rafmacalaba/datause-encoder-data huggingface dataset:
This exercise has two components. The first step is to predict what Bangalore would look like today had it developed in the absence of the FAR restriction. In this counterfactual exercise, particular interest lies in the reduction of Bangalore's spatial size, and the consequent reduction in _~~x~~_ ~~.~~ The second step is to use the implied _~~x~~_ reduction along with a measure of commuting cost per mile to find the commuting-cost savings for the edge resident. These savings measure the welfare gain from the absence of the FAR restriction, and hence the welfare cost of its presence. These two components are discussed sequentially in the next two sections.

# _3.1. Computing the change in Bangalore's spatial size_

Reversing the numerical exercise discussed in section 2, removal of the FAR restriction generates three important impacts on land-use: FAR values rise at central locations, they fall at locations where the restriction is not binding, and dwelling sizes rise throughout the city. In order to predict the change in Bangalore's spatial size, the magnitudes of these three effects must be quantified. Unfortunately, this task involves an element of guesswork, although the numerical results from section 2.3 provide some guidance.

Before delving into the details of this task, it is useful to discuss the available land-use data for Bangalore, which pertain to 1990 (the appendix explains the origins of the data). The city is divided into rings, with each comprising a kilometer's worth of radius. Data for each ring include its total residential population, the average FAR value for its structures, the permitted FAR value, and the ring's built-up land area. In 1990, Bangalore's population extended out to the twelfth ring, whose outer radius is 12 km. The city's total 1990 population was nearly 3.4 million.

Figure 6 shows FAR values for the 12 rings, with the horizontal scale starting at ring 1. The dotted curve shows the permitted FAR value, which is roughly constant across rings, and the solid dark curve shows the prevailing value in each ring, which is estimated using a method explained in 15
fdt = bo+ pfd,, +b1lr, + b2lrt, + c11re, + c21re,1 + d1yt + d2yt1 + yrd, + Pci, + vda, + gt (2) The variables and their data sources are described in Appendix 1.

# **(b) Specification of the Relationship Between Land Registration and Economic Growth**

We specify the land registration/economic growth relationship on the strength of the conceptual framework in which land registration is linked to economic growth through investment and efficiency of resource use. The specification of the economic growth function follows the endogenous growth theory and the related empirical work (Crafts, 1996). It is the same approach used by Torstensson (1994) in his cross-sectional study of the effects of property rights on economic growth (Section I above). In looking for control variables, we face a dilemma in that as many as 50 variables have been used in different cross-sectional economic growth studies, and many of these variables do not hold when subjected to the type of rigorous analysis that Levine and Renelt (1992) undertook. In our study, we will build on three explanatory variables that they found to be robust, namely, investment rate, growth in investment in human capital and population growth. As we are dealing with a single country case study using time series data (as opposed to the bulk of the endogenous growth studies that are cross-sectional), some of the key variables used in the endogenous growth empirical literature have been left out, namely, the initial level of real GDP per capita and the initial human investment stock. Long run economic policy variables such as inflation, real exchange rate, and government expenditures were left out also because they were stable for Thailand during the period covered by the study since macroeconomic stability was the corner stone of economic growth policy (Christensen et al., 1993).

The specification of the economic growth equation is presented in equation 3 as a long-run or equilibrium relationship.

Yt = Po + P LRT, 1 + P2LRE, + P 3A+ N34SCt + 035Tt + 6Pt + WD, + N3MI (3) Where:

- Y - Per capita real GDP

LRT - Land titling

- LRE - Land registration public expenditures

- I - Ratio of investment to GDP

- SC - Investment in human capital (Secondary School Enrolment)

- T - Ratio of Trade to GDP

- P - Population

- FD - Financial development

- M - Ratio of manufacturing output to GDP

6
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**<u>Figure 14: Regulatory and Administrative Opacity</u>**

<!-- Start of picture text --> a. Licenses and permit system b. Communication and simplification of rules<br>4.5 1.6<br>4.0 1.4<br>4.0 1.4<br>3.5<br>1.2<br>3.0 2.7<br>1.0<br>2.5 2.2<br>2.0 2.0 2.0 2.0 2.0 2.0 0.8 0.8<br>1.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5<br>1.0 0.4 0.3 0.3<br>0.5 0.0 0.0 0.0 0.2 0.1<br>0.0<br>Hungary Romania Slovak Republic Albania 2007 Bulgaria Croatia 2007 EU15 Poland OECD MICs Czech Rep. 0.0 Romania Bulgaria Albania 2007 MICs Czech Rep. Hungary Croatia 2007 OECD EU15 Poland Slovak Republic<br><!-- End of picture text --> _Source: see Figure 3. Note: Other MICs are Brazil, Mexico, Turkey. Values refer to 2007 for Croatia and Albania, 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see Annex I._

