rafmacalaba/datause-encoder-data huggingface dataset:
# References

- Alfaro, Laura, Andrew Charlton, and Fabio Kanczuk. 2008. “Plant Size Distribution and CrossCountry Income Differences.” NBER Working Paper 14060.

- Alfaro, Laura and Anusha Chari. 2014. “Deregulation, Misallocation and Size: Evidence from India,” _Journal of Law & Economics_ 57(4): 897-936.

- Asker, John, Collard-Wexler, Alan, and Jan de Loecker. 2014. “Dynamic Inputs and Resource (Mis)Allocation,” _Journal of Political Economy_ 122(5): 1013-1063.

- Banerjee and Duflo. 2004. “Growth Theory through the Lens of Development Economics,” in Aghion Philippe and Steven Durlauf (Eds.) _Handbook of Economic Growth_ , Vol. 1. Amsterdam: Elseveier.

- Bartelsman, Eric, John Haltiwanger, and Stefano Scarpetta. 2009. “Measuring and Analyzing Cross-country Differences in Firm Dynamics,” in Dunne, Timothy, J. Jensen, and Mark J. Roberts (Eds.) _Producer Dynamics: New Evidence from Micro Data_ . Chicago: University of Chicago.

- Bartelsman, Eric, John Haltiwanger, and Stefano Scarpetta. 2013. “Cross-Country Differences in Productivity: The Role of Allocation and Selection,” _American Economic Review_ 103(1): 305-334.

- Bento, Pedro, and Diego Restuccia. 2017. “Misallocation, Establishment Size, and Productivity,” _American Economic Journal: Macroeconomics_ 9(3): 267-303.

- Buera, Francisco J., Joseph P. Kaboski, and Yongseok Shin. 2011. “Finance and Development: A Tale of Two Sectors.” _American Economic Review_ 101(5): 1964-2002.

- Buera, Francisco J. and Joseph P. Kaboski. 2012. “Scale and the Origins of Structural Change,” _Journal of Economic Theory_ 147:684-712.

- Durlauf, Steven D, Paul A. Johnson, and Jonathan R.W. Temple. 2005. “Growth Econometrics,” in Aghion Philippe and Steven N. Durlauf (Eds). _Handbook of Economic Growth,_ Vol. 1. Amsterdam: Elsevier.

- Eaton, Jonathan and Samuel Kortum. 2002. “Technology, Geography, and Trade,” _Econometrica_ 70(5): 1741-1779.

- Eaton, Jonathan, Samuel Kortum, and Francis Kramarz. 2011. “An Anatomy of International Trade: Evidence from French Firms,” _Econometrica_ 79(5): 1453-1498.

- Foster, Lucia, John C. Haltiwanger, and C.J. Krizan. 2001. “Aggregate Productivity Growth: Lessons from Microeconomic Evidence,” in Hulten Charles R., Edwin R. Dean, and

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the five allocation choices; and participants knew that would happen. For each rate of return, the participant set out an allocation of tokens to the dishes, the tokens were translated into Malawi kwacha, and the total was written above each dish on a whiteboard. Having seen the allocation in kwacha, the participant had the opportunity to adjust the allocation. This process was repeated until the participant indicated that she was ready to move on to the next allocation choice.

After completing the first five choices, the participant answered a series of other questions from the baseline survey. (See Appendix A for details.) Then, using the same elicitation method with cup, beans, and dishes, the participant again made five independent choices regarding 2000MK, while facing different rates of return for waiting. This time, each of the five choices concerned the allocation of money between 60 and 90 days from tomorrow (the “far” time frame). Figure 1 presents a schematic of the method used to elicit intertemporal choices under commitment.

The interruption between the five choices in the near period and the five choices in the far period was intentional. We sought to avoid having participants choose the same allocations in both time frames simply for the sake of being (or appearing) consistent. In addition, the order in which the time preference sections of the questionnaire were administered was, in fact, randomly assigned between households within tobacco clubs. With 50 percent probability, a participant was first asked to make decisions about the allocation of money in the “near” time frame, between tomorrow and 30 days from tomorrow. Otherwise, a participant was first asked to make decisions about allocations in the “far” time frame, between 60 and 90 days from tomorrow. Controlling for any effects of these alternative orders does not affect the results, and the order in which time frames were presented does not predict choices.

