rafmacalaba/datause-encoder-data huggingface dataset:
-89-

# References.

(1) Alderman, H. (1989) "New Rese-rch<sup>on Poverty and Malnutrition: What are</sup> the Implications for Research and<sup>Policy," mimeo, Ir-ernational Food Policy</sup> Research Institute, Washington, D.C..

(2) Anand, S. (1983) Ineguality and<sup>Poverty in Malaysia: Measurement and</sup> Decomgosition, Oxford University Press: New<sup>York.</sup> (3) Aspe, P. and Beristain, J. (1984)<sup>"Distribution of Education and Health</sup> Opportunities and Services," in The<sup>Political Economy of Income Distribution</sup> in Mexico, Aspe, P. and Sigmund, P.,<sup>eds., Holmes and Meier: New York.</sup> (4) Aspe, P. and Blanco, H. (1984)<sup>"Macroeconomic</sup> Uncertainty and Employment: The Case of Mexico," in The Political<sup>Economy of Income Distributionin</sup> Mexico, Aspc, P. and Sigmund, P.,<sup>eds., Holmes and Meier: New York.</sup> (5) Atkinson, A. (1987) "On the Measurement<sup>of Poverty," Econt,metrica,</sup> Vol. 55, No. 4, pp. 749-64.

(6) Bardhan. P. (1988) "Sex Disparity<sup>in Child Survival in Rural India," in</sup> Rural Poverty in South Asia, P.<sup>Bardhan and T.N. Srinivasan, eds., Columbia</sup> University Press: New York.

(7) Behrman, J. (1988) "Nutrition, Health,<sup>Birth Order and Seasonality:</sup> Intrahousehold Allocation among Children in Rural<sup>India," JournalI</sup> of Development Economics, Vol. 28, pp. 43-62.

(8) Behrman, J. and Deolalikar, A. (1987)<sup>"Will Developing Country Nutrition</sup> Improve with Income? A Case Study for<sup>Rural South India," mg'rlzEQ2fPolijical</sup> Economy, Vol. 95, No. 3, pp. 492-507.

(9) Behrman, J. and Deolalikar, A. (1988)<sup>"Health and Nut? 'on,' in HanIdboosk</sup> of Development Economics, Vol. I, H. Chenery<sup>and T.N. Srin.v-Asan,</sup> eds., NorthHolland: Amsterdam.

(10) Behrman, J. and Wolfe, B. (1984)<sup>"More Evidence on Nutrition Demand:</sup> Income Seems Overrated and Women's Schooling<sup>Underemphasized,"</sup> Journal of Development Economics, Vol. 14, pp. 105-28.

(11) Besley, T. and Kanbur, R. (1988)<sup>"Food Subsidies and Poverty</sup> Alleviation," Economic Journal, Vol.<sup>98, pp.701-19.</sup> (12) Besley, T. and Coate, S. (1990)<sup>"Workfare vs. Welfare: Incentive</sup> Arguments for Work Requirements in Poverty<sup>Alleviation Programs," mimeo, The</sup> Kennedy School, Harvard University,<sup>February.</sup> (13) Besley, T. and Kanbur, R. (1990)<sup>"The Principles of Targetting," World</sup> Bank Working Paper Series, No. 385,<sup>March, Washington, D.C..</sup>
For this reason, in the case of export reporting, Senegal’s own reports may be preferred. BACI data also seems to prefer these reports over the mirror, mainly because Senegal’s export destinations are much poorer reporters than Senegal’s import partners. This may be counterintuitive to the conventional approach, where import data is always preferred because of reliability assumptions. A more detailed look at Senegal’s trade reporting is given in section A6 of the Appendix.

# 6. Conclusion

This paper offers a review of bilateral trade reporting discrepancies seen in the UN Comtrade database, the largest and most widely used database for empirical investigations of international trade. As such, this paper is intended to serve as a guide for users on when the use of mirror trade data is appropriate. It outlines that (1) discrepancies in international trade reporting can occur because of both logistic and non-logistic factors. Logistic factors relating to reporting guidelines or valuation methods are guaranteed to induce certain types of discrepancies, such as an import valuation that is higher than its corresponding export valuation. Therefore, there is a clear need to define measures of bilateral asymmetry that penalize discrepancies that are least likely to be explained by some logistic constraint.