# **_4.1.3 Regulatory burdens on business entry_**

**Firm entry and exit are two critical parts of the Schumpeterian creative destruction process which is a major prerequisite for a dynamic economy.** High levels of firm turnover (the sum of firms’ entry and exit rates) are associated with higher productivity reflecting the fact that new firms with at least average productivity put competitive pressure on incumbents, leading to more exits but also to increased productivity amongst those that remain<sup>6</sup> . Thus, the replacement of less productive firms by new entrants and the mobility of market share from less productive to more productive firms exert a significant upward push on overall industrial performance. Thus easy entry and quick exit of companies allows for competition to flourish and for an efficient allocation of resources.

**The PMR indicators show that administrative burdens on start-ups are still an issue for sole proprietor firms, especially for the number of mandatory procedures** entrepreneurs have to comply with (16 in Croatia versus 7 on average in the EU15 in 2003). Number of procedures is also an issue to start a corporation (21 in Croatia versus less than 15 on average in the EU15 in 2003). Requirement for specific service sectors -such as road transport and retail distribution, are, on the other hand, less cumbersome than both the EU15 and OECD averages. These sector specific administrative burdens assess the restrictiveness of licensing, registration and notification requirements, as well as technical, health and safety standards.

> 6 Scarpetta, S. _et al._ (2002), "The Role of Policy and Institutions for Productivity and Firm Dynamics: Evidence from Micro and Industry Data", _OECD Economics Department Working Papers_ , No. 329, OECD Publishing.doi:10.1787/547061627526

15
of a country, and _Xi_ is a set of individual characteristics elements of which depend on the propose of the analysis. Following Bjorklund et al. (2010), we include a gender dummy, and, following Bingley and Cappellari (2019), we include cohort dummies, but no other controls are included in _Xi_ .<sup>21</sup> _af_ is the family and neighborhood component shared by all siblings in family _f_ , and _bif_ is the individual specic component for sibling _i_ capturing _i_ 's deviation from the common family and country components. We dene demeaned years of schooling _Sif_ as follows: The focus of the analysis is on the importance of the family and neighborhood component (the family component, for short) _af_ in explaining the variance in demeaned years of schooling _Sif_ .<sup>22</sup> The country mean _μ_ represents the growth and structural change in a country that inuence all children the same way irrespective of their family background. The cohort dummies take out the cohort specic eects shared by the children of a cohort, but may vary across dierent cohorts. The inclusion of the country and cohort specic intercepts in the vector _Xi_ implies that the measure of mobility based on sibling correlation in demeaned schooling refers to relative rather than absolute mobility. Assuming that _af_ is independent of _bif_ , the variance of _Sif_ can be expressed as the sum of variances of the family and individual components as: The sibling correlation in education (denoted by _ρs_ ) then can be expressed as: Sibling correlation thus estimates the share of variance of children's education that can be attributed to common family and neighborhood background.

Sibling correlation is a measure of mobility (more precisely immobility) because the fam-

> 21Some studies on intergenerational mobility in education include age and age squared following the literature on intergenerational mobility in income. However, age and age squared are used in studies on income to mop up life-cycle eects. For education, such life-cycle eects are not relevant.

> 22In the variance components analysis, _af_ is usually called the family component as it represents the family xed eect.

10
In addition, we also constructed indicators through internet searches, for particular city attributes, such as city population, existence of city websites and availability of data on budget and business regulations in such websites. We completed this list of city attributes by including two dummies for whether the city is the country’s capital and/or a port.

At the country level, we draw from the variable “Control of Corruption” from the Worldwide Aggregate Governance Indicators discussed above, where corruption (conventionally defined as the exercise of public power for private gain) is one of its components. The indicator is the aggregate of many individual sources, covering several aspects of corruption, ranging from administrative corruption to “grand corruption” in the political arena and “state capture”. We also constructed an income per capita variable (PPP) by drawing from the Heston-Summers database and the CIA World Factbook (2001). For globalization at the country level, we draw from the A.T. Kearney/Foreign Policy Globalization Index. It tracks and assesses changes in four key components of global integration, incorporating such measures as trade and financial flows, movement of people across borders, international telephone traffic, Internet usage, and participation in international treaties and peacekeeping operations. It covers 62 countries, including industrialized and emerging economies.<sup>14</sup> In Table 1 below we present the legend of all the variables we utilize in the empirical testing of these hypotheses, and their sources and characteristics.

