rafmacalaba/datause-encoder-data huggingface dataset:
negative climate forcing effects of aircraft emissions. See Box 1 below for an overview. The scientific calculation of net radiative forcing is quite complex and no consensus exists on the overall net effect. One problem is that while the climate effect of carbon is moderate but very long-lasting, several other effects (in Box 1) are much more potent but shorter-lasting.

# **Box 1: Principal factors Behind (Positive or Negative) Radiative Climate Forcing (RF) Effects of Aircraft Emissions**

Emissions of CO2, resulting in direct positive RF.

Emissions of NOx result in the formation of tropospheric O3, with positive RF.

Emissions of NOx result in the destruction of ambient CH4 via atmospheric chemistry, with negative RF.

Emissions of sulphate particles arising from sulphur in the fuel result in direct negative RF.

Emissions of soot particles result in direct positive RF. Emissions of water vapour may cause a small direct positive RF.

Persistent linear contrails may form, resulting on net in positive RF.

Formation of cirrus clouds from spreading contrails result in a net positive RF.

Particles emitted from aircraft engines may act as cloud condensation nuclei and seed cirrus cloud formation, affecting magnitude of ice particles, and the albedo and emissivity of cirrus clouds. The net RF effect is uncertain.

Source: Lee (2009).

The other main uncertainty lies in calculating the actual average GHG emissions per traveled distance – which is key to measuring the footprint of a specific air travel profile. This depends on a wide set of parameters which include a) class of travel (since business and first class seats displaces proportionately more economy seats for the same total aircraft space capacity), b) load factors (the degrees of capacity utilization of given aircraft and flights; which differ by class), c) weight factors (as aircraft fuel consumption ultimately depends on aircraft weight), and d) other flight-specific factors (such as flight length and average altitude). Current standard methods for calculating air travel footprints do not reflect the larger footprint attributable to premium travel (through different load factors and weight factors).

very close to unity due to all other significant forcing factors being much more short-lived. The forcing factor could also, in fact, in principle be less than unity (so that some of the basic carbon effect is in fact eliminated at high altitude).

3
<mark>para la Infraestructura Social.” In</mark> _<mark>Decentralized Service Delivery for the Poor</mark>_ <mark>, vol. 2:</mark> _<mark>Background Papers</mark>_ <mark>, 167–222. Report 35692. Mexico City: World Bank.</mark>

- <mark>Woller, Gary M., and Kerk Phillips. 1998. “Fiscal Decentralisation and IDC Economic Growth: An Empirical Investigation.”</mark> _<mark>Journal of Development Studies</mark>_ <mark>34 (4): 139–48.</mark>

- <mark>Xie, Danyang, Heng-Fu Zou, and Hamid Davoodi. 1999. “Fiscal Decentralization and Economic Growth in the United States.”</mark> _<mark>Journal of Urban Economics</mark>_ <mark>45 (2): 228–39.</mark>

- <mark>Yilmaz, Serdar 1999. “The Impact of Fiscal Decentralization on Macroeconomic Performance.” In</mark> _<mark>Proceedings: Annual Conference on Taxation and Minutes of the Annual Meeting of the National Tax Association</mark>_ <mark>, vol. 92, 251–60. Washington, DC: National Tax Association.</mark>

- <mark>Zambrano, Omar, Marcos Robles, and Denisse Laos. 2014. “Global Boom, Local Impacts: Mining Revenues and Subnational Outcomes in Peru 2007–2011.” IDB Working Paper 509 (May), Inter-American Development Bank, Washington, DC.</mark>

- <mark>Zhang, Tao, and Heng-Fu Zou. 1998. “Fiscal Decentralization, Public Spending, and Economic Growth in China.”</mark> _<mark>Journal of Public Economics</mark>_ <mark>67 (2): 221–40.</mark>

27
_Chinese Development Finance: An Application of AidData’s TUFF 2.0 Methodology_ . Williamsburg, VA: AidData at William & Mary.

- Cruces, Juan J. and Christoph Trebesch. 2013. Sovereign Defaults: The Price of Haircuts. _American Economic Journal: Macroeconomics,_ 5(3), 85-117.

- Das, Udaibir, Michael G. Papaioannou and Christoph Trebesch. 2012. Sovereign Debt Restructurings 1950-2010: Literature Survey, Data and Stylized Facts. IMF Working Paper No. 12/203.

- Gardner, Alysha, Joyce Lin, Scott Morris, and Brad B. Parks. 2020. _Bargaining with Beijing: A Tale of Two Borrowers_ . Center for Global Development and AidData at William & Mary.

