rafmacalaba/data-use-mentions-extended huggingface dataset:
# WPS3624

**PESTICIDE POISONING OF FARM WORKERS: IMPLICATIONS OF BLOOD TEST RESULTS FROM VIETNAM**

**Susmita Dasgupta* Craig Meisner David Wheeler Nhan Thi Lam Khuc Xuyen**

## World Bank Policy Research Working Paper 3624, June 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 represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* The authors are, respectively, Senior Economist, Consultant, Lead Economist, Development Research Group, World Bank; Chief, Central Medical Laboratory of the Tien Giang Preventive Medicine Centre and Director, National Institute of Occupational and Environmental Health, Vietnam. We would like to express our appreciation to the medical research team of National Institute of Occupational and Environmental Health, Vietnam for their excellent field work and to Jostein Nygard, East Asia Environment Unit.
# **Figure 19. Investment and governance reform**

_Reform spurts are significantly associated with higher investment growth. Since 2011, improvements in the business climate have continued, but at a slower pace._

## **A. Growth differentials during reform spurts and setbacks**

<!-- Start of picture text --> Growth differential, in percentage points<br>6<br>3<br>0<br>-3<br>-6<br>Reform spurt Reform setback<br><!-- End of picture text -->

## **B. Distance to frontier of Ease of Doing Business**

<!-- Start of picture text --> Distance to frontier score<br>80 2004 2005-2010 2011-2016<br>60<br>40<br>20<br>0<br>Time Cost Procedure<br><!-- End of picture text --> Sources: Doing Business Report, World Bank; Worldwide Governance Indicators, World Bank; Haver Analytics; World Economic Outlook, International Monetary Fund.

A. The columns show the cumulative investment growth differential of economies during a reform spurt or setback episode, relative to those that experienced neither spurts nor setbacks. Spurt (setback) is defined by a two year increase (decrease) by two standard deviations in one or more of indexes of regulatory quality, government effectiveness, rule of law, and control of corruption. Differentials are based on estimates from a panel data regression with time and country fixed effects.The sample includes 75 reform spurt episodes and 71 reform setback episodes among 97 EMDEs over 1996‐2015. The growth differential during reform spurt episodes is significant at the ten percent level. See Annex 2E for more details. B. Indicates proximity in score to country with the highest‐ranking (best) scores for Ease of Doing Business across all time periods with available data. A higher distance to frontier score (DTF)
European countries.

## **While most MENA countries have taken steps to improve financial inclusion, there has not yet been a comprehensive high level commitment to financial inclusion across much of the**

**region.** Financial inclusion is a policy focus for several MENA countries, although it is not yet a priority objective alongside stability for MENA financial regulators and ministries of finance. Five MENA countries now have a financial inclusion strategy, and with three of these adopted since 2006 this seems to be a growing trend for region.

## **The enabling environment for financial inclusion in MENA has improved in the past two**

**years, but is still weak overall** . Deficiencies in financial infrastructure and regulatory frameworks make expanding access to finance costly and risky for banks. Lenders have to rely on collateral that is expensive to register and may not be readily enforceable, and on outdated credit assessment and risk management techniques. Credit information is improving in quality and availability throughout MENA, as credit registries are improved and credit bureaus established, but most microfinance institutions (MFIs) do not yet report to or have access to them. Interest rate caps on micro loans act as a disincentive for banks and investors, and limit

> 1 CAPMAS, 2006

4
>news-based protests*(t-1))|||||0.224***<br>(0.074)||
|Inverse-distance-Common Language<br>weighted news-based protests*(t-1)||||||0.174***<br>(0.051)|
|Constant|Yes|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Month fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|8,184|6,048|6,936|38,718|18,499|41,582|
|Number of countries|95|72|76|162|124|174|
|N_zero|2822|2117|2602|8241|4023|14946|
|Chi-square|21384|13219|18698|31139|19957|19197|
|Log likelihood|-5697|-3658|-4730|-509.1|-289.6|-527.0|

Note: Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1). _Protests pc_ capture the number of protests per million persons. _News-based protests_ captures the number of articles mentioning street protests in a country as percentage of total number of articles covering the country. * denotes foreign.

