rafmacalaba/datause-encoder-data huggingface dataset:
# **Estimating Local Agricultural GDP across the World**<sup>_†_</sup>

_Keywords_ : agriculture, regional economic activity, GDP, cross-entropy _JEL Classification Codes_ : Q10, R12

> _†_ The authors would like to thank the following people for discussions and prior reviews: Claudia Berg (World Bank), Gero Carletto (World Bank), Uwe Deichmann (World Bank), Rashmin Gunasekera (World Bank), Barbro Hexeberg (World Bank), Glenn-Marie Lange (World Bank), Michael Lokshin (World Bank), Eric Roland Metreau (World Bank), Jose Pablo Valdes Martinez (World Bank), Tim Robinson (FAO), Steven Rubinyi (World Bank), Juha Siikam ̈aki (IUCN), Ben Stewart (World Bank), Jeffrey R. Vincent (Duke University). We appreciate the use of non-timber value data from Juha Siikam ̈aki and ports data from Gilles Hosch. The authors would like to thank the participants of conferences including: American Association of Geographers Annual Meeting 2019 in Washington, DC, 3-7 April 2019; United Nations Economic Commission for Europe Workshop on Data Integration: Realising the Potential of Statistical and Geospatial Data in Belgrade, Serbia, 21-23 May 2019; International Institute for Applied Systems Analysis seminar in Laxenburg, Austria, 27 September 2019; IFPRI RISE Workshop in Washington, DC, 19 November 2019; and the Committee for the Coordination of Statistical Activities and United Nations Geospatial Network Joint virtual Workshop on the Integration between Geospatial and Statistical Information, 28 April 2021. We appreciate the support of the World Bank Strategic Research Program on Big Data. The maps displayed in this paper are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of the World Bank Group any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

> _‡_ World Bank. Corresponding author can be reached at: bblankespoor@worldbank.org

> _⋆_ These authors contributed equally to this work.

> _§_ Cornell University. Email: YR97@cornell.edu

> _¶_ International Food Policy Research Institute

> _∥_ International Food Policy Research Institute. Email: T.Thomas@cgiar.org

> _∗∗_ International Food Policy Research Institute. Email: L.YOU@cgiar.org
**<u>Figure 6: Implicit interest rate and maturity.</u>**

<!-- Start of picture text --> Interest rate and maturity structure Interest rate and maturity structure<br>Excluding Central Bank > 0.5<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Long term debt (as % of Domestic Debt) Long term debt (as % of Domestic Debt)<br>.25<br>.25<br>.2<br>.2<br>.15<br>.15<br>.1<br>.1<br>Implicit interest rate<br>Implicit interest rate<br>.05 .05<br>0 0<br><!-- End of picture text --> _Source: our elaboration on the LIC domestic public debt dataset. Note: Correlation is -0.31 in left panel (144 obs.) and -0.15 in right panel (85 obs.)._

**<u>Figure 7: Implicit interest rate, maturity, and investor base.</u>**

<!-- Start of picture text --> Interest rate and investor base Maturity structure and investor base<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Investors other the Central Bank (as % of Domestic Debt) Investors other the Central Bank (as % of Domestic Debt)<br>Source: our elaboration on the LIC domestic public debt dataset.<br>Note: Correlation is 0.25 in left panel (132 obs.) and -0.33 in right panel (133 obs.).<br>Figure 8: Implicit interest rate, maturity, and investor base.<br>Interest rate by share of non-Central Bank holders Maturity structure by share of non-Central Bank holders<br>1 2 3 1 2 3<br>.25 1<br>.2 .8<br>.15 .6<br>.1 .4<br>Implicit interest rate<br>.05 .2<br>Long term debt (as % of Domestic Debt)<br>0 0<br>.4 1<br>.8<br>.3<br>.6<br>.2<br>Implicit interest rate .4<br>.1<br>Long term debt (as % of Domestic Debt) .2<br>0 0<br><!-- End of picture text --> _Source: our elaboration on the LIC domestic public debt dataset._ _Note: Groups 1, 2, and 3 correspond, respectively, to debt portfolios whose share held by non-Central Bank investors is up to one-third, between one- and two-thirds, and more than two-thirds._ 20
48 In sum, this brief review of t'* -rowth literature raises at least four important themes.” First, the broad forces behind econom.. growth - accumulation of produced factors, specialization, economies of scale, and externalities - were sketched-out by the classical economists long ago. Second, these same forces have been used by the development literature to study various aspects of economic growth. Third, by building on the insights of growth and development economists, the "new" theoretical literature on growth is also contributing models that identify specific channels through which national policies may have effects on long-run growth rates. Finally, the empirical work on linking national policies to growth is still evolving, and many basic issues regarding the longrun relationship between policy and growth - including the effects on growth of government size, the allocation of public expenditures, the financing of fiscal expenditures, trade policy, and international capital flows - remain to be more conclusively resolved. There seem to be important opportunities to evaluate empirically the theoretically predicted channels from policy to growth by improving the design of cross-country growth studies and by conducting more detailed longitudinal case studies.

