rafmacalaba/datause-encoder-data huggingface dataset:
||(0.0373)|(0.0367)|(0.038)|(0.037)|(0.038)|(0.037)|(0.038)|(0.037)|
|---|---|---|---|---|---|---|---|---|
|lage|0.0666**|0.0672**|0.067**|0.067**|0.067**|0.067**|0.067**|0.067**|
||(0.0285)|(0.0282)|(0.028)|(0.028)|(0.028)|(0.028)|(0.028)|(0.028)|
|SOE|-0.2701*|-0.2895*|-0.228|-0.260|-0.183|-0.217|-0.208|-0.235|
||(0.1504)|(0.1556)|(0.158)|(0.161)|(0.165)|(0.168)|(0.163)|(0.167)|
|FEM|0.0513|0.0552|0.060|0.061|0.061|0.062|0.055|0.058|
||(0.0825)|(0.0826)|(0.082)|(0.082)|(0.082)|(0.082)|(0.082)|(0.082)|
|Constant|-0.6082***|-0.2123|-0.601***|-0.301|-0.601***|-0.316|-0.605***|-0.245|
||(0.1333)|(0.2622)|(0.135)|(0.289)|(0.135)|(0.284)|(0.134)|(0.269)|
|Observations|8,668|8,668|8,668|8,668|8,668|8,668|8,668|8,668|
|R-squared|0.0827|0.0873|0.086|0.090|0.088|0.092|0.084|0.089|

_Table A- 14: Firms that invest in green activities and government-owned banks_

|VARIABLES|GREEN|GREEN|GREEN|GREEN|GREEN|GREEN|GREEN|GREEN|
|---|---|---|---|---|---|---|---|---|
|SOB|0.0600|0.0350|-0.030|-0.070|-0.060|-0.108|-0.033|-0.095|
||(0.1227)|(0.1233)|(0.272)|(0.285)|(0.287)|(0.303)|(0.370)|(0.386)|
|INTER|||0.221|0.265|0.294|0.360|0.260|0.366|
||||(0.426)|(0.471)|(0.466)|(0.518)|(0.753)|(0.806)|
|ROA||0.0609||0.066*||0.068*||0.065|
|||(0.0383)||(0.040)||(0.040)||(0.040)|
|LTA||0.0131||0.004||0.003||0.009|
|||(0.0413)||(0.045)||(0.045)||(0.043)|
|lsize|0.2824***|0.2815***|0.284***|0.282***|0.284***|0.283***|0.283***|0.282***|
||(0.0540)|(0.0539)|(0.054)|(0.054)|(0.054)|(0.054)|(0.054)|(0.054)|
|lage|0.0737|0.0735|0.073|0.073|0.073|0.073|0.073|0.073|
||(0.0521)|(0.0521)|(0.052)|(0.052)|(0.052)|(0.052)|(0.052)|(0.052)|
|SOE|-0.0656|-0.0081|-0.093|-0.032|-0.114|-0.055|-0.092|-0.040|
||(0.2905)|(0.3007)|(0.290)|(0.300)|(0.290)|(0.297)|(0.285)|(0.293)|
|FEM|0.0625|0.0643|0.059|0.061|0.058|0.061|0.061|0.063|
||(0.1060)|(0.1059)|(0.107)|(0.107)|(0.107)|(0.107)|(0.106)|(0.106)|
|Constant|-0.9156***|-1.1315***|-0.932***|-1.092***|-0.961***|-1.118***|-0.947***|-1.152***|
||(0.2386)|(0.3973)|(0.232)|(0.420)|(0.230)|(0.401)|(0.228)|(0.386)|
|Observations|7,986|7,986|7,986|7,986|7,986|7,986|7,986|7,986|
|R-squared|0.1476|0.1499|0.148|0.151|0.148|0.151|0.148|0.150|

_Table A- 15: Employment growth and government-owned banks_

|VARIABLES|emp_gr|emp_gr|emp_gr|emp_gr|emp_gr|emp_gr|
|---|---|---|---|---|---|---|
|SOB|-0.453|0.058|4.949*|0.147|4.323|-0.769|
||(1.271)|(1.296)|(2.883)|(1.228)|(2.765)|(1.637)|
|INTER||-1.238*|-6.848**|-0.353|-3.584|1.226|
|||(0.689)|(3.296)|(0.557)|(2.981)|(1.145)|
|INTCC||||-2.420*|-6.071|-1.858|
|||||(1.340)|(3.691)|(1.492)|
|INT1||||1.556*|2.654**||
|||||(0.829)|(1.340)||
|INT2||||0.570|2.009|-0.341|