> 14 Specifically, in this index, economic integration combines data on trade, foreign direct investment (FDI), and portfolio capital flows, as well as investment income payments and receipts. Personal contact tracks international travel and tourism, international telephone traffic, and cross-border remittances and personal transfers (including worker remittances, compensation to employees, and other person-to-person and nongovernmental transfers). Technological connectivity counts the number of Internet users, Internet hosts, and secure servers through which encrypted transactions are carried out. Finally, political engagement tracks each country's memberships in international organizations, personnel and financial contributions to U.N. Security Council missions, ratification of selected multilateral international treaties, and the amount of governmental transfer payments and receipts.

15
# **Valuing Global Public Goods: A European Delphi Stated Preference Survey of Population Willingness to Pay for Amazon Rainforest Preservation**

Ståle Navrud and Jon Strand<sup>1</sup> Key words: Delphi expert survey; stated preferences; contingent valuation; global public goods; Amazon rainforest.

JEL classification: Q23; Q51; Q57.

Sector board: Environment 1 Navrud: School of Economics and Business, Norwegian University of Life Scienes, 1432 Ås, Norway; E-mail: stale.navrud@umb.no. Strand: Development Research Group, Environment and Energy Team, the World Bank, Washington D.C. 20433, USA; E-mail: jstrand1@worldbank.org. We thank Ariel Ortiz-Bobea, Michael Toman and Jeff Vincent, and participants at a World Bank seminar, for helpful comments. This research has been supported by a grant from the Bank’s Knowledge for Change Program. The conclusions and viewpoints in this paper are those of the authors and not necessarily the World Bank, including its management, directors and staff.
Table 3: Pooling factors from the Bayesian hierarchical model

||(1)<br>Fertilizer|(2)<br>Cultivated<br>area|(3)<br>Seeds|(4)<br>Pesticides|(5)<br>Crop risk<br>index|
|---|---|---|---|---|---|
|**Pooling factors**||||||
|Conventional pooling factor|0.38|0.38|0.40|0.69|0.76|
||[0.09, 0.87]|[0.06, 0.95]|[0.08, 0.86]|[0.19, 1.00]|[0.26, 1.00]|
|Generalized pooling factor|0.41|0.41|0.44|0.72|0.79|
|Brute force|0.47|0.45|0.51|0.81|0.90|

Notes: Pooling factors are averaged across studies and belong to the interval [0,1], with 0 indicating no pooling and 1 indicating full pooling. 95% posterior intervals are shown in brackets. Computational details are provided in Appendix B.3. Source: Authors’ elaboration using data from Elabed and Carter (2014); Karlan et al. (2014); Cole et al. (2017); Bulte et al. (2019); Hill et al. (2019); Stoeffler et al. (2022).

35
Finally, labor market conditions comprise: (i) the state's female and male unemployment rates (as an indicator of labor demand); (ii) state’s average real hourly earnings of women and men in nonwhite-collar services and industrial production sectors, respectively<sup>8</sup> ; and (iii) state’s share of parttime employees to total employment (to measure access to jobs with flexible conditions). We estimated labor market condition variables using data from the corresponding previous year, that is 2006 or 2016, to control for potential endogeneity. Standard errors are clustered by sampling design substratum,<sup>9</sup> acknowledging a possible correlation between residuals and households’ characteristics within the same substratum.

# **V. Results**

Table 1 summarizes statistics for variables we used in the analysis and sets the stage to understand how the Mexican context changed after one decade. Looking at individual characteristics, we observe that female employment increased between 2007 and 2017. Similarly, substantial improvement in overall education occurred throughout this period: the share of illiterate women decreased 13 percentage points, while higher educational attainment levels increased. Also, the percentage of women being household heads grew slightly from 17 to 19 percent.

Male household heads' educational attainment increased, as well. Household real income decreased, while the share of households with at least one male salaried employee slightly increased during these 10 years. Household dependency rates for children 0-to-4 and 5-to-14 decreased almost 4 and 5 percentage points, respectively; while elderly dependency rates slightly

> 8 The non-white-collar services sectors are traditionally associated with female employment, while the industrial production sector is associated with male employment.

> 9 Substratum is the most detailed ENOE grouping of households in its sample design. Access to public services, the presence of certain goods, and population characteristics, such as education and income, are some variables considered for substratum grouping.

23