Regarding the news discussions of street protests, polity, corruption, and common language are found to have significant effects on the spillover of global protest discussions (Panel B). Between them, _Common Polity_ seems to have the largest effects (columns [4] to [6] of Table 3). The coefficient of 0.467 implies that one standard deviation increase in previous month’s foreign newsbased protests, which is 0.632 percentage point increase, leads to 1.34 percentage points increase in the
individual characteristics of household members, more effective targeting strategies might become possible by relying on the imputed dataset.<sup>4</sup>

**We follow the approach developed by Ruben (1986) and identify matching variables, based on which we concatenate both datasets to ultimately employ multiple imputation methods** . There are many different approaches to statistical matching. Lewaa et al. (2021) provide an overview and divide the different methodologies into parametric, nonparametric, and mixed approaches. We follow early work by Ruben (1986) and first identify potential matching variables. These are variables that are part of both surveys and help to identify households that resemble each other. Nevertheless, not all overlapping variables should form part of the matching variables, but only those relevant to the target variable and similar in distributions across surveys (Serafino and Tonkin, 2017). We harmonize these variables with each other and apply a lasso regression to identify those variables that are most relevant to explain energy spending shares. Next, we concatenate both datasets based on these variables. While the household budget survey contains information on energy spending shares and energy poverty, these variables have missing values in the EU-SILC. After concatenating both datasets, we apply multiple imputation methods.

**To address one of the shortcomings in the literature raised by Lewaa et al. (2021), namely the lack of quality assessments, we explore several different imputation models.** We employ linear regression imputation models, predictive mean matching (PMM), and truncated regression imputation models. For each model, we explore three different imputation specifications by varying the inclusion of survey weights and the survey sampling design as additional matching variables. To evaluate the different models, we apply several validity tests. We compare the joint distribution of the imputed and observed energy spending share (overall and by subgroups), analyze if results differ across imputations, and compare the consistency of mean imputed energy expenditure by variables used in the statistical matching and those not used.

> 4 We
.

When comparing these impacts to what is implied by survey data, we show that survey data overestimate the exclusionary power of religious objectives by about one third, and that religious and social considerations are at least as important as economic factors like loan size and interest rates when we consider the reasons why individuals claim that they are not utilizing credit.

Together, these findings imply several lessons for improving financial inclusion across the world, and lead to several avenues for future research. First, improving our understanding of how non-financial preferences impact financial decisions can be an effective way to increase access to financial services for those that turn them down for social reasons. Second, even if the preferences are for products that are more costly than the conventional alternative, 23
# _Accounting for sample selection_

The above two analyses—using ASER or PISA data—suffer from the potential fact that the profiles of students might be different across grades. This could be, for example, because poorer-performing students drop out at higher grades, or because they are held back and are therefore more concentrated in lower grades. More generally, students who are a grade ahead (or behind) their peers might be different in other ways, and not just in the amount of schooling they have had. In other words, selection on unobservables may bias the results. We therefore now turn to approaches that account for potential selection.

The approaches we use deploy a combination of regression discontinuity and instrumental variables estimation. Specifically, we examine random variation in birth month across school entry cutoffs. Students randomly born a few days after the school cutoff enroll up to a year later than their peers who are just a few days older. This enables us to estimate the causal effect of an additional year of schooling.

