rafmacalaba/datause-encoder-data huggingface dataset:
49

**Table A1.6: India (Cont.)**

|**Politi**<br>**cal Units**|**ate**|**Procedural Rules and**<br>**Transparency Requirements**|**Numerical**<br>**Targets**|**Escap**<br>**e Clauses**|**Sa**<br>**nctions**|
|---|---|---|---|---|---|
|**Maha**<br>**rashtra**|2005|• In each financial year, State Government should lay<br>before both houses of the legislature the Medium-term<br>Fiscal Statement and the Fiscal Policy Strategy<br>Statement. Medium Term Fiscal Plan should set forth<br>three year rolling targets for key fiscal indicators<br>• The Finance Minister should make quarterly review of<br>compliance and lay the outcomes before both houses of<br>the state legislature<br>• Whenever there is a breach of pre-specified level of<br>expenditure or revenue during any period in a year, the<br>government should take appropriate measures to offset<br>the impacts, including curtailing the sum authorized to<br>be paid or applied from and out of the Consolidated<br>Fund of State.|• Eliminate the revenue deficit by<br>31st March 2009 and maintain<br>revenue surplus thereafter at the end<br>of each year<br>• The State Government shall by<br>rules specify the targets for<br>reduction of fiscal deficit (Which<br>are not specified in this act)|•On the grounds<br>of natural<br>calamities or such<br>other exceptional<br>grounds the State<br>Government may<br>specify|N/A|
|**Tamil Nadu**|2003|• The Government shall lay a medium term fiscal plan<br>before the Legislative Assembly along with the budget.<br>The medium term fiscal policy statement should set<br>forth multi-year rolling target for fiscal indicators<br>• The government should disclose a statement at the<br>time of budget presentation including significant<br>changes in accounting policies and their effects and the<br>contingent liabilities created by guarantees<br>• The Finance Minister should review the budget<br>implementation and remedial measures taken to achieve<br>the targets every half-year and explain any deviation as<br>well as proposing remedial measures before legislature<br>• Any measure proposed in the course of the financial<br>year, which may lead to an increase in revenue deficit<br>should be accompanied by remedial measures, which<br>will neutralize such increase|• Reduce the ratio of revenue deficit<br>to revenue receipt every year by 3%<br>to 5%, depending on the economic<br>situation, so as to bring it down to<br>below 5% by 31st March 2008;<br>adhere to it thereafter<br>• Reduce the ratio of fiscal deficit to<br>GSDP beginning from 2002-03<br>financial year to not more than 3%<br>by 31st March, 2008<br>• Cap the total outstanding<br>guarantees to 100 percent of the<br>total revenue receipt in the<br>preceding year, or at 10 percent of<br>GSDP; Cap the risk weighted<br>guarantees to 75 percent of the total<br>revenue receipt in the preceding<br>year, or at 7.5 percent of GSDP|•On the grounds<br>of the unforeseen<br>demand of public<br>finance due to<br>national security,<br>natural calamities<br>or other<br>exceptional<br>grounds specified<br>by the government|N/A|
financed by seignorage have played a crucial<sup>role in developing country</sup> inflation.7 The task for political economy is to specify<sup>tht political correlates of</sup> inflationary fiscal policy. One approach<sup>is to focus directly on the presence</sup> and strength of what Mancur Olson has<sup>called wdistributive</sup> coalitions.68 The extent of urbanization and industrialization has historically been higher in the more developed Latin American countries<sup>and Turkey than it has been until</sup> very recently in the other Asian cases,<sup>and this might be a plausible</sup> indicator of the likely presence of<sup>popular sector groups. (TaMle 1)</sup> Inflation rates from 1960 through 1980<sup>correlate fairly strongly with</sup> urbanization in 1960 (.61) and with<sup>industry's share of GDP in 1960 (.56).</sup> The data on labor organization and income<sup>distribution is much less reliable.</sup> Available estimates suggest more extensive<sup>unionization in Latin America than</sup> in East Asia, though the simple correlation<sup>between unionization</sup> and inflation from 1960-1980 for this sample of countries<sup>is weak (.17). Income distribution</sup> is, in general, also much more skewed in<sup>Latin America as well. This should be</sup> a good predictor of the presence of<sup>popular sector groups and populist</sup> politics, though clearly not of their s,;_cess.9 7
likely to underestimate the protective value of mangroves in the long run because mangrove protection may entail benefits on outcomes such as lives saved, health, and human capital accumulation, which are not well captured by nightlights. Second, while climate change and the resulting intensification of storms may increase the value of conservation for protection purposes, areas designated for conservation or restoration must be carefully chosen given the threat of sea-level rise (Blankespoor et al., 2017). Consequently, an important avenue for future research is the identification of areas that should be prioritized for conservation or restoration.