- Gelpern, Anna, Sebastian Horn, Scott Morris, Brad Parks, and Christoph Trebesch. 2021. How China Lends: A Rare Look into 100 Debt Contracts with Foreign Governments. CEPR Discussion Paper No. 16331.

- Horn, Sebastian, Carmen M. Reinhart, and Christoph Trebesch. 2021. China’s Overseas Lending. _Journal of International Economics,_ 133, 103539.

- IMF. 2020. The International Architecture for Resolving Sovereign Debt Involving PrivateSector Creditors—Recent Developments, Challenges, And Reform Options. IMF Policy Paper No. 2020/043.

- Kratz, Agatha, Allen Feng, and Logan Wright. 2019. New Data on the ‘Debt Trap’ Question. Rhodium Group.

- Kratz, Agatha, Matthew Mingey and Drew D’Alelio. 2020. Seeking Relief: China’s Overseas Debt After Covid-19. Rhodium Group.

- Malik, Ammar, Parks, Brad, Russell, Brooke, Lin, Joyce, Walsh, Katherine, Solomon, Kyra, Zhang, Sheng, Elston, Thai-Binh, and Seth Goodman. 2021. _Banking on the Belt and Road: Insights from a new global dataset of 13,427 Chinese development projects_ . Williamsburg, VA: AidData at William & Mary.

- Reinhart, Carmen M. and Christoph Trebesch. 2016. Sovereign Debt Relief and its Aftermath. _Journal of the European Economic Association,_ 14(1), 215-251.

- Schlegl, Matthias, Christoph Trebesch, and Mark L. J. Wright. 2019. The Seniority Structure of Sovereign Debt. NBER Working Paper No. 25793.

- Sturzenegger, Federico and Jeromin Zettelmeyer. 2007. _Debt Defaults and Lessons from a Decade of Crises_ . MIT Press.

13
populations and 131 PSUs from Kuchi populations were drawn. In the second and final stage, eight households were selected from each PSU.

The implicit stratification over time is a key element of the survey design. The population frame was sorted both spatially and temporally to ensure that (with a systemic interval selection) the selected sample would be seasonally representative. (See Kish (1965, pp. 235-236) for a discussion of implicit stratification.) Thus each quarterly sample of the NRVA survey is representative at the national level. In a country where agriculture is an important form of livelihood, seasonal variations in consumption patterns are to be expected; thus it is critical to capture nationally-representative measures of household food security throughout the year. (Online appendix Table A1 displays key demographic, educational and health, and infrastructure indicators across the four quarters. While we observe some statistical differences in means across quarters, there is little evidence of systematic differences in the samples based on these generally time-invariant characteristics.) Additionally, the year-long fieldwork enabled enumerators to access households in conflict zones without compromising the survey design. Specifically, enumerators would try to secure permission informally from local leaders; when a PSU was considered too insecure to interview at the scheduled time, it would not be replaced immediately, but would be reconsidered at a later date within the quarter.

The NRVA consists of household and community questionnaires and a district market price survey.<sup>2</sup> In this analysis, we exploit two key elements – the food consumption data and the price data. The consumption data include the frequency and quantity of consumption of 91 food items consumed over the previous week, including food bought on the market, produced, or obtained through other methods like food aid and gifts. The NRVA‘s broad coverage of foods, including seasonal varieties, allows for better calculation of caloric and nutrient intake than surveys with fewer items. The price data include prevailing prices of the food items included in the consumption section, as well as domestic and imported grains and fuel. The local price data are important to obtain accurate estimates of price effects in a mountainous country with poor infrastructure, where transportation and transaction costs vary greatly.

> 2 The household questionnaire includes 20 sections – 6 administered by female interviewers to female household members and 14 administered by male interviewers to the male household head.  In Afghanistan, it is important that interviews be conducted among individuals of the same sex.

5
# I. **Introduction**

This paper asks whether or not sex is the primary determinant of labor force participation patterns by using panel data to examine patterns of labor force entry among adult men and women who have different household responsibilities. Labor supply studies differentiate between men and women based on the assumption that individuals are homogeneous by gender (Smith 1983, Psacharopoulos 1994) and that biological differences and the socially ascribed roles due to them may be important factors in determining labor force participation patterns, indicating that models of female labor supply are distinct from those for men (Ashenfelter 1986). This paper suggests that "gender" alone may be a simplistic division, though, since labor supply behavior may be more subject to the constraints imposed by household roles rather than by sex, per se. Of course, gender itself may shape the constraints, but given that they exist, the paper suggest that an individual's role in the household as breadwinnerl and/or caregiver may be a better explanation of differential labor force transition patterns than the sex of the individual.

