rafmacalaba/datause-encoder-data huggingface dataset:
survey respondents. This is not necessarily a handicap – as argued in Kaufmann and Kraay (2008), not only do subjective assessments of corruption provide valuable information, but also policymakers should care about these perceptions because respondents act on them.<sup>9</sup> We draw on seven different measures, all of which are taken from the Worldwide Governance Indicators project (see <u>www.govindicators.org, and Kaufmann, Kraay and Mastruzzi (2009) for descriptions). Five of these are</u> expert assessments of the prevalence of corruption taken from commercial business information providers (Economist Intelligence Unit (EIU), Political Risk Services (PRS), Global Insight Global Risk Service (DRI), Global Insight Business Risk Conditions (WMO), and Cerebus Corporate Intelligence Gray Area Dynamics (GAD)). One additional expert assessment is the World Bank’s Country Policy and Institutional Assessment (CPIA). Finally, we draw on responses from a large cross‐country survey, the Global Competitiveness Report (GCS) survey of firms in 134 countries, that asks firm managers a variety of questions about corruption. Table 1 lists the precise questions about corruption assessed by each of these sources.

Given DB’s emphasis on the business and regulatory environment, we adopt the same strategy as with GII of using closely‐related perceptions of the quality of the business environment as corresponding outcome variables. The DB project provides as evidence of its own relevance strong correlations of the overall DB measure with other leading indicators of the business environment produced by OECD and World Economic Forum.<sup>10</sup> We follow a similar approach here, relating the DB indicators to a set of seven outcome variables that are also taken from the Worldwide Governance Indicators project, and capture a variety of perceptions regarding the quality of the regulatory environment. We use data from the same six expert assessments as we do for GII, but now focused on the regulatory environment, as well as data from the Global Competitiveness Survey. Moreover, we observe that Doing Business is also circumspect about the limited nature of its indicators, sensibly noting that _“Doing Business does not measure all aspects of the business environment that matter to firms or investors—or all factors that affect competitiveness. It does not, for example, measure security, macroeconomic stability, corruption, the labor skills of the population, the underlying strength of_

> 9 In some cases policymakers may very well have the immediate objective of influencing these perceptions directly, either to improve the government’s polling results or to improve the country’s standing in cross‐country rankings based on these corruption assessments.

> 10 See documentation provided at http://www.doingbusiness.org/Documents/DB10_About.pdf

10
- _Foreign creditors._ Foreign depositors’ behavior may differ from that of domestic depositors because of differences in the former’s credit and exchange rate sensitivities and their perceptions of the bank’s financial stability. Liquidity managers should evaluate the cash flows of foreign deposit accounts separately from domestic accounts. The analysis should also distinguish between retail and wholesale foreign deposits.

- **_Net funding gaps_ ,** with emphasis on short-term exposures.

- **_Contractual cash in-flows and outflows_** (the latter was particularly important in the crisis of 2007-2008);

- **_Cash flows and sources and uses of liquidity._** The analysis should not only be retrospective but also prospective, forecasting liquidity needs under different scenarios and under sound assumptions to provide a reasonable basis for planning _._

- **_Reputation of the institution in the local market_** (overall at a particular point in time), as a proxy for the ease of access to money markets (measured as spread over US Treasury securities).

- **_Relative cost of funding_** (access on a cost-effective basis as compared with selected peers).

- **_The functioning of the correspondent, custodian and settlement systems_**<sup>_36_</sup> _._ Banks should actively manage their intraday liquidity positions, taking into account that a large volume of payments go through these systems. Unexpected changes in their flows can have a great impact on the liquidity position and needs of a bank.

- **_Collateral management_**<sup>_37_</sup> _l_ look at the policies in place, to identify and estimate the bank ́s collateral needs and the collateral resources available to meet immediate operational needs in the different jurisdictions where the bank operates (cross-border).

- **_Netting agreements_**<sup>_38_</sup> that reduce credit and liquidity risk are important because they reduce the intraday liquidity needs, grouping all the positions with different participants to a single net position

- **_Opinion of rating agencies_** can force certain type of investors and depositors to flee.

- **_Assessment of the Supervisory Agency_** (CAMEL ratings or equivalent) regarding the general health of the bank.