Before making their choices, each participant knew that one member of the couple would be randomly chosen to have one of his or her choices implemented (also randomly selected). The randomization was performed on site, by rolling dice. Implementation took the form of a voucher, redeemable at the disbursement office in Mponela. The voucher indicated the allocation (some amount at time _t_ and another amount at time _t_  30 ) and was issued to the member of the couple who was randomly chosen. Identity of the recipient was established with a name and a fingerprint placed on the voucher.<sup>24</sup>

> 24 Giné, Goldberg, and Yang (forthcoming) find that fingerprinting is an effective means of economic identification

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**<mark>Hypothesis 5</mark>** <mark>:</mark> _<mark>An increase in the benefit of being included in the emerging market corporate debt index</mark>_

_<mark>causes some medium-sized firms, which previously would not have issued a sufficient amount of debt to gain inclusion in the index, to issue bonds large enough to gain inclusion in the index. The change in the probability of issuing large bonds should be greater for medium-sized firms than for firms in the upper and lower tails of the size distribution.</mark>_ _<mark>lower tails of the size distribution.</mark>_ <mark>Lastly, an increase in the size yield discount D has no effect on the cash holdings of sufficiently large firms, defined as those that would issue 500 or more in debt irrespective of the changes in the yield discount. In contrast, medium-sized firms that prior to the increase in D would have chosen to issue X in debt, respond to the increase in D by choosing to issue 500 in debt, rather than X < 500, and accumulate cash equal to (500 – X). Thus, within the group of firms that choose to issue 500 in bonds, firms of relatively small size will increase their cash holdings more than relatively large issuers of large bonds. We summarize this result in Hypothesis 6:</mark>

**<mark>Hypothesis 6</mark>** <mark>:</mark> _<mark>Within the group of large bond issuers, relatively small-sized firms will increase their cash holdings by more than relatively large-sized firms.</mark>_

# **<mark>3. Data</mark>**

<mark>We use data from different sources. The data on bond issuances come from the Thomson Reuters Security Data Corporation Platinum database (SDC Platinum). This database contains transaction-level information on new issuances of corporate bonds by public and private firms. From this database, we obtain the date a bond is issued, the face value of the bond, and the yield to maturity at issuance. SDC Platinum also contains additional information that we employ, including the rating of the firm at issuance, the country of the firm, the industry of the firm, the market in which the bond is issued, the type of bond (fixed or flexible coupon), the currency of the bond, whether the issuance is public or private, and the maturity at issuance of the bond.</mark> 14
have any signi...cant e¤ect. There are 842 households in the NHSC 2010 survey in large cities (‘metropolitan cities’), out of which 753 availed educational services, and 246 paid bribes. When we regress the amount of bribes paid on the heavy rainfall dummy and a constant, the coe¢ cient is 0.078 with a ’t’statistic equal to 0.35 (column (3) in the lower panel of Table 1). Both the coe¢ cient and the ‘t’statistic are barely a¤ected if we control for income (see column (4) in the lower panel of Table 1). However, one may be concerned that the statistical insigni...cance of the heavy rainfall dummy in this case may be due largely to the small sample size (246 observations). We thus rely on the results for propensity to pay bribes where the sample size is more than three times as large (753 observations) for more credible evidence regarding the e¤ects of the heavy rainfall dummy on corruption in the large cities. The results are presented in columns (1) (without income as a control) and (2) (with income as a control) of the lower panel of Table 1. The evidence is clear and convincing. The heavy rainfall dummy does not have any statistically signi...cant e¤ect on the probability of paying bribes in the case of households that live in large cities; the ‘t’statistic is 0.18 in both the speci...cations. This provides strong evidence in favor of our identi...cation scheme. Also, note that the inclusion of income does not a¤ect the coe¢ cient of heavy rainfall in any of the regressions, which suggests that heavy rainfall does not a¤ect income signi...cantly. This is con...rmed by the results on income in the last column of the lower panel in Table 1; the ‘t’statistic for the coe¢ cient on heavy rainfall is 0.92 (P value 0.36).

# (4.2) Heterogeneous E¤ects of Heavy Rainfall and Interactions-based Instruments

As noted in the introduction, we provide additional evidence by using the interactions of the heavy rainfall dummy with household characteristics as identifying instruments. The interactions as instruments exploit possible heterogeneity across households in the e¤ects of heavy rainfall. For example, we expect that heavy rainfall (and ‡ood) will have stronger e¤ects on the income of those households who rely more on agriculture, such as farming households and agricultural wage laborers (unskilled labor). Thus an obvious way to introduce household heterogeneity is to interact the land owned by a household with the heavy rainfall dummy. However, there are two objections to this. First, to ensure that the exclusion restriction imposed is reasonable, we need to control for direct e¤ects of land (possibly nonlinear), which would nullify a large part of the income e¤ect we are trying to capture using rainfall variations for identi...cation. Second, 21
behalf of their exporters. Second, we provide evidence that the ACWL allows developing countries to pursue the DSU legal process more fully in support of a given market access enforcement interest. Third, we provide preliminary evidence of a “scale” effect, i.e., that the impact of the ACWL may operate through the size of the market access at stake in enforcement actions. Specifically, the ACWL-backed disputes involve smaller amounts of trade relative to otherwise similar non-ACWL-backed cases.

