rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5027

# **Abstract**

This paper solves and estimates a stochastic model of optimal inter-temporal behavior to assess how changes in the design of the unemployment benefits and pension systems in Brazil could affect savings rates, the share of time that individuals spend outside of the formal sector, and retirement decisions. Dynamics depend on five main parameters: preferences regarding consumption and leisure, preferences regarding formal Vs. informal work, attitudes towards risks, the rate of time preference, and the distribution of an exogenous shock that affects movements in and out of the social security system (given individual decisions). The yearly household survey is used to create a pseudo panel by age-cohorts and estimate the joint distribution of model parameters based on a generalized version of the Gibbs sampler. The model does a good job in replicating the distribution of the members of a given cohort across states (in or out of the social security / active or retired). Because the parameters are related to individual preferences or exogenous shocks, the joint distribution is unlikely to change when the social insurance system changes. Thus, the model is used to explore how alternative policy interventions could affect behaviors and through this channel benefit levels and fiscal costs. The results from various simulations provide three main insights: (i) the Brazilian SI system today might generate distortions (lower savings rates and less formal employment) that increase the costs of the system and might generate regressive redistribution; (ii) there are important interactions between the unemployment benefits and pension systems, which calls for joint policy analysis when considering reforms; and (iii) current distortions could be reduced by creating an actuarial link between contributions and benefits and then combining matching contributions and anti-poverty targeted transfers to cover individuals with limited or no savings capacity.

This paper—a product of the Social Protection, Human Development Network—is part of a larger effort in the network to understand the interactions between the social insurance systems and the labor market.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at drobalino@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
indirectly through booming local businesses, this indicator is used as a proxy for the level of decentralization or the extent to which sub-national governments are involved in raising revenues or deciding over parameters of the tax system. We thereby estimate whether the assessment of government performance of respondents living near a mining area varies according to the level of taxing rights granted to sub-national authorities in each country.<sup>2</sup> Measuring the variables of interest respectively at the regional (local institutions) and national level (local government taxing rights) reduces the probability of a bi-directional relationship between these variables and individual assessment of public goods and services.

# **3.4. Additional Covariates**

The empirical estimations account for countries, regional and individual heterogeneity. At the individual level, we consider an array of demographic and socio-economic characteristics, including gender, age, education, employment status, residential area (urban or rural) which are likely to shape their views on public services. Since the Afrobarometer surveys do not include the income level of the respondents, we create a composite index of living standards by relying on questions such as “ _How often have you gone without food (or water, medicine, cooking fuel)?”._ The question points to the level of deprivation of essential public goods and services, including food, water, medication, cooking fuel and cash. The answers are on a Likert-scale as follows: _0=never, 1=just once or twice, 2=several times, 3=many times, 4=always_ . The categorical variables are aggregated using polychoric correlation to derive a single indicator which reflects the level of poverty and neediness of each respondent's in basic necessities.

At the regional or local community level, the models also account for regional fixed-effects in addition to the local quality of institutions described above. At the country level and in addition to the measurement of decentralization, the empirical models include proxies for the overall corruption control with data from the World Governance Indicator and the share of natural resources rents as a percentage of GDP from the World Development Indicators.

**4. Empirical Strategy** Equation (1) is our baseline model. We denote by i i the response of an individual **_i_** living in locality of country and which has been interviewed in survey round **.** Depending on the yy specification, the variable i i indicates the assessment of how _well_ or _badly_ does the government ii ii ii handle public services or the expectation of an individual regarding his/her future living yy conditions. **_NR_active_** is a binary variable that takes the value of _1_ if the respondent is located

> 2 See Appendix B for additional details

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# **Fig 3 Liberia, 2007 (attending school, ages 6-15)**

<!-- Start of picture text --> Share of public expenditure on Share of public expenditure on<br>education by quintiles of assets (2007) education by quintiles of probability (2007)<br>30<br>26<br>24<br>23<br>21 21 20 20 20 21<br>19 16 18 19 19 20 20 20<br>13<br>9<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Population (age 6 to 15) Public expenditure Population (age 6 to 15) Public expenditure<br>40 40<br>30 30<br>20 20<br>Percentage Percentage<br>10 10<br>0 0<br><!-- End of picture text -->

**Fig 4 Tajikistan, 2007 (attending school, ages 7-17)**

<!-- Start of picture text --> Share of public expenditure on Share of public expenditure on<br>education by quintiles of consumption (2009) education by quintiles of probability (2009)<br>24<br>21 20 20 20 21 20 20 20 20<br>20 20 19 19 20 19 20 20 20<br>17<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Population (age 7 to 17) Public expenditure Population (age 7 to 17) Public expenditure<br>30 30<br>Percentage 20 Percentage 20<br>10 10<br>0 0<br><!-- End of picture text -->

