rafmacalaba/datause-encoder-data huggingface dataset:
rural areas, and Zanzibar. The sample households were visited once during the 12-month survey round. The UNPS, on the other hand, has been designed to re-interview 3,123 households that had been interviewed by the Uganda National Household Survey (UNHS) 2005/06. The UNPS 2009/10 sample size was 2,975 households, and the domains of inference include Kampala City, Other Urban Areas, Central Rural, Eastern Rural, Western Rural, and Northern Rural. The sample households were visited twice during a given survey round.

In terms of questionnaire design, the TZNPS and the UNPS share a number of similar features. In each setting, all sample households were administered a multi-topic household questionnaire. The households that were involved in agricultural activities (through ownership and/or cultivation of land and/or ownership of livestock) were also given an agriculture questionnaire. On annual/temporary crop production, the same set of modules were administered separately for the long and short rainy seasons in a single visit in Tanzania, and for the two main agricultural seasons in two different visits in the case of Uganda. At the plot-level, the questionnaires solicited detailed information on land area, physical characteristics, labor and non-labor input use, cultivation, and production.

Both surveys relied on mobile survey teams, each of which was headed by a team leader and was composed of 3-4 enumerators and a data entry operator. In Tanzania, as long as respondent refusal or physical inaccessibility (due to inclement weather or unfavorable road conditions) were non-issues, the field protocol required enumerators to record GPSbased area measurements for agricultural plots that were owned and/or cultivated by the farming household sample that were situated within a 1 hour of travel radius with respect to the dwelling unit, regardless of mode of transportation. Similarly, the field protocol in Uganda required enumerators to take GPS-based plot area measurements as long as the plots were confirmed to be located within the sample enumeration area.

In both settings, the GPS-based plot area measurements were taken following the interview, with the help of respondents that accompanied enumerators to plot locations and identified the plot boundaries. The enumerators were instructed to report nonmeasured plots and the associated reasons to their team supervisors. Each enumerator was assigned a handheld GPS unit (Garmin eTrex HC in Tanzania, and Garmin 12 in Uganda) and was trained extensively on GPS-based area measurement during the field staff training prior to the start of the field work. For a given plot, the enumerator was supposed to walk clockwise the perimeter with the GPS unit active and at a reasonably slow speed, stopping for 10 seconds at every point that the plot outline changed direction. The area of each plot was calculated directly by the GPS unit in acres.

9
_27_ _Ways Out of Poverty - Diffuszn- Best Practices and Creating Capabilities_ ineffective or are abused, in particular subsidized schemes. The key hypothesis that emerges from existing experience is that the schemes with the greatest<sup>chanceof working</sup> well are those aligned with basic market principles. Decent credit support programs<sup>tend</sup> to avoid subsidized credit terms. Sound approaches to advisory services tend<sup>to</sup> emphasize creation of demand for such services and delivery via market mechanisms. In addition, special support schemes seem to require a sound basic overall policy<sup>framework</sup> for markets where they operate.

**Industrial policy.** Special government support for domestic firms of all types<sup>has</sup> typically been discussed under the heading of "infant industry" protection or "industrial policy", even though other sectors might be affected as well, such as services<sup>or</sup> agriculture. Essentially industrial policy consists of a mix of policies that<sup>create room for</sup> domestic firms to leam and to obtain finance so as to improve productivity. In a great number of countries such policies have been a disappointment. The above-cited case<sup>of</sup> the Indian machine tool industry provides an example, where a protected industry<sup>has not</sup> used protection to leam but to be satisfied with substandard performance. However, particularly in East Asian economies like Japan and Korea industrial policy is often<sup>seen</sup> to have helped. A recent study examined productivity performance in Japan and Korea sector by sector (figure 15). It tums out that the sectors that benefited from special support contributed only a modest amount to productivity growth. Overall, even<sup>in the</sup> two arguably most successful cases industrial policy has at best been a small factor<sup>for</sup> success.

**Figure 15: The effect of Industrial policy on growth of total factor**<sup>**productivity in themanufacturing**</sup> **sector and aggregate growth** (average annual rate of growth)

<!-- Start of picture text --> Korea - 1966-1985 Japan - 1960-1979<br>Xt00 900 880 V00 900 867<br>8a00 800-<br>600* 600-<br>400- 32 400- 236<br>200 200 137<br>With industrial policy Without industrial With industrial policy Without industrial<br>policy Policy<br>OTFPgrowth gGDPgrowth OTFPgrowth iGOPgrowth<br><!-- End of picture text --> Note: This analysis is based on the assumption that the growth in manufacturing<sup>sector</sup> accounted for only a third of the growth in GDP in both Korea and Japan. The<sup>TFP</sup> growth figures relate to the manufacturing sector only and not to the whole economy. Source. Pack (2000).