Based on these results, we highlight an additional need of developing countries for support of their WTO self-enforcement efforts. The data suggest that when developing countries have good information regarding a foreign market access violation, they are able to pursue it through the DSU process. Establishment of the ACWL has largely overcome the obstacle posed by the high cost of the legal assistance required for WTO litigation. But inadequate information about possible violations of their market access rights remains an important obstacle that exporters in developing countries face in their WTO enforcement efforts. There is a clear role for further assistance, whether from the private or public sector, through monitoring of policy changes that affect market access of developing country exporters. Informing firms and their governments when their WTO rights may have been violated can strengthen the WTO’s self-enforcement mechanism and thus offer greater market-access security to smaller exporters in poor countries.

# **2. The Data on Developing Countries and WTO Dispute Settlement**

During the period from 1995 through 2008, WTO members initiated 388 formal disputes via requests for consultations.<sup>2</sup> Viewed in historical perspective, WTO member countries initiated almost 50 per cent more disputes during the WTO’s first fourteen years than the GATT Contracting Parties did over the

> 2 In this section we rely on two sources of data.  The first is fundamental data on WTO dispute initiations through the end of 2008.  These data are available on the WTO website.  The second source is a comprehensive database on WTO disputes through 2006.  These data, available on the World Bank website, were compiled in a project organized by Horn and Mavroidis (2008) with funding from the World Bank.

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mainly due to overconfidence in the future and less of a need to invest in human capital (Gylfason, 2001). Rajkumar and Swaroop (2008) demonstrate that efficiency of public spending in education is affected by the quality of governance (measured mainly by quality of bureaucracy and the level of corruption).

In our first step, we test if accountability vis-à-vis citizens can affect the quality of public spending in education. Using the Rajkumar-Swaroop specification, we introduce a measure of voice and accountability<sup>6</sup> extracted from Kaufman et al. governance indicators<sup>7</sup> .

Table 2 gives the results of this first step<sup>8</sup> . As expected, voice and accountability have indeed a strong association with the education outcome (secondary enrolment) even after controlling for spending level, GDP per capita and level of urbanization. The greater is the possibility for citizens to raise their voice, the better is the outcome of public spending. It is worth noting that even though control of corruption is with the expected sign, it is not significant, which may be explained by the fact that control of corruption and voice and accountability are correlated.

> 6 Voice and accountability measures the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media (estimate). Source: Kaufman governance indicators.

> 7 For definition and sources of variables, see annex 1. We used the voice and accountability measure and not the others because of the strong correlation between the four measures.

> 8 Series are not highly correlated with the exception of gdp and living in urban areas.

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# Figure 1: LIC government finances

# (A) Gross government debt

<!-- Start of picture text --> Percent of GDP<br>140 Median<br>120 Interquartile range<br>100<br>80<br>60<br>40<br>20<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

## (B) Gross government debt by LIC category

<!-- Start of picture text --> Percent of GDP<br>60<br>All LICs<br>Commodity exporters<br>Others<br>40<br>20<br>2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->

# (C) Primary fiscal balance

(D) LICs with largest increase in government debt <!-- Start of picture text --> Percent of GDP<br>4 LIC median<br>Commodity exporters<br>2 Others<br>0<br>-2<br>-4<br>-6<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> <!-- Start of picture text --> Percent of GDP<br>150<br>Mozambique Zimbabwe<br>125 Sierra Leone Gambia, The<br>Benin<br>100<br>75<br>50<br>25<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Source: International Monetary Fund, World Bank. A. Dashed blue lines denote the interquartile range, while the solid blue line is the median. A.B.C. The sample includes 30 low-income countries, of which 2 are oil exporters, 8 metals exporters, and the remaining 20 are non-resource-intensive. It excludes Somalia, South Sudan, and Syria due to data restrictions. A.B. Figure shows median gross government debt in percent of GDP. B.C.D. LICs= Low-income countries.