42
**Figure 16: Trend in Inflation in South Asia**

<!-- Start of picture text --> 30.00<br>25.00<br>20.00<br>15.00<br>10.00<br>5.00<br>0.00<br>India Nepal Pakistan Sri Lanka Bangladesh<br>2000 2001 2002 2003 2004 2005 2006 2007 2008Jan2008Feb2008Mar2008Apr2008May2008Jun 2008Jul<br>Inflation rate (%)<br><!-- End of picture text --> _Source: IMF International Financial Statistics and government authorities_ the inflation acceleration was quite intense (Figure 17). With the recent decline in global grain and petroleum prices, inflation rates are coming down in South Asia even though the effects of the decline are partly offset by rapid currency depreciation in India and Pakistan.

**Figure 17: Food and Non-Food Inflation in Afghanistan, January 07-June 08**

<!-- Start of picture text --> 1 4 0 %<br>1 2 0 %<br>b r e a d a n d c e r e a l<br>1 0 0 %<br>8 0 %<br>F o o d<br>6 0 %<br>4 0 %<br>2 0 %<br>O v e r a ll<br>0 %<br>- 2 0 %<br>07-Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec08-Jan Feb Mar Apr May Jun<br><!-- End of picture text --> _Source: Central Statistical Office, Government of Afghanistan_ 18
and sub-national statistics to 5 arcminute pixels based on satellite-derived information of constituents of AgGDP, including forestry, hunting, and fishing, as well as cultivation of crops and livestock production.<sup>1</sup> We make two main contributions. First, we construct a global dataset of gridded AgGDP. This entails a massive effort of data collection and integration. We extend and apply the cross-entropy framework developed in the Spatial Production Allocation Model (SPAM) for crops that pioneered the use of cross-entropy optimization in spatial allocation (You and Wood, 2003; You, Wood, et al., 2014; You, Wood-Sichra, et al., 2018; Yu et al., 2020). We construct and integrate global datasets of the components of agricultural GDP as priors and then reconcile the values with the regional account statistics using cross-entropy optimization. Second, we contribute to efforts assessing the exposure of economic activity to natural hazards with a focus on agricultural GDP. Significant progress has been made to measure physical assets such as built-up area and estimate hazards to quantify its exposure to natural disasters. However, the spatial distribution of agricultural GDP is less known. So, we apply these data to inform efforts quantifying the population and agricultural GDP at risk to drought and water scarcity.

The rest of this paper is structured as follows. The next section provides a detailed description of the methodology and data. Then, we present the model results and data. Then, we discuss the results along with validation followed by usage notes from a fitness-foruse perspective. Finally, we provide concluding remarks.

# **2 Methodology and data**

Following the composite structure of agricultural GDP, we disaggregate the national and sub-national statistics into a global grid through a cross-entropy allocation model. Given the availability of data and the global scope, our efforts varied on adjusting official statistics and creating priors for different components. Below we discuss the construction of each component, AgGDP statistics and the allocation model followed by the global natural hazards data. Given the spatial resolution and year of reference of the input data for the crop value of production, we estimate AgGDP for the year 2010 into 5 arc-minute grids (10x10 km) across the world.

> 1Agriculture, forestry, and fishing corresponds to ISIC divisions 1-3 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production

5
respectively.<sup>17</sup> These results are remarkably consistent with previous studies of registration reforms in two different OECD countries, as discussed in the introduction (see Branstetter, et al., 2010, Bruhn, 2008, Bruhn 2010).<sup>18</sup> We perform several robustness tests to ensure that our main results are not driven by omitted variables. First, previous research noted that the rate of new firm formation is related to industry structure (e.g. Reynolds, et al., 1994 and Baptista, 2008). For example, an economy consisting of mainly self-employed microenterprises has a different structure than one where large-scale manufacturing enterprises account for a large share of employment. Clearly, our sample includes countries with very different industrial composition. However, all level differences between countries are already accounted for in our model with country-specific fixed effects. Nevertheless, we also verify that time-varying differences in industry composition are not affecting our results. In separate regressions (available upon request) we control for industry composition by including annual data on the value added of the manufacturing sector (as a percentage of GDP) and the service sector (as a percentage of GDP) (World Development Indicators, 2012). The results remain robust to these additional controls.<sup>19</sup> Second, differences in the level of economic development have also been found to affect the rate of new firm entry (eg. Wennekers, 2005). To test whether the level of economic development is driving our results we add lagged GDP per capita to our model. We find our

> 17 As an additional robustness check, we also evaluate the impact of “negative” reforms, which refers to instances in which starting a business measures _increase_ . Though the sample size for these reforms is very small (generally less than 10, even at 20 percent), we do observe a significant relationship between increases in procedures and decreases in entry density (results available upon request).

