rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7526

# **Abstract**

This paper estimates slum residents’ willingness to pay for formalized land tenure in Pune, India. In so doing, it offers evidence that the legal assurance of slum residents’ occupancy of their lands could benefit them. Previous studies have discussed legal and non-legal factors that substantially influence the tenure security of residents in informal settlements. However, it remains unclear to what extent, and how, the assignment of legal property rights through the formalization of land tenure improves the tenure security of residents in informal settlements and living conditions, even in the presence of other legal and non-legal factors that also contribute to their tenure security. To address the question, this study focuses on the city of Pune, India, where government agencies have formalized slums by legally ensuring the occupancy of the residents under “slum declaration.” Applying a hedonic price model to an original household survey, this paper investigates how slum residents evaluate formalized land tenure. A spatial econometrics method is also applied to account for spatially autocorrelated unobserved errors. The spatial hedonic analysis finds that the premium of slum declaration is worth 19 percent of the average housing rent in slums. The associated marginal willingness to pay is equivalent to 6 percent of the average household expenditure, although it is heterogeneous depending on a household’s caste and other legal conditions. This finding suggests that the assurance of occupancy rights is a vital component of land-tenure formalization policy even if it does not directly provide full property rights.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The author may be contacted at snakamura2@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
year over GDP, which may as well induce policy reforms. We also add a time trend to control for any tendency of the _Doing Business_ ratings to increase over time. _ηi_ is a set of country fixed effects, and _εi,t_ the error term. A dummy variable, which takes the value one when there is an IMF program in place, is also included in the regression, either separately (in our baseline specification) or interacted with the DPL variable.<sup>7</sup> We estimate the coefficients of this model by employing OLS on a comprehensive country-year panel of aid recipient countries that runs from 2004 to 2014. Standard errors are adjusted for country clustering of observations.

# **3.1 of the cumulative number of DPLs**

We first estimate a linear specification. To assess if there are diminishing returns to additional loans we add a quadratic term, and also estimate a logarithmic model.<sup>8</sup> In Table 2 we present the results obtained for all three functional forms (linear, quadratic and logarithmic). We find strong evidence that Development Policy Financing enhances the business environment. In fact, the coefficient on the cumulative number of DPLs is positive and highly significant. The quadratic and the logarithmic specifications suggest that there are decreasing returns to additional loans. Therefore, in the remainder of our analysis we will focus on the logarithmic specification. Not surprisingly, the _Doing Business_ score is positively correlated with the log GDP per capita, and is improving over time. It appears lower in the presence of an IMF assistance program (which may be explained by the fact that critical macroeconomic conditions may weigh on the business environment), even though the relationship is not statistically significant.

How large is the impact of DPLs on _Doing Business_ ? It is instructive to compute the elasticity

> 7 The IMF assessment in program reviews provides a key input into the World Bank team’s assessment, but the Bank team should make its own assessment (OP 8.60, footnote 4). In cases where there are disagreements over technical or policy issues, Bank and IMF teams are expected to discuss and reach agreement over any differences of opinion and coordinate their policy dialogue. This is facilitated by regular consultations between the Bank and IMF teams and an explicit understanding on the thematic division of labor on macroeconomic and structural issues in each country. However, when there is no IMF program in place, the Bank team’s independent assessment is central. In such cases, the IMF may be asked for a letter of comfort providing its own view of the macroeconomic policies, but such letters are typically much shorter and less detailed than IMF program reviews (IMF 2003). Hence the Bank team’s assessment remains critical.

> 8To retain the zero observations when making the log transformation, we add 1 to the number of cumulative DPLs.

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# **Methodological Appendix**

This appendix describes the calculation of the number of Ebola orphans in detail. It follows the eight steps outlined in the main body of the paper:

1. Divide Ebola deaths by age and gender

2. Estimate deaths among women aged 15-49 3. Calculate surviving children 4. Subtract children over age 15 5. Repeat process for men aged 15-59 6. Adjust for dual parent mortality 7. Adjust for child mortality from EVD 8. Resulting estimate of the total number of orphans

## **1. Divide Ebola deaths by age and gender**

Fatality rates are not uniform across gender and age; however, the most recent country-level mortality estimates from the WHO are not available by gender and age.<sup>**11**</sup> Thus to calculate country-level EVD deaths for these sub-populations we apply the country-level distribution of cases by gender and age to the most recent country-level mortality estimates, and then adjust for the gender- and age-specific mortality/infection differential using the three-country distributional data reported in October 2014.<sup>**2**</sup> The country-level EVD deaths are distributed by age and gender according to case proportions, adjusted by the mortality/infection differential across the sub-groups recorded in Table A1.