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substantial period after the second oil shock. Since it is widely agreed that the life of capital in the power sector is of the order of twenty years, it would be desirable to re-estimate Griffin's model using data which covered all of the 1980s. There have been a large number of studies looking at the comparative economics of different fuels for power generation and trying to identify the break even price but either these are hypothotical greenfield studies or else they are situation specific. Few general lessons can be drawn from these studies particularly since the conclusions are always very dependent on the costs and performance of the technology investigated and the price path assumed for the competing fuels. The method is perfectly suitable for application wherever there is data but, by its very nature, the quantitative aspects of the results cannot be generalized.

Elasticities of Fuel Switchina in Industry Unlike the power sector there has been a continuous interest in econometric modelling of fuel choice in the industrial sector. This may be because the issue of fuel choice appears simpler. Power raises the difficulties of dealing with hydro and nuclear and the crucial distinction in many countries between base and peak load firing. Industry appears to use all major fuels, if a high enough degree of aggregation is used, and time series data suggests that considerable inter fuel substitution has taken place over time.

The first oil shock produced considerable interest in measuring inter fuel substitution (away from oil) as well as energy substitution. Pindyck [19791 is a major example of such studies using translog functions and allowing for the total energy substitution against other factors. The model pooled data on ten industrLalized countries for the period 1959-1973. The coefficients for the parameter of energy substitution against other factors was allowed to vary between countries, but the parameters for inter fuel substitution were constrained to be equal. As in Griffin's work on power, the evaluation of elasticities of country mean shares for each fuel allows the elasticities of substitution to vary between countries. The results for the aggregate industrial sector are extensive but the key values are the partial fuel price elasticities shown ln tables 4.3. and 4.4.
are, in fact, physically separated by quite some distance.<sup>11</sup> More precisely, we use the GPS software to calculate linear distances between firms and find that the average distance from a treated firm to the nearest control firm in our sample is 1.160 kilometers (0.723 miles). Given that linear distances typically underestimate true travel time, it is highly unlikely that there were geographic spillovers from firms located more than a kilometer away.

Next, there is the related issue of general equilibrium effects. Specifically, did the extra profitability for treated firms come at the expense of the control group or other businesses in the economy? We again turn to the map of businesses in our sample and reemphasize that these businesses are spread throughout the Greater Cape Town area, which contains thousands of small businesses outside of our sample. Even in our sample selection process, we started with 10,000 firms that we identified over ten weeks. Our study sample of 852 firms represents less than 10% of this initial listing. Moreover, the actual number of similar businesses in the Cape Town area is significantly greater than 10,000. Hence, it is highly unlikely that treatment effects among the small number of firms in our sample, spread across sixteen industries, led to any significant market or general equilibrium effects.

One final concern is anticipation effects among the control group firms. Since these firms were promised a business training program after the end of the study period, they could have purposefully delayed certain improvement actions, waiting until the training to undertake them. While theoretically possible, the likelihood of such strategic withholding of investments and improvements is small for several reasons. First, these firms would have to wait and forego investment opportunities for eighteen months, which is the timeline they were provided, and for a small business owner that is a very long time to wait for enacting business improvements. Further, these firms were not informed about which type of training they would eventually receive, so it would be impossible for them to pick and choose which business decisions to delay. And ultimately, even if there were some strategic delays among firms in the control group, the main

> 11 These zoom in locations were chosen at random. A fully interactive map of our study sample can be found here: <u><mark>https://www.mapcustomizer.com/map/Map%20Three%20Colors</mark></u> . Each pin on the map represents a business in our sample. <mark>Pink pins represent businesses in the finance treatment and red pins represent those in the marketing treatment. Green pins represent businesses in the control group.</mark>

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Policy Research Working Paper 10216

# **Abstract**

No-till approaches to agricultural soil management have been encouraged as a means of reducing soil erosion, reducing water pollution, and increasing carbon sequestration. An understudied additional benefit of no-till approaches may be improvements in local air quality due to reductions in both machinery use and dust emissions. This paper leverages recent advances in remote sensing and air pollution modeling to examine this question at a landscape scale. Combining data on daily fine particulate matter levels with satellite measures of no-till uptake since 2005, the paper shows a strong association between increasing adoption of no-till agricultural practices and reductions in county average fine particulate matter pollution over more than 28 million hectares of cropland in the U.S. Corn Belt. The reduction in local pollution implies substantial monetary benefits from reductions in mortality that are roughly one-fourth as large as the carbon benefits. The benefits of mortality reductions are also, by themselves, nearly equal to the current monetary costs of subsidizing no-till practices.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at abehrer@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
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