rafmacalaba/data-use-mentions-extended huggingface dataset:
reduces the non-primate prefecture population by 1.7 percent but increases the primate prefecture population by 1.1 percent. Highways also affect patterns of specialization. Regional primates specialize more in manufacturing and services through better regional highways. On the other hand, peripheral areas lose manufacturing but gain in agriculture. Better access to international ports promotes greater population, GDP, and private sector wages on average, effects that are probably larger in the hinterland than in primate prefectures. These findings suggest that highways allow regions to specialize in their comparative advantage in China, meaning that centrally located prefectures focus on manufacturing while those in the hinterland concentrate on agriculture.

Similarly, Roberts et al (2012) also analyze the spatial and national impacts of the construction of the Chinese National Expressway Network. The results indicate that aggregate Chinese real income was approximately 6 percent higher than it would have been in 2007, had the expressway network not been built, although this estimation does not consider the opportunity costs associated with expenditure on the network. The construction not only enhanced intra-national trade, but also accelerated the development of the country's poorest inland regions with the aim of promoting their catch-up with the country's leading coastal areas.

Cosar and Fajgelbaum (2016) introduce internal geography to the canonical model of international trade driven by comparative advantages to study the regional effects of external economic integration. Then they empirically test their model using industry level data from Chinese prefectures, proxying for industries’ export orientation with national export-revenue ratios. The direct effect of distance on economic activity is sizable: moving inland by 275 miles (the median distance from the coast across prefectures) decreases industry employment by 17 percent for an industry with an average export-revenue ratio, and by 13 percent for an industry with average labor intensity. But this negative distance gradient is stronger for export-oriented industries: employment shrinks by 32 percent for an industry that has an export-revenue ratio 1 standard deviation higher than average, and by 21 percent for an industry that has labor intensity 1 standard deviation higher than average.
7 An important implication is that any subsidy programme administered via IDAAN will only reach a minority of the poor people in Panama, and a corollary is that the existing subsidy schemes administered by IDAAN are largely going to benefit people who cannot be regarded as poor. This follows from the fact that (according to the Living Standards Measurement Survey) only 16% of the IDAAN customer base lives either in poverty or extreme poverty; whereas, at least two-thirds of IDAAN's customers benefit from the subsidy programmes reviewed in the preceding section.

Thus, if it is to have any real impact on poverty, any future subsidy scheme directed towards IDAAN's customers would have to be both very small in its scope and very carefully targeted towards the small group of customers who are genuinely poor.

# **The Case for Use of Service Subsidies**

In order to establish the affordability of IDAAN's current water bills to its poorest clients, two different methods were used.

- The first and simplest approach entailed calculating the proportion of income that poor households were actually spending on water and sewerage services. This percentage can then be compared against international benchmarks, which suggest that water bills amounting to between 3% and 5% of income are generally affordable for the poorest households. The data for Panama indicated that a household on the extreme poverty line would on average be assigning less than 3% of its budget to water charges.

- The second and more sophisticated approach entailed asking customers their willingness to pay by means of a household survey conducted as part of the subsidy study. The advantage of a survey is that it is possible to consider the affordability of tariffs above the current level that might be needed in order to finance new investments. However, the survey methodology also brings considerable methodological problems of its own. These include (among others) the possibility that respondents may overstate their willingness to pay, potential biases generated by the structure and ordering of the questionnaire, as well as the sensitivity of the resulting estimates to the
)|[0.51]|3.32|(6.44)|1,276|
|Non-family childcare by Jordanians (Last week)|-0.12|(0.10)|[0.46]|0.07|(0.85)|1,274|
|Non-family childcare by Syrians (Last week)|-0.44*|(0.24)|[0.46]|0.20|(1.68)|1,274|
|Any paid childcare in 2020|0.00|(0.02)|[0.51]|0.01|(0.10)|1,318|
|Months of paid childcare|-0.06|(0.11)|[0.51]|0.06|(0.72)|1,318|
|Hours spent outside the home w/o children (Last week)|-2.00|(2.46)|[0.51]|9.92|(15.95)|1,268|
|Childcare hours by other HH members (Last week)|-17.50*|(10.03)|[0.46]|21.06|(22.46)|243|
|Chore hours by other HH members (Last week)|-11.97|(8.42)|[0.46]|18.78|(20.30)|267|
|Paid childcare hours (Last week)|-0.25|(0.16)|[0.46]|0.06|(0.83)|1,123|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022-2023 phone survey data. Each row is its own dependent variable.

- Domestic work hours
.271*|||2017|0.089|||
||(0.063)||||(0.096)|||
|2018|0.272*|||2018|0.135|||
||(0.113)||||(0.094)|||
|2019|0.095<br>(0.079)|||2019|-0.024<br>(0.113)|||
|ATT|0.153*||0.084|ATT|0.092||0.098|
||(0.054)||(0.057)||(0.079)||(0.099)|

_Source_ : Authors estimation based on the ACC 2012, 2016, and 2019 survey data.