> 36 CEBS, page 25

> 37 CEBS, page 9

> 38 CEBS, page 36
<!-- Start of picture text --> March<br>Baseline + offer of + April<br>savings May<br>accounts: June 2009 harvest<br>March-May 2009 July<br>August<br>September<br>October<br>NovemberDecember + Planting.<br>January<br>FebruaryMarch is oHungry season "<br>April<br>May<br>June 2010 harvest<br>July-Sept. August<br>Endline survey: + July<br>YEP 2010 September<br><!-- End of picture text --> <!-- Start of picture text --> Auction Floors<br>Beg lub Account Bank<br>=a Individual Account<br>Sa<br>Club<br><!-- End of picture text -->
Fouré, J., Bénassy-Quéré, A., & Fontagné, L. 2013. “Modelling the world economy at the 2050” horizon, _Economics of Transition_ 21 (4): 617-54.

- Go, D.S., S. Robinson, K. Thierfilder, and R. Utz (2013). “Dutch disease and spending strategies in resource-rich low-income country: the case of Niger.” World Bank Policy Research Working Paper 6691.

- Go, Delfin S., Marna Kearney, Vijdan Korman, Sherman Robinson, and Karen Thierfelder. 2010. “Wage Subsidy and Labor Market Flexibility in South Africa.” _Journal of Development Studies_ , 46(9), 2011, pp. 1481-1502.   Also, as Economic Research Southern Africa (ERSA) Working Paper 114 (January 2009) and World Bank Policy Research Working Paper 4871 (March 2009).

- Go, Delfin S. and Pradeep Mitra. 1999. “Trade Liberalization, Fiscal Adjustment and Exchange Rate Policy in India.” In Ranis, G. and L. Raut (Eds.) _Trade, Growth, and Development._ North-Holland, Amsterdam, pp. 229-72.

- Gros, D. and C. Alcidi, 2013. _The Global Economy in 2030: Trends and Strategies in 2030_ , Brussels: Centre for European Policy Studies (CEPS).

- Kambou, Gerard, Shantayanan Devarajan, and Mead Over, 1992. “The Economic Impact of AIDS in an African Country: Simulations with a Computable General Equilibrium Model of Cameroon,” _Journal of African Economies_ , 1(1): 109-130.

- KPMG. 2014. _The Global Economic Impact of Antimicrobial Resistance._ A KPMG LLP study at the United Kingdom.

- Keogh-Brown, Marcus Richard, Richard D. Smith, John W. Edmunds, and Philippe Beutels. 2010. “The macroeconomic impact of pandemic influenza: estimates from models of the United Kingdom, France, Belgium and The Netherlands.” _The European Journal of Health Economics._ 11(6):543-54.

- Keogh-Brown, Marcus, Scott McDonald, Richard Smith, Melisa Martinez-Alvarez, and Jo Coast. 2009. “Global CGE modeling of antibiotic resistance: an application of the GLOBE model. Paper presented at the 12<sup>th</sup> Annual Conference on Global Economic Analysis, June 10-12, 2009, Santiago, Chile.

- Hertel, T., R. Keeney, M. Ivanic, and L.A. Winter. 2009. “Why Isn’t the Doha Development Agenda More Poverty Friendly?” _Review of Development Economics._ 13:543-59.

- HM Treasury (2003). _The Green Book: Appraisal and Evaluation in Central Government,_ Treasury Guidance. London.

- Laxminarayan, R., T. Van Boeckel and A. Teillant. 2015. “The Economic Costs of Withdrawing Antimicrobial Growth Promoters from the Livestock Sector,” OECD Food, Agriculture -

- and Fisheries Papers, No. 78, OECD Publishing. <u>http://dx.doi.org/10.1787/5js64kst5wvl en</u>

Lindsay, S., M. Kirby, E. Baris, and R. Bos. 2004. “Environmental Management for Malaria 20
# **<mark>4.2 Costs and Benefits</mark>**

Certain regenerative agricultural practices involve investments in equipment and labor, which can be costly for farmers. For instance, no-till planters and seed drills are needed to plant seeds without disturbing the soil. The costs of obtaining necessary equipment vary by location and size of the farms. In particular, the prevalence of small-scale agriculture, coupled with policies that discourage farm consolidation, can reduce the cost-effectiveness of investing in equipment. Moreover, low farm gate prices and limited market access can make the adoption of regenerative agriculture less appealing due to associated costs. Thus, despite the potential positive impact of regenerative agriculture on productivity and resilience, these practices may not necessarily be profitable for farmers to adopt.

