KHAN AND A.REZAEE (2018) “Data and Policy Decisions: Experimental Evidence from Pakistan,” Stanford University Working Paper.

- [26] CRAWFORD, G. AND C.HARTMANN (2008) “Decentralisation in Africa: A Pathway Out of Poverty and Conflict?” Amsterdam: Amsterdam University Press.

- [27] CRAWFORD, V. P. AND J.SOBEL (1982). “Strategic Information Transmission,” _Econometrica: Journal of the Econometric Society_ : 1431-1451.

- [28] CREMER, J., A.ESTACHE AND P.SEABRIGHT (1994) “The Decentralization of Public Services: Lessons from the Theory of the Firm,” World Bank Policy Research Working Paper 1345

- [29] D’AGOSTINO, R.B., A.J.BELANGER AND R.B.D’AGOSTINO JR (1990) “A Suggestion for Using Powerful and Informative Tests of Normality,” _American Statistician_ 44 (4): 316-321.

- [30] DAL BÓ, E., F.FINAN AND M.A.ROSSI (2013) “Strengthening State Capabilities: The Role of Financial Incentives in the Call to Public Service,” _Quarterly Journal of Economics_ 128 (3): 11691218.

- [31] DAL BÓ, E., F.FINAN, N.Y.LI AND L.SCHECHTER (2018) “Government Decentralization Under Changing State Capacity: Experimental Evidence from Paraguay,” National Bureau of Economic Research Working Paper No. 24879.

- [32] DESSEIN, W. (2002) “Authority and Communication in Organizations,” _Review of Economic Studies_ 69 (4): 811-838.

- [33] DHALIWAL, I. AND R.HANNA (2017) “The Devil is in the Details: The Successes and Limitations of Bureaucratic Reform in India,” _Journal of Development Economics_ 124: 1-21.

- [34] DI PEI, H. (2015) “Communication with Endogenous Information Acquisition,” _Journal of Economic Theory_ 160: 132-149.

- [35] DUFLO, E. (2017) “Richard T. Ely Lecture: The Economist as Plumber,” _American Economic Review_ 107 (5): 1-26.

32
size of the average firm that is younger than five years old. In col. 2, we repeat the analysis in col. 1 using industry dummies and country dummies to control for any time-invariant unobserved heterogeneity across countries and industries. Once again we find that all the age coefficients are positive and significant. Cols. 3 and 4 present results for manufacturing and non-manufacturing sub-samples where we find a similar increase in establishment size with age.

In cols. 1-4, we are comparing the mean size of firms that are 40+ years old with the mean size of firms that are younger than 5 years old. However, young firms could be varying in size across years due to different macroeconomic factors, entry regulations, capital market openness, etc. as well as across countries. In order to see if firms indeed grow as they age over their life cycle, we would want to compare the size of older firms at any given point in time with their size when they were born. The Enterprise Surveys ask firms to report the number of fulltime employees when the firm started operations which allows us to construct Establishment Size Ratio, which is the ratio of each firm’s current employment level to the number of employees when it started operations. Col. 5 presents a regression of Establishment Size Ratio on age dummies and shows that older firms are much larger compared to their size when they first started operations. The constant shows that the average firm that is less than 5 years old is 3.05 times its size when it first started operations whereas the coefficient of the 40+ age bin shows that the average 40+ year old establishment is 11 times its size when it first started operations.

Overall Table 4 shows that firms in developing countries grow as they age and we find no evidence of a declining life cycle in our sample of firms.