To measure these discrepancies, we (2) introduce DI as a measure of bilateral asymmetry and derive indicators that summarize some properties of the distribution of this index. We show that compared to similar measures, the DI is neatly bounded between -1 and 1, penalizes export overreporting more and can directly indicate the direction of discrepancies. Finally, (3) we also show how our different reliability indicators could be used to infer several types of reporting problems at the country, commodity, or trade partnership level. These tools are intended to support any analysis that examines bilateral trade asymmetries on a case-by-case basis and analysis aiming to track reporting discrepancies to specific causes, whether logistic or otherwise.

However, it should be noted that many important limitations remain. The problem of tracking reexports and the non-uniform standard by which they are reported still contribute to many orphan imports and lost exports, which affect the data quality of countries. We note that some indicators proposed here will also be influenced by the number and size of these transactions. To mitigate 40
Some of the selected BAs are market centers, often the largest concentrations of informal business activity and those with higher selection probabilities, in turn. However, this raises logistical challenges due to the sheer number of these businesses, making it daunting for a single enumerator to cover the entire BA in one visit. This may require assigning more than one enumerator per square. Secondly, markets may be open some days of the week and at certain times of the day, which requires visiting the area at the right time. In open markets, operators may sit next to each other, which may result in greater refusal. Finally, it is important to note that the survey may require visiting areas with safety issues. In some cases, the area could be outright dangerous for enumerators; such areas may need to be excluded from the survey with implications in the extent of representativeness (via the definition of ). One last, specific challenge is that policy makers and researchers may want to build panels NN of data for informal businesses (or even of BAs/PSUs), as the dynamics in and out of informality are an important area for analysis. Likewise, implementers may want to recontact businesses in the course of implementing an ISBS-type survey. Revisiting businesses and building such panels is notably difficult when informal businesses are mobile or have no fixed premises. Recontacting such businesses may require the collection of contact information, such as mobile numbers, requiring full compliance with any existing privacy protocols.

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14 poverty line ( _PL_ ) and its non-medical spending ( _NM_ 0) lies below the poverty line.<sup>3</sup> One could get a sense of how far out-of-pocket payments cause impoverishment by comparing the pre-payment poverty headcount (the fraction of households for whom _x_ > _PL_ ) with the post-payment poverty headcount (the fraction of households for whom _NM_ 0< _PL_ ) to get the fraction of the population crossing the poverty line as a result of health expenditures. This approach does not capture _how far_ people are pushed below the poverty line as the result of health spending, and does not capture the possibility that health spending may push households who are already poor in terms of their pre-payment discretionary income even poorer. To get at this, one can compare the pre-payment poverty gap (the aggregate shortfall from the poverty line using pre-payment income _x_ as the living standards measure) with the post-payment poverty gap (the aggregate shortfall from the poverty line using non-medical spending _NM_ 0 as the living standards measure).

# **Impoverishing expenditures: Empirical studies**

Wagstaff and van Doorslaer<sup>3</sup> look at health care payments and poverty in Vietnam in 1993 and 1998. Figure 5 shows their pre-payment income Pen parade for Vietnam in 1998. Also shown in this ‘paintdrip’ chart are the out-of-pocket payments of the households in the chart, along with a food-based poverty line. The difference between the pre-payment and post-payment poverty headcount is around 3.5 percentage points, while the difference between the pre-payment and post-payment (normalized) poverty gaps is around one percentage point. In 1993, the difference between the pre-payment and postpayment poverty headcounts was 4.4 percentage points, so that the fall in the headcount
Another way to check whether IERs calculated from Gini and Theil (0) behave similarly is to use the regression results from Milanovic, Lindert and Williamson (2007) derived within Gini-based framework and check if they “survive” when we use IERs based on Theil. MLW (2007) find, on the sample of 28 pre-industrial societies, that two variables are strongly associated with the level of inequality extraction: being a colony increases the IER by some 25 Gini points; countries that are more densely populated are associated with lower IER (an increase of 10 people per km<sup>2</sup> reduces IER by 1.9 points).<sup>14</sup> As shown in Table 3, both variables remain highly statistically significant when the IER is calculated within the Theil framework.<sup>15</sup> The absolute values of the coefficients are slightly different because the values of Theil indexes and Theil-based IERs differ from those of Gini and Gini-based IERs. But both approaches yield strikingly similar results. In other words, and this is very important, IER results (in this case at least) do not depend on whether we situate ourselves within the Gini or Theil framework.

