rafmacalaba/datause-encoder-data huggingface dataset:
## POLICY RESEARCH WORKING PAPER 2671

# Summary findings

Numerous analysts have linked volunteering and participation to positive economic and political outcomes. Schady uses the 1994 Peru Living Standards Measurement Survey to analyze volunteering patterns in rural Peru. He finds that volunteers in rural Peru have a high opportunity cost of time. They are more educated and more likely to hold a job. Other household characteristics, such as gender, marital status, length of residence, and ethnicity, are also important predictors of the probability of volunteering.

Controlling for household characteristics, communities differ widely in aggregate volunteer levels. These differences seem unrelated to differences in patterns of government expenditure.

Volunteering may have important benefits in building social capital and encouraging greater ownership of development projects. For example, many public programs in rural Peru and elsewhere ask that the intended beneficiaries "participate" as a means of building trust and social capital, increasing the sustainability of investments and helping self-target investments to the poor.

But Schady finds that encouraging participation by potential beneficiaries is unlikely to be an effective form of self-targeting, since people with a higher opportunity cost of time volunteer more. Moreover, social programs that require participation may have difficulty reaching some vulnerable groups, such as women and the illiterate.

This paper-a product of the Poverty Sector Unit, Latin America and the Caribbean Region-is part of a larger effort in the region to understand the factors that promote or inhibit participation and empowerment. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Tania G6mez, room 18102, telephone 202-473-2127, fax 202-522-0054, email address tgomez@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at nschady@worldbank.org. September 2001. (19 pages) _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 represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
# HI. Data and Summary Statistics

The data used in this paper are the LSMS panel of Peru for the years 1994 and 1997. The panel intersection of the two surveys yielded a total of 898 households and 1961 children between the ages of 6 and 18.

As far as children's activities are concerned the data set contains information on three components of time use-housework, self-employment and schooling.27 Housework is not disaggregated fiurther into its various component **s** uch as child care, cooking, energy or water collection, etc. This restricts the manner in which hypotheses can be tested. The lack of disaggregation of the housework variable also prevents me from explicitly testing hypotheses regarding the effects of changes in energy and water infrastructure on time use (these are discussed in detail below). Note however that this limitation does not prevent me from testing the effect of these variables on time use in income-generating activities and schooling.

Reliable community-level data are not available in the Peru LSMS. These variables are constructed from the self-reported household survey data, aggregating up to the segment (cluster) level. Note that in aggregating up to the cluster level I do not just use the panel households but the larger sample in each of the cross sections. The cluster level variables therefore also use information from outside the panel.

Before discussing the summary statistics of the data, let us look at the pattern of time use of boys and girls by per capita consumption decile. Figure I gives time allocated to schooling in the week prior to the survey. As mentioned earlier, time in school is only available in the 1994 data. It is striking that there is very little variation in school time by consumption level-most of the children in the data who attend school are clustered at 25 hours per week. There is no clear difference between boys and girls either. It appears that recall error may be playing a role herethose who attend school are likely to report 5 hours per day for 5 days in a week of schooling.

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Fernández-Villaverde, J., Guerrón-Quintana, P. A., and Rubio-Ramírez. J., (2010). “Reading the Recent Monetary History of the U.S., 1959-2007.” NBER Working Paper 15929, National Bureau of Economic Research, Cambridge, MA.

Ferreira, T., and Shousha, S., (2023), “Determinants of Global Neutral Interest Rates”, Journal of International Economics 145, 103833.

Forbes, K., (2019a), “Commentary: Riders on the Storm”. Challenges for Monetary Policy, Symposium in Jackson Hole, WY organized by the Federal Reserve Bank of Kansas City, pp. 61-81.

Forbes, K., (2019b), “Inflation Dynamics: Dead, Dormant, or Determined Abroad?”, Brookings Papers on Economic Activity, Fall, pp. 257-338.

Forbes, K., Gagnon, J., and Collins, C., (2022), Low Inflation Bends the Phillips Curve Around the World,” Economia Volume Vol. 45, No. 89, pp. 52-72.

Forbes, K., Hjortsoe, I., and Nenova, T., (2018), “The Shocks Matter: Improving Our Estimates of Exchange Rate Pass- Through”, Journal of International Economics, Vol. 114, pp. 255-75.

Forbes, K., Hjortsoe, I. and Nenova, T. (2020), “International Evidence on ShockDependent Exchange Rate Pass-through”, IMF Economic Review, Vol. 68. No. 4, pp. 721-63.

Forbes, K., and Warnock, F., (2012), “Capital Flow Waves: Surges, Stops, Flight and Retrenchment”, Journal of International Economics, Vol. 88, pp. 235-51.