# **References**

- Baird, A. H., Bhalla, R. S., Kerr, A. M., Pelkey, N. W., and Srinivas, V. (2009). Do mangroves provide an effective barrier to storm surges? _Proceedings of the National Academy of Sciences_ , 106(40):E111.

- Barbier, E. B. (2016). The protective service of mangrove ecosystems: A review of valuation methods. _Marine Pollution Bulletin_ , 109(2):676–681.

- Barbier, E. B., Hacker, S. D., Kennedy, C., Koch, E. W., Stier, A. C., and Silliman, B. R. (2011). The value of estuarine and coastal ecosystem services. _Ecological Monographs_ , 81(2):169–193.

- Barbier, E. B., Koch, E. W., Silliman, B. R., Hacker, S. D., Wolanski, E., Primavera, J., Granek, E. F., Polasky, S., Aswani, S., Cramer, L. A., et al. (2008). Coastal ecosystem-based management with nonlinear ecological functions and values. _Science_ , 319(5861):321–323.

- Bertinelli, L., Mohan, P., and Strobl, E. (2016). Hurricane damage risk assessment in the Caribbean: An analysis using synthetic hurricane events and nightlight imagery. _Ecological Economics_ , 124:135–144.

- Bertinelli, L. and Strobl, E. (2013). Quantifying the local economic growth impact of hurricane strikes: An analysis from outer space for the Caribbean. _Journal of Applied Meteorology and Climatology_ , 52(8):1688–1697.

- Blankespoor, B., Dasgupta, S., and Lange, G.-M. (2017). Mangroves as protection from storm surges in a changing climate. _Ambio_ , 46:478–491.

14
We obtain the global conversion factor, as follows: The conversion factor, _gc_ , is dimensioned as tons of CO2 emissions per 1 part per billion of concentration anomaly.

Fires CO2 emissions for each cell are drawn from Van der Werf et al. (2017). These are gross emissions rather than estimated net emissions from land-use change that incorporate both carbon-emitting conversions (e.g., from forest to cropland) and carbon-absorbing conversions (e.g., from pasture to forest).<sup>18</sup> Using the econometric results in Table 1, Figure 7 maps the predicted global distribution of CO2 emissions using grid cell sums of estimated industry-related emissions ( _gc_ * _ICO2i_ ), population-related emissions ( _gc_ * _PCO2i_ ), and fires emissions from Van der Werf et al. (2017). The figure displays mean annual emissions for 2014–21, highlighting the roles of the United States, Europe, China, and the forested regions of South America, Central Africa, and Southeast Asia.

The map illustrates the potential of satellite-based observations for CO2 emissions monitoring at regional, national, and local scales. Until now, observation-based estimates of CO2 emissions by source from every location on the globe have simply not been available.

# **6.2 Emissions from Functional Urban Areas**

Using the data described in Section 6.1, we estimate emissions from urban areas by aggregating estimates for grid cells within each Functional Urban Area. These can be viewed as expected emissions values for FUAs, given their characteristics. In reality, the impacts of different policies, individual economic decisions and more fine-grained structural factors will cause FUAs to deviate from their expected values. These deviations should be reflected in the residual concentration anomalies from estimation of model (1).

We translate these residuals to emissions using the EDGAR global database of gridded CO2 emissions estimated from local activity measures and standard emissions parameters (Crippa et al. 2020). The current EDGAR database terminates in 2018, so we perform the conversion using data for 2015 – 2018, the period of overlap with our OCO-2 database.

> 18  For detailed assessments of net carbon emissions from land-use change, see Gasser et al. (2020) and Winkler et al. (2021).

