rafmacalaba/data-use-mentions-extended huggingface dataset:
- Education: from question 10.5 of the household questionnaire (“How many standard years has [NAME] completed?”).

Follow-up <u>(2011/12):</u>

- Migration: we infer migration status from the current location reported in question 2.12 of the tracking questionnaire (respondent is migrant if reports living “somewhere else”). We further distinguish between intra-state migration, across-state migration, and international migration from the state of residence reported in question 2.23.

- Self-employment: question 2.15 of the tracking module reports the principal activity status. Respondents are classified as self-employed at destination if they report being artisan/independent workers, owning petty shop/small businesses, or owning organized trade/business (with more than 5 employees).

# **B.4 Indonesia Family Life Survey (IFLS)**

Notes: Employment status and occupation type are complemented with corresponding information from section TK of the Proxy Book. Employment status at destination is unreported for international migrants.

Baseline (wave 3 – 2000):

- Employment status: we use information from section TK (employment) of Book 3A. A respondent is classified as not working if the answer to question tk01 (“What was your primary activity during the past week?”) is different than “Working/trying to work/helping to earn income” and questions tk19aa and tk24a have missing answers.

- Self-employed and wage workers: based on answer to question tk24a (“Which category best describes the work that you do?”). Excludes workers that are primarily working in agriculture, forestry, fishing, and hunting (question tk19aa, “In what field of work is this job?”).

- Agricultural workers: those that are primarily engaging in agriculture, forestry, fishing, and hunting according to question tk19aa.

- Income: sum of salary/wage in past year (question tk25a2) and net profit in past year (question tk26ayn). Use
to the Good Governance of Evidence,” Routledge Studies in Governance and Public Policy, Abingdon: Routledge.

- [63] PEPINSKY, T.B., J.H.PIERSKALLA AND A.SACKS (2017) “Bureaucracy and Service Delivery,” _Annual Review of Political Science_ 20: 249-268.

- [64] PETERSEN, M.A. (2004) “Information: Hard and Soft,” Mimeo.

- [65] POMERANZ, D. (2015) “No Taxation Without Information: Deterrence and Self-Enforcement in the Value Added Tax,” _American Economic Review_ 105 (8): 2539-69.

- [66] PRITCHETT, L. AND J.SANDEFUR (2015) “Learning from Experiments When Context Matters,” _American Economic Review_ 105 (5): 471-75.

- [67] QIAN, J., P.E.STRAHAN AND Z.YANG (2015) “The Impact of Incentives and Communication Costs on Information Production and Use: Evidence from Bank Lending,” _Journal of Finance_ 70 (4): 1457-1493.

- [68] RASUL, I. AND D.ROGGER (2018) “Management of Bureaucrats and Public Service Delivery: Evidence from the Nigerian Civil Service,” _Economic Journal_ 128: 413-446.

- [69] RASUL, I., D.ROGGER AND M.WILLIAMS (2018) “Management and Bureaucratic Effectiveness: Evidence from the Ghanaian Civil Service,” Mimeo, University College London.

- [70] ROYSTON, P. (1992) “Comment on sg3. 4 and an Improved D’Agostino Test,” _Stata Technical Bulletin_ 1(3).

- [71] SMITH, A.F.M. (1996) “Mad Cows and Ecstasy: Chance and Choice in an Evidence-Based Society,” _Journal of the Royal Statistical Society_ 159 (3): 367-383.