**Table A1: Gender and age distribution of EVD cases**

||Guinea|Country<br>Liberia|Sierra Leone|Mortality / Infection<br>Differential across Sub-<br>Groups|
|---|---|---|---|---|
|Male|48%|50%|48%|1.01|
|Female|52%|50%|52%|0.99|
||0%|0%|0%||
|0-14|16%|19%|21%|1.03|
|15-44|57%|59%|56%|0.93|
|45+|27%|23%|22%|1.15|

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dependent variable in the estimation also. Thus for the purposes of estimation, the labor variable is discrete; it follows a latent variable-time allocated to labor activities _(m,l_ * )-in the following manmer: mg = _1_ if _mg_ *> O mg =Oifm _*g0_ There are two problems with the time children allocate to schooling. First, it is only available in the 1994 data and not in 1997. This restricts me from constructing a panel. Second, a vast majority of those in school in 1994 report spending 25 hours per week in that activity. The peaks in the data make it more feasible for an analysis of the _decision_ to attend school full-time, using a probit analysis. Thus those who allocate less than 25 hours per week are taken as not in school full-time and those who do are. For the purposes of estimation, the schooling variable is a discrete variable which follows the latent schooling variable _(s, *s)_ in the following form: Since an estimation of the schooling equation would be limited to the 1994 cross-section data, I also estimate a separate set of boy-girl regressions of educational attainment using the panel properties of the data. Following Patrinos and Psacharopoulos (1997), educational attainment is controlled for age by defining a grade-for-age dependent variable: Patrinos and Psacharopoulos (1997) note that since the age of school entry in Peru is about 6, all dispersions in age should be measured from 6. A Grade-for-age measure of 100 indicates complete educational attainment (i.e. no falling behind), and one of zero indicates none (i.e. complete failing behind).

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OLS estimates, column (2) shows fixed effect estimates using industry-level fixed dummies, and column (3) show the results using dirty and clean fixed effects.

**Table 3: Composition of Exports**

|**Dependent variable: Ln**<br>**(Exports as a fraction of**<br>**output)**|**OLS**<br>**(1)**|**FE**<br>**(industry-**<br>**level)**<br>**(2)**|**FE**<br>**(dirty/clean)**<br>**(3)**|
|---|---|---|---|
|Air Pollution Intensity|-0.391***|0|-0.388***|
||[2.83]|[.]|[2.79]|
|Water Pollution Intensity|0.08|0|0.077|
||[1.23]|[.]|[1.18]|
|Toxic Pollution Intensity|0.314**|0.629*|0.354**|
||[2.09]|[1.78]|[1.99]|
|Labor Intensity|10.221***|3.522**|9.951***|
||[3.26]|[2.28]|[3.10]|
|Capital Intensity|0.261|-0.426|0.382|
||[0.47]|[0.35]|[0.60]|
|Constant|-2.889***|3.296|-3.277**|
||[3.25]|[0.95]|[2.54]|
|Observations|132|132|132|
|R-squared|0.21|0.34|0.21|
|Number of VSIC||22||
|Number of Dirty_dum|||2|

Absolute value of t statistics in brackets; significant at 10%; ** significant at 5%; *** significant at 1% The results presented in Table 3 corroborate the descriptive analysis in Figure 4. We find robust evidence that toxic pollution intensive exports increased at a greater rate relative to less pollution intensive exports. The coefficient on toxic pollution intensive exports are significant with a positive sign. On the other hand, OLS results in column (1)and fixed effects results in column (3) show that there was a negative and significant change in air pollution intensive exports during this period. Air pollution intensive exports declined by nearly 40 percent during the period of analysis. Not surprisingly, we find that labor intensive exports have shown a positive and significant increase during this period.

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so we consider analysis of this data as exploratory. In particular, 26% (22%) of the treatment (control) schools missed the baseline questionnaire, 13% (11%) the final paper-based questionnaire, and 59% (63%) the short-term online follow-up. However, the key results are robust to controlling for baseline responses and to dropping T3 schools, which had higher response rates.<sup>7</sup> Finally, the attrition rate is not correlated with the baseline responses, nor with student dropout rates at baseline or follow-up.

## **Qualitative analysis**

We also performed a series of focus groups (12 in total) to gather qualitative information on the challenges of program implementation. We interviewed almost 100 people involved in the program, including 63 teachers and principals from across treatment arms and 34 trainers in 4 different locations.<sup>8</sup> Short paper-based questionnaires were also completed during these focus group sessions.

# Descriptive statistics and balancing tests

Table 2 reports the average baseline characteristics for the students and their schools according to the administrative data available.<sup>9</sup> The table reports the averages for the control arm (column 1), the pooled treatment arms (column 2), and each treatment arm separately (columns 3-5). The administrative data includes detailed academic grades by subject (math, language, natural sciences, etc.), age, gender, school dropout rates, the average dropout probability for the cohort, and regional variables.

