rafmacalaba/datause-encoder-data huggingface dataset:
Proxy Respondents High Income or Wealth Individuals do not respond to the survey and other household members answering on their behalf under-state their incomes or wealth Ex ante solutions for the challenges when using household survey data Following a distinction made by Ravallion (2022), in this section we discuss ex-ante solutions to sparseness, unit non-response and item non-response – solutions that can be implemented in survey design. In the next section we discuss ex-post solutions that use survey and administrative data to address the general challenge of missing top incomes.

A summary of all the solutions to the challenges of measuring the top tail of the income and wealth distributions reviewed in this paper, as well as a description of these solutions and key references, can be found in Table 2 below.

Table 2: Solutions to Measurement Challenges for the Top tail of the Income and wealth distributions

|**Ex Ante**|**Solutions**<br>Oversample rich<br>households|**Challenge**<br>Sparsity|**Data Type**<br>Requires tax data<br>with address<br>information or<br>population<br>census or tax<br>data with low<br>geographic level<br>identifiers|**Description**<br>Use tax data or population<br>census data to oversample<br>rich households|**Key**<br>**references**<br>Kennickel<br>(2019)|
|---|---|---|---|---|---|
||Improve<br>fieldworker<br>protocols|Unit non-<br>response|None|Require multiple visits,<br>improve fieldworker training|Ravallion<br>(2022)|
||Questionnaire<br>design<br>improvement|Item non-<br>response|None|Design questionnaire to<br>obtain at least some income<br>information for those who<br>would otherwise not provide<br>any.|Lepkowski<br>(2005)|
|||||Use PSU or regional|Korinek et|
|**Ex Post**|Reweighting|Unit non-<br>response|survey data only|response rates plus an<br>econometric method to|al. (2006),<br>Ravallion|
|||||adjust sample design surveys|(2022)|

11
# **TABLE A14:  Global Insight's DRI/McGraw-Hill (DRI)**

## **A14: Global Insight's Global Risk Service**

http://www.globalinsight.com DRI is an economic consulting and information company which provides data, analysis, forecasts and expert advice to strategic planners, business and financial analysts, and policy makers. It was founded in 1973 and is based in the United States.

In 1996, DRI launched the Country Risk Review (now referred to as Global Risk Service), a quarterly publication providing country risk assessments to international investors. A first draft of the risk ratings in this publication are produced by country analysts, who then submit their preliminary assessment to regional review committees charged with analyzing and challenging these assessment. The global risk service committee evaluates the reviewed assessments to ensure quality and cross-country consistency. The country analysts then produce the final country risk review.

The CRR assesses the relationship between country risk and its effects on the profitability of investments. For each country, DRI identifies a number of “potential sources of risk”, specifies measurable “risk events”, measures how probable those risk events are, and assesses the severity of impact that each outcome would have. Based on these considerations, DRI produces a risk score for each country.

The CRR identifies a total of 33 “immediate risk events” and 18 “secondary risk events” for 117 developed and developing countries. Immediate risk events are classified into policy risks (tax, and non-tax), and outcome risks (price, and non-price). Secondary risk events are classified into domestic political risks, external political risks, and economic risks. These risk events are described in below.

For each risk event, DRI produces a short run and a long run risk rating. These ratings provide subjective estimates of the likelihood that a particular risk event will occur within one and five years respectively. DRI follows a methodology to ensure that the five year forecasts are consistent with the short-term forecasts. Although these indicators nominally measure the likelihood of future changes in governance concepts, in practice the long-run ratings provide good measures of the current levels of governance.

In the table below we list the variables included in each of the governance indicators. Variable definitions consist of risk events. The actual ratings provide an estimated probability of these events happening. In this paper, we use data for the fourth quarters of 1996, 1998, 2000, and 2002-2005.

62
school, arguing the importance of students to adapt to new technologies rather than focusing on specific skills that become obsolete quickly. On the other hand, there is the “dual system” modeled after Germany with a distinct vocational track including direct involvement of industry through apprenticeships. The underlying rationale for the German model is that by concentrating on specific vocational skills, it is possible to improve the entry of workers into the economy and to make them productive at an earlier point (Hanushek et al. 2017). Most countries are between the two approaches in terms of separating the vocational and general education streams, which is reflected in the starting age of school tracking (Figure 2.9).

