rafmacalaba/datause-encoder-data huggingface dataset:
underlying distribution to produce projections of the population distribution over time (or before and after a shock). The underlying characteristics are held constant, while the weights given to different parts of the sample are changed. It is important to note, however, that reweighting assumes that the characteristics within a weighted group do not change over time. Therefore, if large changes occur in a variable that was not included in the macro weights, errors might arise. For instance, a weighting scheme where weights are applied according to whether a family has children or not would overestimate the number of children if the fertility rate fell as a result of a reduction in the number of large families rather than a smaller number of families with children.

Given that at this stage we want to reweight only with respect to changes in the employment structure without imposing any additional constraints, the procedure is relatively straightforward, because we have only one condition in the minimization problem. The procedure is summarized in Figure 2. We use the same artificial household survey as in the first approach. In a real country case the procedure may be extended to account for other changes in the population structure as well, coming either directly from the Macro-RHG model (as for instance the population growth rate) or taken from external sources such as the United Nations’ demographic projections. Appendix A points out some of these issues and shows also how reweighting procedures could be used to achieve consistency between a Macro-RHG model and real household survey data in the base year.

Under the reweighting procedure, we calculate the household group-specific FGT poverty measures as before, but when calculating it for the urban population and the total population we now account for changes in the employment structure with respect to the initial period. The FGT poverty measures then become: where the index j stands for the household categories and wjt for their respective share in the total population in period t. In the micro-accounting method presented earlier, the implicit assumption was that the coefficients w remain constant over time. jt Likewise, we can calculate the inequality measures by weighing each household with its group and period-specific weight fjt, where fjt = wjt/Nj with wjt is the share of group j in the population at period t and Nj the size 11
are most in need both in terms of resources (e.g. have a low PMT score) and program support (e.g. are at a higher risk of not meeting some program objective, such as having adequate nutritional intake).

We develop a PMT for Sudan based on the latest household survey, the National Baseline Household Budget and Poverty Survey (NHBPS) 2014/2015. We first identify a set of candidate variables that best suit the country’s situation and that we believe serve as good proxies for welfare in Sudan. Based on this set of variables, we then perform statistical analyses to determine the final candidate variables to be included in the PMT. This step includes the estimation of different model specifications. Later, we evaluate the out-ofsample performance of the model across different PMT cutoff scores and use several evaluation metrics to evaluate the targeting performance of the PMT. Given the recent and rapid changes of the Sudan economy, this PMT methodology can readily be applied to new household survey data when it becomes available.

Compared to social program targeting based on a uniform allocation, where everyone has the same chance of being included, the results show that a PMT should be considered for targeting social protection programs in Sudan.<sup>8</sup> The PMT would assist social programs to reach a higher share of the poor and vulnerable, while reducing expenditure going towards households that are less in need, improving both the poverty impact, as well as the sustainability of the program. We explore various specifications of the PMT and find that our proposed Sudan national PMT performs as well as a rural and urban specific PMT, making it easier to implement and manage in practice. Furthermore, costly and time-consuming employment information does not improve the accuracy of the PMT in a meaningful way. Finally, our proposed Sudan PMT relies on about 20 variables that could be collected by a short survey in the field on a large portion of the population.

The rest of the study proceeds as follows. Section 2 briefly describes Proxy Means Tests and provides an overview of program targeting considerations. Section 3 presents the data used in the study and the methodology followed. Section 4 presents the main results of the study, while Section 5 presents robustness analyses. Section 6 concludes.

# 2. Proxy Means Tests (PMT) and Program Targeting

A PMT is an index of observable and verifiable household characteristics that serves as a proxy for household welfare, such as consumption expenditure or income. In general, a PMT can be any weighted

> 8 Uniform allocation is a natural counterfactual for assessing a PMT and has been used as a baseline of comparison in other PMT studies (Brown, Ravallion, and Van de Walle 2018). We discuss this further in the methodology section.

