rafmacalaba/datause-encoder-data huggingface dataset:
characteristics in highly saturated villages are significantly different (Appendix Tables 3 and 5). Of particular note, nonbeneficiary households in above-median saturated treatment areas were significantly smaller (by 0.41 people—mostly adults— with a standardized mean difference of 0.269) than control nonbeneficiary households. This difference may in turn affect the number of caretakers available for young children and, consequently, factors such as household responsiveness to child illness or the available total household resources partially devoted to children.

We address the possible influence of subgroup imbalance in three ways. First, we note that child height and stunting rates were appreciably the same for children ages 36 to 60 months at the time of the follow-up survey (Figure 5 and Appendix Table 10). As these children largely lived their first 1,000 days—a critical growth period in determining child height trajectories—before program introduction, the similar height measures in older nonbeneficiary children between treatment and control villages strongly suggest that this outcome would be balanced at baseline if measured (when these children were 31 months younger). Second, we use baseline data on household composition for a difference-in-differences approach to investigate potential differential changes in household composition across program and non-program villages among nonbeneficiary households with young children. We find no significant differential change in household size or composition in treated villages 31 months after rollout (Annex 3 Table 1). We find this lack of differential change in the full sample and, specifically, with respect to highly saturated villages. Therefore, any baseline difference in household size or composition persists over the study period. If household compositional differences partially determined the child height detriment in comparison villages, then we would also expect to see a persistent height difference among the older child sample, as these children were also raised in somewhat smaller households with somewhat fewer adults. But we do not observe this pattern. Third, we address possible baseline characteristic imbalance more generally by controlling for all baseline characteristics through propensity weighting (Hirano, Imbens, and Ridder 2003). The estimated impacts on child height and weight are appreciably the same as the results presented in Table 6 (Annex 3 Table 2).

# _Adult Labor Supply Responses to Program Presence_

Adult labor market behavior may respond to the _Pantawid_ program with knock-on effects for child development. For example, if real incomes decline for nonbeneficiary households 29
Table 4. National NRA deviation and weighted-average NRAs for 10 key commodities in developing and high-income countries, 1965–1984 and 1985–2010 (percent)

||Devia|tion of n|ational N|RAs|Weig|hted ave|rage of N|RAs|
|---|---|---|---|---|---|---|---|---|
||Devel<br>coun|oping<br>tries|High-i<br>coun|ncome<br>tries|Devel<br>coun|oping<br>tries|High-i<br>coun|ncome<br>tries|
||1965–84|1985–09|1965–84|1985–10|1965–84|1985–09|1965–84|1985–10|
|Rice|32|59|66|186|-20.1|0.9|136.8|351.8|
|Wheat|33|43|52|76|5.5|9.1|12.2|20.5|
|Maize|36|33|40|48|-3.4|2.3|6.9|11.9|
|Soybean|46|120|75|54|2.7|-2.1|0.1|5.2|
|Sugar|53|64|168|152|17.2|18.0|107.6|108.1|
|Cotton|38|32|42|30|-16.0|-2.7|21.3|10.4|
|Coffee|41|29|na|na|-37.3|-11.6|na|na|
|Beef|45|56|84|109|-12.4|2.6|22.7|37.9|
|Pork|81|58|73|69|23.6|-4.6|37.1|15.0|
|Poultry|109|69|91|175|26.3|11.8|24.5|25.4|

Source: Updated from Anderson, Cockburn, and Martin 2010, Table 2.6, using estimates in Anderson and Nelgen 2012b.

Note: Deviation, measured in NRA percentage points, is computed as the absolute value of (residual − trend NRA), where national-trend NRA in each of the two subperiods is obtained by ordinary-least-squares linear regression of the national NRA on time. Estimates shown are an unweighted average of national NRA deviations each year, averaged over the number of years in each period.
data and provides a full reproducibility package, so that interested users can explore alternative organizations of the data that suit their specific purposes.

The WGI data sources cover different sets of economies, and some data sources provide information across many of the six aggregate WGI indicators while others contribute only to one. To get a better sense of the relative importance of the four types of indicators, Table 2 summarizes the number of country-year-source data points in the WGI corresponding to the four types of indicators. The six aggregate indicators over the past 25 years are based on a total of just over 230,000 country-year-source data points. Roughly one-third of these data points, or almost 77,000, come from the four commercial business information providers, which tend to cover the largest sets of countries and more dimensions of governance. Just over one-quarter of the country-year-source data points, or about 62,000, come from data sources produced by NGOs. Finally, surveys and public sector providers account for about 20 percent each of the total number of country-year-source data points or about 45,000 data points each.