- [72] SOMANI, R. (2018) “Information and Public Service Delivery: Evidence from an E-Governance Initiative,” Mimeo
# **1. INTRODUCTION**

For most of the population of Sub-Saharan Africa, farming is a main source of both food and household income. Analysis of the Rural Income Generating Activities (RIGA) study shows that, while non-farm activities are increasing in importance, the vast majority of rural African households remain heavily dependent on agriculture, with on-farm sources of income ranging from 59 to 78 percent of total income (Davis et al., 2010). The comparatively higher importance of agriculture for the African countries included in the RIGA study confirms the critical role that agriculture plays in the economic development and improvement of living standards in the region. This relationship has also been the focus of vast quantities of other literature, including the World Bank’s 2008 World Development Report, _Agriculture for Development_ (World Bank, 2008). However, only rarely is there a focus on issues of underlying data quality and data collection methods. Historically, the data on rural household farming are perceived to be of poor quality, particularly when collected outside the domain of specialized farm surveys, but recently the greater demand for household data on both farming decisions and non-farm activities has led to the expansion of general household surveys to include extensive agricultural modules to capture agricultural production.

Because of the complexity of farming, where salient actions on the farm take place over several months of a season (plot preparation, input application, plot maintenance, harvest and selling), agricultural information would ideally be collected through multiple visits over a farming season to facilitate accurate recall of events. Specialized farm surveys are often designed to visit the household at multiple times, particularly those utilizing resident enumerators (e.g. agricultural extension agents or other Ministry of Agriculture staff). Such specialized surveys, however, offer limited scope to analyze the links between agriculture and non-farm income activities, as well as other socioeconomic outcomes. To meet the demand for integrated data, multi-topic/multipurpose household surveys (such as a Living Standards Measurement Study [LSMS] survey) are often extended
# **5. Concluding discussion**

As our climate changes, preparing for – and responding to - disasters caused by natural hazards in the Caribbean is becoming an increasing priority. World Bank (2018) reported that “within the Caribbean, direct damages due to natural disasters have averaged almost US$1.6 billion per year over the last 20 years.” A cursory comparison suggests that these damages already exceeded the total combined value of net official development assistance and official aid revenue received by Caribbean small states (see World Development Indicators) by a factor of about 2.5. And the damages inflicted by natural disasters will only increase as weather events become more erratic, unpredictable, and extreme.

Governments and development partners frequently cite a need for a people-centered approach, and up-to-date information and statistics, to guide preparations and responses to natural disasters. The Caribbean Disaster Emergency Management Agency (2019), for instance, considers research and data management a foundation of its strategy and evidence-based approaches a core principle to enhance resilience. Household survey data are a central part of the information required to enhance to disaster risk management in the Caribbean. However, as described in detail by Rozenberg et al. (2021), such data are frequently lacking. This paper attempts to help address this gap, by presenting analysis of novel survey data collected in five countries in the Caribbean.

The paper shows that it is possible to integrate disaster risk management in planned household survey data collection at scale and at relatively low cost. The survey module used for the analysis presented in this paper can be adapted for future data collection efforts. Including it in a new household survey will only marginally increase interview time and cost of data collection. One advantage of including a disaster risk module in a broader household survey is that it allows for an analysis of the correlates of exposure to and ability to cope with risks. In the case of this paper, for instance, we look in detail at the correlation between household financial wellbeing and ability to
| POLICY RESEARCH WORKING PAPER 2480

# Summary findings

The introduction of green revolution technologies in wheat and rice production in Asia in the mid-1960s reversed the food crisis and stimulated rapid agricultural and economic growth. But the sustainability of this intensification strategy is being questioned in liglit of the heavy use of external inputs and growing evidence of a slowdown in productivity growth and degradation of the resource base.

Ali and Byerlee address the critical issue of long-term productivity and the sustainability of Pakistan's irrigated agriculture. To estimate changes in total factor productivity in four production systems of Punjab province, they assemble district-level data on 33 crops, 8 livestock products, and 17 input categories.

They find that average annual growth in total factor productivity was moderately high (1.26 percent) for both crops and livestock for the period 1966-94, but observe wide variation in productivity growth by cropping system.

A second, disaggregated data set on soil and water quality reveals significant resource degradation. The authors use the two data sets to decompose the effects of technical change and resource degradation through application of a cost function.