Overall treatment and control groups are relatively balanced. Most of the differences in the variables considered are statistically insignificant. The chi squared test after running seemingly unrelated regressions of each of these baseline variables on treatment assignment shows that the

> 7 These schools had higher response rates in online questionnaires likely because the reminders made schools more prone to see the survey link; furthermore, they explain all the difference on the response rates between treatment and control schools in the online survey used.

> 8 The teachers and principals come from various departments, including Quetzaltenango, Quiché, Totonicapán, Chimaltenango, Sololá, Sacatepéquez, Jalapa, and Jutiapa.

> 9 We did not include in this analysis the complementary online data given that the take-up was roughly 70%.

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Activities (Programa de Incentivo á Redução do Setor Público Estadual na Atividade Bancária-PROES). PROES created mechanisms by which state governments could repair or dispose of their banks with federal resources, and has served as the basis for most of the state bank reforms to date.<sup>12</sup> Intervention of the Central Bank in state banks in the first phase under RAET differed from simple intervention and extra-judicial liquidation in that the normal activities of the state banks continued unaltered. During the period that RAET is in effect (which is flexible and at the discretion of the Central Bank), depositors are protected. At the end of 1994 (on the eve of newly elected governors from the President-elect’s own party (PSDB) taking office in the states of Rio de Janeiro and São Paulo), the Central Bank intervened using RAET in both BANERJ and BANESPA, two of the largest and most indebted banks.<sup>13</sup> Public authorities were careful to stress that intervention was being conducted in accordance with RAET, and promised the preservation of the accounts of depositors. Pedro Malan, president of the Central Bank, asserted that “anyone who wants to go to the banks on Monday to take out their money will encounter plenty of cash at their disposal.”<sup>14</sup> All told, five state banks were placed under RAET through 1995.<sup>15</sup> Some other state financial entities were liquidated outright; at the same time Central Bank intervened in BANERJ and BANESPA, it also closed a relatively small bank in the state of Rio Grande do Norte.<sup>16</sup>

> 12 PROES followed on the heels of a similar program to restructure or close troubled private-sector banks, known as PROER.  Some of the state banks that underwent early intervention were actually privatized under PROER, rather than PROES.

> 13 <u>Folha de São Paulo, 31 Dec 1994.</u>

> 14 Ibid., 5.

> 15 Maia (1999), 111; in addition to BANERJ and BANESPA, state banks in Alagôas, Mato Grosso, and Rondônia were placed under RAET; Ness (2000), 77.

> 16Folha de São Paulo, 31 Dec 1994, 1-2. Later the state bank of Amapá was liquidated by the Central Bank, without recourse to the Regime of Special Temporary Administration.  Though the bank’s liabilities were only modestly in excess of its assets, it had fraudulently issued nearly six billion dollars’ worth of bonds that were

8
_CF= PY(1-u(l-t)-g)-C(Y)(1-u(l-t))-WL(1-u)-Q(l-f)I+oAu(l-t)_ where the accounting capital stock is defined as in (6) and investment is<sup>related to</sup> the real capital stock as in (5).

The firm maximizes the present value of its cash flow discounted by the nominal cost of funds r '- 7r dcbfined by (10) and subject to the following two resource constraints: The nirst states that the total resource extracted cannot exceed the total developed, while the second states that the total resource developed cannot exceed the total found. (In a more general version of this problem, this constraint would have to hold at each point in time.) The solution to this problem yields the following marginal conditions to be satisfied: The first of these is simply the pre-tax marginal product of capital. To convert it to _rg_ simply subtract 6 - _Aqlq_ as before. The second equation is the social value of marginal product per unit of the current input L. An effective tax rate can be obtained directly by subtracting unity from it. The final equation is a form of Hotelling's rule. It gives the pre-tax rate of return to society from not extracting the resource. It can be converted to an effective tax wedge by subtracting rn. These can be used to calculate marginal effective tax rates for a given institutional setting. Notice that the corporate tax and the royalty system interact in each of the decisions of the firm - the current input decision. the depreciable capital input decision and the extraction decision.

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explaining output per worker, they do not directly address the question of what constitutes intangible capital.

In Section 4, we directly investigate the composition of intangible capital, by analyzing the relative contributions of human capital and institutional/social capital using regression analysis. As in World Bank (2006), potential correlation between the regressors and the error term (arising, for example, from measurement error or omitted variable bias) is an issue in the empirical estimation. The intangible capital residual includes, by construction, (i) any assets not accounted for in the tangible capital estimates, for example, some minerals (e.g. diamonds, platinum), fisheries and groundwater, not accounted for in the calculation of natural capital, and (ii) any errors in the estimation of tangible capital and/or of total wealth.