_Note_ : P-value for a pre-test of parallel trends assumption is 0.192 for maize and 0.169 for wheat. Comparison group: “never treated.” Anticipation periods: 0. Estimation method: Doubly Robust. Standard errors are reported in parenthesis. * _p <_ 0.10.

Figure 1 presents a summary of ATTs based on the event-study (dynamic) aggregation and shows results nearly identical to those from aggregation by cohort and survey year. On average, DSM led to a 14-percentage-point increase in the proportion of farmers purchasing maize seed during posttreatment years. Specifically, DSM significantly increased the share of farmers buying maize seed in the second, fourth, and fifth years after its introduction in a district. Though positive, the effect during the same year and the year after is estimated with noise, making it difficult to statistically distinguish from zero. Consistent with the discussion above, the event-study aggregation indicates no evidence of DSM’s impact on the percentage of farmers purchasing fresh wheat seed.

16
administration techniques can reduce sensitivity bias by changing the social context in which sensitive questions are asked so that the identity of the respondent is obscured from interviewers, bystanders, and other potential social referents.

While most of the core topics shared between the PPCS and the Facebook survey could have been considered sensitive, we expect that in this context the questions on conflict exposure and mental health may have been more sensitive than the questions on food insecurity and COVID-19 exposure. Respondents may have perceived the questions on personal exposure to conflict to be sensitive because they brought up memories of traumatic experiences. In the mental health modules, respondents may have perceived questions as sensitive because of the negative attitudes and stigma around mental illness that persist in many parts of the region.<sup>7</sup> Questions about food insecurity may be perceived as sensitive because of their association with poverty, but we expect this sensitivity to be reduced in the specific context of the PPCS because most respondents had participated in the last wave of the Socio-Economic and Food Security Survey and might therefore be accustomed to answering questions about food insecurity. COVID-19 restrictions are not as severe as they were in earlier stages of the pandemic, and surveys about people's experience of COVID have proliferated since early 2020. We might therefore expect the difference in sensitivity bias between the PPCS and the Facebook survey to be more marked in the indicators related to conflict exposure and mental health than in the indicators related to food insecurity and COVID-19 exposure.

# _Hypotheses_

H2a: People tend to underreport conflict exposure, aggressive tendencies, indicators of risk for mental health problems, and symptoms consistent with depression because these are perceived as dangerous or undesirable traits and experiences.

H2b: People are more likely to underreport dangerous or undesirable traits and experiences in an inperson survey than in a self-administered web survey because the interviewer and bystanders are important social referents for respondents.

# _Discussion_

We explore this pair of hypotheses by comparing shares of sensitive responses reported in the Facebook survey and in the PPCS—following the approach of
- **[36]** Hopenhayn, Hugo, 1992.  “Entry, Exit, and Firm Dynamics in Long Run Equilibrium,” _Econometrica_ , Vol.  60 No.  5, pp. 1127-1150.

- **[37]** International Country Risk Guide – ICRG, 1999.  Brief guide to the rating system. ICRG, http://www.icrgonline.com

- **[38]** Investment Climate Survey, 2004.  The World Bank, Washington DC.

- **[39]** Kaufmann, Dani, Aart Kraay and Pablo Zoido-Lobatón, 1999.  “Governance Matters,” Policy Research Working Paper No. 2196, The World Bank, Washington, DC.

- **[40]** Klapper, Leora, Luc Laeven and Raghuram G.  Rajan, 2004.  “Business Environment and Firm Entry: Evidence from International Data,” CEPR Discussion Paper DP4366.

- **[41]** Koedijk, Kees and Jeroen Kremers, 1996.  “Market Opening, Regulation and Growth in Europe,” _Economic Policy_ 23, 445-467.

- **[42]** KPMG, Corporate Tax Rate Survey, March 1998 – January 2003.

- **[43]** La Porta, Rafael, Florencio López-de-Silanes, Andrei Shleifer and Robert Vishny, 1999.  “The Quality of Government,” _The Journal of Law, Economics, and Organization_ 15, pp. 229-79.

- **[44]** Loayza Norman, Pablo Fajnzylber and César Caderon, 2004. _Economic Growth in Latin America and the Caribbean_ , mimeo, The World Bank, Washington DC.

- **[45]** Loayza, Norman, Ana María Oviedo, Fabio Schiantarelli and Luis Servén, 2004. “Regulation and Firm Dynamics,” forthcoming.

- **[46]** Mark A.  Dutz and Aydin Hayri, 1999.  “Does More Intense Competition Lead to Higher Growth?” CEPR Discussion Paper 2249.