They find that continuous and widespread resource degradation (as measured by soil and water quality variables) has had a significant negative effect on productivity, especially in the wheat-rice system, where resource degradation has more than offset the productivity effects of technological change.

Degradation of the health of the agro-ecosystem was related in part to modern technologies, monocropping, and mismanagement of water resources.

The results call for urgent analysis of technology and policy options to arrest the degradation of resources.

This paper-a joint product of the Rural Development Department and the Asian Vegetable Research and Development Center-is part of a larger effort to support the development of sustainable intensification of irrigated agricultural systems. The study was funded by the Bank's Research Support Budget under the research project "Total Factor Productivity Growth in Post-Green Revolution Agriculture of Pakistart and Northwest India." Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Derek Byerlee
.92|657|0.55|0.69|606|34%|28%|
|Census-based|0.76|0.91|750|0.56|0.63|739|58%|36%|
|_C. Existing areal interpolation esti_|_mates_||||||||
|WorldPop 2015|0.99|1.00|312|0.99|0.99|237|13%|13%|
|Facebook HSRL 2015|0.91|0.97|417|0.90|0.93|309|20%|14%|
|GHSL Grid 2014|0.84|0.88|639|0.74|0.79|556|39%|26%|
|GPW 2015|0.68|0.90|1029|0.50|0.78|740|44%|27%|
|GPW 2010|0.29|0.28|1550|0.39|0.48|911|52%|35%|
|WorldPop 2010|0.31|0.81|1206|0.06|0.44|987|64%|34%|
|LandScan 2010|0.04|0.10|2562|0.01|-0.24|2012|135%|92%|

Note: HIES=the Household Income and Expenditure Survey; HRSL= High Resolution Settlement Layer; GHSL=Global Human Settlement Layer; GPW=Gridded Population of the World; SRC=Spearman Rank Correlation; AE= Absolute error; RE=Relative Error (with respect to the census). We retain only the villages in the 55 sub-districts for which the proprietary data are available. Models are estimated on subsample of 414 villages covered in 2012-2013 HIES survey. The satellite indicators used as covariates include all publicly available and all proprietary indicators. Out-of-sample prediction
# **2. Methodology**

2.1 This section describes the construction of the database containing the descriptions of project components, the derivation from it of the network graph, and the econometric analysis.

## **Construction of the component database**

2.2 World Bank projects are described in an appraisal document, which is presented to the Bank’s Board for approval. Phasing in around 1997, the Project Appraisal Document (PAD) has followed a standard format, with evolutionary change over time. These documents are publicly available in pdf or text versions. The format includes an annex entitled “Detailed Project Description” (or a variant).

2.3 To extract the components descriptions, the IEG applied text-analytic methods to all PADs for the period 1997-2012. The project-description annex was extracted from each PAD. The contents of the annex were scanned for headers describing the components. Where these could be identified, the components were parsed out and numbered sequentially. For some of the cases, components could not be distinguished, and the project was considered to have a single component. The extracted components were stored in an Access database, which included descriptors of the the associated project such as beginning and end date, sector, and country.

## **Construction of the network**

2.4 The text data was then preprocessed in four steps. First, the text data was corrected for misspellings and other errors in the OCR process. (Table 2.) Second, words were stemmed –that is, different verb and noun forms (‘visit’, ‘visits’, ‘visited’) were mapped to a common root. Algorithms differ in their aggressiveness in distinguishing roots, and the choice is consequential for determining similarity between components. We chose the Porter (1980) algorithm. (Table 3.) Third, country, city and currency names were automatically identified and excluded, so that network proximity would be based on components’ conceptual content rather than location. Also
what we estimate with self-reported survey data, and may even be larger (Column 1). This holds true for each of the individual sensitive behaviors, save marijuana use. Despite the large standard errors introduced by the small validation sample, the adjusted treatment effect on all sensitive behaviors is larger and significant at the 1% level.

