rafmacalaba/datause-encoder-data huggingface dataset:
attempting to join efforts with each other. This effort translated into the creation of new, infornmal networks, which had a positive effect on firms' performance.

Firm-level survey data has also proven useful in our understanding of the recent East Asian crisis. Detailed information collected on the crisis through surveys administered in 1997 and again in early 1998 offer good examples of how firm-level data can help studying responses to shocks in a way that complements the more traditional macro/aggregate approach. Dollar and Hallward-Driemeier (1998) focus on the adjustment process in the Thai industry following the crisis. Their work highlights the link existing between firms' behavior and (poor) government policies in the year preceding the crisis. As the crisis developed, firms struggled to cope with the existing constraints and to adjust to the new environment.

# **IV. QUESTIONING QUESTIONS**

Having discussed the type of data that would be most useful for firm surveys to gather, we now turn to the subject of questionnaire design itself. Surveys generally ask respondents both about their attitudes towards various issues and their recollection of past factual events. Moreover, in-person surveys pose these questions in a social setting, albeit one with a script and a purpose. Survey designers must realize both that the survey asks the respondent to perform a series of cognitive tasks and that it is an interaction governed by governed by social norms (Sudman, Bradburn, and Schwarz 1996). It is therefore important to consider how the questions themselves can influence responses. This section reviews the literature on questionnaire design. In particular, it seeks to learn how the survey design itself affects responses to questions, and therefore conclusions the researcher may draw from those responses.

# **General Issues**

Rea and Parker (1997) note six general problems to consider when preparing questions: (1) an inappropriate level of wording; (2) ambiguous words and phrases; (3) rnultipurpose questions (i.e., questions that ask two or more opinions simultaneously); (4) rmanipulative information; (5) inappropriate emphasis; and (6) emotional words and phrases.

Additional problems include questions that can be answered differently by people with the same opinion and questions that can be answered identically by people with opposite 24
The age pension continues to be funded on a pay-as-you-go basis. The current expenditures are of the order of 2.4 percent of GDP however, it is expected that this will rise as the population ages. This represents around 32 percent of the total income support arrangements made by the federal government<sup>2</sup> . Expectations for the pension costs have been made in the short run and the long run allowing for the demographic transition and ageing population. These estimates, shown in Table 4, suggest that the increments in age pension payments are of an order of magnitude that is manageable but material.

**Table 4: Projected Age and Service Pensions as a Percentage of GDP**

|**Year**|**%**|
|---|---|
|2003-04|2.88|
|2008-09|3.01|
|2014-15|3.34|
|2024-25|4.01|
|2034-35|4.48|
|2044-45|4.59|

**Source: Productivity Commission 2005, p 203**

The Australian government is required, under its _Charter of Budget Honesty Act 1998,_ to prepare an intergenerational report to assess the long-term sustainability of government policies over a 40 year period, including the financial implications of demographic change. Some of the conclusions of the 2003 Report, and a research report from the Productivity Commission<sup>3</sup> lie at the heart of the government approach to retirement incomes. These reports conclude that while there will be important fiscal challenges arising from the ageing population because of spending pressures in areas such as health, age pensions and age care, “population ageing is not a crisis”<sup>4</sup> , in part due to it being a gradual phenomenon with scope to take some corrective measures. Also because of its system design, Australia does not have large future liabilities associated with age pensions because government funded age pensions are not earnings-related and have been partly replaced by privately funded superannuation. The government has also created a fund, known as the Future Fund, to accumulate sufficient financial assets to offset its own unfunded superannuation liability by 2020.<sup>5</sup> The government expects that the age pension will continue to play a central role in providing income to older Australians. However, there will be a fall in the percentage of the aged population which will seek recourse to the full pension as the superannuation system matures and people accumulate other assets.

Some transitional and parametric changes have been made to eligibility to control costs and reflect societal change. Men qualify for the age pension at age 65 provided that they also meet residency, assets and income based tests. Women had, until more recently, a normal age of 60; however, this is gradually being shifted to age 65 using a progressive scale based on year of birth.

> 2 Source: Productivity Commission 2005 page 193. This figure relates to the 2003-04 budget year.

> 3 Economic Implications of an Ageing Australia

> 4 Ibid p.xxxviii

> 5 As at May 2007, this liability stood at around $103 billion and it is expected to grow to around $148 billion by 2020.  By November 2007 accumulated assets of the fund were $61.5 billion.

