rafmacalaba/datause-encoder-data huggingface dataset:
# Table 1: Definition of variables

|**Variable**|**Definition**|
|---|---|
|N application rate|Nitrogen application rate (Kg/ha)|
|Below recommended N application rate|= 1 if nitrogen application is more than 10% below the recommended application rate|
|Above recommended N application rate|= 1 if nitrogen application is more than 10% above the recommended application rate|
|Applied basal fertilizer on time|= 1 if basal fertilizer was applied within a week after planting|
|Applied organic fertilizer|= 1 if organic fertilizer was applied|
|Seed rate|Seed application rate (Kg/ha)|
|Used hybrid seed|= 1 if hybrid seed was used|
|Pure stand|= 1 if plot was pure stand|
|Plot size|GPS-measured plot size (ha)|
|Labor|Days of labor (family, hired and exchange) used for non-harvesting activities|
|Soil is of good quality|= 1 if plot is of good soil quality; 0 otherwise|
|Soil is of fair quality|= 1 if plot is of poor soil quality; 0 otherwise|
|Plot is not flat|= 1 if plot is not flat; 0 otherwise|
|Plot is swampy|= 1 is plot is swampy; 0 otherwise|
|Soil is sandy-clay|= 1 if soil is sandy-clay; 0 otherwise|
|Plot show signs of erosion|= 1 if plot showed signs of erosion; 0 otherwise|
|Female plot manager|= 1 if plot manager is female; 0 otherwise|
|Age of plot manager|Age of plot manager (years)|
|Years of education of plot manager|Years of education of plot manager|
|African Adult Male Equivalent|African Adult Male Equivalent household size|
|Dependency ratio|Dependency (child and adult) ratio|
|Distance to boma|Distance to district capital (Km)|
|Index of ownership of agricultural tools|1<sup>st</sup>principal component analysis of agricultural tools owned by the household|
|Index of ownership of durable good|1<sup>st</sup>principal component analysis of durable assets owned by the household|
|Annual mean rainfall|Avg 12-month total rainfall(mm) for July-June|
|Annual mean temperature|Annual mean temperature (<sup>o</sup>C*10)|

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||(0.0531)|(0.0632)|(0.0654)|(0.118)|(0.0577)|(0.0645)|
|---|---|---|---|---|---|---|
|Observations|1,418,632|126,994|345,631|2,398|1,073,001|124,596|
|R-squared|0.187|0.079|0.205|0.223|0.174|0.078|
|_F_-stat (HS=HNS)|0.28|0.96|0.98|0.42|0.03|0.88|
|Prob>F|0.596|0.332|0.327|0.518|0.862|0.351|
|_F_-stat (BS=BNS)|6.72|19.12|10.39|0.42|5.01|18.02|
|Prob>_F_|0.012|0.0001|0.002|0.52|0.029|0.0001|
|_F_-stat (FS=FNS)|0.02|0.02|0.14|0.74|0.06|0|
|Prob>_F_|0.882|0.889|0.705|0.394|0.813|0.955|
|_F_-stat (three conditions jointly)|2.61|7.46|3.92|0.32|1.99|6.85|
|Prob>_F_|0.06|0.0003|0.013|0.808|0.126|0.0005|

_Notes_ : Robust clustered standard errors are presented in parentheses. The dependent variable for all regressions is lnTFP. All regressions include firm fixed effect and year dummy variables. HS = subsidized horizontal, and HNS = non-subsidized horizontal; BS = subsidized backward, and BNS = non-subsidized backward; FS = subsidized forward, and FNS = non-subsidized forward.

50
how adjustments are made for household size and composition. Poverty comparisons are sensitive to this choice. Failure to consider that FHHs are typically smaller overstates poverty among them (Quisumbing et al. 2001). There may also be economies of scale in consumption. Not accounting for these—a more common mistake—typically results in an understatement of poverty in FHHs. Allowing for scale economies can reverse conclusions about whether FHHs are richer or poorer (Drèze and Srinivasan 1997 for India; van de Walle 2013 for Mali).

FHHs tend to have higher dependency ratios and a higher share of children. Different demographic composition can be corrected to account for the different consumption needs of adults and children by using adult equivalent scale-adjusted poverty measures. However, this implies knowing the consumption needs of different household members (usually based on actual consumption data in household surveys), which may differ significantly across areas and countries, and not accurately reflect actual biological needs (Quisumbing et al. 2001).

