rafmacalaba/data-use-mentions-extended huggingface dataset:
WPS3777

# Pesticide Traders’ Perception of Health Risks: Evidence from Bangladesh

by SUSMITA DASGUPTA CRAIG MEISNER _Development Economics Research Group The World Bank_ NLANDU MAMINGI _Department of Economics University of the West Indies Cave Hill Campus, Barbados_ ABSTRACT. As pesticide traders are important sources of information about the health impacts of pesticides, a crucial understanding of their perception is necessary to guide further pesticide information dissemination efforts through this channel. To this end, a 2003 survey of 110 Bangladeshi pesticide traders was conducted with questions on the pesticides in stock, knowledge and training in pesticide use and handling, sources of information, protective measures and health effects. A two-equation bivariate probit model was initially estimated for health impairment and trader perception with health effects as an endogenous regressor in the perception equation. Results indicate that pesticide toxicity, exposure in terms of number of years spent in pesticide business, trader’s age (experience) and the interaction between the most harmful pesticides and training received in pesticide use and handling were the significant determinants of health impairment status. Risk perception was determined by actual health impairment status, pesticide toxicity, the average number of hours spent in the shop per day, training and the interaction term between highly toxic substances and training. The evidence suggests that the current information content may not be effective, and thus training programs should be revised with a greater emphasis on health hazards and averting behavior.

_Keywords_ – Trader, pesticide exposure, health impairment, health risk perception, Bangladesh World Bank Policy Research Working Paper 3777, November 2005 _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 view of the World Bank, its Executive Directors, or the countries they
show that tertiary-educated workers have an impact on productivity, but the pool is currently very small in Tanzania compared to its competitors. University-educated top managers are also associated with higher labor productivity. Because they recruit more skilled workers, they are most likely to rate education deficiencies as a big obstacle, possibly because they are aware of the gaps between what is available in Tanzania and the rest of the world. A significant finding is the heavy reliance on Dar es Salaam for tertiary educated workers, which raises costs for firms in other regions, pointing to the need for greater coverage of the country by institutions providing

> 38 Poor learning outcomes at the primary level are well-documented in Tanzania through various learning assessments, such as UWEZO and the Early Grade Reading and Math Assessments. Learning assessments at the lower secondary level are not available. Examinations are not a reliable indicator of student learning, as one of their main aims is to regulate student flow to higher levels. Nevertheless, they provide some information on quality for employers and policy makers.  According to the Government of Tanzania’s education national key results report, pass rates of the Certificate of Secondary Education Examination (CSEE) declined from 90 percent in 2007 to 35 percent in 2012. These data show that Tanzania has a critical issue of quality in service delivery of education (World Bank, 2014b).

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and negative commodity price shocks, using data from Niger.

# **4.3 Sensitivity Analysis**

In this section, we consider changes in some key policy parameters to assess the robustness of the simulated results under the sustainable investing approach _−_ which is taken as benchmark. We focus on the fiscal responses following these changes under an oil production shock. Specially, we look at the fiscal responses by considering different values of the parameter governing the efficiency of public investment and different values of the parameter capturing productivity of public capital.

## **4.3.1 Absorption capacity constraints**

The parameter capturing the efficiency of public investment is a fundamental factor in our framework. In practice, this policy parameter captures all the weaknesses in public-sector management and administration responsible for the failure to translate available resources into effective public investment. Figure 4 presents the macroeconomic responses given an oil production shock for two values of _ε_<sup>_G_</sup> ( _ε_<sup>_G_</sup> = 0 _._ 25 and _ε_<sup>_G_</sup> = 0 _._ 95) around our benchmark value set to 0.6. Figure 4 shows that with much more public investment, households enjoy more consumption under the sustainable investing approach. An interesting result is that without absorption constraints ( _ε_<sup>_G_</sup> _→_ 1), tradable and non-tradable sector outputs decline less under an oil production shock, mitigating the negative impact of a potential Dutch disease effect.

## **4.3.2 Productivity of public capital**

In addition to the absorptive capacity, another important policy parameter for savings and investment decisions is the productivity of public capital _−_ also viewed as the return to public capital. In this subsection, we consider the impact of a higher and a lower return to public capital ( _α_<sup>_G_</sup> = 0 _._ 05 and _α_<sup>
1000 people during 1978, when numerical output also peaked at 284,000 dwelling units. In the next three years, and mirroring general economic conditions in Poland, output fell by about one-third to a level that had been typical of the 1960s. Output has barely recovered from a nadir of 186,000 dwelling units in 1982 (5.1 per 1000 population).