29
# **<u>Panel B: Correlation Matrix between the Firm Innovation Indicators</u>**

||New<br>product line|Upgraded<br>existing<br>product line|New<br>Technology|Discontinued<br>at least 1<br>product|Opened a<br>newplant|Closed at<br>least one<br>existing<br>plant|New JV<br>with foreign<br>partner|New<br>Licensing<br>Agreement|Outsourced<br>a major<br>activity|
|---|---|---|---|---|---|---|---|---|---|
|Upgraded existing product line|0.4612<sup>a</sup>|||||||||
|New Technology|0.3787<sup>a</sup>|0.4424<sup>a</sup>||||||||
|Discontinued at least 1 product|0.2777<sup>a</sup>|0.2297<sup>a</sup>|0.2004<sup>a</sup>|||||||
|Opened a new plant|0.1678<sup>a</sup>|0.1186<sup>a</sup>|0.1485<sup>a</sup>|0.0960<sup>a</sup>||||||
|Closed at least one existing plant|0.0491<sup>a</sup>|0.0366<sup>a</sup>|0.0315<sup>b</sup>|0.2003<sup>a</sup>|0.2148<sup>a</sup>|||||
|New JV with foreign partner|0.1366<sup>a</sup>|0.1112<sup>a</sup>|0.1177<sup>a</sup>|0.0623<sup>a</sup>|0.1340<sup>a</sup>|0.0976<sup>a</sup>||||
|New Licensing Agreement|0.1281<sup>a</sup>|0.0827<sup>a</sup>|0.1066<sup>a</sup>|0.0749<sup>a</sup>|0.1595<sup>a</sup>|0.1080<sup>a</sup>|0.2229<sup>a</sup>|||
|Outsourced a major activity|0.1672<sup>a</sup>|0.1735<sup>a</sup>|0.1882<sup>a</sup>|0.1305<sup>a</sup>|0.1151<sup>a</sup>|0.0881<sup>a</sup>|0.1509<sup>a</sup>|0.1267<sup>a</sup>||
|Brought in-house a previously out-<br>sourced activity|0.1746<sup>a</sup>|0.1699<sup>a</sup>|0.1832<sup>a</sup>|0.1117<sup>a</sup>|0.1292<sup>a</sup>|0.0764<sup>a</sup>|0.1114<sup>a</sup>|0.0966<sup>a</sup>|0.2946<sup>a</sup>|

a b c , , and represent significance at 1%, 5% and 10% respectively

|**Panel C: Correlation Matrix betwe**|**en the Aggregate In**|**novation/Innov**|**ation Indicator**|**s and the Firm**|**Level Variabl**<br>|**es**|Number of<br>||
|---|---|---|---|---|---|---|---|---|
||INDEX8|CORE|INDEX2|LGCAP|Size|Age|Establishments|Corporations|
|CORE|0.8116<sup>a</sup>||||||||
|INDEX2|0.3168<sup>a</sup>|0.2612<sup>a</sup>|||||||
|LGCAP|0.0145<sup>c</sup>|0.0717<sup>a</sup>|0.0326<sup>a</sup>||||||
|Size|0.2405<sup>a</sup>|0.1967<sup>a</sup>|0.1236<sup>a</sup>|0.0711<sup>a</sup>|||||
|Age|0.0513<sup>a</sup>|0.0340<sup>a</sup>|0.1177<sup>a</sup>|0.0604<sup>a</sup>|0.2767<sup>a</sup>||||
|Number of Establishments|0.0477<sup>a</sup>|0.0185<sup>b</sup>|0.0562<sup>a</sup>|0.0280<sup>a</sup>|0.1354<sup>a</sup>|0.0764<sup>a</sup>|||
|Corporations|0.2089<sup>a</sup>|0.2025<sup>a</sup>|0.0776<sup>a</sup>|0.1982<sup>a</sup>|0.2555<sup>a</sup>|0.0148<sup>b</sup>|0.0102||
|CapacityUtilization|0.0172<sup>b</sup>|0.0605<sup>a</sup>|-0.0726<sup>a</sup>|0.1689<sup>a</sup>|0.0937<sup>a</sup>|-0.0563<sup>a</sup>|0.0378<sup>a</sup>|0.0295<sup>a</sup>|

a b c , , and represent significance at 1%, 5% and 10% respectively 35
# FIGURE 5: SHARE OF TRADE, RESTAURANT. AND HOTEL SERVICES IN VALUE ADDED: USSR AND OECD