Meanwhile, the underreporting of gambling does not have a statistically significant association with treatment. Those who received cash alone underreported gambling to the surveyors more often than control group members, and so the measurement error in gambling is probably a combination of a general desirability bias as well as one correlated with treatments. A larger sample size would be needed to separate these more precisely.

In contrast, the slight underreporting of expenditures behaviors in the survey (seen in Table F.3 above) implies that the short term increase in survey-based expenditures due to cash could be due to measurement error correlated with treatment. The adjusted treatment effect of therapy plus cash is generally negative but not statistically significant (Column 6). We see a similar pattern with another expenditure-related item, homelessness, in Table F.4—the survey-reported decline in homelessness tends to disappear with adjustment.

# **References**

- Anderson, M. L. (2008). Multiple inference and gender differences in the effects of early intervention: A reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects. _Journal of the American Statistical Association 103_ (484), 1481–1495.

lvii
drawn from a uniform, one-dimensional policy space [0,1]. This assumption allows for the derivation of a quantitative measure of institutional constraints using a simple spatial model of political interaction.

This initial measure is then modified to take into account the extent of alignment across branches of government using data on the party composition of the executive and legislative branches. Alignment across branches increases the feasibility of policy change, thereby reducing the level of political constraints. The measure is then further modified to capture the extent of preference heterogeneity within each legislative branch. Greater within-branch heterogeneity increases (decreases) the costs of overturning policy for aligned (opposed) branches. Possible scores for the final measure of political constraints range from zero (least constrained) to one (most constrained).

Countries with the greatest level of veto points in the formal policymaking apparatus are those federal states with strong independent judiciaries and either presidential systems or proportional representation electoral rules that tend to yield coalition governments, such as the United States, Germany and Switzerland. Political constraints decrease as the number of veto players declines or as their preferences become more homogeneous, as is the case in moving to a mixed Parliamentary-Presidential system, typified by France or Brazil; to heavily fractionalized Parliamentary systems like those of Belgium, Israel and the Netherlands; to Westminster Parliamentary systems with winner-take-all districts, such as the United Kingdom’s. Nondemocratic countries and those with transitional political regimes have the lowest levels of political constraints because the formal institutional structures in these states provide tremendous discretion to policymakers. Table 4 provides the average observed value of political constraints for each country in our sample.

22
the South in a given year is calculated by multiplying the total value of its international reserve assets in that year by the share that advanced countries allocate to South currencies in that same year.

One consideration in constructing this country-to-region data set for international reserves relates to the classification of countries, as the IMF uses advanced and emerging regions in the COFER database that do not exactly match our definitions of North and South. Advanced economies include several high-income economies that we consider in our analysis to be part of the South (such as Australia; the Czech Republic; Hong Kong SAR, China; and Singapore). This would tend to underestimate investments in the South because countries investing in advanced economies could, in fact, be investing in currencies associated with the South in our classification.

Another consideration relates to the currency classification in COFER. In addition to the wellknown currencies, the data include “unallocated reserves” and “other currencies.”<sup>34</sup> We assume that “unallocated reserves” are allocated across currencies in the same proportion as the “allocated reserves.” We also assume that international reserves held in “other currencies” are held entirely in South currencies. This last assumption is based on the results of the “Survey on the Holdings of Currencies in Official Foreign Currency Assets” conducted by the IMF in 2015. This survey collected year-end data for 2013 and 2014 for 130 countries and provided a more detailed breakdown of the currency allocation of international reserves than COFER. The shares of the world’s international reserves that were unambiguously allocated to South currencies were 3.3 and 3.6 percent in 2013 and 2014, respectively. These estimates provide a lower bound on the allocation of international reserves in South currencies, since the currency breakdown in the survey did not include all South currencies. We match these survey allocations more precisely when allocating “other currencies” entirely to the South (yielding allocations to South currencies of 4.7 and 4.4 percent in 2013 and 2014, respectively
For each crop that the household has cultivated in the past 12 months, the questionnaire enquires about who in the household has worked the most on the crop and about up to two other individuals who worked on the crop in the past 12 months. There are also questions about the amounts sold and the amounts of the crops consumed by the household over the past 12 months to determine whether production was for the market or for subsistence purposes.