**Fig 5 Paraguay, 2009 (attending public school, ages 5-17)**

<!-- Start of picture text --> Share of public expenditure on Share of public expenditure on<br>education by quintiles of incomes (2009) education by quintiles of probability (2009)<br>24<br>22 22 21 21 21<br>20 20 20 20<br>20 20 20 20 20 20 20 19<br>16<br>14<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Population (age 5 to 17) Public expenditure Population (age 5 to 17) Public expenditure<br>30 30<br>Percentage 20 Percentage 20<br>10 10<br>0 0<br><!-- End of picture text --> 38
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The corresponding results from the simulations of responses to U.S. agricultural pollcy changes for the years 1984 and 1985 are presented In tables 3 and 4. In contrast to 1981-82, the responses to a 10 percent Increase In either feedgrain support prices or support and target prices Is weaker or more negative, with maize production Increasing by 0.4 percent and sorghum productlon decreasing by 6.4 percent In the first year and then increasing by 0.9 percent and decreasing by 6.4 percent respectively In the second year. The difference In response between 1984-85 and the earlier period Is explained by changes from 1981-82 levels In both the Mexican macroeconomy and In world feedgrain prices. Thus, In 1984-85, per capita Incomes in Mexico were decreased by 15 percent from the 1981 level (due to the well known Mexican debt crlsis and the macropollcy adjustment response to the crisis) and border real prices for malze and sorghum were down by 10 and 16 percent respectively. The lower Incomes reduced per capita demand for livestock products and hence demand for feedgrains, despite the lower real prices. However, In Mexico maize Is also an Important foodgraln, albelt an Inferior one. Hence, lower incomes stimulated shl s In human consumption In favor of maize offsetting the reduction In feedgrain demand for malze and resulting In a positive production elasticity. The lack of a similar demand for d!rect human consumption of sorghum produced the strong negative production elasticities shown. The strongly positive soybean production elasticities shown in table 3 (over 5 percent In both years) reflect a re atively constant real price and substitution of soybeans for feedgrains In farmer productlon plans.

The elasticities of livestock price response to a 10 percent increase In support or support and target prices are comparable In sign and magnitude to those for the 198182 period although the underlying explanation Is more compllcated,l.e., reductions In per
|**Table 8: Robustness of IV Growth results**<br>(Sample:_English_,_French_and_Socialist_countries)<br> <br> <br>|
|---|
|(1)<br>(2)<br>(3)|
|Variable<br>coefficient<br>_Law_<br>Hansen Test|
|_Initial variables:Law & Si,1990_<br>.209<sup>***</sup><br>.181|
|(.000)<br>(.913)<br>|
|_Continent_<br>.176<sup>***</sup><br>.610|
|(.007)<br>(.737)<br>|
|_Latitude_<br>-.259<br>.214<sup>***</sup><br>1.34|
|(.136)<br>(.002)<br>(.513)<br>|
|Δ_GDP_<br>-.070<br>.223<sup>***</sup><br>.196|
|(.659)<br>(.003)<br>(.907)<br>|
|Δ_Power_<br>.054<br>.207<sup>***</sup><br>.107|
|(.193)<br>(.000)<br>(.948)<br><br>|
|Δ_Roads_<br>.066<sup>**</sup><br>.211<sup>***</sup><br>.285|
|(.295)<br>(.000)<br>(.867)<br>|
|Δ_Pop_<br>.094<br>.218<sup>**</sup><br>.258|
|(.756)<br>(.011)<br>(.879)|
|<br> <br> <br>_All variables above_<br>.228<sup>***</sup><br>.00|
|(.007)<br>(.985)|
|_Base regression for Skill_<br>.174<sup>***</sup><br>.785|
|(sample=68)<br>(.006)<br>(.675)|
|_Skill_<br>-.004<br>.166<sup>**</sup><br>1.18|
|(.885)<br>(.033)<br>(.554)<br>|
|Δ_Skill_<br>-.021<br>.170<sup>***</sup><br>.818|
|(.766)<br>(.009)<br>(.664)<br>p values in brackets. Sample size for the top panel is 94. The lower panel uses the<br>sample for which data on_Skill_is available. Each row shows the second stage IV results<br>from separate regressions. Column (1) shows the estimates of the various variables;<br>column (2) shows the estimate of_Law_coefficient, column (3) provides the Hansen test<br>statistics. “Δ” indicates the value of the relevant variable in 2000 minus the same in<br>1990; Δ_Skill_is the change in_Skill_over 1985-1995._Skill_is for year 1995.|

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# **1. Introduction**

Many studies have estimated the long-term consequences of in utero exposure to stress and illness (see e.g. Almond and Currie 2011; Aizer et al., 2016). In contrast, the long-term consequences of stress experienced during late adolescence and early adulthood has received relatively little attention, despite that this is an important developmental time for the brain (NIMH, 2011; Kaestner and Yarnoff 2011, Dahl 2004).

