rafmacalaba/datause-encoder-data huggingface dataset:
**ameUrIMSf** E **s05!ttLt**

DE **OPMR-PnM**

# 1. n as

In studying **the** use of policy instruments for reducing pollution in the most practical fashlon, attentlon has been focussed on the poselbllity of encouraging fuel substltutlon **as** a **feaslble** approach. Power generatLon and several Lndustrial processes (as well an domestLc heatlng) can use alternatLve fuels. **Since the** pollutlon characterLtLces of fuels vary very wldely, an effectLve fuel awLtching program could often make a substantial contribution to the reduction of pollutLon ln a regLon and ln a country **as** a whole. Two lmportant and lnter-related Lisues are lnvolved ln thle process. The fLrat li the purely technical issue of the extent to whlch a process can be switched to a dLfforent fuel, and **the** second li that of the esze of LncentLves/dLsLncentlves required to brlng about such a substltutlon. By looking at exLitLng experLence lt wlll be poseLble to obtaln evldence on both the extent to whlch fuel swltchlng mlght occur and also the degree of prlce Lntervention that would be needed to bring thLs about. Section 2 reviews the avaLlable evldence on fuel mix and lts change over time: such data w11 form a benchmark for assessing the potentiality for chvnge in a given country or industry.

ThG third section reviews econometrle models of fuel substLtution and their analytical propertles. Thli is compared and contrasted with the engLneerLng appreach whlch, through a form of cost/beneflt analysis, soeks to identlfy for a glven plant the prLces at whlch the optimal fuel choice would swltch. The fourth sectlon revliws actual evidence on prlce elasticLties for fuel swLtching and dLicusses how such data mlght be used ln an assessment of the potentlal for fuel switching. The maln ecaclusions of the paper can be suammrized as follows: (L) The central problem of evaluating a potentlal pollutlon reductlon program through price related incentives in countries **outside the** O.Z.C.D. is lack of informatlon. An economy wlde program **reqpires** data on the whole industrial and power **sectors** Ln varLous forms. For econometrLc analyLsi time se-Les of fuel **sha rs** ln induetry (preferably disaggregated) and ln power **are** requlred, while for mLero-based engineorLng studles data on a large number of plants would be requLred.
out as a country that meets all these criteria and ranks highest out of all countries for pro-poor growth. The opposite situation can also happen, where Senegal and Nigeria rank lowest in term of pro-poor growth because of an expansion in their poor and vulnerable population; these countries also have negative growth in the consumption of the bottom 40 percent.<sup>12</sup> While Table 4 focuses on the increase or decrease of the population size _across_ (of) each welfare groups, Table 5 extends this analysis by probing more deeply into the dynamics _among_ the groups. For the region as a whole, 14 percent of the population moves up one or two welfare categories (i.e., the sum of the upper off-diagonal cells), which is almost half a times higher than the percent of the population that who move down one or two welfare categories (i.e., 10 percent, or the sum of the lower off-diagonal cells). Still, a large degree of immobility exists in the region where as much as 76 percent (=100- 14-10) of the population remains in the same welfare category in both periods.<sup>13</sup>

# **III.3. Profiling Countries and Population Groups**

Following the classification employed in the recent World Bank’s regional report on poverty in Africa (Beegle et al., 2016), we probe more deeply into mobility patterns by dividing countries into four groups: fragile situations, landlocked, resource-poor, and income status. These classifications have also been employed by earlier studies to investigate poverty in the region. For example, Bloom and Sachs (1998) argue that landlocked countries perform worse than coastal countries because of lower competitiveness and fewer trading activities due to higher transport

> 12 In addition, rows 2 to 5 (Table 4) also show that poverty reduction can occur with _reduced_ consumption for the bottom 40 percent. More generally, Table 4 illustrates our earlier discussion that the bottom 40 percent can comprise a wide variety of poverty situations and thus their growth scenarios. Also note that Table 4 provides a discussion of the dynamics of the different welfare categories over time; see Figure 2.1 in the Appendix for the decomposition of these categories in the most recent period. See also Dang et al. (2017) for a more detailed analysis for Senegal. 13 We provide the specific estimates for upward and downward mobility for each country in Table 2.3 in the Appendix. This table also offers estimates for the transitions between the vulnerable group and the middle class.

18
per recipient. As of 2013, total spending reached 87 billion yuan, with an average transfer of 1,609 yuan per recipient.