A second goal of this paper is to identify how households cope with business cycle fluctuations. When the economy slows down, the potential labor force entrant must decide if he/ she will enter the labor force and if he/she does, which sector to enter. The entry decision is based on the income needs of the household, the level of homecare required, and the individual's position in the household, i.e. primary breadwinner or caregiver. If the individual is the primary breadwinner, he/she will enter, regardless of l In this analysis, observations are selected into the sample if they are not working. Nevertheless, if an individual describes him or herself as the household head, we assume that he/she plays a role as a breadwinner or a caregiver. The breadwinner may simply be the person who assures that there is adequate 3
In columns (5) through (8) of Table 2, we present results from specification (1b), namely within-occupation changes in wages. The effects of both offshoring and trade are larger in sign and generally significant at the five percent level, in contrast to the industry-level results reported in the first four columns. In the first row, the coefficients on low-income affiliate employment suggest that a one percent increase in employment abroad is associated with a 0.045 percent reduction in wages at home across all occupations. Among workers in the most routine occupations, we find that a one percent increase in low-income affiliate employment abroad is associated with a 0.073 percent decline in domestic wages, whereas workers in less routine occupations were largely unaffected by offshoring, with no coefficients significantly different from zero. We also find a positive effect of lagged high-income affiliate employment on wages. This is consistent with an interpretation that workers in low income locations perform the same tasks that low-skilled workers perform in the US and are therefore substitutes for workers in the US. In contrast, workers in high-income locations perform tasks that are complementary to workers in the US and so expansion of employment in high-income countries can benefit domestic workers. These results are robust to a range of specification choices, including whether we use prices of imported and exported goods instead of quantities, and our chosen set of control variables.<sup>12</sup> Although these results are statistically significant and economically meaningful in magnitude, it is possible that there are even larger effects within sub-samples of the data, which we explore in Table 3. Krugman (2008) and Feenstra (2008) both write that the effects of international trade and offshoring may have increased recently relative to earlier decades. In Table 3, we split the results into earlier and later time periods. There is no significant association between employment in low-income

> 12 Results are available in the appendix materials. The results indicate that workers with price decreases in their product market have suffered the largest wage declines, with this pattern most pronounced in routine occupations. Similar to our core results, however, this effect is only observed using occupational exposure measures of import price changes. Special thanks to Lawrence Edwards for generous use of his price series data on imports. Other specifications we have tested include removing measures of TFP and controlling for price changes in the service sector using a CPI/PPI index, both of which provide results similar to those presented in Table 2.
prior to the interview. Answers were given on a scale from 0 to 5. Four of the questions were positively worded while the other six were negatively worded. We obtained the PSS score by reversing the scores for the answers to the positively worded items and then summing up the scores across the answers of the ten items. Therefore, a higher score signifies a higher level of perceived stress on a scale of 0 to 40.

To measure depression, we implemented the Center for Epidemiologic Studies Depression Scale (CES-D Scale) designed by Radloff (1977). This instrument measures the current level of depressive symptomatology, including depressed mood, feelings of helplessness and hopelessness, psychomotor retardation, loss of appetite and sleep disturbance. This instrument contains twenty items. Answers were divided into five categories that were then mapped on a scale ranging from 0 to 4. Four of the items were positively worded while the others were negatively worded. We obtained the CES-D score by reversing the scores of the answers to the positively worded items and then summing up the scores across the answers for the twenty items. Therefore, a higher score denotes a higher level of perceived depression, measured on a scale of 0 to 60.

Finally, note that both the PPS and CES-D scales fit the data well. We rely on Cronbach's alpha to measure how well the set of items used measures a single onedimensional latent index (Cornbach, 1951). The alpha for PPS is .79 and for CES-D is .86. Most studies consider a score of .8 or higher to be appropriate.

# **5. Estimation Methods**

We estimate two sets of parameters. First, we respond to the question: What is the impact on the relevant outcomes of offering households 50 square meters of cement floors? In other words, we report the intention-to-treat effect of offering a cement floor to a treatment group relative to a control group. We estimate this parameter by regressing the dependent variables of interest on a variable indicating whether or not the household was offered this benefit and on a large set of control variables. The intention-to-treat dummy variable is equivalent to a dummy variable that indicates whether or not the household resides in the treatment area, since all the households in our sample residing in treatment areas were offered this benefit, whereas it was not offered to any of the households residing in the control areas.