> 18 Kaplan, et al. (2006) also study the impact of the same Mexican reform and find a 4 percent increase in the number of newly registered firms. However, Bruhn (2011) argues that they underestimate the true impact because the authors use data that does not include firms without employees or firms that fail to formally register their employees for Social Security benefits.

> 19 Interestingly, the share of services is mostly significantly negative; in other words, in countries with a greater concentration of service firms (which generally have lower leverage rates, relative to manufacturing firms), we find a lower registration rate of limited-liability companies.

20
# **7. Discussion and Conclusions**

In this paper we use a random sample of CFs matched with NCFs that local experts specifically identified as best possible matches. The forest level propensity score estimates and the comparison of actual and predicted carbon, adjusting for 1990 NDVI, indicate a high degree of balance, suggesting that treatment and control communities are comparable.

To derive results, we use methods that are highly labor intensive, but allow us to carefully estimate carbon for both trees and saplings, count trees, evaluate canopy cover and examine regeneration, which are extremely important measures of forest health and future biomass. Because on-the-ground estimation methods are used, we are also able to gather detailed plot level environmental data like forest type, soil type, soil quality, evidence of fire, altitude, and slope that in many models are shown to be important determinants of forest quality. As our data are collected at one point in time, we include 1990 forest level NDVI estimates to adjust for vegetative baseline. Not surprisingly, we find that baseline vegetation matters for forest quality in 2013.

We find that within the existing institutional environment collective action has very important and generally positive effects on forest quality. Indeed, _in all models, user groups with a well-defined establishment year sequester more carbon compared with NCFs whose leaders could not identify an establishment year_ . We believe well-defined establishment year is an important indicator of collective action. It requires a group decision, which is important, but is not subject to nonrandom sample selection because there is no formal opt-in decision.

A policy conclusion that may be drawn from these results is that as part of a robust REDD+ policy, FCCC Annex 1 funders and non-Annex 1 governments would do well to support community collective action. Such support may be significant for communities, because in

**38 |** P a g e
Appendix A (1999) and calculate the geometric average of the index for a month and for the preceding month as that month’s deflator. After adjusting for the five currency conversion from the Cruzado to the Real, the final values to convert current incomes into 1997 Reais were as follows:<sup>1</sup>

|**Brazilian Tem**|**poral Price Deflators (1997= 1.0)**|
|---|---|
|**Year**|**Temporal Deflator**|
|1981|47.766|
|1983|2192.602|
|1985|129.913|
|1988|63.626|
|1990|67.570|
|1993|0.833|
|1995|0.947|
|1997|1.000|
|1998|1.024|
|1999|1.084|

1

|**Currency Conversion Dat**<br>|**es and Rates:**<br>||
|---|---|---|
|28February1986|1Cruzado|1000 Cruzeiros|
|15 January1989|1Novo Cruzado|1000 Cruzados|
|16March 1990|1Cruzeiro|1Novo Cruzado|
|1 August1993|1CruzeiroReal|1000 Cruzeiros|
|1July1994|1 Real|2750 CruzeirosReais|

50
- 29 -

# Table 11: Proportion of Farmers with Unsatisfled Input Demand (Percent)

|<br>Input|County|Gongzhullng<br>(N-200)|Jurong<br>(N-1 99)|Tal<br>(N-200)|Xiajlang<br>(N-200)|
|---|---|---|---|---|---|
|Fertilizer||10|54|14|23|
|Diesel||10|33|29|67|
|Pesticides||1|32|23|23|
|Herbicides||1|28|26|29|

Note: The numbers show the percentages<sup>of respondents who stated tsat they</sup> were not able to obtaln the<sup>desired quantities of Inputs even though they were</sup> willing to pay higher than market<sup>prices.</sup> Source: Feder et al., 1990.

where Input supply problems<sup>are negligible and farm sizes are larger.</sup> In the other three counties formal credit<sup>does not significantly affect Investment, and It would</sup> thus seem that generally credit<sup>was not a binding constralnt on crop-related</sup> Investment In these counties.<sup>In Tal and Jurong counties, demand for Investment</sup> has likely been low due to the<sup>low marginal productivity of capital and small farm</sup> sizes. In Xlajlang county farm<sup>sizes are larger, but Inadequate Input supplies are</sup> a serous problem, apparently<sup>diminishing the profitability of, and hence the demand</sup> for, farm capital.