-
Costa Rica (as measured in SERCE and TERCE) is relatively strong, secondary education is weaker. They may also suggest that other Central American countries would have even weaker outcomes at the secondary level on the global scale, given Costa Rica’s outperformance in the regional exams.

These weak results corroborate negative perceptions held by citizens of the quality of public education and the value of education in improving labor market outcomes across Central America, as measured in the 2011 Latinobarometro surveys (World Bank 2014b and 2014c). For example, in Honduras, citizens give their public education system one of the lowest rankings in Latin America. At the same time, there is also substantial variation in quality within countries; as is the case with access, outcomes for youth at the top of the socioeconomic distribution are much better than for those at the bottom. For example, in Costa Rica, a 1 standard deviation increase in a socioeconomic index correlates to a 0.3 standard deviation increase in SERCE scores (World Bank 2014d). Low average educational quality in many cases masks large disparities, whereby strong results at the top of the socioeconomic distribution co-exist with very weak ones.

# **3.4 How dropout decisions are actually made, and evidence from qualitative research**

Taken together, human capital theory, the limited causal evidence available, and the empirical correlations presented above show that a host of factors affect the marginal costs and benefits of staying in school, and ultimately the decision to persist or drop out. From a public policy perspective, this could be viewed as promising, in that there are many “levers to pull” or opportunities for policy to impact on the dropout decision. At the same time, neither theory nor empirics provide clear guidance on which factors may be the most important and therefore should be the priorities for policy interventions.

20
and economic context matters. At this juncture, the experimentation continues. However, valuable lessons can be drawn from past and current experience of countries under different power market design options.

This paper undertakes a comprehensive stock-take of the evolution of the power market structure in every economy in the world between 1989 and 2024. We rely on a new unique database, the “Global Power Market Structures Database<sup>1</sup> ” developed by one of the co-authors of this paper. The database examines all 230 global economies, documenting the changes in the power market structure for every year between 1989 and 2024. The database’s objective is to present different designs pursued by countries globally in structuring their power markets. To the best of our knowledge this is the first such database and this is the first paper to analyze power markets in such a comprehensive global manner.

The paper is structured as follows: Section 2 provides a global overview of each power market structure; we also delve into the different design options adopted by countries globally within each structure. Section 3 presents the global experience of countries transitioning between different market structures, highlighting both enablers and challenges countries faced during these transformations. Section 4 discusses the experience of countries that experimented with different designs within their respective power market structure. Section 5 contains our conclusions and distills the key policy recommendations for each power market structure based on 35 years of global experience.

> 1 <u>Global Power Market Structures Database</u> (Akcura 2024).

2
continuity of care. The information about public health services included in this survey is used to assess health benefits.

**_POF._** The national household budget survey ( _Pesquisa de Orçamentos Familiares_ , POF) 2017/18 is a nationally representative budget survey that collects detailed expenditure data from more than 178,000 individuals and 58,000 households. Besides the household budget composition, POF also collects demographics and living conditions data, including the nutritional profile and subjective perceptions of quality of life. POF 2017/18 was collected between July 11, 2017, and July 9, 2018.

**_Indirect taxes._** The indirect taxes estimation is based on Lara Ibarra et al. (2021). They estimate a composite measure of indirect taxes that includes PIS/COFINS or _Programas de Integração Social e de Formação do Patrimônio do Servidor Público_ (PIS), and _Contribuição para Financiamento da Seguridade Social_ (COFINS) 14F<sup>15</sup> ; IPI or _Imposto sobre Produtos Industrializados_ 15F<sup>16</sup> ; ICMS or _Imposto sobre Circulação de Mercadorias e prestações de Serviços de transporte interestadual, intermunicipal e de comunicação_ 16F<sup>_17_</sup> ; and ISS or Imposto Sobre Serviços: a municipal services tax that is regulated at the federal level.  These estimates are based on data from the POF survey.

# B. Administrative sources

**Government expenditure at the federal level.** The Brazilian Ministry of Finance ( _Secretaria do Tesouro Nacional_ , STN) compiles figures about government expenditures in Brazil. This information includes consolidated executed government and revenue collection for the year 2019.

**Social protection data.** Open Data Portal from Brazil’s government has information about the total expenditure and number of beneficiaries for Direct Transfers:  Abono Salarial, Bolsa Família, Benefício de Prestação Continuada, Salário Família, and Unemployment benefits. 17F<sup>18</sup>

**Education expenditure.** We use the estimates of direct investment
such as surveying owners and real estate experts, likely due to poor and biased valuation practices. Furthermore, cadastral information on plots in the tax database is not linked to address information that is visible and known by taxpayers. This lack of data harmonization makes it impossible to send tax notifications to most property owners. As a final challenge, manual practices in collecting new information on properties imply a cumbersome, timeconsuming and error-prone data collection process by tax agents. Ongoing research examines the impact of a new mobile application that helps to determine property values and process billing and collection on tax collection. Box 2 presents a case study from Liberia on how using information from a newly developed electronic tax database increased tax collection.