24
|Road to Europe: Program of AccountingReform and Institutional St Jan Tyl|
|---|
|Road to Europe: Program of AccountingReform and Institutional St Jan Tyl|
|Road to Europe: Program of AccountingReform and Institutional St Jan Tyl|
|SL-Decentralized Service DeliveryProgram<br>Qaiser M. Khan|
|SL-Decentralized Service DeliveryProgram<br>Qaiser M. Khan|
|SL-Decentralized Service DeliveryProgram<br>Qaiser M. Khan|
|SL-Decentralized Service DeliveryProgram<br>Qaiser M. Khan|
|SL-Decentralized Service DeliveryProgram<br>Qaiser M. Khan|
|CO- IDF InstallingBasic Management Capacityin Choco<br>Edgardo Mosqueira Medina|
|China EnergyEfficiencyFinancingII<br>Todd M. Johnson|
|China EnergyEfficiencyFinancingII<br>Todd M. Johnson|
|China EnergyEfficiencyFinancingII<br>Todd M. Johnson|
|China EnergyEfficiencyFinancingII<br>Todd M. Johnson|
|China EnergyEfficiencyFinancingII<br>Todd M. Johnson|
|China EnergyEfficiencyFinancingII<br>Todd M. Johnson|
|China EnergyEfficiencyFinancingII<br>Todd M. Johnson|
|China EnergyEfficiencyFinancingII<br>Todd M. Johnson|
|China EnergyEfficiencyFinancingII<br>Todd M. Johnson|
|China EnergyEfficiencyFinancingII<br>Todd M. Johnson|
|China EnergyEfficiencyFinancingII<br>Todd M. Johnson|
|China EnergyEfficiencyFinancingII<br>Todd M. Johnson|
|China EnergyEfficiencyFinancingII<br>Todd M. Johnson|
|Red Sea and Gulf of Aden Strategic Ecosystem Management GEF ProEnos E. Esikuri|
|Red Sea and Gulf of Aden Strategic Ecosystem Management GEF ProEnos E. Esikuri|
|Red Sea and Gulf of Aden Strategic Ecosystem Management GEF ProEnos E. Esikuri|
|Red Sea and Gulf of Aden Strategic Ecosystem Management GEF ProEnos E. Esikuri|
|Red Sea and Gulf of Aden Strategic Ecosystem Management GEF ProEnos E. Esikuri|
|Red Sea and Gulf of Aden Strategic Ecosystem Management GEF ProEnos E. Esikuri|
|Red Sea and Gulf of Aden Strategic Ecosystem Management GEF ProEnos E. Esikuri|
|Red Sea and Gulf of Aden Strategic Ecosystem Management GEF ProEnos E. Esikuri|
|Red Sea and Gulf of Aden Strategic Ecosystem Management GEF ProEnos E. Esikuri|
|Red Sea and Gulf of Aden Strategic Ecosystem Management GEF ProEnos E. Esikuri|
|SN - Economic Governance Project<br>Jean Michel Noel Marchat|
|SN - Economic Governance Project<br>Jean Michel Noel Marchat|
|SN - Economic Governance Project<br>Jean Michel Noel Marchat|
|SN - Economic Governance Project<br>Jean Michel Noel Marchat|
|SN - Economic Governance Project<br>Jean Michel Noel Marchat|
|SN - Economic Governance Project<br>Jean Michel Noel Marchat|
|SN - Economic Governance Project<br>Jean Michel Noel Marchat|
|SN - Economic Governance Project<br>Jean Michel Noel Marchat|
|SN - Economic Governance Project<br>Jean Michel Noel Marchat|
|SN - Economic Governance Project<br>Jean Michel Noel Marchat|
|SN - Economic Governance Project<br>Jean Michel Noel Marchat|
|SN - Economic Governance Project<br>Jean Michel Noel Marchat|
|SN - Economic Governance Project<br>Jean Michel Noel Marchat|
|Metro Manila Wastewater Management Project<br>Christopher Casuga Ancheta|
|Metro Manila Wastewater Management Project<br>Christopher Casuga Ancheta|
# **I. Introduction**

The significance of group differences in wellbeing is often at the center of the study of inequality. Roemer (1998) suggests that inequality of opportunity occurs when the ability of people to pursue lives of their own choosing depends on predetermined characteristics, such as gender, race, social group, or family background.<sup>2</sup> This perspective implies that it can be instructive to disentangle inequalities due to differences between groups, defined in terms of such predetermined characteristics, from those due to, say, individual differences in effort, talent, or luck. Given two countries with the same overall income inequality, one might worry more about social stability and prospects for inclusive long-term prosperity in the country with higher inequality between groups.