The paper aims to investigate changes in the prevalence of FHHs in Africa and whether FHHs have enjoyed a similar pace of poverty reduction as MHHs. We are not aware on any past work on the latter question taking an Africa-wide perspective.<sup>11</sup> We test the sensitivity of our main results to distinguishing between different types of FHHs and to allowing for scale economies in consumption. The issues noted above may be somewhat less worrying when examining changes instead of levels. Furthermore, in asking these questions we do not assume that female headship is exogenous. Our objective is not to establish the causal effect of headship but to take stock of the correlations found in the data in a methodologically consistent way that is sensitive to the measurement issues across countries.

# **3. Data**

The paper makes use of two types of household surveys. The first is the 100 or so existing Demographic and Health Surveys (DHSs) for Africa and also draws on the World Bank’s Povcalnet data base of harmonized household surveys for Africa. The DHSs are primarily used to briefly describe the prevalence and characteristics of FHHs relative to MHHs, and then to investigate Africa-wide changes in prevalence. The latter can be more effectively examined with DHSs since these surveys contain demographic characteristics that prove key to explaining changes over time. We use the full series of DHSs over the last 25 years. These cover about 89 percent of Africa’s population.

The DHSs have the advantage of administering the same questionnaires (altered only to

> 11 Posel and Rogan (2012) has explored the issue for South Africa for the period 1997-2006.

7
from the government, 57 obtained it for free. In addition, 1,060 manufacturing firms are located in an industrial zone and 453 (out of 3,040) manufacturing firms reported a loan. Table A3 in the Appendix summarizes the descriptive statistics of the different perceived policy variables from the WBES, comparing manufacturing sectors with at least one politically connected firm with all other sectors (with zero connected firms). Firms in connected sectors are more likely to have acquired government land, to be located in an industrial zone, or to have a bank loan. It the following, we test more systematically for the impact of political connections on access to land, industrial zones, and bank loans.

In contrast to non-tariff barriers and energy subsidies, the information on firms’ access to land, industrial zones, and credit is available at the firm level. This allows us to estimate probit regressions that assess whether the probability that a firm has access to land, industrial zones, or credit increases when it is operating in a 4-digit sector with a higher intensity of political connections (i.e., containing more politically connected firms): The dependent policy variable Polij is a dummy variable for firm _i_ in the 4-digit sector _j_ . It equals 1 if the firm bought land from the government or received it for free, it is located in an industrial zone, or it has a bank loan, respectively, and zero otherwise; _connected_ measures the number of politically connected firms by type in the 4-digit sector _j_ . Xij is a matrix of the firm level control variables (firm size and age), and _S_ is a matrix of 2-digit sector dummies.<sup>30</sup>

> connected firms, many of them are often owned by the same politically connected businessmen (or his family members).

> 30 All estimations include standard errors clustered at the 4-digit sector level to account for shocks common to firms in 4-digit sectors.

19
For interventions intended to reach the poor, Pradhan (1996) distinguishes between universal programs (or broad targeting) and narrow targeting. The first provides program accessible to all groups while the second actively targets benefits to the poor while excluding the non-poor. Pradhan emphasizes that targeting be seen as a potential instrument and not as an objective. Thus in many developing countries where poverty is widespread and administrative capacity is low, a combination of broad and narrow targeting will be required.

# _Some Practical Constraints_

Country experiences with pro-poor expenditure policy also suggest that there are important resource and informational constraints in the proper identification and implementation of pro-poor interventions. Mackinnon and Reinikka (2000) review Uganda’s experience with the selection of its package of universal basic services and suggest that the most difficult aspect is limiting the commitment to what is “feasibly affordable.” The selection process is undermined if the package is, in fact, not affordable; however, defining what is affordable is constrained by the limited information on the costs of services. They also identify the main controversies in Uganda’s experience, including (i) the role of the state and (ii) the scope for charging and/or exempting the poor. The second source of controversy is also related to some of the tensions between national and local guidelines for providing services.