12
# **Does sharecropping affect productivity and long-term investment?**

# **Evidence from West Bengal’s tenancy reforms**

Klaus Deininger<sup>1</sup> , Songqing Jin<sup>2</sup> , Vandana Yadav<sup>2</sup> 1 World Bank, Washington DC, USA 2 Michigan State University, East Lansing MI, USA Keywords: India, land reform, share cropping, investment, productivity.

JEL Codes: O13, O15, Q12 Insightful comments by H. Binswanger, T. Hanstad, T. Haque, M. Gautam, H.K. Nagarajan, Gershon Feder, Michael Carter, and three anonymous reviewers are gratefully acknowledged. The data for this study were collected by EIT, Calcutta under the FAOWorld Bank Cooperative Program. We thank D. Gustafson and P. Munro-Faure for their support and A.K. Roy and D. Mazumdar for outstanding efforts in data collection and processing and the World Bank’s Knowledge for Change Program and Global Land Tools Network for financial support. The views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.
# **Table 8:  Global Trends in Governance 1996-2006 for Selected Sources**

||**Sample**|**A**<br>**1996**|**verag**<br>**2002**|**e**<br>**2006**|**S**<br>**1996**|**td.Dev**<br>**2002**|**.**<br>**2006**|**Change**|**t-Statistic**|
|---|---|---|---|---|---|---|---|---|---|
|**Voice and Accountability**||||||||||
|EIU|120|0.41|0.43|0.47|_0.25_|_0.25_|_0.26_|0.06|1.79|
|FRH|196|0.56|0.58|0.58|_0.31_|_0.30_|_0.30_|0.02|0.79|
|GCS (Press Freedom / Parliament)|95|..|0.57|0.55|_.._|_0.15_|_0.15_|-0.01|-0.59|
|PRS|140|0.63|0.63|0.67|_0.25_|_0.26_|_0.25_|0.04|1.33|
|HUM|155|0.63|0.66|0.58|_0.34_|_0.35_|_0.34_|-0.05|-1.34|
|RSF|137|..|0.73|0.73|_.._|_0.23_|_0.23_|0.01|0.26|
|WMO|181|..|0.55|0.57|_.._|_0.26_|_0.24_|0.04|1.55|
|**Political Stability**||||||||||
|DRI|106|0.82|0.68|0.84|_0.18_|_0.27_|_0.16_|0.02|1.01|
|EIU|120|0.56|0.56|0.59|_0.25_|_0.24_|_0.23_|0.03|0.99|
|GCS (cost of terrorism)|95|..|0.66|0.67|_.._|_0.17_|_0.14_|0.01|0.57|
|PRS|140|0.70|0.75|0.73|_0.13_|_0.12_|_0.11_|0.03|1.98|
|HUM|177|0.65|0.63|0.62|_0.29_|_0.25_|_0.26_|-0.04|-1.27|
|WMO|181|..|0.67|0.68|_.._|_0.24_|_0.22_|0.01|0.54|
|**Government Effectiveness**||||||||||
|DRI|106|0.57|0.46|0.72|_0.28_|_0.30_|_0.21_|0.15|4.46|
|EIU|120|0.43|0.39|0.37|_0.31_|_0.31_|_0.27_|-0.06|-1.67|
|GCS (infrastructure quality)|95|..|0.53|0.51|_0.22_|_0.24_|_0.23_|-0.02|-0.66|
|PRS|140|0.58|0.54|0.54|_0.24_|_0.29_|_0.28_|-0.04|-1.38|
|WMO|181|..|0.56|0.57|_.._|_0.23_|_0.23_|0.04|1.62|
|**Regulatory Quality**||||||||||
|DRI|106|0.82|0.76|0.86|_0.16_|_0.20_|_0.13_|0.04|2.22|
|EIU|120|0.54|0.54|0.56|_0.22_|_0.22_|_0.22_|0.02|0.73|
|GCS (burden of regulations)|95|..|0.30|0.35|_0.15_|_0.13_|_0.12_|0.06|3.17|
|HERITAGE|153|0.56|0.56|0.60|_0.12_|_0.12_|_0.12_|0.04|3.22|
|WMO|181|..|0.58|0.59|_.._|_0.25_|_0.25_|0.04|1.57|
|**Rule of Law**||||||||||
|DRI|106|0.71|0.63|0.81|_0.20_|_0.25_|_0.17_|0.09|3.61|
|EIU|120|0.49|0.51|0.53|_0.27_|_0.26_|_0.24_|0.04|1.30|
|GCS (organized crime / police / independent judiciary)|95|..|0.56|0.57|_.._|_0.21_|_0.20_|0.00|0.09|
|HERITAGE|153|0.57|0.49|0.46|_0.23_|_0.24_|_0.24_|-0.11|-4.07|
|PRS|140|0.72|0.62|0.63|_0.23_|_0.24_|_0.22_|-0.09|-3.46|
|QLM|115|0.45|0.46|0.44|_0.29_|_0.30_|_0.30_|-0.01|-0.17|
|HUM|159|0.59|0.57|0.51|_0.36_|_0.36_|_0.40_|-0.08|-1.92|
|WMO|181|..|0.58|0.59|_.._|_0.23_|_0.23_|0.02|0.77|
|**Control of Corruption**||||||||||
|<br>DRI|106|0.58|0.52|0.66|_0.26_|_0.32_|_0.27_|0.08|2.24|
|EIU|120|0.35|0.35|0.38|_0.33_|_0.34_|_0.31_|0.02|0.51|
|GCS (bribe frequency)|95|..|0.64|0.65|_.._|_0.22_|_0.18_|0.01|0.46|
|PRS|140|0.59|0.41|0.42|_0.21_|_0.19_|_0.20_|-0.17|-7.10|
|QLM|115|0.39|0.40|0.37|_0.29_|_0.29_|_0.29_|-0.02|-0.47|
|WMO|181|..|0.52|0.53|_.._|_0.27_|_0.26_|0.03|1.08|
|**Notes:**||||||||||

