rafmacalaba/datause-encoder-data huggingface dataset:
## **Table 7: Alternative II – Valuation of Public Land**

## ***Please note that one crore rupees is equal to 10 million rupees, or about USD 180,000 at the rate of INR 55 per USD**

According to Alternative I (a) valuation of public land is Rs. 20,478 Crores at Jantri rates. At Market Rates – Alternative I (b) the valuation is Rs. 43,630 Crores. According to Alternative II (a) valuation of public land is Rs. 27,930 Crores at Jantri rates. At Market Rates – Alternative II (b) the valuation is Rs. 54,539 Crores.

# **3 Conclusions: Significance of Revenue Potential**

The financial resources that could be generated by monetizing public land range between Rs. 20,000 Crores and Rs. 54000 Crores (about 3.6 billion and 9.8 billion USD, respectively).<sup>18</sup> The population of AMC (the expanded jurisdiction as of 2006) is 5,570,585. This implies per capita availability of fiscal resources amounting to Rs. 36000 to Rs. 97000 (about USD 700 and 1800, respectively)<sup>19</sup> . The amounts involved may be compared to investment requirements for urban infrastructure—a well-recognized government investment priority. HPEC at 2009-10 prices had estimated the per capita investment cost of Rs. 43386 for the entire range of physical urban infrastructure for the next twenty years.<sup>20</sup> The significance of the value of potentially marketable public land, at 82 percent of this requirement in the low case, and more than double this requirement in the high case can readily be appreciated in this context.

AMC in 2005 had prepared a City Development Plan under Jawaharlal Nehru National Urban Renewal Mission (JnNURM). The capital investment needs projected in the plan for a seven-year period were about Rs.9000 crores. The minimum value of public land is over twice this estimate. Thus study thus clearly demonstrates that monetizing public land could significantly contribute to investments in infrastructure and other high priority government goals.

# **4 Conclusions: Further Applications**

Unlocking the value of public land has been recognized as a potentially highly significant source of finance for urban infrastructure. The present study is the

> 18 Since potentially marketable public lands amount to about 15 percent of total developable land, releasing all these lands on the market at the same time could depress equilibrium prices and thus lower these estimates. However such a rapid release of lands both unlikely to be feasible and is perfectly avoidable with proper management of these valuable investments.

> 19 The per capita measure of availability is conservative in that the population of the expanded AMC is considerably larger than the old AMC which is our study area. Due to the jurisdictional expansion prior to the most recent census in 2011, we do not have recent population data for the old AMC. The numbers are nonetheless very substantial when compared to GDP per capita at factor cost of about USD1200 in 2011-2012 using a USD exchange rate of 55.

> 20 HPEC Table 3.2 _op.cit._

29
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24
Figure B2. Topic content of the presidential speeches of Peru using country-specific LDA, 1940-2016 <!-- Start of picture text --> 100%<br>75%<br>Social development<br>50%<br>Infrastructure<br>Leftist<br>revolution<br>Public services<br>25%<br>0%<br>1940 1945 1950 1955 1960 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016<br>Topic proportion<br><!-- End of picture text --> Note: This Figure shows the topic probabilities estimated with LDA for the presidential speech given each year. The topic probabilities can be thought of as representing the proportion of each document that belongs to each topic. Only topics whose average document probability exceeds 10% in at least one decade are presented, the rest are grouped as a residual.

Table B2. Top probability terms defining the topics in Peru _(In descending order of probability of term within the topic)_

|“Public services”|“Infrastructure”|“Revolutionary left”|“Social development”|
|---|---|---|---|
|service|national|revolution|national|
|national|Public works|The people|program|
|Public works|education|policy|investment|
|jobs|production|development|social|
|school|plan|social|health|
|department|social|process|resources|
|direction|construction|national|Public works|
|object|activities|economic|growth|
|studies|ministry|revolutionary|project|
|services|bank|companies|security|

Note: This Table shows the words that have the highest probability of belonging to each topic. Because LDA defines topics as distributions over words, the lists of terms define the subject of each topic.

