rafmacalaba/datause-encoder-data huggingface dataset:
countries in the world, the model needs to allow for cross-country heterogeneity in the degree of risk sharing.

This paper extends the existing literature by developing an empirical model that ...ts those two requirements. In the model, countries contribute possibly di¤erent fractions of their income to a common pool, in exchange for a claim on the aggregate income contributed to the pool by all countries. The fraction of income contributed to the global pool by each country can be naturally interpreted as its respective degree of risk sharing. Solution of the model yields a system of equations in which each country’s consumption path depends on the innovations to the permanent income of all countries. Moreover, the system features nonlinear parameter restrictions across equations.

The model is implemented empirically using panel data for industrial and developing economies. Estimation results show that consumption risk sharing varies signi...cantly across countries. On the whole, rich countries exhibit higher degrees of risk sharing than developing countries, and the gap between both country groups appears to have widened over the period of ...nancial globalization. Moreover, the pattern of consumption risk sharing across countries is signi...cantly related to their degree of ...nancial openness. Countries possessing larger stocks of international assets and/or liabilities exhibit larger degrees of risk sharing. Countries whose foreign asset stocks are more tilted towards FDI assets show higher consumption risk sharing, while the opposite happens with countries whose foreign liability stocks involve larger FDI liabilities. Once these measures of international ...nancial integration are taken into account, trade integration, domestic ...nancial development and the level of GDP per capita do not contribute signi...cantly to explain the cross-country pattern of consumption risk sharing.

26
Yu, H., and D. Robinson. “The New Ambiguity of Open Government,” _UCLA. L. Rev. Disc_ (2012): 59, 178–208.

# **_Books_**

Arrow, Kenneth J. 1951. 2nd ed., 1963. _Social Choice and Individual Values_ . Yale University Press.

Black, Duncan. 1958. _The Theory of Committees and Elections_ . Cambridge: Cambridge University Press.

Borowiak, Craig T. 2011. _Accountability and Democracy: The Pitfalls and Promise of Popular Control_ . Oxford: Oxford University Press.

Buchanan, James M. and Gordon Tullock. 1962. _The Calculus of Consent: Logical Foundations of Constitutional Democracy_ . University of Michigan Press.

Downs, Anthony. 1957. _An Economic Theory of Democracy_ . Cambridge: York: Cambridge University Press.

Keohane, Robert. 2011. _Global Governance and Democratic Accountability_ .

Lindblom, Charles E. and Robert A. Dahl. 1976. _Politics, economics, and welfare_ . Chicago: University of Chicago Press.

Olson, Mancur, Jr. 1965. 2nd ed., 1971. _The Logic of Collective Action_ . Cambridge: Harvard University Press.

Ostrom, Elinor. 2005. _Understanding Institutional Diversity_ . Princeton, NJ: Princeton University Press.

de Tocqueville, Alexis. 1956. _Democracy In America_ . Edited and Abridged Edition by Richard Heffner. Mentor Books.

Trapnell, Stephanie (ed.) 2014. _Right to Information: Case Studies on Implementation_ . World Bank.

# **_Reports_**

Global OGD Initiative. “Global OGD Initiative Survey and Interview Report.” Accessed March 23, 2015. <u>www.opendatainitiative.org/survey-and-interview-report/.</u> Hania Farhan, Debra D’Agostino, and Henry Worthington. _Web Index 2012_ , World Wide Web Foundation.

Hazell, R., and B. Worthy. “Impact of Freedom of Information on Central Government,” unpublished End of Award Report to the Economic and Social Research Council, University College London, Constitution Unit (2009).

Jesuit Hakimani Centre. “Open Government Data for Effective Public Participation: Findings of a Case Study Research Investigating Kenya’s Open Data Initiative in Urban Slums and Rural Settlements,” Nairobi Kenya (2014).

45
34

# Table **5**

# **Incidence Analyses of the Incidence of Ivorian Taxes** under Elasticity Sensitiv.Wtv Analysis for Table _5_ **Results**

(Capital-Labor Substitution Elasticities in all Industries Set Equal to 0.5) (Hicksian EVs expressed as percentage of **benchmark** gross income)