Due to the diversity of China’s rural economy and the difficulty of measuring income for rural households, the dibao program’s implementation has varied among localities and evolved over time. Local variation and flexibility was explicitly built into the central dibao policy regulations (Poverty Alleviation Office of the State Council 2010). According to reports based on fieldwork (World Bank Social Protection Group 2010, World Bank 2011), variation exists in the extent to which applications are open versus by invitation of local officials. In practice village leaders often identify potential beneficiaries and invite them to apply. Village committees, which include village leaders and other community members, play a central role in identifying and screening potential beneficiaries. Members of village committees live in close proximity to and have local knowledge of potential beneficiary households. Applications or nominations for dibao benefits are submitted to the township government and forwarded to the county Department of Civil Affairs. Decisions are made by township and county officials, who review the documentary evidence submitted by households and villages, and who sometimes visit the households to check on, or to collect additional, information. The names of applicants are, in principle, made public in the villages and are subject to community review and feedback.

National policy permits, and local officials in practice make use of, a range of information to evaluate eligibility. This might include information about household income, assets, and housing conditions, as well as the presence of household members who are able or unable to work, or of illness or disability (Poverty Alleviation Office of the State Council 2010; World Bank Social Protection Group 2010; World Bank 2011).

In principle the dibao program tops up the income of recipients to the level of the local dibao threshold. The amount of the dibao benefit, then, should depend on the level of the dibao threshold and the level of household per capita income. Table 1 shows the national average dibao threshold, which has increased over time. Dibao thresholds vary substantially among provinces and counties. Practices regarding how to determine the amount of the transfer also vary. In some areas local officials estimate the gap between the household’s income and the local dibao threshold and decide on the transfer accordingly. Due to difficulties accurately 6
**Figure 1: Pupil-Teacher Ratio in Indonesia: 2001-2006**

<!-- Start of picture text --> 24.0<br>22.2<br>22.0<br>21.0 20.7 20.7<br>20.0 19.5<br>19.0<br>18.0<br>16.0 15.7 16.0<br>15.3<br>14.0 13.9 13.1 13.5<br>13.5 13.7 13.7 13.4 12.5<br>12.0<br>10.0<br>2001 2002 2003 2004 2005 2006<br>primary junior secondary senior secondary<br><!-- End of picture text --> However, in recent years, concern with the composition of the teaching force has been more prominent, particularly with the general recognition that learning outcomes are influenced by the ability of teachers who guide the learning process. There is an often expressed dissatisfaction with the distribution of ability within the present teaching force, with the public sentiment that public policies should induce desirable changes. With this line of thought, teachers’ minimum qualification, in terms of educational attainment, has been raised several times during the past decade, even though there is little consensus on whether educational attainment is a good measure of “ability” relevant for teachers.

The most significant policy change in teacher employment is the latest Teacher Law (UU14/2005) passed in 2005. It caught much attention with the 100 percent teacher salary increase for certified teachers. One of the key requirements for certification is four or more years of college education (S1 or D4). The Ministry of Education set the goal that by 2015, the whole teaching force should be comprised of only certified teachers. The magnitude of the potential change will be large given that a large proportion of incumbent teachers are below the minimum qualification level (Table 1). For example, by early 2006, over 80 percent of the primary school teachers were without college education. Given that there are nearly 2.7 million teachers in 6
of food or quantities consumed per day (or meal), nor does it account for intra-household distribution of food. With these limitations in mind, we adopt the FCS as a measure of food consumption and later show that it proves itself to be a sufficient metric to unveil the effect of the quality of the rainy season on food consumption.

In Mauritania, Food Security Monitoring Surveys (FSMS) are conducted twice a year and provide first-hand information on the food situation for intervention planning purposes, including FCS-based food insecurity rates at sub-national levels. We focus mainly on the first round that is carried out during lean seasons when food consumption is usually at the lowest in the annual cycle.<sup>5</sup> In our study case, we use a pooled sample of 10,969 household observations from five FSMS rounds collected during lean seasons between 2011 and 2015.<sup>6</sup> Each household observation has consumption frequencies in the food groups required for the calculation of the FCS as well as key household characteristics. Over the five lean seasons, we observe large variations in the moderate food insecurity rates in rural areas, from 29% in 2013 to 42% in 2012 (see Figure 1). Note that the worst two years (2012 ans 2015) followed the poorest rainy seasons (2011 and 2014) observed in the period in terms of aggregate rainfall.<sup>7</sup> Moreover, our data clearly reveal seasonal patterns between lean and post-harvest seasons with an average food insecurity rate of 34% for the former against 22% for the latter.

> 5Another round is conducted during the post-harvest period (December-January).

6There was no survey conducted in 2016 and, from 2017 onwards, data were collected late in the rainy season which poses comparability issues with the interaction of the preceding and ongoing rainy season quality in food consumption outcomes.

7We calculate a rural population-weighted average of department-level 5-month precipitation z-scores and find a value of -0.7 and -0.3 in 2011 and 2014, against 2.1, 1.7, 0.2 in 2010, 2012 and 2013 respectively.