32
Table 1: Relationship between mentioning green technologies in ECTs and firm performance, 2012-2021

|Dep. var.: green frm = 1|(1)|(2)|
|---|---|---|
|**Construction**|||
|log sales|0.021***<br>(0.005)||
|log operating income||0.018***<br>(0.004)|
|**Energy**|||
|log sales|0.013***<br>(0.004)||
|log operating income||0.007**<br>(0.003)|
|**Farming and Forestry**|||
|log sales|0.001<br>(0.001)||
|log operating income||0.002<br>(0.001)|
|**PMP**|||
|log sales|0.005***<br>(0.002)||
|log operating income||0.002<br>(0.001)|
|**Transportation**|||
|log sales|0.010***<br>(0.003)||
|log operating income||0.006***<br>(0.002)|
|Year FE|Yes|Yes|
|Sector FE|Yes|Yes|
|Country FE|Yes|Yes|
|Observations|21,490|19,388|

Notes: Table reports OLS estimates on the relationship between firm attributes and the probability of having an ECT mentioning keywords related to green technology adoption by WIPO Green sector. The dependent variable takes the value of 1 in the year the firm mentioned keywords related to a green technology of the WIPO green sector, and in all subsequent years. Standard errors clustered at the firm level in parentheses. Significance levels at the 1, 5, and 10 percent levels are denoted by ***, **, and *, respectively. All models include controls for the country’s GDP per capita (USD).

22
<!-- Start of picture text --> Stock of Silver: Model Baseline<br>600<br>500<br>400 \NN<br>2 NN<br>& 300 _—<br>Br SN<br>200 NN aoe———<br>100 —<br>0 2020 2040 2060 2080 2100<br>. | . Year<br><!-- End of picture text -->

<!-- Start of picture text --> Stock of Indium:Model Baseline<br>16<br>14 XY<br>12 N\<br>10 NNONNS<br>n _~<br>& 8 oS<br>Br —<br>6 NN——<br>4 ———<br>2<br>0<br>2020 2040 2060 2080 2100<br>Year<br><!-- End of picture text -->
# **The World Bank’s Early Reflections on Development: A Development Institution or a Bank?**

Michele Alacevich<sup>*</sup>

> * University of Palermo. For contacts: <u>Michele.alacevich@unimi.it</u> or <u>michele.alacevich@unipa.it</u> I wish to thank Pier Francesco Asso, Jean-Jacques Dethier, Valentina Kalk, Branko Milanovic, Gary Mongiovi, Paul Streeten, Paola Villa, Michael Ward, two anonymous referees, and participants at the Workshop “Research Bank on the World Bank”, held at the Central European University, Budapest, April 1-2, 2005, for insightful comments on a previous draft of this paper. The staff of the World Bank Group Archives, of the Rare Book, Manuscript, and Special Collections Library of Duke University, Durham, NC, and of the Joint World Bank-IMF Library provided invaluable assistance. Sangeeta Sharma of the Joint World Bank-IMF Library was particularly helpful. The usual disclaimer applies. This paper is part of a larger research on “The Evolution of Development Economics and the Role of the World Bank: Archival Inquiries 1948-1968”, supported by the Research Committee and the Archives of the World Bank.
2 composition of incomes. But there is an acuLte lack of empirical analysis on the effects of taxes in the course of economic transition. Some exceptions include studies by Coulter, Heady, Lawson and Smith (1993), and Milanovid (1992). Partly, this is due to the fact that in a rapidly changing environment, it is difficult to isolate a set of factors influencing a naricular phenomenon studied. Nevertheless, as mere reliable data emerge, there is a need to put them to use to analyzing and assessing tangible economic issues of the transition. In particular, household budget survey data, with all their shortcomings, can serve multiple purpose of analyzing a host of distributional issues.

In this paper, we attempt to use the data on household income, expenditure, and taxes from the most recent Bulgarian household budget survey to analyze the i'npacl of income tax on household income. The present income tax system with marginal rates ranging from 20 percent to 52 percent is the most important tax affecting the individual and household financial positions. Furthermore, we are interested in addressing questions such as these: who pays income tax in Bulgaria? What proportion of households are paying most of these taxes, and how progressive is the income tax? Does the current tax system contribute to achieving economic equality? Finally, we also examined the effective tax rates paid by households in rural and urban areas, as well as their distributional impacts.

