rafmacalaba/datause-encoder-data huggingface dataset:
equation 1 with the monthly average emissions rate of each of these three pollutants and re-estimate the same difference-in-differences model on the national sample.

# **7.1 Direct effect of MNREGA on pollutant emissions**

To begin I present the event study for the emissions rate of each of the three pollutants in Figure 4. Each panel shows the trend in emissions rates for black carbon, organic carbon, and SO2 before and after the implementation of MNREGA. In all three panels there is a clear increase in emissions rates in the year of MNREGA implementation that continues to grow in the year after implementation before leveling off and remaining above pre-MNREGA levels. For both black carbon and organic carbon the pre-trends are relatively flat and not-distinguishable from zero. SO2 shows more of an increasing trend but this is driven primarily by the estimates on the earliest years in the sample.<sup>27</sup> For all three pollutants that I examine, the implementation of MNREGA significantly increases the emissions rate from biomass burning. Beginning with black carbon, column 1 of Table 6 indicates that MNREGA increased the emissions rate from biomass burning by 38%. Organic carbon emissions also increase by 37% while SO2 emissions increase by an estimated 49%.

These estimated effects indicate substantial increases in the average monthly emissions rates as a result of the implementation of MNREGA. They provide some confirmatory evidence that MNREGA increased the number of fires; if fires increase one would expect to see a corresponding increase in emissions from biomass burning.

The size of the change in emissions rates is substantially larger than the estimated change in the number of fires. There are two explanations for this. The first is that the quantity of biomass burned per fire may have increased. This would be consistent with the model explored below of increased fires being driven by increased combine use. Previous estimates suggest fields cleared with combines have roughly 100% more biomass than fields cleared manually (Yang et al., 2008). To the extent that fires that consume more biomass have higher emissions (Smil, 1999) this increase in biomass is comparable to the difference between the estimated increase in emissions rates and the increase in fires.

> 27I pool the early and late years of the sample because I do not have a balanced sample in event time. In other words, the estimates of the coefficient on event time -4 are only identified by districts in Phase 2 or Phase 3. As a result, I pool event time -5 and -4 with -3 in Figure 4. If I do not do this pooling, and drop event time -4 and -5 instead, the slight trend disappears.

22
Andrews-Johnson (2004) also concluded that IPAs appear to play a useful role in attracting FDI. However, all three studies were based on cross-sectional data and a very small number of observations (between 36 and 58).

Contributions studying FDI inflows into the US have relied on more detailed data and employed a more rigorous methodology. Head, Ries and Swenson (1999) estimated a location choice model on a sample of 760 Japanese manufacturing establishments in the US between 1980 and 1992. In addition to agglomeration factors and other controls, the authors examined the effects of profit taxes, factor subsidies, the existence of a foreign trade zone in the state, the use of unitary taxation by the state, and the presence of an investment promotion office in Japan. They did not find a significant effect of investment promotion offices in Japan. Bobonis and Shatz (2007) analyzed determinants of the FDI stock in US states from eight source countries using ArellanoBond dynamic panel data estimator. They measured investment promotion with the number of years a state had a full-time state trade or investment office in each of the eight countries. They found that a one-percent increase in the number of years with an investment office increased the FDI stock by between 0.14 and 0.27 percent. In their sample, the latter figure corresponded to a one million USD (at the median) or a 2.6 million USD (at the mean) increase in the value of the FDI stock.<sup>8</sup> The most rigorous analysis to date performed in a multi-country setting was done by Charlton and Davis (2006). The authors focused on the question of whether IPAs have been more successful in attracting FDI inflows into industries they explicitly target. Industry-level data on FDI inflows into 19 industries in 22 OECD countries during the 1990-2001 period combined with information on targeted industries collected through a survey of IPAs provided the basis for their study. Using propensity score matching and the difference-in-differences specification, the authors found that targeting of an industry increased the growth rate of FDI inflows into that industry by 41 percent.

