rafmacalaba/datause-encoder-data huggingface dataset:
# **2.4 Dependent informality rates by worker and job characteristic**

Table 4 reports, for each of the four European regions and for Europe as a whole, proportion of informal employees among all employees (the _dependent informality rate_ ), broken down by gender, age, educational attainment, origin, occupation, size of establishment, and economic activity. The estimates refer to 2008-2009 and are non-weighted averages of country-specific estimates. Apart from the South as a whole, Table 4 includes a separate column for Spain, Portugal, and Greece<sup>17</sup> . It appears that in Southern Europe prevalence of work without contract is higher among females (23% vs. 19% among males), whilst elsewhere the difference is fairly small (larger differences exist at the country level though). Plausibly, this has to do with the fact that the share of hospitality, personal and household services (sectors which are female-dominated and feature high informality rates) in dependent employment is higher in the South than elsewhere.

In all parts of Europe, the lowest dependent informality rate is found among tertiaryeducated workers, whilst the highest rate is found among medium-educated in the South and among low-educated elsewhere. Overall average is 14.5% for low-educated workers, 8.4% for medium-educated, and 5.7% for those with higher education. Likewise, the smallest proportion of workers without contract (5% on average, ranging from 1% in the North to 17% in the South) is found among those holding highly-skilled non-manual occupation, whilst the highest informality rate is associated with elementary occupations (17% on average, from 8% to 10% in the North and East, to 15% in the West to 30% in the South). For other occupations, the overall informality rate is about 10%, ranging from 4% in the North to 6% in the East to 9% in the West to 21% in the South. To sum up, dependent informality rate is inversely related to skills (measured in terms of either schooling or occupation). These findings are in line with theoretical expectations (see, e.g. Perry et al., 2007: pp. 6, 9; PfauEffinger, 2009: Table 1): motivation to go informal is strongest for low-skilled, lowproductive workers both on the supply side (as their alternative in the formal sector is not much better) and on the demand side (small firms find it too costly to hire formally lowproductive workers), as well as with empirical findings from Latin America (e.g. Henley et al., 2006: Table 5) and Italy (Boeri and Garibaldi, 2005: Table 2).

The age-informality profile is U-shaped: The informality rate is 17% among the youth, 7% for the prime age workers, 9% for the 55-64 year olds, and 16% among those of retirement age. In the West and (to a smaller extent) in the South, the dependent informality

> 17 Recall that for Italy the latest available data refer to 2006, whilst two other Southern countries in our data, Cyprus and Israel, are small.

17
independent of the diet quality of FAFH in neighborhoods with mid- and high socioeconomic status, both factors are related in poor neighborhoods. So, it seems that, after one controls for a number of determinants of excess body weight, the low diet quality of FAFH is found to be substantially associated with high obesity and overweight among women who live in poorer neighborhoods.

**Table 3: Socioeconomic status specific effects: diet quality and BMI categories** _ordered logit model, average marginal effects_

||**Low**<br>**socioeconomic**<br>**status**|**Mid–low**<br>**socioeconomic**<br>**status**|**Mid**<br>**socioeconomic**<br>**status**|**High**<br>**socioeconomic**<br>**status**|
|---|---|---|---|---|
|_Obesity_|||||
|PDRNI restricted|0.366***|0.028|‐0.034|0.023|
||(0.068)|(0.096)|(0.148)|(0.183)|
|_Overweight_|||||
|PDRNI restricted|0.048***|0.009|‐0.011|0.014|
||(0.013)|(0.033)|(0.052)|(0.133)|
|N|804|535|533|234|
|Control variables|Yes|Yes|Yes|Yes|

_Note:_ For the control variables, see table 1. The variable _PDRNI restricted_ is the diet quality index that measures the proportional difference of relative nutrient intake (PDRNI). A one-unit increase expresses a higher distance to the ideal value and thus implies a less nutritious diet. * p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses are clustered by district.