The paper is organized as follows. Section II provides the background to the analysis through a brief overview of the evolving tax structure and government revenue performance in Bulgaria. Section III describes the data and methodology, and section IV discusses the income levels and income distribution. In section V, we ask whether the present income tax system is progressive. The section provide estimates of the overall effective tax rates as well
# <u>Description of step</u>

It is suggested that when the level of the IU has changed in the last ten years, with limited data at the new IU-level, the calculation procedure outlined in sections 2, 3, and 4 is directly applied to data at the level of the new IU, where any missing new-IU level data for the last ten years is filled in with the corresponding data for the old IU. For example, for groundnut in Gram Panchayat GP1 of Andhra Pradesh, yield data was collected at the mandal level from 2000 to 2006, with GP-level yield data recorded from 2007 to 2009 (see Table 16). If products are to be offered at the level of the GP, the data to be used in de-trending, calculation of the threshold yield and pure premium rate is therefore the concatenation of mandal-level data from 2000 to 2006 and GP-level data from 2007 to 2009.

Table 16. Groundnut in one gram panchayat in Andhra pradesh: data for threshold yield and pure premium rate calculations

|**District**||**H**|**istorica**|**lyield**|**data, bef**|**ore de-**|**trendin**|**g (kg/h**|**a)**||
|---|---|---|---|---|---|---|---|---|---|---|
||**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|
|New insurance unit:<br>village cluster (gram<br>panchayat)|-|-|-|-|-|-|-|612|1,243|978|
|Old insurance unit:<br>subdistrict(mandal)|167|916|1,061|840|1,682|738|1,143|-|-|-|
|Data to use in threshold<br>yield and pure premium<br>rate calculations|167|916|1,061|840|1,682|738|1,143|612|1,243|978|

Having calculated the pure premium rate, as described in the previous section, the commercial premium rate should be calculated as suggested in Section 0, but with an additional multiplicative load at the crop/district-level to allow for the degree of missing data. One simple option would be to use a multiplicative load of the following form: where:

- denotes the number of years of data at the new insurance unit level, up to a maximum of

- 7

- is the Credibility Factor in the probable yields step

- and are constants, to be chosen based on expert judgment.

# <u>Actuarial justification</u>

The suggested procedure involves two key steps. First, probable yields and pure premium rates are calculated as described above. These calculations are unbiased estimates of the average nondrought year yield and average loss cost under the following assumptions:

1. Yields are homogenous across the district.  Specifically, for any possible yield threshold, the ‘true’ probability that yields will fall below that yield threshold does not vary significantly across the district; and

2. The number of crop cutting experiments conducted per insurance unit has remained constant, even as the size of the insurance unit has changed.

Second, rates within each district with be universally increased to account for the possibility that the above two assumptions do not hold.

28
<!-- Start of picture text --> (a) (b)<br>U U<br>U<br>U U<br>TUR BRA U<br>U * * U<br>U * * U * * *<br>*<br>U * L U *<br>L<br>* L * L L L<br>L L<br>L<br>L<br>L L<br>1 2 4 8 16 32 1 2 4 8 16 32<br>(c) (d)<br>U U<br>U U<br>IND U PAK U<br>U<br>U<br>U * * * U * * *<br>*<br>*<br>U * U *<br>* L L * L L<br>L L<br>L L<br>L<br>L<br>L L<br>1 2 4 8 16 32 1 2 4 8 16 32<br>Wavelet Scale (months)<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>−0.5 −0.5<br>−1.0 −1.0<br>1.0 1.0<br>Wavelet Correlation with USA 0.5 0.5<br>0.0 0.0<br>−0.5 −0.5<br>−1.0 −1.0<br><!-- End of picture text --> Figure 9: Wavelet cross-correlation between the monthly Industrial production index changes of USA (a) TUR, (b) BRA, (c) IND, and (d) PAK.

of industrial production series between Turkey and United States at 2 month frequency and by different lead and lag times. As displayed in the fourth panel, there is no significant association between the two series at this scale. In comparison, the changes in industrial production of the United States lead changes in the industrial production of Turkey on the scales between 4 and 16 months (d2 – d4) in a statistically significant way. These lead 21
The advantage of using generalised impulse responses when compared to standard Cholesky ones is that they do not depend on the ordering of the variables in the system. Generalized impulse responses compare the conditional expectation of a variable in the model, given a shock and the history of the model, to the conditional expectation of that variable given the historically observed information of the model (Koop, Pesaran & Potter, 1996 for details).