Figure 2.9 Starting age of school tracking <!-- Start of picture text --> US<br>Canada<br>Iceland<br>Finland<br>Norway<br>Australia<br>Sweden<br>UK<br>Denmark<br>Latvia<br>Spain<br>New Zealand<br>Russian Federation<br>Ireland<br>Slovenia<br>Switzerland<br>Poland<br>Portugal<br>China<br>France<br>Greece<br>Japan<br>Bulgaria<br>Italy<br>Korea<br>Netherlands<br>Luxembourg<br>Chile<br>Belgium<br>Mexico<br>Czech Republic<br>Turkey<br>Hungary<br>Austria<br>Slovakia<br>Germany<br>5 6 7 8 9 10 11 12 13 14 15 16 17 18 19<br><!-- End of picture text --> _Source_ : Brunello and Checchi 2007.

Notably, during the past decades, there has been a tendency in many OECD countries toward adopting more comprehensive school systems and delaying general and vocational education tracks. In many countries today, the entry age for vocational schooling has largely coincided with the start of upper secondary education or the end point of compulsory education. In a few countries, however, either at the beginning or sometime during lower secondary education, parents must choose (or schools decide) an educational pathway or a specific type of schooling for students. For example, this takes place from the age of 10 in most of the regions in Germany and in Austria, at age 11 in Liechtenstein, and at age 13 in Luxembourg and the Netherlands. Together with the trend of postponed tracking, there has been increased enrollment share of the general track at the upper secondary level (Figure 2.10). One important point, though, is that there are large variations of how the tracked system works in practice. For example, Switzerland is well-known for its thriving 17
22 project.17 Three groups were then formed: (i) countries that seemed to have prograssed steadily from phases I to IV (Brazil, Israel, South Korea); (ii) countries that cycled back and forward between phases II and IV (Chile, Colombia, Ghana, and the Philippines); and (iii) countries that remained in Phase II for long periods (India, Turkey, and Egypt).

Using the wealth of information generated in the ten country studies Krueger (1978) and Bhagwati (1978) attempted to determine whether, on average, the relaxation of the anti-export bias had affected exports and the overall economic performance. We will review first Krueger's synthesis of this project. An important finding is that devaluations -- that is, the movement from Phase II to Phase III -- generally resulted in important reductions in the premium on import licenses and, thus, in a lower anti-export bias. She also investigated the extent to which changes in the trade orientation of a country contributed to an expansion of exports and whether this phase movement implied some costs in terms of output growth. From a regression analysis Krueger then found that while the real effective exchange rate played a significant role in determining exports, the dummy variables included to control for the different liberalization phases were not significant. With respect to real output she found that on average, higher exports were associated with higher GNP, with no significant costs being related to the transition towards a more liberalized trade regime. Perhaps Krueger's most important conclusion from this monumental cross-country study was that in order to attain a sustainable growth in exports (and, thus, GNP) 17Recall from Section II that the phases were defined as follows: (I) systematic imposition of across-the-board QRs; (II) control system becomes more discriminatory with strong antiexport bias; (III) devaluation and relaxation of some QRs; (IV) continued liberalizativ , with greater emphasis on the price mechanism than on QRs; (V) fully liberalized and convertible regime, no QRs remain.
**_Potential problems associated with free entry_**

**_Section 4_**

economies of scale or scope in a sub-set of the monopolist's outputs, a competitor could profitably supply that sub-set of the market. A descriptive illustration of this case is presented in Box 4-1, which also illustrates why we call this the "1/3, 2/3 problem".

# **Box** 4-1: Ineffiei nt entry

Three towns desire water supplies in given quaantities. The cost of supplying any one of these towns by itself is $300, of supplying two of them via one facility is $400, and of all three towns jointly is $660. From society's perspective it is cheaper for any one company to supply all three towns, since $660 is less than the cost of supply by three sepate plants ($900), or of supply by any two towns by one plat ithe other town supplied by a secd plant ($700).

If however, a monopolist supplies all three towns at a price per town of ($660!3=X$220) an entrant could supply two of the towns more cheaply (at-a cost of $400/2=$200 each).

Source; Baumol, Panzarand Willig 1982, reference to Faulhaber 1975 Box 4-1 shows that free entry could result in a situation in which there is a natural monopoly (in terms of subadditivity of production costs), but in which monopoly supply may be difficult to preserve in the free market place. This could provide a rationale for restricting entry to natural monopolies.