> 78 We should emphasize again the narrow selectivity of this review, and for that matter, of the "new" growth literature. For example, important issues such as income distribution (Kuznets (1956-57), Adelman and Morris (1973), Robinson (1976), Nelson (1981), Sen (1981a,b), Kanbur (1987), Adelman and Robinson (1978)}, health and nutrition {Selowsky and Taylor (1973), Stiglitz (1976), Lau, Lin, Yotopoutos (1978), Bliss and Stern (1978), and Behrman and Deolalikar (1987, 1988)}, and the structural evolution of economies (Kuznets (1966), Taylor (1969, 1983), Chenery and Taylor (1968)}, have not yet received much attention by the "new" growth literature.
# **5.3. Alternative measures of educational outcomes**

Figure 5.3 Proportion completing Primary schooling (CEPE) <!-- Start of picture text --> 10 15 20 25 30 10 15 20 25 30<br>Age Age<br>Others Income declined Others Displaced<br>10 15 20 25 30 10 15 20 25 30<br>Age Age<br>Others Lost Ownership Others Lost job<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>% completed CEPE<br>0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>% completed CEPE<br>0 0<br><!-- End of picture text --> In the previous analysis we used only total years of education as an educational outcome variable. As a sensitivity analysis, we propose to look at another potential outcome variable that measures the percentage of the population that completed CEPE (six years of primary education). This is justified by the fact that the average years of education based on the ENV-2008 data is recorded as being a little over 7 years and almost 40 percent of the population fail to complete the CEPE. We estimate nonparametric kernel-weighted local polynomial regressions of percent ever completing six years of primary education against age using Epanechnikov kernel. We ran the regressions separately for the war victims and the rest of the sample as identified by the victimization indicators. Figure 5.3 provides the outcomes of non-parametric estimation for four victimization categories. Of the victimized groups, individuals whose family members suffered from income loss or job loss are less likely to complete CEPE. This is evident particularly in the age-cohort with individuals aged between 10 and 22. The internally displaced individuals do not show a different trend in the successful completion of CEPE. Among households that suffered 22
Chart 19 Share of Countries in Easing and Tightening Phases, including Holds <!-- Start of picture text --> (Percent of sample)<br>100<br>80<br>60<br>40<br>20<br>0<br>1975 1979 1984 1989 1994 1999 2004 2009 2014 2019 2024<br>% Tightening in Tightening Phase % Holding at End of Tightening Phase<br>% Easing in Easing Phase % Holding at End of Easing Phase<br><!-- End of picture text --> Sources: Authors’ calculations based on data from January 1970 through May 2024. Notes: Chart shows share of sample in easing and tightening phases, including the portion of each for which policy is “on hold” before a turning point. “On hold” is the period after the last rate increase during a tightening phase, or after the last rate decrease and end of any QE programs during an easing phase. Movements in the policy interest rate <0.1 percentage point do not qualify as a change.

Is this longer holding period at the end of the current tightening phase unusual? Or is it typical of historical periods when a large share of economies simultaneously tightens monetary policy, and the shocks behind the tightening have a larger global component (as shown in Section IV.2)? To better place today’s long holding period in context, the right side of Chart 18 compares holding periods during the Highly Synchronized Tightening periods (initially introduced in Section IV1.1) to all other holding periods before the pandemic (with results for the pandemic already shown).

Before the post-pandemic tightening, the median length of holding periods did not vary based on the degree of global synchronization (although the bottom panel shows that these holding periods comprised a shorter fraction of the overall tightening phase for the more synchronized periods, which tend to be shorter). The most recent post-pandemic, Highly Synchronized Tightening continues to stand out as involving an unusually long holding period (by both measures), however, even when compared to earlier Highly Synchronized Tightening periods.

# VI.2 Premature Adjustments

Next, we examine another measure related to transitioning between phases in a cycle: “premature adjustments.” These are periods when the policy rate is adjusted in the opposite direction of the phase (i.e., increasing rates during an easing phase or decreasing rates during a tightening phase), but the rate change does not subsequently meet the criteria to be a turning point denoting the transition to the next phase. In most cases, this is because the rate change is reversed soon afterward.