7
9 decades to forms of support at least somewhat decoupled from production. Notwithstanding this limitation, below we estimate the trade- and welfare-reducing effects of individual policy instruments. We assume that border measures are applied first, and this may be supplemented by additional domestic distortions. This allocation assumption provides an upper-bound on welfare losses from border measures and a lower bound on welfare losses from domestic measures. An attempt is made in the empirical section below to gauge the potential importance of non-product-specific support measures which are excluded from the formal ITRI and IWRI measures. We also report commodity level TRI and WRI indexes below, which give the aggregate trade- and welfare-reducing effects of OECD member country policies to individual commodity markets. These indices are computed using a methodology similar to that in Box 1, but where distortions are summed across countries for an individual commodity, instead of across commodities for an individual country. Croser, Lloyd and Anderson (2009) provide a detailed exposition of the methodology as it applies to individual commodity markets globally. Below we provide them for the subset of countries that are OECD members.

# **Data**

This study makes use of data from the World Bank’s Distortions to Agricultural Incentives database (Anderson and Valenzuela 2008). For high-income countries that database drew on the OECD’s PSE and CSE series (OECD 2009) for the period since 1986, but extended the time series back to 1955 for many countries. In the case of the European Union, whose membership expanded several times over the past half century, the World Bank study provides distortion estimates by country on the assumption that the estimated EU-wide PSE and CSE for each product applied in each member country (see Josling 2009). Differences across EU countries in the overall sector distortion indicators are thus due to differing commodity shares in sector production and consumption.

We focus on a subset of OECD and other countries in the World Bank database (hereafter called the focus countries): 15 Western European countries, all of
# The Consumption, Income, and Wealth of the Poorest: Cross-Sectional Facts of Rural and Urban Sub-Saharan Africa for Macroeconomists<sup>_∗_</sup>

Leandro De Magalh ̃aes Ra ̈ul Santaeul`alia-Llopis University of Bristol Washington University in St. Louis Universitat de Val`encia

## JEL: E20; E21; O10; O55; I32

Keywords: Inequality, Consumption, Income, and Wealth, Sub-Saharan Africa

> _∗_ We thank Kathleen Beegle, Taryn Dinkelman, Pascaline Dupas, Francisco Ferreira, John Kaboski, Talip Kilic, and John Temple for comments and suggestions at different stages of this project. We also thank the seminar participants at the World Bank LSMS group and the SED in Seoul 2013.
Reassuringly the point estimates remain positive and statistically strong than the ones reported in column (3) of table 3.

We investigate next, whether the positive correlation between firm size and the investment in job training could be driven by the sector of activity. This is plausible since small and large firms are likely to differ in the technology use, which in turn is also likely to determine the demand for investing in job training. Even though in the baseline specification reported in column (3) of table 3, we already control for differences across firms in the sector of activity (with country-sector dummies) it is possible that the returns to the investment are different by sector of activity. If the latter holds, we would expect the results to be different across different samples with differing technologies, levels of technological adoption and/or capital/labor rations. Moreover, since in our sample micro and small firms are more concentrated in non-manufacturing sectors, this sector composition could also be driving the results.<sup>19</sup> Column (2) reports the findings for the baseline specification when restricting the sample only to manufacturing firms, which account for 73% of the sample. Reassuringly the magnitude and significance of the point estimates are almost not affected. In columns (3) and (4) we also analyze whether our findings are systematically different across low-tech and high-tech sectors, respectively.<sup>20</sup> In our sample, the low-tech manufacturing sectors have a larger share of smaller firms than the high –tech sectors, probably due to the large fixed costs of starting up high-tech activities. Reassuringly, restricting the sample across these two groups still yields quantitatively large and statistical significant differences in the intensity to train across firm sizes in the two the manufacturing sectors. However, the point estimates in columns (3) and (4) show that the differences across firm sizes are slightly larger for firms operating in the low-tech sectors than for those firms in the high –tech sectors. Finally, one could argue that the differences across firms in the technology used are still not adequately captured by the sector composition of firms. In particular, there is robust

> 19 We also worry more with the possible non-representativeness of the _Enterprise Surveys_ in the nonmanufacturing sectors than for the manufacturing sectors. Moreover, in most countries, the _Enterprise Surveys_ do not cover so exhaustively the non-manufacturing sectors. 20 We follow Parisi et al (2006) and consider high technology manufacturing sectors the following 3-digit ISIC sectors: Auto or Auto-component, Chemical and Pharmaceutical, Electronics or Metals and Machinery industries. The low technology manufacturing sectors are the Beverages, Food, Garment and Leather, Non-metallic/Plastic Materials, Wood and Paper, Textiles.