Table 3. Explaining the Inequality Extraction Ratio: dependent variable = measured inequality (Gini or Theil) / maximum feasible inequality (Gini or Theil)

||**Gini-framework**|**Theil-framework**|
|---|---|---|
|GDP per capita (in ln)|128.22<br>(0.34)|151.26<br>(0.49)|
|Squared (ln) GDP per capita|-9.81<br>(0.32)|-11.46<br>(0.48)|
|Percent of urban population|0.31<br>(0.24)|0.21<br>(0.65)|
|Population density per km<sup>2</sup>|-0.19**<br>(0.000)|-0.20**<br>(0.001)|
|Colony dummy|26.61**<br> (0.000)|24.18**<br>(0.002)|
|Constant|-344.46**<br>(0.46)|-455.26<br>(0.54)|
|Adjusted R<sup>2</sup> (F)|0.67(12.1)|0.53(6.6)|
|No. of observations|28|28|

Note: ** Indicates statistical significance at less than 1%. _p_ -values between brackets.

> 14 The finding regarding the role of population density is probably the most interesting because it lends itself to several interpretations (see MLW, 2007). The effect of colonies is rather expected.

> 15 In both cases, IERs are calculated under the assumption that the subsistence minimum is fixed in real terms at $PPP 300 (in 1990 international dollars).

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maturities, without significantly affecting the overall volume [see Edwards (1999)]. Focusing on the recent experience of Brazil, Forbes, Fratzscher, Kostka and Straub (2011) report that the regulations induced capital outflows from Brazil, in line with the intention of the Brazilian authorities.<sup>28</sup> Experience suggests that the private sector reacts over time to the regulations and to changing global and local circumstances. Therefore, proper prudential regulation and supervision should be a dynamically evolving framework. This is vividly reflected in the recent experience of Korea and other countries.<sup>29</sup> The interaction between the regulator and the private sector suggests that some regulations tend to lose their bite overtime. Hence, policy makers may adopt a mixture of temporary measures dealing with unanticipated transitory shocks, and more enduring policies [reserve requirements, taxes, etc.] at rates that evolve overtime, responding to changing risk exposure and to the size of capital flows.

# **CONCLUSIONS**

A salient macroeconomic trend during the 1990s-2000s was the massive financial globalization of emerging markets. While the links between faster growth and the external financial integration of these countries has been tenuous at best, an unintended consequence has been their growing exposure to financial turbulence associated with sudden stops of inflows of capital, capital flights, and deleveraging crises. The significant output and social costs associated with financial crises, estimated on average at more than 10 percent of GDP, has highlighted the need for going beyond the three policy goals framed by the original trilemma. In response to the crises of 1997-2001, several emerging market countries added financial stability as an explicit goal, built up reserves and adopted a public finance approach to financial integration incorporating the control of private sector balance sheet vulnerabilities and the attendant contingent fiscal liabilities into their policy formulation.

The subsequent resilience emerging markets displayed during the global crisis of 2008-09 is an eye-opener, not just for developing countries but OECD countries as well. A key lesson is

> 28 They looked at the impact of the March 2008 1.5% tax on fixed income investment, and the October 2009 2 % tax on all foreign portfolio investments).  Using fund level capita flows data (fixed income), they found that the regulations induced capital outflows from Brazil.

29 From July 25 2011 onward, financial institutions operating in Korea are no longer allowed to buy FXdenominated bonds issued onshore by Korean companies (―Kimchi bonds‖) who swap proceeds into local currency. A Bank of Korea official explained the policy intent saying that ―local firms should raise funds in won when they use the money here.‖ Kimchi bond issuance had risen considerably in the months before the policy change, because all-in borrowing costs are lower than for comparable won-denominated debt. [ _Financial times_ , ―Seoul takes kimchi bonds off the menu,‖ July 19, 2011].