||lntroduciin|! an equal**X-**|**ld. non-dis**|**tortiny**cons|umption tax|to replace:1|
|---|---|---|---|---|---|---|
|Households|Personal<br>income tax|Production<br>tax|Export<br>tax|Import<br>duties|Stabili-<br>zation<br>Fund|Subsidies|
||1|~2|3|4|**_5_**|_6_|
|Food croppers<br>in Savannah|-0.64|-6.41|0.27|0.77|0.56|-3.58|
|Export croppers|-0.25|4.68|1.18|24.20|28.01|0.87|
|**Small**<br>businesses|-1.57|1.32|0.12|-0.06|-1.45|0.23|
|Other food<br>croppers|_-1.50_|-7.82|0.08|0.39|-2.76|-3.17|
|**Inactive**|2.65|-2.58|0.39|1.01|5.18|-2.82|
|**Government**<br>**employees**|3.40|2.37|-0.71|-8.98|-10.89|1.50|
|**Households in**<br>**formal sector**|**-0.62**|3.39|-0.40|-10.77|-8.71|1.71|
or two firms. The additional increase in concentration is meaningful in the context of the United States Department of Justice’s guidelines for horizontal mergers, which state that HHI increases of more than 100 points in already concentrated markets are likely to enhance market power. In contrast, and also in line with our theoretical framework, there is no detectable change in the number of firms from the introduction of TBT measures.

In Appendix B, we estimate the impact of quality regulation on similar indicators for competitive dynamics from the perspective of the _importing_ country. Although this is outside the scope of our theoretical framework, our rich firm-level data allows us to examine these outcomes. Overall, quality regulation does not meaningfully reduce the number of importing firms or increase their concentration. This suggests that the costs of quality regulation are primarily borne by exporting firms, particularly in lower-income countries.

# **4.5 Heterogeneous Benefits of Information**

As a final test of the importance of the information channel, we examine heterogeneity in the potential benefits of information about product quality embedded in quality regulations by allowing the impacts of such regulations to differ across differentiated and non-differentiated products. The expectation is that differentiated products have a greater scope for quality information (Rauch, 1999). Columns 1 and 2 of Table 3 present estimates of Equation (1), while columns 3 and 4 present estimates of Equation (4). Quality regulation stimulates strongly the imports of differentiated products (column 1), but deters weakly the imports of non-differentiated products (column 2). In contrast, non-technical measures deter trade across both types of products. Furthermore, differentiated products drive the finding of reallocation of trade—toward higher-income countries in the case of SPS measures and toward lower-income countries in the case of TBT measures (column 3). There is no such reallocation of trade for non-differentiated products (column 4).

In some cases, SPS and TBT measures are implemented in the context of a deep preferential trade agreement (PTA). In addition to reducing tariffs between participating countries, PTAs may contain provisions for harmonizing or mutually recognizing quality regulations across partner countries.<sup>32</sup> The information benefits and compliance costs of quality regulations may depend on the degree to which regulations are harmonized across countries due to PTAs.

To examine this possibility, we combine our data with information on deep PTAs and

> 32These provisions can involve mutual recognition, whereby country A implements distinct NTMs but recognizes compliance with the NTMs of country B, or equivalence, whereby country A implements NTMs that are legally identical to those of country B or the adoption by all preferential partners of international standards.

19
# **4.2 Simple Model Specification.**

A very simple model specification to analyze what emerges from these hypotheses tests is advanced as follows:

# (1)  Y = f(X, Z, c, C) + ε

Y = city performance as measured by a vector of qualitative service variables which include access to water supply, sewerage, electricity, and telephones from the UN dataset, as well as the overall quality of infrastructure services and electricity supply, access to telephones and/or cell phones and access to internet in schools, using the EOS database; X = measure of globalization at the city level, which we specify using the Taylor dataset as the total number of offices of major international advertising, accounting, and financial firms in a city; Z = vector of governance indicators, which includes control of corruption, bribery in utility, and state capture, largely drawn from the EOS database; c = other city characteristics, such as city size, whether it is the capital city, and/or a port, as well as availability of websites and availability of information on city budget and business regulations in such websites; C=country characteristics, such as income level, size, extent of urbanization, and degree of globalization at the country level, and, ε = error term.

# **4.3 Data description and Mapping of Variables**

The KLM database, which integrates existing data with newly collected data and indicators, covering a total of 412 cities in 134 countries, was constructed by drawing from a variety of sources. Each variable we utilize in this paper is listed in Table 1 below. From the UN Observatory (1998), we downloaded 4 service access/performance variables, covering about 80 cities in 60 countries. From the enterprise-specific EOS survey database (2003), we construct city-specific averages for 12 governance indicators, as well as 5 service access/performance variables, covering 271 cities in 101 countries (see Table 1 below for details). For instance, two governance-related variables for which we constructed city-wide averages, were frequency of bribery in utility, and the extent of state capture (from firms’ reports on the distortionary impact on competition of illegal payments made by certain firms) to influence government policies, laws or regulations. From the Taylor database, we downloaded the number of offices of major advertising, accounting and financial firms in each city and we constructed a “global city” variable by adding up the three variables, covering a total of 261 cities in 114 countries.