There are three major s 3yrces of housing in Poland: state, cooperative, and private housing.- Over time the relative importance of supply from each of these sources has varied as a reflection of the state's role in the housing supply system. Before World War II, most housing was supplied privately. After the war, the state began to

- 3/ A fourth sJurce of housing--built by industrial establishments for their employees--was an important source of supply during the 1940s and 1950s. It is still used where housing shortages are critical. We have no separate statistics to identify this source of housing, but it could be as much as 12 percent of the 1978 total housing stock if it is the same as the category "other socialized housing" in Poland's statistical yearbook. Ciechocinska (1987) reported that, as of 1978, 7.7 percent of Warsaw's apartments were owued by enterprises.
incorporating a machine learning approach and expanding the analysis to the LAC region. This fills a geographical gap raised by some authors (see Rizzo, 2021), who also point out the predominance of macro-level, case-based studies that fail to account for citizens' individual perspectives.

Most importantly, our work provides an innovative approach to analyzing the relationship between political trust and citizen participation by investigating nonlinearities and complexities in the way explanatory variables interact and have an impact on the dependent variable—that is, citizen participation in the various forms considered in this study. Looking beyond linearity and simple relationships imposed by traditional econometric models can provide deeper insights into the complexity of such sociopolitical relationships. More concretely, basic Logit models—used as baseline for our analysis—need to be supplemented by decision trees and regularization methods such as Lasso. These methods are especially useful in predicting whether or not an individual in the Latin America and Caribbean region will participate politically based on her trust in government institutions and socioeconomic characteristics.<sup>4</sup> Traditional Logit models are unable to detect complex nonlinear interactions between independent variables.<sup>5</sup> In order to overcome this limitation, we advance the use of decision tree models, which are especially useful for disentangling complex, nonlinear patterns between explanatory variables that influence the outcome variable. Our key finding is that trust variables predict political participation in non-linear ways. Trust is a significant driver of participation for an individual only after certain levels of education, age, and political interest are combined with experiences of discrimination and violence.

We rely on data from the Latinobarómetro 2020 and explore the existence of a relationship between trust in government and citizen participation in the LAC region. We also investigate the extent to which additional factors (for example, socioeconomic, demographic, and attitudinal) might be influencing citizen participation. A large body of literature has identified socioeconomic status as the primary factor determining citizen participation. In this literature, the higher an individual’s socioeconomic level, the greater their
is where probabilistic catastrophe risk models can add value (see introduction and Appendix). Measures of meteorological drought therefore serve our objectives. Since we are using crop yield loss indices based on rainfall variability and tables of stationary data on crop and soil characteristics<sup>12</sup> , our regression analysis described in Section 3 asks the question:

- “What is the relationship between our selected poverty indicator (reduction in household consumption) and crop yield losses arising from rainfall variability?”

The household survey data forming the basis of the regression methodology contains self‐reported crop damage arising from sources other than rainfall variability. This field is controlled for in the regression methodology in order to extract as direct as possible a relationship between household consumption and meteorological drought.

Using a rainfall‐based index also confers the advantage of objectivity in the measure, removing challenges such as reporting bias that are present when working with reported crop yield statistics (Verma et al, 1988, Fermont and Benson, 2011). It is also more plausibly exogenous than a self‐ reported measure. However, we acknowledge the limitations of working with a modelled estimate rather than a direct measure of yield impact.

The relative importance of the Belg and Meher seasons for each Woreda was accounted for by taking a weighted‐index of the two seasonal values for crop yield shortfall. The regional summary is shown in table 2.1 below:

**Table 2.1: Weighting of cropping seasons, by region**

||Cropping<br>|Season<br>|
|---|---|---|
||Meher|Belg|
||Prod. Share|Prod. Share|
|Tigray|0.99|0.01|
|Afar|0.85|0.15|
|Amhara|0.99|0.01|
|Oromia|0.95|0.05|
|Somali|0.89|0.11|
|B.G
as shown in Figure C1(c) and the share of BTF in total transport expenditure also fell as seen in Figure C1(d).

In Table A2, we present the average treatment effects of the scheme on household expenditures, as estimated using equation (7). Across columns 1 to 5, it is evident that households in and Tamil Nadu less on com- Punjab spend considerably transportation pared to their counterparts in neighboring states during the post-treatment periods relative to the pre-period. Specifically, following the scheme implementation, expenditure on BTF as a proportion of total transport expenses for treated households decreased by an average of 6.6 percentage points compared to the control group (column 5).