Unlike World Bank (2006), we have a panel dataset with observations for 115 countries for the years 1995, 2000 and 2005. The use of country and time fixed effects helps us to mitigate omitted variables bias as long as the unobserved variables are constant over time and/or across countries. In addition, we take a number of steps to reduce measurement error in the variables used in the analysis. Our indicator of intangible wealth accounts for net foreign financial assets; we use several indicators of human capital that are a function of health status in addition to years of schooling; we analyze the robustness of the results to alternative measures of institutional capital and to the exclusion of outliers. Finally, we estimate an instrumental variables regression in which differences in European settler mortality rates (from Acemoglu, Johnson and Robinson, 2001), distance from the equator, and the percentage of the population speaking a Western European language as their first language today (from Hall and Jones, 1999), are used as instruments for current institutions and human capital. Overall, our results show that human capital is an important factor, in both statistical and economic terms, in explaining intangible capital.

# **2. Data**

# _2.1 Intangible capital_

We compute intangible capital for over 100 countries for the years 1995, 2000 and 2005 by subtracting produced capital, natural capital and net foreign financial assets from total (comprehensive) wealth. Our estimates of comprehensive wealth and its "tangible" components are from World Bank (forthcoming).

Applying expression (1), World Bank (forthcoming) calculates comprehensive wealth as the present discounted value of future consumption.<sup>4</sup> To test whether these comprehensive wealth

> 4 The computation is performed for a time horizon of  25 years,  which roughly corresponds to a generation. Assuming that the elasticity of utility with respect to consumption is one and that consumption grows at a constant

4
**Table 9.B. Model comparison: Root mean squared error of tin price forecasts**

(U.S. dollars per metric tonne)

|||||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|---|---|
|**Approach**<br>|**Horizon**|**Consensus**|**Futures**|**correlations**|**BVAR**|**OEM**|**learning**|
|Consensus|3|2171|...|...|...|...|...|
|Futures|3|*|1425.1|...|...|...|...|
|Bivariate correlations|3|||1575.7|...|...|...|
|BVAR|3||*|*|2409.7|...|...|
|OEM|3|||||1959.1|...|
|Machine learning|3||||||1805.3|
|Consensus|6|2537.4|...|...|...|...|...|
|Futures|6|*|1280.3|...|...|...|...|
|Bivariate correlations|6|*||1722.6|...|...|...|
|BVAR|6||*|*|3241.5|...|...|
|OEM|6|*|||*|1166.9|...|
|Machine learning|6||*||*||1969.2|
|Consensus|9|3021.2|...|...|...|...|...|
|Futures|9|*|1217.4|...|...|...|...|
|Bivariate correlations|9||*|2376.8|...|...|...|
|BVAR|9|*|*|*|4907.3|...|...|
|OEM|9|*||*|*|1395.1|...|
|Machine learning|9||*||*|*|2887|
|Consensus|12|3456.8|...|...|...|...|...|
|Futures|12|*|1180.5|...|...|...|...|
|Bivariate correlations|12||*|3080.5|...|...|...|
|BVAR|12|*|*|*|5481.7|...|...|
|OEM|12|*|*||*|1989.4|...|
|Machine learning|12||*|||*|4114.3|
|Consensus|15|3742.7|...|...|...|...|...|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15|...|...|...|...|...|...|
|BVAR|15|*|...|...|6794.4||...|
|OEM|15||...|...|*|2672.3|...|
|Machine learning|15||...|...||*|5243.3|
|Consensus|18|3955.5|...|...|...|...|...|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations<br>|18<br>|...<br>|...|...|...<br>|...|...|
|BVAR|18|*|...|...|7065.7|...|...|
|OEM|18||...|...|*|3277|...|
|Machine learning|18|*|...|...||*|6103.1|
|Consensus|21|4077.3|...|...|...|...|...|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations|21|...|...|...|...|...|...|
|BVAR|21|*|...|...|6695.9|...|...|
|OEM|21|*|...|...|*|2368.6|...|
|Machine learning|21|*|...|...||*|6639.7|
|Consensus|24|4188.2|...|...|...|...|...|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24|...|...|...|...|...|...|
|BVAR|24|*|...|...|6632.2|...|...|
|OEM|24||...|...||4022.5|...|
|Machine learning|24|*|...|...||*|6879.6|

_Source_ : World Bank.

_Note_ : The forecast period is 2015Q1-2022Q1. Forecasts for OEM are only available at the semi-annual frequency; forecasts for all other approaches are available at the quarterly frequency. Diagonal entries indicate average RMSE estimates for the respective approach. * indicates significant difference between the forecasts of any pair of approaches, at the 5 percent significance level according to an F-test (RMSE).

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