# 3. Constructing Aggregate Governance Indicators

In this section we summarize the methodology used to combine the individual indicators of governance, as organized into six dimensions described in the previous section, into six composite measures of governance. The discussion here closely follows the presentation of the WGI methodology in Kaufmann, Kraay and Zoido-Lobaton (1999), Kaufmann, Kraay and Mastruzzi (2004, 2011), as our basic methodology has not changed since these earlier presentations.

We rely on a statistical method known as an “Unobserved Components Model” (UCM) to construct the aggregate indicators.<sup>14</sup> The basic premise underlying this approach is straightforward. Each individual indicator of governance provides an imperfect signal of the broader concept of governance to which it is assigned. Since this broader concept of governance cannot be measured directly, we have a signal extraction problem: how do we best extract the most informative signal of the underlying concept of governance from the available observed data on individual governance indicators? The UCM provides a solution to this signal extraction problem. It generates estimates of governance that combine the information in the individual indicators into the most precise possible estimate of governance, conditional on a set of assumptions detailed below. Naturally, this estimate will be imperfect, and the

> 14 The Unobserved Components Model was introduced in the econometrics literature in Goldberger (1972).  It is a special case of a wider class of latent variable models.  It also is closely related to the empirical Bayes estimator discussed in Efron and Morris (1972).

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21 ̳Medical insurance‘) and U CA Berkeley and Chicago U, both of which show a fairly high degree of specialization in ̳Determinants of health and ill-health‘. Harvard U is dramatically less specialized than U York, which specializes heavily in ̳Economic evaluation‘ and to a lesser extent ̳Health and its value‘ and ̳Efficiency and equity‘, and the World Bank, which is fairly specialized in ̳Determinants of health and ill-health‘ and to a lesser extent ̳Health and the economy‘.

# **Countries of origin**

Unsurprisingly in view of Table 6, the US dominates health economics. As table 9 shows, the US comes top by a substantial margin on total publications, total citations, the _h_ -index, and the _I_<sup>3</sup> index with  =0.5. (We base the country of origin on the author‘s affiliation at the time of publication of each publication.) The UK comes second on all four indices, but the UK‘s _h_ -index is just half that of the US‘s. Canada comes third on all four measures; it is some way behind the UK and only marginally ahead of Australia, the Netherlands and Sweden whose rankings vary across the four indices and who all come fourth or fifth on at least one index. The US, of course, may do less well on a population-adjusted basis, but it is not obvious whether such an adjustment makes sense. Arguably the US _ought_ to be doing more research than other countries since its health system is so different from those of other countries—there may be thus less complementarity of research between the US and the OECD countries than there is among the other OECD countries.

Unsurprisingly, few non-OECD countries make it into the top-25. Taiwan (China) is the highest-ranking non-OECD territory, the next being China and S Africa. Table 10 shows the top 25 institutions on the _h_ -index from non-OECD countries. U Cape Town comes out top on publications, but the National Taiwan U comes out top on total citations, the _h_ -index and the _I_<sup>3</sup> index with  =0.5. Taiwan (China) and S Africa both have four institutions in the top-25, while China and India both have three, two of China‘s being in Hong Kong SAR, China.

Table 11 shows the topical distribution of the top four countries‘ top-50 publications, along with the (normalized) Herfindahl index computed as in Table 8. The United Kingdom is the most specialized country, focusing heavily on the topic ̳Economic evaluation‘. The
schooling, which is consistent with an earlier study from Indonesia showing female advantage in mathematics at age 11 (Suryadarma 2015).

In addition, we find gender gaps in three out of five EDI domains: social competence, emotional maturity, and language and cognitive development. In all cases, we find evidence of female advantage. In the physical health and well-being and communication skills and general knowledge domains, gender differences are not consistently observed across ages 6 to 8. This is consistent with other studies using the EDI, which have found that girls score higher on the EDI in at least two of these domains in kindergarten (Australian Government 2013, Janus and Duku 2007).