Moreover, accessibility is another key factor influencing adoption. For instance, in Malawi and Zimbabwe, the adoption of regenerative agricultural practices is negatively associated with access to fertilizers (Makate, et al. 2019). Applying fertilizers is more profitable when they are accessible in this context.

The potential for carbon mitigation through regenerative agriculture, though supported by mixed evidence, presents a promising avenue to increase the benefits through soil carbon credits. Farmers engaged in carbon markets can receive financial incentives or credits for adopting and maintaining these sustainable practices. This introduces an additional source of income for farmers, which can make regenerative agriculture more attractive.

# **<mark>4.3 Lack of Local Knowledge Support and Information</mark>**

The effectiveness of regenerative agriculture is intricately tied to specific local climate and soil conditions, which are subject to change over time. Hence, there is a crucial need for local knowledge support to assist farmers in adopting the most suitable practices based on their evolving conditions.

With the help of the growing accessibility of satellite data and advancements in machine learning techniques, the costs associated with local knowledge support can significantly diminish, enabling widespread availability. For instance, e-extension services can utilize predictive machine learning technology to offer insights based on highly detailed soil nutrient status.<sup>10</sup> Even when regenerative agriculture is suitable and profitable based on the local climate and soil conditions, it can still take time to raise awareness among farmers and train farmers to learn how to utilize the technologies. Demo plots may

> 10 For example, iSDAsoil provides soil and agronomy maps with a 30-meter resolution covering the entire African continent. In addition, Global Agro-Ecological Zones (GAEZ), supported by the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA), can identify suitable agricultural land utilization options at a  9 x 9 km grid cell level for the entire globe.

17
- B. Scientific and creative property

- Research and Development (R&D) in science and technology (spending for the development of new products and production processes, usually leading to a patent or license)

- Mineral exploration (spending for the acquisition of new reserves)

- Copyright and license costs (spending for the development of entertainment and artistic originals, usually leading to a copyright or license); and development costs in the motion picture, radio and television, sound recording, and book publishing industries

- Other product development, design, and research expenses (not necessarily leading to a patent or copyright), such as new product development costs in the financial services industry, new architectural and engineering designs, and R&D in the social sciences and humanities

# C. Economic competencies

- Brand equity (advertising expenditures and market research for the development of brands and trademarks)

- Firm-specific human capital (spending on developing workforce skills; for example, on-the-job training and tuition payments for job-related education)

- Organizational capital (costs of improvement in organizational structures).

In the US, the Income and Product Accounts (NIPA) already provide data on business expenditures on computer software (own account spending and purchases) and databases (own account spending). For other intangible expenditures, CHS estimations are based on a mixture of public data, private data, and derivative measures.

Official firm surveys are an important source of direct information for CHS. For example, the Census Bureau‘s Services Annual Survey (SAS) gives firm-level data on purchases of computer databases. Surveys conducted by the Bureau of Labor Statistics (BLS) collect data on firm expenses on workforce skills development (in-house trainers, outside trainers, tuition reimbursement, and outside training funds). R&D data are from firm R&D surveys conducted by the National Science Foundation (NSF).

But several intangible expenditures are not elicited in any firm survey in the US. In some cases, CHS use data on the revenue of the industry which produces the intangible asset to measure the expenditure on that asset. For example, spending on new architectural and engineering designs is estimated as half of the revenue of the architectural and engineering design industry as reported in SAS. Similarly, outlays on 8
Table A27: Robustness of impact on Aspirations index to differential attrition