Statistical methods are often used to ‘decompose’ economic inequality into constituent parts. Sub-group decomposable measures of inequality can be written as the sum of inequality that is attributable to differences in mean outcomes across population sub-groups and that which is due to inequality _within_ those sub-groups.<sup>3</sup> Many have used such decompositions to ‘understand’ economic inequality and guide the design of economic policy. Indeed, Cowell (2000) argues: “It is almost essential to attempt to ‘account for’ the level of, or trend in, inequality by components of the population.” Although decompositions of inequality, as described above, have long been the workhorse in this literature, empirical implementation has tended to find little evidence of significant between group differences. For example, in a classic reference, Anand (1983)

> 2 World Development Report 2006, entitled “Equity and Development,” adopts a notion of equity that combines the concept of equality of opportunities with the avoidance of absolute deprivation – a Rawlsian form of inequality aversion in the space of outcomes.

> 3 See Bourguignon (1979), Shorrocks (1980, 1984) and Cowell (1980). Cowell (2000) provides a recent survey of methods of inequality measurement, including a discussion of the various approaches to sub-group decomposition.

3
states. They account for a little more than three-fourths of the total population of India. At the other extreme, eleven **small** states (excluded from our analysis and therefore not shown in Table 6) have populations of less than ten million (13 million according to the Census 2011) each. The remaining seven states, which we call **medium** -size states, have populations ranging from 36 million in Orissa to 22 million in Chhattisgarh (42 million in Orissa to 25.4 million in Chhattisgarh, according to the Census 2011).

**Table 6** : Rural and urban population in the largest 17 states of India, 2009-10

|State|Percent<br>Rural|Percent<br>Urban|Total<br>Million)|
|---|---|---|---|
|Uttar Pradesh|80|20|175|
|Maharashtra|58|42|97|
|Bihar|90|10|84|
|Andhra Pradesh|72|28|77|
|West Bengal|76|24|75|
|Tamil Nadu|55|45|64|
|Madhya Pradesh|76|24|62|
|Rajasthan|76|24|62|
|Gujarat|62|38|54|
|Karnataka|65|35|53|
|Orissa|86|14|36|
|Kerala|74|26|31|
|Assam|90|10|28|
|Jharkhand|80|20|26|
|Haryana|70|30|23|
|Punjab|65|35|23|
|Chhattisgarh|82|18|22|
|Total (17 largest states)|74|26|993|
|Total(all India)|73|27|1,043|

Among the large states, Tamil Nadu, Maharashtra, Gujarat and Karnataka, in that order, are the most urbanized with 35 percent or higher rate of urbanization. Bihar is the least urbanized among the large states and has an urbanization rate of just 10 percent. Among the medium-size states, only Punjab has 35 percent urban population, with the rest having urbanization rates of 30 percent or less. Assam and Orissa, with just 10 and 14 percent urban populations, respectively, are the least urbanized medium-size states.

# _7.2. Rural and Urban Poverty_

We now turn to the estimates of rural and urban poverty in the 17 largest states. To conserve space, throughout the text of the paper, we confine ourselves to presenting the estimates at the Tendulkar line. In the appendix, we report the estimates at the Lakdawala lines. Recall that the estimates at the Tendulkar line are available for three years: 1993-94, 2004-05 and 2009-10. Disregarding 1973-74 and 1977-78, which are 18
Policy Research Working Paper 9488

# **Abstract**

With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. This project set out to test whether an openly available dataset (Twitter) could be transformed into a resource for urban planning and development. The hypothesis is tested by creating road traffic crash location data, which are scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over age five and young adults. The research project scraped 874,588 traffic-related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. The project geolocated 32,991 crash reports in Twitter for 2012–20 and clustered them into 22,872 unique crashes to produce one of the first crash maps for Nairobi. A motorcycle delivery service was dispatched in real-time to verify a subset of crashes, showing 92 percent accuracy. Using a spatial clustering algorithm, portions of the road network (less than 1 percent) were identified where 50 percent of the geolocated crashes occurred. Even with limitations in the representativeness of the data, the results can provide urban planners useful information to target road safety improvements where resources are limited.