In this paper, we estimate the long-term health consequences of stress during adolescence and early adulthood. We use a novel measure of stress: the risk of being inducted into the military during the Vietnam War.<sup>3</sup> We exploit the substantial year-to-year variation in induction risk between 1955 (after the Korean War) and 1973 (when the US switched to an all-volunteer force) that resulted from both changes in demand for military manpower and changes in which ages were eligible for the draft. We focus on the entire period of the war (1955-72), not just the period of the draft lottery (1970-72), which is important because the pre-lottery years were a particularly morbid and lethal time for American soldiers.<sup>4</sup> We argue that the prolonged uncertainty about whether one would be conscripted into military service between the ages of 181⁄2 and 26 caused large amounts of stress on young men. Highly publicized calls for increased troop levels in Vietnam and increasingly large numbers of U.S. servicemen being killed or wounded were well documented by media outlets, which made this risk particularly salient.

> 3 “The Vietnam War” is the most common name given in English to the conflict that took place in Vietnam between 1955 and 1975. It is the term we are using in this paper. The conflict is also called the Second Indochina War and in Vietnam it is commonly known as the “Resistance War against America”.

> 4 Over 80 percent of US casualties were suffered before the first draft lottery occurred on December 1, 1969. Public outcry and a shift from explicitly pro-US government media coverage to a more nuanced approach covering public dissent and occasionally showing explicit, disturbing war images suggest the salience to risk of being inducted persisted during the draft lottery years as well (The U.S. National Archives and Records Administration, 2013, Griffin, 2010, Hallin, 1984).

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in the hands of the private sector. There is a distinct pattern of trade over the season that is partly influenced by the timing of the harvest, liquidity constraints of farmers, and ADMARC’s participation in the market (Jayne et al. 2008).

By far the largest component of the cost of domestic maize trade is transport costs, with an estimated share of 48 to 57 percent of total transaction costs (Fafchamps et al. 2005). Transport costs in Malawi are high because the secondary road infrastructure (the so-called feeder roads) is not well developed (Lall et al. 2009). The quality of the major trunk road network (international routes) is good and is not a constraint, but for rural areas, the conditions are fundamentally different. Average unit transport costs per ton per kilometer from rural to urban areas are approximately 20 times higher than from urban to urban areas, which is caused by differences in the average distance (85 km in rural transport, more than 2000 km in international transport), the average truckload (2.5 tons in rural transport, more than 20 tons in international transport), the demand for transport services and the availability of backhaul cargo (Lall et al. 2009). The type of trucks used on the international routes is also not appropriate for transport in rural areas (Lall et al. 2009). Transport costs are also high because fuel for transport is expensive. The import and distribution of fuel for transport are managed by a parastatal of the Government of Malawi, which also sets pan-territorial pump prices. Domestic pump prices are high relative to import unit values and thus relative to world market prices, and they have increased stepwise over the years. The gap between import unit values and fuel prices at the pump is 80 to 220 percent. Annual increases in pump prices average approximately 26 percent and reached a peak between June 2008 and January 2009. High transport costs clearly restrict the maize trade. Survey data indicate that the average distance between the purchase and sale location of maize transactions is approximately 55 km, with a maximum of 200 km (Fafchamps et al. 2005). Not surprisingly, the problem of high transport costs is particularly acute in remote and isolated areas.

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**Table 7. Tax compliance: "cell" approach, women (measured at median income)**

|Age/region|<30|30 – 39|40 - 49|50-59|
|---|---|---|---|---|
|RO1 (North)|111.38%|92.07%|93.91%|91.52%|
|RO2 (East)|96.84%|95.00%|96.80%|111.93%|
|RO3 (South)|105.33%|101.04%|103.25%|109.95%|
|RO4 (West)|127.60%|97.92%|98.90%|110.19%|

_Source: Own estimation based on the imputed EU-SILC data._

**Our results show that tax compliance is highest among workers younger than 30 and older than 60, possibly due to the selection bias where people with better jobs tend to stay longer in the labor market** . The research also reveals that tax compliance varies across regions, with Macro region One (North) having the lowest compliance rate. In contrast, the compliance rate is medium in Macro region.

factors affecting tax compliance, such as the size of the informal economy or the quality of tax administration, may exist. Further investigation may be necessary to understand these regional differences and address the compliance issues.

