rafmacalaba/data-use-mentions-extended huggingface dataset:
programs within and without the health sector. A second critical assumption is that the units of analysis can be the "average encounter" and the "average vaccination" and that the chosen integration strategy is independent of the mix or impacts of these average events. A third assumption is that the national health objective in rural areas is to maximize healthy-life-days.

Given these assumptions and the additional assumption that the effects of **diseases** and health interventions are additive, the parameters of the objective function can be "guess-timated" from fundamental epidemiologiLal data organized according to the pattern of the Ghana Health Assessment Team study, as is done here in Tables 1 and 3. Since each of the objective function parameters (a and b) represents the net impact of an intervention **oi** an index of overall health status, rather than its impact on any single disease, it would be feasible to estimate the objective function for a region by setting up two experimental groups, one with only the vaccination program and
# **1. Introduction**

The Belt and Road Initiative (BRI) is a development strategy proposed by China that focuses on connectivity and cooperation on a trans‐continental scale. It roughly follows and expands the old Silk Road on the land side and complements it with a maritime part to build a series of economic corridors with the goal of boosting trade and stimulating economic growth across Asia, Europe and Eastern Africa.<sup>2</sup> The range of initiatives and activities that will be part of the BRI is very wide, including policy coordination, infrastructure, trade and investment, financial and people‐to‐people exchanges. In this paper, we focus on the consequences of transport infrastructures linked with the Belt and Road Initiative and quantify the associated decrease in shipment times and ultimately trade costs. We build two new databases with estimates of trade cost changes across sectors and country‐pairs and provide an analysis of the systemic impact that the BRI may have on trade costs for all countries in the world.

The new databases presented in this paper are composed of two sets of data that can be used to address different research questions. The global database contains information based on an analysis of 1,000 cities in 191 countries and 47 sectors. The regional database contains the same variables but focuses on the 71 economies that are part of the Belt and Road Initiative and is computed with a higher degree of granularity.<sup>3</sup> In particular, the focus on fewer countries allows to include in the analysis a larger number of cities.

In terms of method, we first use a combination of geographical data and network algorithms to compute the reduction in shipping times between all city pairs in our analysis. As a starting point, we use the current network of railways and ports across the world and employ a shortest path algorithm to compute an estimate of the current shipping times between every pair of cities. We use assumptions regarding the average speed for different transportation modes as well
so, the tariff data cover only 43% of the entire sample for which trade and institutional data are available. This restriction hardly changes the overall representation of country-pairs in the panel. However, using tariff data the sample is

> 23 We chose not to use the World Bank’s “Governance Matters” (see Kaufmann, Kraay and Mastruzzi, 2005) institutional quality indicators because its country- and especially year-coverage is less complete than our institutional indicators. Nevertheless, these indicators are highly correlated with our institutional indicators.

17
# **3. Data and methodology**

# _Data_

Baseline data for the evaluation were collected in April-May 2005. A first follow-up survey was collected in July-August 2006 - nine months after households started receiving benefits. Due to implementation delays, the business grant had been disbursed only 2 to 3 months prior to this survey and vocational training courses has been selected but had not yet started. A second follow-up survey was collected between August 2008 and May 2009 (henceforth referred to as 2008). At this point, households had stopped receiving transfers and any related program benefits for an average of two years. As the first follow-up survey was done while some program activities were still under implementation, this paper primarily focuses on the second follow-up survey.

Thanks to thorough tracking procedures, attrition compared to the baseline was minimal, less than 2.4 percent in 2008, and is uncorrelated with treatment status (P-value = 0.54). Differences in attrition rates between treatment groups are also very small and not significant (P-value = 0.67). The sample includes 972 households eligible for the vocational training, 990 households eligible for the business grant, and 996 households in the control.<sup>7</sup> The surveys included comprehensive information on household socioeconomic status, including detailed modules for expenditures and economic activities.<sup>8</sup> The baseline survey included specific questions regarding households demand and preferences for potential programs that could help increase their income. It also included detailed modules on household asset endowments (durables, land, livestock), credit access, demographics, dwelling characteristics, education, and remoteness. We use this information, together with the information on baseline economic activities, to analyze the heterogeneity of program impacts. The second follow-up survey in addition included a labor market history module as well as a specific module on households’ nonagricultural self-employment activities, which together with the module on economic activities in the last 12 months is used to define the outcome variables.

Finally, we draw
and refugees, were most affected (Dang and Nguyen, 2021; Sumner et al., 2022; Li _et al._ , 2023). In this regard, the importance of measurement of statistical capacity has been recognized in the SDG indicator framework with the recent formal population of SDG indicator 17.18.1 with the SPI pillars 4 and 5 indicators and the ODIN data availability indicators is a step in the right direction.<sup>2</sup> Since better statistical capacity could help collect more data, such as on gender-disaggregated data and climate change, Tichenor (2022) argues that assessment tools such as the SPI can be regarded both as a tool for monitoring development and as a development goal itself.