_PRS Country coverage in 1996: 130, all other periods 140. GCS Country coverage in 2002:80; 2003-2006: 95. change calculated over 2002-2006 Heritage Country coverage in 1996: 142; all other periods 153. WMO series begins in 1998, change calculated over 1998-2006 HUM series begins in 2002, change calculated over 2002-2006 RSF series begins in 2002, change calculated over 2002-2006_ 36
envelope calculation produces a rough estimate of the global welfare gains that would have arisen if the agreement had already been fully implemented in 2012. We estimate gains of approximately US $101 billion for imports and $109 billion for exports.<sup>7</sup> To put these estimates in context we note that the estimates of global welfare gains represent US $16.01 and $17.30 per WTO resident per year, respectively. Roughly one quarter of the estimated welfare gain arises because of implementation in China alone.

The paper proceeds as follows. Section 2 provides a short review of the relevant empirical literature on trade and logistics. Section 3 describes the data, especially the measures that describe border outcomes and the quantitative representations of border management procedures. Section 4 describes the estimating framework. Section 5 provides the estimation and counterfactual results. Section 6 provides some back-of-the envelope calculations of the economic value of the Bali agreement. Section 7 concludes.

# **2 Literature**

Most of the literature that relates to our work treats TAB measures of time-to-trade as independent variables in an econometric model in which the dependent variable is the value of trade. We differ in that we treat a time-to-trade measure as the dependent variable, and attempt to explain it with other data including the OECD policy data. It is nonetheless useful to review the literature with the TAB variables on the right hand side.

Most of the empirical literature using TAB data to study international trade uses the time-totrade measures rather than estimates of the monetary costs of trading. This is true of the initial study using these data, Djankov et al. (2010), which focuses its attention on a single component of the TAB indicators, _days spent in inland transit_ . Using a modified gravity regression, Djankov et al. (2010) estimate that a one-day increase in inland transport times reduces exports by approximately one percent. Subsequent regressions indicate that exports of time sensitive goods are more sensitive to inland transit time than are other goods.<sup>8</sup> Subsequent studies using these and related data have linked longer trading times to reduced

> 7Our estimates for exports are more speculative than for imports, because countries’ export bundles are much more specialized than are their import bundles. The estimates of the value of time in Hummels and Schaur (2013) are cross-commodity average estimates, and are thus less suitable for valuing changes that affect highly specialized export bundles. We view our estimates for imports as a rough lower bound on the gains from the TFA, and the export figures as indicative of the possible gains through an additional (less likely) channel. There are additional channels possible but these are more speculative and/or more difficult to quantify.

> 8Djankov et al. (2010) also provides a useful review of the TAB time-to-trade data that we use.

4
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