This study extends the existing literature in several directions. First, it applies a rigorous approach, similar to that pioneered by Charlton and Davis (2006), to a large sample of developed and developing countries. As mentioned before, little is known about the effectiveness of investment promotion in developing countries. One could argue that investment promotion could

> 8 The literature on location determinants of FDI in the US goes back to at least the 1980s. See Bobonis and Shatz (2007) and Coughlin and Segev (2000) for a review.

10
Table 10: Effects on women’s time use

||(1)<br>Taking care<br>of children|(2)<br>Cooking/washing/<br>housework|(3)<br>At school|(4)<br>Doing<br>paid work|
|---|---|---|---|---|
|**Full sample**|||||
|ITT estimate|-0.280|-0.130|0.018|0.613***|
||(0.396)|(0.148)|(0.100)|(0.200)|
|Observations|1961|1964|1963|1963|
|_R_<sup>2</sup>|0.249|0.184|0.527|0.135|
|Control mean|4.800|2.810|0.402|3.461|
|**Have child aged 0-2**|||||
|ITT estimate|-0.069|-0.006|0.044|0.650**|
||(0.387)|(0.153)|(0.094)|(0.253)|
|Observations|1122|1125|1124|1124|
|_R_<sup>2</sup>|0.266|0.170|0.581|0.125|
|Control mean|4.765|2.723|0.418|3.266|

_Notes:_ Each column reports the number of hours spent on a given activity in the last 24 hours. Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

Table 11: Effects on women’s psychological well-being

||(1)|(2)|(3)|
|---|---|---|---|
||Depression score|Depressed (score_>_10)|Unhappiness|
|**Full sample**||||
|ITT estimate|-0.631|-0.075*|-0.143**|
||(0.476)|(0.040)|(0.056)|
|Observations|1957|1957|1957|
|_R_<sup>2</sup>|0.114|0.089|0.077|
|Control mean|10.920|0.604|2.315|
|**Have child aged 0-2**||||
|ITT estimate|-0.350|-0.054|-0.127**|
||(0.412)|(0.035)|(0.055)|
|Observations|1121|1121|1121|
|_R_<sup>2</sup>|0.111|0.087|0.102|
|Control mean|10.701|0.592|2.288|

_Notes:_ Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

39
An ordered logit estimation technique was used in the following analysis to examine what factors determined the quality of developing country MBA graduates. Four points should be noted. First, there are four separate econometric analyses based on the technical skills, communication skills, language skills, and overall strength of local MBA students. Second, the significance of the coefficients is based on z-statistics (rather than the usual t-statistics) because of the ordered logit estimation procedure. Third, any numerical interpretation of the coefficients is difficult due to the nature of the regression analysis. Finally, to determine the ‘goodness of fit’ the results include chi-squared values (based upon the likelihood ratio), since a maximum likelihood estimation procedure is being used.

# _D.  Analysis of the Technical Skills of Local MBA Students_

The MBA survey asked firms to rank the technical skills of local MBA graduates (discussed in Section IV). The purpose of the econometric analysis is to see what firmspecific and country factors can be isolated in terms of their effect on firm ratings of technical skills.

The results of the ordered logit regression are given in Table 6. The first thing to note is that all of the firm-specific variables are found to be insignificant in the analysis. Thus firm size, sector, skill requirements and number of MBAs hired yearly do not seem to affect the rating of local MBA technical skills.

On the other hand, the country-specific and region-specific variables do influence the technical skills rating. As the table shows, three regions have significantly lower ratings for MBAs in the area of technical skills: Africa, the Middle East, and South Asia. But GDP per capita was not found to be a significant factor in determining the technical skills of local MBAs, nor was the number of business students in the country (as a proportion of population).

These results are in keeping with the previous analysis, in which the only significant factor in the analysis of the technical skills of local MBA graduates was the regional location of the firms and the schools which produced these MBAs.