18
<!-- Start of picture text -->
[Public<br>|<br>|Overal Schools | Charter Schools |<br>| OutsideWard3 [oT<br>| High | 30.91 | 37.00 |<br>|Wards7and8| |<br>[High TSSOT 39.69 |<br>Notes: the unit of observation is a campus-year. Observations weighted by Fall enrollment. “Elementary” encompasses elementary,<br>elementary/middle and elementary/middle/high schools; “Middle” refers to middle middle schools; “High” encompasses “middle/high” “middle/high” and “high” “high” schools.<br><!-- End of picture text -->

Notes: the unit of observation is a campus-year. Observations weighted by Fall enrollment. “Elementary” encompasses elementary, elementary/middle and elementary/middle/high schools; “Middle” refers to middle middle schools; “High” encompasses “middle/high” “middle/high” and “high” “high” schools.
household surveys as methods that used either replacement or reweighting. Replacement involves replacing some part of the top of the income distribution in the household surveys parametrically- e.g. with values from the Pareto distribution with the parameter estimated using the rest of the survey distribution. Reweighting involves increasing the weights of those at the top of the distribution to account for unit non-response. We now discuss reweighting for unit non-response as well as imputation and parametric replacement, both of which can be classified as methods using replacement. Lustig (2020) highlighted that the replacement and reweighting distinction can also be applied to solutions that use administrative data, and we discuss solutions using administrative data in the following section.

# _Using survey data only_

In this section we first discuss reweighting as a solution to non-random unit non-response and imputation to solve non-random item non-response. These are methods with long histories in the statistics literature and solve very specific problems with specific solutions. We then discuss using consumption data and imputing top incomes using the survey data.

# Reweighting for unit non-response

Unit non-response is unlikely to be MCAR. Ravallion (2022) discusses solutions for non-random unit non-response. One is to find replacement observations, but this is unlikely to solve non-random nonresponse, since presumably the same biases affecting original non-response will affect which households respond as replacements. One benefit of replacement is that it does raise the sample size, which may be important for studying the top of the income distribution since it can reduce sparseness problems.

A commonly used method to solve unit non-response is reweighting, where the respondents in different groups (“weighting classes”) are upweighted to represent themselves and those in the same group that did not respond. The reweighting groups are often regions (Korinek et al. (2006)), which implies that for the method to be successful non-response must be random within regions, or that non-response is “ignorable” within regions (Ravallion, 2022). The size of the regions used is likely to affect the proportion of bias the reweighting can solve, since non-ignorability is likely to be less of a concern when using smaller geographical regions. The US CPS uses 254 cells for the entire country. It is common for the PSU, a much smaller region, to be the area used to undertake the reweighting (Hlasny and Verme (2018) document this for the Arab Republic of Egypt, Kerr and Wittenberg (2015) document this for South Africa).

Korinek et al. (2006) argue that non-response is unlikely to be ignorable and provide an econometric method to adjust survey weights for selective compliance in responding. Korinek et al. (2007) show 14
# **_B. Poverty Projections and Past Trajectories_**

How would using the 2011 PPPs at face value along with the $1.82 poverty line affect our view of the evolution of poverty in recent decades? And, would the prospects for reaching the goal of ending extreme poverty in 2030 change? Projections of global poverty so far into the future are very uncertain, regardless of the poverty line or PPPs used. Nevertheless, for the purpose of comparison with existing forecasts, we replicate the methodology used for the most recent projections of global poverty in World Bank (2014b). We conduct simulations for two separate growth scenarios – the first is based on each country’s average 10-year historic growth, which may represent an optimistic scenario; and the second is based on average growth rates over the last 20 years, which are lower on average.<sup>32</sup> The projection method assumes distribution neutral growth, holding inequality unchanged within each country and that populations grow in line with projections available in the WDI The results of the projections are shown in Figure 7 and suggest that global poverty measured and projected using the $1.82 and $1.92 lines at 2011 PPPs follow a very similar trajectory to the one for the $1.25 line at 2005 PPPs from 1981 until 2030. Figure 8 details the projections to 2030 under alternate growth scenarios. For the $1.25 line and 2005 PPPs, we estimate that the poverty rate would decline from 14.5 percent in 2011 to 4.7 percent by 2030 under the 10-year historic growth scenario.<sup>33</sup> Our projection based on the $1.82 line suggests a trajectory of global poverty declining from 14.9 percent in 2011 to 4.3 percent in 2030. The

> 32 The growth rates used in the projections are the same as used in World Bank (2014b) and Lakner, Negre and Prydz (2014), which are based on annualized real growth rates of GDP per capita (countries in Sub-Saharan Africa) or national accounts l household final consumption per capita (all other countries). These growth rates are adjusted by a factor based on observed differences between national accounts growth and survey growth found by Ravallion (2003). A factor of 0.87 is used for all countries except China (0.72) and India (0.57). The 10 year growth rates are based on observations between 2001 and 2011, while the 20 year growth rates are based on 1990 to 2010.

> 33 This is very similar to the trajectory from 14.5 to 4.8 estimated by World Bank (2014b).

25