14
**Table 11: Including Ethno-linguistic Fragmentation, with GOVFRAC**

||(1)|(2)|(3)|
|---|---|---|---|
||Total<br>Expenditure|Total<br>Tax<br>Revenues|Deficit|
|GOVFRAC|-0.069<br>(0.033)**|-0.039<br>(0.02)*|-0.016<br>(0.012)|
|Ethno-linguistic|-0.075|-0.048|0.046|
|Fragmentation|(0.062)|(0.037)|(0.023)**|
|Turnout|0.045<br>(0.041)|0.036<br>(0.032)|0.019<br>(0.017)|
|Majoritarian System|-0.003|-0.012|-0.006|
|(=1, 0 if Proportional)|(0.017)|(0.012)|(0.006)|
|Presidential System|-0.035|-0.031|-0.004|
|(=1, 0 if Parliamentary)|(0.018)*|(0.014)**|(0.008)|
|Regular|-0.016|-0.001|-0.011|
|Elections<br>(=1, 0 otherwise)|(0.013)|(0.01)|(0.007)|
|Observations|174|174|191|
|R-squared|0.51|0.64|0.24|

Robust standard errors in parentheses, clustered by country.

* significant at 10%; ** significant at 5%; *** significant at 1%. Controls as in Table 1. Countries included in the sample as listed under Table 10.

37
# **1. Introduction**

Subjective welfare measures, such as satisfaction with particular welfare dimensions, have become increasingly prevalent in the past decade.<sup>1</sup> Importantly, subjective welfare metrics have advantages over more traditional measures of welfare such as monetary poverty (e.g., Ravallion 2013), and sharp changes in peoples’ subjective perceptions of their welfare have been demonstrated to be particularly important to many instances of political change that have occurred across the world (e.g., Ianchovichina 2017; etc.). Furthermore, given their simplicity compared to more traditional consumption and employment-based metrics of deprivations, subjective metrics are often used to track changes in consumption and employment during times of crises.<sup>2</sup> Despite the importance of subjective welfare metrics and the greater ease in fielding subjective welfare modules during crises, there are well-known problems with using such measures. Most importantly, answers to subjective welfare questions depend on respondent-specific scales that are potentially not comparable across individuals, are potentially subject to frame-of-reference effects, and suffer from potential measurement error in ways that could differ from more traditional welfare metrics (e.g., Ravallion 2013).<sup>3</sup> However, even with these potential sources of biases that make the levels of subjective welfare metrics difficult to interpret, one common applied microeconomic technique to help better interpret subjective welfare metrics is to focus on sharp changes in response to arguably exogenous shocks.

This paper focuses on potential difficulties in interpreting changes in subjective mea-

> 1For example, a number of Sustainable Development Goals can be tracked using subjective welfare metrics.

> 2For example, traditional consumption modules typically capture the quantity and value of consumption of potentially hundreds of food and non-food items over reference periods varying from the past week to the past year. Such expansive modules are unable to be implemented via mobile phone. However, the Food Insecurity Experience Scale (FIES), which is a subjective measure of food access, is composed of an 8-question module that is amenable to phone surveys (e.g., Ballard 2013).

> 3A separate literature investigates the degree to which food security indicators, including subjective food security indicators, overlap with each other. There is growing evidence of a lack of alignment between subjective measures of food security and more traditional measures, such as undernourishment, obtained from the same respondents. However, there is also a lack of alignment between some traditional measures and other non-subjective food security indicators (e.g., Headey and Ecker 2013; Maxwell et al. 2014; Broussard and Tandon 2016; etc.). Similarly, Beegle et al. (2012) report subjective welfare assessments by expenditure per capita, and demonstrate that the two measures do not perfectly align. However, these differences could be driven by imperfections in either the subjective welfare question or in the measurement of expenditure.

2
Policy Research Working Paper 9841

# **Abstract**

An accurate understanding of how input use affects agricultural productivity in smallholder farming systems is key to designing policies that can improve productivity, food security, and living standards in rural areas. Studies examining the relationships between agricultural productivity and inputs typically rely on land productivity measures, such as crop yields, that are informed by self-reported survey data on crop production. This paper leverages unique survey data from Mali to demonstrate that self-reported crop yields, vis-à-vis (objective) crop cut yields, are subject to non-classical measurement error that in turn biases the estimates of returns to inputs, including land, labor, fertilizer, and seeds. The analysis validates an alternative approach to estimate the relationship between crop yields and agricultural inputs using large-scale surveys, namely a within-survey imputation exercise that derives predicted, otherwise unobserved, objective crop yields that stem from a machine learning model that is estimated with a random subsample of plots for which crop cutting and self-reported yields are both available. Using data from a methodological survey experiment and a nationally representative survey conducted in Mali, the analysis demonstrates that it is possible to obtain predicted objective sorghum yields with attenuated non-classical measurement error, resulting in a less biased assessment of the relationship between yields and agricultural inputs. The discussion expands on the implications of the findings for (i) future research on agricultural intensification, and (ii) the design of future surveys in which objective data collection could be limited to a subsample to save costs, with the intention to apply the suggested machine learning approach.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at tkilic@worldbank.org or iyacouboudjima@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
in terms of market competition, gender equality, upward mobility, and trust. As in OECD (2011), however, they base the analysis on self-perceived status.