25
# **1. Executive Summary**

This paper provides a statistical analysis of a survey carried out by the Foreign Investment Advisory Service (FIAS, a multi-donor facility of the World Bank Group) in the Russian Federation about businesses’ access to land and real estate. The survey was carried out at the request of the Ministry of Economic Development and Trade and the Federal Anti-Monopoly Service. The survey data were used to test a number of hypotheses about the administrative procedures for businesses’ access to land, as described below.

The “Survey of Land and Real Estate Transactions in the Russian Federation”<sup>2</sup> investigated the problems faced by businesses in carrying out land and real estate transactions. The survey covered 15 regions which represent all 7 Federal districts of the Russian Federation. In the course of the survey information was collected from 517 business intermediaries that helped clients with land and real estate transactions in 2004 and 1188 legal entities and sole proprietors that attempted, underwent or completed locating procedures (i.e. land and real estate transactions) in 2004.

The Survey, in turn was inspired by past FIAS projects investigating administrative barriers to investment, including businesses’ access to land and real estate, and by a report on “Privatization of Enterprise Land in the Russian Federation: 1992 – 2003”<sup>3</sup> prepared by Khakhalin and Butler for USAID, which described the legal history and economics of the issue and its implications for further policy development.

Between the survey, past research and a mass of anecdotal evidence about businesses’ access to land in the Russian Federation, we developed a number of “stylized facts” as follows:

- Land privatization in the Russian Federation has a checkered history, with a clear policy in favor of land privatization not firmly established until the enactment of the 2001 Land Code. Even after the enactment of the Land Code, many vital parameters of land privatization, including pricing parameters, are still not settled.

- The new Land Code of the Russian Federation explicitly calls for land to be privatized as follows:

- Land under buildings that were privatized earlier is supposed to be sold (or leased) to the owner of the building at an administered price within parameters set by federal legislation; (this category accounts for the great majority of land transactions involving businesses in the Russian Federation);

- Vacant land intended for new construction is supposed to be privatized by transparent auction or tender procedures (although there have been relatively few transactions of this type to date).

> 2  http://www.fias.net/ifcest/fias.nsf/Content/FIAS_Resources_Country_Reports

> 3  Khakhalin, Andre A. and Stephen B. Butler, “Privatization of Enterprise Land in the Russian Federation: 19922003” prepared for USAID.

1
More recent concern is associated with the extra-regional climate effects of large-scale forest loss. A key issue here is that the Amazon forest influences the regional climate of areas such as the U.S. Midwest via climatic tele-connections (Avissar & Werth, 2003). There is an enormous accumulation of data from the LBA and associated NASA supported studies (Davidson et al. 2012), but little in the way of conclusive results to suggest the magnitude of rainfall availability in critical crop production regions that could be affected by a given proportionate loss in forest area. This is a line of inquiry that is attracting considerable attention from researchers connected with the UK funded ESPA program, as a basis for international payments for ecosystem services. How much more we can add to this kind of analysis is the question.

# **3. Opportunity Costs of Amazon Forest Protection**

# **3.1 Agricultural Outputs on Cleared Land**

Cattle herd expansion has been the single factor contributing most to Amazon rainforest destruction. The Brazilian cattle herd grew from 147 million head of cattle in 1990 to about 200 million by 2007. Eighty-three percent of this expansion occurred in the Amazon, and it is to be feared that this trend could continue as the industry bounces back from a recent agricultural downturn (Bowman et al., 2012). An estimated 18.5% of the Brazilian Amazon has been converted from forest to other uses, most during the past 30 years. Although 80% of this area is dedicated to beef cattle ranching on extensive pastures, its direct impact on deforestation is unknown.