A concern might be that differences in COVID regulation stringency measures across treatment and control groups could drive changes in household expenditures. Although we have controlled for COVID-19 cases in our main specification, as a robustness check, we control for the share of the state had either recom- additionally days government mended or required closing public transport in a month and the share of days the state government had either recommended or required individuals not to leave the house in a month. We present the results in Appendix Table D6. Estimated coefficients remain robust to adding these controls with magnitudes and standard errors similar to those in the baseline Table A2. Finally, the significance levels of the baseline estimates remain robust when the standard errors are clustered at the district level (see Appendix Table D7).

# **5.2 Evidence from the Delhi Primary Survey**

We next utilize the sample from the Delhi Primary survey data to examine changes in monthly transportation expenditure, including bus expenses, at the _individual_ level.<sup>10</sup> Among non-users, 64% spent |1 _∼_ 1,000 per month on transportation and the remaining spent more than |1,000. Of users, both new and continuous, 55% did not spend any money on transportation, including buses, per month in the period after
# **INTRODUCTION**

Mobile phones are increasingly ubiquitous even in low-income regions, allowing people to leapfrog legacy communication technologies and traditional information systems. Globally, the number of mobile subscribers has surpassed the human population [1]. In the developed world, mobile phone users include nearly everyone. In low-income countries, 61 percent of the population has access to mobile phones [2].

Mobile network data, which for the sake of this report refers to the vast quantity of information that is produced as a passive byproduct of using mobile services—holds great promise as a transformative resource for social good. Numerous researchers have demonstrated that, if used properly, mobile phone data can be used in ways that complement traditional data collection approaches. Such data can also be used to fill in data gaps where traditional approaches cannot provide data, and in some cases replace traditional data sources. For instance, in transport planning, the following studies, [3]–[6], have shown that elements such as origin and destination (OD) matrices can be generated from mobile phone data. Besides these academic studies, there are a handful of case studies of the use of mobile data applied to transport projects. The examples include Seoul City [7] and Kampala, Uganda [8]. Despite these few studies and proofs of concepts, it is difficult to find material that tries to answer practical questions on the use of mobile data in transport such as “What are the best practices for accessing data and interfacing with mobile network operators?” or “How do you select the best models for generating OD matrices?” Under this context, the World Bank team in Sierra Leone, together with researchers at the University of California Berkeley, aimed to use this technology to understand insight in the urban mobility pattern in the city of Freetown, capital of Sierra Leone.

Among research question under investigation in this study are:

- (1) what are the impacts of rainy season on urban mobility?

- (2)
the respective thresholds in the period 2000 to 2017 in Bangladesh and the US. Each entry of the climate data corresponds to the average daily wind speed recorded at the centroid of one division or state. In Bangladesh, 0.07% of the days between 2000 and 2017 are classified under the strongest storm (wind above 35km/h), while it is 0.4% in the US during the same period.

**Table 4.4: Number of storms per country.**

||> 15km/h|>20km/h|**_Wind speeds_**<br>>25 km/h|>30km/h|>35km/h|
|---|---|---|---|---|---|
|**_Bangladesh_**|7,356|2,528|703|166|35|
|**_US_**|77,250|30,119|11,433|4,329|1,407|

_Source: authors based on ERA5 data._ We then match the dates on which these storms occurred to the dates on which storm-induced power outages were reported by the energy agencies. This allows us to compute the share of storms that caused power outages in Bangladesh and the US.

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for highly educated workers. Also, program participants may have been more negatively selected in Russia, because not everyone unemployed was registered there.

_ALMP programs in Slovakia_ : Lubyova and Ours (1999) use administrative data from 20 Slovak districts to analyze whether it was beneficial for unemployed workers who wanted a regular job to accept a temporary ALMP job or enter a retraining program. Specifically, they study the effects of two ALMP programs in Slovakia targeted at registered unemployed workers on their exit rate from unemployment. The programs were targeted especially toward older workers, disabled workers and the long-term unemployed. The program provided retraining and counseling services as well as wage subsidies in two types of jobs: socially purposeful jobs (SPJ) and publicly useful jobs (PUJ). The subsidy in SPJ, which could be at private sector firms, had a minimum duration of 2 years and the subsidy at PUJ, which were typically public works jobs, had a maximum duration of 6 months.