This problem is most acute when the conditions for perfect contestability hold (see Section 3.1). As one moves further away from these conditions, the problem diminishes. The greater the level of sunk costs, and barriers to entry, and the greater the incumbent's ability to adjust prices in response to the threat of entry, and the less likely this problem is to exist.

One would not expect to encounter this problem often in the real world of electricity and water supply. Generally, there will already be an incumbent supplying the three towns. Water and electricity supply have high sunk costs. The incumbent will generally be able to drop prices low enough to make entry uneconomic, while still covering variable and fixed costs, and eaming a return on non-sunk assets. Knowing the incumbent can do this, potential entrants will not invest.

Where the sunk costs and lead time associated with entry are significant and competition law does not prohibit it, the incumbent could also price-discriminate to deter entry. The incumbent could deter entry by dropping its price to the two towns where entry is threatened to $199. It could then charge the third town $262. In this way it could cover its full costs while offering each town a better deal than their next best altemative.

Of course the entrant could also change its entry strategy in response, and target the town which is now charged most. Whether changing the pricing policy will work depends on the response times and the commitment of the entrant and the incumbent. Where an incumbent is in place, and the entrant has yet to invest, the advantage should lie with the incumbent. The entrant will have a long lead time between _33_
16 other parts of the town. Inequality appears to be considerably more widely dispersed _within_ these two broad groups.

# **7. Three Robustness Checks**

We noted in Section 1 an important caveat that attaches to the broad findings in this paper, namely the possibility that there may exist important cost of living differences between urban conurbations of different sizes. The findings reported above have not attempted to adjust for such cost-of-living differences, because spatial price indices across city-size categories are not generally available. It is well recognized in the literature, however, that observed differences in poverty rates between urban and rural areas can be significantly attenuated once one corrects for the fact that the cost of living in urban areas may be much higher than in rural areas (generally because of the higher cost of food and housing). The possibility exists that our broad findings of lower poverty in small towns than in metropolitan areas might also be driven, at least in part, by our failure to allow for a higher cost of living in metropolitan areas.

While household survey datasets are not generally large enough in sample size to permit the construction of a cost-of-living index across different city-size categories, our survey data for Brazil constitute an important exception. We are able to draw on the 2002 POF data (see Table 1) to construct a cost-of-living index across the broad city size categories employed in this paper, and can check whether our findings for Brazil, reported in previous sections, are robust to this correction.

There are many ways in which spatial price indices can be constructed. We follow here the approach applied by Ferreira, Lanjouw and Neri (2003) to the construction of a _regional_ price index (that distinguished also between urban and rural areas) using 1996 PPV data for Brazil. This approach was subsequently applied in World Bank (2007) to produce a regional price index based on the 2002 POF data, and is based on unit-value information provided in the POF survey on food items, as well as a hedonic model of rent. We re-apply the method here, but focus solely on urban areas and construct a Laspeyres price index that captures price differences across city-size categories (and regions). Because of the limited sample size of even the unusually large POF household
# **1. Introduction**

A key issue confronted by policy makers designing policies to cope with the recent food price crisis is whether policy responses should be geographically targeted or not. As noted by Zaman et al. (2008; see also World Bank, 2008a and 2008b; IMF, 2-008, and Wodon and Zaman, 2008), three main types of policies have been advocated to respond to the crisis. Firstly, economy-wide policies aim to stabilize domestic food prices typically through indirect tax cuts and broad-based subsidies. Secondly, social protection and human development programs aim to help households cope with the shock induced by higher food prices. The third set of policies aims to boost domestic food production through a focus on agricultural productivity with the hope that a food supply response will help reduce food imports, put downward pressure on prices, and at the same time bringing in additional income for domestic food producers.

A recent survey of 118 country teams and country economists carried out by the World Bank in March 2008 suggests that in sub-Saharan Africa, the reduction in foodgrain taxes and the expansion of existing safety nets and social protection programs were the most commonly adopted policies to deal with the crisis. In this paper, our focus is implicitly on safety nets. A substantial body of research has shown that safety nets can be well targeted, but that this is by no way guaranteed (e.g., Subbarao et al., 1997; Braithwaite et al., 2000; Coady et al., 2003 and 2004). One of the easiest ways to achieve good targeting to the poor is to rely on geographic targeting through poverty maps (Elbers et al., 2002, 2003). Poverty maps can be especially useful in countries which have limited capacity to implement proxymeans testing mechanisms, while at the same time being constrained in terms of the likely gains from self-targeting. Many countries in sub-Saharan Africa clearly fall in these categories.