5
_it t it_<sup>_v_</sup> _j_ = μ _i_ +η _i_ + ε _j_ , (9) where _μi_ is an unobservable country-specific effect, assumed to be i.i.d. with zero mean and variance σμ2 ;<sup>η</sup> _it_<sup>denotes an effect specific to the</sup><sup>_t_th survey for the</sup><sup>_i_th country, also</sup> assumed i.i.d. with zero mean and varianceση2 , and ε _itj_ denotes the residual disturbance, assumed i.i.d. with zero mean and variance σε2 .<sup>11</sup> The _μi_ ’s,<sup>η</sup> _it_<sup>’s, and the</sup> ε _itj_ ’s are assumed mutually independent. Under these assumptions, the covariance structure of the error term is: These expressions define a two-way error-components model in which the survey- _t_ 2 specific effect<sup>η</sup> _i_<sup>is nested in the country-specific effect</sup><sup>_μi_. If</sup> ση = 0, (9) reduces to the standard error-components model (8).

The parameters of the nested error components model given by (4) and (9)-(10) can be estimated in a variety of ways, ranging from ANOVA-type to minimum quadraticnorm and maximum likelihood estimation.<sup>12</sup> On the whole, Monte Carlo evidence reported by Baltagi _et al_ . (2001) suggests that the method chosen makes little difference for the estimates of the regression coefficients in (4). However, when -- like in our case -- one is also interested in the standard errors of the parameter estimates, as well as in the variance components themselves, maximum likelihood estimation offers the best performance, especially if the sample is severely unbalanced.<sup>13</sup>

# _II.3 Data_

We implement the empirical approach described above using the Dollar and Kraay (2002) dataset, which comprises 794 country-year observations for which both the Gini coefficient and the quintile shares are available.<sup>14</sup> The dataset combines observations for which both the Gini coefficient and the quintile shares refer to gross (i.e. before taxes and transfers) income (47 percent of the observations); net (i.e. after taxes and transfers) income (29 percent of the observations); and expenditure (24 percent).

Table 1 presents some descriptive statistics. The average Gini coefficient is roughly the same in the income and expenditure subsamples -- 0.37 for income and 0.38 for expenditure. This is somewhat surprising since on the basis of conventional

> 11 These assumptions can be further relaxed to allow for heteroskedasticity of

_it_ ε . _j_

> 12 See Baltagi _et al_ . (2001) and Davis (2002) for discussion.

> 13 See Rabe-Hesketh _et al._ (2004) on the computational aspects of ML estimation in this context.

> 14 We discard the 158 extra observations for which Dollar and Kraay construct the quintile shares from the Gini coefficient using equations (1)-(3), and thus assuming that the distribution of income is lognormal -- which is precisely what our regressions aim to test.

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However, even if respondents are able to identify the intended reference period, they may still encounter problems when it comes to accurately assigning memories to the reference period. This is because autobiographical memory tends to be organized in the context of ongoing life experiences, rather than in relation to specific dates (Belli 1998). Thus, temporal-based attempts at anchoring—common phrases used such as “in the last month” or the “during the last 12 months”—may be relatively ineffective. Instead, prompting respondents with locally-relevant recall cues (e.g., public events, religious services) or graphical aids (e.g., personal event timelines) to help anchor the reference period to respondents’ personal experiences (Loftus and Marburger 1983; van der Vaart and Glasner 2007). The use of shorter recall periods (last week vs. during the last 12 months) also increases the chances that respondents will be able to recall specific activities that occurred during the reference period, whereas longer recall periods tend to encourage guessing and estimation based on established patterns of activity (Brown 2002). Even then, respondents may exhibit “telescoping” behavior, particularly at longer recall periods, in which distant events may be reported as having happened more recently than they did (“forward telescoping”) and recent events as occurring much longer ago (“backward telescoping”) (Sudman and Bradburn 1973).