Lubyova and Ours (1999) base their analysis on the administrative records of 100,000 individuals who entered unemployment in 1993. The records allow construction of detailed labor market histories. Using multivariate duration analysis, the authors jointly model the duration of unemployment and the duration of staying in an ALMP program, controlling for observable and unobservable heterogeneity among people. The focus of the study is on whether participation in ALMP programs affected the exit rate from unemployment to regular jobs. For women, 40% exited unemployment by finding jobs, 9% exited by entering ALMP and 51% had right-censored spells. For males, 47% exited unemployment by finding a job, 8% exited it by entering an ALMP program and 45% had a right-censored spell. On average, workers that entered the ALMP programs are found to have had a 150% increase in the exit rate into a regular job, with similar estimated program impacts for men and women. From additional analyses allowing the ALMP program effect to depend on the type of program, the authors conclude that there were positive
that our results are probably not driven by gender differences in occupational structure but more likely by childcare needs.

# **5 Heterogeneities According to Social-Distancing Measures**

This section explores potential heterogeneities in the previous results depending on the social-distancing measures adopted by the countries in our sample. We start by describing the indices that we use to capture government policies in this regard.

## **5.1 The Stringency Index**

Immediately after the onset of the pandemic governments around the world imposed strong measures aimed at containing the spread of the disease, including lockdowns, travel restrictions, school closures, and various social-distancing measures. The socioeconomic impact of the pandemic is expected to be affected by the stringency of these measures. Economies in which these non-pharmaceutical interventions were stricter probably experienced a stronger shock, at least in the short run, than those where the containment measures were less rigorous.

Measuring the stringency of the policy reactions to the pandemic is however a tricky task as it involves all sorts of data and aggregation issues. In this section we use the Stringency Index of the Oxford COVID-19 Government Response Tracker (OxCGRT). The OxCGRT collects information on policy measures that governments have taken to tackle COVID-19. The different policy responses cover more than 180 countries and are coded into 23 indicators, recorded on a scale to reflect the extent of government action. This initiative, explained and documented in Hale et al. (2021), has become the most widely used database of the containment measures taken around the world.<sup>18</sup> In this section we use the Stringency Index of the OxCGRT. This index records the strictness of lockdown-style policies that restrict people’s behavior. It is calculated using all ordinal containment and closure policy indicators, plus an indicator recording public information campaigns. The index is computed as a simple average of the 9 following indicators rescaled to a maximum of 100: school closing, workplace closing, cancel of public events, restrictions on gatherings, close of public transport, stay-at-home requirements
**Figure 1.8. Total inequality and inequality within top income population, RLMS 1994-2015**

<!-- Start of picture text --> Panel A: Imputing Top 10% Panel B: Imputing Top 5% Panel C: Imputing Top 2.5%<br>.6<br>.5<br>.4<br>.3<br>.2<br>Combined Ginni Combined Ginni Combined Ginni<br>Ginni for top 10% Ginni for top 5% Ginni for top 2.5%<br>.1<br>Gini coefficient<br>1994 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 1994 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 1994 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

**Note:** Estimation results are obtained based on total household income per capita. All numbers are deflated with December to December regional CPIs and weighted with population weights. We use the repeated cross sections for each year. The combined Gini index includes top income inequality, non-top incomes inequality and inequality between these two population groups. Inequality measures are calculated for positive incomes, non-top income and between groups inequality are calculated with trimmed income data.

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2 The Data Revolution in Justice 19

# **2 The Data Revolution in Justice**

Recent years have witnessed an increase of data availability in judicial systems around the world. Many countries have developed electronic case management and e-filing systems. This has led to the creation of massive databases that track every characteristic of each case. In fact, this is by definition an element of the fair administration of justice: everything that happens in a case needs to be tracked to demonstrate the evidence behind facts-based decisions. Even though this data is readily available to policy makers, it has rarely been exploited to evaluate policies or improve the functioning of the judiciary.

The underutilization of data raises many questions: What if we could harness its power to understand what sectors of the population lack access to justice? Taking it one step further, what if we could use the text of decisions to reduce judicial biases, to revamp justice systems that unfairly target gender and racial minorities? What if we could use high-frequency data to understand and address why cases get backlogged, thereby removing a main barrier to justice in the developing world? In the era of COVID-19, what if we could use high-frequency data to predict the future congestion of courts, and plan ahead to be prepared for what is yet to come? Ultimately, what if we could use data to assess the specific impact of new laws on individuals and firms, and promote best practices among different countries?

By responding to these questions, the data revolution can help the rule of law reclaim its role in development planning. More importantly, by using data more effectively, judiciaries around the world, and particularly those in developing countries, will be able to improve their performance, address deficits in the quality and accessibility of justice, and contribute to prosperity. In this section, we explore the opportunities that arise from the expanded data systems in justice. We discuss how data can help assess social prejudice and integrity in justice systems in ways that have not been possible until recently. As COVID-19 moves