9
|Province FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes<br>Yes|Yes|
|---|---|---|---|---|---|---|---|---|
|Observations|630|630|628|630|630|628|630<br>630|628|
|Number of provinces<br>|63|63|63|63|63|63|63<br>63|63|
|R2<br><br>|0.873|0.892|0.160|0.550|0.716|0.839|0.507<br>0.647|0.741|
|σu<br><br>|0.0584|0.0609|0.317|0.0430|0.0335|0.0213|0.0217<br>0.0173|0.0140|
|σe<br>|0.0724|0.0609|0.0542|0.0247|0.0175|0.0163|0.0129<br>0.0101|0.00946|
|ρ<br>Notes: i) Authors’ estimation fro|0.394<br>m VHLSSs using provin|0.501<br>ce FE models, i|0.971<br>i) Robust standar|0.752<br>d errors in parenth|0.786<br>eses clustered at th|0.631<br>e province level, i|0.739<br>0.746<br>ii)*** p<0.01, ** p<0.05, * p<0.1.|0.686|

40
significant underreporting of conflict. PODES did not pick up many of the conflicts observed in the villages where the in-depth research was conducted.

It should not be surprising that in-depth qualitative evaluations would yield more instances of local conflict. A better understanding of _why_ conflict is underreported provides some insights for improvements for future survey design (e.g., in the context of the 2005 PODES), but also whether under-reporting is systemic or whether it varies across area. If errors in reporting levels are correlated systematically with particular features of these localities, this introduces bias in a regression framework.

There are at least two reasons why conflict might be under-reported in PODES. The first concerns cognition: how respondents understand the questions asked. The PODES instrument asks whether a village has experienced conflict in the past year, but it does not say what is meant by conflict. Does conflict only include large-scale incidents with large numbers of deaths or material destruction? Is violent crime a kind of conflict? In many places village head elections result in the polarization of villages into two hostile groups. If violence appears to be latent, but has not yet erupted, should this be counted as being an example of conflict?

Preliminary evidence from PODES suggests that village heads tend to define conflict narrowly, only reporting conflict as having occurred if it was violent and had significant human or physical impacts. In 62.7% of villages reporting conflict, respondents explicitly identified deaths, injuries or material damage.

A second explanation for the lower levels of conflicts in PODES is functional and relates to the incentives, or lack thereof, for village heads to give an accurate and honest response to questions about conflict in their village. In the absence of clear knowledge about the purposes of the survey – and how the resulting data will be used – village heads may understate problems in 19
# **8.  Summary and Conclusions**

In this paper, we have investigated individuals’ exposure to indoor air pollution (IAP) in Bangladesh. We have analyzed exposure at two levels: differences within households attributable to family roles, and differences across households attributable to income and education. Within households, we have related individuals’ exposure to pollution in different locations during their daily round of activity. We find high levels of exposure for children and adolescents of both sexes, with particularly serious exposure for children under 5. Among prime-age adults, we find that men have half the exposure of women (whose exposure is similar to that of children and adolescents). We also find that elderly men have significantly lower exposure than elderly women.

Across households, we draw on results from our previous paper (Dasgupta, et al., 2004), which relates pollution variation across households to choices of cooking fuel, cooking locations, construction materials and ventilation practices. We find that these choices are significantly affected by family income and adult education levels (particularly for women). Overall, we find that the poorest, least-educated households have twice the pollution levels of relatively high-income households with highly-educated adults.

To summarize, we find that young children and poorly-educated women in poor households face pollution exposures that are four times those of men in higher-income households organized by more highly-educated women. In our previous paper, we recommended feasible changes in cooking locations, construction materials and ventilation practices that could greatly reduce average household pollution levels. In this paper, we consider measures for narrowing the exposure gap within households. We focus particularly on changes for infants and young children, since they suffer the worst mortality and morbidity from indoor air pollution, but our findings also apply to women and adolescents. Our recommendations for reducing their exposure are based on a few simple, robust 20
48 (12.2) jI where t varies from 1 to n. Then the per capita GNP of m countries<sup>will be</sup> Xe which immediately gives their total growth rate as where t varies from 2 to n. These yearly growth rates are further aggregated using the weights wt given in (2.6). This procedure will be referred to as the WDR method.

The above procedure appears to be intuitively reasonable but it suffers from several drawbacks. First, _RW_ depends on the exchange rate used. It<sup>is</sup> obvious that one has to use a fixed exchange rate for all years, i.e., et is fixed for all t. Since exchange rates for countries vary widely over time,<sup>it</sup> raises the most diff!cult problem of selecting the base year. This<sup>problem is</sup> recognized by the World Bank as can be seen from the following<sup>quote:</sup> "The national accounts commonly reported for SubSaharan Africa are based on 1980 prices and exchange<sup>rates,</sup>
point estimate is much smaller among women and, hence, not significant. (The standard errors are the same.) The next three columns show the corresponding results for individuals 70 years of age or older. All the results are much smaller in magnitude—in the case of women, they have a negative sign—and statistically insignificant. (Among men, the result is similar to the result among younger women.) This establishes that the positive effect on total labor force participation is driven by beneficiaries younger than 70, who, based on their age, can be expected to be still economically active, and, in particular, by men.