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Main|Trimmin|g treated|Im|puting cont|rol|IPW|
||spec.|Bottom<br>UB|Top<br>LB|95th pc.|75th pc.|50th pc.||
|T1 (Safe Spaces)|-0.18|-0.11|-0.22*|-0.19|-0.19|-0.18|-0.18|
||(0.13)|(0.11)|(0.13)|(0.13)|(0.13)|(0.13)|(0.13)|
|T1+Leaders|-0.16|0.09|-0.32***|-0.20*|-0.18|-0.16|-0.16|
||(0.12)|(0.09)|(0.12)|(0.12)|(0.12)|(0.12)|(0.12)|
|T2 (Safe Spaces + IGA)|0.13|0.29***|0.04|0.10|0.11|0.12|0.12|
||(0.11)|(0.09)|(0.11)|(0.11)|(0.11)|(0.11)|(0.11)|
|T2+Leaders|-0.04|0.23***|-0.20*|-0.09|-0.07|-0.04|-0.04|
||(0.10)|(0.08)|(0.11)|(0.10)|(0.10)|(0.10)|(0.10)|
|T3 (Safe Spaces + Husbands Clubs)|0.01|0.23**|-0.14|-0.06|-0.02|-0.00|0.02|
||(0.10)|(0.10)|(0.09)|(0.09)|(0.09)|(0.10)|(0.10)|
|T3+Leaders|-0.00|0.10|-0.06|-0.02|-0.01|-0.01|-0.01|
||(0.16)|(0.11)|(0.15)|(0.16)|(0.16)|(0.16)|(0.16)|
|T4 (control spillover)|-0.03|-0.02|-0.03|-0.03|-0.03|-0.03|-0.03|
||(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|
|N|2991|2990|2990|2992|2992|2992|2991|
|Control Mean|0|0|0|.002|.001|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. Column (1) replicates the main results presented in tables 3, 4 and 5. Columns (2)-(3) present results equivalent to Lee bounds: we re-run the estimations after dropping _K_ observations from the top/bottom of the distribution in the treatment group to calculate the lower/upper bound, where _K_ is the difference between the number of attriters in the treatment groups and the control group. Columns (4)-(6) present similar results, but instead of dropping _K_ observations in the treatment group, we impute the outcomes of _K_ attriters in the control group using the 5th, 25th and 50th percentiles of the observed distribution of the treatment group. Column (7) presents the Inverse Probability Weighting (IPW) estimates.

59
from the World Development Indicators (the World Bank). We use the data for the period 19802009 since the GDP data are available only after 1980 and the latest data available in FAOSTAT are for 2009.

The first column in Table 2 reports a cross-section regression result using a 5-year average of CE consumption and GDP for the period 2005-2009. The fitted CE consumption curve based on the regression result is shown in Figure 4 along with actual CE consumptions for the sample countries, including data for China in red. The second column in Table 2 reports an alternative regression using all the available data points for each year during the period 19802009. In this regression, the standard errors are adjusted for within-country correlation (clustering). The results turn out to be similar between the two regressions.

Table 2: Regression Results for Cereal Equivalent Consumption

||(1)<sup>a</sup>|(2)<sup>~~b~~</sup>|
|---|---|---|
|_A1_|2.2***|2.2***|
||(0.17)|(0.16)|
|_A2 _|1.7***|1.7***|
||(0.16)|(0.15)|
|_k_|4.6 × 10<sup>-5</sup>***<br>|5.8 × 10<sup>-5</sup>***<br>|
||(9.5 × 10<sup>-6</sup>)|(1.1 × 10<sup>-5</sup>)|
|R<sup>2</sup>|0.74|0.71|
|Observations|154|4100|

Source: Authors’ regression results Notes: *** indicates that the coefficients are significant at the one percent level.<sup>a</sup> Regression (1) is based on 2005-2009 averages of CE consumption and real GDP for 154 countries. Robust standard errors are given in parentheses. b Regression (2) shows the results using all the available data points for each year during the period 1980-2009. The standard errors in parentheses are based on heteroskedasticity-consistent estimates of the variance-covariance matrix and corrected for within country correlation (clustering).