The magnitude of the gender gap also ranges between the EDI domains, with the largest gender gap found in the emotional maturity domain followed by the social competence domain. This too is consistent with previous studies of the EDI in higher-income contexts, which have found that girls tend to develop faster than boys in these two domains (Australian Government, 2013, Janus and Duku 2007). The gender gaps in EDI observed in our study are somewhat large compared to results from other gender gap studies that use the EDI. Studies from Australia and Canada using the EDI show that boys have about a 0.09 standard deviation lower score than girls (Australian Government, 2013, Janus and Duku 2007). In contrast, we find gender gaps as large as 0.247 SD in the social competence domain and as large as 0.296 SD in the emotional maturity domain.

Our findings also show that there are significant gender gaps in enrollment both in terms of rate and duration in early childhood education and primary school. The magnitude of this gap varies by the age of the children. For preschool, both the rate and duration of enrollment for girls is higher than for boys in the prime preschool-going ages of 3 to 5. In primary school, gender gaps in enrollment only exist at ages 6 and 7 and close subsequently. The magnitude of these gaps is larger when average quality of preschool services in the village is higher. Taken together, these results suggest that girls are more likely to be enrolled in early childhood education programs at the intended age. This suggests that girls are likely to benefit more from early childhood education programs because they are exposed to better quality, age appropriate developmental activities. Suryadarma (2015) presents evidence to support that the Indonesian school system favors girls to boys. In addition, gender gaps are observed in parenting practices for children age 7 to 9, 19
# **8   References**

Abramsky, Tanya, Karen M Devries, Lori Michau, Janet Nakuti, Tina Musuya, Nambusi Kyegombe, Charlotte Watts. 2016. “The impact of SASA!, a community mobilisation intervention, on women's experiences of intimate partner violence: secondary findings from a cluster randomised trial in Kampala, Uganda.” _Journal of Epidemiology and Community Health_ 0:1-8.

<mark>Agarwal, B., 1997. ''Bargaining''and Gender Relations: Within and Beyond the Household.</mark> _<mark>Feminist economics</mark>_ <mark>,</mark> _<mark>3</mark>_ <mark>(1), pp.1-51.</mark> Agarwala, Rina, and Scott M. Lynch. 2006. “Refining the Measurement of Women’s Autonomy: An International Application of a Multi-dimensional Construct.” Social Forces 84(4): 2077–98 Allendorf, Keera. 2007. “Couples' Reports of Women's Autonomy and Health-Care Use in Nepal.” _Studies in Family Planning_ 38(1): 35-46.

<mark>Almås, I., Armand, A., Attanasio, O. and Carneiro, P., 2015.</mark> _<mark>Measuring and Changing Control: Women’s empowerment and targeted transfers</mark>_ <mark>(No. w21717). National Bureau of Economic Research.</mark> Ambler, Kate, Cheryl Doss, Caitlin Kieran, and Simone Passarelli. 2016. “He Said, She Said: Exploring Patterns of Spousal Agreement in Bangladesh and Nepal.” Working Paper, IFPRI and Yale University.

Anderson, Siwan, and Mukesh Eswaran. 2009. “Determinants of female autonomy: evidence from Bangladesh.” _Journal of Development Economics_ 90(2).

Asante, Kwaku O., and Paul N. Doku. 2010. “Cultural adaptation of the Condom Use Self Efficacy Scale (CUSES) in Ghana.” _BMC Public Health_ 10 : 227.

Ashraf, Nava. 2009. “Spousal control and intra-household decision making: an experimental study in the Philippines.” _American Economic Review_ , 99(4): 1245-77.

Ashraf, Nava, Erica Field, and Jean Lee. 2014. "Household Bargaining and Excess Fertility: An Experimental Study in Zambia." _American Economic Review_ , 104(7): 2210-37.

Balk, Deborah. 1994. “Individual and Community Aspects of Women’s Status and Fertility in Rural Bangladesh.” Population Studies 48(1): 21–45.

Bandiera, Oriana, Niklas Buehren, Robin Burgess, Markus Goldstein, Selim Gulesci, Imran Rasul and Munshi Sulaimany. 2015. “Women’s Empowerment in Action: Evidence from a Randomized Control Trial in Africa.” Working Paper.

Banerjee, Abhijit, Esther Duflo, Nathanael Goldberg, Dean Karlan, Robert Osei, William Parienté, Jeremy Shapiro, Bram Thuysbaert, and Christopher Udry. 2015. “A Multi-faceted Program Causes Lasting Progress for the Very Poor: Evidence from Six Countries.” _Science_ .

Bandura, Albert. 1982. “Self-efficacy mechanism in human agency.” _American Psychologist_ 37(2): 122147.