It is also important to stage the time use interview in a way that complements, rather than competes against, the chronological ordering of autobiographical memory. For example, asking respondents to recall when and for how long they engaged in each activity listed in the time diary may be particularly challenging for respondents, whereas encouraging the respondent to recount the previous day as narrative account may, in fact, enhance recall accuracy.

Guidance for the WEAI time diary provides an example of one way to structure time diary interviews to promote better recall (see Box 3 for another example from the United States).<sup>9</sup> The WEAI time diary interview begins with the enumerator asking the respondent to recall what time he or she woke

> 9 These guidelines were developed based on the protocol developed for the WEAI by Data Analysis and Technical Assistance Limited (DATA) in Bangladesh. A video tutorial for the method is available online: <u>https://www.youtube.com/watch?v=jr8ebiKUkbQ.</u>

9
The issue here is whether these differences will be reflected in national poverty lines. Such lines must invariably pass a test of their social relevance in the specific country context.<sup>23</sup> A poverty line that is too frugal by the standards of society will no doubt be seen as such by those constructing that line, and so be rejected. Similarly, a line that is too generous will not be easily accepted either. As argued by Ravallion (1998, 2008b), the very process of setting a national poverty line entails enumerable choices that appear to be guided in large part by a desire for the line to be accepted in the specific context.

This argument would seem more plausible for the capabilities-based approach than the welfarist approach based on relative deprivation. Some set of “capabilities” is an (implicit or explicit) foundation for most poverty lines used in practice. Nutritional needs for good health and normal activities are commonly identified, although there is considerable discretion in terms of how such needs are mapped into the consumption space. In a poor country, it is socially acceptable, and common, to allow a poverty line to attain almost all of the stipulated nutritional requirements (2100 calories per day, say) with one or two starchy food staples, while in a middle-income or rich country the stipulated diet is far more diverse (and palatable). Allowances for non-food consumption introduce even more discretion, and it seems plausible that ideas about social inclusion needs in specific societies would come to play an important role, particularly (but not only) for the non-food allowances. It would hardly seem credible that the national poverty lines that emerge from the choices made in their calibration would not come to reflect prevailing views about what poverty means in the specific context.

The apparent stickiness of real national poverty lines over time sits uncomfortably with this view. While relative poverty lines used in OECD countries and by Eurostat are automatically adjusted over time in line with the changes in the mean (or median), it does not appear to be common to see official poverty lines in growing developing economies being revised upwards in real terms. However, it is not necessarily inconsistent with our approach to find that the real level of the poverty line is resistant to change in some growing economies. For one thing, it may well be the case that a (positive) minimum aggregate income gain to a low-income country is needed before upward pressure on the poverty line emerges; in fact that is implied by our weakly relative poverty lines based on equation (4). It must also be acknowledged that there can be a strong

> 23 This is no less true of the poverty lines constructed for World Bank _Poverty Assessments_ , which emerge out of close collaboration between the technical team (often including local statistical staff and academics) and the government of the country concerned.

10
Surveys of poor rural coastal households confirm this diversified livelihood strategy. In Bangladesh, the most dominant primary occupation of households surveyed is fishing (34%), followed by agriculture (29%) and daily wage laborer (15%); however, more than half (52%) of the respondents do not own any farmland and 64% are illiterate (Paul and Routray 2011). In Tanzania and Zanzibar, 34% of coastal households identified fishing as their most important economic activity, and 38% responded farming/livestock keeping – but 73% identified both activities as important to their livelihoods (Tobey and Torell 2006). In Thailand, although only a few coastal households surveyed engage in agriculture, 31% reported having at least one male member undertaking outside work and 17% reported one female member (Barbier 2008). All the households engaged in some form of mangrove-dependent activity for their main livelihoods, such as offshore fishing or aquaculture (16%), 31% collected wood and other mangrove products (31%), and more than half (52%) engaged in both activities.