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the sample reported that they had previously gambled.

During the course of the study we measured gambling outcomes at three stages, immediately after the treatment during baseline, after six months, and after one year.

i) Immediate outcome: Following the baseline survey, participants received 10 Rand ($1.00) and were offered to spend part of this compensation in the following game. First, they were shown a bag with fifty balls, in which five balls were red. Participants were then offered the opportunity to pay 1 Rand from their show-up fee to draw a ball (which was replaced after each round). If they drew a red ball they would receive 5 Rand in return. They were allowed to play up to ten times. On average, participants played 0.8 rounds. 57% of people decided not to play this gamble.

ii) Midline outcome: About six months after the debiasing treatment, participants were contacted via cell phone to collect an intermediate gambling outcome. Participants were offered the choice of a lottery in which they could win R250 or to receive R15 for certain (all prizes were delivered as cell phone airtime). To mimic the experience of playing the lottery, participants were asked to guess which six numbers between 1 and 49 were drawn at the national lottery at an undisclosed particular date over the past six months. The winning odds were randomly assigned within each group:<sup>15</sup> half of the participants were told they would win if they picked all six numbers correctly (odds 1:9,355,819), while the other half were told they would win if they picked at least four correct numbers (odds 1:800). These exact winning odds were not disclosed to participants. Overall, 26.6% of the participating sample chose to play the lottery, of which nobody won.

iii) Endline outcome: Between June and November 2012, approximately one year after the debiasing treatments were administered, an in-person endline survey was administered that collected data on gambling behavior of participants in the last six months. Information was also collected on a range of financial behaviors and attitudes. Note that although endline outcome measures are self reported, our empirical strategy exploits variation in the intensity of treatment rather than the binary effects of treatment. Since all respondents in this sample received the same treatment (but experienced different intensity due to the roll of dice) we are not concerned about reporting biases.

> 15Within the treatment group, random assignment was stratified along treatment intensity (above vs. below median number of rolls).

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# 1. Introduction - summary

Pension systems around the world are facing serious, multidimensional challenges. Policy makers are grappling with the demographic phenomenon of aging societies, a low-interest financial environment that provides insufficient returns on pension savings, and policy legacy issues such as low contribution rates, generous retirement age and early withdrawal rules. All these issues affect the replacement rate of pensions and raise the risk of old-age poverty.

Policy makers in many developing countries must take additional factors into account. Higher rates of informality and greater fluctuation in the labor market lead to relatively low contribution density, and a considerable portion of society is not covered by public pension systems at all. In addition, the risk of old-age poverty is rising as urbanization and demographic changes lead to an erosion of traditional family support systems.

The academic literature on the topic suggests that reverse mortgages (RM) can be a welfare-enhancing tool to supplement pension income, or as a form of insurance **.** In particular, low-income senior homeowners may tap into their accumulated housing wealth to smooth consumption and increase their resilience against financial shocks. The welfare-enhancing potential of this instrument has been modeled in academic studies using data from a variety of jurisdictions. While estimates of potential benefits vary widely by location and model calibration, RM are estimated to be especially beneficial for the very elderly, women (due to higher longevity and lower pensions on average), and “house-rich, cash poor” households. They might also serve to prevent financial exclusion of retirees who can no longer meet the income requirements of a common mortgage.

However, this brief lays out market failures and risk factors that act as constraints on the supply side of the RM market. The risk that housing equity falls below the outstanding loan balance, adverse selection, moral hazard, the high cost of insurance based on imperfect actuarial models, the absence of risk pooling mechanisms that reduce the economic viability of RM for lenders, regulatory disincentives in some countries and market barriers. The combination of these factors may help explain why some banks, both in rich and developing countries, have exited the RM market in recent years, and why only a small subset of financial intermediaries chose to enter it in the first place.