2
**Table 11.** Effects of the lottery incentive intervention one year after the intervention ended

|Dep variable|HIV incidence|HIV prevalence|STI prevalence|HIV incidence|HIV prevalence|STI prevalence|
|---|---|---|---|---|---|---|
|Years|2011-2013|2011-2013|2011-2013|2011-2013|2011-2013|2011-2013|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Any lottery|-0.029<sup>**</sup>|-0.038<sup>**</sup>|-0.031<sup>**</sup>|0.006|0.003|-0.039<sup>**</sup>|
||(0.012)|(0.019)|(0.012)|(0.021)|(0.027)|(0.016)|
|Any lottery×Risk-loving||||-0.132<sup>***</sup>|-0.112<sup>**</sup>|-0.018|
|||||(0.047)|(0.049)|(0.028)|
|Risk-loving||||0.116|0.098|0.014|
|||||(0.036)|(0.034)|(0.024)|
|Mean control group|0.171|0.320|0.065|0.187|0.187|0.070|
|Observations|2317|2783|2745|1364|1639|1618|

Note: OLS regressions. Sample of individuals aged 18-32 at baseline and tested at 24 and 36 months. Columns (1) and (4): Dependent variable equal to one if the individual tested HIV positive at least twice after 36 months, and tested HIV negative at baseline, zero if the individual tested HIV negative at baseline and after 36 months. Columns (2) and (5): Dependent variable equal to one if the individual tested HIV positive at baseline or after 36 months, and zero otherwise. Columns (3) and (6): Dependent variable is equal to one if the individual tested positive for any of the two STIs (syphilis and trichomoniasis) after 36 months, and zero otherwise. All regressions include village fixed effects. Robust standard errors in parentheses, clustered at the village level. *** 1% , ** 5% , * 10% significance.
- 64 -

# **Table** A.8: **Changig ComPosition ot** Nontarm Activity Across States with **Differing** Agricultural hicomes, **hdba** 1971-81

|||1971-8|1 Cha|nge In N|onfarm|Employ|ment De|nsity|
|---|---|---|---|---|---|---|---|---|
||Totl<br>Nonfarm|Mining|House<br>Hold<br> <br>Mft.|Non<br>- House<br>Hold<br>Mfht|-<br>.Constr|. Comm|. Tran|011w<br>s. Services|
|||(Fu|ll-Time|Workers|per 1|,000 Pop|ulatlo|n)|
|A.<br>Rural Areas|||||||||
|High Agricultural Income|||||||||
|PunJab<br>|7.2<br>|0<br>|-3.3<br>|5.4<br>|0|2.4|3.1|-. 4|
|Haryana|9.6|-.3|-1.9|6.0|1.4|2.1|2.4|<br>-.2|
|Medium Agricultural Incomo|||||||||
|Karnataka|4.2|.2|.6|5.3|.2|1.6|1.0|<br>-4.7|
|Gujarat|6.5|-. 2|-7.2|8.7|1.0|1.0|1.6|<br>1.7|
|Low Agricultural Income|||||||||
|Madhya Pradesh|8.2|1 2|.3|2.8|2.5|1.1|.5|-.1|
|Blhar|5.4|.5|-.2|2.7|.5|1.3|.6|-.1|
|B. Urban Areas|||||||||
|Hljh Agricultural Income|||||||||
|r'unJab<br>|1 1.6|.1|3.4|4.9|.2|-. 2|2.4|.8|
|Haryana|24.9|.3|4.1|18.5|4.9|.7|-1.9|-1.7|
|Medium-AgrIcultural Income|||||||||
|Karnataka|6.0|0|-2.2|10.4|2.3|1.2|-5.9|.4|
|Gujarat|14.1|-.4|-.4|14.2|-1.7|2.5|5.3|-5.4|
|Low Agricultural Income<br>|||||||||
|Madhya Pradesh|.2|-.6|-1.9|6.3|4.4|3.2|.9|-12.1|
|Bihar|-14.6|-5.9|-4.9|3.1|-.1|-1.7|-2.4|-2.7|

a/Karnataka was called Mysore prior to 1971. b/Urban includes all locaities over 5,000 in population. It encompasses both rural towns and large urban areas. Source: India (1971a-b, 1981c). See Appendix Table A.9 for th raw data.
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