There is an extensive literature using random variation in birth month across school-entry cutoffs to identify the effect of _relative age_ differences on learning.<sup>21</sup> This literature relies on estimating test score variation across exogenously determined relative ages. Our approach is inspired by this literature and repurposed to assess _relative grade_ rather than _age_ since we care about how much is learned over the course of a year of schooling.<sup>22</sup> Specifically, we use PISA 2015 data, which assesses 15-year-olds across multiple grades. This dataset includes cohorts born in 1999 and 2000. Since PISA data only includes birth month and year, we are unable to examine precise effects around exact school entry dates as is typical in the literature. Instead, we explore effects around school entry months, which proxies for specific entry dates. An advantage of the PISA data is that it codes a variable for the gap between the grade a student is in and the expected grade given that country’s school entry
|**Country**|**Title**|**Findings **|**Comment**|**Source**|
|---|---|---|---|---|
|||worse than expected for 7%<br>and 26%, respectively.|||
|Uganda|Developing<br>hospital<br>accreditation<br>standards in Uganda|Among accreditation items (1)<br>physical infrastructure and (2)<br>infection control and HCWM<br>are relevant to WASH. 27.5%<br>(11/40) hospitals were not<br>tracking infection rates and<br>32.5% (13/40) had functional<br>sterilisation equipment.|Good performance was measured<br>in equipment and running water, 24<br>hours staff calls systems, clinical<br>guidelines and waste segregation.<br>Poor performance was measured in<br>care for the vulnerable, staff living<br>quarters, physician performance<br>reviews, patient satisfaction surveys<br>and sterilising equipment.|Galukande et<br>al (2016)|
|Uganda|Quality of antenatal<br>care services in eastern<br>Uganda:<br>implications<br>for interventions|74.6% (217/291) respondents<br>rated the ANC service as<br>satisfactory. Infection control<br>was available in 73.4% (11/15)<br>facilities.<br>Cleanliness<br>was<br>dissatisfactory<br>for<br>4.1%<br>(12/291), fairly satisfactory<br>for<br>25.8%<br>(75/291)<br>and<br>satisfactory<br>
//anticorrp.eu/publications/quantitative-report-on-causes/ (2014).

88 Claudio Ferraz and Frederico Finan. "Exposing Corrupt Politicians: The Effects of Brazil’s Publicly Released Audits On Electoral Outcomes." _The Quarterly Journal of Economics_ (2008).

89 Horacio A. Larreguy, John Marshall, James M. Snyder, Jr. "Revealing Malfeasance: How Local Media Facilitates Electoral Sanctioning of Mayors in Mexico." NBER Working Paper No. 20697 (2014).

90 A. Joshi, “Background Paper on Service Delivery: Review of Impact and Effectiveness of Transparency and Accountability Initiatives,” Institute of Development Studies (2013).

91 These include, among others, citizen report cards, organizational score cards, and asset discloures of public officials, as well as more “participatory” mechanisms such as community monitiring, social audits, public expenditure tracking surveys, and participatory budgeting processes.

> 92 J. Gaventa and R. McGee, “The Impact of Transparency and Accountability Initiatives,” _Development Policy Review_ 31: s3–s28 (2013).

93 T. Carothers and S. Brechenmacher. “Accountability, Transparency, Participation, and Inclusion: A New Development Consensus?” Carnegie Endowment for International Peace (2014). p. 26.

14
Municipal|42|20|
|<br>Cooperative|9|0|
|<br>Other|1|0|

**Source: LpVaa,** 1996. * = **as** of **Janary,** 1994; ** = **as** of July, 1996.

As of July 1996, the share of enterprise and municipal housing fell to 20%/, while the private (including former cooperative) stock constituted 80%; in all, 66% of the housing subject to privatization was privatized. The share of privatized apartments varies depending on the region of Estonia: in some it reaches 98%/e, while in others it is as low as 35% (Eastern Estonia, a region with a large population of ethnic Russians, many of whom are not Estonian citizens). The enterprise housing stock has all but disappeared, having either been tranderred to the municipalities or privatized by tenants.

In 1993 and 1994, selective surveys on the level of willingness to privatize were conducted. In 1993, 30% of respondents said they were not interested, while in 1994 the equivalent figure was only 12%.

According to Estonian researchers, the rental sector reform, and in particular the removal of state subsidies, promoted the process of privatization, which they generally consider to have success.