# **6 Conclusion**

Rising obesity rates are one of the most undervalued public health issues in many emerging economies. High rates of excess body weight have detrimental health effects and generate a burden on health systems. A unique representative restaurant survey that includes objective measures of the micronutrients in the most widely consumed meals in Metropolitan Lima allows us to shed light on the relationship between the nutritional quality of FAFH and individual excess body weight among the population in urban Peru. Eating away from home is a frequent occurrence across socioeconomic groups in Peru and represents over a quarter of household food expenditures. We build a new diet quality index, the PDRNI, to account for overnutrition in specific micronutrients that are most relevant in terms of excess body weight. The index overcomes shortcomings of other diet quality indexes.

The findings indicate that lower diet quality in FAFH in restaurants located in the individual’s food environment is significantly associated with excess body weight. This relationship holds after accounting for a number of factors that correlate with body weight, including socioeconomic fixed effects which control for unobserved factors that correlate with body weight and the nutritional quality of FAFH. Among the various nutrients, the main drivers of poor nutritional quality appear to be sodium, especially in poorer areas. Sodium intake is positively associated with the consumption of energy-rich sugar-sweetened beverages, increasing higher total daily energy intake and contributing to weight gain.

Furthermore, the lower quality of readily available food is found to explain part of the 19
23 Table 6: Expenditure share of monthly electricity consumption of 30 kWh by grid connection status and current spending

||Sh|are of ho<br>|usehold ex<br>|penditur<br>|e<br>|% of|househol<br>|ds for wh<br>|ich share<br>|5%<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||All|Expend<br>elec|iture on<br>tricity|Gr<br>conne|id<br>ction|All|Expend<br>elect|iture on<br>ricity|G<br>conn|rid<br>ection|
|Country||Yes|No|Yes|No||Yes|No|Yes|No|
|Angola|0.1|0.0|0.1|0.1|0.1|100|100|100|100|100|
|Botswana|3.7|1.3|4.9|1.7|5.4|86|98|80|96|77|
|Burkina Faso|8.8|4.2|9.5|4.5|9.5|32|72|26|71|26|
|Côte d'Ivoire|1.8|1.2|2.4|—|—|95|99|92|—|—|
|Ethiopia|1.3|0.9|1.4|1.1|1.3|98|99|98|99|98|
|Ghana|1.4|1.1|1.9|1.2|2.0|98|99|96|99|95|
|Madagascar|9.6|4.0|10.4|—|—|25|75|18|—|—|
|Malawi|2.2|0.7|2.3|0.8|2.3|96|100|95|100|95|
|Mali|1.4|1.1|1.5|0.8|1.6|99|99|99|100|99|
|Mozambique|2.3|0.6|2.5|0.0|0.0|93|100|92|—|—|
|Niger|3.6|1.7|3.8|1.6|3.8|82|98|80|98|81|
|Nigeria|0.3|0.3|0.4|0.3|0.4|100|100|100|100|100|
|Rwanda|8.9|2.2|9.7|2.4|9.7|31|92|25|90|24|
|São Tomé and Príncipe|1.7|1.3|1.9|—|—|97|99|96|—|—|
|Senegal|2.9|2.1|4.1|1.9|4.1|88|94|79|95|79|
|Sierra Leone|5.1|2.9|5.5|2.9|5.5|61|90|56|90|56|
|South Africa<sup>a</sup>|1.2|1.0|1.5|1.0|2.2|98|99|97|99|95|
|Swaziland|1.9|0.9|2.4|—|—|94|100|92|—|—|
|Tanzania|1.9|0.9|2.1|0.9|2.1|95|100|94|100|94|
|Togo|6.9|3.6|9.0|3.6|9.0|54|81|37|81|37|
|Uganda|4.4|1.7|4.7|2.0|4.7|71|97|69|96|68|
|Zambia|4.1|1.0|4.6|1.2|4.9|71|100|66|99|63|
|Median|2.2|1.1|2.4|1.2|3.0|93|99|92|99|81|

_Source_ : World Bank staff calculations based on utility information and household survey data. _Note_ : — = Grid connection status not available in the survey. a. These numbers are for Johannesburg. Depending on eligibility, poor households are provided with 50, 100, or 150 kWh of free electricity a month in Johannesburg.