Bandura, Albert. 2006. “Guide for constructing self-efficacy scales.” in Frank Pajares & Tim Urdan (eds.). _Self-efficacy beliefs of adolescents_ , (Vol. 5., Chapter 14, pp. 307-337).

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***** The census measures poverty on the basis of ‘unsatisfied basic needs’. _Source_ : Census, 1992

**Figure 3.12 : Geographical distribution of poor**

<!-- Start of picture text --> La Paz<br>(Centro/Sur)<br>6%<br>El Alto<br>46%<br>La Paz<br>(Norte/Laderas)<br>48%<br><!-- End of picture text -->

***** The census measures poverty on the basis of ‘unsatisfied basic needs’. _Source_ : Census, 1992

It is therefore of relevance to examine how the new connections made during this period were distributed between richer and poorer neigborhoods (Figure 3.13). While the fact that a new connection takes place in a poorer area is certainly no guarantee that it went to a poor household, the probability that it did so is at least substantially higher than in a more affluent areaindeed at least three times as high in El Alto as in the central or southern neighborhoods of La Paz.

**Figure 3.13: Geographical distribution of new connections**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70% La Paz (South)<br>60% La Paz (Center)<br>50%<br>40% La Paz (North)<br>30% El Alto<br>20%<br>10%<br>0%<br>ElectricityFixed telephone Water* Sewerage*<br>Distribution of new connections by area<br><!-- End of picture text --> _Sources:_ Electropaz, AISA and COTEL, 2000 The analysis reveals that in the case of water and sewerage, more than 80% of new connections were made in the poorest areas: El Alto and the northern ‘laderas’ of La Paz. While this undoubtedly reflects the contractual obligations on AISA, it is important to 19
## **Using machine learning to assess yield impacts of crop rotation: Combining satellite and statistical data for Ukraine**

# **1. Introduction**

While agronomic benefits from crop rotation are well documented, economic factors together with the ability to manage nutrients as well as pest, weed, and insect populations by applying synthetic fertilizers or pesticides, often prompt farmers to implement short rotations or plant the same crop in continuation. This raises issues for policy if there are externalities, either across space -e.g., from harmful pests migrating to neighboring fields or run-off from excessive fertilizer application contaminating rivers- or if, possibly due to insecure tenure, land users do not fully account for longterm effects of their actions on soil fertility, thus contributing to natural resource degradation.

Studies suggests that, in cases where such external effects are present, relatively modest incentives could prompt producers to adjust their behavior. Yet, studies to explore this issue are limited, mainly because of limitations in routine statistical data that fail to report on the crops grown in the same field in different years. Most studies are thus based on information from agronomic trials that is infrequent in nature, often removed from field realities, and not available for the production systems practices in many parts of the globe.

In this paper we explore whether use of crop cover information derived from freely available satellite imagery can fill this gap. We generate a crop map for all of Ukraine in three consecutive years and use the resulting data to estimate impacts of crop rotations on yields for the country’s four main crops (maize, soybeans, cereals, and sunflower) at the village level. Results are statistically significant, economically meaningful, and very different from what is reported in the US-based literature. Contrary to the literature that is mostly focused on corn(maize)-soybean rotations, we find that continued soybean cultivation incurs only a limited yield penalty and that the magnitude of yield reductions from monocropping is often below that of unsuitable predecessor crops, mainly sunflower, that deplete the soil horizon of water or increase disease pressure.

To allay concerns about measurement error or replicability of this approach in settings where yield information is not at all or less regularly collected, we repeat the regressions using indices of vegetative development that can be derived directly from satellite imagery (EVI, LAI, FAPAR, and LSWI). Using crop masks for the crops considered here produces results that are very similar to those obtained for yields. As such indices can be constructed at levels of spatial and temporal resolution well beyond what is within reach of statistical systems, this offers tremendous potential for research.

2
survey data until a new survey becomes available. Specifically, we use the survey data of 1999 for years 1996 to 2001, the survey data of 2002 for years 2002 to 2004, and the survey data of 2005 for years 2005 to 2009.