‐ Marine fisheries, especially small scale fisheries, are an importation source of nutrition, employment and income for many of the world’s coastal and rural poor. For example, the smallscale fisheries sector is responsible for half of the catch in developing countries, employs 25-27 million full or part-time people in fishing, and another 68-70 million in post-harvest activities (World Bank 2008). Fishing households are some of the poorest in coastal areas, and for many other poor coastal households, fishing provides an important source of food and occasional income (Béné et al. 2010; Campbell et al. 2006; Mills et al. 2011; Kronen et al. 2012; Nayak et al. 2014; Solaymani and Kari 2014; World Bank 2008).

Because of their lack of assets, their dependence on exploiting marine and coastal resources, and their need for diversified sources of income, the rural coastal poor are susceptible to a _poverty-environment trap_ that is commonly faced by rural poor households in less favored areas of developing countries (Barbier 2010 and 2012). Figure 3 illustrates the elements of the trap that can be a vicious cycle for poor households in rural coastal areas.

Increased use of coastal and marine resources, including available agricultural land, causes extensive degradation. The result is falling productivity and incomes for the households dependent on these resources. This leads to more people searching for available outside work to boost or supplement incomes. If there are large numbers of households seeking outside employment in coastal areas, the supply of labor for paid work could exceed demand. The market wage for hired labor will decline. For some households, the wage rate will fall to the level of the reservation wage or even lower, and these households would now forego outside employment opportunities and instead allocate all of their labor back to production activities dependent on coastal and marine resources. The danger for these households is that, if this process degenerates into a vicious cycle, then the dynamics of a poverty-environment trap may ensue (see Figure 3).

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As a visual illustration of this measure, in Figure 6 we show, for Argentina and Ecuador, the speech proportion in 2010 of the top three topics in 1940. We can readily observe that policy volatility was much higher in Argentina. In 1940, a topic concerned with the development of infrastructure dominated speech content with a topic proportion of 48%. However, by 2010, this same topic had nearly disappeared. The fall in prevalence is similarly pronounced for the second and third most prevalent topics of 1940. On the other hand, policy volatility in Ecuador was considerably lower. In 1940, a topic discussing a wide range of subjects, such as social policy, the economy and justice, had a speech prevalence of 47%. Although the importance of the topic had fallen by 2010, it remained a significant topic with a proportion of 29%. In other words, policy, as reflected in presidential speeches, was less discontinuous in Ecuador than in Argentina. These are precisely the cross-country differences our measure attempts to capture.

Figure 6. Speech proportion in 2010 of the top three topics in 1940 in Argentina and Ecuador <!-- Start of picture text --> (a) Argentina (b) Ecuador<br>50% 50%<br>40% 40%<br>Top 1 topic in<br>30% 1940 30%<br>Relevance in<br>20% 2010 of the top 1 20%<br>topic in 1940<br>10% 10%<br>0% 0%<br>1940 2010 1940 2010<br><!-- End of picture text --> Note: This Figure shows the speech proportion in 2010 of the top three topics of the presidential speech in 1940. The topics were selected by the LDA using pooled data of all the countries.

One of our alternative measures of policy volatility is based on the R-squared obtained by regressing �� _—_ the topic proportions of the speech from year _t—_ on ���� _—_ the topic proportions of the speech from the preceding year. The R-squared of these regressions provides an indicator of how similar the topic distributions in a speech from a given year _t_ are compared to the topic distributions of the speech from the preceding year _t+1_ , with a high R-squared indicating a high degree of similarity between the two years. In the context of our research question, a high R-squared suggests high policy stability. To illustrate this measure, in Figure 7, we show how the R-squared measure captures policy volatility in Costa Rica throughout the 1878-2015 period.

17
social and political) cohesion found in China, compared to SSA. Being one large and relatively homogeneous country—rather than many smaller, ethnically diverse and geographically dispersed countries—brings economic advantages in ( _inter alia_ ) public administration, the provision of domestic public goods, external trade negotiations, access to external markets, labor migration (which raises aggregate output by reducing geographic disparities in labor productivity) and in reducing and managing conflict.