Forbes, K., and Warnock, F., (2021), “Capital Flow Waves—or Ripples? Extreme Capital Flow Movements Since the Crisis”, Journal of International Money and Finance, Vol. 116, 102394.

Frankel, J., and Rose, A., (1998), “The Endogeneity of the Optimum Currency Area Criteria”, Economic Journal, Vol. 108, pp. 1009–25.

Friedman, M. (1982). Monetary policy: Theory and practice. Journal of Money, Credit and Banking, Vol. 14. No. 1, pp. 98-118.

Fratto, C., Vannier, B. H., Mircheva, B., de Padua, D., Poirson Ward, H., (2021), “Unconventional monetary policies in emerging markets and frontier countries”, IMF Working Paper 2021/014.

Friedman, M. (1961), “The lag in effect of monetary policy,” Journal of Political Economy, Vol. 69, pp. 447–66.

Fry-McKibbin, R., McKinnon, K., and Martin, V. L., (2022), “Measuring Global Interest Rate Comovements with Implications for Monetary Policy Interdependence”, RBA Annual Conference Papers, Reserve Bank of Australia.

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# **1. INTRODUCTION**

Citizen participation is crucial in increasing transparency and accountability in the government budget-making process. Traditionally citizens are informed about budget decisions through statements from government officials or from media coverage. However, an active citizenry engagement with the budget-making process enhances government accountability and ensures the needs of various groups of society are considered (Ahmad & Weiser, 2006; Im, Lee, Cho, & Campbell, 2014; Irvin & Stansbury, 2004; Khagram, Fung, & de Renzio, 2013; Wampler, 2012).

Increasing understanding of the importance of citizen engagement, especially at the local level, encourages governments and nongovernmental organizations to conduct budget-making process with greater transparency and accountability. The residents whose daily lives are positively affected by budget-related decisions are likely to remain engaged and active in community life (Khagram et al., 2013).

This study contributes to the growing literature on participatory budgeting by looking at the demand side of participatory budgeting, quantifying the level of awareness and participation in the budget process by rural households in the Kyrgyz Republic and demonstrating the effect of characteristics of the active and non-active citizens. This paper used the results of the nationally representative data from the Life in Kyrgyzstan survey for 2012 to analyze the extent of citizen awareness and participation in the local budget processes, to identify the socioeconomic groups that participate most and least in the budget process, and to explore the determinants of awareness and participation.

The study argues that civic awareness and interest in local budget processes is relatively high while the participation rate is relatively low. Interest, awareness and participation are positively associated with age, education, employment, risk-taking attitude, trust, and social capital. The findings suggest as well that lack of awareness and non-participation is attributable to females, non-Kyrgyz ethnic groups, inactive in labor market, internal migrants, and the residents of communities with poor infrastructure.

This paper is a result of collaboration between the World Bank and the research consortium led by the German Institute for Economic Research (DIW Berlin) aimed at measuring attitudes, determinates and changes in the citizen participation in the local budget processes started under the World Bank project "Information Matters - Transparency and Accountability in the Kyrgyz Republic" (IMTAK). This project focused on rural areas and provided training to officials in village administrations (Ayil Okmotu), local elected leaders and representatives of civil society (active residents and members of 2
Three groups of variables are used in the analysis as posited by the ecological risk framework: protective factors, risk factors, and behaviors/outcomes.<sup>8</sup> The empirical findings from the US, Latin America, and Caribbean studies guided our selection of risk and protective factors to include in the Mexico and Chile analysis. The twelve risk factors in the analysis include low socioeconomic status (parental education level);<sup>9</sup> rural residence, indigenous ethnicity, low healthcare access, social exclusion, weak family cohesion, physical or psychological abuse by a parent, household substance abuse, poor parental response to bad behavior, positive parental influence on smoking and alcohol, perceived high neighborhood violence; and experienced discrimination. The fifteen protective factors considered include trust in institutions (government & community); live with both parents; connectedness (overall, with mother, with father, with other adult); engage in activities with parents; church attendance and spirituality; school quality; feeling of preparedness for the future; optimism towards employment; and sense of wellbeing. The independent variables in the stepwise regressions are these risk and protective factors. The data permit us to analyze seven positive behaviors/outcomes: not inactive,<sup>10</sup> secondary school completion, older age at first job, safe sexual behavior (not sexually active, using contraception if sexually active), older age at first pregnancy/parenthood, participation in activities, and healthy attitudes towards alcohol. These are the dependent variables in the stepwise regressions. Table 1 shows which variables are used in the Mexico analysis and which are used for Chile, defines the variables, and discusses the methodology for the creation of composite proxy variables.