<!-- Start of picture text --> 25%-<br>PRT N<br>20% -_<br>NiR<br>AWT AlAI4S NZa<br>ThR A URAUSA<br>DNK AWS<br>15%-_<br>yWD<br>.~~~~~~~P GR _ G_ DEU NeBR (ER NEiRC<br>_ I I:IN I CBR FI S<br>YUG~~~~~~~~US<br>USSR<br>5%- lll<br>7 8 9 10<br>LOG OF PER CAPITA INCOME<br><!-- End of picture text -->
6

# TABLE 2.3

# Rates of Business Tax

# May 1988

|Industry|Tax Rates|
|---|---|
|Retail<br>|3Z|
|Who'lesale|lOZ|
|Transport<br>|3Z-<br>15X|
|Construction<br>|3Z|
|Banking and Insurance|5%|
|Telephone; and Telecommunications<br>|3Z|
|Publishing<br>|3Z|
|Utilities<br>|3Z|
|Entertainment<br>|3Z|
|Other Services|3X-<br>10%|

Note: Tax is levied on gross sales value, except for wholesale where it is levied on gross mark-up.

Source: General Taxation Bureau

# The Rate Structure: Historical Rationale

The highly differentiated tax rate structure is a consequence of the change in 1983 from profit remittance to profit taxacion for state-owned enterprises. Under the profit remittance system that prevailed up to 1983, the fact that state-controlled prices did not reflect costs of production was not of any particular significance. However, under profit taxation, enterprises are allowed to retain a certain proportiorn of their profits, so that the pricing system has the potential for generating considerable inequality between enterprises, with attendant consequences on their investment and growth prospects. As the price of a good to its purchaser was fixed by the State, the product tax rate could be set to reduce the price received by the producer and thus reduce excess profits. Indeed the main consideration in setting product tax rates in 1984 was to equalize profits (as a proportion of sales value) across sectors, but with modifications to encourage or discourage the production of particular goods. It is this which explains the large number of different product tax rates and, in cases where they were subsequently replaced, of VAT rates.

In principle, prices could have been adjusted to equalise profitability between industries. However, the government has been unwilling to undertake a thorough revision of prices, and price changes have been confined to a fairly small number of goods. Instead, the use of taxation rather than price reform has been seen as being politically more acceptable.
Policy Research Working Paper 6455

# **Abstract**

It is becoming increasingly apparent that the post2004, across-the-board, commodity price increases, which initially appeared to be a spike similar to the ones experienced during the early 1950s (Korean War) and the 1970s (oil crises), have a more permanent character. From 1997–2004 to 2005–12 nominal prices of energy, fertilizers, and precious metals tripled, metal prices went up by more than 150 percent, and most food prices doubled. Such price increases, especially in food commodities, not only fueled a debate on their key causes, but also alarmed government officials, leading to calls for coordinated policy actions. This paper examines the relative contribution of various sector and macroeconomic drivers to price changes of five food commodities (maize, wheat, rice, soybeans, and palm oil) by applying a reduced-form econometric model on 1960–2012 annual data. The drivers include stock-to-use ratios, crude oil and manufacturing prices, the United States dollar exchange rate, interest rate, and income. Based on long-run elasticity estimates (approximately −0.25 for the stock-to-use ratios, 0.25 for the oil price, −1.25 for the exchange rate, and much less for others), the paper estimates the contribution of these drivers to food price increases from 1997–2004 to 2005–12. It concludes that most of the price increases are accounted for by crude oil prices (more than 50 percent), followed by stock-to-use ratios and exchange rate movements, which are estimated at about 15 percent each. Crude oil prices mattered most during the recent boom period because they experienced the largest increase.