We consider three groups of bank regulation/institutional variables. The first group of regulatory variables is related to state policies that enable or restrict competition. Entry barrier index, _entry_bar,_ measures bank entry requirements, which is constructed based on eight questions in the Barth, Caprio, and Levine surveys regarding legal submissions required to obtain a banking license in a given country. Application denied, _ap_denied_ , is the percentage of applications to set up a bank which were denied in the past five years. Government ownership, _gov_own_ , measures the fraction of banks that are 50% or more owned by the government. The second group of variables measure bank regulation and supervision. Activity restrictions index, _activity_restriction_ , measures the degree to which the national regulatory authorities allow banks to engage in securities, insurance, and real estate businesses. Capital stringency index, _capital_stringency_ , measures the amount of capital a bank must maintain. Supervisory power index, _supervisory_power_ , indicates whether the supervisory authorities have the power and the authority to take specific preventive and corrective actions. Diversification index, _diversification_index_ , captures whether there are explicit, verifiable, quantifiable guidelines for bank asset diversification and whether banks are allowed to make loans outside of national borders. Deposit insurance coverage ratio, _covratio,_ is the amount of deposit insurance coverage divided by deposits per capita. It is set to 1 if a country offers full coverage. We obtain this variable from Demirguc-Kunt, Kane, and Laeven (2008). Since the data ends in year 2003 and most countries did not change their deposit insurance coverage till the recent financial crises, we use the deposit insurance coverage in 2003 for years 2003 to 2007.<sup>9</sup> Finally, we also use data on the investor protection index ( _investor_protection_ ), depth of credit information sharing ( _credit_info_depth_ ), and the existence of a private information bureau ( _private_bureau_ ) from Djankov, McLiesh, and Shleifer (2007) and the World Bank Doing Business Survey.<sup>10</sup> These variables measure the strength of private monitoring and information

> 9 See Demirguc-Kunt, Kane, and Laeven (2008) for a detailed analysis of various features of deposit insurance mechanisms.

> 10 Details on how these variables are constructed are available on World Bank’s Doing Business Survey website at <u>http://www.doingbusiness.org/methodology.</u>

10
Fafchamps, Marcel, David McKenzie, Simon Quinn, and Christopher Woodruff (2014) “Microenterprise Growth and the Fly-paper Effect: Evidence from a Randomized Experiment in Ghana”, _Journal of Development Economics_ , 106: 211-226.

Fafchamps, Marcel and Simon Quinn (2015) “Aspire”, Mimeo. Stanford University.

Fafchamps, Marcel and Christopher Woodruff (2014) “Identifying Gazelles: Expert panels vs. surveys as a Means to Identify Firms with Rapid Growth Potential”, Mimeo. University of Warwick.

Grimm, Michael and Anna-Luisa Paffhausen, (2015) “Do Interventions Targeted at Microentrepreneurs and Small and Medium-sized Firms Create Jobs? A Systematic Review of the Evidence for Low and Middle Income Countries”, _Labour Economics_ , 32: 67-85.

Hall, Robert and Susan Woodward (2010) “The Burden of the Nondiversifiable Risk of Entrepreneurship”, _American Economic Review_ 100: 1163-1194.

Haltiwanger, John, Ron Jarmin, and Javier Miranda (2013). “Who creates jobs? Small vs large vs young”, _Review of Economics and Statistics_ , 95(2), 347–361.

Henrekson, Magnus and Dan Johansson (2010) “Gazelles as job creators: a survey and interpretation of the evidence”, _Small Business Economics_ 35: 227-44.

Howell, Sabrina (2015) “Financing Constraints as Barriers to Innovation: Evidence from R&D Grants to Energy Start-ups”, Mimeo. Harvard University.

Hsieh, Chang-Tai and Peter Klenow (2014) “The Life Cycle of Plants in India and Mexico”, _Quarterly Journal of Economics_ 129(3): 1035-84.

Hsieh, Chang-Tai and Benjamin Olken (2014) “The Missing “Missing Middle””, _Journal of Economic Perspectives_ 28(3): 89-108.

International Labour Organization (ILO) (2012), _Global Employment Trends 2012: Preventing a deeper jobs crisis_ , ILO, Geneva.

Karlan, Dean, Ryan Knight and Christopher Udry (2014) “Consulting and Capital Experiments with Micro and Small Tailoring Enterprises in Ghana”, Mimeo. Yale.

Kerr, William, Ramada Nanda and Matthew Rhodes-Kropf (2014) “Entrepreneurship as Experimentation”, _Journal of Economic Perspectives_ 28(3): 25-48.

Klinger, Bailey and Matthias Schündeln (2011) “Can Entrepreneurial Activity be Taught? QuasiExperimental Evidence from Central America”, _World Development_ 39(9): 1592-1610.

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