The World Bank’s (2001a) Ethiopia Public Expenditure Review (PER) finds that there are few cases, if any, where “a simple, direct and unambiguous connection” can be made between an intervention and a poverty outcome. It suggests that the problem be approached at three levels: (a) strengthening the availability and use of poverty information; (b) strengthening public expenditure management and the quality of information on public expenditure; and (c) making a more effective link between poverty information and expenditure management.” (51) The criteria for comparisons across sectors have a special emphasis on _implementation constraints_ in the Zambia PER (World Bank 2001b). In particular, it recognizes that some social services may by design be intensive in skilled labor and require higher wages. Administrative constraints may also warrant alternative delivery techniques rather than “best practice.” For example, where the implementation of a comprehensive primary health care program is difficult, dedicated immunization programs by visiting (i.e., non-permanent) staff may be more appropriate.

33
Beyond providing substantive insights regarding the impact of rotational practices, our paper is related and contributes to a growing body of evidence on machine learning and the use of remotely sensed data for analysis of agricultural production relations. We document the potential of machine learning by showing that a modest effort of training data collection allows generation of a crop map for the entire agricultural area of Ukraine. Although field-level crop cover data at slightly coarser resolution are freely available for the United States in the form of the crop data layer (CDL), there are few, if any studies outside the United States that have achieved national coverage with field-level data on crop cover.

We also contribute to the evidence on use of remotely sensed data by showing that use of common indices of crops’ vegetative development not only produces results consistent with those from combining crop cover data with statistical information on yields, but also affords insights (e.g., on soil water content) in their own right. The scope for disaggregation inherent in such data is a promising avenue for future research, including exploration of implications on profitability and implications for potential policy interventions to eliminate negative externalities resulting from rotational choices.

The rest of the paper is organized as follows. Section 2 briefly reviews available literature as well as the relevance of the topic within the context of Ukraine’s agricultural development. Section 3 describes the data sources, provides descriptive statistics on the incidence of crop rotations and yields, and outlines our econometric approach. Section 4 reviews evidence on the impact of rotational practice on yield, commonly used vegetation indices, and soil water content. Section 5 concludes by drawing out implications for future research as well as policy.

# **2. Literature and context**

Insights into the trade-offs involved in on-farm decision making regarding crop choice and sequence derived from detailed study of the productivity of different rotations can help identify economic effects of rotational choices, including whether such choices are likely to trigger external effects and, if yes, help identify suitable instruments to internalize such externalities. Yet, data limitations have implied that study of this topic remained extremely limited, especially outside the United States. The nature of Ukraine’s transition from collectivized agriculture as well as recent policy reforms make such analysis particularly relevant.

# **2.1 The rationale for studying crop rotation effects**

Field-level management of the soil’s nutrients, physical structure, and weed as well as pest populations via rotation of crops has long been a key source of agricultural productivity, efficiency and resilience. The underlying principle is simple: as crops differ from each other in terms of their susceptibility to disease, the 3
# **<mark>References</mark>**

1. ONUSIDA/09.36S. 2009. “Situación de la epidemia de sida, diciembre de 2009”. <u>http://data.unaids.org/pub/Report/2009/jc1700_epi_update_2009_es.pdf</u>

2. UNAIDS/WHO. 2002. “Initiating Second Generation HIV Surveillance System: Practical Guidelines. UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance”. - <u>http://data.unaids.org/Publications/IRC pub02/jc742 initiatingsgs_en.pdf</u> 3. UNAIDS/WHO. 2004. “Second Generation Surveillance for HIV: The Next Decade”. - <u>http://data.unaids.org/publications/irc pub01/jc370 2ndgeneration_en.pdf</u> 4. Rodríguez-García R, World Bank Group. 2007. “Planning and Managing for HIV/AIDS Results. A Handbook”. <u>http://siteresources.worldbank.org/INTHIVAIDS/Resources/375798 1103037153392/Results_Handbook_31Oct2007.pdf</u> 5. Atkins D, Best D, Briss PA, et al. 2004. "Grading quality of evidence and strength of recommendations". BMJ 328 (7454): 1490 6. World Health Organization. 2009. “HIV Triangulation Resource Guide”. <u>http://data.unaids.org/pub/Manual/2009/20090915_hiv_triangulation_resource_guid e_en.pdf</u> 7. Downs S, Black N. 1998. “The feasibility of creating a checklist for the assessment of the methodological quality both of randomized and non-randomised studies of health care interventions”. J Epidemiol Community Health 1998; 52; 377-384.