Clearly, to be successful industrial policy needs to expose the firms that are supported to some form of serious competition, for example, by tying continued support to export success in truly competitive markets as in the case of Korea and Japan.59 Openness to

**59** Stiglitz, 1998b.
Policy Research Working Paper 10774

# **Abstract**

This paper explores the evolution and resilience of global value chains (GVCs) in the agrifood sector, which intensified since the 1994 Uruguay Round Agreement. Using unique data from the FactSet database, along with Fortune 500 lists, the comprehensive analysis of approximately 17,500 agribusiness companies worldwide examines more than 150,000 supplier and customer connections from 2014 to 2022. The findings reveal that large corporations, acting as central nodes, have increased their network centrality in global value chains, particularly through geographic diversification and a concentrated supply strategy. The study also indicates that there is a correlation between the complexity and depth of firm-to-firm linkages and increased resilience, suggesting that firms with greater connectivity are less likely to exit the industry. The analysis not only contributes new insights into the structure and dynamics of agribusiness networks, but also highlights the role of firm linkages in navigating recent disruptive global events, such as the United States-China Trade War, the COVID-19 pandemic, extreme weather episodes, and geopolitical tensions.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at abeck@worldbank.org; slim@agcenter.lsu.edu; and dtaglioni@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
<u>Table A17. Female Non-Production Workers, GVCs and Barriers</u>

|||Finance|Tax rates|Pol. Inst.|Corrup.|Comp.|Elec.|Inad. Edu.|
|---|---|---|---|---|---|---|---|---|
|||||(a) All Region|s||||
||(1) GVC|0.277***|0.311***|0.229***|0.264***|0.299***|0.259***|0.285***|
|||(0.0325)|(0.0470)|(0.0336)|(0.0296)|(0.0415)|(0.0378)|(0.0358)|
||(2) Obstacle|-0.0342|0.0453|0.00585|0.0534|-0.182|-0.198*|0.281**|
|GVC1||(0.114)|(0.131)|(0.120)|(0.151)|(0.136)|(0.112)|(0.141)|
||(1)*(2)|-0.257**|-0.313**|0.00194|-0.148|-0.377**|-0.106|-0.292**|
|||(0.125)|(0.134)|(0.140)|(0.130)|(0.166)|(0.137)|(0.133)|
||Observations|41,324|41,324|41,324|41,324|41,324|41,324|41,324|
||R-squared|0.538|0.538|0.538|0.538|0.538|0.538|0.538|
||(1) GVC|0.507***|0.523***|0.524***|0.499***|0.491***|0.511***|0.603***|
|||(0.0449)|(0.0647)|(0.0500)|(0.0456)|(0.0468)|(0.0610)|(0.0680)|
||(2) Obstacle|-0.110|-0.0615|0.0250|0.0251|-0.297*|-0.221**|0.245*|
|GVC4||(0.112)|(0.150)|(0.134)|(0.163)|(0.162)|(0.104)|(0.145)|
||(1)*(2)|-0.411|-0.349|-0.372|-0.315|-0.296|-0.271|-0.834***|
|||(0.302)|(0.283)|(0.238)|(0.246)|(0.266)|(0.203)|(0.264)|
||Observations|41,324|41,324|41,324|41,324|41,324|41,324|41,324|
||R-squared|0.537|0.537|0.537|0.537|0.537|0.537|0.537|
|||||(b) MENA regi|on||||
||(1) GVC|0.339**|0.206|0.255**|0.245*|0.464***|0.307**|0.392***|
|||(0.140)|(0.134)|(0.116)|(0.138)|(0.130)|(0.139)|(0.0908)|
||(2) Obstacle|-0.467**|-0.133|-0.816**|-0.684*|-0.227|-0.283*|-0.0198|
|GVC1||(0.190)|(0.298)|(0.319)|(0.336)|(0.183)|(0.163)|(0.210)|
||(1)*(2)|-0.435|0.0825|-0.0635|-0.0464|-0.985**|-0.281|-0.972**|
|||(0.397)|(0.456)|(0.207)|(0.320)|(0.456)|(0.332)|(0.363)|
||Observations|6,861|6,861|6,861|6,861|6,861|6,861|6,861|
||R-squared|0.470|0.469|0.469|0.469|0.470|0.469|0.470|
||(1) GVC|0.643**|0.762*|0.509|0.632*|0.436|0.632*|1.046***|
|||(0.292)|(0.428)|(0.332)|(0.358)|(0.387)|(0.331)|(0.164)|
||(2) Obstacle|-0.519***|-0.148|-0.833**|-0.683**|-0.471***|-0.347**|-0.190|
|GVC4||(0.186)|(0.258)|(0.306)|(0.326)|(0.158)|(0.160)|(0.209)|
||(1)*(2)|-1.299|-1.610|-0.390|-0.768|-0.414|-1.109|-3.474***|
|||(1.006)|(1.389)|(0.894)|(0.971)|(1.319)|(0.884)|(0.658)|
||Observations|6,861|6,861|6,861|6,861|6,861|6,861|6,861|
||R-squared|0.466|0.466|0.466|0.466|0.466|0.466|0.468|