In _Latvia_ between 1990 and 1994, the share of state-owned and municipal housing decreased from 64% to 54%, while the share of private dwellings rose from 22% to 39% (Tosics and Hogedus, 1996). In _Ukraine,_ housing privatization produced the following housing structure by form of ownership in 1996:
|||||||
|_Nominal_|14,166|90%|14,166|90%|90%|13,998|93%a|87%a|
|_Effective_|14,166|90%|14,166|90%|90%|13,998|93%a|87%a|

Notes:

1. Statistical significance is denoted ‘a’ :(sig. at <=1%); ‘b” (sig. at <=5%); “c” (sig. <=10%)

2. For phones, there was no measurable difference between nominal and effective access. A very small percentage of landlines were nonworking, but these households also owned a working mobile phone.

However, as discussed earlier, nominal access data can be misleading as they do not accurately reflect either the functionality of service, or whether the service is used. Figure 3.1 depicts both nominal and effective levels of access, showing the significant disparity that can occur between variables. For example, we find that while 93% of households are located within a 20-minute walk of public transport facilities (or nominal access), only 52% of households have a member who regularly uses these facilities. This suggests that while there has been considerable investment, mostly by the private sector, in expanding the public transport network, only about a half of households need to use it. It illustrates not only the potential pitfalls of relying solely on nominal access data as a measure of service coverage, but 9
on the non-R&D output reduces to 61 percent. The impact of capital investment-specific shocks on labor reduces to 27.3 percent in the R&D sector and 17.4 percent in the non-R&D sector while the combined e↵ect of technology shocks on labor in the non-R&D sector reduces to 35.3 percent. These results show that even under the extreme assumption of constant individual hours, the significant e↵ects of R&D and capital investment-specific shocks on the output of the non-R&D sector and R&D investment remain.

# **8 Conclusion**

In this paper we examine sources of the excessive volatility in the R&D sector as well as the role and contribution of the sector to aggregate fluctuations. In doing so, we consider the e↵ects of productivity and capital investment-specific shocks in the R&D and non-R&D sectors using a VAR and data from the BEA-NSF satellite account for the period 19592007. The shocks are identified by imposing long-run restrictions which are justified by a two-sector general equilibrium model. We show that introducing exogenous changes in sectoral productivities, in addition to investment-specific technical change, into an RBC model motivates three long-run identifying restrictions. First, the model predicts that the change in capital investment-specific technology is the unique source of the secular trend in the real price of capital investment goods. Second, changes in capital investment-specific technology along with changes in R&D-specific technology are the only sources of permanent shocks to labor productivity in the R&D sector. Third, changes in productivity in the R&D sector and capital investment-specific technology along with changes in technology in the non-R&D sector are the only sources of permanent shocks to labor productivity in the non39
|
|Radio|0.57|0.49|0.56|0.50|0.49|7,321|
|Time to school (hours)|0.17|0.81|0.17|0.81|0.97|7,321|
|Expenditure transportation to school|54.69|63.63|53.19|61.97|0.31|7,321|
|Urban|0.46|0.50|0.46|0.50|0.99|7,321|
|_Panel B: Other characteristics_|||||||
|Income|634.79|496.06|631.09|490.81|0.75|7,321|
|Age|15.26|0.44|15.27|0.44|0.39|7,220|
|Male|0.55|0.50|0.56|0.50|0.40|7,220|
|Spanish score ENLACE 2009|0.01|1.01|-0.01|0.99|0.30|7,321|
|Math score ENLACE 2009|0.01|1.01|-0.01|0.99|0.50|7,321|

Note: p-value derive from a OLS regression on the treatment variable 13
48|0.29|
|Wheat/barely|0.21|0.20|0.22|0.21|0.24|0.26|
|<br>Sorghum/maize|0.30|0.34|0.23|0.23|0.20|0.31|
|<br>Pulses|0.13|0.12|0.14|0.16|0.04|0.10|
|Oilseeds|0.05|0.05|0.04|0.04|0.04|0.03|
|Number of observations|5443|3360|2083|1728|238|102|

_Source:_ Own computation from AU/WB/UG land tenure survey. _Note:_ Only plots with cereals, pulses and oilseeds included.