58
projects delivering perceptible benefits and the concerned population assigning credit to the government for those benefits.

One of the main challenges in identifying the impact of projects on counterinsurgency outcomes is the non-random assignment of projects, which leads to spurious correlations if project placement is dependent on local security conditions, as is often the case. Two recent studies which address the question use different empirical strategies for inference and arrive at different results. Berman, Shapiro, and Felter (2011) examine development projects undertaken by the U.S. military in Iraq and, after controlling for region-specific characteristics and pre-existing trends, find that projects reduced violence, although only after a significant increase in troop strength in 2007. Crost, Felter, and Johnston (2011) employ a regression discontinuity design to examine the effect of development projects in the Philippines and find that projects exacerbated violence in the short run and had no effect in the long run.

Our study differs from these existing works in two important ways. First, we use randomized assignment of projects across villages to eliminate selection bias. Of 500 villages in our sample, half were randomly assigned to receive a development program in 2007, with the other half not receiving it until after 2011. Second, in addition to events data on security incidents, we use survey measures of household-level economic outcomes and of individual perceptions and attitudes. This allows us to test the specific mechanisms through which projects affect security.

We find that projects improve villagers’ perceptions of economic well-being and attitudes towards central and sub-national government, NGOs, and U.S. military forces. Projects also improve villagers’ perceptions of the local security situation and cause a reduction in the number of security incidents recorded by the International Security Assistance Force (ISAF) a year or more after project implementation. However, there are no effects on incidents occurring within a year of project implementation and no effects on the number of incidents reported by villagers in surveys.

In areas facing high levels of violence, no effects are observed on attitudes to government or on security incidents, despite positive effects on perceptions of economic well-being. These results indicate that, in areas where attitudes to government are unfavorable either due to a failure of the government to ensure security or to broad-based support for the insurgency, even projects that deliver perceptible benefits are insufficient to sway attitudes. Thus, while projects can prevent the spread of 3
|Economic growth|Weaker-than-expectedeconomicperformancereduces tax<br>revenue and increases thecost of unemployment insurance and <br>othersocialprotection programs|Environmental changes and extreme weather<br>events threaten to disrupt activity across a wide<br>rangeofeconomicsectors|
|---|---|---|
|Commodity<br><br>prices|I suddenchangesincommoditypricesaffectgovernment<br>spending,customsduties,andenergy,agricultural,orfood<br>subsidies.|Theincreasedfrequencyandseverityofextreme<br>weathereventsincreasethevolatilityofglobal<br>commodityprices|
|State-owned<br>Piieigiattd618)<br>29 e|The poor financial or<sup>commercial performance</sup><br>ofSOEsmay<br>yBeNeratecontingentliabilities|Climate change intensifies<br>risks to SOE<br>performance|
|Public-private<br><br>partnerships(PPPs)|i pppsmayentailcontractualobligationsand/orimplicitpublic<br>(iicuiCswithimportantfiscalimplications|Climatechangethreatensthefinancialviabilityof<br>PPPs|
|Ee eennein|Disasters<br>can disrupt production in fiscally important sectors<br>andmay require large-scale reliefandreconstructionspending|Climate change increases thefrequency<br>and<br>severityofnaturaldisasters|
|Public health<br>emergencies|Epidemicdiseasecan radically increase healthspendingand<br>mayadverselyaffectemployment,production,andtrade|Risingtemperaturesand extremeweatherevents<br>increasetheriskofepidemics|
|Judicial awards|Court judgments against the government may result in<br>unexpectedspending|Courts maydetermine<br>that agovernment<br>is liable<br>forclimate-adaptationmeasures|
<!-- Start of picture text --> wf a<br>6<br>5<br>:<br>\<br>24 \<br>Cc<br>o \<br>OQ 3 \<br>2 \<br>RN<br>| y ><br>— —_—_—_—_—_—.<br>0 =— = ‘“ —,<br>bo) &* He) v N iS) \ v % b ©<br>z-score<br><!-- End of picture text -->

—— Weight-for-age, Literate moms- --— Weight-for-age, Illiterate moms == Height-for-age, Literate momsTM= === Height-for-age, Illiterate moms
individual, household characteristics, and cluster variables as well as controlling for thLe subregion in which the individual lives (again through the inclusion of dummy variables).