Concerns regarding land reallocation<sup>did not hinder productive Investment</sup> signlficantly (none of the coefficients are statistically significant). This Is, apparently because farmers<sup>expected (at least at the time of the survey) that the</sup>
_C O O R D I N AT I N G TA X R E F O R M S I N T H E P O O R E S T C O U N T R I E S : C A N L O S T TA R I F F S B E R E C O U P E D ?_ swarnim waglé<sup>1</sup> _Abstract_ A revenue-neutral switch from trade taxes to domestic consumption taxes is fraught with implementation challenges in countries with a large informal sector. It is shown for a sample of low-income countries over 25 years that they have had a mixed record of offsetting reductions in trade tax revenue. The then the case of a data paper analyzes specific Nepal, using unique set compiled from unpublished customs records of imports, tariffs and all other taxes levied at the border. It estimates changes to revenue and domestic production associated with two sets of reforms: i) proportional tariff cuts coordinated with a strictly enforced value-added tax; and ii) proposed tariff cuts under a regional free trade agreement. It is shown that a revenue-neutral tax reform is conditional on the effectiveness with which domestic taxes are enforced. Furthermore, loss of revenue as a result of intra-regional free trade can be minimized through judicious use of Sensitive Lists that still cover substantially all the trade as required by Article XXIV of the GATT.

Keywords: tariff, tax revenue, trade adjustment, Nepal JEL Classification: F13, F21, H20, O17 Sector Board: EPOL

> 1 Consultant, The World Bank. I am grateful to Prema-chandra Athukorala for helpful comments and guidance. I thank Paul Brenton, Mombert Hoppe and Olivier Jammes for facilitating the use of the Tariff Reform Impact Simulation Tool (TRIST) developed by the World Bank. I also thank Nepali officials in the Ministry of Finance and the Department of Customs for granting me access to the Automated System for Customs Data (ASYCUDA). And I acknowledge with gratitude Thomas Baunsgaard and Michael Keen of the International Monetary Fund (IMF) for sharing their data set on taxes. All errors are mine.
2007 study suggests that PROPER has reduced emissions intensity, with a particularly rapid and strong impact on firms that have poor initial compliance records. Their 2009 study finds a strong reactive response during the first six months of disclosure, followed by a more moderate, but still significant, longer-run response as management adjusts to the new regime.

This study extends PRD assessment to China, using panel data on pollution from rated and unrated firms, before and after implementation of the Green Watch program. Our work offers two main contributions to the literature. First, we exploit the panel structure of the data to control for confounding factors such as time-variant technology improvement and selection bias between rated and unrated firms. Second, we go beyond a single measure of environmental performance to consider the impact of ratings disclosure on several measures, including emissions intensity and effluent concentrations for a variety of air and water pollutants.

The remainder of the paper is organized as follows. Section 2 reviews the relevant literature, focusing on the role of PRD programs in developing countries. Section 3 describes China’s Green Watch program, while Section 4 describes our survey instrument and provides descriptive statistics for major variables. Section 5 presents our estimation model and results, and Section 6 summarizes and concludes the paper.

# **2. Previous research**

The literature on pollution control policies includes extensive work on command-andcontrol, market-based and information-based instruments [6]]. Command-and-control instruments are often inefficient and ineffective in developing countries, because firms may fail to report adequately, regulators may lack the technical and administrative capacity for effective monitoring and enforcement, and judicial systems may be weak and/or corrupt. These same
6. By the late 1960s and early 1970s, concern had shifted to preventive health care and access to rural health services. The expansion in the number of health centers and subcenters<sup>created a</sup> need for more auxiliary health workers, such as medical assistants, nurses, and health<sup>and sanitation</sup> inspectors. In the 1970s and 1980., the increased emphasis on primary health care meant that<sup>health</sup> personnel development became concerned primarily with commuity health workers<sup>and traditional</sup> birth attendants working in village-based services. Associated issues were<sup>community participation,</sup> intersectoral coordination, and local-level management.