19
=0) is determined on the basis of PAC spending summed over this period. Goods are ordered by PAC spending and sets of I are defined, each set corresponding to a percentile cutoff value. Models (4)-(6) are estimated using all these 32 Kee, Nicita and Olarreaga (2004a). See e.g. Kohli (1991) for the GDP function method. In this method imports are treated as inputs into domestic production, given exogenous world prices, productivity and endowments.

33 Fuller (1986). The idea behind this correction is to limit the influence of estimates that are large and also have large standard errors. Without the correction, these large estimates would grossly overstate the true elasticity. The correction mutes their effect.

34 <u>www.fec.gov.</u> 35 Mapping PACs into SIC-based agriculture-related sectors was done via a concordance constructed by Beaulieu and Magee (2002). In most cases many PACs mapped into one SIC code, so SIC level contributions are simply the aggregate of the mapped PACs. For one-to-many maps, political contributions from each PAC were fractionally assigned equally to each SIC code into which the PAC mapped. To check for consistency we compared our data with the data on the opensecrets.com website. For the few sectors in which opensecrets.com reports such information our data closely matched theirs.

15
<br>**Name**|**Distance**<br>**to site**<br>**(km)**|**Number of**<br>**mauzas**|**HHs**<br>**(census)**|**Pop**<br>**(census)**|**Number of**<br>**intended**<br>**clusters**|**Number of**<br>**actual**<br>**clusters**|**Number of**<br>**intended**<br>**households**|**Number of**<br>**actual**<br>**households**|
|---|---|---|---|---|---|---|---|---|---|
|**0**|Sites|Zero||213,198|909,120|200|192|2600|2496|
|**1**|High<br>Exposure|0.1 to 15|33|83,482|463,528|99|99|1287|1287|
|**2**|Low<br>Exposure|15.1 to<br>+45|33|256,776|1,381,963|99|96|1248|1248|
|**Total**|||**66**|**553,456**|2,754,611|**398**|**387**|**5174**|**5031**|

Note: HH = household. Each cluster includes
result of household investment choices. Rather, throughout the analysis, private and public expenditures are assumed to be made in equal proportions.

In the model, education enhances labor productivity through two channels. First, more education improves the skill composition of the labor force, resulting in a greater supply of skilled labor and lesser supply of unskilled labor. Second, for each skill level, more education yields a higher level of average schooling attainment, thereby improving its average labor productivity. The second channel is captured by linear increasing functions between labor efficiency and the average years of schooling for the three types of labor.

As noted above, we model the off-farm labor supply decision of rural households as a CET function of the ratio of the shadow value of labor in agriculture, relative to the off-farm wage rate. We assume that the elasticity of transformation is a linear, increasing function of average years of schooling within the labor skill groups. This specification, which implies that rural households with higher educational attainment respond more effectively to farm/non-farm wage gaps, is based on recent empirical evidence (Zhang, Huang and Rozelle, 2002).

# **2.1 Model Calibration and Choice of Parameters**

Detailed data on the population and labor force is necessary to calibrate the education block of the model. We calculate the age distribution of the rural and urban populations, respectively, according to their mortality tables from the 2000 national population census, under the assumption of a stationary population, i.e. no change in the age structure. The age distribution of each of the 40 rural household groups is assumed to be the same, and similarly with the 60 urban household groups. Age-specific labor participation rates by urban and rural classifications are also obtained from the 2000 national population census. We scaled them up/down to the aggregated labor force participation rate of each representative household to 11
Our data on prices of imports and exports are recorded by the Bureau of Labor Statistics using the Standard Industrial Trade Classification scheme and are weighted by purchases of goods within each industry. These are converted to the 3-digit SIC coding scheme, and then made compatible with the CPS industrial codes using code provided by David Autor. These concordances between SIC coding and CPS coding are available in the online appendix to Autor et al. (1998).

G. Panel Data Set of Current Population Survey Workers We construct a panel data set from the CPS Merged Outgoing Rotation Groups by matching individuals surveyed in two consecutive years between 1983 and 2002. Each month, one quarter of survey respondents are asked questions about their job including wage, occupation and industry. One year later, half of these individuals are again asked these same job-related questions. We use Madrian and Lefgren's (2000) matching algorithm to first match an individual based on their