This paper is a product of the Development Impact Evaluation 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 smilusheva@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
the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA; <u>www.worldbank.org/lsms). Historically, plot-level soil statistics derived from household surveys have</u> relied on subjective farmer assessments of soil quality or on linking with soil raster data (when plots are geo-referenced). Direct systematic measurement of soil fertility as part of a large-scale household-level data collection operation has rarely been attempted due to the high costs of soil sampling and analysis. Recently developed rapid low-cost technology for assessing soil characteristics using infrared spectroscopy, however, has increased the potential for direct soil fertility characterization in large studies.

The value of soil data is unquestionable, but the sources, quality, and resolution of such data vary widely. And while platforms like EthioSIS provide invaluable information on soil from an agronomic perspective, having soil data integrated with household-level or plot-level data on input use, farm management practices, agricultural labor, agricultural production, and household socioeconomic characteristics holds extensive analytical value. Soil data from household and farm surveys also provide great opportunities for validation of information obtained through other means. However, the quality of the subjective soil data that are most often found with such inclusive agricultural household surveys has rarely been validated. In this paper, we seek to compare subjective farmer assessment of plot-level soil quality against objective laboratory analyses, by utilizing the data purposively collected for methodological validation under the LSMS Methodological Validation Program.

Using a unique plot-level data set collected by the Living Standards Measurement Study (LSMS) of the World Bank in collaboration with the World Agroforestry Centre (ICRAF) and the Central Statistical Agency of Ethiopia, and with funding from UK Aid, which consists of a menu of subjective farmerestimated indicators of soil quality and results from objective conventional and spectral soil tests, this paper analyzes the impacts of relying on subjective farmer estimates of soil quality for policy-based decision making through comparison of subjective and objective measures of soil properties. Results from the methodological experiment data suggest that smallholder farmers are unable to clearly discriminate between soil fertility levels, which we hypothesize may partially explain the slow adoption of improved agricultural practices and inputs often observed in Africa.

Building on the few previously existing studies, such as those by Dawoe et al. (2012), Desbiez et al. (2004), Odendo et al. (2010), and Gray and Morant (2003), we aim to validate the use of subjective soil quality indicators against objective measures. Specifically, we compare a multidimensional farmer assessment of soils with plot-level soil analysis conducted using conventional and spectral testing, similar to the data used by Marenya and Barrett (2009).

4
**Table 22. Transport Practices and Means**

|||Ben|in|Malawi||
|---|---|---|---|---|---|
|||N %|Traders|N %|Traders|
|**Transport Activity**|No transport|124|19%|150|20%|
||Only with own vehicle|36|5%|14|2%|
||Only with transporter|502|76%|532|72%|
||With own vehicle and transporter||0%|39|5%|
||Total|662||735||
|**Means of Transport**||||||
||foot|4|1%|7|1%|
||bicycle||0%|53|9%|
||handcart|74|14%|5|1%|
||oxcart|2|0%|16|3%|
||motorbike|11|2%||0%|
||I tontruck|213|41%|243|42%|
||7 ton truck|178|34%|189|32%|
||large truck|37|7%|43|7%|
||train||0%|4|1%|
||other|2|0%|22|4%|
||Total|521||582||

**Table 23. Transported Distances and Rates on Selected Routes**

|||||Benin|||
|---|---|---|---|---|---|---|
||Kilom|eters|$/|tona|Peak rate ratio|N|
||Mean|S.d.|Mean|S.d.|||
|Route 1|64.05|114.67|11.23|9.82|1.11|504|
|Route 2|40.74|95.96|13.65|12.73|1.11|275|
|Route 3|17.08|64.73|12.15|7.82|1.03|119|
|||||Malawi|||
||Kilom|eters|$/|tona|Peak rate ratio|N|
||Mean|S.d.|Mean|S.d.|||
|Route 1|55.27|80.20|16.04|15.62|1.18|563|
|Route 2|34.95|69.71|19.91|28.62|1.16|337|
|Route3|13.10|50.64|20.06|41.25|1.17|122|

a At the time of survey, exchange rates used are I US $= CFA 600 and I US $ = MK 45.