Multinomial Logit estimates can be derived from what is known as the "randoim utility" model. For example, the utility from choicej for individual i am given as

# (2)

where Z refers to all the regressors in the model (see equation 1). Under the assumption that the error terms (v) are identically distributed with a specific distribution, one can derive the expressions for the coefficients for each outcome, specifically: The model is unidentified (i.e., there are many solutions yielding the same set of probabilities) and the usual way of estimating it is under the restriction that the coefficients for the "reference choice" are all equal to zero. The resulting coefficient estimates are therefore only interpretable relative to this base category. In this analysis the reference category has been set to those who sought no modem sector advice or treatment in response to an episode of fever.<sup>22</sup> There are two major assumptions that are being made in this estimation. First, since the model is estimated conditional on the set of those reporting fever, the estimates will be biased if unobserved factors that determine fever (u s from equation 1) are correlated with unobserved factors determining treatment choice (v s from equation 2). In addition, the model assumes that the error terms from the different choices are uncorrelated (i.e., the v s from the different choices in equation 2), also known as the Independence of Irrelevant Alternatives (IIA) assumption. Therefore the Multinomial Logit model is estimated under the assumptions of no sample selectivity and independence of the errors in the choice model.

Table 12 reports the results from the MNL estimation for the specification that excludes average cluster wealth and fever. The results confirm that the relationship between being in the richer quintiles in Western and Central Africa and the use of public care is significantly different from zero. In the MNL results, higher wealth is significantly associated with more public facility use-substantially more so for higher-level services.

> 22. Note that seeking care from traditional healers is grouped with no treatment in this part of the analysis. This is mostly because the multivariate choice analysis is difficult to identify when only few cases choose one particular choice.

_24_
and whether the firm is a beneficiary of such programs, and if so, what type of support the firm received.

While the approach of the survey is as much as possible to ask factual questions, it is also important to understand the perceptions that entrepreneurs and managers have about what are the main barriers and drivers of the decisions to adopt new technologies. To this end, the questionnaire asks the respondent to select the most important obstacle and driver for adoption from a closed list of options. As barriers we include: lack of information and technical skills, uncertainty about demand, cost, lack of finance, government regulations or lack of infrastructure. As drivers, we include competition, adoption by other firms, production of new products, accessing new markets, cost reductions or adjusting to regulations. The survey also asks managers to benchmark their business technology sophistication level in relation to other firms in the country, and also vis-a-vis more advanced firms internationally. This helps to understand the role of beliefs of the main managers in technology adoption decisions and potential behavioural biases and overconfidence.

## **A.1.5 Financial Statements and Workers**

In addition to the information on the technologies used by firms, the survey also collects financial statements information, information on the business owners, employees, and on potential drivers of and barriers to technology adoption.

**Financial statements** . The survey asks the establishment about its total sales, material inputs, replacement value of capital stock, energy consumption, wages and employment. This allows to construct measures of nominal value added per worker, and capital per worker.

**Workers** . Beyond the number of employees, the survey asks questions that provide information on the education of the workers (share of workers with primary, secondary and tertiary education), and about the occupation composition of the labor force (share of Managers, Professionals, and Technicians; Clerical support workers and sales workers; Production workers and Service workers).

# **A.2 Validation of the Ranking of Technology Sophistication**

To evaluate the coherence of the rankings of technology sophisticated from industry experts, we implement a multi-stage validation exercise. First, we select 14 business functions for which we compare the technologies in the grid along three dimensions that define their sophistication and that summarize some of the arguments used by experts to justify their rankings: functionality, integration, and automation. Functionality refers to the capabilities a technology offers to handle more complex tasks, in a faster way, on a larger scale, with 77
Policy Research Working Paper 8172

# **Abstract**

Arsenic contamination in shallow groundwater aquifers remains a major barrier to providing access to safe drinking water in Bangladesh. Chronic exposure to arsenic has been shown to cause serious health impacts, including various cancers, skin lesions, neurological damage, heart disease, and hypertension. Numerous epidemiological studies have shown cognitive impacts on memory, linguistic-abstraction, attention, learning, and physical ability. The neurotoxic effects of arsenic could be particularly harmful for children during their critical growth periods and have impacts on early childhood development. This study uses cross-sectional data from the nationally representative 2012–13 Bangladesh Multiple Indicator Cluster Survey to investigate the effects of arsenic contamination in drinking water on early childhood development outcomes in a sample of around 7,500 children ages 3–5 years. Early childhood development is measured in four skills domains: literacy-numeracy, physical, social-emotional, and learning using the Early Childhood Development Index. Arsenic contamination is measured in source drinking water at the cluster-level. After controlling for a range of demographic, social, and economic characteristics of households, the results show that arsenic contamination is significantly and negatively associated with the overall Early Childhood Development Index, on outcomes within the physical, social-emotional, and learning skills domains. Further, there is a clear dose-response relationship, where those children with exposure to higher concentrations of arsenic have worse developmental outcomes.

This paper is a product of the Water Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gjoseph@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team