2
recall periods, and varying levels of disaggregation of the listed commodity items. One test design included a long list of consumption items and a 7-day recall period. Another test design maintained everything the same, but switched the recall period to 14 days.<sup>22</sup> This simple change resulted in a reduction in mean consumption by about 12 percent, and an increase in poverty by 8 percentage points. Similarly, maintaining the 7-day recall period, but collapsing the food items prompt into fewer, broad food category questions, produced a mean consumption estimate that was 28 percent less than the personal diary module.<sup>23</sup> These findings are not unique to the population examined by Beegle et al. (2012). Jolliffe (2001) presents findings from another between-groups experiment where two subsamples from the population of El Salvador were administered different questionnaires. Both questionnaires were designed to elicit the same information, but one was longer with more prompts for different consumption items. The other, shorter instrument, asked about all the same food types, but collapsed the prompts into broad food categories.<sup>24</sup> The recall period was the same, as well as other aspects of the instrument. The key finding from the experiment is that even though the samples were the same in all relevant dimensions, due to the differences in the questionnaire design, the short-questionnaire sample had a measured poverty rate that was 46 percent greater (due to lower measured consumption) than the long-questionnaire sample.

An important implication of these studies is that differences in household survey questionnaires over time (and between countries) will result in limited comparability in consumption and absolute poverty levels. This finding is true whether the differences is in how the survey is administered (for example, diary or enumerated), the extensiveness of the survey (short but comprehensive list of items compared to long list of items), the recall period (for example, 7-day compared to 14-day), or time of interview (for example, whether in the lean or harvest season). For more discussion on this point, and the importance of data quality in general,

> 22 In more detail, the 7-day recall would ask whether (and how much) a particular item was consumed in the last seven days and then this value would be annualized. The 14-day recall asks exactly the same question, but simply inquires whether (and how much) consumed in the last 14 days and then this value is annualized.

> 23 However, it is far from obvious that shorter recall periods and closer observation are always preferable for all consumption items (see Deaton and Kozel, 2005b)

> 24 The long questionnaire contained a detailed list of 75 food items and 25 nonfood items, while the short questionnaire collapsed these items into 18 broad-category food items and 6 nonfood items.

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Rajkumar et al. [4] evaluate the targeting of efforts to reduce undernutrition in Ethiopia and conclude, among other things, that:

- 1) Most nutrition-related programs focus exclusively on a subset of the country’s woredas and these are difficult or impossible to identify accurately because of a poor nutrition information system.

- 2) Many programs use food insecurity as a proxy for nutrition insecurity, even though these are not highly correlated. Further, woreda targeting for food insecurity is often based on dated information.

They conclude: “ _Ethiopia’s malnutrition rate could probably be much reduced by shifting some of the programs from the woredas with a high concentration of major programs into woredas with high malnutrition_ ”. This paper addresses this shortage of information by estimating levels of stunting and underweight for all woredas.

Levels of undernutrition often have important spatial dimensions to them. As in many other developing countries, this is also the case in Ethiopia. The urban-rural divide is large, but there are also notable variations, particularly in underweight, across regions [4]. The importance of the spatial dimension has also been observed in other countries, and the result is often strong even after controlling for other correlates. Undernutrition regressions for Vietnam, South Africa, Pakistan, and Morocco suggest that community-level efforts are of great importance [15]. Fuji [16], for instance, in his undernutrition regressions for Cambodia, finds that individual-level and household-level variables explained 20 to 30 percent of the variation in the z-score, while he was able to increase the explanatory power of the model to about 40 to 60 percent by including geographic variables and interaction terms. Alderman and Christiaensen [8] is an exception, as they find in their analysis of Ethiopia that location does not matter significantly once they control for other correlates.

Estimating levels of wasting at local levels is also desirable; however, an imputation model with reasonable accuracy for wasting was not found. Previous country studies using small area estimation (henceforth SAE) for undernutrition in Bangladesh [17], Cambodia [16,18], Ecuador, Panama, Dominican Republic [19] and Tanzania [20], were also unable to find a suitable model for wasting. Other undernutrition SAE country studies include stunting in Brazil [21]; and stunting, underweight and wasting in Nepal [22].

3