8. World Health Organization. 1999. “Sexually Transmitted Infections Prevalence Study Methodology. Guidelines for the implementation of STI prevalence surveys”. <u>http://www.wpro.who.int/internet/resources.ashx/HSI/docs/Guidelines_for_the_Impl ementation_of_STI_Prevalence_Surveys.pdf</u> 9. Aschengrau, A. 2003. “Essentials of Epidemiology in Public Health”. Sudbury Mass.: Jones and Barlett.

10. Gordis, L. 2004. “Epidemiology”. Third Edition, Elsevier Saunders.

11. Girden, E. 2001. “Evaluating Research Articles from Start to Finish”. Second Edition, Sage Publications.

12. Hernandez-Avila, M. y col. 2000. “Diseño de estudios epidemiológicos”. Salud pública de México / vol.42, no.2, marzo-abril de 2000.

13. Szklo M. 2006. “Quality of scientific articles”. Rev Saúde Pública 2006; 40 (N Esp):30-5.

14. Velasco, V. y col. 2003. “Muestreo y Tamaño de Muestra. Una guía práctica para personal de salud que realiza investigación”. e-libro.net. Buenos Aires 15. Salganik M, Heckathorn D. 2004. “Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling”. Sociological Methodology, Vol. 34. (2004), pp. 193-239 16. Muhib F, Lin L, et al. 2001. “A venue-based method for sampling hard-to-reach populations”. Public Health Rep. 2001; 116(Suppl 1): 216–222.

17. UNAIDS/WHO/UNICEF. 2008. “Epidemiological fact sheets on HIV and AIDS”. <u>http://www.unaids.org/en/KnowledgeCentre/HIVData/Epidemiology/epifactsheets.as p</u> 40
years of schooling, _e_ , by expressing learning gained relative to learning in the status quo: where _β_ is the learning gain produced by the intervention expressed in standard deviations ( _σ_ ) per year in country _i_ ; _test_ denotes the test used to measure learning; _δ_ is the status-quo learning rate in standard deviations ( _σ_ ) per year in country _i_ ; and _x_ denotes the population for which this status-quo learning rate is calculated. This population _x_ could represent the control group of the same study from which the _β_ estimates are drawn; alternatively, _x_ could be the student population in country _i_ , in which case _δ_ becomes the average learning trajectory for the country as a whole. The choice of _x_ will affect our interpretation. If we choose the control group, then the resulting value for equivalent years of schooling gained is relevant to the study sample only. If we choose national-average learning trajectories, we can interpret the value as the equivalent years of schooling gained at the national level. In this paper’s main calculations, we use national-level learning trajectories _n_ , and in the robustness section, we explore the trade-offs involved in using a different measure of status quo learning. We estimate micro-LAYS derived from impacts on learning, denoted by superscript _l_ . To derive these estimates, we adjust equivalent years of schooling, e, gained in country _i_ by the quality of learning _L_<sup>_h_</sup> _i_<sup>in that country relative to learning in a high-performance global benchmark country</sup> _h_ : We substitute in terms for _L_<sup>_h_</sup> _i_ = _LLhi_ . This is analogous to the quality adjustment used in macro-LAYS. We further specify that both the numerator and denominator of the learning-adjustment term are expressed in terms of standard deviations ( _σ_ ) on a test that is representative at national level _n_ for each country, such that: For the next step we invoke two assumptions. First, we assume that learning is constant along a local trajectory. This assumption, explored in depth in Filmer et al. (2020) for macro-LAYS, enables conversion of relative levels _L_<sup>_h_</sup> _i_<sup>=</sup> _L_<sup>_<u>L</u>_</sup> _h_<sup>_<u>i</u>_intorelativerates</sup><sup>_Lh_</sup> _i_<sup>=</sup> _δ_<sup>_<u>δ</u>_</sup> _h_<sup>_<u>i</u>_,sincetherelationship</sup> is constant. (This assumption is discussed in more detail in Section 5.) Second, we assume that standard deviations across tests and samples are comparable. This assumption is strong, but not novel: it is implicitly invoked whenever any time standard-deviation effect sizes are compared across studies, which is the dominant practice in the literature on education interventions. We note that this assumption is most robust when learning gains in a given study are based on similar tests to 10
- O’Muicheartaigh, Colm & P. Campanelli. 1998. The relative impact of interviewer effects and sample design effects on survey precision. _Journal of the Royal Statistical Society: Series A (Statistics in Society),_ 161(1): 63-77.

- Schnell, Rainer & F. Kreuter. 2005. Separating Interviewer and Sampling-Point Effects. _Journal of Official Statistics,_ 21(5): 389-410.