7. Although the emphasis from the 1950s to the 1980s shifted in turn<sup>from hospitals to health</sup> centers and hedth posts, hospitals and physicians retained their central and dominant<sup>roles in sub-</sup> Saharan Africa, as is evident in the allocation of financial resources. Despite<sup>a decade of policies in</sup> support of primary health care, most hospitals have maintained or even expanded<sup>their share of national</sup> health budgets, and their favored stus is promoted by the medical profession and the urba& establishment. The main supporters of changes in health personnel policies have often been public health physicians in ministries of health, despite considerable opposition from<sup>their clinical colleagues.</sup> 8. The _production_ phase of health personnel development has received by far the greatest attention. During the 19508 and 1960s, all countries in sub-Saharan Africa were critically<sup>short of</sup> health workers, and their health worker to population ratios were much lower than those in<sup>other parts</sup> of the world (WHO 1988a). Since then, many African countries have successfiuly expanded<sup>the</sup> availability of health workers and have tried to achieve a good balance or mix between different cadres of health personnel. Women make up more than half of the health work force in mnany countries. Despite these efforts, sub-Saharan Africa is still very short of health workers of all grades<sup>(WHO</sup> 1988a: Roemner 1988; see Table 1).

Table 1: Number of physicians per 10,000 inhabitants, by WHO Region

|[WHO<br>REGION|PHYSICIANS|DENTISTS<br>l________|PHARMACISTS<br>___________|<br>MEDICAL<br>ASSISTANTS|NURSES<br> <br>MIDWIVES|
|---|---|---|---|---|---|
|Africa|1.0|0.1|0.2|0.4|11.9|
|Americas|15.5|4.5|_5.1_|0.0|42.0|
|Eastern<br>Mediterranean|_5.1_|0.6|1.0|1.4|12.5|
|Europe|24.4|2|4.0|0.2|54.5|
|Southeast Asia|3.3|0.1|1.5|2.9|8.2|
|Westem Pacific|6.3|0.5|2.4|3.7|27.0|
|All Regions|9.4|1.1|_2.5_|2.1|26.7|

Source: Fulop 1 , based on data available **to WHO** m 986.

9. Of the six World Health Organization regions, Africa was the only one that<sup>showed a</sup> deterioration in the doctor to population ratio from 1980 to 1986 (Table 2). This trend<sup>has been</sup> exacerbated by the lack of training centers in many African countries, the emigration oi senior professionals, and high rates of population growth.

_5_
sector. Successful marketing requires the flexibility to respond to changing business conditions and investor needs, calls for close interactions with the private sector and relies on the ability to generate and implement consistent promotional strategies throughout a long period of time. Usually, government entities are neither flexible nor immune to political interference from changing governments. Investor servicing also requires a good understanding of the needs of the private sector. Other private sector advantages in investment promotion include cost containment, flexibility in hiring and firing and ability to pay salaries above the civil service levels which allows the agency to hire highly skilled and motivated staff.

Our results confirm the intuition of Wells and Wint. In regressions not reported here, we find that agencies with a more autonomous status (i.e. agencies which are not subunits of a ministry) are more effective in attracting FDI inflows than subunits of a ministry.<sup>32</sup> In Table 20 (the first two columns), we show that this pattern is confirmed when we consider cases of the status change. We find that agencies starting out as subunits of a ministry become more effective when they gain more autonomy (that is they change their status to being either an autonomous public body, semiautonomous agency reporting to a ministry, joint public-private or private entity), compared to those that remain subunits of a ministry.

In a related exercise (also Table 20), we find that IPAs accountable to external entities (in addition to or instead of being accountable to the agency’s board) tend to be more effective. Further, we find that agencies accountable to entities dealing with economic issues rather than political issues (ministry of finance versus ministry of foreign affairs for example) are more effective in attracting FDI.<sup>33</sup> An explanation for this result might be that the work of an IPA is of economic nature and its involvement in the design of economic policy might make the IPA more effective in its mission.

> 32 We define “more autonomous” as having a status other than a sub-unit of a ministry.  In the Census, 30 IPAs had the status of a sub-unit of ministry, 26 an autonomous public body, 43 a semi-autonomous agency reporting to a ministry, 3 a joint public-private entity and 2 a private entity (figures for 2004).

> 33 We defined economic institutions as ministry of finance, ministry of industry, ministry of commerce or other related institutions. The group of political institutions consists of president, prime minister and ministry foreign affairs. A surprising result was that agencies reporting to the highest political level (president or prime minister) were not found to be more effective than others, which contradicts the conclusions of Morisset (2003).

27