> 2 SDG 17.18.1 is made up of one ODIN indicator (component a) and two SPI indicators (components b and c).

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# **The Evolution of City Form:**

# **Evidence from Satellite Data**

Somik V. Lall* Mathilde Lebrand Maria Edisa Soppelsa World Bank

## JEL: O18, R52, R14

Key words: internal city structure; transport networks; satellite data; land-use policy

**Acknowledgments:** The authors thank Hogeun Park for supporting the spatial data analysis. They also thank Richard Damania, Lewis Dijkstra, William Maloney, Forhad Shilpi, Daniel Sturm, and Anthony Venables for helpful discussions. The authors appreciate the partnership with Thomas Esch and Mattia Marconcini of the German Aerospace Center (DLR) in developing measures of building heights. The authors acknowledge the generous financial support from the Global Platform for Sustainable Cities and the Government of Switzerland (SECO). The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. 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.

*Author for correspondence: Somik Lall, slall1@worldbank.org; Lead Urban Economist, World Bank
12 Figure 2: Comparison of Africa in 2012 with other regions in 1990 <!-- Start of picture text --> Latin America & Caribbean 1990<br><!-- End of picture text --> _Source_ : World Bank staff analysis using data from World Bank 2016 and World Bank and IEA 2015. _Note_ : Bubbles are in proportion to population.

# Descriptive Statistics

Household surveys in the 22 countries show that, on average, two‐thirds of all people lived in rural areas (see Table A.2 in the annex). In terms of numbers of households, one‐third of households lived in rural areas in South Africa, but in all other countries the percentage was 42 or higher. In 16 countries, the average per capita expenditure in every rural quintile (calculated using population weights) was lower than in the corresponding urban quintile, and in seven countries household expenditure in every rural quintile (calculated using household weights) was similarly lower. The median for the percentage of people who were officially classified poor across the 22 countries was 46 percent. In rural areas, the poor constituted a majority, with a median of 56 percent of all people and a weighted average of 50 percent. About one‐fifth of all people lived in households headed by a woman. The median of the percentage of people living in female‐ headed households was 23 in urban areas, 20 in rural areas, and 21 percent of the total population. At the highest end of the spectrum, Botswana stands out with half of all people living in female‐headed households; at the opposite end is Mali with only 4 percent reporting living in female‐headed households. Across the 22 countries studied, the median monthly per capita expenditure in 2014 U.S. dollars<sup>8</sup> was $73 in urban areas and $37 in rural areas, giving a national median of $49 per capita per month.

# Measuring Access in Household Surveys

What information about
2000).<sup>17</sup> It is difficult to tell whether those households who had high incomes or engaged in a particular activity in one month would also have high incomes or would they have been engaged in similar activities in different months. Measurement errors also occur with occupational choice, since the individual level 7 day recall status can deviates from the 12 month recall status. These measurement errors will result in inconsistent estimates in regressions using earnings at both the individual and household level as well as analysis involving individual employment variables.

In sum, while the recall period has a negligible impact on aggregate indicators of primary employment, it matters a lot for indicators capturing multiple activities both at aggregate and household/individual level analysis. A short recall period biases any kind of individual and household level analysis because it understates participation in secondary activities, and by implication gives a misleading picture of livelihoods and overstates underemployment. It also makes it difficult, if not impossible, to measure annual earnings for important segments of the labor force – likely the poor. It follows then that indicators based on the 12 month recall period are the more reliable, especially for earnings and livelihood level analysis<sup>18</sup> .

# **4. Suggestions on minimizing measurement error when calculating LFPR in low-income countries**

The Uganda analysis, as well as other analysis cited above, showed that there is significant variation in the LFPR depending on the questionnaire design. There are categories of respondents – specifically women and those in school - whose labor force participation is systematically understated. The key question is how to construct a meaningful indicator for LFP in a given country, in the presence of non-sampling measurement error traced to the type of questionnaire or the implementation. The first step is to recognize that the bias of the error is to underestimate the labor force. But then, what can the analyst do after the data have been collected?

Guarcello et al (2010) offer one option. Using data from 24 countries, they
possible. One would have to investigate the institutional arrangements in some of these markets to see if these high NTB estimates are reflecting true policy-related barriers to imports, or are reflecting missing costs that would raise marketing margins and so reduce the true NTB wedge. Another question regards the specific policy instruments that are generating these barriers. If these NTB estimates are approximately true, it would indicate a rather aggressive industrial policy, favoring certain activities (firms) relative to others. We note that there is a correlation between higher NTB ad valorem wedges and formal tariffs. From the subset of activities for which we have estimates, the price impacts of NTBs tend to increase with the domestic share of value added, which suggests that NTB might be protecting activities involving high levels of employment. This hypothesis could not be test in this study.