All variables were normalized in the range 0 to 1. Behaviors were normalized such that a 1 indicates good behavior and a 0 indicates “risky” behavior. For factors, a high level of a risk factor would get a 1 and a high level of a protective factor would be 1.

> 8 The classification of a variable as a “risk factor” or “protective factor” may seem like an artificial distinction. For example, poverty is a risk factor while lack of poverty may be a protective factor.  However, there are some risk factors without corresponding protective factors and vice versa.  Rather than enter the debate in this paper, we simply classify a factor as “risk” or “protective” based on how the question was asked in the survey. 9 Various proxies for poverty were tested, including household earnings, household luxury/durable goods ownership, and socio-economic indicators generated by the government.  All had similar results as the “parental education” variable, but the parental education was used since the first alternative is a poor measure due to earnings being only a temporal measure of wealth, there was not sufficient variance in the second measure, and the algorithm for the third variable was not available.

> 10 Inactivity is defined as not being in school or work and not searching for a job.

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higher in the core, respectively. There is also a stronger perception of corruption in the core, measured as the percentage of manufacturing firms expected to pay informal payments or gifts to public officials or tax officials and to give gifts to obtain an operating license. All these indicators are about twice as high as in non-core regions. The perception of corruption is also higher in the core, but to a lesser extent. Regarding finance, the percentage of manufacturing firms in the core with a line of credit or loans in the core is 20 percent higher. In addition, the share of manufacturing firms using banks to finance investments is more than 30 percent higher. Nevertheless, the perception of manufacturing firms regarding access to finance is more pessimistic in the core.

# **2.3 Services Firms**

The analysis of services covers 7,847 firms which represent 19 percent of the overall sample (see Appendix 3). We dropped retail or wholesale trade firms and hotels and restaurants as these do not represent typical firms for which cross-border trade would be significant. Two-thirds of the remaining services firms focus on business services including IT services, telecommunications, accounting & finance, advertising & marketing, and other business services. Another third of the firms is in construction or transportation. On average, 48 percent of services firms are located in the core. Table 2 shows the indicators for services firms. Fewer indicators show a statistically significant difference between core and non-core regions, which might be due to the low number of observations for some indicators.

Regarding firm characteristics, services firms in the core have a higher share of exports in sales than services firms in non-core regions. Export participation, on the other hand, does not show a significant difference. Contrary to the findings in manufacturing, this implies that the threshold of export participation might be lower, but differences in export intensity are more difficult to achieve in non-core regions. Other trade-related indicators show that the average time of inventory of the most important input is more than 20 days in the core compared to 12 days in non-core regions. While ownership variables show no significant difference, it is worth to point out the generally lower FDI share of services versus manufacturing firms.

Unlike manufacturing firms, there seem to be no significant differences between core and noncore regions with regard to technology. Regarding the workforce, the average number of permanent full-time employees is 70 percent in core-regions than non-core regions. The percentage of firms identifying labor regulations and labor skill levels as a major constraint is lower in the core for services firms.

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Council and European Parliament, 2009, GDP and Beyond. Measuring Progress in a Changing World, COM(2009) 433, Brussels, 20.8.2009 CMEPSP, 2009. Report of the Commission on the Measurement of Economic Performance and Social Progress, September, 14, 2009 Dercon, S. (2004), “Growth and Shocks: Evidence from Rural Ethiopia”, Journal of Development Econom‐ ics 74, 309–329.

Fehr, E (1998), « Reciprocity and economics : the economic implications of “Homo reciprocans” », Euro‐ pean Economic Review, n° 42, p. 845‐859.

Freeman, P. K., L. A. Martin, J. Linnerooth‐Bayer, R. Mechler, S. Saldana, K.Warner and G. Pflug (2002), Financing Reconstruction: Phase II Background Study for the Inter‐American Development Bank Regional Policy Dialogue on National Systems for Comprehensive Disaster Management, Washington DC: Inter‐ American Development Bank.

Flegg A.T. and C.D. Webber, 1997. On the appropriate use of location quotients in generating regional input‐output tables: reply. Regional Studies 31, pp. 795‐805 Flegg A.T. and T. Tohmo, 2010. Regional Input‐Output models and the FLQ formula: a case study of Fin‐ land, Regional Studies (in revision) FLOODsite, 2009. Flood risk assessment and flood risk management. An introduction and guidance based on experiences and findings of FLOODsite (an EU‐funded Integrated Project). Deltares | Delft Hydraulics, Delft, the Netherlands

Gordon, P., Richardson, H., & Davis, B. (1998). Transport‐related impacts of the northridge earthquake. Journal of Transportation and Statistics, 1, 21–36 Government of Queensland, Guidance on the Assessment of Tangible Flood Damages, Sep 2002, The State of Queensland (Department of Natural Resources and Mines) 2002 Haimes, Y., Horowitz, B., Lambert, J., Santos, J., Lian, C., & Crowther, K. (2005). Inoperability input‐output model for interdependent infrastructure sectors. I: Theory and methodology. Journal of Infrastructure Systems, 11, 67–79.