Robust standard errors clustered by country and year in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Notes: i) Each regression controls for firms’ age, city of operation and share of government ownership. ii) All the regressions include country, year, sector and size fixed effects. iii) Country-year-sector averages are used to reduce the risk of endogeneity between the business environment and firm-level.

iv) Each column for each GVC definition represents a separate regression. v) All the variables are in log.

vi) The intercept is included.

44
**<mark>Effect of Stress on Later-Life Health: Evidence from the Vietnam War Draft</mark>**<sup>**1**</sup>

<mark>John Cawley, Cornell University Damien de Walque, Development Research Group, The World Bank Daniel Grossman, West Virginia University</mark><sup>2</sup>

**Keywords:** Stress, Obesity, Long-term Health Outcomes, Vietnam War Draft **JEL codes: I12, I18, I31**

> 1 Cawley gratefully acknowledges an Investigator Award in Health Policy Research from the Robert Wood Johnson Foundation. We thank Adam Wagstaff and participants at European Health Economic Association Congress in Hamburg for helpful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.

> 2 Corresponding Author: Daniel Grossman, Business and Economics Building, West Virginia University, Morgantown, WV 26506-6025, email: daniel.grossman@mail.wvu.edu.
Thus, we can write the above conditions as follows Let us denote the maximum level of debt that induces repayment with _b_<sup> ̄</sup><sup>_∗_</sup> . As _zi_<sup>_∗>zi_,</sup> thus _y_ 2<sup>_∗_</sup> _i_<sup>_>y_2</sup><sup>_i_,for</sup><sup>_i_=1, 2byProposition2,thisobservationconcludesthatthereexists</sup> an interval 2 _b_<sup> ̄</sup> 1 = 2 _b_<sup> ̄</sup> 2 _< b < b_<sup> ̄</sup><sup>_∗_</sup> . Therefore, lending to the central government of a union does not lead to a default, yet the same magnitude of debt triggers debt distress in a union with incomplete integration when shared equally between the regions. We summarize this finding in the following proposition.

**Result 3:** _In a union with symmetric regions and spillovers, the level of debt that can be accumulated without triggering debt distress is greater if integration is complete._

Intuitively, for a given interest rate the cost of defaulting is greater in a centralized system. This is mainly because higher returns due to productivity spillovers lead to greater investments under a centralized system, thus to greater second period income. This, in turn, increases the value of punishment, _i.e._ the share of output that could be captured by the creditors, deterring the borrower from default.

# **5 Debt Dynamics and Contagion of Shocks**

Next, we compare the complete and incomplete integration schemes in terms of their resilience against productivity shocks. In particular, we are interested in investigating if there are differences between how these two systems either magnify or alleviate debt distress triggered by productivity shocks.

We begin by emphasizing that the timing of the shock is an important factor. When a productivity shock hits in the second period, the only decision the borrower can make is to choose between defaulting and repaying the debt. However, revelation of information about the shock prior to the investment decision will cause the borrower to reconsider her investment decision. We now turn to these issues.

## **5.1 Shocks with Irreversible Debt and Investment Decisions**

Unexpected falls in productivity lead to debt distress in both complete and incomplete schemes if investments are irreversible and levels are at re- integration borrowing fixed spective threshold levels before the shock hits. This observation follows from the debt 17
10% respectively. Tapwater supply is limited to provincial towns and large metropolitan cities such as Dhaka, Chittagong, Rajshahi, Sylhet, Barisal and Rangpur (Figure 1).

Access to groundwater-fed water supplies for irrigation is an important indicator for measuring food security in Bangladesh. Upazila-level, groundwater use for irrigation for the year of 2006– 07 Boro rice season is shown in Figure S2c. Groundwater irrigation has been estimated using reported information on irrigated area and the number of irrigation pumps surveyed under the minor irrigation campaign by the Bangladesh Agricultural Development Corporation (BADC). Additional information on irrigation requirement for dry-season rice cultivation under various soil types and their infiltration capacity (Ravenscroft, 2003) has been used to estimate groundwater irrigation.

The social vulnerability is defined here by the lack of nutrition in children under the age of five. Four indicators measuring the level of under-nutrition in children: the number of underweight and severely underweight, and stunted and severely stunted children at the upazila level of Bangladesh (Figure S3) are used here to characterize social vulnerabilities. According to the 2012 Undernutrition Maps of Bangladesh, underweight and severely underweight children are those whose standardized weight-for-age is less than two and three standard deviations, respectively, below the median for the international reference following the WHO standard. Similarly, stunted and severely stunted children are those, whose standardized heightfor-age is less than two and three standard deviations, respectively, below the median for the international reference following the WHO standard.