Second, technology is important for facilitating and monitoring compliance with real estate taxes. The ideal technology system will not only support property identification using a geospatial database and valuation, but will also integrate billing, payments, appeals and enforcement. Dzansi et al. (2022) report from a census of local governments in Ghana that only 17 percent of them have a digital database of properties or a revenue software of any kind to assist with billing, and find that use of technology is associated with higher revenue collection. In addition, using a randomized experiment in a large municipal government, they find that revenue collectors using electronic tablets with geospatial data and navigational directions to more easily locate properties collected twice as much revenue as control group collectors.

Technology solutions can also be used to reduce compliance costs and revenue leakage. Weigel (2020) and Bal ́an et al. (2022) describe the use of a payment technology in door-todoor property tax campaigns in Kananga, Democratic Republic of Congo. Taxpayers made cash payments to tax collectors instead of traveling to the tax office, thereby reducing their compliance costs. The tax collectors used handheld printers to issue receipts to taxpayers and each transaction was recorded in the device’s memory and downloaded to the government database on a regular basis. To monitor leakages, supervisors confirmed that the revenues
**Food Prices, Access to Markets and Child Undernutrition in Ethiopia**

_Paul Brenton_<sup>_1_</sup> _and Mike Nyawo_<sup>_2_</sup>

# _Keywords_

Food prices; Child undernutrition; Stunting; Wasting; Ethiopia, Livings Standards Measurement Survey

> 1 Global Trade and Regional Integration, World Bank Group

> 2 Global Trade and Regional Integration, World Bank Group We are grateful to Carlo Azzarri, Tom Bundervoet, Chris Garbers, Mombert Hoppe, Neil Rankin, Asmelash Tsegay and Manex Bule Yonis for comments and suggestions. This research benefited from the Umbrella Facility for Trade trust fund that is supported by the Governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom.
economies and across different social groups, the data cover sub-national units and distinguish by gender and skill level.

We divide the Western Balkans group into 54 sub-national regions corresponding to the ISO3166 international standard division at the two-digit level<sup>1</sup> . For the European Union, we take each of the 27 countries as our unit of analysis. In total, our QSM has 81 spatial units that respond heterogeneously to the policies we simulate.

To use the model and perform our counterfactual analysis over these locations, we construct a database with basic information on wages and employment for four groups of workers: high-skilled males, low-skilled males, high-skilled females, and low-skilled females, divided into three economic sectors: agriculture and mining, manufacturing, and services.

We employ two international databases: the International Income Distribution Database (I2D2) and the Working Time Statistics (WTS) to compute the national and sub-national level measures needed. In addition, we use the Global Roads Inventory Project (GRIP) for information on road infrastructure.

For EU countries, we use the WTS database to calculate the country-specific wage ratio across skill levels and the wage ratio across gender. Then, we multiply the wages in each country and sector by the skill and gender ratios to have data at the skills-gender level in each sector. We follow the same procedure to obtain employment levels.

Meanwhile, we use the I2D2 database for the Western Balkans because it allows us to analyze the data at the sub-national geographical level. This database enables cross-Balkan comparisons by standardizing representative household surveys conducted separately in each economy. However, given the independence of the data sources, not all the variables are available for all the Western Balkan economies. For example, Serbia and North Macedonia do not collect information about the economic sector from the workers. To overcome this problem, we combine the I2D2 with the WTS database by calculating the aggregated salaries and employment levels for every region and multiplying them by the proportion of wages across the three economic industries calculated at the national level.
Policy Research Working Paper 9160

# **Abstract**

This paper proposes a methodology for identifying urban areas that combines subjective assessments with machine learning, and applies it to India, a country where several studies see the official urbanization rate as an under-estimate. For a representative sample of cities, towns and villages, as administratively defined, human judgment of Google images is used to determine whether they are urban or rural in practice. Judgments are collected across four groups of assessors, differing in their familiarity with India and with urban issues, following two different protocols. The judgment-based classification is then combined with data from the population census and from satellite imagery to predict the urban status of the sample. The Logit model, and LASSO and random forests methods, are applied. These approaches are then used to decide whether each of the out-of-sample administrative units in India is urban or rural in practice. The analysis does not find that India is substantially more urban than officially claimed. However, there are important differences at more disaggregated levels, with “other towns” and “census towns” being more rural, and some southern states more urban, than is officially claimed. The consistency of human judgment across assessors and protocols, the easy availability of crowd-sourcing, and the stability of predictions across approaches, suggest that the proposed methodology is a promising avenue for studying urban issues.

This paper is a product of the Office of the Chief Economist, South Asia Region, and the Office of the Chief Economist, Latin America and the Caribbean Region. 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 yli7@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