Finally, for the production of other agricultural products such as dairy, eggs, oil, or fish, the questionnaire only enquires about sales of these products over the past 12 months and about up to three household members who may have been involved in their production. Unfortunately, the questionnaire does not enquire about those engaged in this other agricultural production exclusively for purposes of household consumption.

In our analyses, any household member that is mentioned as being involved in either a nonagricultural household enterprise, in the production of a type of livestock or crop that has been sold by the household in the previous 12 months, or in the production of the other agricultural products, is considered to be engaged in “market added” work or “added employment” even if they were not reported as being employed in the individual questionnaire. This is therefore our most comprehensive measure of “market work” or employment.

Any individual engaged in the production of livestock or crops exclusively for household consumption in the past 12 months is included in the “extended added” work category even if they did not report being involved in subsistence work in the individual questionnaire. By necessity, the “market added” and “extended added” statuses are for a 12-month reference periods since these were the reference periods used in the crops and other agricultural products modules.<sup>9</sup> Our main focus in this paper is to compare these “added” employment and work definitions using project data to the market and extended work definitions obtained exclusively from an approach that focuses on individual responses on their activities.

The
**LEGAL EFFECTIVENESS AND EXTERNAL CAPITAL: THE ROLE OF FOREIGN DEBT**

# **Abstract**

Previous research has documented weak, and sometimes conflicting, effects of legal quality on measures of firm debt. Using WorldScope data for 1,689 firms as well as more detailed proprietary data for 315 firms across nine East Asian countries, we find that access to foreign financing appears to loosen borrowing constraints associated with poor legal systems. This helps resolve inconsistencies in prior findings as well as explains how legal protection is important for debt use. In particular, we find that the legal effectiveness is important for determining the amount, the maturity, and the currency denomination of debt. We discuss several mechanisms by which firms can avoid the costs of poor legal systems with foreign borrowing. Finally, this paper contributes to the policy debate surrounding the importance of creditor rights for domestic lending.

2
# **1. INTRODUCTION**

Social interactions allow for information dissemination about technologies, products, job opportunities, and beliefs (Jackson, 2010; Jackson, 2011). In finance, substantial evidence shows that information sharing with peers plays an important role for investment decision making, including stock market participation and portfolio choice (e.g., Duflo & Saez, 2003; Hong, et al., 2004; Bursztyn, et al., 2014; Li, 2015; Hvide & Ostberg, 2015). In social settings, however, individuals are selective about the information they want to share with others. For example, people may tend to boast about good stock trades, favoring the transmission of appealing but inaccurate ideas about active trading (Shiller, 1995; Shiller, 2015), or they may try to convince others that they are wealthier or more intelligent than what they really are.<sup>3</sup> Despite extensive research on the effects of social interactions on investment choices, the role of selective communication in the diffusion of trading strategies remains largely unexplored.<sup>4</sup> In this paper, we use data from a large-scale financial training program to study the effects of noisy word-of-mouth communication on stock market participation. We show that biased signals from peers disproportionally attract uninformed individuals to equity trading. These investors overestimate the value of active trading and generate inferior returns once they enter the stock market.

Canonical models of social learning assume that peer choices are perfectly observed by members of the social network (Banerjee, 1992; Bikhchandani, et al., 1992). In practice, peer preferences and outcomes are not always observed directly, and such information is often transmitted via informal conversations. When negative outcomes (e.g., trades that generated low or negative returns) are filtered out to present a positive self-view to others, less information is available to those who are forming an opinion

> 3 This behavior has long been recognized and studied in sociology and