In Angola (effective unit charge of US$0.012/kWh), Ethiopia (US$0.022), Ghana (US$0.069), Mali (US$0.12), Nigeria (US$0.026), and South Africa (US$0.10 used in the calculation, although it could be zero in some municipalities), virtually the entire population should find electricity consumption at the subsistence level affordable. At the opposite end of the spectrum are Burkina Faso (US$0.28/kWh), Madagascar (US$0.12), and Rwanda (US$0.23), where only one‐quarter or less of those not yet connected to the grid can afford electricity.

The definition of affordability in the multi‐tier framework is based on the share of household income, arguably making cash expenditures more suitable as the basis for computing the expenditure share. Data on cash expenditures excluding home‐grown food were available in 18 countries. The results are shown in Table 7.
# Appendix: Further tables

## **Table A1 Main results with other productivity estimators**

|**Dependent:**||**Productivity (3**|**‐year growth)**||
|---|---|---|---|---|
||**TFP**|**(FE)**|**TFP (Wo**|**oldridge)**|
|HGF Downstream share|0.040***|0.039***|0.044***|0.043***|
||(9.704)|(9.467)|(10.628)|(10.423)|
|HGF Upstream share|0.015***|0.014***|0.012***|0.012***|
||(3.643)|(3.598)|(3.087)|(3.048)|
|HGF Intra‐industry share||0.010***<br>(2.846)||0.010***<br>(2.921)|
|Region‐Year FE|yes|yes|yes|yes|
|Sector‐Year FE|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|
|Observations|140,594|140,594|140,594|140,594|
|R‐squared|0.208|0.208|0.187|0.187|

Note: The reported coefficients are beta coefficients, t‐values in parentheses. The explanatory variables are measured at time t, while the dependent variables are 3‐year changes between t and t+3. Firm controls are log employment, labor productivity and log capital per labor ratio. Standard errors are clustered at the county‐industry (2‐digit NACE)‐year level. The definition of HGF is the 3‐year Birch employment definition.

24
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 represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Correspondence should be addressed to: Susmita Dasgupta, MC2-205, World Bank, 1818 H Street, NW, Washington, DC 20433, <u>Sdasgupta@worldbank.org.</u>
8 coordinating tax reforms: can lost tariffs be recouped?

SITC Section 3 and Division 27, 28 and 68).<sup>12</sup> Exports, however, could be misleading as a measure of natural resource abundance because a country that is too poor to consume its own natural resources exports much of its output, compared with a richer country which exports less but produces just as much. Therefore, I also use a second measure – oil and gas rents per capita – taken from the World Bank’s Adjusted Net Savings data center.<sup>13</sup>

# 1.2.3 _Estimation Method_

I use four different estimation methods. The first method uses the fixed effects “within” estimator in equation 1.1 where the dependent variable – domestic taxes (net of trade taxes) – is regressed on a set of explanatory variables explained earlier. The fixed effects model removes the correlation between time-invariant unobserved effects and the explanatory variables. The main explanatory variable – tax revenue as a share of GDP – is, however, possibly endogenous. Both the collection of non-trade tax and trade tax revenues could, for example, be driven by a reformed customs administration.

The second method, therefore, addresses the potential endogeneity of trade tax by using instrumental variables which are its own first and second lags. Despite these corrections, a bigger problem in the first two models as specified in 1.1 equation is that the presence of the lagged dependent variable as one of the explanatory variables regressor (DTit−1) renders the estimates inconsistent because of its correlation with the fixed effect, causing a dynamic panel bias (Nickell 1981). There could also be serial correlation in the error term. Roodman (2009) offers a useful guide on the use of dynamic panel estimators in these situations.<sup>14</sup>

> 12 These are primarily fuel, metals, and ores, whose total export values for the years 1982-2006 I obtained from partner country records in COMTRADE. Because the values are inclusive of cost, insurance, and freight (c.i.f.), I use an ad hoc coversion factor of 1.1 to bring them closer to their f.o.b. values.