Conversely, only 13–18 percent of deforestation is caused by conversion to soy crops (Morton et al., 2006; Lima et al., 2011), of which less than 6 percent can be attributed to biodiesel (Lima et al., 2011). However, there is a growing concern that soy expansion fuels deforestation elsewhere as it displaces cattle ranchers to inner Amazon frontiers, where land is still cheap (Nepstad et al., 2006; Arima et al., 2011). Soy crops are expanding specially on older frontiers; during the 20012004 period in Mato Grosso, more than one-third of the area where soybean plantings expanded was previously in pasture (Morton et al., 2006), and this increased demand for land plays an important role in driving the expansion of extensive ranching at the deforestation margin along with the more intensive cultivation of soy and other crops on older frontiers, both directly and indirectly (Bowman et al., 2012). Today, soy and sugarcane occupy large swaths of the southern and central-western Brazilian Cerrado and have expanded into the Amazon region despite the official ban on growing sugarcane there (Manzatto et al., 2009), raising concerns about environmental and social consequences.

23 | P a g e
<!-- Start of picture text --> Figure 3a Sources of India's Imports 2007 Figure 3b Destinations of India's Exports 2007<br>NAFTA<br>SSA<br>2%<br>ROW USA 5% ROW<br>USA<br>13% 9% SAFTA 9%<br>18%<br>4%<br>SSA<br>NAFTA<br>SAFTA<br>6%<br>2%<br>1% EU27 ASEAN<br>20% 6%<br>ASEAN<br>10%<br>MENA<br>EU27<br>14%<br>LAC 29%<br>3%<br>MENA China China<br>Japan<br>24% 9% 7%<br>3%<br>Japan LAC<br>2% 4%<br><!-- End of picture text --> ASEAN = Association of South East Asian Nations, EU = European Union, LAC = Latin America and the Caribbean, MENA = Middle East and North Africa, SA = South Asia, SSA = Sub-Saharan Africa, NAFTA = North American Free Trade Agreement, SAFTA = South Asian Free Trade Area.

# _Source_ : GTAP 8 Database.

Notes: NAFTA share consists of Canada and Mexico and does not include the United States. SAFTA share does not include India.

8
**Table 3.4. Estimation results with investment-grade and non-investment grade samples**

|**Dep. var. = CDS spread, log**|**1A**|**1B**|**2A**|**2B**|
|---|---|---|---|---|
||**Investment grade N**|**on-investment grade**|**Investment grade**|**Non-investment grade**|
|Public investment (share of GDP)|-1.470|-1.116|-5.233*|-8.919**|
||(1.660)|(2.103)|(2.750)|(3.537)|
|PIQ-F|-0.647|-2.875***|||
||(0.534)|(0.963)|||
|PIQ-F × Public investment (share of GDP)|-4.150|-35.450***|||
||(18.991)|(10.700)|||
|PIQ-D|||0.144|-0.930|
||||(0.508)|(0.650)|
|PIQ-D × Public investment (share of GDP)|||9.463|-57.489**|
||||(15.494)|(25.532)|
|General government gross debt, percent of GDP|<br>-0.000|0.008**|0.005|0.005|
||(0.003)|(0.003)|(0.009)|(0.011)|
|Total reserves (percent of GDP)|-0.002|-0.013|-0.005|-0.015|
||(0.005)|(0.008)|(0.009)|(0.010)|
|Exchange rate (LCU per USD), log|-0.026|-0.020|0.019|1.322**|
||(0.026)|(0.028)|(0.361)|(0.616)|
|Inflation, consumer prices (percent)|0.067***|0.025**|-0.011|0.012|
||(0.023)|(0.012)|(0.014)|(0.012)|
|Real GDP growth (percent)|-0.042*|-0.031**|-0.031*|-0.006|
||(0.023)|(0.011)|(0.014)|(0.011)|
|Real GDP per capita, log|-0.109|-0.375|-0.973|-2.791**|
||(0.142)|(0.230)|(0.733)|(1.261)|
|Current account balance (percent of GDP)|0.008|-0.036*|-0.028*|-0.029***|
||(0.008)|(0.018)|(0.015)|(0.009)|
|Systemic banking crisis DV|0.236|1.896***|0.785***|2.674***|
||(0.211)|(0.529)|(0.221)|(0.485)|
|Currency crisis DV|0.136|0.128|-0.255**|-0.354|
||(0.149)|(0.248)|(0.104)|(0.305)|
|Debt crisis DV||2.176**||1.547**|
|||(0.939)||(0.714)|
|Debt restructuring DV||0.915***<br>(0.230)||-|
|Year FE|yes|yes|yes|yes|
|Country FE|no|no|yes|yes|
|Observations|230|278|95|128|
|Adj. R-sq.|0.708|0.449|0.865|0.846|

# _Source:_ Authors’ estimations.

_Note:_ *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. Country-level clustered standard errors are in parentheses.