Haimes, Y., & Jiang, P. (2001). Leontief‐based model of risk in complex interconnected infrastructures. Journal of Infrastructure Systems, 7, 1–12.

Hallegatte, S. (2008), “An Adaptive Regional Input‐Output Model and Its Application to the Assessment of the Economic Cost of Katrina”, Risk Analysis 28, 779–799.

Hallegatte S., J. C. Hourcade and P. Dumas (2007), Why Economic Dynamics Matter in Assessing Climate Change Damages: Illustration on Extreme Events”, Ecological Economics 62, 330–340.

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The key result from these data is that the medium-term component of the credit cycle for the MENA countries is not more important and volatile than the short-term component – unlike the Western economies and Japan where the medium-term component plays the primary role in driving the overall credit.

# **b) Are the credit cycles synchronized across MENA countries?**

The next characteristic of the credit cycles that we examine for the MENA countries is the synchronization of these cycles among the countries. Figure III-6 displays the medium-term credit cycles in MENA countries, separating oil-exporters (left figure) from oil-importers (right figure). Except for a few countries, the medium-term component of the credit cycles is not synchronized. The average correlation is 0.21 for oil-exporters, which does not indicate a high synchronization among these countries. This number is even lower (0.08) for oil-importers. The average correlation for credit cycles in Western countries and Japan is 0.43.

# **Figure III-6: Medium-term Credit Cycle Country Groups**

<!-- Start of picture text --> Medium-term Credit Cycle in Oil Exporters<br>(in percent) Medium-term Credit Cycle in Oil Importers<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>1967 1977 1987 1997 2007 2017 year<br>year<br> Jordan Morocco<br> Saudi Arabia Kuwait Egypt Lebanon<br> Iran Algeria Tunisia West Bank and Gaza<br> Qatar Bahrain Yemen Syria<br> UAE Oman Libya<br>Average correlation: 0.21 Average correlation: 0.08<br>40<br>20<br>20<br>10<br>0 0<br>-20 -10<br>-20<br>-40<br><!-- End of picture text --> 20
|PIabPIITRN|-0.008*|0.002*|-0.006*|Continuous|
|---|---|---|---|---|
|PIabPIIPROT|0.005|-0.001|0.004|Continuous|
|SUPPLIER|0.037||0.037|Binary|

The significant marginal effects from the perception equation include sickness, pesticide toxicity, time spent in the shop, training and the interaction effect. Health impairment has the most sizable direct impact on trader’s perception, followed by training, where traders who fell sick were 39.7% more likely to view the pesticide as hazardous (perception=1). Those receiving training were 33.7% more likely to view the pesticides as hazardous. Higher proportional use of WHO II pesticides increases risk perception by 0.014%, whereas higher use of WHO III reduces risk perception by 0.002%. In regards to the time spent in the shop, a one hour increase increases a traders’ perception of health risk by 0.051 (or 5.1%). The interaction term, although found to be negatively signed in the regression, has an overall effect of decreasing risk perception by 0.006%.

# **6.  Summary and conclusion**

Systematic studies of pesticide exposure on trader’s health and health risk perception are scarce in developing countries. Drawing on a new survey conducted in Bangladesh, this paper is an attempt to address this gap. To model trader health risk perception, we initially used a 2-equation, bivariate probit framework with health impairment effects as an endogenous dummy variable. Wald and likelihood ratio tests rejected the presence of endogeneity, and thus the two equations were estimated separately.

Even casual inspection during the survey revealed that traders work, and surround themselves, with toxic substances. When questioned, 54% of the traders reported frequent health symptoms commonly associated with acute pesticide poisoning such as eye irritation, headaches, dizziness, vomiting and skin effects. Yet, 92% openly admitted that they do not take any protective measures during handling of pesticides. The probit analysis strongly indicates that the persistent exposure to toxic pesticides increases the probability of sickness. It is widely known that the health effects of pesticide use can be reduced significantly by averting behavior – such as wearing gloves, eye glasses or a mask while handling pesticides and washing hands after touching pesticides. There is an 17