41
|**Table 9: Imports by Product and by Fe**|**deral Okrug**(i|n x 2001 rubles)||||||Sector|
|---|---|---|---|---|---|---|---|---|
|**Sector**<sup>**b/**</sup>|Northwest|Central|South|Volga|Urals|Siberia|Far East|total|
|Railway transportation|0.1|0.4|0.1|0.3|0.2|0.2|0.1|1.5|
|Truck transportation|0.4|1.3|0.4|0.8|0.8|0.5|0.2|4.3|
|Pipelines transportation|||||||||
|Maritime transportation|||||||||
|Air transportation|0.3|1.2|0.3|0.5|0.4|0.3|0.1|3.1|
|Other transportation|0.1|0.5|0.1|0.3|0.3|0.2|0.1|1.5|
|Telecommunications|0.7|2.7|0.7|1.4|1.2|0.9|0.4|8.0|
|Science & science servicing|0.0|0.1|0.0|0.1|0.0|0.0|0.0|0.3|
|Financial services|1.6|5.8|1.4|3.0|2.6|1.9|0.8|17.1|
|Ferrous metallurgy|4.3|16.7|6.1|6.8|3.1|2.0|1.2|40.2|
|Non-ferrous metallurgy|3.4|11.0|3.1|4.6|2.8|1.2|0.7|26.7|
|Chemical & oil-chemical industry|12.3|49.1|6.6|14.0|9.3|14.2|4.7|110.4|
|Mechanical engineering & metal-working|58.0|192.9|26.0|35.7|30.6|13.9|14.5|371.5|
|Timber & woodworking & pulp & paper industry|5.3|21.2|3.1|1.2|0.8|0.5|0.5|32.4|
|Construction materials industry|3.0|12.9|2.3|3.6|1.7|2.9|0.6|27.0|
|Food industry|31.3|78.3|11.9|16.3|9.6|11.1|5.6|164.2|
|Other industries|0.8|2.5|0.9|1.7|1.1|1.0|0.4|8.4|
|Public services, culture and arts|1.0|3.8|0.9|1.8|1.5|1.2|0.5|10.7|
|Agriculture & forestry|3.7|12.5|4.4|6.4|3.0|3.9|1.8|35.8|
|Coalmining|0.6|1.2|0.4|0.7|0.8|1.3|0.3|5.1|
|Housing and communal services|3.5|14.2|3.5|6.4|4.2|4.2|1.8|37.9|
|Construction|9.8|23.3|9.0|16.9|20.9|8.6|5.6|94.0|
|Electric industry|0.3|0.9|0.3|0.6|0.4|0.4|0.1|3.0|
|Gas|0.1|0.3|0.1|0.2|0.3|0.1|0.0|1.<br>2|
|Crude oil extraction|1.1|2.2|1.2|5.2|1.1|2.2|0.6|13.6|
|Oil refining and processing|3.5|10.5|3.5|7.4|5.4|4.1|1.5|36.0|
|Other goods-producing sectors|1.6|5.4|1.5|3.3|3.0|2.0|0.8|17.6|
|Post|0.1|0.2|0.1|0.1|0.1|0.1|0.0|0.<br>7|
|Wholesale and retail trade|1.0|3.4|1.0|2.1|2.1|1.3|0.5|11.5|
|Textiles and apparel|25.9|108.8|26.0|44.4|27.3|29.7|12.7|274.8|
|Okrug Total:|174.0|583.1|115.1|185.8|134.5|109.6|56.3||

43