West, Brady T. & A.G. Blom. 2017. Explaining Interviewer Effects: A Research Synthesis. _Journal of Statistics and Methodology,_ 5: 175-211.

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# **4. Data**

For Step1 of the regression we use data from the BankScope database for the years 1995-2009. We have collected individual bank data on net interest margin, non-interest expense, other operating income over average assets, total assets, non interest earning assets and total capital ratio. We use these data to calculate the four variables used in our estimation namely the net interest margin, the implicit interest payments/total average assets (non-interest expense – other operating income), the cost of reserves (non interest earning assets/total assets) and the extra capital held, total capital/total assets. We have data for 197 countries and areas, a total of 231,834 bank observations.

For Step2 we use data from Thomson Reuters’ DataStream regarding interest rates for 47 countries. We are using weekly data from 1995-2009 to calculate the volatility of interest rates measured as the yearly standard deviation of the weekly observations. For Step2 we use short term interest rates defined as three month or 90 days interest rates. We use yearly data for the gross domestic product growth, and inflation from the International Monetary Fund’s _World Economic Outlook_ database.

For many countries we have data that span fifteen years (1995-2009), but some of the countries do not provide data for all of the years assessed. We present findings for these countries by concentrating on the most recent years. For most countries we present results for 2009 in this paper, but for countries that we do not have data for 2009, we present results for the year immediately preceding 2009.

For Step1 we used countries with 5 or more bank observations per year to make sure we had enough observations to make reasonable conclusions about the micro and macro situation in the country. The dataset adds significant value to the literature since there is no previous attempt to calculate spreads in as many countries and offer comparisons among countries, regions, degrees of development and reforms. The dataset is very extensive with a wide coverage and comparable data. The data have been collected from BankScope.

In terms of representativeness of the data, it is important that a large share of the assets in the banking sector is accounted for. If that was not the case, the results for a given country would be less reliable as the sample would not be a good representation of the banking sector in that country. As a result, we evaluate the assets of the banks in our sample as a percent of total assets in each country’s banking system (Figure 15b).

We find that our data set shows an overwhelming degree of representativeness since 100 percent of assets are accounted for in almost all countries in the sample. For countries where this is not the case, the country is excluded from the analysis in order to ensure reliability of the cross-country (world-wide) analysis. In many countries especially developing countries the banking sector owns much of the financial assets in the country, which is a key ingredient in ensuring the representativeness of the data set. The total data set is displayed in Annex 3.

12
# **Table A22:  Latinobarometro (LBO)**

**Data Provider** Latinobarometro **Description** Nongovernmental organization based in Santiago, Chile **Website** www.latinobarometro.org **Data Source** Latinobarometro surveys **Type** Survey **Respondents** Households **Frequency** Annual since 1995 **Coverage** Sample of Latin American countries **Public Access** Full dataset is commercially available.  Averages of sub-indicators are publicly available in this spreadsheet. **Description** Latinobarometro administers a common questionnaire to households in Latin America with questions on areas such as Economy and International Trade, Integration and Regional Trading Blocks, Democracy, Politics and Institutions, Social Policies, Civic Culture, Social Capital and Social Fraud, the Environment, and Current Issues.

|**Voice and Accountability**|2008|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|---|
|Satisfaction with democracy|X|X|X|X|X|X|X|X|X|X|
|Trust in Parliament|X|X|X|X|X|X|X|X|X|X|
|**Political Stability and Absence of Violence**|||||||||||
|NA|||||..||||||
|**Government Effectiveness**|||||||||||
|Trust in Government|X|X|X|X|X|X|X|..|..|X|
|**Regulatory Quality**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||||
|Trust in Judiciary|X|X|X|X|X|X|X|X|X|X|
|Trust in Police|X|X|X|X|X|X|X|X|X|X|
|Have you been a victim of crime?|X|X|X|X|X|X|X|X|X|X|
|**Control of Corruption**|||||||||||
|Frequency of corruption|X|X|X|X|X|X|X|X|||
|**Country Coverage**|**18**|**18**|**18**|**18**|**18**|**17**|**17**|**17**|**17**|**17**|
|**Year of publication**|**2008**|**2007 **|**2006 **|**2005 **|**2004 **|**2003**|**2002 **|**2000**|**1998 **|**1996**|

61