**Finally, we discuss the impact of non-compliance on the share of minimum-wage earners.** Estimating the share of minimum wage earners is important for several reasons. Firstly, it allows for assessing the prevalence of low-wage work in the economy, a crucial indicator of income inequality and poverty. Secondly, it can inform policy decisions regarding minimum wage setting and targeting social protection programs for low-income households. Additionally, understanding the characteristics of minimum wage earners, such as their age, gender, education level, and employment industry, can provide insights into the broader labor market dynamics and help identify vulnerable groups needing support.

**The proportion of minimum wage earners in Romania is notably high when estimated from tax data. Over a quarter of all employees are minimum wage earners, and in some sectors, this share exceeds**

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## **–** Nurses at District Hospitals or equivalent

The survey team was provided with a list of randomly selected candidates for the above categories of respondents for all the PHCs and higher-level health facilities (such as District Hospitals) across the 64 blocks of the study area. However, the survey team reports substantial difficulty in adhering to this list because the personnel were not found at the health facilities. We describe the numbers of respondents that the survey team was able to reach in the sections below, and highlight the fact that we were not able to reach a random sample of providers appointed at these positions.

# **3.3 Description of the questionnaire**

The questionnaires were designed to understand whether the characteristics or traits (such as, public service motivation and integrity) of frontline health workers in the public health system, and how they are managed by their supervisors within the bureaucracy, correspond to the insights of economic theory discussed above. All of the questionnaires are posted at https://sites.google.com/view/stutikhemani/public-health-systems-survey. Below we briefly summarize some of the novel aspects of the data.

The concepts of intrinsic motivation and social pressure among peers are at the heart of economic theory of managing complex organizations tasked with delivering public goods, but are very difficult to measure. The difficulties arise not only because they are intangible and subjective concepts which makes it hard for researchers to design the questions that would go into a survey, but also because it is hard for respondents to understand and answer the questions. In our extensive field-testing of these survey modules in Bihar we found that the abstract nature of the questions made it difficult for respondents to grasp what we were asking. For example, the module to measure "integrity" comes from literature in psychology and aims to assess the extent of moral disengagement (Moore et al, 2012). In field testing these questions in rural Bihar, we found that respondents struggled to understand what we were asking. The very first question of this module, for example, asks respondents to indicate the extent to which they agree or disagree with the statement "It is okay to spread rumors to de16
the surveyed public schools may systematically differ from those using other schools (such as, the users of private schools<sup>10</sup> ).

Selection bias would be more of a concern in analyzing satisfaction with (and usage of) basic services, as opposed to utilities. Basic utilities like water or electricity tend to be universally used and often have single providers servicing entire areas, with limited scope for users to exercise choice. In the case of basic services such as health and education these conditions are less likely to hold. This increases the likelihood of selection bias and makes the typical approach of matching users’ satisfaction with services with objective data from public service providers problematic.

To address these issues, a modified version of the disconfirmation model is presented in the next section to identify the determinants of satisfaction, using the restricted sample of households who can be matched with the objective data for each type of facility. A reducedform version of this model is estimated using the matched sample of household and facility level data. In addition, to examine whether selection bias is a significant concern, a Heckman 2- stage selection model is estimated where satisfaction is estimated on the matched dataset only _after_ the factors that determine a household’s choice of a facility are taken into account.

# **IV. Results**

An accurate analysis of the determinants of reported satisfaction will require modeling the determinants of satisfaction levels, _after_ taking into account the household specific indicators 10 For example, Lankford et al (1995) shows that socio-economic characteristics, including income and parental education and family composition, along with the location of a household and school characteristics, influence strongly the choice between public and private schools in the United States.

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# Alternative Instruments for Smoothing the Consumption of Primary Commodity Exporters

by Kenneth M. Kletzer David M. Newbery and Brian D. Wright

# Table of Contents

|1.|The Costs of Income Variability|3|
|---|---|---|
||1.1 The magnitude of the problem|6|
|2.|Buffering the Export Price Instability Using<br>Conventional Instruments|9|
||2.1 Loans and savings<br>2.2 Consumption smoothing with serial cerrelation<br>2.3 Price stabilization<br>|9<br>1 3<br>1 5<br>|
||2.4 Futures contracts|16|
|3.|Commodity<br>Bonds|21|
||3.1 Smoothing with commodity bonds under full<br>commitment|2 2|
||3.2 Sovereign borrowing and default prevention|25|
|4A|. Overview of Dynamic Smoothing Strategies|27|
||4.1 Default constraint nonbinding<br>4.2 Default constraint binding|27<br>3 0|
|5.|Condusions|4 3|
|Ta|bles|4 6|
|Re|ferences|5 1|
|Ap|pendix|5 5|
|Fig|ures|6 3|

This Is a revised version of a paper presented at the World Bank, Commodity Economics Division, June 1990.