None of this implies that Africa cannot learn from China. This paper will argue that the most important lessons are found at a level deeper than China’s specific policies; rather they are found by understanding how the country’s radical reform process came about—borne out of a time of internal upheaval and crisis—and why it succeeded. Some of the lessons are obvious enough, but there are possible surprises too. The paper begins by looking more closely at the causes of China’s success against poverty, before turning to the lessons for Africa.

# **Understanding China’s (uneven) progress against poverty**

Many visitors to China’s booming capital city, including those at the Beijing Olympics of 2008, will no doubt be surprised to hear that even 10% of China’s population is roughly as poor as the poorest 40% of Africa’s. Those visitors would have to venture out to the inland rural areas to see the extreme poverty that still remains in China. Like most developing countries, living standards tend to be lower in rural areas of both China and Africa, but the disparities between rural and urban areas are particularly large in China. In 2002, 22% of China’s rural population lived below $1 a day, while that was only true of less than 1% of the urban population. By contrast, 51% of SSA’s rural population lived under $1 a day, versus 40% in urban areas.<sup>17</sup> The large difference in living standards between China’s urban and rural areas stems in part at least from the longstanding (pre-reform) registration ( _hukou_ ) system—a kind of internal passport system that started around 1950. Under the _hukou_ system a rural migrant has little hope of using urban services at the destination without obtaining registration there, which can be difficult and costly—particularly for the poor. These restrictions have been relaxed somewhat over time. Indeed, despite of the restrictions on migration, China’s transition entailed substantial urbanization; the urban population share rose from 19% in 1980 to 44% in 2006. This pace of urbanization was part of a process that helped reduce poverty nationally. Analytic

> 17 These estimates of the urban and rural poverty measures are from Ravallion et al. (2007).

6
# **Petroleum Subsidies in Yemen: Leveraging Reform for Development**

Clemens Breisinger<sup>1</sup> , Wilfried Engelke<sup>2</sup> , and Olivier Ecker<sup>1</sup>

**Keywords:** Petroleum subsidy, reform, development, growth, poverty, Yemen.

## **Acknowledgements**

This paper has been prepared as a background paper of the World Bank’s Private Sector Growth and Social Protection Development Policy Grant for Yemen. The authors’ gratefully acknowledge the comments and suggestions from James Thurlow (UN-WIDER), Manfred Wiebelt (IFW), Karl Pauw, Paul Dorosh and Margaret McMillan (all IFPRI). We also thank Abdulmajeed Al-Bataly from the Ministry of Planning and International Cooperation, and Amir M. Althibah for the provision of data and the fruitful discussions on potential reform scenarios.

> 1 International Food Policy Research Institute (IFPRI.

> 2 World Bank, MNSPR.
Table A.5: Pre-intervention SLM adoption by treatment status (recalled)

|Variables|Treated mean|Control mean|Dierence in mean|
|---|---|---|---|
|Contact farmers: before 2010||||
|Adopted mulching|0.489|0.405|0.084|
|Adopted strip-tillage|0.248|0.214|0.034|
|Adopted pit planting|0.190|0.167|0.023|
|Adopted contour farming|0.007|0.000|0.007|
|Adopted crop rotation|0.314|0.262|0.052|
|Adopted row planting|0.124|0.095|0.029|
|Adopted improved fallowing|0.036|0.024|0.013|
|Number of observations|137|42|179|
|Other Farmers: before 2010||||
|Adopted mulching|0.181|0.203|-0.022|
|Adopted strip-tillage|0.087|0.118|-0.031|
|Adopted pit planting|0.059|0.036|0.023|
|Adopted contour farming|0.002|0.000|0.002|
|Adopted crop rotation|0.121|0.132|-0.011|
|Adopted row planting|0.055|0.059|-0.005|
|Adopted improved fallowing|0.005|0.005|0.000|
|Number of observations|4,385|1,499|5,884|

Source: Household survey, 2012. Notes: T-test inferences are based on standard errors clustered at the community level. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. SLM=sustainable land management.

48