Fischer and Torgler (2007) use data for 26 countries from the 1998 International Social Survey Programme to examine the association between relative income position and several measures of social capital, such as generalized trust, confidence in institutions, compliance with social norms, and civic engagement. Relative position is measured as the difference between an individual’s income and the reference group income at the aggregate level (national or regional). They find that relative income matters for most measures of social capital, such that social capital rises with an improved relative income position and declines with a disadvantage in the relative income position. The strongest effect is observable for happiness, compliance with social norms, and generalized trust.

Cárdenas, Kharas and Henao (2011) are among the few studies looking at how absolute income levels relate to values. They define the middle class as people with per capita daily income falling between $10 and $100 in PPP terms, and analyze the middle class’ attitudes and values using the World Values Survey data for Peru. Contrary to previous findings, the authors suggest that the middle class in that country sees taxes as a redistributive transfer from them to the poor and are not inclined to support them. Furthermore, the middle class was found to have a high preference for free markets and support for private industry and competition. Their results also show a relative decline in the support for state-owned enterprises and the government as the owner of business, and a primacy of growth policies, which suggests that the middle class may be optimistic about their own potential but skeptical of the role of the state. The income variable is however characterized by strong measurement errors as it is based on large income bands. Moreover, only 1 percent of the Peruvian population earns above U$100 a day, which questions the representativeness of the population sample they use to estimate the values of the upper class.

Tóth and Keller (2011) analyze how the redistributive preferences of middle classes relate to actual income and to its distribution, in contexts with different levels of income inequality. Using principal component analysis, they construct a composite index of redistribution preference based on the desirability of redistribution and state involvement in providing jobs, education and social expenditures. The analysis suggests that redistributive preferences of those in the middle are higher if they live in a society where many people feel poor and only a few feel rich. In a related paper, in line with individuals’ attitudes towards redistribution, Reed-Arthurs and Sherin (2010) employ data on taxation and fairness to find that Americans’ demand for redistribution to the poor is influenced 5
21 shares of NTRI2, which includes NTRI as well as household services and personal services (using I2D2’s classification). Finally, the results indicate significantly higher self/unpaid employment in DEINDU countries compared to MFGSERV countries. While I2D2 allows us to consider more countries, the database consists of surveys, not censuses, and classical measurement error in the dependent variable should affect precision.

Notwithstanding, relying on various measures, specifications, and databases, the evidence presented here suggests that cities in NRXGDP and DEINDU countries may be disproportionately consumption cities relative to the cities in MFGSERV countries. **5. Mechanisms: Urban Construction and Consumption Cities** We now explore some of the mechanisms behind the rise of consumption cities. In particular, cross-country differences in urban construction may stem from “white elephant” projects financed by resource rents. In the absence of a global historical data base on “white elephants”, we examine whether exports of natural resources correlate with the over-construction of tall whose economic rationale be very buildings, might questionable as discussed below. As shown by Ahlfeldt and Barr (2020), construction costs increase with tall are also more as non-linearly height. Very buildings inefficient they require more occupiable square feet to be dedicated to essential building functions (e.g., due to elevators).

We use a novel data set that inventories all the world’s “tall buildings”, with information on their of construction and The database is maintained year height.<sup>26</sup> by the _Council on Tall Buildings and Urban Habitat_ (CTBUH) and is publicly available online.<sup>27</sup> The database mostly captures buildings above 80 meters (Jedwab et al., 2020b). Since some countries have no such buildings, to avoid having their stock of heights equal to 0 when using logs, we consider for each country buildings above 80 meters as well as their 10 tallest buildings, even if some of them are below 80 meters. In the end, we use 16,369 tall buildings.<sup>28</sup> We use as our dependent variable log _urban height density_ , which is the

> 26To our knowledge, the only other econometric study on white elephant projects and tall building construction is a non-economics study on autocracies and skyscrapers by Gjerlow and Knutsen (2019). The study finds that autocratic regimes are more likely to build super-tall and possibly “vanitous” buildings.

> 27The full online database can be found here: http://www.skyscrapercenter.com/. As one example, the webpage for the Burj Khalifa in Dubai is: http://www.skyscrapercenter.com/building/burj-khalifa. According to their website, the data have been “collected by the Council for more than 40 years [...] The Council relies on its extensive member network [of academics, land developers, architectural firms, builders, city administrations, and banks] to maintain” the database with the help of “an Editorial Board”.

> 28As discussed in Jedwab et al. (2020b, 2021c), measurement error is more likely for smaller buildings