For the estimation of ERPs with NTB effects in the case of specific industries (Section 6), we use the ad valorem estimates of Dr. Hanafy for the outputs, and for tradable inputs we use the average of 9 percent NTB equivalent associated with low-value added products. (Cement is the only exception, where there appears to be little applied tariff or NTB restrictions on product or raw materials.) For ERP estimates for the aggregate sectors from the input-output matrix (Section 7), there are only six sectors to which we add to the applied tariffs the estimates of the ad valorem effects of NTBs (see Table 5b). The sectors to which were applied NTB estimates were those that corresponded most closely to the specific industries and products for which tariff-equivalent estimates were available at the disaggregated industry level from Dr. Hanafy. There are three sectors to which we applied the average tariff-equivalent of 9 percent (i.e., excluding the highvalue-added goods mentioned above); and there are three sectors that closely matched the industries for which we apply the NTB estimates directly (textiles, fertilizers, and iron and steel).<sup>9</sup> For the five non-tradable sectors NTB
into account spatial differences in transmission of global price shocks and diet compositions. The analysis uses Indonesian household panel data collected in 2007 and 2010. The first round of 2007 was fielded from the second to third quarter, immediately before the initial food price run‐up late in 2007. The follow‐up survey was conducted in 2010 after the crisis had subsided. The timings offer us an ideal setting to assess the impact of food price spikes on child growth. The geographic coverage of the survey over seven provinces in five macro regional islands lets us take advantage of significant variations in village‐level food price changes.

We find disheartening evidence that soaring prices had a significant and negative impact on child growth among non‐farming households, despite government‐backed support programs targeting the most vulnerable. At the same time, a corresponding effect on child growth was undetectable for food‐producing households, suggesting that some measure of self‐sufficiency remains a prudent course of action for poor rural households. The result remains robust when
heterogeneous, so that a larger number of additional voters may be attracted from a reduction in the gasoline price, even when this price is initially quite low.<sup>24</sup> Thus overall, one cannot say too much about the (directional) relationship between car ownership and motor fuel subsidies from theoretical arguments alone. It could be that, when comparing democracies and more autocratic governments, subsidies to motor fuels are likely to be relatively more prevalent in the former relative to the latter, when broader groups own motor vehicles; while the opposite could be the case when the group possessing motor vehicles is small. The reason is that with a broad group of vehicle owners, a large number of voters are affected by a given fuel subsidy rate. This may be the dominating factor in many democracies where leaders may need a large core group to stay in power. In autocracies, by contrast, the power base of leaders is often narrower: this makes fuel subsidies attractive for autocracies when few (basically, all in the core support group) own cars. Moreover, the fiscal cost of paying these off through low fuel prices will then be relatively low.

Thus while these relative effects are difficult to sort out in a theoretical model, they can be tested, which I intend to do on our compiled data set. In particular, one can test a) if the propensity to subsidize motor fuels is greater or smaller when the group of vehicle owners is larger; and b) whether this relationship differs between democracies and autocracies, and according to the “strength” of democracy as defined in our data.

Note finally the argument, made by some observers, that political competition models as presented here yield meaningful explanations of political outcomes only when the group of voters at stake (here, the group of car owners) is relatively large. But note that there are here both political benefits (more favorable voters) and costs (fiscal expenditures) associated with fuel subsidies when this group is large; and
such as Azerbaijan, Tajikistan and Uzbekistan, there was a belief that the overall performance of their national governments improved over the past three years. Taking into account differences across countries, consumption levels, receipt of government benefits and other demographic factors, those respondents who were affected “a great deal” were, on average, 11 percent more likely than those unaffected to say that the overall government performance worsened in the previous three years.

**Figure 10. Crisis severity and perceptions of national government performance**

<!-- Start of picture text --> Georgia<br>40<br>Uzbekistan<br>Azerbaijan<br>Tajikistan<br>30<br>Turkey<br>Bulgaria<br>20<br>Armenia Hungary Sweden MacedoniaKazakhstanMongolia Belarus<br>Montenegro<br>Albania<br>Moldova<br>Estonia Poland<br>Slovenia<br>10 Ukraine Czech RepublicUnited Kindgom<br>Slovak Rep. SerbiaKyrgyz Rep.<br>Latvia Russia Bosnia-Herzegovina<br>Lithuania CroatiaItaly GermanyFrance<br>Romania<br>0<br>-10 -5 0 5 10<br>Average GDP growth in 2009-2010 (percent)<br>Performance rated as better than 3 years ago (per cent respondents)<br><!-- End of picture text --> Source: LiTS II (2010), IMF WEO October 2010 Update.