The graph presented in Figure 4 shows a concave relationship between food consumption in cereal equivalents and real income levels (Rask and Rask, 2004, 2011). This curve is steep in the early stage of economic development since consumers are likely to spend a large proportion of increases in their incomes on food. This CE consumption continues to increase, albeit at a 14
However, this annualized figure will certainly under-predict the _actual_ increase in imposed measures in 2Q through 4Q of 2009. The increase in the rate of imposed measures is expected to be _larger_ than 11.9 per cent higher than the number imposed in 2008, given the sharp increase in newly initiated investigations in 2Q through 4Q of 2008 (see again figure 1a). To highlight this point, in figure 1b we illustrate the _projected_ imposition of definitive new import restrictions at the product-level for these countries between 2Q 2009 and 1Q 2010. These projections are computed from assumptions and parameters estimates from the 2007 data: 79 per cent of new initiations result in definitive measures, it takes four quarters to reach imposition of a measure after initiation of an investigation, and the number of new investigations initiated each quarter between 2Q 2008 and 1Q 2009 are as in figure 1a. While this approach is admittedly crude and does not control for the likelihood that the share of investigations that may result in definitive measures may rise during the crisis, as a conservative benchmark it suggests at least an 18.5 per cent increase in imposed measures in 2009 when compared to 2008.

# **2.2 The countries that are using the trade remedies to limit imports**

In addition to documenting the time trend of trade remedy use, figures 1a and 1b also illustrate the relative frequency with which these actions are being taken by developed versus developing economy users of import protectionist instruments. The figures indicate that increasingly these actions are being undertaken by developing countries.

Table 1 provides further information on the country-level use of these trade policy instruments between 1Q 2007 and 1Q 2009. Most striking is that since the onset of the crisis – or roughly between 1Q 2008 and 1Q 2009, developing countries initiated 74 per cent of all new investigations. This use has been dominated by India (20 per cent), Argentina (12 per cent), Turkey (8 per cent), Brazil (7 per cent), and China (5 per cent). Developed economies initiated only 26 per cent of the new investigations during this time, although most of those derive from initiations by the United States (8 per cent) and the European Union (7 per cent).

7
Table 1: Descriptive statistics of the analytical sample

||||Survey Wav<br>|e<br>|
|---|---|---|---|---|
||Pooled|NRVA 2007|ALCS 2014|IEL 2020|
||(1)|(2)|(3)|(4)|
|Annualized income (USD)|1891.25|1241.84|2276.80|1952.63|
||(1390.15)|(976.44)|(1532.45)|(1259.11)|
|Female|0.10<br>|0.14<br>|0.08<br>|0.08<br>|
||(0.30)|(0.34)|(0.28)|(0.27)|
|Experience|18.81|21.10|18.44|16.93|
||(14.04)|(13.92)|(14.12)|(13.69)|
|Experience square|551<br>|638.66<br>|539.41<br>|473.99<br>|
||(654.69)<br>|(683.36)<br>|(650.14)<br>|(618.06)<br>|
|Public sector|0.66|0.67|0.67|0.64|
||(0.47)|(0.47)|(0.47)|(0.48)|
|Age|34.07<br>|35.90<br>|33.37<br>|33.24<br>|
||(11.82)|(12.21)|(11.84)|(11.10)|
|Compulsory law|0.22|0.01|0.22|0.45|
||(0.41)|(0.10)|(0.41)|(0.50)|
|_N_|13135|3811|5869|3455|

_Note_ : Standard deviation in Parenthesis. Compulsory law indicates the share of respondents born in or after 1991 who were on the verge of starting 7 grade and affected by the 2004 constitution—increased compulsory education from 6 to 9 grade.

Finally, for the economic analysis, we use macro-level historical data on average years of schooling and gross national income per capita in constant 2017 purchasing power parity (PPP) terms from the Human Development Index country profile for Afghanistan (https://hdr.undp.org/data-center/ specific-countrydata#/countries/AFG). We also compile data on population growth, labor force, and female-to-male labor force participation ratio from the World Bank Open Data repository (https://data.worldbank.org).

While these surveys are among the few nationally representative repeated cross-sectional household surveys reporting on the labor activities of adults in the household, there are some potential limitations. For instance, labor market outcomes for those aged 14 and 15 years old are missing for 2007. Additionally, the coverage of women working as salaried workers is limited, corresponding to the lower labor force participation rate of women in the country. Furthermore, the absence of information on the school type and degree of salaried workers restricts our ability to analyze differential returns to education by school type and/or degree, if any. However, we provide the first rigorous evidence on the monetary rate of return to schooling in Afghanistan and highlight the scale of economic loss the country will experience if women’s access to education and work remains restricted. We hope this will serve as a starting point for future work.

7