> 13 See Bolt et al. (2002).

> 14 Roodman (2009) states that dynamic panel estimators are suitable in the following situations: (i) panels that have a relatively small number of years but large number of countries; (ii) the depen-
security. In many island nations, climate change also poses threats to water supply and overall socio-economic stability and health risks, which may arise as climate change influences the spread of diseases. Many island economies feature large tourism sectors, and services’ contribution to GDP, therefore, disproportionately affects the economy’s development.

# 4.1. Natural disaster shock scenarios

We devise natural disaster shocks to explore the potential effects of climate risks on such a model economy. In particular, we are interested to know:

- how the severity of climate risks in the form of a natural disaster can have non-linear impacts on macroeconomic performance,

- how the presence of other economic risks (for example, shocks to trading partners, internal business cycle fluctuations, or shocks in currency markets) compounds the risks posed by climate change, and

- how fiscal policy can ameliorate the impacts of climate shocks and threshold and compound risks, in particular.

To do so, we adapt one of the World Bank’s macroeconomic models. MFMod is a structural econometric model that captures the macroeconomic income and product accounts and is capable of representing an economy’s flow of funds across sectors and for major income types (Burns et al. 2019). Similarly, the economy’s interactions with its trading partners and specific sectors (for example, the labor market or the energy sector) are modeled in detail. MFMod’s long-run behavioral relationships are derived from economic theory, while short-run adjustment to these long-run relationships is estimated empirically from economic data.<sup>3</sup> MFMod permits the forecasting of its main economic variables (like economic growth, consumer inflation, and balance of the current account) and policy implications (for example, debt sustainability). We also draw on the World Bank’s Climate Change and Development Reports (CCDR) framework. CCDRs introduce climate change via changes in hazard rates, exposure, and impacts in MFMod and analyze how these changes impact a country’s development path.<sup>4</sup> In addition to focusing on increasing resilience via adaptation, they also study how a transition to a low-carbon economy can be financed, thereby identifying cost-effective and equitable climate policy options. CCDRs utilize

> 3 Several accounting identities and market clearing conditions (so-called ‘adding-up constraints’) close the model.

> 4 MFMod is a flexible macroeconomic model that is continuously improved and adjusted. This includes many fit-forpurpose versions of MFMod that are used for country-specific and topic-specific work by World Bank staff.

11
For instance, Hale et al. (2018) found that highlighting top petitions on the UK petitions website changed the distribution of signatures across petitions likely due to the social information provided (Margetts _et al._ , 2011). However, the decision to allow these types of interactions is a platform decision that affects the way the government considers demands.

The relationship between demands made and the outcomes of political processes is highly variable in offline settings as well. Some literature suggests governments are highly responsive to the ideological preferences of citizens (Caughey and Warshaw, 2017) and that—once citizens participate—their voices are heard relatively equally <mark>(Hindman, 2009, p. 17). However, other research suggests the demands o</mark> f <mark>certain privileged groups</mark> i <mark>n society, such as rich donors, hold far more sway over policy than t</mark> hose of <mark>other voters (Bartels, 2010). Offline research also point</mark> s t <mark>o the importance of institutions</mark> i <mark>n mediating how demands translate into policy, such</mark> a <mark>s studies finding proportional electoral systems e</mark> xhibit <mark>higher income redistributi</mark> o <mark>n because governments ne</mark> e <mark>d more of the populati</mark> on’s support t <mark>o gain power (Austen-smith, 2000).</mark>