# **Conclusion**

Subjective perceptions confirm the widespread impact of the global economic crisis, with twothirds of respondents in Europe and Central Asia being subjectively affected. Households were affected primarily through the labor market by job losses and reduced wages and remittances. Findings from the Life in Transition Survey underscore the limitations of comparisons of 21
questions used for analysis. For example, analyzing life satisfaction or minimum income data

> 5 The old OECD scale assigns a value of 1 to the first household member, 0.7 to each additional adult, and 0.5 to each child. The corresponding figures for the modified OECD scale are 1, 0.5, and 0.3. We discuss the definitions of the square root and other scales in Section 3.

5
**Figure 1: Comparison of reported secondary jobs by activity type: 7 day versus 12 month recall, 2009/10**

<!-- Start of picture text --> 12 month recall 7 day recall<br>Family worker-farm 317 533<br>Family worker-nonfarm 119 125<br>Own account-farm 435<br>359<br>Own account-nonfarm 401<br>437<br>Public wage 18 58<br>Private non-farm wage 133 444<br>Private farm wage 246 684<br>0 100 200 300 400 500 600 700 800<br>Number of observations<br><!-- End of picture text -->

**Source:** Authors calculations based on the UNPS 2009/10.

Using only short recall affects other indicators and analyses as well. One common indicator prepared as an alternative to the ILO defined unemployment measure is _underemployment_ . This concept is used because few people in a low-income country can afford to be completely without work, although they may not

> 16The Uganda National Panel Survey (UNPS) 2009/10 is a follow up of a randomly selected sub-sample of the UNHS 2005/06. It is the latest survey with available data that captures multiple activities in different recall periods. However, the national representativeness of this data is compromised by high attrition, which could be non-random from the perspective of employments characteristics of those who attritted.  While the structure of primary employment [weighted with attrition adjusted weights] from the data is similar to that in the UNHS 2009/10, the paper uses unweighted statistics from the survey and only provides a sample interpretation of the statistics to analyze how respondents in the sample varied their responses to questions with different reference periods.

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interprovincial migration accelerated in the past decade as the number of people aged five and older who had migrated across provinces within the census period increased from 2.0 million individuals (or 2.9 percent of population) in the 1999 census to 3.4 million (4.3 percent) in the 2009 census (GSO, p.21, 2011). The rest either moved intra provincially or stayed in the same location. The rise in interprovincial migration is not inconsistent with the theory since a large majority of people do not migrate and it appears to be precisely the disparity in earnings opportunities which would induce some individuals to migrate (Fukase, 2013b).

> **22** Chiquiar (2008) used one percent samples of male individuals taken from Mexico’s 1990 and 2000 population censuses and complemented them with site-specific data for globalization-related variables. Specifically, “globalization” is measured in Chiquiar’s study by the shares of state employment in agriculture and industry, the distance from the U.S., the share of FDI in the state GDP, the shares of _maquiladora_ employment and imports, and migration rates. Controlling for personal characteristics in his cross section data, Chiquiar (2008) finds that Mexican states more exposed to globalization experienced a decrease in the skill premium relative to the other states.

13
to assume that differences between public and private sector would be even more pronounced if data on benefit coverage were also collected for workers other than regular paid employees, casual paid employees and contract / piece paid workers in stateowned or private enterprises.

# **4.2. Selection Equation, Wage Regressions and Baseline Decomposition**

Turning to the initial step of the three-step procedure outlined above, Table 3 reports outputs from an empirical estimation of the sector selection equation (2). These outputs provide insights into the determinants of self-selection into labor force participation and into public and private sector. Not directly reported in Table 3 but available upon request are the coefficients of the multinomial logit model used to estimate equation (2). What is displayed in Table 3 are marginal effects on the likelihood of self-selection into the public sector, the private sector or inactivity. This is because marginal effects

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br>Impact evaluation Systematic review<br><!-- End of picture text -->

**Figure 4 Economic Outcomes reporting from the DHI Evidence base**

# 3.1.7 Evaluation methods

Most studies (n=583, 91%) evaluated the interventions through an RCT framework. The remainder of the studies used quasi-experimental designs. Among the studies with an explicit economic evaluation framework, the majority did not adopt any specific economic evaluation method but instead simply reported the cost incurred or saved. Cost-effectiveness analysis was the most frequently used economic evaluation method (n=63), followed by cost utility analysis (n=28). Cost minimization and cost consequence analyses were reported in one and two studies, respectively. None of the included studies used cost-benefit analysis.