This paper is a joint product of the Development Prospects Group and the Trade Department of the Poverty Reduction and Economic Management Network. 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 jbaffes@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
Table 1: Effect of Climate Change on Poverty.

|(1)|(2)|
|---|---|
|VARIABLES<br>Asset Poverty|Asset Poverty|
|(in log)|(in log)|
|**GDP per capita 2000-2005 (in log)**<br>**-1.684*****|**-1.746*****|
|(0.0923)|(0.104)|
|Geographic Coordinates<br>yes|yes|
|State efects|yes|
|Year efects|yes|
|Constant<br>11.72***|10.81***|
|(0.345)|(0.903)|
|Observations<br>4,192|4,192|
|R-squared<br>0.4523|0.5222|
|Wald chi2<br>(4) 1548.84|(36) 2440.30|
|_Prob > chi_2<br>0.0000|0.0000|
|**First Stage Instrumental Variables**||
|(1)|(2)|
|GDP per capita 2000 - 2005|GDP per capita 2000 - 2005|
|(in log)|(in log)|
|Precipitation<br>-0.0133**|-0.0288***|
|(0.00646)|(0.00618)|
|Precipitation squared<br>0.000932|0.00158***|
|(0.000568)|(0.000527)|
|Temperature<br>-0.0536***|-0.0576***|
|(0.00980)|(0.00945)|
|Temperature squared<br>0.00162***|0.00150***|
|(0.000240)|(0.000230)|
|Distance to nearest road<br>yes|yes|
|Distance to nearest road<br>yes|yes|
|Geographic Coordinates<br>yes|yes|
|State efects|yes|
|Year efects (2005)|yes|
|Constant<br>3.474***|4.140***|
|(0.141)|(0.436)|
|Observations<br>4,192|4,192|
|R-squared<br>0.234|0.360|
|Test<br>(9,2059) 73.64|(41,2027) 27.67|
|_Prob > F_<br>0.0000|0.0000|

Notes: ( Standard errors). *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05. 2SLS Instrumental Variable Regression decreases poverty. Our study finds poverty elasticity to growth of -1.74%. In other words that, when average per capita GDP increases by 1 percent, the proportion of municipal poverty declines by around 1.74 percent. Past studies have found a growth elasticity of poverty ranging 13
significant impact of both in-kind and cash on the intake of vitamin C. When looking at iron and zinc, two key nutrients for brain development, we find large and statistically significant impacts for the in-kind modality, but not for cash. For zinc, the difference between the two treatment types is statistically significant at conventional levels. Similarly the in-kind transfer has a significantly larger impact than the cash transfer on the intake of calcium and retinol. In order to provide an overall estimate of the program effect on nutritional intakes, we study the impact on the first component of the Principal Component Analysis (PCA) of the six nutrients. The in-kind modality shows a larger impact than the cash one with the difference between the two treatment types being non-trivial in terms of size (about 0.10 s.d.) and marginally significant (p-value = 0.09). The results for the overall sample and the sample of those with at least one ENLACE are remarkably similar and this is consistent with the fact that the program had no statistically significant impact on the probability of taking the exam. In order to rule out the possibility that the results are driven either by children with extremely low intakes or those with very high intakes, we study the treatment effects on the probability of having intakes equal or above the RDAs. Results presented in Appendix Table A6 are in line with those discussed above.

Overall, the evidence provided in this section suggests that children, who had been exposed to the PAL program for at least 18 months, did not exhibit better health status compared to those who were not exposed. However, they do display better nutritional intakes, especially if they receive the transfer in-kind.

# **5.2 Student**

We use two distinct data sources to study the impact of PAL on child labor. First, we use information from the evaluation follow-up survey in order to study the attendance and labor outcomes of children age 12 and 13 who were enrolled in primary education. These children would have been eligible to take the ENLACE in 2005, if the test had been in place at the time.<sup>20</sup> We focus on five different outcomes: 1) whether the child only reports having attended school the week prior the interview; 2) whether she was both working and attending school; 3) whether she was only working; 4) whether she was neither working nor attending; 5) the number of hours of work (including zeros). Results are reported in columns 1 to 5 in Table 10. In the control group 84 percent of the children aged 12 to 13 in primary education,

> 20Because of the almost universal enrollment in primary education and the lack of any effect of PAL on the probability of attending a grade that is appropriate for student age, the results are unlikely to be driven by a selection effect. While 12 is the standard age for completing primary school in Mexico, over-age is quite common and we include 13 year old in order to improve statistical power. Results restricted to 12 year old are qualitatively similar.

18