**Table 15: Results on Labor Force Participation, by Age-Group**

|_Variable_|_All_|_< 70 years_<br>_Males_|_Females_|_All_|_> 70 years_<br>_Men_|_Women_|
|---|---|---|---|---|---|---|
|Clbi M|0.091***|0.108**|0.066|0.008|0.060|−0.021|
|ooma ayor|(0.032)|(0.044)|(0.044)|(0.022)|(0.040)|(0.032)|
|Dependent variable by year|0.646***<br>(0.046)|0.313***<br>(0.079)|0.821***<br>(0.071)|0.429***<br>(0.056)|0.492***<br>(0.085)|0.349***<br>(0.061)|
|Dependent variable by area|0.713***<br>(0.049)|0.501***<br>(0.064)|0.792***<br>(0.079)|0.616***<br>(0.045)|0.676***<br>(0.077)|0.385***<br>(0.066)|
|Observations|12,093|3,977|7,356|9,778|4,530|5,042|
|R-squared|0.300|0.333|0.123|0.287|0.342|0.070|
|Municipalities, number|270|214|254|280|256|262|
|F|236.0|446.7|80.55|103.8|105.2|18.35|
|Partial R-squared|0.105|0.119|0.102|0.0945|0.0976|0.0917|
|Kleinbergen-Paap statistic|1,045|624.9|665.0|1024|495.9|721.1|
|OIR test|0.927|0.679|0.428|0.469|0.966|0.448|
|Endogeneity test|0.00702|0.0384|0.212|0.383|0.104|0.810|
|_First stage_|||||||
|Dd ibl b|0.001|0.005|−0.004|0.003|0.001|0.002|
|epenent varae y year|(0.042)|(0.074)|(0.052)|(0.053)|(0.070)|(0.079)|
|Dd ibl b|0.004|0.008|0.002|−0.001|−0.001|−0.007|
|epenent varae y area|(0.037)|(0.060)|(0.050)|(0.048)|(0.069)|(0.087)|
||1.011***|1.015***|1.009***|0.987***|0.981***|0.983***|
|Instrument from probit|(0.031)|(0.041)|(0.039)|(0.031)|(0.044)|(0.037)|

_Note:_ The table illustrates a linear probability model relying on a three-stage procedure for binary treatment variables on the dependent variable indicating participation in the labor force. Only the OIR test is derived from a two-step procedure. All specifications include a complete set of control variables. Standard errors are shown in parenthesis.

To understand the dynamics that drive the positive effect of the benefit on labor force participation, we have to delve deeper and take a more granular look at the kind of economic activities that are particularly affected. In table 16, we explore the results for two age-groups and for gender and location-specific subgroups within each of these. The method employed is the same as above, that is, we have estimated a three-stage linear probability model for the binary outcome. One could argue that a multinomial model would be more appropriate; yet, no such models for IV estimation are available.

31
the 2003-2008 price period. They do, however, ...nd it plausible that there was some in‡uence in previous years 1979, 1986, and 1990. Knitell and Pyndick (2016) analyze the oil market using a simple static partial equilibrium model with inventory markets and again ...nd no relation between speculation and the oil price peak in 2008. Fattouh, Kilian, and Mahadeva (2013) summarize the literature that examines oil markets and conclude that there is no evidence that speculation is the main driver of price increases. Baumister and Hamilton (2019) also test a VAR approach for oil market but introducing priors for elasticities, ...nding that supply disruptions tend to be a much bigger factor than in previous research.

Another branch analyzes commodity ...nancial markets from a portfolio point of view. Bohl and Stephan (2012) study the e¤ect of increased trading in future markets for six top traded agriculture and energy commodities using a GARCH model approach and ...nd no evidence of a causal relation between future trading and price volatility. Chary, Lochstoer, and Ramadorai (2013) show that restrictions in ...nancial markets can alter spot prices through hedging decisions, a¤ecting real outcomes. Sockin and Xiong (2015) study the possibility of information frictions in commodity markets, and demonstrate the importance of prices as signals for both demand and supply and the weakness of assuming that shocks are publicly known. In line with their results, this paper incorporates information shocks and allows for some shocks to be unknown.

The work presented in this paper is mostly related to Knitell and Pyndick (2016), who estimate non fundamental shocks as deviations in inventory levels. The paper follows a similar methodology except that it consider corn markets (instead of oil) and a dynamic framework (rather than static).