# **2.5 Groundwater risk mapping at the national scale**

We construct two multi-hazard groundwater risk maps based on the following selection criteria applying on groundwater arsenic (As), salinity (i.e. electrical conductivity, EC) and dry-season depth to shallow groundwater levels (GWD) at the national scale in Bangladesh. Mapped areas of groundwater risks represent four zones with specific ranges of groundwater As and salinity concentrations that are considered risks to public health, and depth to dry-season groundwater levels below or greater than a certain threshold (4-8 m bgl) that is considered unsustainable to low-lift pumping technologies and economically expensive. Cut-off values of hazard parameters such as arsenic, salinity and groundwater depletion are based on various drinkingwater standards and lifting capacity of traditional pumping technologies used in Bangladesh. For instance, drinking water standards for arsenic are 10 μg/L and 50 μg/L according to the 7
Demographic and Health Surveys, which have collected nationally representative data on domestic violence for a variety of countries, state that they follow a modified version of the WHO guidelines (Kishor and Johnson, 2004). However, given the extremely large samples involved (ranging from 2,000 to 90,000), it seems fair to ask if such guidelines can be effectively implemented at reasonable cost.

# _What Does Privacy Mean?_

Beyond the difficulty of conducting private interviews illustrated with the challenging example of studying domestic violence there are more general issues of privacy to consider. For example, once data is collected the researcher often has limited say in regards to the subsequent dissemination of the data. Indeed, while the ethical rationale for privacy is readily apparent, there are also ethical considerations that encourage wide data access. For instance, students, particularly in low income countries, generally have limited access to research grants and other funds that could be used to collect data that will further their research. From this perspective broad access is less extractive than exclusive rights to use a data set granted to a single research team, often from outside the countries where the data has been generated. Moreover, as field research is not as easily replicated as are clinical studies, public posting of data provides a check against inadvertent research errors or excessive data manipulation. Thus, it is common policy that funders of research require that the data collected as part of the project be made publically available within a specified period.<sup>5</sup>

> 5 The research department at the World Bank has such a policy.  Additionally, the World Bank strongly encourages governmental statistical offices to make data collected using instruments it has designed, such as the Living Standards Measurement Survey, publically available.  It often provides a draft letter of data protocol for the country statistical director as part of technical assistance.  However, the ultimate decision on data access to such surveys remains within the statistical office and is governed by local laws and the preference of the government.

11
events, which are associated with flooding and waterlogging (Trancoso et al. 2024; Letsch, Dasgupta, and Robinson 2023; Otto et al. 2023; Nanditha and Mishra 2024).

Our analysis draws on newly available, spatially granular data on relative wealth - the Relative Weath Index (RWI) (Chi et al. 2022) - and variation in extreme heat and flood exposure across South Asia. While most of South Asia is very hot, sizable areas of the region are at a high elevation and have low average temperatures. Even in non-mountainous areas, average maximum temperatures range from 28 to 34 degrees (figure 1). Similarly, while flooding affects almost a third of South Asia, exposure to it varies within provinces and districts (figure 1). Given this variation, we estimate regressions on high-resolution spatial data to examine the association of the RWI with heat and floods.

Our preferred specification regresses the RWI on indicators for ranges of average annual maximum temperature, allowing for the possibility of non-linearities in the relationship between heat and wealth or firm size. We use an indicator for any flooding as well as the number of floods as our measure of exposure to flooding. Given the observation difference in the distribution of the RWI in urban and rural places, we estimate separate regressions for urban and rural locations.

We find that places with lower wealth are more exposed to heat, in both urban and rural areas of South Asia. Compared to places at an average temperature of 30 degrees Celsius, places with an average temperature of 34 degrees Celsius have 0.5 standard deviations (SDs) lower RWI in urban areas and 0.3 SDs in rural areas. Additionally, in urban areas, places with lower wealth are _more_ exposed to flooding. An additional flood in urban areas is associated with a 0.004 SDs lower RWI. The reverse is the case in rural areas: in rural areas, places with lower wealth are _less_ exposed to flooding.

In low- and middle-income countries, a large share of poor households rely on small, informal firms for work. Hence, to further understand exposure to climate shocks among the poor, we study whether exposure to extreme heat and flooding varies by firm size. This analysis uses spatially granular data on firm size from the Economic Census of India.

Among firms in India, smaller non-agricultural firms are more exposed to floods and heat than larger firms. On average, places with an average temperature of 33 degrees Celsius have 0.25 fewer employees, about 12.5 percent smaller, than a place with an average temperature of 31 degrees 3