Table 1 shows the variance decomposition for the VAR model estimated above. One should note that only a small percentage of the forecast errors in the Chilean and Colombian pressure indexes can be attributed to EMBI shocks. In fact, the figures are 0.07% and 1.17% for the first month for each country respectively. Nevertheless, when we look at the Argentinean and Mexican indexes for the same horizon, those figures become much larger (15.69% and 40.26% respectively).

These results indicate that the unremunerated reserve requirements (combined with other capital account polices) might have helped to insulate the Chilean and Colombian economies from certain types of global external financial shocks, namely the ones captured by the EMBI spread. Evidently, at this stage, we cannot distinguish whether this difference is due to capital account policies, other macroeconomic policies, or simply the type of exchange rate regime adopted by the different countries. One also has to note that the precise role played by the capital controls in insulating those economies was not clarified in our empirical analysis so far.

In order to confirm the validity of our results we decided to estimate models for Chile and Colombia individually, including a wider selection of macroeconomic variables. The theoretical foundations of those VAR models are standard New Keynesian sticky-price models of the monetary transmission mechanism applied to small open economies, assuming that the domestic central bank responds (possibly with a lag due to information frictions and measurement limitations) to deviations of domestic inflation from the inflation target, to the output gap and to external conditions captured by the terms of trade, the real exchange rate gap, the sovereign risk premium and the EMBI spread.

First, we estimated a model of the Chilean economy using monthly data from January 1991 to December 2000, including as endogenous variables the EMBI spread, the output gap, the difference between inflation in the past 12 months (change in the consumer price index) and the inflation target, the exchange rate indexed deposit rate (this is the policy instrument), the Chilean sovereign risk premium (this variable was 7
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21
estimates increase from 16% in 2020 to 27% in 2100. On the other hand, African agriculture will benefit if climate change turns out to be mild with a small increase in temperature and an increase in precipitation.

Looking across different agro-ecological zones, farms in moist savannah and dry savannah are the most vulnerable to higher temperature and reduced precipitation regardless of the elevation of these farms. On the other hand, the farms in sub-humid or humid forest gain even from this severe climate change. These results indicate that major agricultural areas in Africa will shift in the future. Farmers will reduce farming in the currently productive moist savannah and dry savannah to the sub-humid AEZ which is currently less populated by farmers.

Current climate already limits the incomes of African farmers. The results suggest that unless warming is severe, farmer incomes will not fall much further. Farmer incomes will even rise with the PCM scenario. These results should be understood in terms of what farmers can do in the case of climate change. Previous studies revealed that farmers can change livestock species, crop varieties, adopt irrigation, and change farm types to adapt to climate change. These adaptations will reduce the damage from climate change substantially (Seo and Mendelsohn 2008a, 2008b, Mendelsohn and Seo 2007). The results also indicate that farmers will even change locations in the case of a severe climate change.

In Figures 2 and 3, we examine the spatial distribution of impacts from the two climate scenarios based on Model 3 with country fixed effects. The maps show the percentage loss of agricultural profits across Africa for each AEZ. Under the CCC scenario, lowland AEZs in general gain from climate change. However, desert areas, mid elevation AEZs and high elevation AEZs are predicted to lose a large percentage of net revenue. Predictions from the PCM scenario are quite different. All places would gain except for the deserts. However, the largest benefits from climate change would fall on the mid elevation AEZs and highlands. Thus even in scenarios where the continental average income may not fall, farmers in selected region may be damaged by climate change.

# 6. Conclusion and Policy Implications

This paper examines the impact of climate change on different Agro-Ecological Zones in Africa. Agro-ecological zone data were obtained from FAO and combined with the economic surveys 12