26
Policy ReseaRch WoRking PaPeR 4290

# **Abstract**

This paper summarizes the methods and findings of the hydrological assessment component of the project studying likely impacts of climate change on water resources and agriculture in Africa.

The first phase of the study used a version of a conceptual rainfall-runoff model called WatBal (Water Balance) applied to gridded data to simulate changes in soil moisture and runoff across the whole continent of Africa rather than to any particular catchment or water resource system. The model inputs were the climate variables of the 1961–90 climatology and physiological parameters (such as soil properties and land use) derived from global datasets for each of the 0.5<sup>o</sup> latitude/ longitude cells across the continent. The primary model output comprised a time series (monthly time step) of simulated runoff for all the grid cells for each of the districts in the countries of interest.

The second phase of the study extended the hydrology analyses to update the above hydroclimatic series to the year 2000 using updated input data. To ascertain the possible impacts of climate change within the districts being investigated this study used synthetic or GCMbased climate change scenarios as input to the WatBal model. The WatBal model was used to determine the impact of these different scenarios on runoff and actual evaporation and hence flow in the districts under study. The generated hydroclimatic series and scenario analyses were used as inputs into various Ricardian regressions in other analyses measuring likely impacts of climate change on the agricultural economies of Africa.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the group to mainstream climate change research. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Pauline Kokila, room MC3-446, telephone 202-4733716, fax 202-522-1151, email address pkokila@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at strzepek@colorado.edu. July 2007. (62 pages) _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
31 traffic between day and night by railways companies or to model allocation between public and private vehicles in urban settings. These models are generally too aggregate to meet the needs of a regulator but they are used anyway because they can provide useful initial insights. What matters from the viewpoint of a regulator is that the cost account for many of the variables that will be subject to regulatory supervision such as tariff/tolls or government control such a fuels but these are seldom take into account in a sensitive enough way to be useful because they do not account well enough for the different characteristics of the various types of users.

The main aggregate methods can be classified in 2 groups: qualitative or intuitive methods and "mathematical or statistical" methods. Among the qualitative methods, it is worth pointing to a series of techniques based on expert assessment or field surveys of expectations designed to complement the quantitative estimates. The expert assessment is least refined since it assumes an expert can assess the impact of mutliple factors on a potential _new_ business environment-since in the context of privatization, new business opportunities are typically being created.

Field surveys of expectation are targeted to anyone who can be affected by the project and is in a priviledged position to assess the prospects. A refined version of these surveys is provided by the techniques called _Delphi,_ where the experts forecast and get a _feed-back_ on the results from the full sample of people surveyed allowing a series of iterations until some degree of convergence is achieved. This approach is used by IATA or EUROCONTROL for instance for airline traffic forecast. These techniques can be useful at the national or regional level but are unlikely to be useful to forecast traffic and traffic risk on a given corridor. Finally, the technique of _brain-storming,_ quite common in socioliogy, consists in putting together a group, for instance users, and through in a series of question to be discussed by the group. This technique is used by Puertos del Estado in Spain complement the more analytical forecasts of the spanish ports.

The more quantitative methods to forecast aggregate demand can be classified into: trends extrapolations-assuming a linear, exponential, quadratic or some other function that seems to fit past observations well--, fancier time series analysis and more sophisticated econometric work. Extrapolation is the weakest but also the most commonly used around the world. The future is assumed to be determined by what happened in the past. It is mostly useful for very short run needs and has a better chance of working on monthly data than on annual data. But this is