# **Other goals of civic engagement**

In this paper we only consider the benefits of digital civic engagement in terms of policy outputs. While policy change is a key outcome of civic engagement, we do not claim that it is the only important outcome of participation. First, civic participation of any kind is often seen as a first step on a ladder of participation (Arnstein, 1969), with initial success or even just contact encouraging further and deeper engagement. Indeed, this rationale is explicitly cited by MySociety for encouraging participation on FixMyStreet (Stempeck, 2012), a widely replicated (FixMyStreet.Org, 2015) civic tech platform. Second, participants in digital civic platforms cite a wide range of outcomes as a success beyond simply directly changing policy including raising awareness of a cause, creating solidarity and fulfilling a sense of civic duty (Wright, 2016). Future work should also examine how the design of civic engagement platforms affects these wider goals.

8
[19] China Electricity Council. China Power Statistics Compilation. 2012. Beijing: 2012

[20] China Electricity Council. China Power Statistics Compilation. 2013. Beijing: 2013

[21] China Electricity Council. China Power Statistics Compilation. 2014. Beijing: 2014

[22] Tsinghua University.Wang.China's Energy Science and Technology Development Outlook for the 21st Century.Tsinghua University Press Publishing.

[23] National Electricity Regulatory Commission. Electric Power Supervision Annual Report 2010

[24] National Bureau of Statistics. 2006. China Statistical Yearbook 2006. Beijing: China Statistics Press, 2006.

[25] National Bureau of Statistics. 2007. China Statistical Yearbook 2007. Beijing: China Statistics Press, 2007.

[26] National Bureau of Statistics. 2008. China Statistical Yearbook 2008. Beijing: China Statistics Press, 2008.

[27] National Bureau of Statistics. 2009. China Statistical Yearbook 2009. Beijing: China Statistics Press, 2009.

[28] National Bureau of Statistics. 2010. China Statistical Yearbook 2010. Beijing: China Statistics Press, 2010.

[29] National Bureau of Statistics. 2011. China Statistical Yearbook 2011. Beijing: China Statistics Press, 2011.

[30] Department of Industrial Statistics, National Bureau of Statistics. China Industrial Statistical Yearbook 2010. Beijing: China Statistics Press, 2010.

[31] Department of Industrial Statistics, National Bureau of Statistics. China Industrial Statistical Yearbook 2011. Beijing: China Statistics Press, 2011.

[32] Department of Industrial Statistics, National Bureau of Statistics. China Industrial Statistics Yearbook 2012. Beijing: China Statistics Press, 2012.

[33] Department of Industrial Statistics, National Bureau of Statistics. China Industrial Statistics Yearbook 2013. Beijing: China Statistics Press, 2013.

[34] China Iron and Steel Association. China Steel Statistics 2011. Beijing: China Iron and Steel Industry Association Information Statistics Department, 2011.

[35] Editorial Office of China Chemical Industry Yearbook. China Chemical Industry Yearbook 2005-2006. Beijing: China Chemical Information Center, 2007.

[36] Editorial Office of China Chemical Industry Yearbook. China Chemical Industry Yearbook 2010 (upper, middle and lower). Beijing: China Chemical Information Center, 2012.

[37] China Nonferrous Metals Industry Association. China Nonferrous Metals Industry Yearbook 2006. Beijing: China Nonferrous Metals Industry Yearbook Editorial Board, 2006.

46
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**Figure 2.1 : Average of all flow (m3/s per month, y-axis) and precipitation (mm per month, x-axis) in sample catchment area of the Mekong river basin in a nonlinear regression. gression. ression.**

**Figure 2.2 : Predicted flow (m3/s per month, x-axis) versus calculated average flow (m3/s per month, y- axis) in sample catchment area of the Mekong river** **<u>basin from precipitation model.in from precipitation model.om precipitation model.m precipitation model.precipitation model.recipitation model.ecipitation model.ipitation model.pitation model.itation model.tation model.ion model.on model.n model.odel.l..</u>**

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