# 1.2 A preliminary analysis

Empirical models have been used extensively in the commodity’s literature, though mostly for energy and metals. Given the lack of similar approaches used for corn, this section explores the use of these same techniques in corn markets The goal is to motivate the theoretical models used in this paper.

The estimation proceeds to construct a VAR model with sign restrictions based on Kilian and Murphy’s (2014) model for oil. The endogenous variables are the same ones to be used later in the theoretical models: real price, consumption, production, and storage. Real prices, consumption, 7
Through the early 1970s, Mozambique was also a major exporter. Its decline has been paralleled by a major expansion in Brazilian exports. While Brazil doesn't obtain the technical results which prevail in India's manual processing system, it has been competitive in the U.S. market due to its lower transportation costs and the unique larger-sized variety of the nut produced there. Other suppliers, including Tanzania, Kenya, and China have a reputation for irregular quality, contributing to substantial price discounts in international markets (of 25-30%). Table 2 traces the levels of kernel exports by the leading suppliers, while Table 3 illustrates the substantial price premium obtained by Indian suppliers.

||Table 2: Ex|ports of Cashew Kernels<br>('000<br>Tons)|by Major Prod<br>_|ucers|
|---|---|---|---|---|
|Country|1975|1980|1985|1988-90 (Ave.)|
|India|_58.0_|37.4|31.6|34.9|
|Mozambique|24.4|17.1|2.5|3.2|
|Brazil|7.6|11.9|25.0|21.7|
|Tanzania|4.0|_5.5_|_0.5_|1.4|
|Kenya|0.0|2.7|1.9|_0.5_|

Source: Gill and Duffuis, Edible Nut Market Reports, various years.

Table 3: Price Comparisons of Major Cashew Kernel Suppliers (United States Import Values (FOB) All Grades; 1989-90 Averages)

|Country|Quantity<br>C(ons)|Unit Value<br>($/Ton)|% of Indian<br>Price|Comments|
|---|---|---|---|---|
|India|19,833|4835|100|Manual Processing;<br>_HighQuality|
|Brazil|19,429<br>___________<br>_M|4205<br>edium|87|Mechanical Processing;<br>Quality|
|Mozambique|2,845|3577|74|Mechanical Processing;<br>Low Quality|
|Tanzania|1,332|3673|76|Mechanical Processing;<br>Low Quality|
|China|744|3568|74|Manual Processing;<br>Low Quality|
|Kenya|403|3262|68|Mechanical Processing;<br>Low Quality|

Source: Agriconsult/AMEC (1992) 9
country and year. Each of the unshaded bars illustrates the average pre-reform ratios, and the shaded bars illustrate similar averages for the post reform period. These are simple unconditional averages, but they are suggestive of an increase in the value of royalty payments relative to sales increases around the time of IPR reform. Furthermore, it is clear that affiliates in the high patent sample make more extensive use of intangibles provided by the parent and that the ratio of royalty payments to sales increases by the largest amount for this sub-sample when reforms occur.<sup>27</sup> Of course, it is necessary to move beyond this figure to estimation of regressions that control for other factors influencing technology transfer and affiliate performance. Results of regression analysis of royalty payments made by affiliates located in the reforming countries to their parents based on specification (1) are reported in Table III.<sup>28</sup> Columns (1)-(3) report the impact of IPR reform on log of the value of recorded licensing payments to the parent firm.<sup>29</sup> The specification in the first column is run on the full sample of affiliates, and the positive and statistically significant coefficient on the post reform dummy indicates that intrafirm royalty payments increase at the time of reforms. As additional regressors, all specifications in the table include the difference between the corporate income tax in the host country and the U.S., the host country withholding tax

> 27 Recall that the high patent sample includes affiliates of parents that, over the four years prior to a particular reform, average at least as many U.S. patent applications as the parent of the median affiliate in the reforming country over the same period.

> 28   The sample used in generating Table III uses data on affiliates of U.S. firms in the reforming countries listed in Table II as well as data on their parent firms.  That is, data are drawn from countries that undergo IPR reform at some point over the 1982-1999 period.  An expansion of the sample to include affiliates based in all countries yielded results very similar to those shown in Table III.  These results and others referred to in the text but not shown in the tables are available from the authors upon request. 29   A large number of affiliates, particularly those in low-tech industries, report zero values for technology transfer in most years.  We thus added 1 to all observations and took the log of the resulting values.  This transformation is standard in the older R&D and productivity literature.  Robustness checks described in the text remove affiliates that do not make royalty payments from the sample.

16
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19